the models firms [ failure risk of firms. we analyze...
TRANSCRIPT
1
Financial Distress Predictions in an International Context A Review and Empirical Analysis of Altmanrsquos Z-Score Model
ABSTRACT
The purpose of this paper is to assesses the classification performance of the Z-Score model in
the predictionng of bankruptcy and other types of firm distress firms encounter The goal is in
order aiming to confirm the modelrsquosits usefulness for all parties especially banks that operate
internationally and that are interested in assessing the firmsrsquo failure risk of firms We analyze the
Z-Score performance for firms from 31 European and 3 non-European countries using different
modifications of the original model This study is the first to offer such akind of comprehensive
international analysis has not been presented thus far Except for the USUS and China the firms
in the sample are primarily private and cover non-financial companies across all industrial
sectors are included Thus we use the version of the Z-Score model developed by Altman (1983)
for private manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) is used in our testing
The literature review shows that Z-Score models have been outperformed by competing market-
based or hazard models but in some studies they Z-Score models still performed very well None
of the reviewed studies performedwas based on a comprehensive international comparison
which makinges the results difficult to generalize The analysis in the currentis study shows that
while a general international model works reasonably well for most countries (AUC area under
curve prediction accuracy levels of approximately 075) and exceptionally well for some (above
090) the classification accuracy may can be considerably improved with country-specific
estimations especially with the use of additional variables In some country models the
2
information provided by additional variables helps boost the classification accuracy to a much
higher level
JEL codes G15 G32 G33
Keywords Z-Score bankruptcy failure default financial distress
3
1 Introduction
The first multivariate bankruptcy prediction model was developed by EI Altman (1968) in the
late 1960s After this pioneering work the multivariate approach to failure prediction spread
worldwide among researchers in finance banking and credit risk Failure prediction models are
important tools for bankers investors asset managers rating agencies and even the distressed
firms themselves The banking industry as the main provider of financing in the economy is
especially interested in minimizing the level of non-performing loans in order to maximize profit
on the credit activity and banks seek to reduce their own risk of default Another issue of interest
for bankers is capital adequacy and an the internal ratings-based approach encouraged by Basel
2 The Z-Score model has become a prototype for many of these models Asset managers and
investors need to have reliable tools for that can help them the select ion appropriate of
companies to be added forto their portfolios Financial distress of companies is on the one hand
detrimental to investor returns but on the other hand risk may provide opportunities for high
returns on short-sale strategies Rating agencies assess the risk of the entities and of securities
issues and thus they need to have a tool to predict default In addition Altman (1983 1993 and
2006) has suggested that the management of distressed firms can utilize the Z-Score model as a
guide to a financial turnaround
The approach used for bankruptcy prediction has been evolving evolved over time Beaver (1966
1968) used univariate analysis for selected ratios and found that some of them had very good
predictive power Altman (1968) moved significantly forwardmade strides by developing a
multiple discriminant analysis model (MDA) called the Z-Score model The next two decades
Commented [AFS1] Per the journal guidelines each citation must have a corresponding reference in the reference list Please note that there is no corresponding reference for this citation Please supply the missing information where indicated in the reference list at the end of the text
4
brought saw additional contributions to financial distress research For example such as Ohlson
(1980) with proposed a logit model1 Taffler (1984) with offered a Z-Score model for the UK and
Zmijewski (1984)2 who used a probit approach Dimitras et al (1996) reviewed 47 studies on
business prediction models They summarizinged the methods employed and the variety of
ratios used Discriminant analysis was the prevailing method and the most important financial
ratios came from the solvency category with profitability ratios being also being important
The next summary of different approaches to credit risk analysis was given by Altman and
Saunders (1998) Balcaen and Ooghe (2006) reviewed models of business failure prediction and
classified 43 models presented in the literature into four categories (univariate models 1 risk
index models 2 MDA models 21 and conditional probability models 19) However they
omitted the rapidly fast-growing type of number of models based on the option pricing theory
and contingent claims (eg Vassalou and Xing 2004 commercialized into the KMV model) and
they omittedas well as hazard models (eg Shumway 2001) Kumar and Ravi (2007) reviewed
128 statistical and artificial intelligence models for bank and firm bankruptcy predictions of banks
and firms with paying special attention paid to the techniques used in the different models
These authorsy noted that neural networks were the most popular intelligence technique In
their review Jackson and Wood (2013) presented the frequency of occurrence of specific
forecasting techniques in the prior literature The five most popular techniques were as follows
1 Altmanrsquos Z-score and Ohlsonrsquos O-score have beenwere compared by Dichev (1998) 2 A re-estimation of Ohlsonrsquos and Zmijewskirsquos models was presented by Grice and Dugan (2003)
Commented [S Ed2] Abbreviations and acronyms are often defined the first time they are used within the main text and then used throughout the remainder of the manuscript Please consider adhering to this convention
5
(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and
(5) univariate analysis
Recent reviews of the efficacy of these models have been delivered offered by Agarwal and
Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into
account the performance of accounting-based models market-based models and hazard
models These three model types of models prevail in the literature According to Agarwal and
Taffler (2008) there is little difference in the predictive accuracy of accounting-based and
market-based models however the usage of accounting-based models allows for a higher level
of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based
models perform comparably to the Merton structural market-based approach for CDS spread
estimation However the comprehensive model which used both sources of variables
outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and
market information (Shumway 2001 and Campbell et al 2008) were compared with two other
approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and
Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach
Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy
ROC (Receiver Operating Characteristic) ROC analysis and information content
Despite the vast amount of research on failure prediction the Z-Score model introduced by
Altman (1968) has been applied worldwide Thus although the Z-Score model has been in
existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or
6
financial distress prediction or and analysis both in research and in practice Therefore question
arises of why focus on the accounting-based model which is at least occasionally outperformed
by other models Most of the firms operating on the market are privately held and therefore
there are no market data (eg stock prices) are available Theose firms are usually financed by
banks which are obligated to assess their creditworthiness and monitor their performance In
the case of internationally active banks it is especially important to use a universal model across
the banking group for distress prediction for provisioning and for economic capital calculation
In following subsequent regulatory requirements they need to validate models and prove their
efficacy Thus we found it useful to analyze the performance of an accounting-based model in
an international context
In our study we use a large international sample of firms to assess the classification performance
of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for
firms from 31 European and three non-European countries (China Colombia and the USUS) T
these firms are mostly privately held and aA large number of firms are from non-manufacturing
industries Therefore we use the version of the model developed by Altman (1983) for private
and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive
international analysis of the performance of the Z-Score modelrsquos performance has not been
presented thus farto date We regard our review and analysis as important contributions to the
economic literature
3 We use bankruptcy failure default and financial distress as equivalents
7
The paper is structured as follows In the introduction we present a brief summary of the
development of failure prediction research starting beginning with Beaver (1966) and Altman
(1968) In the second section we summarize the original Z-Score model (1968) and its extension
for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we
present the results and conclusions from the literature review on these models The fourth
section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for
empirical analysis In the fifth section the empirical data and statistical methods are discussed
while the sixth section presents empirical findings Finally the seventh section summarizes the
study
2 Classic Z-Score models
21 Z-Score model for public firms
Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two
groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy
petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period
The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259
million USD Altman recognized that this group was not homogenous with respect to size and
industry although all firms were relatively small and from manufacturing industries Therefore
he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2
consisted of a paired sample of manufacturing firms chosen on a stratified random basis These
firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD
Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of
8
a lack of data and the very large firms because of a lack of data because offor small firms and the
rarity of bankruptcies among these firms in that period of large firms He did not match the asset
size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than
those in Group 1 The data collected for the firms in both groups were from the same years For
Group 1 the data were derived from financial statements one reporting period prior to
bankruptcy
Using financial statements Altman compiled a list of 22 potentially important financial ratios
based on financial statements for evaluation He classified these variables into five standard ratio
categories liquidity profitability leverage solvency and activity ratios These ratios were
chosen based on their popularity in the literature and their potential relevancey to the study The
final discriminant function estimated by Altman (1968) is as follows
Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)
where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms
The original Z-Score model was based on the market value of the firm and was thus applicable
only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a
intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
2
information provided by additional variables helps boost the classification accuracy to a much
higher level
JEL codes G15 G32 G33
Keywords Z-Score bankruptcy failure default financial distress
3
1 Introduction
The first multivariate bankruptcy prediction model was developed by EI Altman (1968) in the
late 1960s After this pioneering work the multivariate approach to failure prediction spread
worldwide among researchers in finance banking and credit risk Failure prediction models are
important tools for bankers investors asset managers rating agencies and even the distressed
firms themselves The banking industry as the main provider of financing in the economy is
especially interested in minimizing the level of non-performing loans in order to maximize profit
on the credit activity and banks seek to reduce their own risk of default Another issue of interest
for bankers is capital adequacy and an the internal ratings-based approach encouraged by Basel
2 The Z-Score model has become a prototype for many of these models Asset managers and
investors need to have reliable tools for that can help them the select ion appropriate of
companies to be added forto their portfolios Financial distress of companies is on the one hand
detrimental to investor returns but on the other hand risk may provide opportunities for high
returns on short-sale strategies Rating agencies assess the risk of the entities and of securities
issues and thus they need to have a tool to predict default In addition Altman (1983 1993 and
2006) has suggested that the management of distressed firms can utilize the Z-Score model as a
guide to a financial turnaround
The approach used for bankruptcy prediction has been evolving evolved over time Beaver (1966
1968) used univariate analysis for selected ratios and found that some of them had very good
predictive power Altman (1968) moved significantly forwardmade strides by developing a
multiple discriminant analysis model (MDA) called the Z-Score model The next two decades
Commented [AFS1] Per the journal guidelines each citation must have a corresponding reference in the reference list Please note that there is no corresponding reference for this citation Please supply the missing information where indicated in the reference list at the end of the text
4
brought saw additional contributions to financial distress research For example such as Ohlson
(1980) with proposed a logit model1 Taffler (1984) with offered a Z-Score model for the UK and
Zmijewski (1984)2 who used a probit approach Dimitras et al (1996) reviewed 47 studies on
business prediction models They summarizinged the methods employed and the variety of
ratios used Discriminant analysis was the prevailing method and the most important financial
ratios came from the solvency category with profitability ratios being also being important
The next summary of different approaches to credit risk analysis was given by Altman and
Saunders (1998) Balcaen and Ooghe (2006) reviewed models of business failure prediction and
classified 43 models presented in the literature into four categories (univariate models 1 risk
index models 2 MDA models 21 and conditional probability models 19) However they
omitted the rapidly fast-growing type of number of models based on the option pricing theory
and contingent claims (eg Vassalou and Xing 2004 commercialized into the KMV model) and
they omittedas well as hazard models (eg Shumway 2001) Kumar and Ravi (2007) reviewed
128 statistical and artificial intelligence models for bank and firm bankruptcy predictions of banks
and firms with paying special attention paid to the techniques used in the different models
These authorsy noted that neural networks were the most popular intelligence technique In
their review Jackson and Wood (2013) presented the frequency of occurrence of specific
forecasting techniques in the prior literature The five most popular techniques were as follows
1 Altmanrsquos Z-score and Ohlsonrsquos O-score have beenwere compared by Dichev (1998) 2 A re-estimation of Ohlsonrsquos and Zmijewskirsquos models was presented by Grice and Dugan (2003)
Commented [S Ed2] Abbreviations and acronyms are often defined the first time they are used within the main text and then used throughout the remainder of the manuscript Please consider adhering to this convention
5
(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and
(5) univariate analysis
Recent reviews of the efficacy of these models have been delivered offered by Agarwal and
Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into
account the performance of accounting-based models market-based models and hazard
models These three model types of models prevail in the literature According to Agarwal and
Taffler (2008) there is little difference in the predictive accuracy of accounting-based and
market-based models however the usage of accounting-based models allows for a higher level
of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based
models perform comparably to the Merton structural market-based approach for CDS spread
estimation However the comprehensive model which used both sources of variables
outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and
market information (Shumway 2001 and Campbell et al 2008) were compared with two other
approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and
Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach
Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy
ROC (Receiver Operating Characteristic) ROC analysis and information content
Despite the vast amount of research on failure prediction the Z-Score model introduced by
Altman (1968) has been applied worldwide Thus although the Z-Score model has been in
existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or
6
financial distress prediction or and analysis both in research and in practice Therefore question
arises of why focus on the accounting-based model which is at least occasionally outperformed
by other models Most of the firms operating on the market are privately held and therefore
there are no market data (eg stock prices) are available Theose firms are usually financed by
banks which are obligated to assess their creditworthiness and monitor their performance In
the case of internationally active banks it is especially important to use a universal model across
the banking group for distress prediction for provisioning and for economic capital calculation
In following subsequent regulatory requirements they need to validate models and prove their
efficacy Thus we found it useful to analyze the performance of an accounting-based model in
an international context
In our study we use a large international sample of firms to assess the classification performance
of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for
firms from 31 European and three non-European countries (China Colombia and the USUS) T
these firms are mostly privately held and aA large number of firms are from non-manufacturing
industries Therefore we use the version of the model developed by Altman (1983) for private
and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive
international analysis of the performance of the Z-Score modelrsquos performance has not been
presented thus farto date We regard our review and analysis as important contributions to the
economic literature
3 We use bankruptcy failure default and financial distress as equivalents
7
The paper is structured as follows In the introduction we present a brief summary of the
development of failure prediction research starting beginning with Beaver (1966) and Altman
(1968) In the second section we summarize the original Z-Score model (1968) and its extension
for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we
present the results and conclusions from the literature review on these models The fourth
section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for
empirical analysis In the fifth section the empirical data and statistical methods are discussed
while the sixth section presents empirical findings Finally the seventh section summarizes the
study
2 Classic Z-Score models
21 Z-Score model for public firms
Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two
groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy
petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period
The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259
million USD Altman recognized that this group was not homogenous with respect to size and
industry although all firms were relatively small and from manufacturing industries Therefore
he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2
consisted of a paired sample of manufacturing firms chosen on a stratified random basis These
firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD
Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of
8
a lack of data and the very large firms because of a lack of data because offor small firms and the
rarity of bankruptcies among these firms in that period of large firms He did not match the asset
size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than
those in Group 1 The data collected for the firms in both groups were from the same years For
Group 1 the data were derived from financial statements one reporting period prior to
bankruptcy
Using financial statements Altman compiled a list of 22 potentially important financial ratios
based on financial statements for evaluation He classified these variables into five standard ratio
categories liquidity profitability leverage solvency and activity ratios These ratios were
chosen based on their popularity in the literature and their potential relevancey to the study The
final discriminant function estimated by Altman (1968) is as follows
Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)
where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms
The original Z-Score model was based on the market value of the firm and was thus applicable
only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a
intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
3
1 Introduction
The first multivariate bankruptcy prediction model was developed by EI Altman (1968) in the
late 1960s After this pioneering work the multivariate approach to failure prediction spread
worldwide among researchers in finance banking and credit risk Failure prediction models are
important tools for bankers investors asset managers rating agencies and even the distressed
firms themselves The banking industry as the main provider of financing in the economy is
especially interested in minimizing the level of non-performing loans in order to maximize profit
on the credit activity and banks seek to reduce their own risk of default Another issue of interest
for bankers is capital adequacy and an the internal ratings-based approach encouraged by Basel
2 The Z-Score model has become a prototype for many of these models Asset managers and
investors need to have reliable tools for that can help them the select ion appropriate of
companies to be added forto their portfolios Financial distress of companies is on the one hand
detrimental to investor returns but on the other hand risk may provide opportunities for high
returns on short-sale strategies Rating agencies assess the risk of the entities and of securities
issues and thus they need to have a tool to predict default In addition Altman (1983 1993 and
2006) has suggested that the management of distressed firms can utilize the Z-Score model as a
guide to a financial turnaround
The approach used for bankruptcy prediction has been evolving evolved over time Beaver (1966
1968) used univariate analysis for selected ratios and found that some of them had very good
predictive power Altman (1968) moved significantly forwardmade strides by developing a
multiple discriminant analysis model (MDA) called the Z-Score model The next two decades
Commented [AFS1] Per the journal guidelines each citation must have a corresponding reference in the reference list Please note that there is no corresponding reference for this citation Please supply the missing information where indicated in the reference list at the end of the text
4
brought saw additional contributions to financial distress research For example such as Ohlson
(1980) with proposed a logit model1 Taffler (1984) with offered a Z-Score model for the UK and
Zmijewski (1984)2 who used a probit approach Dimitras et al (1996) reviewed 47 studies on
business prediction models They summarizinged the methods employed and the variety of
ratios used Discriminant analysis was the prevailing method and the most important financial
ratios came from the solvency category with profitability ratios being also being important
The next summary of different approaches to credit risk analysis was given by Altman and
Saunders (1998) Balcaen and Ooghe (2006) reviewed models of business failure prediction and
classified 43 models presented in the literature into four categories (univariate models 1 risk
index models 2 MDA models 21 and conditional probability models 19) However they
omitted the rapidly fast-growing type of number of models based on the option pricing theory
and contingent claims (eg Vassalou and Xing 2004 commercialized into the KMV model) and
they omittedas well as hazard models (eg Shumway 2001) Kumar and Ravi (2007) reviewed
128 statistical and artificial intelligence models for bank and firm bankruptcy predictions of banks
and firms with paying special attention paid to the techniques used in the different models
These authorsy noted that neural networks were the most popular intelligence technique In
their review Jackson and Wood (2013) presented the frequency of occurrence of specific
forecasting techniques in the prior literature The five most popular techniques were as follows
1 Altmanrsquos Z-score and Ohlsonrsquos O-score have beenwere compared by Dichev (1998) 2 A re-estimation of Ohlsonrsquos and Zmijewskirsquos models was presented by Grice and Dugan (2003)
Commented [S Ed2] Abbreviations and acronyms are often defined the first time they are used within the main text and then used throughout the remainder of the manuscript Please consider adhering to this convention
5
(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and
(5) univariate analysis
Recent reviews of the efficacy of these models have been delivered offered by Agarwal and
Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into
account the performance of accounting-based models market-based models and hazard
models These three model types of models prevail in the literature According to Agarwal and
Taffler (2008) there is little difference in the predictive accuracy of accounting-based and
market-based models however the usage of accounting-based models allows for a higher level
of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based
models perform comparably to the Merton structural market-based approach for CDS spread
estimation However the comprehensive model which used both sources of variables
outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and
market information (Shumway 2001 and Campbell et al 2008) were compared with two other
approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and
Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach
Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy
ROC (Receiver Operating Characteristic) ROC analysis and information content
Despite the vast amount of research on failure prediction the Z-Score model introduced by
Altman (1968) has been applied worldwide Thus although the Z-Score model has been in
existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or
6
financial distress prediction or and analysis both in research and in practice Therefore question
arises of why focus on the accounting-based model which is at least occasionally outperformed
by other models Most of the firms operating on the market are privately held and therefore
there are no market data (eg stock prices) are available Theose firms are usually financed by
banks which are obligated to assess their creditworthiness and monitor their performance In
the case of internationally active banks it is especially important to use a universal model across
the banking group for distress prediction for provisioning and for economic capital calculation
In following subsequent regulatory requirements they need to validate models and prove their
efficacy Thus we found it useful to analyze the performance of an accounting-based model in
an international context
In our study we use a large international sample of firms to assess the classification performance
of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for
firms from 31 European and three non-European countries (China Colombia and the USUS) T
these firms are mostly privately held and aA large number of firms are from non-manufacturing
industries Therefore we use the version of the model developed by Altman (1983) for private
and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive
international analysis of the performance of the Z-Score modelrsquos performance has not been
presented thus farto date We regard our review and analysis as important contributions to the
economic literature
3 We use bankruptcy failure default and financial distress as equivalents
7
The paper is structured as follows In the introduction we present a brief summary of the
development of failure prediction research starting beginning with Beaver (1966) and Altman
(1968) In the second section we summarize the original Z-Score model (1968) and its extension
for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we
present the results and conclusions from the literature review on these models The fourth
section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for
empirical analysis In the fifth section the empirical data and statistical methods are discussed
while the sixth section presents empirical findings Finally the seventh section summarizes the
study
2 Classic Z-Score models
21 Z-Score model for public firms
Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two
groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy
petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period
The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259
million USD Altman recognized that this group was not homogenous with respect to size and
industry although all firms were relatively small and from manufacturing industries Therefore
he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2
consisted of a paired sample of manufacturing firms chosen on a stratified random basis These
firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD
Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of
8
a lack of data and the very large firms because of a lack of data because offor small firms and the
rarity of bankruptcies among these firms in that period of large firms He did not match the asset
size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than
those in Group 1 The data collected for the firms in both groups were from the same years For
Group 1 the data were derived from financial statements one reporting period prior to
bankruptcy
Using financial statements Altman compiled a list of 22 potentially important financial ratios
based on financial statements for evaluation He classified these variables into five standard ratio
categories liquidity profitability leverage solvency and activity ratios These ratios were
chosen based on their popularity in the literature and their potential relevancey to the study The
final discriminant function estimated by Altman (1968) is as follows
Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)
where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms
The original Z-Score model was based on the market value of the firm and was thus applicable
only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a
intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
4
brought saw additional contributions to financial distress research For example such as Ohlson
(1980) with proposed a logit model1 Taffler (1984) with offered a Z-Score model for the UK and
Zmijewski (1984)2 who used a probit approach Dimitras et al (1996) reviewed 47 studies on
business prediction models They summarizinged the methods employed and the variety of
ratios used Discriminant analysis was the prevailing method and the most important financial
ratios came from the solvency category with profitability ratios being also being important
The next summary of different approaches to credit risk analysis was given by Altman and
Saunders (1998) Balcaen and Ooghe (2006) reviewed models of business failure prediction and
classified 43 models presented in the literature into four categories (univariate models 1 risk
index models 2 MDA models 21 and conditional probability models 19) However they
omitted the rapidly fast-growing type of number of models based on the option pricing theory
and contingent claims (eg Vassalou and Xing 2004 commercialized into the KMV model) and
they omittedas well as hazard models (eg Shumway 2001) Kumar and Ravi (2007) reviewed
128 statistical and artificial intelligence models for bank and firm bankruptcy predictions of banks
and firms with paying special attention paid to the techniques used in the different models
These authorsy noted that neural networks were the most popular intelligence technique In
their review Jackson and Wood (2013) presented the frequency of occurrence of specific
forecasting techniques in the prior literature The five most popular techniques were as follows
1 Altmanrsquos Z-score and Ohlsonrsquos O-score have beenwere compared by Dichev (1998) 2 A re-estimation of Ohlsonrsquos and Zmijewskirsquos models was presented by Grice and Dugan (2003)
Commented [S Ed2] Abbreviations and acronyms are often defined the first time they are used within the main text and then used throughout the remainder of the manuscript Please consider adhering to this convention
5
(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and
(5) univariate analysis
Recent reviews of the efficacy of these models have been delivered offered by Agarwal and
Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into
account the performance of accounting-based models market-based models and hazard
models These three model types of models prevail in the literature According to Agarwal and
Taffler (2008) there is little difference in the predictive accuracy of accounting-based and
market-based models however the usage of accounting-based models allows for a higher level
of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based
models perform comparably to the Merton structural market-based approach for CDS spread
estimation However the comprehensive model which used both sources of variables
outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and
market information (Shumway 2001 and Campbell et al 2008) were compared with two other
approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and
Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach
Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy
ROC (Receiver Operating Characteristic) ROC analysis and information content
Despite the vast amount of research on failure prediction the Z-Score model introduced by
Altman (1968) has been applied worldwide Thus although the Z-Score model has been in
existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or
6
financial distress prediction or and analysis both in research and in practice Therefore question
arises of why focus on the accounting-based model which is at least occasionally outperformed
by other models Most of the firms operating on the market are privately held and therefore
there are no market data (eg stock prices) are available Theose firms are usually financed by
banks which are obligated to assess their creditworthiness and monitor their performance In
the case of internationally active banks it is especially important to use a universal model across
the banking group for distress prediction for provisioning and for economic capital calculation
In following subsequent regulatory requirements they need to validate models and prove their
efficacy Thus we found it useful to analyze the performance of an accounting-based model in
an international context
In our study we use a large international sample of firms to assess the classification performance
of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for
firms from 31 European and three non-European countries (China Colombia and the USUS) T
these firms are mostly privately held and aA large number of firms are from non-manufacturing
industries Therefore we use the version of the model developed by Altman (1983) for private
and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive
international analysis of the performance of the Z-Score modelrsquos performance has not been
presented thus farto date We regard our review and analysis as important contributions to the
economic literature
3 We use bankruptcy failure default and financial distress as equivalents
7
The paper is structured as follows In the introduction we present a brief summary of the
development of failure prediction research starting beginning with Beaver (1966) and Altman
(1968) In the second section we summarize the original Z-Score model (1968) and its extension
for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we
present the results and conclusions from the literature review on these models The fourth
section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for
empirical analysis In the fifth section the empirical data and statistical methods are discussed
while the sixth section presents empirical findings Finally the seventh section summarizes the
study
2 Classic Z-Score models
21 Z-Score model for public firms
Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two
groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy
petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period
The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259
million USD Altman recognized that this group was not homogenous with respect to size and
industry although all firms were relatively small and from manufacturing industries Therefore
he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2
consisted of a paired sample of manufacturing firms chosen on a stratified random basis These
firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD
Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of
8
a lack of data and the very large firms because of a lack of data because offor small firms and the
rarity of bankruptcies among these firms in that period of large firms He did not match the asset
size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than
those in Group 1 The data collected for the firms in both groups were from the same years For
Group 1 the data were derived from financial statements one reporting period prior to
bankruptcy
Using financial statements Altman compiled a list of 22 potentially important financial ratios
based on financial statements for evaluation He classified these variables into five standard ratio
categories liquidity profitability leverage solvency and activity ratios These ratios were
chosen based on their popularity in the literature and their potential relevancey to the study The
final discriminant function estimated by Altman (1968) is as follows
Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)
where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms
The original Z-Score model was based on the market value of the firm and was thus applicable
only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a
intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
5
(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and
(5) univariate analysis
Recent reviews of the efficacy of these models have been delivered offered by Agarwal and
Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into
account the performance of accounting-based models market-based models and hazard
models These three model types of models prevail in the literature According to Agarwal and
Taffler (2008) there is little difference in the predictive accuracy of accounting-based and
market-based models however the usage of accounting-based models allows for a higher level
of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based
models perform comparably to the Merton structural market-based approach for CDS spread
estimation However the comprehensive model which used both sources of variables
outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and
market information (Shumway 2001 and Campbell et al 2008) were compared with two other
approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and
Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach
Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy
ROC (Receiver Operating Characteristic) ROC analysis and information content
Despite the vast amount of research on failure prediction the Z-Score model introduced by
Altman (1968) has been applied worldwide Thus although the Z-Score model has been in
existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or
6
financial distress prediction or and analysis both in research and in practice Therefore question
arises of why focus on the accounting-based model which is at least occasionally outperformed
by other models Most of the firms operating on the market are privately held and therefore
there are no market data (eg stock prices) are available Theose firms are usually financed by
banks which are obligated to assess their creditworthiness and monitor their performance In
the case of internationally active banks it is especially important to use a universal model across
the banking group for distress prediction for provisioning and for economic capital calculation
In following subsequent regulatory requirements they need to validate models and prove their
efficacy Thus we found it useful to analyze the performance of an accounting-based model in
an international context
In our study we use a large international sample of firms to assess the classification performance
of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for
firms from 31 European and three non-European countries (China Colombia and the USUS) T
these firms are mostly privately held and aA large number of firms are from non-manufacturing
industries Therefore we use the version of the model developed by Altman (1983) for private
and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive
international analysis of the performance of the Z-Score modelrsquos performance has not been
presented thus farto date We regard our review and analysis as important contributions to the
economic literature
3 We use bankruptcy failure default and financial distress as equivalents
7
The paper is structured as follows In the introduction we present a brief summary of the
development of failure prediction research starting beginning with Beaver (1966) and Altman
(1968) In the second section we summarize the original Z-Score model (1968) and its extension
for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we
present the results and conclusions from the literature review on these models The fourth
section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for
empirical analysis In the fifth section the empirical data and statistical methods are discussed
while the sixth section presents empirical findings Finally the seventh section summarizes the
study
2 Classic Z-Score models
21 Z-Score model for public firms
Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two
groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy
petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period
The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259
million USD Altman recognized that this group was not homogenous with respect to size and
industry although all firms were relatively small and from manufacturing industries Therefore
he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2
consisted of a paired sample of manufacturing firms chosen on a stratified random basis These
firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD
Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of
8
a lack of data and the very large firms because of a lack of data because offor small firms and the
rarity of bankruptcies among these firms in that period of large firms He did not match the asset
size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than
those in Group 1 The data collected for the firms in both groups were from the same years For
Group 1 the data were derived from financial statements one reporting period prior to
bankruptcy
Using financial statements Altman compiled a list of 22 potentially important financial ratios
based on financial statements for evaluation He classified these variables into five standard ratio
categories liquidity profitability leverage solvency and activity ratios These ratios were
chosen based on their popularity in the literature and their potential relevancey to the study The
final discriminant function estimated by Altman (1968) is as follows
Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)
where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms
The original Z-Score model was based on the market value of the firm and was thus applicable
only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a
intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
6
financial distress prediction or and analysis both in research and in practice Therefore question
arises of why focus on the accounting-based model which is at least occasionally outperformed
by other models Most of the firms operating on the market are privately held and therefore
there are no market data (eg stock prices) are available Theose firms are usually financed by
banks which are obligated to assess their creditworthiness and monitor their performance In
the case of internationally active banks it is especially important to use a universal model across
the banking group for distress prediction for provisioning and for economic capital calculation
In following subsequent regulatory requirements they need to validate models and prove their
efficacy Thus we found it useful to analyze the performance of an accounting-based model in
an international context
In our study we use a large international sample of firms to assess the classification performance
of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for
firms from 31 European and three non-European countries (China Colombia and the USUS) T
these firms are mostly privately held and aA large number of firms are from non-manufacturing
industries Therefore we use the version of the model developed by Altman (1983) for private
and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive
international analysis of the performance of the Z-Score modelrsquos performance has not been
presented thus farto date We regard our review and analysis as important contributions to the
economic literature
3 We use bankruptcy failure default and financial distress as equivalents
7
The paper is structured as follows In the introduction we present a brief summary of the
development of failure prediction research starting beginning with Beaver (1966) and Altman
(1968) In the second section we summarize the original Z-Score model (1968) and its extension
for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we
present the results and conclusions from the literature review on these models The fourth
section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for
empirical analysis In the fifth section the empirical data and statistical methods are discussed
while the sixth section presents empirical findings Finally the seventh section summarizes the
study
2 Classic Z-Score models
21 Z-Score model for public firms
Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two
groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy
petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period
The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259
million USD Altman recognized that this group was not homogenous with respect to size and
industry although all firms were relatively small and from manufacturing industries Therefore
he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2
consisted of a paired sample of manufacturing firms chosen on a stratified random basis These
firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD
Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of
8
a lack of data and the very large firms because of a lack of data because offor small firms and the
rarity of bankruptcies among these firms in that period of large firms He did not match the asset
size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than
those in Group 1 The data collected for the firms in both groups were from the same years For
Group 1 the data were derived from financial statements one reporting period prior to
bankruptcy
Using financial statements Altman compiled a list of 22 potentially important financial ratios
based on financial statements for evaluation He classified these variables into five standard ratio
categories liquidity profitability leverage solvency and activity ratios These ratios were
chosen based on their popularity in the literature and their potential relevancey to the study The
final discriminant function estimated by Altman (1968) is as follows
Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)
where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms
The original Z-Score model was based on the market value of the firm and was thus applicable
only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a
intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
7
The paper is structured as follows In the introduction we present a brief summary of the
development of failure prediction research starting beginning with Beaver (1966) and Altman
(1968) In the second section we summarize the original Z-Score model (1968) and its extension
for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we
present the results and conclusions from the literature review on these models The fourth
section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for
empirical analysis In the fifth section the empirical data and statistical methods are discussed
while the sixth section presents empirical findings Finally the seventh section summarizes the
study
2 Classic Z-Score models
21 Z-Score model for public firms
Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two
groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy
petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period
The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259
million USD Altman recognized that this group was not homogenous with respect to size and
industry although all firms were relatively small and from manufacturing industries Therefore
he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2
consisted of a paired sample of manufacturing firms chosen on a stratified random basis These
firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD
Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of
8
a lack of data and the very large firms because of a lack of data because offor small firms and the
rarity of bankruptcies among these firms in that period of large firms He did not match the asset
size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than
those in Group 1 The data collected for the firms in both groups were from the same years For
Group 1 the data were derived from financial statements one reporting period prior to
bankruptcy
Using financial statements Altman compiled a list of 22 potentially important financial ratios
based on financial statements for evaluation He classified these variables into five standard ratio
categories liquidity profitability leverage solvency and activity ratios These ratios were
chosen based on their popularity in the literature and their potential relevancey to the study The
final discriminant function estimated by Altman (1968) is as follows
Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)
where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms
The original Z-Score model was based on the market value of the firm and was thus applicable
only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a
intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
8
a lack of data and the very large firms because of a lack of data because offor small firms and the
rarity of bankruptcies among these firms in that period of large firms He did not match the asset
size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than
those in Group 1 The data collected for the firms in both groups were from the same years For
Group 1 the data were derived from financial statements one reporting period prior to
bankruptcy
Using financial statements Altman compiled a list of 22 potentially important financial ratios
based on financial statements for evaluation He classified these variables into five standard ratio
categories liquidity profitability leverage solvency and activity ratios These ratios were
chosen based on their popularity in the literature and their potential relevancey to the study The
final discriminant function estimated by Altman (1968) is as follows
Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)
where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms
The original Z-Score model was based on the market value of the firm and was thus applicable
only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a
intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
9
Therefore Altman (1983) advocated a complete re-estimation of the model substituting the
book value of equity for the market value in X4 Using the same data Altman extracted the
following revised Zrsquo-Score model
Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)
where X4 = Book value of equityBook value of total liabilities with the other variables being
the same as those in the original (1968) Z-Score model
DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a
secondary sample due to the lack of a private firm data base However he analyzed the accuracy
of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the
revised model because of a potential industry effect The industry effect that is more likely to
take place when this kind of industry-sensitive variable (asset turnover) is included in the model
Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)
Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)
The EBITTotal assets ratio X3 made the highest contributioncontributed most to the
discrimination power in this version of the model The classification results for the Zrsquorsquo-Score
model were identical to the revised five-variable Zrsquo-Score model In the currentis study our
empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting
bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for
both privately held and publicly listed firms and for both manufacturing and non-manufacturing
firms
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
10
3 Literature review
We searched for papers published from the year 2000 onward 4 in prominent international
journals or and books We selected 31 articles in which the Z-Score was either used either as a
failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies
Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength
In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and
in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score
was also used for other purposes such as the evaluation of the costs and benefits of covenants
in bonds the choice of the type of debt type (bank vs non-bank private or public) and the
relationship between investment and internal funds We focused on this part of the literature
that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score
model foras a measuringe of financial distress and performingas well as for the robustness checks
indicates that it is acceptabilityed as a reasonable simple and consistent measure of the
distressed firms
In the case of the modification of the The most common modification to the Z-Score model
model the most common change was the use of other estimation techniques or country-specific
data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved
the its prediction capability TAdditionally the application of new data of on both US and non-US
4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
11
firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear
Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US
bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The
MCLP approach performed better than Altmanrsquos original model and gave results similar to or
better than those of Ohlsonrsquos original model The original models were not recalculated and the
authors referred solely to their original prediction rates only Merkevicius et al (2006) using data
on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA
and an unsupervised learning artificial neural network (called the SOM-Altman model) with the
use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high
prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and
Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models
are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-
score model They then introduced variables unique to Japan to check whether corporate
structure variables have any impact on the probability of bankruptcy t They called this the model
X-score model These two models were useful for Japanese firms in predicting bankruptcy
prediction but the market-based model was the most successful In summary the developed C-
score and X-score (with country-specific variables) models improved bankruptcy prediction
Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the
performance of their model developed for UK listed companies with combined accounting
market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy
in the case of financially distressed firms (81 vs 87 for the new model) however it was less
correct for financial health prediction Another modification was introduced by Lyandres and
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
12
Zhdanov (2013) who posed the question of whether the inclusion of variables related to
investment opportunities improved the predictive power of three models (Altmanrsquos Z-score
model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment
opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment
opportunities were linked to the likelihood of default The inclusion of either of these measures
improved the out-of-sample forecasting ability of all three
The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a
comparison with other accounting-based market-based or hazard models However although
the original Z-Score model was not solely an accounting-based approach because the market
value of equity was utilized we classify it here as an accounting -based approachbecause the
market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US
firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that
the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of
the model had significantly changed which means that the relation between the financial ratios
and the signs of financial distress had changed over time The model was sensitive to industry
classification (more efficient for manufacturing firms than for non-manufacturing firms) but was
not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan
(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models
Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and
updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
13
relative information content tests to compare the out-of-sample performance BSM-Prob
outperformed accounting-based models The conclusions were robust to various modifications
of accounting-based models such as updated coefficients industry effects and the separation
of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed
firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos
(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted
bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first
decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and
for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed
accounting-based models in terms of the ROC curve (091)
Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its
discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The
dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors
proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up
to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier
option model outperformed the other models Pindado et al (2008) developed an ex -ante model
for the estimation of financial distress likelihood (FDL) using awith the use of panel data
methodology and they presented a financially (not legally) based definition of distress Their
sample covered 1583 US companies and 2250 companies from other G-7 countries for the
period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model
outperformed the Z-Score model in terms of stability and classification power for different
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
14
countries and time periods In the case of the re-estimated Z-Score model only profitability and
retained earnings kept maintained their significance for different years and countries
Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980
Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for
US listed firms Based on these models the authors built their own integrated model that is a
multi-period logit model with an expanded set of variables The integrated model which
combined accounting and market data as well as firmsrsquo characteristics outperformed the other
models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models
Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos
and Zmijewskirsquos models performed adequately although their performance deteriorated over
time
Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their
efficacy using the ROC curve They selected three single- variable models three accounting-based
models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural
network approach) and four contingent claims models The contingent claims models and the
latter group outperformed the other models The best four best models were contingent claims
models based on European call and barrier options Although tThe predictive performance
improved withIn the case of the application of the neural network to accounting-based models
the predictive ability improved but it was still lower than within the case of the market-based
models
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
15
Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz
information criterion (GASIC) for variable selection combined with the logit model for bankruptcy
prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model
evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed
firms but the type II error was high For two- and three- steps- ahead forecasts the performance
of the models was similar forin terms of failed firms but forin the case of non-failed firms and
the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC
model outperformed the other models forin the case of failed firms and but it performedwas
comparablye forin the case of non-failed firms
In general Altmanrsquos Z-Score model underperformed in compared withison to market-based
models but there was also evidence indicated that it performed well for short-term distress
prediction The question of whether market-based models are perform better than accounting-
based models has been raised many times (eg discussion in Das et al 2009 Bauer and d
Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on
the accounting-based approach In this study we analyze primarily analyze privately held firms
so there are by definition no market data for these firms In this case aAn accounting-based
approach is the only solution that could be appliedapplicable in this case by banks as lenders or
by investors holding debt securities of firms not listed on the stock exchange Thus far most of
the studies have concentrated on the US market only a few of them have used data from other
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
16
countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant
extension of the previous research
4 Methodology and research hypotheses
The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model
(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly
traded manufacturing and non-manufacturing firms) have been adapted for different purposes
In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score
model in classifying bankrupt and non-bankrupt firms in an international context with special
focus on the European market However we also validate the results in a set of non-European
countries to generalize the results for circumstances outside Europe Second we will re-estimate
the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a
benchmark for assessing the effects of different factors on the modelrsquos performance in terms of
classification accuracy We will assess the effects of the following five factors on this
performance year of bankruptcy size of firms age of firms industry and country of origin WIn
all we will test a set of hypotheses based on the effects of the model on performance on two
different levels First we will test a set of hypotheses on a pooled set of all firms and second on
data from each country individually CIn compared withison to the previous research our this
studyrsquos contribution is the focus on an international context not just simply the model
application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the
performance of the Zrsquorsquo-Score mModel when using ain large body of international data the
research hypotheses are of a technical nature and are presented given as follows
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
17
H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the
same sample of firms that was used to develop the original Z-Score mModel The bankruptcies
in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest
observations are from nearly seventy years ago during the post-war period Altman (1983)
recommended utilizing data as near to the present as possible when developing a bankruptcy
prediction model It is obvious that firmsrsquo the financial behavior of firms and their business
environment have significantly changed since then Thereforen potentially making the
importance of the financial ratios as reflected by the coefficients of the model may differ from
their original importance reflected by the coefficients of the model Therefore we suggest as
the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables
of the Zrsquorsquo-Score model will improve the classification performance of the model in an
international context This hypothesis is supported by the previous research (eg Grice and
Ingram 2001) and by practice H1 aims to support this evidence on the international level and is
expressed as follows
H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its
classification accuracy of the model
H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However
MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of
multinormality homoscedasticity and linearity which are not often met in empirical financial
ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis
(LRA) to assess the effect of the method of estimation method LRA does not require most of the
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
18
restricting assumptions of MDA In LRA the multivariate normality of the independent variables
is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more
useful than LRA for small samples such as the original sample of 66 firms used in the estimation
of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this
study we use large samples which is advantageous for LRA Therefore our second hypothesis
(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be
improved when it is estimated using LRA instead of MDA The model re-estimated for the original
variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The
performance of this re-estimated model is used in this study as the benchmark for further
analyses Thus the second hypothesis is as follows
H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher
than that of the multiple discriminant analysis version
H3 Bankruptcy year The model based on the relationship between bankruptcy and financial
ratios is likely to be affected by the macroeconomic environment These effects may significantly
decrease the classification accuracy of the model If the model is estimated using data from one
year and it will be applied to data from another year the validity of the model can be questioned
Business cycles inIn terms of economic growth credit policy and interest rates business cycles
can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-
Score model is estimated using data from the period 1946ndash1965 period which includes several
business cycles Therefore the model is not focused on any specific stage of a cycle and does not
explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
19
firms for the last most recent few years when developing a prediction model In this study the
benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original
estimation although it covers several recent years on for different stages of the business cycle in
different countries Therefore the third hypothesis (H3) assumes is that the classification
accuracy of the benchmark model can be increased by explicitly taking into account of the year
of bankruptcy in the estimation of the model estimation The third hypothesis is presented as
follows
H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the
effect of the year of bankruptcy is included is higher than that of the model without the
effect
H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small
and larger firms which decreases the performance of the model estimation when estimated for
data from one size category and isare applied to for data from another size category For the
bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range
of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not
include very small or very large firms Altman (1983) regarded the suitability of the original Z-
Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable
because of this omission it did not scrutinize very large or very small firms In the currentis study
the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from
very small firms to very large firms The fourth hypothesis (H4) assumes that the classification
performance of the uniform benchmark LR -model based on the original four financial variables
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
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46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
20
of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into
account Thus we present the fourth hypothesis as follows
H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is
includede is higher than that of the model without this effect
H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a
function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-
Score model does not explicitly take age into account age However Altman (1983) noted that
the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which
was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will
probably show a low ratio because it has not had time to build up cumulative profits Thus a
young firm is to some degree discriminated against in the model and its likelihood of being
classified as bankrupt is relatively higher than that of an older firm The incidence of failure is
much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is
implicitly taken into account in X2 we expect that an explicit consideration of the age will improve
the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis
(H5) proposes that the performance of the uniform benchmark model based on the original four
financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken
into account The fifth hypothesis is can be presented as follows
H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm
age is higher than that of the model without this effectincluded
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
21
H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms
Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a
homogenous group of bankrupt firms If we are interested in a particular industry grouping we
should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies
show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)
Firms in different industries tend to report different levels of the same financial ratios which may
have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect
may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio
which showed the least significance on a univariate basis while making but it had made a very
significant contribution to the discriminant power of the multivariate model Altman (1983)
recognized the potential industry effect due to a the wide variation among industries in asset
turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was
also estimated using the original sample of manufacturing firms In our analysis the uniform
benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is
estimated for a statistical sample representing different industries Therefore the sixth
hypothesis (H6) assumes that an explicit consideration of industry will improve the classification
accuracy of this benchmark model H6It can be expressed in the following form
H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is
higher than that of the model without this effectincluded
H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It
can be expected that the international applicability of the model to other countries is affected by
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
22
country-specific differences The eEconomic environment legislation culture financial markets
and accounting practices in a country may affect the financial behavior of firms and the boundary
between bankrupt and non-bankrupt firms These factors may potentially weaken the
classification performance of the model in for other countries outside other than that e one fore
country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the
seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm
into account will improve the classification accuracy of the benchmark model In our empirical
study the country effect will beis assessed by including a variable of for country risk Therefore
the seventh hypothesis is presented as follows
H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country
risk is higher than that of the model without this effectincluded
5 Empirical data and statistical methods
51 Sample of firms
The principal data of for this study are were extracted from the ORBIS databases of Bureau Van
Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the
moment of sampling contained administrative information on more than 50 million European
firms However income statement and balance sheet information was available for
approximately 8 million companies More than 99 of the companies covered in this database
are private companies from different various industries justifying the use of the Zrsquorsquo-Score model
instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all
industrial groupings and for both private and public entities (Altman 1983 124 Altman and
Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
23
of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all
industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly
used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that
international comparability may be a problem when administrative firm-level data are
internationally pooled For administrative data Although the definition of variables is usually less
harmonized for administrative data tThis is less of a problem in the ORBIS database because of
the common international format of balance sheets For example although some discrepancies
in profitloss statements may arise because of differences in fiscal systems across countries
balance sheet variables largely adhere to international standards
AThere are a number of factors that influence the international applicability of bankruptcy
prediction models These factors include accounting legislation and practice creditor rights and
investor protection judicial efficiency corporate governance bankruptcy protection and
insolvency management and firm risk-taking These factors strongly differ between European
and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-
Score model outside Europe First it is especiallyparticularly important to include the USUS as
because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It
is include d in our study as well because it has the largest market capitalization in the world
Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it
applicable to emerging market companies Therefore we include firms from China and Colombia
which represent two very culturally and institutionally very dissimilar emerging market
countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
24
firms ie the limit we set for European countries) from ORBIS World were not
available Thus the results are also estimated and tested for three non-European countries (the
United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-
Score modelrsquos performance The samples of firms from these countries are were extracted from
ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms
from around the world
SFor statistical sampling several requirements are set for the statistical sampling of the empirical
data First we require that the firm to be selected be an industrial (non-financial) company
Second its owners must have limited liability (so partnerships and sole proprietors are left out
of the study) Third we set a minimum requirement for the size of the firm Because the financial
ratios of very small firms are generally too unstable for a failure prediction model these firms
are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have
exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we
include in our estimation sample firms from all European countries and 3 pre-selected non-
European countries where the number of failed firms is more greater than 60 If the number of
failed firms for any European country is less than 60 the firms from this country are only included
only in the test sample For qualifying European countries the firms are randomly classified in
the estimation and test samples so that the number of firms is approximately equal in both
samples Thus our estimation data include firms from 28 European and 3 non-European
countries Fifth all failed firms that fulfill the above requirements are included in our samples
However if the number of non-failed firms in a country is very high a sample is randomly
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
25
selected from that country Finally the time span of fiscal years potentially available for this study
ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in
the database are from a financial period within 2007 and 2010 earlier years are also excluded
for non-failed firms for comparability All qualifying observations of non-failed firms from the
years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses
of failed firms to the last most recent financial statements available before failure The four
independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize
outliers
Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test
data by country The estimation sample includes data from 2602563 non-failed and 38215
failed firms from 28 European and 3 non-European countries The test sample is slightly larger
because it includes data from 31 European and 3 non-European countries From For the country
of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt
firms The available US data that were available consist only of listed (and delisted) firms From
China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are
included in the estimation sample although there are only 32 such firms altogether7 The Chinese
datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status
are separately analyzed separately only in for the test data8 ST firms are listed firms suffering
6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
26
from serious financial difficulties Excluding the special US data and the two non-private Chinese
datasets 994 of the observations in the data are private firms
(Table 1 here)
52 Status of failed firms
ORBIS has five classes for potentially active firms (active default of payment receivership
dormant and branch) and seven classes for inactive firms that no longer carry out business
activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation
branch and no precision) From Among these classes only active is selected to represent non-
distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by
considering (with exceptions described below) a firm failed if its status in ORBIS is stated as
bankruptcy However because of the small number of bankrupt firms in some countries we also
consider receivership (active) firms as failed even if they are active These firms generally suffer
from serious financial distress However firms in liquidation are generally not included in the
sample of failed firms Firms in liquidation may depending on the country contain firms that
have ceased activities due to reasons other than failure (mergers discontinuing the operations
of a daughter company or of a foreign branch etc) Therefore for most countries we select only
firms that are coded as being bankrupt or under receivership However there are a number of
special cases where failed firms are coded under a different status heading These countries or
samples are the following
Country Status categories Bulgaria In liquidation Bankruptcy
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
27
Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the
study (for example Switzerland) If a country had only a very small number of failed firms it was
dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries
including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status
classes (including the bankruptcy category) are not completely homogenous within European
countries due to different legislations although there are obvious similarities in insolvency acts
(Philippe et al 2002) China is a special case because it includes samples with three different
criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are
two different samples (liquidation and receivership)
53 Statistical methods
In this study seven research hypotheses are drawn for statistical testing The statistical analysis
will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The
classification performance of the original model is assessed by the AUC (Area Under Curve)
measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close
connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
28
model and 05 for a model with an average classification performance In SAS software is used
for all statistical analysess SAS software is used
The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is
tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical
method (multiple discriminant analysis or MDA) The problem is that the estimation sample
includes different numbers of failed and non-failed firms from 31 countries In the original Z-
Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from
the USUS Following the characteristics of these data we weight the firms so that the weights
for the failed and non-failed firms are equally In so doingthis way the non-proportional
sampling in from different countries does will not affect the re-estimated model The number of
firms from different countries however varies significantly leading to greater weights for larger
countries To avoid this problem the observations are also weighted so that each country has an
equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using
these weighted data and the resulting AUC is compared with that based on the original model
The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score
model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is
based on less- restrictive statistical assumptions than MDA In this estimation the dependent
variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA
does not require that independent variables be multivariate normal or that groups have equal
covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
29
creates a score (logit) score L for every firm It is assumed that the independent variables are
linearly related to L This score or logit is used to determine the conditional probability of failure
as follows
= (eq 4)
where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the
original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing
the statistical significance of the difference between AUCs for this LR model and for the re-
estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it
is used as a benchmark for further statistical AUC comparisons because LR is applied as the
principal method in the testing of the remaining research hypotheses
The third hypothesis (H3) is associated with the performance effect of taking account of the
bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on
the following logit
(eq 5)
where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model
bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and
D1 = 1 when year = 2008 0 otherwise
D2 = 1 when year = 2009 0 otherwise
D3 = 1 when year = 2010 0 otherwise
)|1( XYp )( 441101
1
1
1XbXbbL ee
3
1
4
10
jjji
ii DcXbbL
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
30
The dummy variables do not directly refer to the bankruptcy year which that is not given in the
data basedatabase but rather to the last available year before bankruptcy For failed firms there
is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is
the base category If the AUC of this extended LR model statistically significantly exceeds the AUC
of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3
Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third
hypothesis above However for each hypothesis appropriate variables are used instead of the
year dummies Hypothesis H4 addresses the performance effect of taking size into account the
size and it will beis tested using by adding two additional variables measuring firm size in to the
LR -model In this LR model size is measured by the natural logarithm of total assets and its
squared form In this way the effect of logarithmic size can be reflected by in a function following
the second-order parabola Hypothesis H5 predicts that the classification performance improves
when the age of the firm is explicitly taken into account When testing this hypothesis the
category 6ndash14 years category is used as the base category and two dummy variables are
incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis
H6 looks at whether is associated with the classification performance is affected bythe effect of
the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt
is tested here using dummy variables for seven industries (D1 restaurants and hotels D2
construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and
water production D7 information technology) with all other industries acting as the base
category
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
31
Hypothesis H7 predicts that the explicit consideration of the country of origin will improves
classification performance This hypothesis is tested by using country risk measures instead ofnot
using dummy variables for countries but using country risk measures instead The country risk of
each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the
annual closing of accounts The rating is numerically recoded such that the best rating AAA
equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating
D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial
ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-
modelLR models with the original four financial ratios and the additional variables specified in
the hypotheses are estimated for all data In addition an LR model including all additional
variables is estimated for all data to assess the simultaneous effect of all variables Finally six of
the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not
included in this country- level testing hypothesis H7 is not tested because the additional variable
(country risk) is constant within the country
6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models
Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score
model for all data The variation in the ratios is significant as shown by the standard deviation
and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-
failed firms are close to each other indicating a symmetry of distributions However this is not
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
32
the case for the failed firms For the faileddistressed firms the median exceeds the mean for
these three ratios indicating negatively skewed distributions For X4 (BVETD) the means
significantly exceed the median for both failed and non-failed firms indicating a positively
skewed distribution For each of the four variables both the mean and the median are higher for
non-failed firms than for failed firms which is consistent with the expectations The difference
between the means of the non-failed and failed firms is larger in the original USUS data than in
our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is
approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of
EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may
indicate lower classification accuracy than in the original sample
(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates
(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the
large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model
Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same
statistical method or MDA The coefficients here are negative because the models are estimated
using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight
than in the original USUS data while the weights of WCTA and BVETD have proportionally
decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the
logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR
model These coefficients are directly comparable with those of the MDA model which isas
expected for the this exceptionally large sample The differences in the coefficients of the original
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
33
four variables among the eight LR-modelLR models (Models 1-8) are small For each model the
coefficient of BVETD is very close to zero This indicatinges that the original four variables and
the additional variables are quite independent of each other
(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-
modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate
that after 2007 (the base category) this risk of failure significantly decreased year by year The
base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due
to as a result of the global financial crisis The crisis played a significant role in the failure of key
businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The
effects were especially felt in Europe The coefficients of Model 4 for the size variables show that
the contribution of size on to the logit (risk measure) is atreaches its maximum value when
logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model
5 confirms the riskiness of young firms because the risk of failure is very high for newly founded
firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The
coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky
industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is
statistically significant (because of the large sample) but negative and very close to zero Finally
the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7
62 All data performance of the Zrsquorsquo-Score models
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
34
Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1
refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of
all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average
accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)
Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its
performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The
lower part of the table shows the AUCs for the countries included only in the test data The
performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)
samples It is also low and for the liquidation firms in the United Kingdom
The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5
presents the differences of these medians between the non-failed and failed firms by country
This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of
medians This correlation which is high for each financial ratio showing that the effects of the
ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large
except for EBITTA which This implies with the exceptionally high AUC that the ST firms
systematically differ from the non-ST firms although the differences are not extremely large The
differences between the medians are very large in Poland for each ratio justifying the high AUC
They are also large and in Finland and in the Czech Republic where the difference in EBITTA is
average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in
all four ratios are below the average a result thatwhich is obviously associated with a low AUC
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
35
In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even
negative In Iceland and Ireland the differences only in EBITTA are exceptionally small
(Table 4 here)
(Table 5 here)
Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by
MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)
supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is
at approximately the average level The re-estimation of the coefficients has led to improved
classification accuracy in a number of countries especially in Bosnia China (ST) Norway and
Greece However it has impaired the classification accuracy in the UK and China (delisted)
Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-
modelLR model) showing the effect of the estimation method For the benchmark model the
AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between
among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR
model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country
This result was expected because the coefficients of the models are directly comparable The
similar results for the models may also indicate that the independent variables conform to
multinormality This last latter result confirms what most researchers in the field of default
classification models have concluded that the accuracy levels of MDA and logistical regression
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
36
models are extremely similar despite any statistical violations such as normality between MDA
and logistical regression models
Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)
than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects
are not positive for all countries The effects are positive for example for Russia Estonia
Germany Ireland and Latvia but these effects are not statistically significant There are
statistically significant negative effects for China (ST) and Serbia There are notable negative
effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are
due to the exceptional annual distributions of failed firms in these countries For the countries
with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the
countries with positive effects it is only a few percent For each group non-failed firms are quite
equally distributed over years When the coefficient of D3 is very low (-0666) it strongly
decreases the risk estimates of most failed firms in the former countries but of only a few failed
firms in the latter countries This which leads to the observed effects H3 is supported by
the level of the whole sample However in some country samples (at the level of individual
levelies) H3 is not supported
Model 4 (LR-modelLR model with size variables) performs better than the benchmark model
which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads
and to significant improvements in the AUC for eg China (delisted) and Austria For China
(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
37
also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs
in a few countries but thise decrease is not significant Model 4 is based on the four original
variables and the size effect following a second-order parabola This kind of size effect is very
small for micro firms but increases when approaching its maximum value in middle-sized firms
The countries with a positive change in AUC typically have data where in which the percent of
failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized
failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In
contrast the countries with a negative change in AUC have data where in which the percentage
of failed micro firms is exceptionally high (70-80)
Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly
the same AUC as the benchmark model (AUC = 0748) However the difference is positive and
statistically significant giving at least marginal support to H5 (age effect) For nearly all countries
the effect of the age on AUC is small For Austria however thise effect is however positive and
significant For Austria the percentage of non-failed young firms (less than 6 years) among non-
failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed
firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong
positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only
a few non-failed firms which This obviously leads to an improvement in AUC
Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC
supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
38
AUC is notably higher than the benchmark AUC only in only a few countries such asfor example
France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg
restaurants and hotels and the information technology industryies but it has a positive effect
for on the construction and manufacturing industries For the countries with a positive effect on
AUC the percent of non-failed firms in restaurants and hotels and information technology is
high while that of failed firms is low For the risky industries (construction and manufacturing)
these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many
failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)
firms Therefore the AUC will be increasesd This situation is reversed for the countries with a
negative effect on AUC (Austria and Slovenia) The samples of these countries include a high
percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC
Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher
classification performance (AUC = 0749) than the benchmark model This result gives only very
weak support for H7 (country of origin effect) However for each country the resultant AUC is
nearly identical to that given by the benchmark model This result was expected due to the
negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7
This result implies that country risk hasdoes not have noany effect on the boundary between
bankrupt and non-bankrupt firms
Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional
variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
39
the benchmark AUC in all test data However the effect on the AUCs largely varies and is either
negative or positive in different countries The positive effect is large in several countries such
as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative
effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show
that the inclusion of additional variables in the original model will usually increase the AUC but
not in every country
The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with
the USUS firms being unlike the majority of other firms in this study being listed or delisted
companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese
firm samples is associated with very small differences between the medians of the non-failed and
failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable
with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed
firms is very high Prior studies based on Chinese ST -firms have also demonstrated good
predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for
additional research and modeling work regarding unlisted and delisted Chinese firms
63 Country-level data performance of the Zrsquorsquo-Score models
The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform
all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the
different models estimated for each country separately (country-level models) In this table the
ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
40
from the country data this benchmark will beis clearly outperformed by the resulting MDA
(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and
Sweden) However these results give only weak support for H1 at the country level because the
effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and
2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs
given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed
by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation
method)
Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC
than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3
leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United
StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in
nearly every country supporting H4 This improvement is significant however only in the United
StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark
model but for which it is only 0710 for the benchmark model It This improvement is also notable
in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age
category dummies) gives higher AUCs than the benchmark model for several countries but none
of the improvements are significant The positive effect is particularly strong especially in
Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only
weakly supports H5 because these negative effects are relatively small
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
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Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
41
Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects
on AUCs when compared with the benchmark However the only significant effect is the negative
effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the
industry dummies However there are notable positive effects on the AUC in for example
France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak
support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in
AUC compared with the benchmark model in Colombia Iceland and the Unites States It also
leads and to notable improvements in AUC at least in Belgium Denmark Estonia France
Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are
found only in Austria and Slovenia Thus as a conclusion we can concludestate that the
classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can
remarkably increases in most countries when different effects are taken into account by
additional variables This increase is found in most European countries and in Colombia and the
United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-
modelLR model and it can only be slightly improved only slightly by additional variables
(Table 6 here)
7 Summary of the study and a suggested extension
The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model
originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is
assessed using a very large international data set We test how the original version of the Zrsquorsquo-
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
42
Score model performs in different countries and how re-estimations using another statistical
method and different additional variables affect the classification performance when the data
are very heterogeneous For this kind of testing seven research hypotheses on classification
performance are extractedformulated These research hypotheses are tested for all data and
separately for country data (country-level analysis) The estimation data are from 31 countries
and the results are validated for 34 countries The countries are mainly from Europe but three
non-European countries are included (China Colombia and the United StatesUS) The statuses
used in the classification are mainly bankruptcyactive but receivership firms are also considered
to have failed In the Chinese data ST (special treatment) and delisted firms are also separately
analyzed as failed firms
The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very
satisfactorily in an international context The effects of the four financial ratios on performance
are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small
contribution in re-estimation The original model performs very well in several countries such as
Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only
marginally improved the classification performance thus weakly supporting the obsolescence
hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust
across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion
holds for the re-estimation of the model using LRA because the performance results are very
similar with to those of MDA (H2) The use of additional variables in the model generally
improvesd the classification accuracy of the original model but the results for countries are
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
43
dependent on the distribution of failed and non-failed firms When the coefficients are estimated
for all data the effects on performance in a country depend on how the distributions in that
country correspond to the distributions in all data For all sets of additional variables the
performance is generally improved but the improvement is not strong and the effects vary
country by country Thus the evidence gives weak support to for the effects of all additional
variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the
variations in the effects between countries are also stronger The effects of age (H5) industry
(H6) and country (H7) are marginal When all additional variables are included in the same model
the performance generally significantly increases but at the same time the variations among
countries become stronger
In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an
international context It is however possible to extract a more efficient country model for most
European countries and for non-European countries using the four original variables
accompanied with by a set of additional background variables Considering practical applications
it is obvious that while a general international model works reasonably well for most countries
the classification accuracy may be somewhat improved with country-specific estimation (a
conclusion similar to Xu and Zhang 2009) In a country model the information provided even by
simple additional variables may help boost the classification accuracy to a much higher level
In finance and accounting research failure prediction models may be utilized as risk measures in
many different contexts Where failure prediction modeling is not the primary focus it would be
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
44
time-consuming uneconomical and superfluous to first estimate a failure prediction model (or
models) and only then proceed to study the phenomenon of interest In such instances a well-
tested general model that works reliably and consistently across different countries is highly
desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-
estimated version containing the four Altman (1983) study variables with coefficients re-
estimated using a large data set work consistently well internationally and are easy to implement
and interpret Thus this kind of accounting-based model may can be used by all interested
parties especially internationally active banks or other financial institutions not only for failure
or distress prediction but also for other managerial purposes such as provisioning or and
economic capital calculation Internationally active banks need to develop a universal tool that
can be applied in all subsidiaries and branches to control risk across the whole banking group
Further research should focus on other modifications and extensions than those presented in our
paper such as using alternative modeling techniques (eg panel data analysis) the
introducingction of new variables (eg macroeconomic data) and testing its usefulness on with
data from other countries (eg emerging markets)
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
45
References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via
Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157
Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based
Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551
Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo
Journal of Finance 23 (1968) pp 589ndash609
Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing
with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)
Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy
Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p
253
Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo
Journal of Banking and Finance 21 (1997) pp 1721ndash1742
Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley
2006)
Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic
Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38
(2006) pp 63ndash93
Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction
Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash
442
Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
46
Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)
pp 71ndash111
Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review
43 (1968) pp 113ndash122
Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo
Review of Financial Studies 21 (2008) pp 1339-1369
Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939
Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8
(2004) pp 537ndash569
Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo
Journal of Banking and Finance 33 (2009) pp 719-730
Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-
1147
Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and
Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash
513
Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction
Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93
Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction
Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61
Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9
(2004) pp 5ndash34
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
47
Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice
Hall International 1989)
Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models
in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202
Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for
Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash
173
Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of
Financial Markets 16 (2013) pp 439ndash476
Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International
Conference on Computational Science 3994 (2006) pp 364ndash371
Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of
Accounting Research 18 (1980) pp 109-131
Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using
a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76
Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start
Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International
Insolvency Institute 2002)
Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61
(2008) pp 995ndash1003
Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of
Financial Stability 3 (2007) pp 85ndash131
Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
48
Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data
in the OECD No 20101 (OECD Publishing 2010)
Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of
Business 74 (2001) pp 101-124
Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22
(2007) pp 376-391
Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and
Finance 8 (1984) pp 199-227
Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed
Companies Using Accounting Market and Macroeconomic Variablesrsquo International
Review of Financial Analysis 30 (2013) pp 394-419
Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent
Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28
Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-
868
Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo
Journal of Business and Management 16 (2010) pp 75-88
Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of
Contemporary Accounting and Economics 6 (2010) pp 34ndash45
Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of
Accounting Studies 14 (2009) pp 534ndash558
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
49
Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating
for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236
Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress
Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
50
Tables
Table 1 Number of observations by country
Country Estimation data Test data
Non-failed Failed Non-failed Failed
Austria (AT) 7430 55 7526 44
Belgium (BE) 179979 2994 179818 2944
Bosnia (BA) 29391 35 29139 32
Bulgaria (BG) 50041 48 42351 44
China (CN) 39315 198
China delisted dataset DL 16291 29
China ST data 846 16 1020 16
Colombia (CO) 8366 139 6982 125
Greece (GR) 51763 28
Croatia (HR) 59541 249 58478 275
Czech Republic (CZ) 92835 556 92562 564
Denmark (DK) 167934 1334 168538 1398
Estonia (EE) 34313 234 34196 242
Finland (FI) 90878 481 91227 459
France (FR) 160749 6124 161653 6318
Germany (DE) 98814 910 99496 921
Hungary (HU) 19421 303 20155 313
Iceland (IS) 17399 248 17624 243
Ireland (IE) 6665 121 6406 139
Italy (IT) 167113 8101 166258 8124
Latvia (LV) 8064 433 8241 477
Lithuania (LT) 10000 56
Netherlands (NL) 20885 154 15854 147
Norway (NO) 172467 1294 170985 1206
Poland (PL) 87200 291 86233 264
Portugal (PT) 180114 3390 178646 3422
Romania (RO) 161992 97 164259 93
Russian Federation (RU) 116903 2534 115711 2481
Serbia (RS) 100100 68
Slovakia (SK) 7856 120 7788 124
Slovenia (SI) 14419 59 14081 41
Spain (ES) 156746 3036 158122 2991
Sweden (SE) 169810 2256 169999 2314
Ukraine (UA) 133342 1787 133980 1765
United Kingdom (GB) 171493 760 170930 716
UK (GB) liquidation dataset 342423
4990
United States (US) 9557 56 9929 53
TIn total 2602563 38215 3148079 43664
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
51
Table 2 Descriptive statistics (all data)
WCTA RETA EBITTA BVETD
Statistic Non-failed Failed Non-failed Failed
Non-failed Failed
Non-failed Failed
Median 0152 -0059 0189 -0024 0041 -0020 0451 0025
Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703
Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712
Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215
Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240
Maximum 0956 0956 0958 0958 0785 0785 68606 68606
Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
52
Table 3 The coefficients of the different models estimated for all data
Coefficients for different statistical models
Variable Zrsquo-Score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant 325 -0042 0035 0207 -
13466 0007 0048 0049 -
13302
WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459
RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160
EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682
BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013
Year dummies
Year 2008 -0055 -0034
Year 2009 -0179 -0150
Year 2010 -0666 -0631
Size variables
Total assets (log) 1830 1837
Total assets squared (log) -0061 -0061
Age dummies
Less than 6 years 0135 0186
15 years or more -0058 -0099
Country risk
SP cCountry rating rank -0003 -0014
Industry dummies
Restaurants and hotels -0653 -0628
Construction 0445 0365
Whole sale and retailing -0112 -0157
Agriculture -0180 -0176
Manufacturing 0139 0095
Energy and water production -0454 -0472
Information technology -0913 -0915
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
53
Significance
Coefficients are statistically significant at 00001 Models
Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients
Model 1 = The MDA model
Model 2 = The LR model
Model 3 = The LR model estimated for all data with year dummies
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
54
Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++
Countries with estimation data
Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819
Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758
Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784
Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632
China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968
Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757
Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801
Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838
Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796
Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890
Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853
France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799
Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688
Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696
Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716
Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712
Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849
Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724
Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787
Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
55
Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904
Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785
Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703
Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814
Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808
Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721
Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774
Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800
Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722
United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729
United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723
Countries only in test data
European
Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718
Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778
Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738
United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635
Non-European
China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556
China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The original Altman (1983) Zrsquorsquo-Score Model
Model 2 = The MDA model estimated for all data
Model 3 = The LR model estimated for all data with year dummies
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
56
Model 4 = The LR model estimated for all data with size variables
Model 5 = The LR model estimated for all data with age category dummies
Model 6 = The LR model estimated for all data with industry dummies
Model 7 = The LR model estimated for all data with country risk rankings
Model 8 = The LR model estimated for all data with all variables
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
57
Table 5 Differences of medians between non-failed and failed groups
Country WCTA RETA EBITTA BVETD AUC of
Z-Score
Austria (AT) 0448 0405 0126 0487 0788
Belgium (BE) 0223 0264 0077 0431 0760
Bosnia (BA) 0106 0249 0088 0580 0805
Bulgaria (BG) 0114 0291 0094 0436 0630
China (CN) 0068 0032 0016 0280 0570
China delisted data DL 0089 -0061 -0032 0083 0546
China ST data 0298 0293 0139 0468 0911
Colombia (CO) 0226 0235 0099 0705 0724
Croatia (HR) 0275 0335 0044 0274 0812
Czech Republic (CZ) 0404 0397 0069 0667 0813
Denmark (DK) 0267 0356 0046 0733 0798
Estonia (EE) 0331 0388 0113 1033 0827
Finland (FI) 0402 0606 0207 0907 0864
France (FR) 0140 0213 0065 0424 0723
Germany (DE) 0131 0136 0032 0262 0658
Greece (GR) 0171 0262 0049 0349 0670
Hungary (HU) 0175 0224 0052 0587 0746
Iceland (IS) 0246 0261 0051 0337 0674
Ireland (IE) 0181 0266 0039 0560 0672
Italy (IT) 0277 0164 0073 0207 0799
Latvia (LV) 0117 0120 0042 0254 0691
Lithuania (LT) 0246 0218 0051 0569 0782
Netherlands (NL) 0204 0253 0077 0432 0754
Norway (NO) 0157 0219 0115 0329 0694
Poland (PL) 1340 0920 0124 1351 0904
Portugal (PT) 0215 0200 0052 0318 0724
Romania (RO) 0222 0271 0056 0298 0740
Russian Federation (RU) 0350 0242 0069 0245 0802
Serbia (RS) 0120 0148 0045 0389 0713
Slovakia (SK) 0256 0184 0061 0431 0774
Slovenia (SI) 0111 0172 0035 0326 0725
Spain (ES) 0143 0143 0076 0285 0713
Sweden (SE) 0255 0346 0099 0663 0801
Ukraine (UA) 0204 0200 0031 0449 0714
United Kingdom (GB) 0211 0245 0033 0472 0719
UK lLiquidation dataset 0156 0234 0031 0447 0621
United States (US) 0195 0378 0248 0722 0701
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
58
Average of column items 0245 0265 0073 0481 0740
Correlation with Z-Score AUC 0611 0681 0516 0574 1000
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal Assets
BVETD = Book Value of EquityTotal Liabilities
SALTA = SalesTotal Assets
AUC = Area under the ROC curve
Z-Score = Altman (1983) Z-Score in the test data
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
61
61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
59
Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)
Test data AUC for different models
Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764
Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834
Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853
Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707
China ST companies 0985 0984 0982 0980 0986 0986 0986 0989
Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+
Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863
Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842
Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843
Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899
Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894
France (FR) 0739 0773 0799 0805 0826 0800 0810 0845
Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762
Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820
Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+
Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724
Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868
Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771
Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807
Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814
Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913
Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797
Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774
Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862
Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
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Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
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United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
60
Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698
Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858
Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901
Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758
United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785
United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+
Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823
Significance
AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +
AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -
Models
Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables
Model 1 = The MDA model estimated for country data
Model 2 = The LR model estimated for country data
Model 3 = The LR model estimated for country data with year dummies
Model 4 = The LR model estimated for country data with size variables
Model 5 = The LR model estimated for country data with age category dummies
Model 6 = The LR model estimated for country data with industry dummies
Model 7 = The LR model estimated for country data with all variables
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61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
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United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
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61
Appendix Medians of the Altman (1983) model (Z) variables by status and country
Non-failed Failed
Non-failed Failed
Non-failed Failed Non-failed Failed
Country WCTA
RETA
EBITTA
BVETD
Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022
Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029
Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000
Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068
China (CN) 0105 0037 0064 0032 0064 0048 0870 0590
China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840
China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308
Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235
Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000
Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076
Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025
Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136
Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163
France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077
Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073
Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111
Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130
Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141
Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168
Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029
Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088
Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117
Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079
Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069
Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501
Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025
Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048
Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051
Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000
Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065
Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108
Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051
Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086
Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007
United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107
UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132
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62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages
62
62
United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078
Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075
Legend
WCTA = Working CapitalTotal Assets
RETA = Retained EarningsTotal Assets
EBITTA = EBITTotal assets
BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the
calculation of the averages