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TRANSCRIPT
Constructing a Powerful Profitability Factor:
International Evidence
Matthias X. Hanauer and Daniel Huber ∗
First draft: January 31, 2017
This draft: September 25, 2018
Recent findings for the U.S. stock market indicate that cash-based profitability
measures (i.e., profitability measures that exclude accounting accruals) outper-
form measures of profitability that include accruals. We demonstrate that this
result also holds for international markets. In a comparison of different profitabil-
ity definitions, we find that a factor based on cash-based gross profitability (gross
profitability adjusted for accounting accruals) subsumes other popular profitabil-
ity factors based on time-series, factor-spanning, and cross-sectional asset pricing
tests. We therefore propose that a profitability factor based on cash-based gross
profitability should be used in international factor models.
Keywords: Factor models, Profitability, International Markets, Anomalies
JEL Classifications: G11, G12, G15∗Department of Financial Management and Capital Markets, Technische Universitat Munchen (TUM),
Arcisstr. 21, 80333 Munich, Germany.Email: [email protected], [email protected] appreciate helpful comments from David Blitz, John Doukas, Jurgen Ernstberger, Ralf Elsaß, RudigerFahlenbrach, Stefano Giglio, Heiko Jacobs, Christoph Kaserer, Laurens Swinkels, Milan Vidojevic, andMichael Weber, as well as conference and seminar participants at the EFMA Doctoral Seminar 2017,the Robeco Research Seminar, the TUM Research Seminar 2017, and the Munich Finance Day 2018.Matthias is also employed by Robeco, an asset management firm that among other strategies offers activefactor investing strategies. The views expressed in this paper are solely those of the authors and do notnecessarily express the views of TUM or Robeco. Earlier versions of this paper were circulated under thetitle “Dissecting Profitability: Evidence from International Stock Markets”. Any remaining errors are ourown.
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1 Introduction
This paper investigates the relation between average stock returns and various prominent
profitability measures in international stock markets. We document that average returns
increase with profitability in countries outside of the U.S., where recent findings indicate
that cash-based profitability measures (i.e., profitability measures that exclude accounting
accruals) outperform measures of profitability that include accruals. Furthermore, we find
that internationally, a newly introduced profitability factor, based on cash-based gross prof-
itability, has the highest marginal power in a range of time-series, factor-spanning, and
cross-sectional asset pricing tests.
One of the economic motivations for including a profitability factor in an asset pricing
model stems from the rewritten dividend discount model, as stated by Fama and French
(2015):1
Mt
Bt
=∑∞τ=1 E(Yt+τ − dBt+τ )/(1 + r)τ
Bt
. (1)
In the above equation, for a given firm, Mt refers to the total market value at time t, Bt
to the book equity at time t, Yt+τ to the total equity earnings for period t + τ , and r to
the internal rate of return on expected dividends (i.e., approximately the long-term average
expected stock return). The equation states that if current valuation, Mt
Bt, and expected
future investments, dBt+τBt
, are fixed, a higher expected future profitability, Yt+τBt
, implies a
higher expected return, r.
Using current profitability as a proxy for expected future profitability, various researchers
(e.g., Novy-Marx 2013, Ball et al. 2015 and Fama and French 2015) confirm this relationship
empirically for the U.S. stock market, showing that higher stock returns are associated with
higher profitability ratios. This so-called profitability effect is one of the most prominent
asset pricing phenomena documented in the finance literature. However, in contrast to other
1Please note that the q-factor model of Hou, Xue, and Zhang (2015) also contains a profitability factor, butthe economic rationale for the inclusion is different.
2
popular effects—like value, size, and momentum—there is no generally accepted definition
of profitability in the literature.
For instance, Haugen and Baker (1996) show that profitability measured as net income to
book value of equity (hereinafter return on equity or ROE) is positively correlated with future
returns.2 Novy-Marx (2013) shows that gross profitability, defined as gross profits deflated
by the book value of total assets, predicts the cross-section of expected stock returns and
has a higher predictive power than ROE. Ball et al. (2015) confirm these results and point
out that the difference in the marginal predictive power is mainly due to the deflator in the
measures. Furthermore, they develop a new measure—operating profitability—and claim
that it reflects more closely the actual expenses incurred to generate revenue for a given
period. Operating profitability is defined as gross profit less selling, general and administra-
tive expenses (SG&A), excluding research and development (R&D) expenditures, deflated
by the book value of total assets. In a comparison with gross profitability, the authors find
that operating profitability is better suited for predicting the cross-section of expected stock
returns. Another definition of operating profitability by Fama and French (2015) was intro-
duced within the framework of the Fama-French five-factor model: gross profits less SG&A,
less interest expenses, deflated by the book value of equity.
The four measures of profitability introduced so far have something in common: They
include accruals, which are accounting adjustments of operating cash flows used to more pre-
cisely measure periodic firm performance (Ball et al. 2016). Accruals, however, are negatively
correlated with the cross-section of expected returns (Sloan 1996); this phenomenon, which is
also referred to as the accrual anomaly, was confirmed by numerous studies (e.g., Fama and
French 2008, Hirshleifer, Hou, and Teoh 2009, and Polk and Sapienza 2009). Ball et al. (2016)
account for this issue by correcting any accounting accruals adjustments that were made to
operating profitability;3 the result is cash-based operating profitability. They find that this
2Also Hou, Xue, and Zhang (2015) use a factor based on ROE in their q-factor model. However, in order tocalculate this factor, they use more timely information on net income taken from the most recent publicquarterly earnings announcement.
3These adjustments are changes in accounts receivable, inventory, prepaid expenses, deferred revenue, ac-
3
measure dominates operating profitability in the explanation of the cross-section of expected
returns and it even subsumes the accrual anomaly. Furthermore, Fama and French (2018)
also acknowledge that cash-based operating profitability dominates operating profitability
as defined in Fama and French (2015). Consequently, Fama and French (2017) hint at the
possibility that future research might further refine the definition of the profitability factor
in asset pricing models, such as their five-factor model.
Barillas et al. (2017) compare various prominent asset pricing models by their maximum
squared Sharpe ratios and state that “the choice of profitability factor is a key”. More
specifically, they find that models that include a cash-based profitability factor are superior
to models with the more traditional, accruals-based profitability factors. However, all of
the aforementioned results are based on U.S. data only and evidence for a broader set of
profitability measures in international markets is currently rare.4 To investigate if these
findings can be extended to international markets or are a country-specific phenomenon,
we concentrate on international markets outside the U.S.5 The international evidence for
the profitability effect is of particular interest because U.S. studies that investigate periods
prior to the original sample periods (i.e., pre-sample studies) led to conflicting results. For
instance, Linnainmaa and Roberts (2018) find profitability returns that are statistically not
distinguishable from zero between 1926 and 1963, while Wahal (2018) reports a significant
profitability premium over the period from 1940 to 1963, controlling for value.
Drawing on the research findings presented so far, we investigate the following profitability
measures: (i) return on equity (ROE), (ii) gross profitability by Novy-Marx (2013), (iii)
operating profitability by Ball et al. (2015), operating profitability defined in an alternative
way (iv) by Fama and French (2015), and (v) cash-based operating profitability by Ball
counts payable, and accrued expenses.4In this context, Karolyi (2016) speaks of a large U.S. “home bias” in the empirical Finance literature. We
later discuss the papers by Cakici, Chatterjee, and Tang (2017) and Chen et al. (2018) that also compareprofitability measures outside the U.S.
5Examples for findings that do not hold internationally are the momentum echo and the post-publicationdecline in anomalies documented by Novy-Marx (2012) and McLean and Pontiff (2016), respectively, forthe U.S. In contrast, Goyal and Wahal (2015) and Jacobs and Muller (2018) find no robust evidence forthese effects in more than thirty non-U.S. countries.
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et al. (2016), with two major goals: Evaluating their return predictive power, both (1)
standalone; and (2) in the presence of other factors, including other profitability factors.
We collect monthly equity market data from Datastream and yearly accounting data from
Worldscope from 07/1989 to 06/2016 on firm level for 49 countries and apply common quality
filters that are proposed in the literature. Generally, we include all countries in our analysis
for the periods in which they are classified by MSCI as a developed or an emerging market.
We provide strong evidence that the profitability effect is globally prevalent. Interestingly,
we find that in contrast to the results of Ball et al. (2015), the step from (ii) gross profitability
to (iii) operating profitability (i.e., subtracting SG&A excluding R&D expenditures) results
in a lower forecasting power; whereas, correcting for accounting accruals leads to the same
positive effect as presented by Ball et al. (2016) for the U.S. Thus, in a new approach, we
abstain from the SG&A correction and propose a new profitability measure that adjusts
gross profitability for accounting accruals: (vi) cash-based gross profitability. By performing
a range of factor, portfolio-based, and cross-sectional asset pricing tests, we show that all
profitability definitions besides ROE are robustly priced outside the U.S. Even more striking,
cash-based gross profitability emerges as the strongest of all the profitability measures. We
perform several comparative tests on the profitability measures, which lead to the same
principal result—cash-based gross profitability can subsume the other profitability measures.
Finally, we also provide evidence that comparatively stronger profitability measures are better
suited for the explanation of future earnings growth, which hints at the possibility that future
earnings growth is an important driver of the profitability premium.
Our main goal is to determine the most suitable profitability factor from a global perspec-
tive (excluding the U.S.). However, for robustness reasons, we also perform an analysis on
the basis of regional and country levels. We find that all profitability factors exhibit positive
premiums across regions and that cash-based gross profitability is the dominant profitability
factor for developed and emerging markets as well as for Japan and Asia Pacific excluding
Japan (as part of developed markets). Only for Europe, does cash-based operating profitabil-
5
ity show a slightly better performance than cash-based gross profitability, which is second
in rank. In addition, we find that the cash-based gross profitability factor exhibits positive
average returns and spanning test alphas in the majority of developed and emerging market
countries.
The documented results have important implications for the definition of a powerful em-
pirical asset pricing model. Harvey, Liu, and Zhu (2016) catalog 316 published anomalies as
potential asset pricing factors in the cross-section of stock returns. Researchers have tried
to shrink this list of anomalies using factor models that only include a small number of
characteristic-based factors, e.g., Fama and French (2015) with the five-factor model and
Hou, Xue, and Zhang (2015) with the q-factor model. Both models notably include a prof-
itability factor. Although both papers motivate the inclusion by an economic rationale, the
factor definitions differ from each other, which implies that further empirical tests are re-
quired to contest or to strengthen the underlying rationales. In case of a parsimonious factor
model, however, it is crucial that the implemented factors exhibit high power, that is they (i)
independently display economically substantial and significantly abnormal returns; and (ii)
cannot be subsumed by the remaining factors of the model. Moreover, (iii) small alterations
in the factor definitions (variants) should not lead to abnormal returns relative to the default
factor definitions. Finally, a powerful factor model should also be better suited for reduc-
ing the number of remaining anomalies in the cross-section of stock returns. For instance,
Ball et al. (2016) demonstrate that a model that incorporates a cash-based profitability factor
subsumes the accrual anomaly. Taking these requirements into account, our findings indicate
that a profitability factor based on cash-based gross profitability is more powerful than the
other profitability factors analyzed. We follow that cash-based gross profitability is preferable
for factor models that focus on international markets.
The papers closest to ours are Cakici, Chatterjee, and Tang (2017) and Chen et al. (2018).
In both of these papers, the authors compare various profitability factors on international
markets; however, they analyze the factor returns only on a standalone basis and do not
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control for other predictors of realized stock returns. In contrast, we investigate whether the
profitability factors are distinct factors that can expand the efficient frontier constructed by
means of other well-known factors in the literature. Cakici, Chatterjee, and Tang (2017)
document that gross profits are a superior numerator compared to operating income and
earnings before interest and taxes (EBIT), but recommend scaling them by enterprise value
or market value of equity. In line with these results, we also find that a factor based on
cash-based gross profits scaled by enterprise value carries a higher standalone return than
the standard factor variant based on cash-based gross profits scaled by total assets. This
superiority, however, disappears when we control for other factors, like a value factor that
already incorporates a valuation effect, which was induced prior solely by using the enterprise
value as a denominator in the profitability ratio. Chen et al. (2018) find that monthly portfolio
sorts based on quarterly ROE lead to the highest return spreads in most of the countries
of their sample. We do not calculate profitability factors based on quarterly accounting
data as this would substantially reduce our sample and make the factors incomparable.6
Furthermore, Novy-Marx (2015) argues that using quarterly accounting data could induce
a look-ahead bias, because quarterly earnings might include revisions and thus might be
different from the earnings that are actually announced. For the stated reasons, we calculate
all profitability ratios based only on annual accounting information in order to put them on
an equal footing. As a result, we find that a factor based on ROE has only weak predictive
power compared to the factors based on the other profitability ratios. One of the reasons
for this outcome seems to be the mean-reverting character of ROE that we document in an
analysis of future profitability growth. Finally, both of these thematically-related papers are
also different from our analysis in that they do not include a cash-based profitability factor.
Our study also adds to the consistently expanding literature on international evidence for
6Exemplary comparing yearly and quarterly earnings-per-share (EPS) data from Worldscope, we find thatusing quarterly data would imply a total loss of more than 50% of the observations per year, rangingfrom more than 80% at the beginning of the sample to less than 20% at the end of the sample. AlsoWalkshausl and Lobe (2014) state that the reporting of quarterly accounting data has just started and isnot (yet) common practice in many non-U.S. countries of their sample.
7
cross-sectional return predictors that were initially only documented for the U.S. Previous
research in this context includes the studies by Fama and French (1998), Rouwenhorst (1998),
McLean, Pontiff, and Watanabe (2009), and Watanabe et al. (2013), who analyze value,
momentum, share issuance and asset growth, respectively.
The paper is structured as follows: Section 2 describes the data and variables. Section 3
outlines the theoretical framework behind the analysis. Section 4 presents empirical results,
Section 5 contains robustness tests, and Section 6 concludes.
2 Data
We collect monthly equity market data from Datastream and yearly accounting data from
Worldscope from 07/1989 to 06/2016 on firm level for the following 49 countries: Argentina,
Australia, Austria, Belgium, Brazil, Canada, Chile, China,7 Colombia, Czech Republic, Den-
mark, Egypt, Finland, France, Germany, Great Britain, Greece, Hong Kong, Hungary, In-
donesia, India, Ireland, Israel, Italy, Jordan, Japan, Korea, Morocco, Mexico, Malaysia,
Netherlands, Norway, New Zealand, Pakistan, Peru, Philippines, Poland, Portugal, Russia,
Singapore, Spain, South Africa, Sri Lanka, Sweden, Switzerland, Thailand, Turkey, Taiwan,
and Venezuela. As in Jacobs (2016), we include all countries in our analysis for the periods in
which they are classified by MSCI as a developed or an emerging market. Mexico, Sri Lanka,
and Venezuela are later dropped from the sample because they do not fulfill our requirements
to either have the variables needed to construct the profitability measures or to have at least
25 stocks available during any month of the sample period.
The result is a comprehensive, international sample. We do not include the U.S., because
the analysis of profitability for this region is already available, and obviously, including the
U.S. would have a considerable effect on any findings. Table 1 provides an overview of the
sample.
7Chinese “A” shares are excluded from the sample because they were not accessible for international investorsfor a large part of our sample period.
8
[Table 1 about here.]
The sample start date of 07/1990 was chosen specifically, because this is the time when
cross-sectional data becomes more available in Datastream.8 Moreover, other international
studies (e.g., Fama and French 2012 and Fama and French 2017) start in 1990 as well, which
allows for an easier comparison of the results.
As we want to restrict our sample to common stocks, exclusively, and to ensure high data
quality, we conduct the recommended static and dynamic screens proposed by Ince and Porter
(2006), Griffin, Kelly, and Nardari (2010) and Schmidt et al. (2017). More specifically, (i)
we demand that companies are located and securities are listed in the respective domestic
country; (ii) only primary quotations of a security are analyzed; (iii) for firms with more
than one equity security, only the one with the biggest market capitalization and liquidity
is chosen; (iv) securities with quoted currency or with ISIN country codes different from
those of the associated countries are disregarded; (v) following Karolyi, Lee, and Dijk (2012),
Schmidt et al. (2017), and Griffin, Kelly, and Nardari (2010), we also apply name filters in
order to exclude any non-common equity securities like ADRs, investment trusts, REITs,
mutual funds, preferred stocks, and warrants from our sample. Finally, we perform a manual
check of all removed stocks to ensure that none of them was deleted by error. The details of
this comprehensive screening process are provided in Appendix A.1.
Moreover, we consider both active and dead stocks, in order to obviate survivorship
bias. Following Fama and French (1992), Novy-Marx (2013), Ball et al. (2015), and Ball
et al. (2016) among others, all financial firms are dropped.
For the remaining firms, we calculate accruals and cash-based operating profitability ac-
cording to Ball et al. (2016), operating profitability according to Fama and French (2015)
and Ball et al. (2015), and gross profitability according to Novy-Marx (2013). We also calcu-
late cash-based gross profitability, which is defined in a similar way to cash-based operating
profitability; the only difference is that the starting point is gross profitability instead of8The data collection already starts in 07/1989 as we require the returns in the prior twelve months of the
sample start date to calculate momentum.
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operating profitability.9 Table 2 contains summary statistics of these variables as well as
the following five control variables: the natural logarithm of the book-to-market ratio, with
the book value of equity defined as shareholder’s equity plus balance sheet deferred taxes (if
available), the natural logarithm of the monthly market capitalization lagged by one month,
momentum calculated as the cumulative return from months t − 12 to t − 2, the current
return lagged by one month and the growth rate of total assets. As in McLean, Pontiff, and
Watanabe (2009), we winsorize each of the variables at the top and bottom 1% to eliminate
the effects of outliers. We then calculate the summary statistics as the time series averages
of the measures.
[Table 2 about here.]
For a firm to be part of our empirical analysis, we demand the following variables to be
available: the associated stock return, momentum, sales and costs of goods sold. Moreover,
the following variables must be larger than zero: the one-month lagged market capitalization,
the book-to-market ratio, total assets, and total assets lagged by one year. The remaining
number of firm-months after the application of all these filters is 3,727,579.
We find that the average annual gross profitability is 24%. If we subtract SG&A expenses
(net of R&D expenditures), to calculate operating profitability, this value drops to 11%. If we
additionally account for both interest expenses and R&D expenditures and divide by book
equity, as in the case of the Fama and French (2015) definition, we observe an average value of
22%. If we further perform cash adjustments on operating or on gross profitability, the result
decreases only by 1%, in either case. As before with the non-cash-based measures, there is
a clear effect in the deduction of SG&A expenditures and the addition of R&D expenses.
Accruals are on average -3% of total assets. The values for operating profitability, cash-based
operating profitability, and accruals are close to the values reported in Ball et al. (2016) for
the U.S. (13%, 12%, and -3%, respectively). However, the average gross profitability for the
9The details of the variable constructions are provided in Appendix A.2. To ensure that accounting infor-mation is known before we use it to explain the stock returns, we match accounting information for thefiscal year y − 1 with stock returns from July of year y to June of year y + 1 throughout the paper.
10
U.S. as reported by Ball et al. (2015) is much higher (37%). It seems that SG&A expenses
(net of R&D expenditures and scaled by total assets) have a more dominant role in the U.S.
than in markets outside the U.S.
[Table 3 about here.]
Table 3 displays time series averages of the correlations between the profitability measures
and accruals. The correlation between gross profitability and operating profitability is 0.64
and between gross profitability and operating profitability according to Fama and French is
0.56. Accruals are slightly negatively correlated with operating profitability, as opposed to the
U.S. findings, where the coefficient is positive (cf. Ball et al. 2016). The correlation between
gross profitability and cash-based gross profitability is, however, highly positive, as expected
(0.88). Operating profitability is highly correlated with cash-based operating profitability
(0.78), but not as much with cash-based gross profitability (0.52). The correlation between
cash-based operating profitability and cash-based gross profitability of 0.69 is rather low,
which is similar to the correlation between operating profitability and gross profitability. This
documents once more the notable influences of SG&A and R&D expenses on the profitability
definitions. The table also shows that all profitability measures are negatively correlated with
the book-to-market ratio. This finding indicates that profitable firms exhibit high valuations
and therefore, it is important to control for other factors, such as the value factor, in the
following analyses.
3 Methodology
Based on the results in Ball et al. (2016) and Barillas et al. (2017), we would expect asset
pricing models that incorporate a cash-based operating profitability factor to perform better
than those which do not, or those which rely on another profitability factor that is different
from cash-based operating profitability. In order to test this expectation, we (1) evaluate
different factors by means of descriptive statistics and factor spanning tests and (2) perform
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asset pricing tests that build on the insights of (1) with regard to the choice of the profitability
measure in the factor creation.
To be more specific, we analyze the following set of factors: the size factor (SMB, small
minus big), the value factor (HML, high minus low), the momentum factor (MOM), the
investment factor (CMA, conservative minus aggressive), several profitability factors (RMW,
robust minus weak) based on the following profitability definitions: ROE, gross profitability
(GP), operating profitability as defined by Ball et al. (2015, OP), operating profitability
as originally defined by Fama and French (2015, OPFF), cash-based operating profitability
(CbOP) and cash-based gross profitability (CbGP), and an accruals factor (ACC).
SMB and HML are constructed as follows: At June of every year, the stocks of every
country are sorted independently into two size groups, Big (B) and Small (S) and three
book-to-market (BM) groups, High (H), Medium (M) and Low (L). At the intersection of
the 2 x 3 size and book-to-market groups, six portfolios are created. SMB is calculated as
the difference between the average monthly portfolio returns of the three small stock and
the three big stock portfolios and HML is calculated as the difference between the average
monthly returns of the two high and the two low BM stock portfolios.
RMW and CMA are constructed analogously to HML, except for the sorting variable
besides size, which is profitability in the case of RMW (measured as ROE, GP, OPFF, OP,
CbOP, or CbGP), and the growth rate of the book value of total assets from year y − 2 to
y − 1, in the case of CMA.
In order to construct MOM, the stocks are sorted every month t by their cumulative past
performance from month t− 12 to month t− 2 into winners (W) and losers (L). In addition,
similar to HML, the stocks are allocated every month t to the two size portfolios, B and S.
Apart from that, the calculation of MOM is analogous to HML.
With regard to the size breakpoints in the 2 x 3 sorts, we follow the common approach of
Fama and French (2012) for international data: the stocks in the top 90% of the aggregate
market capitalization of a country are classified as big and the stocks in the bottom 10% are
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classified as small. For the other sorting variable besides size, we calculate the breakpoints
as the 30th and 70th percentiles of big stocks per country (cf. Fama and French 2012, 2017).
Moreover, we aim to investigate if international asset pricing models can be improved
substantially by adding a profitability factor if it was not already included, or by the choice
of the profitability measure underlying the factor. We analyze the following asset pricing
models: the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model
(FF3FM), the Fama-French-Carhart four-factor model (FFC4FM), the Fama-French five-
factor model (FF5FM), a modified Fama-French five-factor model based on CbGP instead
of OPFF (FF5FMCbGP), and the modified model plus momentum (FF5FMCbGP+MOM).
4 Empirical Results
4.1 Factor Analysis
Table 4 provides an overview of the average monthly returns, the monthly standard deviations
and the t-values of the aforementioned factors. The returns of the traditional four factors are
in line with earlier international evidence as, e.g., in Fama and French (2012). The average
returns for the market and size premiums are 31 bp and -3 bp, respectively, however, both are
not significantly different from zero (t-values of 1.12 and -0.28). In contrast, the value and
momentum premiums are both economically and statistically significant with average returns
of 46 bp (t-value of 3.70) and 70 bp (t-value of 3.74), respectively. The average returns of 26 bp
and 14 bp for the investment and accruals factors, respectively, are smaller but still more than
two standard errors away from zero. Among the profitability factors, the premiums range
from 13 bp for RMWROE to 36 bp for RMWCbGP. These premiums are also highly significant,
with t-values ranging from 3.09 for RMWOPFF to 4.67 for RMWCbGP. Only the premium that
is based on ROE (t-value of 1.86) is below the traditional threshold of two standard errors.
We can also confirm the results of Novy-Marx (2013) and Ball et al. (2016) that top-line
profitability measures, such as gross profitability and operating profitability, are superior
13
to bottom-line net income and that correcting for accounting accruals (the step from gross
profitability or operating profitability to their cash-based variants) leads to higher average
returns. In contrast to the findings in Ball et al. (2015), the step from (cash-based) gross
profitability to (cash-based) operating profitability leads to lower premiums in international
markets. Overall, all profitability factors besides RMWROE exhibit strong statistical and
economical significance and RMWCbGP shows the highest premium individually.
[Table 4 about here.]
To understand which of the factors are more important than others, or which of them are
possibly redundant, we perform factor spanning tests as follows: The asset pricing model to
be tested in a single test is used to explain a factor, which by definition is not part of the
explanatory variables of the model. If the model generates sizable and statistically significant
alphas, the omitted factor contains important information, which is not covered by the model
under observation.
For the first set of spanning regressions (Panel A) we use the FFC4FM complemented
by the investment factor CMA in order to explain RMW, based on ROE, GP, OPFF, OP,
CbOP, CbGP and, ACC. For the second set of regressions (Panel B), we add RMWCbGP to
the model and then rerun the tests. In the final set of regressions (Panel C), RMWCbGP
is the dependent variable, and we add separately RMWOPFF , RMWOP, and RMWCbOP as
independent variable instead. The results are presented in Table 5.
[Table 5 about here.]
Panel A shows that each of the six profitability factors and the accruals factors exhibit
significant alphas when regressed on the remaining factors. The accruals factor has an al-
pha of 11 bp (t-value of 2.15) and the profitability factor alphas range from 0.20 bp for
RMWROE and RMWOPFF (t-values of 3.08 and 3.32, respectively) to 46 bp for RMWGP and
RMWCbGP (t-values of 7.23 and 7.60, respectively). These results confirm our hypothesis,
that profitability is a separate factor in the cross-section of realized stock returns and con-
14
tains valuable information, which is not already included in the FFC4FM complemented by
CMA. When we augment the explanatory factors with RMWCbGP in Panel B, the augmented
model captures the average returns of all other profitability factors and the accruals factor.
The remaining alphas range from 0 bp to 6 bp and are all insignificant (maximum t-value
of 1.16). Our third set of regressions, presented in Panel C, shows that the converse is not
true. When RMWCbGP is the dependent variable and RMWOPFF or RMWOP or RMWCbOP is
added to the independent variables, the alpha stays significant and ranges between 19 bp and
39 bp, with t-values greater than 4.23.10 This confirms the previous results, that RMWCbGP
is superior to the other profitability measures.
To evaluate the consistency of the profitability premium over time, Figure 1 plots the
cumulative sum of factor returns and spanning alphas over time. The plot uses the full
sample estimates of the spanning betas to estimate the monthly alphas.
[Figure 1 about here.]
Figure 1 shows that the returns for the cash-based gross profitability factor are quite
consistent over time with high returns from 1990 to 2000 and from 2008 to 2016. Between
2000 and 2008, the performance is rather flat until 2005 and the years between 2005 and
2008 are the only period with a prolonged negative performance. This period, however, also
exhibits a strong positive performance for the value factor (not shown in the graph). As
the value and profitability factors are typically negatively correlated, we also analyze the
cumulative performance of the spanning alpha. When controlling for the other factors, the
cumulative performance becomes even stronger and more stable with only a short period of
negative to flat returns between 2005 and 2008.
In order to measure the economic significance of our findings, we follow Ball et al. (2016)
and calculate the ex-post maximum Sharpe ratios associated with various factor combinations
from the viewpoint of an investor who is trading these factors. In this analysis, the lower
10In unreported tests, we find that the RMWCbGP alpha also remains positive and significant when RMWROEor RMWGP is added to the independent variables.
15
bound for the weights is zero and the sum of the weights has to add up to 1. Table 6 displays
the factor weights of the factor portfolios and the corresponding maximum Sharpe ratios.
[Table 6 about here.]
The (annualized) Sharpe ratio on the international market portfolio (CAPM) is 0.22 and
it increases to 0.83 when we extend the factor set with the classical size and value factors
(FF3FM). Further adding the momentum factor (FFC4FM) or the two new factors of the
Fama and French (2015) five-factor model (FF5FM) leads in both cases to a Sharpe ratio
of 1.28. When the factors are jointly added instead (FF5FM + MOM), the Sharpe ratio
amounts to 1.48. An investor who holds the securities underlying the previously mentioned
factors could only marginally profit by adding an accruals factor (FF5FM + MOM + ACC)
as the Sharpe ratio increases only to 1.53 in this case. However, replacing the profitability
factor of the Fama and French (2015) five-factor model by an alternative profitability factor
would be clearly beneficial. The highest Sharpe ratio with a value of 2.07 can be achieved by
adding a cash-based gross profitability factor (FF5FMCbGP + MOM + ACC). The investment
opportunity set that consists of all possible factors (ALL) on the other hand, only obtains
a marginally higher Sharpe ratio of 2.09 and the associated tangency portfolio places the
highest weight on the cash-based gross profitability factor (34%).
The results above indicate again that a cash-based gross profitability factor (RMWCbGP)
is superior to alternative profitability factor definitions. Next, we investigate if this result
also holds in the context of finer portfolio sorts.
We form quintile portfolios and the associated 5-1 (high-minus-low) portfolios based on
every sorting profitability variable at June of every year, from 07/1990 to 06/2016, and
calculate value-weighted monthly excess returns from July of year t until June of year t+1,
respectively. As in the factor analysis, the breakpoints are estimated based on big stocks.
Panel A in Table 7 presents the average value-weighted excess returns of these portfolios.
The 5-1 portfolios will be analyzed in greater detail, so we also state the respective t-values in
this case. In Panel B, we investigate if the 5-1 portfolio excess returns can be explained by the
16
CAPM, the FF3FM, the FFC4FM, the FF5FM, a modified FF5FM based on CbGP instead
of OPFF (FF5FMCbGP), and the modified model plus momentum (FF5FMCbGP+MOM).
[Table 7 about here.]
The excess returns on the 5-1 (high-minus-low) quintile portfolios created based on ROE,
gross profitability (GP), operating profitability (OP), cash-based operating profitability (CbOP),
and cash-based gross profitability (CbGP) are between 29 bp (OP) and 40 bp (CbGP), and
the associated t-values range from 2.47 (OP) to 3.46 (CbGP). Only the 5-1 portfolio based
on operating profitability by Fama and French (OPFF) has a low return of 4 bp, with an
associated t-value of 0.40—it seems that deducting interest rate expenses generally has also
a considerable effect on the profitability definition. The return on the 5-1 accruals (Accr)
portfolio is -0.10 bp with a t-value of -1.06.
The regressions in Panel B show that neither the CAPM, nor the FF3FM, nor the FFC4FM,
nor the FF5FM can capture the average 5-1 portfolio return for most of the profitability
measures. The only notable exception is the 5-1 Accr portfolio return, which can be explained
by each of the models, but especially well by the FF5FM (alpha = -0.01, t-value -0.09). The
biggest improvement in the models appears if we look at the FF5FMCbGP. All the alphas,
except for the one based on ROE (which is 22 bp), are below or equal to an absolute value
of 10 bp, and not significant. If we further add momentum to the model, the alphas are
almost unchanged. We conclude, that in particular the substitution of OPFF by CbGP can
be recommended to improve on the FF5FM definition on international markets.
4.2 Fama-MacBeth (1973) Regressions
In this subsection, we investigate the suitability of the six profitability measures to predict
future returns in cross-sectional regressions. Every month, from 07/1990 until 06/2016,
we perform Fama and MacBeth (1973) regressions of monthly stock returns on one of the
six profitability measures (models 1 to 6), on accruals (model 7) and on cash-based gross
17
profitability in combination with each of the other profitability definitions and accruals,
respectively (models 8 to 13). Moreover, every model includes the five control variables
introduced in Section 2 and country dummies to control for potential country effects. The
results are shown in Table 8.11
[Table 8 about here.]
We find that the most prominent profitability definitions in the literature, namely gross
profitability, operating profitability (according to Fama and French 2015 as well as Ball et
al. 2015), and cash-based operating profitability, exhibit positive average regression slopes
that are significantly different from zero. Our newly introduced measure, cash-based gross
profitability, however, has the highest statistical significance in either the individual tests
(models 1 to 6), or when testing against other profitability ratios (Panel B). Model 7 confirms
the existence of the accrual anomaly in our sample and Panel B shows that this anomaly
becomes less prevalent if we add cash-based gross profitability as explanatory variables to
the model.
We can confirm the result by Novy-Marx (2013) internationally: that gross profitability
is a superior profitability measure to ROE. Moreover, similar to Ball et al. (2016), there
is a clear positive effect in the adjustment for accounting accruals; in other words, cash-
based operating profitability according to Ball et al. (2016) is superior to its non-cash-based
predecessor. We can also confirm Fama and French (2018) in their assertion that cash-based
operating profitability beats their original version of operating profitability. Furthermore, we
infer, in particularly from models 5, 6 and 12, that cash-based gross profitability is superior
to cash-based operating profitability.
4.3 Current profitability and future profitability
The profitability variables analyzed in this study refer to the most recent reporting period at
a time. However, Yt+τBt
in equation (1) stands for the (expected) future profitability of a given11The coefficients for the country dummies are not reported for the purpose of conciseness.
18
firm. It follows that if a profitability variable is priced in the cross-section of stock returns,
it should be also an indicator of future profitability.
In order to test this assertion, we follow Novy-Marx (2013) and Kyosev et al. (2018), and
perform monthly Fama-MacBeth regressions to predict three-year growth in ROE. Table 9
reports the results. The analysis is performed from 07/1990 to 06/2016 for a global sample
of developed market and emerging market countries (as in Section 4.2). As ROE growth
exhibits some persistence by construction, the t-values are calculated using Newey and West
standard errors with 35 lags.
[Table 9 about here.]
The first column in Table 9 depicts that high positive ROE implies negative future ROE
growth, or in other words, there is a mean reversion in ROE. This possibly explains why a
profitability factor based on ROE has shown the weakest results in our previous analyses.
By controlling for this effect, we observe that the other profitability variables have a positive
relation and accruals have a negative relation with future profitability. Gross profitability
and cash-based gross profitability carry the highest t-values, which might indicate why the
factors based on these measures have shown particularly strong results. These results are
in line with the recent findings of Bouchaud et al. (2018) that indicate that sticky earnings
expectations drive the profitability anomaly.
The important role of the ROE factor in the Hou, Xue, and Zhang (2015) q-factor model
might appear inconsistent with these findings at first sight. However, as Novy-Marx (2015)
points out, the ROE factor in the q-factor model is based on the most recently announced
quarterly earnings; as a consequence, it profits from the post-earnings-announcement drift. In
contrast, our ROE factor is based on the most recently announced annual earnings, because
of the limited data availability on international markets. Therefore, it cannot be directly
compared to the ROE factor in the model of Hou, Xue, and Zhang (2015), which has the
advantage of utilizing more timely and more frequently updated information.
19
5 Robustness
5.1 The Role of the Denominator
In the previous section, we have focused on the numerator and have scaled the respective
measures of profit by the current book value of total assets (equity) for measures before
(after) interest expenses. Zhang (2017) argues that with scaling by current total assets and
not by one-year-lagged assets, the profitability effect is contaminated by a hidden investment
effect. Although we demonstrated in Section 4 that the profitability effect is also present after
controlling for the investment effect in factor and portfolio sorts as well as in cross-sectional
regressions, we also show the results for scaling cash-based gross profits by one-year lagged
assets in this section.
Novy-Marx (2013) scales gross profits by the book value of total assets and not a market-
based measure to avoid causing conflict in the productivity measure with a valuation measure
(book-to-market). Also in equation (1) profits are scaled by a book measure and company
valuation is reflected by the book-to-market ratio. Nevertheless, Cakici, Chatterjee, and Tang
(2017) find that for average portfolio returns that do not control for other factors such as
book-to-market, the relation of firm profitability and stock returns is more pronounced when
profits are scaled by enterprise value or market value of equity. Therefore, we also present the
results for scaling cash-based gross profits by enterprise value.12 We scale cash-based gross
profits by enterprise value, not market value of equity, because cash-based gross profits are
measured before interest expenses and are independent of leverage; therefore, they represent
an asset level measure of earnings.
Table 10 shows results for profitability factors based on cash-based gross profits scaled by
current total assets as in the previous analyses (RMWCbGP), but also scaled by one-year-
lagged assets (RMWCbGP/LagA) and by enterprise value (RMWCbGP/EV). Panel A reports
the summary statistics. Echoing the results from Table 4, the average monthly return for
12Enterprise value is defined as market capitalization plus preferred stock plus minority interest, plus long-and short-term debt, less cash and short-term investments.
20
RMWCbGP is 36bp (t-value of 4.67). When cash-based gross profits are scaled by one-year-
lagged total assets, the average premium for RMWCbGP/LagA decreases to 26 bp (t-value of
3.14) but remains highly significant. Therefore, we can confirm that a profitability measure
can benefit from the investment effect by measuring the denominator timely and we can also
rule out that the profitability effect is just an investment effect in disguise. When cash-based
gross profits are scaled by enterprise value, the RMWCbGP/EV premium increases to 63 bp
(t-value of 7.57). This confirms the standalone factor results of Cakici, Chatterjee, and Tang
(2017). However, standalone factor returns do not provide evidence on the predictive power
of factors in the presence of other factors that also predict stock returns. Therefore, we also
implement factor spanning tests and Sharpe ratio analyses.
[Table 10 about here.]
Panel B reports the results for factor spanning regressions. The first regression shows that
the RMWCbGP/LagA returns are completely spanned by the FF5FMCbGP complemented by a
momentum factor and an accruals factor. The negative loading of CMA on RMWCbGP/LagA
demonstrates that scaling by lagged assets instead of current assets implies a negative invest-
ment effect. In contrast, RMWCbGP/EV cannot be spanned by these factors and a significant
spanning alpha of 10 bp (t-value of 2.36) remains. This raises the question of whether
RMWCbGP/EV also dominates RMWCbGP. We find that this is not the case: By switching the
positions of RMWCbGP and RMWCbGP/EV in the third regression, an even bigger spanning
alpha of 17 bp (t-value of 3.57) remains. These findings imply that both RMWCbGP and
RMWCbGP/EV contain important information that is not covered by the other profitability
factors as well as other model factors.
Panel C finally answers the question of which profitability factor variant adds the most
value in a parsimonious factor model that only includes one profitability factor. Echoing
the results of Table 6, the investment opportunity set that comprises the factors of the
FF5FMCbGP, complemented by a momentum factor and an accruals factor achieves an (an-
nualized) Sharpe ratio of 2.07. When we replace RMWCbGP by RMWCbGP/LagA and by
21
RMWCbGP/EV, the maximum ex post Sharpe ratios decrease to 2.01 and 1.91, respectively.
Therefore, we conclude that a profitability factor based on cash-based gross profits scaled by
current total assets exhibits the highest predictive power in the presence of other factors.
5.2 Regional Analysis
In this section, we analyze how the results from our global analysis (excluding the U.S.) for
the different profitability factors can be attributed to different regions, namely developed
markets (DM), emerging markets (EM), Europe, Asia Pacific ex Japan, and Japan. We are
particularly interested in which of these regions CbGP dominates CbOP. Thus, in Table
11 we repeat our analysis from section 4.1 on a regional basis. Panel A shows the average
monthly returns for the profitability factors based on ROE, GP, OPFF, OP, CbOP, and CbGP.
CbGP yields significant profitability premiums for all regions and dominates all the other
profitability factors in the following regions: DM, EM, Asia Pacific (excluding Japan), and
Japan. The results for Asia Pacific and Japan are remarkable in that the profitability factor
of the Fama and French (2015) five-factor model does not exhibit a significant premium (cf.
Fama and French 2017). In Europe, however, CbOP has the highest average return and
CbGP ranks only second.
[Table 11 about here.]
Panel B presents the results from factor spanning regressions. The dependent variables
are the profitability factors listed above, with the exception of RMWCbGP. The indepen-
dent variables are the factors of the FF5FMCbGP complemented by momentum, as in the
previous analysis for the global sample in Table 5, panel B. The independent factors (includ-
ing RMWCbGP) nearly completely span the returns of the different profitability factors that
are tested in each of the regions. The spanning alphas range from -2 bp to 11 bp (with a
maximum absolute t-value of 1.77). The only exceptions are emerging markets and Europe,
where OPFF and CbOP exhibit significant alphas of 18 bp (t-value of 1.98) and 11 bp (t-value
22
of 2.35), respectively. Based on these results, we conclude that our global findings can be
attributed to the majority of regions.
5.3 Cash-based Gross Profitability Factors per Country
So far, we analyzed global (excluding the U.S.) and regional profitability factors, but we
have not broken down our analysis on a country level. By doing this we intend to determine
whether our results are mainly driven by a handful of countries with many big firms (based
on market capitalization) or whether the profitability effect can be verified in the majority of
the countries of our sample. As shown in Section 4.1, cash-based gross profitability dominates
the other profitability measures internationally based on a portfolio analysis. For compari-
son, we now create country-specific RMWCbGP factors and analyze the associated monthly
average returns and associated t-values. Moreover, we also perform factor spanning tests,
by regressing the country-specific RMWCbGP factors on the following (also country-specific)
factors: RMRF, SMB, HML, CMA, and MOM. We report the alphas and the associated
t-values; the results are displayed in Table 12.13
[Table 12 about here.]
The average RMWCbGP returns are positive in 36 out of 42 countries and range from -64
bp for Hungary to 131 bp for Argentina. The associated t-values are larger or equal to 2.0
in the following countries: Argentina, Denmark, France, Germany, Hong Kong, Indonesia,
Japan, Korea, Poland, Portugal, Singapore, Switzerland, Thailand and Taiwan. In general,
the spanning test alphas are even higher. They are positive in 39 out of 42 countries and
range from -23 bp for Belgium to 134 bp for Indonesia. In addition to the countries stated
previously, the following countries also carry t-values of the spanning alphas larger or equal
to 2.0: Australia, Brazil, Canada, Great Britain, India, Malaysia, Philippines, and South
Africa. This implies that in 22 out of 42 countries, the cash-based gross profitability factor
13To be included in the country-specific analysis, we require at least ten years of available factor return data.Therefore, Colombia, Czech Republic, Jordan, and Morocco drop out of the sample in this analysis.
23
contains statistically significant information that is not already covered by the other factors.
For the remaining 20 countries, the alpha is positive in 17 cases; however, in most cases it
is not statistically significantly different from zero, although some of the alphas are sizable
(e.g., Greece, Israel, and Peru have alphas of more than 50 bp). Shorter factor return time-
series and/or less diversified factor portfolios due to a lower number of stocks per portfolio
could lead to power issues in case of smaller countries. Figure 2 provides an overview of the
spanning test alphas per country. It appears that the cash-based profitability factor exhibits
positive average returns and spanning test alphas in a majority of the investigated countries.
Thus, we conclude that the profitability effect presents a broad and global phenomenon.
[Figure 2 about here.]
6 Conclusion
In this study, we analyze the most prominent profitability measures in the literature, namely
(i) return on equity (ROE), (ii) gross profitability by Novy-Marx (2013), (iii) operating
profitability by Ball et al. (2015) and defined in an alternative way (iv) by Fama and French
(2015), (v) cash-based operating profitability by Ball et al. (2016) and (vi) cash-based gross
profitability, for a total of 46 countries, from 07/1990 to 06/2016. We intentionally exclude
the U.S. as it has already been analyzed thoroughly in the past.
To compare the profitability measures, we perform time-series, factor-spanning, and cross-
sectional asset pricing tests. We calculate the six profitability factors following the standard
procedures of Fama and French (1993, 2012, 2015). Based on the average factor returns and
the associated t-statistics, cash-based gross profitability exhibits the best performance. We
verify this assertion by performing a series of factor spanning and mean-variance spanning
tests.
Moreover, we form quintile portfolios and the associated 5-1 (high-minus-low) portfolios
for all six profitability measures as well as accruals. We then test if the 5-1 portfolio re-
24
turns can be explained by either of the following models: the CAPM, the Fama-French
three-factor model, the Fama-French-Carhart four-factor model, the Fama-French five-factor
model, a modified Fama-French five-factor model based on cash-based gross profitability (in-
stead of operating profitability) and the modified model plus momentum. Based on alphas
and t-statistics, we find that the modified Fama-French five-factor model (with and without
momentum) explains the 5-1 portfolio returns particularly well. Thus, in an international
context, we recommend the substitution of operating profitability according to Fama and
French (2015) by cash-based gross profitability, to improve on the Fama and French five-
factor model definition.
We also conduct Fama and MacBeth (1973) cross-sectional regressions of monthly stock
returns on (i) individual profitability measures plus a set of control variables; and (ii) the
individually best profitability measure from (i) jointly with each of the other profitability
measures plus control variables. We find that cash-based gross profitability has the highest
marginal power to explain future stock returns in the individual as well as in the simultaneous
tests. With regards to accruals, we find similar results to the U.S. in that accruals negatively
predict future returns individually; however, in the presence of any cash-based profitability
measure, its predictive power substantially decreases.
Several robustness checks are performed. First, we analyze the role of the denominator
and scale cash-based gross profits (i.e., the numerator) by total assets, by one-year lagged
assets, and by enterprise value. We find that a factor based on cash-based gross profits scaled
by enterprise value (by one-year lagged assets) has a higher (a lower) standalone return than
the factor variant based on cash-based gross profits scaled by current total assets. However,
when controlling for other factors, we find that the standard cash-based profitability factor
(based on current assets) adds the most value to the investment opportunity set. Second, we
perform a regional factor analysis in order to see if our results are mainly driven by certain
regions or are more evenly distributed among regions. The following regions are analyzed:
Developed markets, emerging markets, Europe, Asia Pacific (excluding Japan), and Japan.
25
We find that a profitability factor based on cash-based gross profitability dominates the other
profitability factors in all regions except Europe, where, similar to the U.S., a factor based on
cash-based operating profitability shows a slightly better performance than the factor based
on cash-based gross profitability (which ranks second in Europe). Regional factor spanning
tests confirm this finding. Thus, we conclude that our global (excluding the U.S.) findings
can be attributed to the majority of regions. Finally, we perform a country-based analysis to
determine if our results are only driven by a subsample of rather big and influential countries
or are more evenly distributed between countries. We expect a higher influence the larger the
size, because usually this implies more data coverage, higher data quality and generally, more
listed stocks, which, in turn leads to larger weights in a value-weighted return analysis. More
specifically, we (i) calculate country-specific robust-minus-weak factors based on cash-based
gross profitability and (ii) perform factor spanning tests by regressing these factors on the
following (also country-specific) factors: RMRF, SMB, HML, CMA and MOM. Next, in case
of (i), we analyze the mean factor returns and in case of (ii), the respective alphas. Based
on this analysis, we document that cash-based gross profitability is a priced factor in the
majority of developed and emerging markets countries, controlling for other common factors,
such as market, size, value, investment and momentum.
The findings of this paper have important implications for the specification of a powerful
empirical asset pricing model. Parsimonious factor models that only include a small num-
ber of characteristic-based factors and are able to shrink the list of asset pricing anomalies
significantly should only include factors with a high power. These factors should (i) exhibit
substantial and significant returns, (ii) not be subsumed by the other model factors, and (iii)
not be susceptible to small alterations in the factor definitions (variants). We show that a
profitability factor based on cash-based gross profitability should be used in factor models for
international markets because it robustly outperforms the other profitability factors analyzed
in the study.
26
References
Annaert, J., M. D. Ceuster, and K. Verstegen. 2013. “Are extreme returns priced in the stock
market? European evidence”. Journal of Banking & Finance 37 (9): 3401–3411.
Ball, R., J. Gerakos, J. T. Linnainmaa, and V. V. Nikolaev. 2015. “Deflating profitability”.
Journal of Financial Economics 117 (2): 225–248.
Ball, R., J. Gerakos, J. T. Linnainmaa, and V. V. Nikolaev. 2016. “Accruals, cash flows,
and operating profitability in the cross section of stock returns”. Journal of Financial
Economics 121 (1): 28–45.
Barillas, F., R. Kan, C. Robotti, and J. A. Shanken. 2017. “Model Comparison with Sharpe
Ratios”. Rotman School of Management Working Paper No. 3013149.
Bouchaud, J.-P., P. Krueger, A. Landier, and D. Thesmar. 2018. “Sticky expectations and
the profitability anomaly”. Journal of Finance , forthcoming.
Cakici, N., S. Chatterjee, and Y. Tang. 2017. “Alternative Profitability Measures and Cross
Section of Expected Stock Returns: International Evidence”. SSRN Working Paper no.
2969687.
Campbell, C. J., A. R. Cowan, and V. Salotti. 2010. “Multi-country event-study methods”.
International Financial Integration, Journal of Banking & Finance 34 (12): 3078–3090.
Chen, T.-F., L. Sun, K. C. J. Wei, and F. Xie. 2018. “The Profitability Effect: Insights from
International Equity Markets”. European Financial Management , forthcoming.
Fama, E. F., and K. R. French. 1992. “The Cross-Section of Expected Stock Returns”. Journal
of Finance 47 (2): 427–465.
– . 1993. “Common risk factors in the returns on stocks and bonds”. Journal of Financial
Economics 33 (1): 3–56.
– . 1998. “Value versus Growth: The International Evidence”. Journal of Finance 53 (6):
1975–1999.
27
– . 2008. “Dissecting Anomalies”. Journal of Finance 63 (4): 1653–1678.
– . 2012. “Size, value, and momentum in international stock returns”. Journal of Financial
Economics 105 (3): 457–472.
– . 2015. “A five-factor asset pricing model”. Journal of Financial Economics 116 (1): 1 –22.
– . 2017. “International tests of a five-factor asset pricing model”. Journal of Financial
Economics 123 (3): 441–463.
– . 2018. “Choosing factors”. Journal of Financial Economics 128 (2): 234–252.
Fama, E. F., and J. D. MacBeth. 1973. “Risk, Return, and Equilibrium: Empirical Tests”.
Journal of Political Economy 81 (3): 607–636.
Fong, K. Y. L., C. W. Holden, and C. A. Trzcinka. 2017. “What Are the Best Liquidity
Proxies for Global Research?” Review of Finance 21 (4): 1355–1401.
Goyal, A., and S. Wahal. 2015. “Is Momentum an Echo?” Journal of Financial and Quanti-
tative Analysis 50 (6): 1237–1267.
Griffin, J. M., P. J. Kelly, and F. Nardari. 2010. “Do Market Efficiency Measures Yield Correct
Inferences? A Comparison of Developed and Emerging Markets”. Review of Financial
Studies 23 (8): 3225–3277.
Harvey, C. R., Y. Liu, and H. Zhu. 2016. “. . . and the cross-section of expected returns”.
Review of Financial Studies 29 (1): 5–68.
Haugen, R. A., and N. L. Baker. 1996. “Commonality in the determinants of expected stock
returns”. Journal of Financial Economics 41 (3): 401–439.
Hirshleifer, D., K. Hou, and S. H. Teoh. 2009. “Accruals, cash flows, and aggregate stock
returns”. Journal of Financial Economics 91 (3): 389 –406.
Hou, K., C. Xue, and L. Zhang. 2015. “Digesting Anomalies: An Investment Approach”.
Review of Financial Studies 28 (3): 650–705.
28
Ince, O. S., and R. B. Porter. 2006. “Individual equity return data from Thomson Datastream:
Handle with care!” Journal of Financial Research 29 (4): 463–479.
Jacobs, H. 2016. “Market maturity and mispricing”. Journal of Financial Economics 122 (2):
270–287.
Jacobs, H., and S. Muller. 2018. “Anomalies across the globe: Once public, no longer exis-
tent?” SSRN Working Paper no. 2816490.
Karolyi, A. G. 2016. “Home bias, an academic puzzle”. Review of Finance 20 (6): 2049–2078.
Karolyi, G. A., K.-H. Lee, and M. A. van Dijk. 2012. “Understanding commonality in liquidity
around the world”. Journal of Financial Economics 105 (1): 82 –112.
Kyosev, G., M. X. Hanauer, J. Huij, and S. Lansdorp. 2018. “Does Earnings Growth Drive
the Quality Premium?” SSRN Working Paper no. 2794807.
Linnainmaa, J. T., and M. R. Roberts. 2018. “The History of the Cross-Section of Stock
Returns”. The Review of Financial Studies 31 (7): 2606–2649.
McLean, R. D., and J. Pontiff. 2016. “Does Academic Research Destroy Stock Return Pre-
dictability?” Journal of Finance 71 (1): 5–32.
McLean, R. D., J. Pontiff, and A. Watanabe. 2009. “Share issuance and cross-sectional re-
turns: International evidence”. Journal of Financial Economics 94 (1): 1–17.
Novy-Marx, R. 2012. “Is momentum really momentum?” Journal of Financial Economics
103 (3): 429–453.
– . 2013. “The other side of value: The gross profitability premium”. Journal of Financial
Economics 108 (1): 1–28.
– . 2015. “How can a q-theoretic model price momentum?” NBER Working Paper No. 20985.
Polk, C., and P. Sapienza. 2009. “The Stock Market and Corporate Investment: A Test of
Catering Theory”. The Review of Financial Studies 22 (1): 187–217.
29
Rouwenhorst, K. G. 1998. “International Momentum Strategies”. Journal of Finance 53 (1):
267–284.
Schmidt, P. S., U. Von Arx, A. Schrimpf, A. F. Wagner, and A. Ziegler. 2017. “On the Con-
struction of Common Size, Value and Momentum Factors in International Stock Markets:
A Guide with Applications”. Swiss Finance Institute Research Paper 10.
Sloan, R. G. 1996. “Do Stock Prices Fully Reflect Information in Accruals and Cash Flows
about Future Earnings?” The Accounting Review 71 (3): 289–315.
Wahal, S. 2018. “The profitability and investment premium: Pre-1963 evidence”. Journal of
Financial Economics , forthcoming.
Walkshausl, C., and S. Lobe. 2014. “The Alternative Three-Factor Model: An Alternative
beyond US Markets?” European Financial Management 20 (1): 33–70.
Watanabe, A., Y. Xu, T. Yao, and T. Yu. 2013. “The asset growth effect: Insights from
international equity markets”. Journal of Financial Economics 108 (2): 529–563.
Zhang, L. 2017. “The Investment CAPM”. European Financial Management 23 (4): 545–603.
30
Tab
le1:
Des
crip
tive
stat
isti
csT
heta
ble
pres
ents
sum
mar
yst
atist
ics
for
the
46co
untr
ies
ofou
rD
atas
trea
man
dW
orld
scop
esa
mpl
e.C
olum
n2
stat
esth
em
arke
taffi
liatio
nac
cord
ing
toM
SCI,
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30
[Con
tinue
don
next
page
]
31
Cou
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30
32
Table 2: Summary statistics for profitability measures and control variablesThe table presents time-series averages of the cross-sectional means, standard deviations and1%-, 25%-, 50%-, 75%- and 99%-quantiles of the following variables: (1) Gross profitability(GP) according to Novy-Marx (2013), defined as revenues minus costs of goods sold, dividedby the book value of total assets, (2) Operating profitability (OPFF) according to Fama andFrench (2015), defined as gross profit minus selling, general, and administrative expenses,interest expenses and costs of goods sold, divided by the book value of equity, (3) Operatingprofitability (OP) according to Ball et al. (2016), defined as gross profit minus selling, general,and administrative expenses (excluding R&D expenditures) and costs of goods sold, dividedby the book value of total assets, (4) Cash-based operating profitability (CbOP) accordingto Ball et al. (2016), defined as OP minus the change in accounts receivable, the changein inventory, and the change in prepaid expenses, plus the change in deferred revenues, thechange in accounts payable, and the change in accrued expenses, deflated by the book valueof assets, (5) Cash-based gross profitability (CbGP), defined the same way as CbOP, butstarting with GP instead of OP, (6) Accruals (Accr), defined as the change in current assetsminus the change in cash, the change in current liabilities, the change in current debt, thechange in income taxes payable, and depreciation, divided by the book value of total assets,(7) the natural logarithm of book-to-market (log(B/M)), (8) the natural logarithm of the 1-month lagged market value (log(MV)), (9) the 1-month lagged return (r1,1), (10) momentum(r12,2) and (11) the growth in total assets (dA/A) from t−1 to t. The analysis is performedfrom 07/1990 to 06/2016.
GP OPFF OP CbOP CbGP Accr log(B/M) log(MV) r1,1 r12,2 dA/Amean 0.24 0.22 0.11 0.10 0.23 -0.03 -0.37 11.59 0.01 0.10 0.17sd 0.21 0.44 0.14 0.16 0.22 0.10 0.95 1.93 0.13 0.52 0.521st -0.18 -1.51 -0.39 -0.47 -0.29 -0.38 -3.57 7.30 -0.32 -0.73 -0.4625th 0.11 0.06 0.04 0.02 0.09 -0.07 -0.90 10.27 -0.06 -0.21 -0.0250th 0.20 0.18 0.10 0.09 0.20 -0.03 -0.31 11.49 -0.00 0.01 0.0575th 0.33 0.34 0.17 0.17 0.33 0.01 0.23 12.80 0.06 0.29 0.1899th 1.05 2.12 0.60 0.66 1.08 0.28 1.92 16.57 0.51 2.43 3.71
33
Table 3: Correlation coefficientsThe table reports the time-series averages of the cross-sectional spearman correlation coeffi-cients between the following variables: (1) Gross profitability (GP) according to Novy-Marx(2013), (2) Operating profitability (OP) according to Ball et al. (2016), (3) Cash-based oper-ating profitability (CbOP) according to Ball et al. (2016), (4) Cash-based gross profitability(CbGP), (5) Operating profitability (OPFF) according to Fama and French (2015), and (6)Accruals (Accr). For details with regard to variable construction, see Table 2. The sampleis described in Table 1. The analysis is performed from 07/1990 to 06/2016.
GP OP CbOP CbGP OPFF Accr log(B/M) log(MV) r1,1 r12,2 dA/AGP 1.00 0.64 0.52 0.88 0.56 -0.07 -0.19 0.14 0.07 0.10 0.03OP 1.00 0.78 0.52 0.88 -0.08 -0.20 0.21 0.07 0.17 0.17CbOP 1.00 0.69 0.66 -0.45 -0.11 0.16 0.07 0.14 -0.10CbGP 1.00 0.44 -0.33 -0.14 0.11 0.08 0.08 -0.16OPFF 1.00 -0.07 -0.23 0.23 0.07 0.16 0.17Accr 1.00 -0.01 0.01 -0.02 -0.03 0.27log(B/M) 1.00 -0.32 0.07 -0.12 -0.19log(MV) 1.00 0.08 0.22 0.14r1,1 1.00 0.07 -0.03r12,2 1.00 0.06dA/A 1.00
34
Table 4: Factor summary statisticsThe table reports the average monthly returns, the monthly standard deviations and thet-values of the following factors: robust minus weak (RMW), based on (1) ROE, (2) GP,(3) OPFF, (4) OP, (5) CbOP and (6) CbGP, accruals (ACC), small minus big (SMB), highminus low (HML), conservative minus aggressive (CMA), momentum (MOM) and the marketreturn minus the risk-free rate (RMRF), from 07/1990 to 06/2016. The factors are createdat June of every year (except for MOM, which is created at every month t) based on 2x3sorts of size and the second sorting variable of the respective factor. The holding period is 12months (except for MOM, where it is 1 month). SMB is calculated as the difference betweenthe average monthly value-weighted portfolio returns of the three small stock and the threebig stock portfolios. The other factors are calculated as the difference between the averagemonthly value-weighted returns of the two highly and the two lowly ranked portfolios withregard to the respective sorting variable.
mean return standard deviation t-valueRMRF 0.31 4.89 1.12SMB -0.03 1.97 -0.28HML 0.46 2.20 3.70MOM 0.70 3.33 3.74CMA 0.26 1.52 3.02ACC 0.14 0.97 2.53RMWROE 0.13 1.25 1.86RMWGP 0.33 1.43 4.11RMWOPFF 0.19 1.11 3.09RMWOP 0.27 1.41 3.44RMWCbOP 0.30 1.26 4.14RMWCbGP 0.36 1.35 4.67
35
Table 5: Factor spanning testsThe table presents the results from factor spanning regressions. The dependent variablesare the monthly factor returns of robust minus weak (RMW), based on (1) ROE, (2) GP,(3) OPFF, (4) OP, (5) CbOP, (6) CbGP and (7) ACC. The independent variables are theexcess return of the market (RMRF), small minus big (SMB), high minus low (HML), conser-vative minus aggressive (CMA), momentum (MOM), RMWCbGP, RMWOPFF , RMWOP andRMWCbOP. The analysis is performed from 07/1990 to 06/2016.
Intercept RMRF SMB HML CMA MOM RMWCbGP RMWOPFF RMWOP RMWCbOP
Panel A: Spanning tests w/o other profitability factorsRMWROE 0.20 −0.09 −0.10 −0.05 −0.26 0.08
(3.08) (−7.02) (−3.17) (−1.64) (−5.51) (4.17)RMWGP 0.46 −0.13 −0.17 −0.23 −0.14 0.08
(7.23) (−10.17) (−5.47) (−6.95) (−2.99) (3.98)RMWOPFF 0.20 −0.06 −0.14 −0.03 −0.11 0.08
(3.32) (−5.03) (−4.98) (−0.90) (−2.40) (4.44)RMWOP 0.35 −0.11 −0.27 −0.15 −0.21 0.10
(5.81) (−8.37) (−9.20) (−4.64) (−4.65) (5.47)RMWCbOP 0.35 −0.10 −0.24 −0.15 −0.07 0.08
(6.26) (−8.84) (−8.80) (−5.08) (−1.60) (4.73)RMWCbGP 0.46 −0.13 −0.15 −0.24 −0.04 0.08
(7.60) (−10.23) (−5.21) (−7.59) (−0.86) (4.17)ACC 0.11 −0.02 −0.14 −0.10 0.23 0.02
(2.15) (−1.36) (−5.37) (−3.65) (5.77) (1.07)Panel B: Spanning tests with RMWCbGP
RMWROE 0.06 −0.05 −0.05 0.02 −0.25 0.06 0.31(0.83) (−3.65) (−1.66) (0.54) (−5.48) (3.00) (5.30)
RMWGP 0.00 −0.01 −0.02 0.01 −0.10 0.00 1.00(0.02) (−1.25) (−1.60) (0.75) (−6.92) (0.06) (52.76)
RMWOPFF 0.04 −0.02 −0.09 0.05 −0.09 0.05 0.33(0.72) (−1.42) (−3.27) (1.63) (−2.23) (3.12) (6.30)
RMWOP 0.04 −0.02 −0.17 0.01 −0.18 0.05 0.67(0.89) (−1.76) (−7.35) (0.55) (−5.47) (3.51) (15.75)
RMWCbOP 0.05 −0.02 −0.14 0.01 −0.04 0.03 0.66(1.16) (−1.96) (−6.91) (0.36) (−1.41) (2.45) (17.40)
ACC 0.02 0.01 −0.11 −0.05 0.24 0.00 0.21(0.29) (1.00) (−4.05) (−1.71) (6.14) (0.06) (4.38)
Panel C: Spanning tests to explain RMWCbGP
RMWCbGP 0.39 −0.11 −0.10 −0.23 −0.00 0.05 0.35(6.75) (−8.70) (−3.59) (−7.72) (−0.05) (2.74) (6.30)
RMWCbGP 0.22 −0.06 0.03 −0.14 0.10 0.01 0.67(4.73) (−5.62) (1.13) (−5.83) (2.93) (0.64) (15.75)
RMWCbGP 0.19 −0.05 0.03 −0.13 0.01 0.02 0.76(4.23) (−5.03) (1.26) (−5.42) (0.39) (1.13) (17.40)
36
Tab
le6:
Max
imum
expo
stSh
arpe
rati
osT
heta
ble
pres
ents
the
max
imum
ex-p
ost
annu
aliz
edSh
arpe
ratio
sth
atca
nbe
achi
eved
byva
rious
fact
orco
mbi
natio
nsan
dth
ein
divi
dual
fact
orwe
ight
s.T
hefa
ctor
sar
ede
scrib
edin
Tabl
e4.
We
anal
yze
the
follo
win
gas
set
pric
ing
mod
els:
The
Cap
ital
Ass
etPr
icin
gM
odel
(labe
lled
asR
MR
F),
the
Fam
a-Fr
ench
thre
e-fa
ctor
mod
el(F
F3FM
),th
eFa
ma-
Fren
ch-C
arha
rtfo
ur-fa
ctor
mod
el(F
FC4F
M),
theF
ama-
Fren
chfiv
e-fa
ctor
mod
el(F
F5FM
),th
elat
terp
lusm
omen
tum
(FF5
FM+
MO
M)a
ndth
ela
tter
plus
mom
entu
man
dac
crua
ls(F
F5FM
+M
OM
+A
CC
),a
mod
ified
Fam
a-Fr
ench
five-
fact
orm
odel
base
don
the
resp
ectiv
epr
ofita
bilit
ym
easu
rest
ated
inth
ein
dex
(e.g
.FF
5FM
CbG
Pis
base
don
CbG
Pin
stea
dof
OP F
F)
plus
mom
entu
man
dac
crua
ls(e
.g.
FF5F
MC
bGP+
MO
M+
AC
C).
We
also
stat
eth
ere
sults
for
the
com
bina
tion
ofal
lava
ilabl
efa
ctor
s(A
LL).
The
anal
ysis
ispe
rform
edfro
m07
/199
0un
til06
/201
6.R
MR
FSM
BH
ML
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LC
MA
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WF
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WR
OE
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PR
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000.
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501.
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5FM
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09
37
Table 7: Quintile Portfolio AnalysisThis table reports the results from a quintile portfolio analysis based on different profitabilitymeasures. Panel A shows the average monthly value-weighted excess returns of the quintileportfolios as well as the 5-1 portfolios and the associated t-values in brackets, based on(1) ROE, (2) GP, (3) OPFF, (4) OP, (5) CbOP, (6) CbGP and (7) Accr, respectively. InPanel B, we test if the 5-1 portfolio excess returns can be explained by the following assetpricing models: (1) the CAPM, (2) the Fama-French three-factor model (FF3FM), (3) theFama-French-Carhart four-factor model (FFC4FM), (4) the Fama-French five-factor model(FF5FM), (5) a modified Fama-French five-factor model based on CbGP (FF5FMCbGP) and(6) the modified model plus momentum (FF5FMCbGP+MOM). The panel reports the alphasand the associated t-values in brackets. The analysis is performed from 07/1990 to 06/2016.
ROE GP OPFF OP CbOP CbGP AccrPanel A: Monthly excess returns
1 (low) 0.04 0.09 0.22 0.12 0.11 0.10 0.372 0.36 0.24 0.37 0.33 0.31 0.22 0.353 0.47 0.41 0.31 0.37 0.34 0.35 0.284 0.40 0.38 0.48 0.36 0.35 0.37 0.345 (high) 0.37 0.47 0.26 0.41 0.43 0.50 0.275-1 0.33 0.38 0.04 0.29 0.32 0.40 −0.10
(2.77) (3.36) (0.40) (2.47) (2.82) (3.46) (−1.06)Panel B: Alphas and t-values from asset pricing tests
CAPM 0.37 0.41 0.06 0.31 0.35 0.43 −0.11(3.31) (3.71) (0.57) (2.73) (3.22) (3.94) (−1.22)
FF3FM 0.24 0.62 0.13 0.45 0.47 0.64 −0.14(2.33) (6.76) (1.34) (4.78) (5.26) (7.04) (−1.54)
FFC4FM 0.18 0.53 0.05 0.40 0.43 0.56 −0.13(1.72) (5.69) (0.54) (4.11) (4.59) (6.01) (−1.41)
FF5FM 0.13 0.49 −0.22 0.21 0.26 0.54 −0.01(1.34) (5.48) (−3.44) (2.67) (3.17) (5.90) (−0.09)
FF5FMCbGP 0.22 0.00 −0.03 0.10 0.08 0.01 0.04(1.94) (−0.06) (−0.28) (1.06) (0.90) (0.09) (0.43)
FF5FMCbGP+MOM 0.19 −0.01 −0.06 0.10 0.08 0.00 0.02(1.63) (−0.17) (−0.57) (1.00) (0.93) (0.05) (0.27)
38
Tab
le8:
Aco
mpa
riso
nof
profi
tabi
lity
mea
sure
san
dac
crua
lsin
Fam
a-M
acB
eth
regr
essi
ons
The
tabl
ere
port
sav
erag
eFa
ma-
Mac
Beth
prem
ium
s(m
ultip
lied
by10
0)an
dth
eirt
-val
ues
from
mon
thly
cros
s-se
ctio
nalr
egre
s-sio
nsto
pred
ict
stoc
kre
turn
s(m
odel
1to
13).
The
regr
essio
nsar
epe
rform
edfro
m07
/199
0to
06/2
016
for
agl
obal
sam
ple
ofD
Man
dEM
coun
trie
s,as
defin
edin
the
lege
ndof
Tabl
e1,
cont
rolli
ngfo
rpot
entia
lcou
ntry
effec
tsw
ithco
untr
ydu
mm
ies.
The
inde
pend
entv
aria
bles
aret
he1-
mon
th-la
gged
stoc
kre
turn
(r1,
1),m
omen
tum
(r12,2
),th
enat
ural
loga
rithm
ofth
eboo
k-to
-mar
ket
ratio
(log(
B/M
)),t
hena
tura
llog
arith
mof
the
lagg
edm
arke
tva
lue
(log(
MV
)),t
hegr
owth
rate
ofto
tala
sset
s(d
A/A
),re
turn
oneq
uity
(RO
E),g
ross
profi
tabi
lity
(GP)
,ope
ratin
gpr
ofita
bilit
yac
cord
ing
toFa
ma
and
Fren
ch(O
P FF),
oper
atin
gpr
ofita
bilit
y(O
P),c
ash-
base
dop
erat
ing
profi
tabi
lity
(CbO
P),c
ash-
base
dgr
ossp
rofit
abili
ty(C
bGP)
and
accr
uals
(Acc
r).F
urth
erde
tails
onth
eva
riabl
eco
nstr
uctio
nar
egi
ven
inTa
ble
2.T
hela
stro
wco
ntai
nsth
eav
erag
ead
just
edR
2 .
Pane
lA:I
ndiv
idua
lfor
ecas
ting
pote
ntia
lofp
rofit
abili
tym
easu
res
Pane
lB:M
argi
nale
ffect
sfor
cash
-bas
edgr
ossp
rof-
itabi
lity
and
the
othe
rpr
ofita
bilit
ym
easu
res
Mod
1M
od2
Mod
3M
od4
Mod
5M
od6
Mod
7M
od8
Mod
9M
od10
Mod
11M
od12
Mod
13r 1,1
−4.
69−
4.74
−4.
70−
4.72
−4.
71−
4.74
−4.
68−
4.76
−4.
75−
4.75
−4.
76−
4.75
−4.
74(−
10.9
0)(−
11.0
2)(−
10.9
2)(−
10.9
4)(−
10.9
2)(−
11.0
1)(−
10.8
4)(−
11.1
0)(−
11.0
4)(−
11.1
0)(−
11.0
8)(−
11.0
6)(−
11.0
4)r 1
2,2
0.33
0.30
0.32
0.32
0.32
0.30
0.33
0.29
0.30
0.30
0.30
0.30
0.30
(1.7
0)(1.5
8)(1.6
9)(1.6
6)(1.6
6)(1.5
8)(1.7
3)(1.5
5)(1.5
8)(1.5
7)(1.5
6)(1.5
8)(1.5
8)lo
g(B/
M)
0.27
0.33
0.28
0.29
0.29
0.33
0.29
0.32
0.33
0.32
0.33
0.33
0.33
(6.8
8)(8.3
0)(6.8
9)(7.1
7)(7.3
0)(8.3
2)(7.3
4)(8.2
7)(8.3
1)(7.8
7)(8.1
3)(8.3
2)(8.2
4)lo
g(M
V)
−0.
18−
0.17
−0.
18−
0.18
−0.
18−
0.17
−0.
17−
0.17
−0.
17−
0.18
−0.
17−
0.17
−0.
17(−
6.79
)(−
6.27
)(−
6.78
)(−
6.95
)(−
6.82
)(−
6.21
)(−
6.24
)(−
6.52
)(−
6.24
)(−
6.54
)(−
6.55
)(−
6.37
)(−
6.23
)dA
/A−
0.48
−0.
45−
0.49
−0.
47−
0.40
−0.
38−
0.41
−0.
37−
0.39
−0.
39−
0.38
−0.
37−
0.35
(−8.
63)
(−8.
55)
(−9.
08)
(−8.
95)
(−7.
72)
(−7.
12)
(−7.
70)
(−6.
67)
(−7.
20)
(−7.
19)
(−7.
10)
(−7.
05)
(−6.
65)
ROE
0.12
0.01
(1.3
4)(0.0
6)G
P1.
100.
34(7.9
6)(1.6
4)O
P FF
0.33
0.16
(5.9
5)(3.1
2)O
P1.
130.
37(5.8
0)(2.0
8)C
bOP
1.09
0.22
(7.5
3)(1.5
2)C
bGP
1.05
1.04
0.78
0.94
0.93
0.95
0.97
(9.0
6)(9.5
2)(5.0
4)(8.3
8)(8.9
9)(7.5
1)(7.7
6)A
ccr
−1.
18−
0.52
(−7.
82)
(−3.
06)
R2
12.8
6%12.8
8%12.8
4%12.8
6%12.8
5%12.8
7%12.8
1%12.9
2%12.8
8%12.8
9%12.8
9%12.8
7%12.8
7%
39
Tab
le9:
Pro
fitab
ility
mea
sure
san
dR
OE
grow
thT
heta
ble
repo
rts
aver
age
Fam
a-M
acBe
thpr
emiu
ms
(mul
tiplie
dby
100)
and
thei
rt-
valu
esfro
mm
onth
lycr
oss-
sect
iona
lre-
gres
sions
topr
edic
tth
ree-
year
grow
thin
profi
tabi
lity,
mea
sure
dasy t
=IBt+
3−IBt
BEt
,with
IB
asin
com
ebe
fore
expe
nditu
res
and
BE
asbo
okeq
uity
.T
here
gres
sions
are
perfo
rmed
from
07/1
990
to06
/201
6fo
ra
glob
alsa
mpl
eof
DM
and
EMco
untr
ies,
asde
fined
inth
ele
gend
ofTa
ble
1,co
ntro
lling
for
pote
ntia
lcou
ntry
effec
tsw
ithco
untr
ydu
mm
ies.
The
inde
pend
ent
varia
bles
are
mom
entu
m(r
12,2
),th
ena
tura
llog
arith
mof
the
book
-to-
mar
ket
ratio
(log(
B/M
)),t
hena
tura
llog
arith
mof
the
lagg
edm
arke
tva
lue
(log(
MV
)),t
hegr
owth
rate
ofto
tala
sset
s(dA
/A),
retu
rnon
equi
ty(R
OE)
,gro
sspr
ofita
bilit
y(G
P),o
pera
ting
profi
tabi
l-ity
acco
rdin
gto
Fam
aan
dFr
ench
(OP F
F),
oper
atin
gpr
ofita
bilit
y(O
P),c
ash-
base
dop
erat
ing
profi
tabi
lity
(CbO
P),c
ash-
base
dgr
oss
profi
tabi
lity
(CbG
P)an
dac
crua
ls(A
ccr)
.Fu
rthe
rde
tails
onth
eva
riabl
eco
nstr
uctio
nar
egi
ven
inTa
ble
2.T
hela
stro
wco
ntai
nsth
eav
erag
ead
just
edR
2 .T
het-
valu
esar
eca
lcul
ated
usin
gN
ewey
and
Wes
tst
anda
rder
rors
with
35la
gs.
Mod
1M
od2
Mod
3M
od4
Mod
5M
od6
Mod
7r 1
2,2
0.09
0.09
0.09
0.09
0.09
0.09
0.09
(4.4
8)(4.1
5)(4.3
0)(4.4
6)(4.4
5)(4.1
7)(4.4
7)lo
g(B/
M)
−0.
010.
010.
000.
000.
000.
00−
0.01
(−0.
69)
(0.3
1)(0.2
5)(0.1
2)(−
0.23
)(0.1
6)(−
0.72
)lo
g(M
V)
0.02
0.02
0.02
0.02
0.02
0.02
0.02
(4.6
6)(4.9
6)(4.9
8)(4.9
1)(4.8
0)(4.9
0)(4.6
5)dA
/A−
0.06
−0.
05−
0.05
−0.
05−
0.03
−0.
03−
0.05
(−5.
33)
(−4.
54)
(−5.
14)
(−4.
70)
(−2.
47)
(−2.
99)
(−4.
44)
ROE
−0.
65−
0.68
−0.
74−
0.73
−0.
69−
0.67
−0.
64(−
14.2
8)(−
15.6
7)(−
21.6
9)(−
19.7
1)(−
17.0
1)(−
15.1
4)(−
13.9
3)G
P0.
24(9.9
6)O
P FF
0.19
(6.3
7)O
P0.
52(8.2
4)C
bOP
0.37
(8.2
1)C
bGP
0.22
(9.7
3)A
ccr
−0.
22(−
8.23
)R
226.2
4%26.9
3%27.6
2%27.4
7%27.2
0%26.9
1%26.3
9%
40
Tab
le10
:T
hero
leof
the
deno
min
ator
The
tabl
esh
ows
resu
ltsfo
rpr
ofita
bilit
yfa
ctor
sba
sed
onca
sh-b
ased
gros
spr
ofits
scal
edby
diffe
rent
deno
min
ator
s.R
MW
CbG
Pus
escu
rren
tto
tala
sset
sas
deno
min
ator
,RM
WC
bGP
/Lag
Aus
eson
e-ye
arla
gged
tota
lass
ets,
and
RM
WC
bGP
/EV
uses
ente
rpris
eva
lue.
Pane
lAre
port
sth
eav
erag
em
onth
lyre
turn
s,th
em
onth
lyst
anda
rdde
viat
ions
,and
the
t-va
lues
ofth
eth
ree
fact
ors.
Pane
lBsh
owss
pann
ing
test
s.Pa
nelC
pres
ents
the
max
imum
ex-p
osta
nnua
lized
Shar
pera
tiost
hatc
anbe
achi
eved
byad
ding
the
diffe
rent
varia
ntso
fthe
cash
-bas
edpr
ofita
bilit
yfa
ctor
toth
eot
herf
acto
rsan
dth
ein
divi
dual
fact
orwe
ight
s.T
here
mai
ning
fact
ors
are
desc
ribed
inTa
ble
4.T
hean
alys
isis
perfo
rmed
from
07/1
990
until
06/2
016.
Pane
lA:F
acto
rsu
mm
ary
stat
istic
sfo
rdi
ffere
ntde
nom
inat
ors
mea
nst
.dev
.t-
valu
eR
MW
CbG
P0.
361.
354.
67R
MW
CbG
P/L
agA
0.26
1.45
3.14
RM
WC
bGP
/EV
0.63
1.47
7.57
Pane
lB:S
pann
ing
test
sA
lpha
t-va
lR
MR
FSM
BH
ML
WM
LC
MA
AC
CR
MW
CbG
PR
MW
CbG
P/E
V
RM
WC
bGP
/Lag
A0.
000.
22-0
.01
-0.0
1-0
.04
-0.0
0-0
.23
0.07
0.92
RM
WC
bGP
/EV
0.10
2.36
0.02
0.09
0.61
0.03
0.10
-0.0
40.
56R
MW
CbG
P0.
173.
57-0
.09
-0.1
3-0
.58
0.02
-0.1
40.
190.
75Pa
nelC
:Max
imum
expo
stSh
arpe
ratio
sR
MR
FSM
BH
ML
WM
LC
MA
AC
CR
MW
CbG
PR
MW
CbG
P/L
agA
RM
WC
bGP
/EV
SRFF
5FM
CbG
P+
MO
M+
AC
C0.
100.
080.
220.
050.
070.
020.
462.
07FF
5FM
CbG
P/L
agA
+M
OM
+A
CC
0.09
0.07
0.22
0.05
0.15
0.00
0.41
2.01
FF5F
MC
bGP
/EV
+M
OM
+A
CC
0.11
0.03
0.00
0.11
0.00
0.19
0.57
1.91
41
Table 11: Factor summary statistics - Regional AnalysisThe table shows results for the different regions. Panel A reports the average monthly returnsand the t-values of the profitability factors (RMW, robust minus weak) based on (1) ROE,(2) GP, (3) OPFF, (4) OP, (5) CbOP and (6) CbGP. Panel B presents the results from factorspanning regressions. The dependent variables are the factors listed above with the exceptionof RMWCbGP. The independent variables are the market, size, value, investment, momentumfactors plus the profitability factor based on CbGP. The factors are constructed as in Table 4.The regions besides emerging markets are defined as in Fama and French (2012) or Famaand French (2015). The analysis is performed from 07/1990 to 06/2016.
DevelopedMarkets
EmergingMarkets Europe Asia Pacific
ex Japan Japan
Panel A: Monthly average returns
RMWROE 0.13 0.19 0.18 0.09 -0.01(1.69) (1.54) (2.06) (0.57) (-0.05)
RMWGP 0.33 0.38 0.29 0.24 0.26(3.94) (2.8) (3.83) (1.31) (2.02)
RMWOPFF 0.19 0.32 0.19 0.24 0.03(2.82) (2.71) (2.58) (1.42) (0.3)
RMWOP 0.29 0.24 0.28 0.22 0.2(3.52) (1.85) (3.68) (1.25) (1.35)
RMWCbOP 0.31 0.27 0.34 0.32 0.12(4.11) (2.35) (4.59) (2.29) (1.04)
RMWCbGP 0.35 0.41 0.31 0.35 0.26(4.44) (3.39) (4.27) (2.35) (2.2)
Panel B: Spanning alphas
RMWROE 0.05 0.04 0.02 0.14 0.03(0.65) (0.49) (0.26) (1.13) (0.41)
RMWGP 0 0.09 0.04 0.04 0(-0.13) (1.48) (1.41) (0.44) (-0.03)
RMWOPFF 0.04 0.18 -0.02 0.11 -0.05(0.58) (1.98) (-0.27) (1.17) (-0.6)
RMWOP 0.06 0.04 0.08 0.11 0.05(1.17) (0.45) (1.58) (1.17) (0.56)
RMWCbOP 0.07 -0.08 0.11 0.1 -0.06(1.44) (-1.13) (2.35) (1.77) (-0.77)
42
Table 12: Country-specific RMW-factors based on cash-based gross profitabilityThe table reports the average monthly returns of country-specific RMW-factors based onCbGP (column 2) and associated spanning test alphas, employing the following (country-specific) factors: the excess market return and the size, value, investment, and momentumfactors (column 4), and the associated t-values one column to the right, respectively (column3 and 5). The analysis is performed from 07/1990 to 06/2016.
Country averagereturn t-value spanning
alpha t-value
Argentina 1.31 2.50 1.20 2.05Australia 0.27 1.64 0.44 3.22Austria 0.16 0.74 0.23 1.03Belgium -0.29 -1.40 -0.23 -1.14Brazil 0.81 1.92 1.04 2.56Canada 0.46 1.95 0.74 4.28Chile 0.03 0.17 0.22 1.17China 0.39 1.11 0.54 1.57Denmark 0.85 3.35 1.02 4.24Egypt 0.12 0.18 0.38 0.68Finland 0.24 0.74 0.32 1.18France 0.38 2.93 0.49 4.19Germany 0.61 4.44 0.64 4.50Great Britain 0.22 1.69 0.40 4.08Greece 0.47 1.69 0.51 1.95Hong Kong 0.54 2.47 0.82 4.69Hungary -0.64 -1.40 0.24 0.48India 0.44 1.52 0.71 2.90Indonesia 1.03 3.02 1.34 4.00Ireland -0.04 -0.08 -0.06 -0.12Israel 0.57 1.50 0.57 1.65Italy 0.19 0.99 0.27 1.52Japan 0.26 2.20 0.40 3.85Korea 0.73 2.57 0.98 3.81Malaysia 0.49 1.96 0.73 4.05Netherlands 0.20 0.92 0.33 1.60New Zealand -0.06 -0.22 0.20 0.79Norway 0.42 1.62 0.45 1.80Pakistan 0.13 0.27 0.50 1.23Peru 0.28 0.49 0.79 1.07Philippines 0.33 0.82 0.85 2.30Poland 0.64 2.00 0.73 2.82Portugal 0.63 2.18 0.72 2.61Russia -0.23 -0.54 -0.08 -0.20Singapore 0.56 2.60 0.73 4.21South Africa 0.45 1.87 0.63 2.85Spain 0.19 0.96 0.29 1.57Sweden 0.19 0.94 0.28 1.61Switzerland 0.47 2.59 0.55 3.23Taiwan 0.50 2.28 0.51 3.29Thailand 0.60 2.10 0.89 3.68Turkey -0.07 -0.20 0.48 1.36
43
Figure 1: Cumulative Performance of the Cash-based Gross Profitability FactorThe figure plots the cumulated performance of the monthly time-series of CbGP and thespanning alpha of CbGP. The sample period starts in 7/1990 and ends in 06/2016.
0
50
100
150
1990 2000 2010
Year
Cum
. Per
form
ance
variable RMWCbGP αRMWCbGP
44
Figure 2: Spanning test alphas for country-specific cash-based gross-profitabilityfactorsThe figure shows country-level alphas obtained from regressing the profitability factorsbased on cash-based gross-profitability on the excess market return (RMRF), small minusbig (SMB), high minus low (HML), conservative minus aggressive (CMA) and momentum(MOM). The analysis is performed from 07/1990 to 06/2016.
0.0
0.5
1.0
Indo
nesi
aA
rgen
tina
Bra
zil
Den
mar
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rea
Thai
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nes
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g Ko
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ece
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ance
Turk
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ain
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and
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gium
45
A Appendix
A.1 Sample definition
Constituent lists
Datastream comprises three types of constituent lists: (1) research lists, (2) Worldscope lists
and (3) dead lists. By using dead lists, we ensure to obviate any survivorship bias. For
every country we use the intersection of all available lists and eliminate any duplicates. As a
result, we have one remaining list for every country, which can subsequently be used in the
static filter process. Table A.1 and Table A.2 provide an overview of the constituent lists for
developed markets and emerging markets, respectively, used in our study.
Static screens
We restrict our sample to common stocks exclusively by performing several static screens, as
displayed in Table A.3. Screen (1) to (7) are standard filter rules that are straightforward
to apply. Screen (8) is performed as follows: Ince and Porter (2006), Campbell, Cowan, and
Salotti (2010), Griffin, Kelly, and Nardari (2010), and Karolyi, Lee, and Dijk (2012), among
others, have provided generic filter rules for the exclusion of non-common equity securities
in Datastream. The authors provide certain keywords, which are searched for in the name
of the Datastream securities. We follow these studies and delete a stock only, if a keyword
within the name of the stock fulfills the following conditions: (1) There is a whitespace (or
tab) directly before the keyword; (2) There is a whitespace or a dot after the keyword, or
the name of the stock ends with the keyword. We use the following three Datastream items
as name, respectively: “NAME”, “ENAME”, and “ECNAME”. This process prevents that
stocks are deleted by accident, which otherwise could happen, if a keyword was part of a
regular, larger word that actually was to be accepted. Finally, we also perform a quality
review of all deleted stocks in order to make sure that all the deletions have been justified.
Table A.4 provides an overview of the keywords used in our study. Furthermore, Griffin,
46
Kelly, and Nardari (2010) have introduced country-specific keywords that we also apply in
our study. In general, this process is very similar to the generic filter rules presented above,
but now we have to filter for a certain country before we can apply the associated keyword
tests. Table A.5 provides an overview of the country-specific keywords used in our study.
Dynamic screens
Moreover, we perform several dynamic screens in order to ensure high data quality and to
limit data errors, which have become standard in the literature on the analysis of international
stock markets. Table A.6 provides an overview.
A.2 Variable definitions
In this section, we first state the variable definitions used within this study and then give a
tabular overview of all the required Datastream items.
Return on equity (ROE):
ROE = Income before expendituresBook equity
with:
Book equity = Common equity + Deferred taxes
Gross profits (GP), according to Novy-Marx (2013):
GP = Sales − COGSTotal assets
47
Operating profitability according to Ball et al. (2015):
OP = Sales − COGS − Reported SG&ATotal assets
with:
Reported SG&A = SG&A− R&D expenditures
Operating profitability according to Fama and French (2015):
OPFF = Sales − COGS − SG&A− Interest expensesBook equity
Cash-based Operating profitability according to Ball et al. (2016):
CbOP = OP + Cash-based adjustmentsTotal assets
with:
Cash-based adjustments = −∆Accounts receivable −∆Inventory −∆Prepaid expenses
+ ∆Deferred revenue + ∆Trade accounts payable + ∆Accrued expenses
Cash-based gross profitability:
CbGP = GP + Cash-based adjustmentsTotal assets
48
Accruals according to Ball et al. (2016):
Accr = ∆Current assets −∆Cash −∆Current liabilitiesTotal assets
+ ∆Debt in current liabilities + ∆Income tax payable −∆DepreciationTotal assets
The accounting variables used so far and their Datastream equivalents are depicted in
Table A.7.
49
Table A.1: Constituent lists: Developed marketsThe table contains the Research lists, Worldscope lists and Dead lists of developed marketscountries in our sample.
Country Lists Country Lists
Australia DEADAU Japan DEADJPFAUS FJASDAQWSCOPEAU FOSAKA
Austria ALLAS FTOKYODEADOE FUKUOKAFOST JAPALLWSCOPEOE JAPOTC
Belgium DEADBG WSCOPEJPFBEL Netherlands ALLFLWSCOPEBG DEADNL
Canada DEADCN1 FHOLDEADCN2 WSCOPENLDEADCN3 New Zealand DEADNZDEADCN4 FNWZDEADCN5 WSCOPENZDEADCN6 Norway DEADNWFTORO FNORFVANC WSCOPENWLTTOCOMP Portugal DEADPTWSCOPECN FPOM
Denmark DEADDK FPORFDEN FPSMWSCOPEDK WSCOPEPT
Finland DEADFN Singapore DEADSGFFIN FSINWSCOPEFN FSINQ
France ALLFF WSCOPESGDEADFR Spain DEADESFFDOM FBILFFOTC FBRCLFFRA FSPDOMWSCOPEFR FSPN
Germany DEADBD1 FSPNQDEADBD2 FVALDEADBD3 WSCOPEESDEADBD4 Sweden DEADSDDEADBD5 FSWDDEADBD6 WSCOPESDFGERDOM Switzerland DEADSWFGERIBIS FSWAFGKURS FSWSWSCOPEBD WSCOPESW
Greece DEADGR Great Britain DEADUKFGREE FBRITFGRMM LSETSCOSFGRPM LSETSMMFNEXA LUKPLUSMWSCOPEGR WSCOPEJE
Hong Kong DEADHK WSCOPEUKFHKQWSCOPEHK
Ireland DEADIRFIRLWSCOPEIR
Italy DEADITFITAWSCOPEIT
50
Table A.2: Constituent lists: Emerging marketsThe table contains the Research lists, Worldscope lists and Dead lists of emerging marketscountries in our sample.
Argentina FPARGA Morocco DEADMORWSCOPEAR FMORDEADAR WSCOPEMC
Brazil DEADBRA Pakistan DEADPAFBRA FPAKWSCOPEBR WSCOPEPK
Chile DEADCHI Peru DEADPEFCHILE FPERUFCHILE10 WSCOPEPEWSCOPECL Philippines DEADPH
China DEADCH FPHIFCHINA FPHILAWSCOPECH FPHIMN
Columbia DEADCO FPHIQFCOL WSCOPEPHWSCOPECB Poland DEADPO
Czech Republic DEADCZ FPOLFCZECH WSCOPEPOWSCOPECZ Russia DEADRU
Egypt DEADEGY FRUSFEGYPT WSCOPERSWSCOPEEY South Africa DEADSAF
Hungary DEADHU FSAFFHUN WSCOPESAWSCOPEHN Sri Lanka DEADSL
India DEADIND FSRILAFBSE WSCOPECYFINDIA Korea DEADKOFNSE FKORWSCOPEIN WSCOPEKO
Indonesia DEADIDN Taiwan DEADTWFINODB FTAIQFINOMB WSCOPETAFINOQ Thailand DEADTHWSCOPEID FTHAQ
Israel WSCOPEIS WSCOPETHFISRAEL Turkey DEADTKDEADIS FTURK
Jordan FJORD WSCOPETKWSCOPEJO Slovakia DEADSLODEADJO FSLOVAK
Malaysia DEADMY FSLOVALLFMAL WSCOPESXFMALQ Venezuela DEADVEWSCOPEMY WSCOPEVE
Mexico DEADME FVENZFMEXMEX101WSCOPEMX
51
Table A.3: Static ScreensThe table displays the static screens applied in our study, mainly following Ince and Porter(2006), Schmidt et al. (2017) and Griffin, Kelly, and Nardari (2010). Column 3 lists theDatastream items involved (on the left of the equality sign) and the values which we setthem to in the filter process (on the right of the equality sign). Column 4 indicates thesource of the screens.
Nr. Description Datastream item(s)involved
Source
(1) For firms with more than onesecurity, only the one with thebiggest market capitalization andliquidity is used.
MAJOR = Y Schmidt et al. (2017)
(2) The type of security must be eq-uity.
TYPE = EQ Ince and Porter(2006)
(3) Only the primary quotations of asecurity are analyzed.
ISINID = P Fong, Holden, andTrzcinka (2017)
(4) Firms are located in the respec-tive domestic country.
GEOGN = countryshortcut
Ince and Porter(2006)
(5) Securities are listed in the respec-tive domestic country.
GEOLN = countryshortcut
Griffin, Kelly, andNardari (2010)
(6) Securities with quoted currencydifferent from the one of the as-sociated country are disregarded.a
PCUR = currencyshortcut of the coun-try
Griffin, Kelly, andNardari (2010)
(7) Securities with ISIN country codedifferent from the one of the asso-ciated country are disregarded.b
GGISN = countryshortcut
Annaert, Ceuster,and Verstegen (2013)
(8) Securities whose name fields indi-cate non-common stock affiliationare disregarded.
NAME, ENAME,ECNAME
Ince and Porter(2006), Campbell,Cowan, and Salotti(2010), Griffin, Kelly,and Nardari (2010)and Karolyi, Lee, andDijk (2012)
a In this filter rule also the respective pre-euro currencies are accepted for countries withinthe euro zone. Moreover, in Russia “USD” is also accepted as currency, besides “RUB”.
b In Hong Kong, ISIN country codes equal to “BM” or “KY” and in the Czech RepublicISIN country codes equal to “CS” are also accepted.
52
Table A.4: Generic Keyword DeletionsThe table reports the generic keywords, which are searched for in the names of all stocks ofall countries. If a harmful keyword is detected as part of the name of a stock, the respectivestock is removed from the sample.
Non-common equity Keywords
Duplicates 1000DUPL, DULP, DUP, DUPE, DUPL, DUPLI,DUPLICATE, XSQ, XETa
Depository Receipts ADR, GDRPreferred Stock PF, ’PF’, PFD, PREF, PREFERRED, PRFWarrants WARR, WARRANT, WARRANTS, WARRT, WT, WTS,
WTS2Debt %, DB, DCB, DEB, DEBENTURE, DEBENTURES, DEBTUnit Trusts .IT, .ITb, INV, INV TST, INVESTMENT TRUST,
RLST IT, TRUST, TRUST UNIT, TRUST UNITS, TST,TST UNIT, TST UNITS, UNIT, UNIT TRUST, UNITS,UNT, UNT TST, UT
ETFs AMUNDI, ETF, INAV, ISHARES, JUNGE, LYXOR, X-TRExpired securities EXPD, EXPIRED, EXPIRY, EXPYMiscellaneous (mainly taken fromInce and Porter, 2006)
ADS, BOND, CAP.SHS, CONV, CV, CVT, DEFER,DEP, DEPY, ELKS, FD, FUND, GW.FD, HI.YIELD,HIGH INCOME, IDX, INC.&GROWTH, INC.&GW,INDEX, LP, MIPS, MITS, MITT, MPS, NIKKEI, NOTE,OPCVM, ORTF, PARTNER, PERQS, PFC, PFCL, PINES,PRTF, PTNS, PTSHP, QUIBS, QUIDS, RATE, RCPTS,REAL EST, RECEIPTS, REIT, RESPT, RETUR, RIGHTS,RST, RTN.INC, RTS, SBVTG, SCORE, SPDR, STRYPES,TOPRS, UTS, VCT, VTG.SAS, XXXXX, YIELD, YLD
53
Table A.5: Country-specific Keyword DeletionsThe table reports the country-specific keywords, which are searched for in the names of allstocks of the respective countries. If a harmful keyword is detected as part of the name of astock, the respective stock is removed from the sample.
Country Keywords
Australia PART PAID, RTS DEF, DEF SETT, CDIAustria PC, PARTICIPATION CERTIFICATE, GENUSSSCHEINE,
GENUSSCHEINEBelgium VVPR, CONVERSION, STRIPBrazil PN, PNA, PNB, PNC, PND, PNE, PNF, PNG, RCSA,
RCTBCanada EXCHANGEABLE, SPLIT, SPLITSHARE, VTG\\.,
SBVTG\\., VOTING, SUB VTG, SERIESDenmark \\)CSE\\)Finland USEFrance ADP, CI, SICAV, \\)SICAV\\), SICAV-Germany GENUSSCHEINEGreat Britain PAID, CONVERSION TO, NON VOTING,
CONVERSION ’A’Greece PRIndia FB DEAD, FOREIGN BOARDIsrael P1, 1, 5Italy RNC, RP, PRIVILEGIESKorea 1PMexico CPO, ’L’, ’C’Malaysia ’A’Netherlands CERTIFICATE, CERTIFICATES, CERTIFICATES\\),
CERT, CERTS, STK\\.New Zealand RTS, RIGHTSPeru INVERSION, INVN, INVPhilippines PDRSouth Africa N’, OPTS\\., CPF\\., CUMULATIVE PREFERENCESweden CONVERTED INTO, USE, CONVERTED-,
CONVERTED - SEESwitzerland CONVERTED INTO, CONVERSION, CONVERSION SEE
54
Table A.6: Dynamic ScreensThe table displays the dynamic screens applied in our study, following Ince and Porter (2006)and Schmidt et al. (2017). Column 3 lists the Datastream items required and column 4 statesthe sources of the screens.
Nr. Description Datastream item(s)involved
Source
(1) We delete the zero returns atthe end of the return time-series,which exist, because in case ofa delisting Datastream displaysstale prices from the date ofdelisting until the end of the re-spective time-series. We alsodelete the associated market cap-italizations.
TRI, MV Ince and Porter (2006)
(2) We delete the associated returnsand market capitalizations in caseof abnormal prices (unadjustedprices > 1000000).
TRI, MV, UP The screen originallystems from Schmidt etal. (2017), but con-trary to this study,we employ the unad-justed price instead ofthe price index for thedetermination of ab-normal prices
(3) We delete returns and the asso-ciated market capitalizations incase of return spikes (returns >990%).
TRI, MV Schmidt et al. (2017)
(4) We delete returns and the asso-ciated market capitalizations incase of strong return reversals, de-fined as follows: Rt−1 or Rt >=3.0 and (1 + Rt−1)(1 + Rt)− 1 <0.5.
TRI, MV Ince and Porter (2006)
55
Tab
leA
.7:
Acc
ount
ing
vari
able
san
dD
atas
trea
mid
enti
fiers
The
tabl
eco
ntai
nsal
lacc
ount
ing
varia
bles
used
inou
rst
udy
aswe
llas
the
asso
ciat
edid
entifi
ers
used
byD
atas
trea
m.
Acc
ount
ing
vari
able
Dat
astr
eam
item
Nee
ded
for
defin
itio
n(s)
ROE
GP
OP
FFO
PC
bOP
CbG
PA
ccru
als
EVIn
com
ebe
fore
expe
nditu
res
WC
0155
1x
Com
mon
equi
tyW
C03
501
xx
Def
erre
dta
xes
WC
0326
3x
xSa
les
WC
0100
1x
xx
xx
CO
GS
WC
0105
1x
xx
xx
Tota
lass
ets
WC
0299
9x
xx
xSG
&A
WC
0110
1x
xx
Res
earc
han
dde
velo
pmen
tco
sts
WC
0120
1x
xIn
tere
stex
pens
esW
C01
251
xA
ccou
nts
rece
ivab
leW
C02
051
xx
Inve
ntor
yW
C02
101
xx
Prep
aid
expe
nses
WC
0214
0x
xD
efer
red
reve
nue
WC
0326
2x
xTr
ade
acco
unts
paya
ble
WC
0304
0x
xA
ccru
edex
pens
esA
ccru
edpa
yrol
lW
C03
054
xx
Oth
erac
crue
dex
pens
esW
C03
069
xx
Cur
rent
asse
tsW
C02
201
xC
ash
&sh
ort
term
inve
stm
ents
WC
0200
1x
xC
urre
ntlia
bilit
ies
WC
0310
1x
Deb
tin
curr
ent
liabi
litie
s=
shor
t-te
rmde
btW
C03
051
xIn
com
eta
xpa
yabl
eW
C03
063
xD
epre
ciat
ion
WC
0115
1x
Pref
erre
dst
ock
WC
0345
1x
Min
ority
inte
rest
WC
0342
6x
Shor
t-te
rmde
btan
dcu
rren
tpo
rtio
nof
long
-ter
mde
btW
C03
051
xLo
ng-t
erm
debt
WC
0325
1x
56