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Abstract

Almost 20 years after its publication, Piotroski’s (J Account Res 38:1–41, 2000) FSCORE, the composite measure of the firm’s fundamental strength remains a strong predictor of subsequent stock returns and future profitability in international markets over the 2000–2018 period. Across developed non-US countries as well as emerging countries, high-FSCORE firms significantly outperform low-FSCORE firms by about 10% per year. Furthermore, FSCORE preserves its return-predictive power in all size segments after controlling for established cross-sectional return determinants, such as firm size, book-to-market, momentum, operating profitability, and investment. The findings are consistent with the view that fundamental information is only gradually incorporated into prices by investors.
Vol:.(1234567890)
Journal of Asset Management (2020) 21:106–118
https://doi.org/10.1057/s41260-020-00157-2
ORIGINAL ARTICLE
Piotroski’s FSCORE: international evidence
ChristianWalkshäusl1
Revised: 20 February 2020 / Published online: 9 March 2020
© The Author(s) 2020
Abstract
Almost 20years after its publication, Piotroski’s (J Account Res 38:1–41, 2000) FSCORE, the composite measure of the
firm’s fundamental strength remains a strong predictor of subsequent stock returns and future profitability in international
markets over the 2000–2018 period. Across developed non-US countries as well as emerging countries, high-FSCORE firms
significantly outperform low-FSCORE firms by about 10% per year. Furthermore, FSCORE preserves its return-predictive
power in all size segments after controlling for established cross-sectional return determinants, such as firm size, book-to-
market, momentum, operating profitability, and investment. The findings are consistent with the view that fundamental
information is only gradually incorporated into prices by investors.
Keywords FSCORE· Fundamental analysis· Stock returns· Return predictability· International markets
Introduction
In his seminal work, Piotroski (2000) develops an account-
ing-based composite measure of the firm’s fundamental
strength, the FSCORE, which employs historical financial
statement information to identify fundamentally weak and
strong firms among value stocks. His results on the US mar-
ket reveal a significantly positive FSCORE-return relation
among firms with high book-to-market ratios that is robust
to standard controls of that time.
Since then, the FSCORE has become particularly popular
as a stock screening tool among US investors (Novy-Marx
2014) but also has been used for various purposes in the
academic US literature. For instance, it has been applied for
predicting future firm profitability (Fama and French 2006),
institutional investor demand (Choi and Sias 2012), and as
an instrument variable for testing how public fundamental
information is incorporated into prices (Turtle and Wang
2017). In the latter vein, Piotroski and So (2012) and Ahmed
and Safdar (2018) show that investors’ expectation errors
concerning the firm’s fundamental strength, as proxied by
FSCORE, cause the US value and momentum premiums and
therefore help to explain these anomalies.
Besides, a recently growing strand of the literature also
documents the usefulness of FSCORE in diverse applica-
tions outside the USA. Consistent with Piotroski and So
(2012), Ng and Shen (2016) reveal that FSCORE helps to ex
ante separate subsequent winners from losers among Asian
value and growth firms. Walkshäusl (2017, 2019) finds sup-
portive evidence that the FSCORE also adds to our under-
standing of the value and momentum effects in European
stock returns that can be traced back to investors’ expecta-
tion errors concerning firm fundamentals. Tikkanen and Äijö
(2018) show that incorporating the information contained in
FSCORE improves the performance of various long-only
value investing strategies in Europe that are formed on valu-
ation ratios other than book-to-market. Finally, Hyde (2018)
and Ng and Shen (2019) provide evidence on the market-
wide FSCORE-return relation in Australia and five Asian
equity markets.
In this paper, we revisit the FSCORE and study its return-
predictive ability in the broad cross section of international
firms drawn from 20 developed non-US markets and 15
emerging markets in a unified framework of analysis over
the post-publication period 2000–2018. We follow the path
recently traveled by Hyde (2018) and Ng and Shen (2019)
who also extend the scope of Piotroski (2000) by not just
focusing on the application of FSCORE among value stocks
but across all sample firms to shed light on its global eco-
nomic importance as an average-return predictor.
* Christian Walkshäusl
christian.walkshaeusl@ur.de
1 Center ofFinance, University ofRegensburg,
Universitätsstraße 31, 93053Regensburg, Germany
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107Piotroski’s FSCORE: international evidence
For readers who associate the FSCORE only with value
investing, this may warrant some discussion. Though
Piotroski (2000) initially has tested the FSCORE among
value stocks because ‘[h]igh book-to-market firms offer
a unique opportunity to investigate the ability of simple
fundamental analysis heuristics to differentiate firms’ (p.
2) due to their nature of being largely neglected by inves-
tors and thinly followed by analysts, the application of
FSCORE may not be limited to firms with high book-to-
market ratios. In fact, given that the FSCORE captures
information about the firm’s fundamental strength or fun-
damental quality, it represents a return-predictive device
on its own that can be analyzed across all types of firms.
Our approach is in line with Piotroski and So (2012) as
well as prominent replication studies like Hou etal. (2018)
who also investigate the FSCORE-return relation among
all sample firms and not just among value stocks in the
USA. By studying the pure FSCORE-return relation, we
aim to provide a clearer and more general perspective on
the genuine return-predictive power of FSCORE in inter-
national markets that is not influenced by another variable.
Our framework of analysis is inspired by Fama and
French (2008) and employs the two most common tech-
niques in studying variable-return relations: portfolio sorts
and firm-level cross-sectional regressions in the manner of
Fama and MacBeth (1973). The first approach gives a good
impression of how average returns vary with FSCORE,
while the second approach helps to assess the incremental
power of FSCORE for predicting subsequent stock returns
in the presence of established determinants of the cross
section. We take into account the most recent develop-
ments in asset pricing that explicitly consider controls for
the fundamental aspects of the firm based on operating
profitability and investment behavior (Fama and French
2015, 2018). In further robustness tests, we additionally
investigate whether our key findings hold when the con-
trols of the q-factor model of Hou etal. (2015) are applied
as an alternative way of risk-adjusting returns. Through-
out our main return analysis, we also study the FSCORE-
return relation in three different size segments (small-cap,
mid-cap, and large-cap stocks) to evaluate its pervasive-
ness across firm size. This is important from a practical
point of view to examine whether the excess returns asso-
ciated with FSCORE are a market-wide phenomenon or
mostly concentrated among low-capitalization stocks and
therefore probably not realizable by international inves-
tors. Finally, we revisit the proposition that the return pre-
dictability of FSCORE arises due to its ability to forecast
the firm’s future profitability. Under the assumption that
investors tend to underreact to changes in firm fundamen-
tals (e.g., Lakonishok etal. 1994), FSCORE should pos-
sess unique information about subsequent fundamental
performance that governs the positive FSCORE-return
relation.
The remainder of the paper is organized as follows. The
next section reviews the existing literature in more detail and
provides a synthesis of our contribution in comparison with
previous works. After describing the data and variables used
in this study, the subsequent section presents the empirical
results with respect to (1) the FSCORE-return relation, (2)
FSCORE’s incremental cross-sectional return predictability,
and (3) its ability to forecast future firm profitability. After
that, further robustness tests are provided before the final
section concludes the paper.
Literature review andsynthesis
ofcontribution
Before we present our empirical analysis, we review the
existing research on FSCORE in investment strategies in
more detail with the aim to synthesize the contribution of
our study in comparison with previous works. For ease of
assessment, Table1 summarizes methodological aspects and
performance-related findings of the literature.
The FSCORE in subsamples and in combination with
other variables Though the FSCORE represents a return-
predictive device on its own, it has previously been inves-
tigated largely in subsamples of firms (value stocks) or in
combination with other variables, such as book-to-market
and momentum. In the spirit of Piotroski’s (2000) original
study, Tikkanen and Äijö (2018) show that the performance
of European long-only value investing strategies that employ
valuation ratios other than book-to-market for the classifi-
cation of value stocks, such as the earnings-to-price ratio,
dividend yield, and enterprise multiple, can be significantly
improved by incorporating the information contained in
FSCORE.
Piotroski and So (2012), Ng and Shen (2016), and Walk-
shäusl (2017) document for the USA, seven Asia–Pacific
markets, and Europe that there exists a strong performance-
related interaction between FSCORE and the full spectrum
of book-to-market ratios, i.e., value and growth stocks. They
find that the positive value-growth returns are concentrated
among value stocks with high FSCORES and growth stocks
with low FSCORES, but absent among value stocks with
low FSCORES and growth stocks with high FSCORES.
Hence, consistent with a mispricing-based explanation, their
results suggest that the value premium is the result of price
corrections arising from the reversal of investors’ expecta-
tion errors for those firms, where market-based performance
expectations implied by the book-to-market ratio are incon-
gruent with the actual fundamental strength of the firm as
measured by FSCORE.
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108
C.Walkshäusl
In an analogous manner, Ahmed and Safdar (2018) and
Walkshäusl (2019) present evidence for the US and Euro-
pean equity markets that the FSCORE also helps to explain
the momentum premium by finding strong interactions
between FSCORE and the firms’ past price performance.
In line with the notion that investors tend to underreact to
changes in firm fundamentals, they find that the positive
winner–loser returns are concentrated among those firms
where past price performance is congruent with the firms’
fundamental strength but absent among those firms where
past price performance is incongruent with the firms’ fun-
damental strength.
The pure FSCORE-return relation and size segmentation
As shown in Table1, the analysis of different size segments
is not uncommon in this strand of the literature. However,
only three studies have explicitly investigated the pure
FSCORE-return relation in detail without any complement-
ing variables.1 These studies consider the USA (Turtle and
Table 1 Research on FSCORE in investment strategies
This table summarizes methodological aspects and performance-related findings of research on FSCORE in investment strategies. The table
reports the sample of the given study and the emphasis of the analysis. Return measurement: equal-weighted (EW), market-adjusted, stock return
minus the market return (MKT-Adj.), risk-adjusted, abnormal return after controls (Risk-Adj.), size-adjusted, stock return minus the return on
its matching size group (SZ-Adj.), and value-weighted (VW). The FSCORE premium is the return difference between high- and low-FSCORE
firms based on the given return measurement per month (p.m.) or per year (p.a.)
The asterisk (*) specifies that the reported value is a calculated average across individual countries or from bivariate sorts. ‘Size segmentation’
indicates that results for different size segments are reported in the given study. Risk adjustment: 3F (controls for firm size and book-to-market),
4F (controls for firm size, book-to-market, and momentum), 5F (controls for firm size, book-to-market, operating profitability, and investment),
and 6F (controls for firm size, book-to-market, momentum, operating profitability, and investment). ‘Significant’ indicates whether the return
effect associated with FSCORE is significant after risk adjustment
Study Sample Emphasis Return measure-
ment
FSCORE premium Size
segmen-
tation
Risk
adjust-
ment
Significant
Piotroski (2000) USA (value firms),
1976–1996
FSCORE among
value firms
EW, MKT-Adj. 23.5% p.a. (EW),
23.0% p.a.
(MKT-Adj.)
Yes 4F Yes
Piotroski and So
(2012)
USA, 1972–2010 Interaction between
book-to-market
and FSCORE
SZ-Adj. 10.03% p.a. No 4F Yes
Ng and Shen
(2016)
7 Asia–Pacific
markets,
2000–2015
Interaction between
book-to-market/
firm size and
FSCORE
VW, Risk-Adj. 0.83% p.m. (Risk-
Adj.)*
Yes 4F Yes
Turtle and Wang
(2017)
USA, 1973–2014 FSCORE as an
information
instrument
EW 6.73% p.a. Yes 6F Yes
Walkshäusl (2017) Europe, 1990–2013 Interaction between
book-to-market
and FSCORE
SZ-Adj. 0.84% p.m. Yes 4F Yes
Ahmed and Safdar
(2018)
USA, 1973–2015 Interaction between
momentum and
FSCORE
SZ-Adj. 8.59% p.a. No 3F Yes
Hyde (2018) Australia,
1993–2013
FSCORE as a qual-
ity measure
EW, VW 1.31% p.m. (EW),
0.52% p.a. (VW)
Yes 4F Yes (EW), No
(VW)
Tikkanen and Äijö
(2018)
Europe (value
firms), 1992–
2014
FSCORE among
value firms
EW 8.00% p.a. to
17.33% p.a.
Yes 5F Yes
Ng and Shen
(2019)
5 Asian markets,
2000–2016
FSCORE as a qual-
ity measure
EW, VW 0.71% p.m. (EW)*,
0.26% p.m.
(VW)*
No 4F Yes
Walkshäusl (2019) Europe, 1990–2017 Interaction between
momentum and
FSCORE
SZ-Adj. 1.03% p.m.* Yes 6F Ye s
1 Though the emphasis of Piotroski and So (2012) and Walkshäusl
(2017) is on the impact of FSCORE in value-growth strategies, they
also present (introductory) results on the pure FSCORE-return rela-
tion across all US firms in their Table1 (p. 2850) and across all Euro-
pean firms in Table II (p. 852).
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109Piotroski’s FSCORE: international evidence
Wang 2017), where the FSCORE has been initially discov-
ered, Australia (Hyde 2018), and five individual Asian mar-
kets (Ng and Shen 2019).2 Except for Ng and Shen (2019),
who also focus on the post-2000 era, the two other studies
investigate more extended sample periods that also include
the years before 2000.
Risk adjustments and control variables The inference of
abnormal returns is generally model-specific. The majority
of previous works use, like the original study of Piotroski
(2000), four-factor model adjustments (4F) with controls for
firm size, book-to-market, and momentum in the spirit of
Carhart (1997). Given the more recent asset pricing exten-
sions of Fama and French (2015, 2018), there exist so far
only three studies that additionally control for operating
profitability and investment. These studies focus on the USA
(Turtle and Wang 2017) and Europe (Tikkanen and Äijö
2018; Walkshäusl 2019).
Synthesis of contribution In light of the reviewed research
on FSCORE in investment strategies, our focus on the pure
FSCORE-return relation in the broad cross section of inter-
national firms, including the regions of developed EAFE
markets, Asia–Pacific, Europe, and emerging markets in a
unified framework of analysis, fills a gap in the existing liter-
ature. By taking into account the most recent developments
in asset pricing and a thorough size segmentation analysis,
our study offers a clear perspective on FSCORE’s unique
pervasiveness and persistence as a return-predictive device
in international non-US equity markets in the post-2000 era
after its publication.
Data andvariables
The dataset in this study consists of firms from 20 developed
non-US equity markets and 15 emerging markets. The selec-
tion of developed countries resembles the countries included
in the well-known EAFE (Europe, Australasia, and the Far
East) stock market benchmark from MSCI that measures the
foreign stock market performance outside of North America.
Among the countries classified as emerging by MSCI, we
select those for which data coverage enables us to calcu-
late valid FSCORES from the start of the sample period,
which basically corresponds to the 15 largest markets in this
region. We collect monthly total return data on common
stocks from Datastream and firm-level accounting informa-
tion from Worldscope. To ensure that accounting informa-
tion is known before the returns are calculated, we match
the latest accounting information for the fiscal year ending
in the previous calendar year with stock returns from July of
the current year to June of the following year throughout the
paper. All data are denominated in US dollars. To ensure that
tiny or illiquid stocks do not drive our results, we follow Ang
etal. (2009) and exclude very small firms by eliminating the
5% of firms with the lowest market equity in each country.
In addition, we exclude firm-year observations with nega-
tive book equity and financial firms with Standard Industrial
Classification (SIC) codes between 6000 and 6999 (Piotroski
2000; Piotroski and So 2012). Since we focus on examining
FSCORE’s post-publication performance, the sample period
is from July 2000 to June 2018 (henceforth 2000–2018),
and the dataset comprises on average 6787 firms per month
from developed countries and 5016 firms per month from
emerging countries. Table2 shows distributional statistics
of sample firms across individual countries (Panel A) and
reports time-series averages of cross-sectional statistics of
the employed variables for perspective (Panel B).
The construction of our key variable of interest, the
FSCORE, follows Piotroski (2000). The composite measure
of the firm’s fundamental strength is based on the sum of
nine binary indicator variables measuring different aspects
of the firm’s financial condition. An indicator variable is
equal to one if the underlying condition holds for a firm and
zero otherwise. The nine conditions are defined as follows.
(1) Net income before extraordinary items is positive, (2)
cash flow from operations is positive, (3) the annual change
in return-on-assets (net income before extraordinary items
divided by lagged total assets) is positive, (4) cash flow from
operations is greater than net income before extraordinary
items, (5) the annual change in leverage (long-term debt
divided by total assets) is negative, (6) the annual change in
liquidity (current assets divided by current liabilities) is pos-
itive, (7) the firm did not issue stocks, (8) the annual change
in gross margin (sales minus cost of goods sold divided
by sales) is positive, and (9) the annual change in turnover
(sales divided by lagged total assets) is positive. High val-
ues on FSCORE indicate strong fundamentals, whereas low
values on FSCORE indicate weak fundamentals.
The further variables used in this study are defined
as follows. A firm’s size (SZ) is its market equity (stock
price multiplied by the number of shares outstanding)
measured as of June of each year in million US dollars.
Book-to-market (BM) is the ratio of book equity to mar-
ket equity for the fiscal year ending in the previous cal-
endar year. Momentum (MOM) is the cumulative prior
12-month stock return, skipping the most recent month
(Jegadeesh and Titman 1993). Following Fama and French
(2015), operating profitability (OP) is revenues minus cost
of goods sold and interest expense, all divided by book
equity.3 Investment (INV) is the annual change in total
3 We do not include selling, general, and administrative expenses,
as this item is not broadly available among international firms. The
return predictability of operating profitability is, however, not affected
by this adjustment.
2 The five Asian markets in Ng and Shen (2019) are Hong Kong,
Japan, Korea, Singapore, and Taiwan.
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110
C.Walkshäusl
assets divided by lagged total assets. For the later analy-
sis of the association of FSCORE with future firm profit-
ability, we follow Piotroski (2000) and define profitability
based on return-on-assets (ROA), which is net income
before extraordinary items divided by lagged total assets.
The control for profitability in the q-factor model of Hou
etal. (2015) that is applied as a further robustness test
is based on return-on-equity (ROE) and defined as net
Table 2 Summary statistics, 2000–2018
Panel A reports the average number of firms per month in each country over the sample period from July 2000 to June 2018. Panel B reports
time-series averages of cross-sectional statistics of the variables, including the mean, standard deviation, and median. FSCORE is the composite
measure of the firm’s fundamental strength. Firm size (SZ) is market equity (stock price multiplied by the number of shares outstanding) meas-
ured as of June of each year in million US dollars. Book-to-market (BM) is the ratio of book equity to market equity for the fiscal year ending in
the previous calendar year. Momentum (MOM) is the cumulative prior 12-month stock return, skipping the most recent month. Operating profit-
ability (OP) is revenues minus cost of goods sold and interest expense, all divided by book equity. Investment (INV) is the annual change in total
assets divided by lagged total assets. Return-on-assets (ROA) is net income before extraordinary items divided by lagged total assets. Return-on-
equity (ROE) is net income before extraordinary items divided by lagged book equity
Developed EAFE markets Emerging markets
Country Firms Country Firms
Panel A: Sample countries
Australia 763 Brazil 140
Austria 38 Chile 100
Belgium 59 China 762
Denmark 83 India 812
Finland 86 Indonesia 202
France 413 Malaysia 567
Germany 362 Mexico 72
Hong Kong 545 Philippines 84
Ireland 30 Poland 143
Italy 154 Russia 64
Japan 2328 South Africa 157
Netherlands 87 South Korea 956
New Zealand 61 Taiwan 539
Norway 104 Thailand 298
Portugal 34 Turkey 120
Singapore 364
Spain 66
Sweden 212
Switzerland 136
United Kingdom 862
Variable Developed EAFE markets Emerging markets
Mean SD Median Mean SD Median
Panel B: Variables
FSCORE 5.54 1.70 5.83 5.77 1.60 5.94
SZ 1311 3918 141 793 2409 131
BM 0.99 0.82 0.78 1.15 1.06 0.83
MOM 0.15 0.53 0.06 0.20 0.59 0.07
OP 0.75 0.91 0.52 0.44 0.57 0.32
INV 0.14 0.50 0.04 0.13 0.39 0.06
ROA 0.00 0.18 0.03 0.05 0.10 0.04
ROE 0.03 0.36 0.07 0.09 0.26 0.09
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111Piotroski’s FSCORE: international evidence
income before extraordinary items divided by lagged book
equity.
Empirical results
Return behavior ofhigh‑ andlow‑FSCORE rms
We begin our analysis of the FSCORE-return relation at
the portfolio level. Each June, all firms in the considered
regional sample are assigned to three portfolios based on
their FSCORE characteristic from the fiscal year ending in
the previous calendar year. A firm is assigned to the low,
medium, or high portfolio if its FSCORE is between zero
and three, between four and six, or between seven and nine.
Monthly size-adjusted returns on the equal-weighted port-
folios are calculated for the subsequent 12months, and the
portfolios are rebalanced each year. For the size adjustment,
the monthly return on a stock is measured net of the return
on its matching country-specific size quintile portfolio.4 We
present market-wide results for four regions: (1) developed
EAFE markets, (2) Asia–Pacific, (3) Europe, and (4) emerg-
ing markets. Asia–Pacific includes Australia, Hong Kong,
Japan, New Zealand, and Singapore, while Europe encom-
passes the remaining developed equity markets. To shed fur-
ther light on the economic importance and pervasiveness of
FSCORE for predicting subsequent stock returns across the
full firm size spectrum, we also report outcomes for three
different size segments in each region. A firm is classified as
a small-cap, mid-cap, or large-cap stock if its firm size is in
the bottom, middle, or top tercile of the country-specific firm
size distribution, measured as of June of each year.
Table3 shows average monthly size-adjusted returns
for the outlined FSCORE portfolios along with the aver-
age number of sample firms per month and the average firm
size characteristic for perspective. The column ‘High–Low’
reports the spread return between high- and low-FSCORE
firms for testing whether the return difference is significantly
different from zero.
We find that high-FSCORE firms are rewarded with
positive subsequent stock returns, while low-FSCORE are
penalized with negative returns. The resulting (high–low)
FSCORE premiums are economically large and statisti-
cally highly significant across all considered regions and
size segments. Thus, international evidence for FSCORE is
strong. Furthermore, the size-segmented results document
that the positive FSCORE-return relation is not limited to
smaller firms but likewise present among the largest and
economically most important firms in non-US countries.
With monthly values of 0.79% (developed EAFE markets)
and 0.95% (emerging markets), the average market-wide
FSCORE premiums correspond to about 9.9% and 12.0%
on an annual basis, which are just in the same range of mag-
nitude as their US counterpart of 10.03% per year, reported
in Piotroski and So (2012, Table1).5 In addition, the market-
wide results for Europe are also in line with Walkshäusl
(2017, 2019) and suggest that the return effect associated
with FSCORE is even somewhat stronger in the post-2000
era.
Incremental return predictability ofFSCORE
Portfolio sorts represent a very useful approach to investigate
how average returns vary with different levels of the variable
of interest. However, the portfolio-level analysis also has
the potential shortcoming that much of the individual stock
information is lost through aggregation. In addition, show-
ing that there exists a positive FSCORE-return relation does
not rule out the possibility that the identified return effect is
just a manifestation of already known determinants of the
cross section.
To examine the incremental power of FSCORE for pre-
dicting subsequent stock returns, we conduct cross-sectional
return regressions at the individual firm level using the Fama
and MacBeth (1973) methodology, which provides a test set-
ting that easily allows for multiple control variables. Specifi-
cally, we estimate a firm-level cross-sectional regression of
monthly stock returns on FSCORE and common return con-
trols. Taking into account the most recent developments in
asset pricing (Fama and French 2015, 2018), the set of com-
mon controls includes firm size, book-to-market, momen-
tum, operating profitability, and investment for measuring
the abnormal return effect associated with FSCORE. Except
for momentum, which is measured monthly, we update the
explanatory variables each June to predict monthly stock
returns from July to the following June. In the regression,
firm size and book-to-market are measured in natural logs,
and the regression includes country dummies to control for
possible country effects.
Table4 shows average slopes from the outlined firm-
level cross-sectional regression. To gauge the strength of
the abnormal return effect associated with FSCORE, the
last column ‘Premium’ translates the corresponding slope
into an abnormal return estimate by multiplying the average
slope with the difference in average FSCORE characteristics
between high and low firms.6
4 The size benchmark portfolios are formed each June by allocating
all firms in a given country to quintiles based on firm size. Monthly
raw returns on the equal-weighted size portfolios are calculated for
the subsequent 12 months, and the portfolios are rebalanced each
year.
5 Formally, e.g., (1 + 0.0079)12 − 1.
6 For perspective, across regions and size segments, the difference in
average FSCORE characteristics between high and low firms is, with
values close to five, very similar.
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112
C.Walkshäusl
Table 3 Average monthly size-
adjusted returns on FSCORE-
sorted portfolios, 2000–2018
Each June, all firms in the considered regional sample are assigned to three portfolios based on their
FSCORE characteristic from the fiscal year ending in the previous calendar year. A firm is assigned to
the low, medium, or high portfolio if its FSCORE is between zero and three, between four and six, or
between seven and nine. Monthly size-adjusted returns on the equal-weighted portfolios are calculated
for the subsequent 12months, and the portfolios are rebalanced each year. For the size adjustment, the
monthly return on a stock is measured net of the return on its matching country-specific size quintile port-
folio. ‘High–Low’ provides the spread return between high- and low-FSCORE firms. The t statistic for
the average monthly return is given in parentheses. The results are reported for all firms in the considered
region (market) and for three different size segments. Asia–Pacific includes Australia, Hong Kong, Japan,
New Zealand, and Singapore, while Europe encompasses the remaining developed equity markets (see
Table2). A firm is classified as a small-cap, mid-cap, or large-cap stock if its firm size is in the bottom,
middle, or top tercile of the country-specific firm size distribution, measured as of June of each year. The
table also reports the average number of sample firms per month and the average firm size characteristic for
perspective
Low Medium High High–Low Firms SZ
Developed EAFE markets
Market − 0.58 0.01 0.20 0.79 6787 1311
(− 6.3) (0.6) (6.1) (6.5)
Small − 0.50 0.02 0.28 0.78 2229 41
(− 5.3) (0.8) (5.8) (6.0)
Mid − 0.73 − 0.02 0.23 0.96 2308 195
(− 6.5) (− 1.1) (5.4) (6.6)
Large − 0.50 0.03 0.12 0.62 2250 3713
(− 3.7) (1.6) (3.7) (4.0)
Asia–Pacific
Market − 0.43 0.01 0.14 0.57 4061 1000
(− 4.6) (0.8) (4.0) (4.8)
Small − 0.29 0.01 0.19 0.47 1337 47
(− 2.5) (0.2) (3.3) (3.2)
Mid − 0.58 0.00 0.14 0.73 1381 183
(− 4.9) (0.1) (3.1) (5.0)
Large − 0.49 0.03 0.10 0.59 1343 2788
(− 3.2) (1.0) (2.2) (3.3)
Europe
Market − 0.78 − 0.01 0.30 1.08 2726 1860
(− 7.2) (− 0.5) (6.2) (7.1)
Small − 0.82 0.02 0.46 1.28 892 42
(− 7.0) (0.5) (6.2) (7.4)
Mid − 0.93 − 0.04 0.34 1.27 927 250
(− 6.5) (− 1.4) (5.6) (6.9)
Large − 0.39 0.00 0.15 0.54 907 5293
(− 2.4) (0.2) (3.2) (2.8)
Emerging markets
Market − 0.71 − 0.04 0.24 0.95 5016 793
(− 8.8) (− 2.8) (9.1) (9.6)
Small − 0.70 − 0.02 0.36 1.07 1647 73
(− 7.0) (− 0.6) (8.5) (8.5)
Mid − 0.78 − 0.06 0.25 1.03 1706 226
(− 5.9) (− 2.3) (6.5) (6.8)
Large − 0.63 − 0.03 0.13 0.76 1663 2089
(− 4.8) (− 1.3) (3.9) (5.2)
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113Piotroski’s FSCORE: international evidence
The results make clear that the return-predictive power
of FSCORE is not explained away in the presence of estab-
lished cross-sectional benchmark variables. The return
difference between high- and low-FSCORE firms remains
economically and statistically significant after controlling for
firm size, book-to-market, momentum, operating profitabil-
ity, and investment in all considered regions and size seg-
ments. Though we note the tendency of decreasing abnor-
mal returns from small-cap to large-cap stocks, the monthly
FSCORE premiums among large caps preserve a meaningful
Table 4 Average slopes from
monthly cross-sectional return
regressions with controls,
2000–2018
This table shows average slopes from firm-level cross-sectional regressions of monthly stock returns on
FSCORE in combination with common return controls. The set of common controls includes firm size
(SZ), book-to-market (BM), momentum (MOM), operating profitability (OP), and investment (INV).
Except for momentum, which is measured monthly, the explanatory variables are updated each June to pre-
dict monthly stock returns from July to the following June. In the regressions, firm size and book-to-market
are measured in natural logs, and all regressions include country dummies to control for possible country
effects. The tstatistic for the average slope is given in parentheses. The R2 values are adjusted for degrees
of freedom. ‘Premium’ gives the monthly abnormal return associated with the average FSCORE slope
FSCORE SZ BM MOM OP INV R2Premium
Developed EAFE markets
Market 0.106 − 0.016 0.365 0.375 0.164 − 0.441 0.079 0.53
(6.0) (− 0.6) (6.8) (1.7) (5.9) (− 8.0)
Small 0.129 − 0.320 0.307 0.437 0.110 − 0.373 0.068 0.65
(6.1) (− 5.6) (5.8) (2.3) (3.3) (− 4.1)
Mid 0.115 − 0.002 0.398 0.509 0.198 − 0.441 0.091 0.57
(5.4) (0.0) (6.3) (2.4) (5.3) (− 6.2)
Large 0.066 − 0.019 0.345 0.316 0.157 − 0.359 0.113 0.32
(3.3) (− 0.5) (5.0) (1.1) (4.2) (− 5.1)
Asia–Pacific
Market 0.079 − 0.099 0.386 0.116 0.211 − 0.427 0.080 0.39
(3.9) (− 2.7) (6.0) (0.6) (4.7) (− 6.2)
Small 0.087 − 0.711 0.323 0.082 0.158 − 0.409 0.071 0.44
(3.2) (− 7.6) (4.2) (0.4) (2.9) (− 3.1)
Mid 0.092 − 0.119 0.425 0.222 0.221 − 0.439 0.093 0.46
(3.6) (− 1.4) (5.7) (1.1) (4.0) (− 4.2)
Large 0.059 − 0.003 0.399 0.280 0.263 − 0.265 0.106 0.29
(2.4) (− 0.1) (5.2) (1.2) (3.6) (− 3.1)
Europe
Market 0.141 0.056 0.294 0.905 0.111 − 0.372 0.050 0.69
(7.0) (1.7) (5.2) (3.3) (3.5) (− 5.2)
Small 0.173 − 0.027 0.301 1.110 0.091 − 0.364 0.036 0.86
(6.4) (− 0.4) (5.3) (4.9) (2.0) (− 3.1)
Mid 0.144 0.106 0.325 1.049 0.156 − 0.339 0.056 0.70
(5.7) (1.7) (4.4) (3.9) (3.2) (− 3.6)
Large 0.061 − 0.045 0.194 0.503 0.071 − 0.478 0.083 0.28
(2.6) (− 1.0) (2.2) (1.2) (1.6) (− 4.2)
Emerging markets
Market 0.135 − 0.064 0.416 0.185 0.336 − 0.243 0.163 0.66
(8.3) (− 2.0) (7.7) (0.9) (7.0) (− 4.7)
Small 0.189 − 0.296 0.369 0.119 0.252 − 0.190 0.157 0.93
(8.5) (− 3.9) (6.1) (0.7) (3.7) (− 1.8)
Mid 0.129 − 0.148 0.466 0.352 0.363 − 0.215 0.181 0.63
(5.8) (− 1.6) (7.8) (1.8) (4.8) (− 2.5)
Large 0.083 − 0.010 0.377 0.100 0.374 − 0.237 0.194 0.40
(4.4) (− 0.2) (5.7) (0.4) (7.2) (− 3.4)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
114
C.Walkshäusl
magnitude of about 0.30% in the regions of developed mar-
kets and 0.40% in emerging markets.
The outcome that the information contained in
FSCORE remains significant after risk-adjusting returns
is consistent with the broad majority of prior studies that
have examined the FSCORE mainly in combination with
other variables (Piotroski 2000; Piotroski and So 2012; Ng
and Shen 2016; Turtle and Wang 2017; Walkshäusl 2017;
Ahmed and Safdar 2018; Tikkanen and Äijö 2018; Ng
and Shen 2019; Walkshäusl 2019). Our results extend this
finding to the major regions in international markets based
on the most recent return controls (Fama and French 2018)
and underscore that the FSCORE represents a return-pre-
dictive device on its own.
The market-wide slopes on the control variables echo
in general prior results in the literature. International
stock returns are significantly positively associated with
book-to-market and operating profitability, while they are
significantly negatively related to investment. In contrast,
we mostly do not find reliable firm size effects or momen-
tum effects (except for Europe) during the sample period.
These observations are, however, also in line with recent
international evidence (Fama and French 2017) and the
generally weak performance of momentum strategies since
the late 1990s (Bhattacharya etal. 2017).
FSCORE andfuture rm protability
To investigate the association of FSCORE with future
firm profitability, we follow the methodology described in
Bradshaw etal. (2006) and conduct Fama–MacBeth-type
regressions based on annual realizations of fundamentals.
Specifically, we estimate a firm-level cross-sectional regres-
sion of the firm’s future profitability using return-on-assets
(short-term or long-term) on current profitability, firm size,
and FSCORE, which all can be observed before the future
firm performance is realized. Our investigation is inspired by
Piotroski (2000), Piotroski and So (2012), and Walkshäusl
(2017), who have stressed a positive univariate relation
between FSCORE and future firm profitability. Control-
ling for current profitability and firm size helps to uncover
the genuine incremental effect of FSCORE. The existing
Table 5 Average slopes
from annual cross-sectional
regressions to predict future
profitability, 2000–2018
This table shows average slopes from firm-level cross-sectional regressions of future profitability using
return-on-assets (short-term or long-term) on current profitability (ROA), firm size (SZ), and FSCORE.
The explanatory variables are updated each year to predict the firm’s 1-year-ahead profitability (short-term)
or the average profitability over the 4-year period after the short-term horizon (long-term). In the regres-
sions, firm size is measured in natural logs, and all regressions include country dummies to control for pos-
sible country effects. The tstatistic for the average slope is given in parentheses. The R2 values are adjusted
for degrees of freedom. ‘Difference’ provides the annual difference in future profitability between high-
and low-FSCORE firms based on the average FSCORE slope
Intercept ROA SZ FSCORE R2Difference
Developed EAFE markets
Short term − 0.078 0.591 0.009 0.006 0.446 0.031
(− 15.5) (32.5) (17.0) (11.3)
Long term − 0.050 0.338 0.008 0.004 0.319 0.018
(− 5.4) (26.2) (9.5) (7.6)
Asia–Pacific
Short term − 0.085 0.577 0.011 0.006 0.441 0.030
(− 11.5) (31.7) (15.2) (8.3)
Long term − 0.062 0.345 0.011 0.003 0.337 0.017
(− 6.1) (20.5) (9.9) (5.3)
Europe
Short term − 0.065 0.634 0.006 0.006 0.462 0.028
(− 9.9) (26.4) (16.4) (6.7)
Long term − 0.030 0.382 0.005 0.003 0.288 0.014
(− 4.2) (11.9) (9.4) (5.1)
Emerging markets
Short term − 0.059 0.582 0.007 0.005 0.401 0.023
(− 16.5) (32.4) (22.6) (12.3)
Long term − 0.028 0.304 0.006 0.003 0.246 0.013
(− 10.6) (18.8) (17.9) (5.7)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
115Piotroski’s FSCORE: international evidence
literature shows that the current level of profitability is eco-
nomically the most important determinant of future profit-
ability because profitability is only slowly mean-reverting
(e.g., Fama and French 2006). The addition of firm size
to the explanatory variables is motivated by our previous
size segmentation and takes into account the evidence that
smaller firms tend to be less profitable (Fama and French
1995).
Table5 shows average slopes from the outlined firm-level
cross-sectional regression to forecast profitability. We evalu-
ate the firm’s future profitability both over short-term and
long-term horizons, where the former is the 1-year-ahead
profitability, and the latter is the average profitability over
the 4-year period after the short-term horizon. As before,
firm size is measured in natural logs, and the regression
includes country dummies. The last column ‘Difference’
provides the annual difference in future profitability between
high- and low-FSCORE firms based on the corresponding
slope.
First and expectedly, current profitability exerts the most
substantial impact on the firm’s subsequent fundamental per-
formance. Over the short-term horizon, the current level of
profitability accounts on average for about 60% of the future
level and still more than 30% over the long-term horizon.
Second, as indicated by the significantly positive firm size
slope, larger firms are also in international markets, on aver-
age, more profitable than smaller firms. Third and finally,
we observe that FSCORE captures additional information
about subsequent fundamental performance in all considered
regions and therefore helps to forecast profitability. Over the
short-term horizon, the difference in 1-year-ahead profit-
ability between high- and low-FSCORE firms amounts to
3.1 percentage points among developed countries and 2.3
percentage points among emerging countries, which appears
economically sizable given the mean and median return-on-
assets profitability of the typical sample firm (see Panel B in
Table2). The long-term horizon results document that the
positive relation between FSCORE and subsequent funda-
mental performance remains intact over extended periods,
causing an average annual difference in future profitability
of at least 1.3 percentage points between high and low firms
over the 4years following the short-term horizon.
These findings are altogether consistent with the view that
investors tend to underreact to changes in firm fundamen-
tals (e.g., Lakonishok etal. 1994). Since FSCORE measures
the improvement or deterioration in the firm’s fundamental
strength, the positive FSCORE-return relation arises because
investors do not fully anticipate the positive association of
FSCORE with future firm profitability. Such investor behav-
ior should result in predictable return patterns for high- and
low-FSCORE firms, and this is indeed what we find here.
Further robustness tests
In this section, we further test the robustness of our key
findings using value-weighted returns that overweight larger
firms and alternative methods for risk-adjusting returns
based on the CAPM and q-factor model.
First, we repeat our market-wide portfolio-level analy-
sis of Table3 employing value-weighted returns. Hou etal.
(2018) recently show that many of the previously docu-
mented anomalies on the US equity market fail to hold
when value-weights are used. Second, we measure abnor-
mal returns on the FSCORE-sorted portfolios relative to the
market in a CAPM setting because investors still base their
capital allocation decisions primarily on this model, as found
by Barber etal. (2016) and Berk and van Binsbergen (2016).
The market excess return is the value-weighted return of all
firms in the considered region in excess of the risk-free rate,
the 1-month US Treasury bill rate. To obtain the abnormal
return relative to the market (CAPM alpha), the portfolio
excess returns are regressed on the market excess return.
Third, we consider the controls of the q-factor model of Hou
etal. (2015) as an alternative to the applied risk adjustment
based on the Fama and French (2015, 2018) approach. The
q-factor model is motivated by the q-theory of investment
and controls for firm size, investment, and return-on-equity
in the cross section of average returns. Hou etal. (2015)
found that when returns are adjusted by these controls, the
difference between high- and low-FSCORE firms in the USA
is rendered insignificant using value-weights. To examine
whether this is also the case in non-US equity markets, we
proceed as follows. In each region, we estimate a weighted
least squares cross-sectional regression of monthly stock
returns on firm size, investment, and return-on-equity that
uses firm size as the weights (value-weights).7 The residuals
from this regression are then sorted on the firm’s FSCORE
characteristic into the low, medium, and high groups. Within
each group, the residuals are value-weighted and then aver-
aged across months. In this way, we obtain abnormal returns
that are adjusted for effects associated with firm size, invest-
ment, and return-on-equity. If the controls of the q-factor
model can describe the spread return between high- and low-
FSCORE firms, the corresponding abnormal return should
be statistically indistinguishable from zero.
Table6 shows average monthly value-weighted returns
for market-wide FSCORE portfolios (Panel A), their abnor-
mal returns relative to the market (Panel B), and their
abnormal returns relative to the q-factor model (Panel C).
Except for the region of Asia–Pacific, we find significantly
positive FSCORE-return relations using value-weighted
7 As before, the explanatory variable firm size is measured in natural
logs, and the regression includes country dummies.
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116
C.Walkshäusl
returns. Among firms from developed EAFE markets, the
average (high–low) spread return amounts to 0.44% per
month and 0.60% per month in emerging markets. Second,
the abnormal returns relative to the market are very similar
to our size-adjusted return results among large-cap stocks,
as reported in Table3. All considered regions display now
significantly positive return differences between high- and
low-FSCORE firms. When the controls of the q-factor model
are applied, the FSCORE premiums are reduced but remain
economically meaningful and statistically significant. Thus,
in contrast to the US findings of Hou etal. (2015, 2018),
the return predictability of FSCORE appears to be more
robust outside the USA even when value-weights are used.
However, from an investment perspective, we note that the
resulting FSCORE premiums are more driven by the signifi-
cant underperformance of low-FSCORE firms than by the
outperformance of high-FSCORE firms after controlling for
firm size, investment, and return-on-equity.8
In light of the fact that investors tend to underreact to
changes in firm fundamentals, and the finding that the return
predictability of FSCORE largely can be traced back to its
ability to forecast the firm’s future profitability, adjusting
returns for profitability effects will likely reduce abnormal
returns.9 This particularly has to be expected when larger
firms are overweighted in the analysis since the largest
firms are regularly followed by more analysts, leading to
more timely incorporation of fundamental information into
prices (Hameed etal. 2015). The observation that this has
a more significant impact on the long leg of the FSCORE
premium than on its short leg is consistent with the concept
of arbitrage asymmetry (Stambaugh etal. 2015). Buying
the (undervalued) high-FSCORE firms is for most investors
easier than shorting the (overvalued) low-FSCORE firms.
Circumventing firms with low-FSCORE characteristics
may be advisable to investors regardless of the considered
weighting scheme and irrespective of the applied control
variables given their persistent underperformance. Never-
theless, the value-weighted results also document that long-
only investors that are only benchmarked against the market
would have been able to display significantly positive alphas
by investing in the largest high-FSCORE firms in the major-
ity of regions over the sample period 2000–2018.
Conclusions
In this paper, we have studied the pure FSCORE-return
relation in the broad cross section of international firms
with the aim to shed light on the genuine return-predictive
power of Piotroski’s (2000) FSCORE when used on its own.
We find that the FSCORE is an economically meaningful
Table 6 Robustness of FSCORE using value-weights, 2000–2018
Panel A reports average monthly value-weighted returns on
FSCORE-sorted portfolios using all firms in the considered region
(market-wide sorts). The portfolio formation is analogous to Table3.
‘High–Low’ provides the spread return between high- and low-
FSCORE firms. Panel B reports abnormal returns relative to the mar-
ket (CAPM alphas). The abnormal returns are obtained by regressing
the monthly portfolio returns in excess of the risk-free rate (1-month
US Treasury bill rate) on the market excess return, the value-
weighted excess return of all firms in the considered regional sam-
ple. Panel C reports abnormal returns relative to the controls of the
q-factor model. The abnormal returns are based on the residuals from
weighted least squares cross-sectional regressions of monthly stock
returns on firm size, investment, and return-on-equity in each region
that are sorted into the three FSCORE groups. Within each group, the
residuals are then value-weighted and averaged across months. The
weighted least squares regressions use firm size as the weights. In the
regressions, the explanatory variable firm size is measured in natural
logs, and all regressions include country dummies to control for pos-
sible country effects. The tstatistic for the average monthly return or
abnormal return is given in parentheses
Low Medium High High–Low
Panel A: Average returns
Developed EAFE markets 0.28 0.61 0.72 0.44
(0.7) (1.9) (2.5) (2.4)
Asia–Pacific 0.15 0.48 0.53 0.38
(0.4) (1.6) (1.9) (1.6)
Europe 0.30 0.71 0.92 0.62
(0.7) (2.0) (2.7) (3.4)
Emerging markets 0.37 0.86 0.97 0.60
(0.7) (2.1) (2.4) (2.6)
Panel B: Abnormal returns relative to the market
Developed EAFE markets − 0.47 − 0.03 0.14 0.61
(− 3.7) (− 1.3) (2.7) (3.9)
Asia–Pacific − 0.44 − 0.01 0.08 0.52
(− 2.4) (− 0.3) (1.5) (2.4)
Europe − 0.56 − 0.04 0.20 0.76
(− 4.1) (− 2.3) (3.3) (4.6)
Emerging markets − 0.59 − 0.02 0.12 0.71
(− 2.8) (− 0.5) (2.1) (3.1)
Panel C: Abnormal returns relative to the q-factor model
Developed EAFE markets − 0.27 − 0.01 0.06 0.33
(− 2.5) (− 0.8) (1.7) (2.7)
Asia–Pacific − 0.36 0.00 0.03 0.39
(− 2.4) (0.0) (0.6) (2.2)
Europe − 0.32 − 0.02 0.09 0.41
(− 2.8) (− 1.2) (1.7) (2.9)
Emerging markets − 0.40 0.03 0.00 0.40
(− 2.5) (0.9) (0.1) (2.4)
8 In unreported tests, we have also applied the risk adjustment based
on Fama and French (2015, 2018) using value-weights. The obtained
results are very similar to those presented here.
9 Profitability exhibits a strong autocorrelation over several years,
i.e., lagged profitability is a strong predictor of future profitability
(Fama and French 2006).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
117Piotroski’s FSCORE: international evidence
and statistically significant predictor of the cross section
of international stock returns. Its return-predictive ability
is similarly present among developed non-US markets and
emerging markets, pervasive across small and large firms,
and remains robust after controlling for established deter-
minants of the cross section, such as firm size, book-to-
market, momentum, operating profitability, and investment.
The FSCORE premium also preserves its significance when
benchmarked against the market or the controls of the q-fac-
tor model using value-weights that overweight larger firms
in the market. All in all, our results imply that the FSCORE
remains a rather global phenomenon around the world. Fur-
thermore, in light of the fact that it seems implausible that
fundamentally strong firms may be considered riskier than
fundamentally weak firms, our findings are still consistent
with the view that fundamental information is only gradu-
ally incorporated into prices by investors, which has been
emphasized by Piotroski (2000) almost 20years ago.
Acknowledgments Open Access funding provided by Projekt DEAL.
Open Access This article is licensed under a Creative Commons Attri-
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tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
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permitted by statutory regulation or exceeds the permitted use, you will
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