the study on corporate financial distress forecasting ... · financial distress forecasting model...

The Study on Corporate Financial Distress

Forecasting Model: An Application of Dynamic

Distress Threshold Value 1Kuo-ChingChiou,

2*Jian-Fa Li,

3Ming-Min Lo and

4Guo-Wei Wu

1Chaoyang University of Technology.

2Chaoyang University of Technology. [email protected]



Abstract

In this study, we have employed factors such as Financial Ratios,

Corporate Governance variables and Intellectual Capital to construct a

financial distress forecasting model by Logistic Regression. The data

consists of 54 electronics companies listed in Taiwan Stock Exchange (TSE)

and Over the Counter (OTC) during year 2012 to 2015 to be our observation

samples. 18 companies out of the 54 has been financially distressed in 2015.

Instead of the traditional one-half criteria (the probability of success is 0.5),

we adopted the criteria of the Dynamic Distress Threshold Value (0.01~0.99)

to improve the maximizing prediction accuracy of model. In empirical

results, we have found out the maximizing prediction accuracy (MPA) will

be higher with Dynamic Distress Threshold Value than the Tradition

(threshold value =0.5) model. This study has also found that the prediction

accuracy would be even higher after including Intellectual Capital into our

prediction model.

Key Words:Financial distress, intellectual capital, dynamic distress

threshold value.

International Journal of Pure and Applied MathematicsVolume 119 No. 18 2018, 853-863ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

853

1. Introduction

The recent financial markets have developed improvement, but there were many

hidden crisis. Such as the 2000 Internet bubbles, the 2008 financial tsunami, the

2011 European debt crisis. Many companies encountered a financial distress. It

often results in substantial loss of investors. Therefore, we would build a

corporate financial distress model. In Taiwan, the electronics industry

contributes about 60% of the total exports. The electronics industry has a great

influence on the economy in Taiwan. Intellectual Capital was to be valuable

factor of electronics industry since that professional know-how and expertise

employee contributes greatly to the competitiveness of electronic companies.

Some previous literature only adopted Financial Ratio and Corporate

Governance to be important factors of financial distress warning system. This

study will further more to include Intellectual Capital to be another important

factor. Intellectual Capital consists three parts - human resource capital,

structural capital and customer capital. These three parts shall cover four

different variables which are human resource capital -sales per employee and

equipment per employee, structural capital - operation expense ratio and

customer capital - sales growth ratio.The purposes of this study, one is to

improve the prediction accuracy of the model by adding the Intellectual Capital.

Second,we attempt to maximize the prediction accuracy of the financial distress

model through introducing the criteria of the Dynamic Distress Threshold Value.

2. Literature Review

The definition of a financial distress firm is the company with bankruptcy, bond

default, an overdrawn bank account, and nonpayment of a preferred stock

dividend, reduction in the principal and interest or change in the full delivery of

shares (Beaver, 19661; Deakin, 1972

2; Lee and Yeh

3, 2004; Ohlson

4, 1980).

1Beaver, W.H., Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4,

71-111; 1966 2Deakin, E.B., A Discriminate Analysis of Predictor Business Failure. Journal of Accounting

Research, 10(1), 167-179; 1972 3Lee, T.S., Yeh, Y.H., Corporate Governance and Financial Distress: Evidence from Taiwan.

Corporate Governance, 12(3), 378-388; 2004 4Ohlson, J.A., Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of

Accounting Research, 18(1), 109-131; 1980

International Journal of Pure and Applied Mathematics Special Issue

854

Stewart5 (1997) proposed that Intellectual Capital is the sum of all the

knowledge and ability of each employee and team to bring the company a

competitive advantage. Masoulas6 (1998) pointed out that Intellectual Capital is

the sum of all the intangible assets, so that the products provided by the

enterprise have a more added value. Chen7 (2001) empirically showed that

Intellectual Capital, knowledge, experience, ability, wisdom and innovation

have relationship.

Beaver (1966) employed the single variable model to construct the financial

early-warning model, the paper obtained 79 financial distress firms during the

periods from 1954 to 1966, the result found "cash flow / total liabilities" to be the

best forecast. Using financial ratio to construct the logistic regression model,

Ohlson (1980)empirically found business size, financial structure, business

performance and liquidity are useful variables. Shleifer and Vishny8 (1997)

confirmed that when the major controlling shareholders have the ability to

effectively dominate the company, their decision would affect the interests of

other smaller shareholders. Claessens et al9. (2000) found that many controlling

shareholders of the listed companies use pyramid structure, cross-shareholding

and other methods to increase their voting rights of the company to achieve the

purpose of controlling the company. Lin et a10

l. (2010) have shown that with the

chosen corporate governance, the prediction model provided higher prediction

accuracy. Liang et al11

. (2016) and their study results shown that the Financial

ratios categories of solvency and profitability and the corporate governance

categories of board structure and ownership structure are the most important

5Stewart, T.A.. Intellectual Capital: The New Wealth of Organizations. New York: Bantam

Doubleday Dell Publishing Group, Inc; 1997 6Masoulas, V., Organizations Requirements Definition for Intellectual Capital Management.

International Journal of Technology Management, 16(2), 126-143; 1998 7Chen, M.C., The Effect of Information Technology Investment and Intellectual Capital on

Business Performance. Dissertation Thesis, National Central University; 2001 8Shleifer, A., Vishny, R.W., A Survey of Corporate Governance. Journal of Financial, 52(2),

737-783; 1997 9Claessens, S., Djankov, S., Lang, L.H.P., The Separation of Ownership and Control in East Asian

Corporations. Journal of Financial Economics, 58(1), 81-112; 2000 10

Lin, F., Liang, D., Chu, W.S. The Role of Non-Financial Features Related to Corporate

Governance in Business Crisis Prediction. Journal of Marine Science and Technology, 18(4),

504-513; 2010 11

Liang, D., Lu, C.C., Shih, G.A., Financial Ratios and Corporate Governance Indicators in

Bankruptcy Prediction: A Comprehensive Study. European Journal of Operational Research,

252(2), 561-572; 2016


855

features in bankruptcy prediction. Sullivan12

(2000) found that the distinction

between human capital and Intellectual Capital is particularly important for the

owner of a knowledge-based company. Lee and Tang13

(2004) found that the

Intellectual Capital can improve the financial distress model forecasting ability.

Liang et al14

. (2005) found that the efficiency of model could be improved by

using the criteria of dynamic distress threshold value.

3. Empirical Methodology

This paper has collected samples from the period of 2012 to 2015 in Taiwan

Stock Exchange (TSE) and Over the Counter (OTC) listed electronic industries

in Taiwan. Based on the logistic regression model, this paper uses the data of

2012, 2013 and 2014 for observation periodto determine whether a company is a

financial distress company or a financial normal company in 2015. The following

definitions of financial distress company are given by Taiwan Economic

Journal(TEJ) to distinguish financial crisis companies, bounced cheque,

bankruptcy, CPA comments, restructuring, relief-financial distress, take-over,

full-cash delivery stock, finance tight or net negative. In 2015, there are 18

financial distress companies provided by TEJ and otherwise, we have taken

another 36 normal companies with similar size to for comparison.

In our study, the logistic regression model was established. If the company is

financial distress then Y is set to “1” and otherwise, Y is set to “0”. The

independent variables consist of Financial Ratios (including current ratio (CR),

debt ratio (DR),average collection turnover ratio (ACTR), fixed asset turnover

ratio (FATR), net profit margin (NPM), and cash reinvestment ratio (CRR)),

Corporate Governance factors (Stocks Holding ratio by directors and

supervisors (SHRDS), Intellectual Capital factors (including sales per

employee(SPE),equipment per employee (EPE), operating expense ratio (OER),

sales growth rate (SGR).

12

Sullivan, P.H., Harrison, S., Profiting from Intellectual Capital: Learning from Leading

Companies. Journal of Intellectual Capital, 1(1), 33-46; 2000 13

Lee, T.S., Tang, H.C., Integrating Financial Ratios and Intellectual Capital Indices in

Constructing Corporate Distress Diagnostic Systems -Application of MARS and Neural Network.

Master Thesis, Fu Jen Catholic University; 2004 14

Liang, L.Y., Lu, Y.C., Lee, C.J., Dynamic Distress Threshold, Corporate Governance and

Construction of the Predicting Model of Financial-Distress Probability in Taiwan. Master Thesis,

Ming Chuan University; 2005


856

This paper has selected 11 variables from Financial Ratio information, Corporate

Governance, and Intellectual Capital to examine the significance of financial

crisis companies and financial normal companies by using t testfrom each

observation year.

Most of the data presented are significant in Table 1, which indicating it is

feasible to identify the company as financial distress company or financial

normal company.

Table 1The Differences Analysis in Financial Structures between Financial Crisis

Companies and Financial Normal Companies

Note: FNC is financially normal company, FCC stands for financial crisis

company, “*”, “** “, “*** “denote that these are significantly different from 0

under the significance level of 10%, 5%, 1%, respectively.

The logistic regression model is stated as follows.

lnp

1−p= α + β

iXi

ni=1 (1)

Where p = p (Y = 1), 1-p = (Y = 0),Y is 1 for the financial distress company, Y

is 0 for the financial normal company, Xi represents independent variable, n is

the number of independent variables.

An optimal model could accurately forecast a company with a financial distress.

According to a logistic regression model, usually using a fifty-fifty rule (0.5) as

the threshold value, that any company with p value larger than 0.5 would be

identified as a possible financial crisis firm. Otherwise it would be considered as

a financial normal firm. This study of the Dynamic Distress Threshold Value

Mean S.d p value Mean S.d p value Mean S.d p value

FNC 208.556 127.408 201.774 126.269 231.319 189.671

FCC 125.082 206.956 97.505 116.905 105.653 113.419

FNC 1126.844 2928.928 798.413 1830.346 930.261 1860.798

FCC 398.667 639.763 232.236 175.966 337.953 618.288

FNC 31.193 114.548 73.018 357.275 73.099 332.897

FCC 33.825 90.828 7.103 8.296 6.997 6.423

FNC 4.598 12.687 2.663 14.051 -1.896 19.249

FCC -44.639 84.452 -32.918 45.004 -23.727 39.487

FNC 33.573 67.752 22.753 53.672 21.7 50.016

FCC -42.543 153.938 -13.186 101.602 -0.786 81.985

FNC 2.281 3.68 2.433 2.687 1.628 2.281

FCC 0.993 1.771 0.481 2.933 0.249 2.284

FNC 0.993 1.771 1.008 1.701 0.863 1.125

FCC 1.174 2.876 1.289 2.822 1.553 3.002

FNC 560.583 2095.680 370.417 1121.511 -42.722 1023.179

FCC 18.167 2089.234 -401.389 759.405 -630.833 1393.632

FNC 22.618 20.700 22.466 20.407 22.109 19.386

FCC 52.761 88.434 45.167 55.788 32.544 35.954

FNC 7.181 9.791 7.206 10.137 6.458 8.394

FCC 7.102 10.237 8.233 14.461 6.531 12.214

FNC 6.049 22.258 4.086 21.723 -4.508 20.017

FCC -4.068 68.480 -20.503 26.419 -8.941 38.495

Intellectual Capital

0.170

0.980

0.578

0.776 0.65 0.225

0.374

0.056*

0.978

0.421

0.011**

0.034**

0.763

0.001***SGR

0.217

0.205 0.018** 0.041**

0.084*

0.933 0.44 0.406

0.001*** 0*** 0.008***

0.005***

0.304 0.198

0.014** 0.094*

Financial Ratios

Corporate Governance

ACTR

FATR

2014 2013 2012

CR

DR

Variables Companies

0.196

NPM

CRR

SHRDS

SPE

EPE

OER

0.073* 0.013**

classification


857

constructs 99 threshold values from 0.01 to 0.99 in order to maximize the

prediction accuracy of the model. In logistic regression model, predictive

accuracy can be done using a Classification Table (Chen et al15

. 2005). In Table

2, Samples A and D are the correct number of the observations, B and C are the

wrong number of the observations. The Overall Correct Percentage is A

N+

D

N ×

100% , N = A + B + C + D.

Table 2: Classification Table

Item Predicted Samples Correct Percentage

Observed 1 (financial crisis) 0 (financial normal)

1(financial crisis) A B A/(A+B)

0(financial normal) C D D/(C+D)

Overall Correct Percentage (A+D)/ (A+B+C+D)

The maximizing prediction accuracy of model is as follow.

Maximizing Prediction Accuracy (p) =

𝐼𝑗 𝑒

α+ βi X ini=1

1+𝑒α+ βi X i

ni=1

> p 𝑁1𝑖=1

N +

𝐼𝑗 𝑒

α+ βj X jnj=1

1+𝑒α+ βj X j

nj=1

< p 𝑁0𝑗=1

N (2)

Where p is Dynamic Distress Threshold Value, I j is indicator function.

If𝑒α+ βi X i

ni=1

1+𝑒α+ βi X ini=1

> p, it is financial distress company. If𝑒α+ βi X i

ni=1

1+𝑒α+ βi X ini=1

< p,it is

financial normal company. xi is the explanatory variable of the financial distress

company i, xj is the explanatory variable of the financial normal company j, N1

is the total number of financial distress companies, N0 is the total number of

financial normal companies, N is N1 plus N0.

4. Empirical Results

This paper has constructed two logistic regression models. Model I selected

financial ratio and corporate governance with a total of 7 independent variables.

Model II selected financial ratio corporate governance and Intellectual Capital

factor with a total of 11 independent variables. The purpose of this research is to

determine whether or not taking in Intellectual Capital factors would be able to

improve our financial crisis early warning model. The empirical results from

15

Chen, J.C., Cheng, B.L., Chen, S.F., Liou, Z.J. Multivariate Analysis. Taiwan: Five South Book

Publishing Co., Ltd.; 2005


858

Model I and Model II (Fig 1~6), we could find out the maximizing prediction

accuracy (MPA) would be higher in the model with Dynamic Distress

Threshold Value than those on the basis of the Tradition Threshold Value (0.5).

This study has also found that the prediction accuracy would be even higher

after including Intellectual Capital into our prediction model.

Model I: Financial Information + Corporate Governance

Fig. 1: The Year before the Crisis Fig. 2: Two Year before the Crisis

Fig. 3: Three Year before the Crisis

Model II: Financial Information+Corporate Governance+Intellectual Capital

Fig. 4: The Year before the Crisis Fig. 5: Two Year before the Crisis

0.8703 0.8333

00.20.40.60.8

1

0.0

1

0.1

2

0.2

3

0.3

4

0.4

5

0.5

6

0.6

7

0.7

8

0.8

9

Co

rrect rate

threshold value(p)

p=0.5

p=036~0.43

0.8703 0.8333

00.20.40.60.8

1

0.0

1

0.0

9

0.1

7

0.2

5

0.3

3

0.4

1

0.4

9

0.5

7

0.6

5

0.7

3

0.8

1

0.8

9

0.9

7

Co

rrect rate

threshold value(p)

p=0.5p=0.4~0.42

0.7777 0.7777

0

0.2

0.4

0.6

0.8

1

0.0

1

0.0

9

0.1

7

0.2

5

0.3

3

0.4

1

0.4

9

0.5

7

0.6

5

0.7

3

0.8

1

0.8

9

0.9

7C

orrect rate

threshold value(p)

p=0.5

p=0.51~0.52，

0.57~0.6

0.9259

0.9074

0

0.2

0.4

0.6

0.8

1

0.0

1

0.1

6

0.3

1

0.4

6

0.6

1

0.7

6

0.9

1

Co

rrect rate

threshold value(p)

p=0.5p=0.4

0.8888 0.8518

0

0.2

0.4

0.6

0.8

1

0.0

1

0.1

1

0.2

1

0.3

1

0.4

1

0.5

1

0.6

1

0.7

1

0.8

1

0.9

1

Co

rrect rate

threshold value(p)

p=0.5p=0.33~0.35、

0.39~0.4、0.42


859

Fig. 6: Three Year before the Crisis

5. Concluding Remarks

In this study, we have taken 54 electronics companies listed in TSE and OTC in

Taiwan during year 2012 to 2015 for samples, 18 companies out of the 54 has

been financial distress in 2015. We have selected 11 variables from Financial

Ratio information, Corporate Governance information, and Intellectual Capital

information to construct the Corporate Financial Distress Forecasting Model.

We used Logistic Regression Model and the data of 2012, 2013 and 2014 to

determine whether a company is possibly financial distress or financial normal in

2015.

In Table 3, the Maximizing Prediction Accuracy (MPA) for model I and model

II would be higher in Dynamic Distress Threshold Value than those by

employing the criteria of one half rule 0.5. We could find out that Dynamic

Threshold Valueis better than Tradition Threshold Value.

Finally, as shown in Table 4, after we join the Intellectual Capital factors, the

Maximizing Prediction Accuracy (MPA) will be higher in Model II than Model

I. The improvement of prediction accuracy will be increased 1.85% to 11.11%.

The Intellectual Capital could improve the prediction accuracy of the financial

distress model.

0.8888 0.7777

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.0

1

0.0

7

0.1

3

0.1

9

0.2

5

0.3

1

0.3

7

0.4

3

0.4

9

0.5

5

0.6

1

0.6

7

0.7

3

0.7

9

0.8

5

0.9

1

0.9

7C

orrect rate

threshold value(p)

p=0.5p=0.42


860

Table 3: Comparison of Tradition Distress Threshold Value and Dynamic Distress

Threshold Value

Year 2014 2013 2012

Method Model I Model II Model I Model II Model I Model II

Tradition Threshold Value 83.33% 90.74% 83.33% 85.18% 77.77% 77.77%

Dynamic Distress Threshold Value 87.03% 92.59% 87.03% 88.88% 77.77% 88.88%

Table 4: The Prediction Accuracy of Models and the Best Threshold Value

Year Results 2014 2013 2012

Model I Prediction Accuracy

(best threshold value) 87.04%

(p=0.36~0.43) 87.04%

(p=0.4~0.42) 77.78%

(p=0.5~0.52, 0.57~0.6)

Model II Prediction Accuracy

(best threshold value)

92.59%

(p=0.4)

88.89%

(p=0.33~0.35, 0.39~0.4, 0.42)

88.89%

(p=0.42)

References

[1] Beaver, W.H., Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111; 1966

[2] Chen, M.C.,The Effect of Information Technology Investment and Intellectual Capital on Business Performance.Dissertation Thesis, National Central University; 2001

[3] Chen, J.C., Cheng, B.L., Chen, S.F., Liou, Z.J. Multivariate

Analysis. Taiwan: Five South Book Publishing Co., Ltd.; 2005

[4] Claessens, S., Djankov, S.,Lang, L.H.P.,The Separation of Ownership and Control in East Asian Corporations. Journal of Financial Economics, 58(1), 81-112; 2000

[5] Deakin, E.B., A Discriminate Analysis of Predictor Business Failure. Journal of Accounting Research, 10(1), 167-179;1972

[6] Lee, T.S.,Yeh, Y.H., Corporate Governance and Financial Distress: Evidence from Taiwan. Corporate Governance, 12(3), 378-388;2004

[7] Lee, T.S., Tang, H.C., Integrating Financial Ratios and Intellectual Capital Indices in Constructing Corporate Distress Diagnostic Systems -Application of MARS and Neural Network. Master Thesis, Fu Jen Catholic University; 2004

[8] Liang, D., Lu, C.C., Shih, G.A., Financial Ratios and Corporate Governance Indicators in Bankruptcy Prediction: A Comprehensive Study. European Journal of Operational Research, 252(2), 561-572;2016

[9] Liang, L.Y., Lu, Y.C., Lee, C.J.,Dynamic Distress Threshold,

Corporate Governance and Construction of the Predicting Model


861

of Financial-Distress Probability in Taiwan. Master Thesis, Ming

Chuan University;2005

[10] Lin, F., Liang, D., Chu, W.S. The Role of Non-Financial Features Related to Corporate Governance in Business Crisis Prediction. Journal of Marine Science and Technology, 18(4), 504-513;2010

[11] Masoulas, V., Organizations Requirements Definition for Intellectual Capital Management. International Journal of Technology Management, 16(2), 126-143;1998

[12] Ohlson, J.A., Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131; 1980

[13] Shleifer, A., Vishny, R.W., A Survey of Corporate Governance. Journal of Financial, 52(2), 737-783;1997

[14] Stewart, T.A.. Intellectual Capital: The New Wealth of Organizations. New York: Bantam Doubleday Dell Publishing Group, Inc;1997

[15] Sullivan, P.H., Harrison, S., Profiting from Intellectual Capital: Learning from Leading Companies. Journal of Intellectual Capital, 1(1), 33-46;2000


862

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