An Exploration of Two Accounting-Based Models for Earnings
Misstatements and Their Implications for Stock Returns
By
Suzie Noh
B.A. Economics and Mathematics
Emory University, 2013
SUBMITTED TO THE MIT SLOAN SCHOOL OF MANAGEMENT IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE
OF
MASTER OF FINANCE
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
A
JUNE 2014
LiBRARIES
© 2014 Suzie Noh. All Rights Reserved.
The author hereby grants to MIT permission to reproduce and to distribute
publicly paper and electronic copies of this thesis document in whole or in part
in any medium now known or hereafter created.
Signature of Author:
Signature redacted
MIT Sloan School of Management
May 9, 2014
Signature redacted
Certified By:
S.P. Kothari
Dean
Deputy
Management,
of
Professor
Billard
Gordon Y
Thesis Supervisor
Accepted By:
Signature redacted
Heidi Pickett
Program
Finance
of
Program Director, MIT Sloan Master
Management
of
MIT Sloan School
I
An Exploration of Two Accounting-Based Models for Earnings
Misstatements and Their Implications for Stock Returns
By
Suzie Noh
Submitted to the MIT Sloan School of Management on May 9, 2014 in partial
fulfillment of the requirements for the degree of Master of Finance
ABSTRACT
Using two popular accounting-based models for earnings manipulation (i.e., the Beneish M-Score and the Dechow
F-Score) and the financial data of public companies from 2004 to 2012, 1 find that the M-Score (F-Score) predicts
less (more) earnings overstatements during the recent financial crisis in 2007-2008 than other sample years.
However, a detailed investigation at the industry level reveals that this does not hold in all industries. I further show
that the potential misstating firms flagged by the M-Score tend to underperform the market both at the aggregate and
the industry level, and some of those flagged by the F-Score underperform at the industry level. Finally, by running
Fama-French three-factor regressions at the aggregate level, I provide evidence that the firms flagged by the MScore generally yield negative risk-adjusted stock returns. The evidence suggests public availability of financial
statements alone does not ensure that all the elements of financial statements are fully integrated into prices in a
timely manner. Overall, this study provides substantial support for the use of quantitative accounting analysis in
equity trading.
Thesis Supervisor: S. P. Kothari
Title: Gordon Y Billard Professor of Management, Deputy Dean
2
TABLE OF CONTENTS
1.
Intro d u ctio n ...................................................................................................................................................
5
2.
Literature Review ...........................................................................................................................................
9
3.
4.
5.
2.1.
Earnings M anagement M odels...............................................................................................9
2.2.
Predicting Stock Returns Based on the Signs of Earnings M anipulation ..................................
Two Scoring M odels .................................................................................................................................
12
144.
3.1.
The Beneish M -Score................................................................................................................
14
3.2.
The Dechow F-Score.................................................................................................................
15
Data and Sample Formation..........................................................................................................................
17
4.1.
Calculations of M -Scores and F-Scores ..................................................................................
17
4.2.
Stock Returns of Potential Overstating Firms.........................................................................
18
Em pirical Results .........................................................................................................................................
19
The Number of Potential Overstating Firms...........................................................................
19
5.1.1.
Aggregate Percentage Changes over Time ...........................................................................
19
5.1.2.
Industry Percentage Changes over Time .............................................................................
20
One-, Two-, and Five-year Stock Return Analysis ..................................................................
21
Aggregate Comparison of M -Score and F-Score ..................................................................
22
5.2. 1.1.
Stock Returns of Firms Flagged in a Given Year .....................................................
22
5.2.1.2.
Stock Returns of Firms Flagged by Both Scores in a Given Year ............................
24
5.2.1.3.
Stock Returns of Firms Flagged in Two Consecutive Years .....................................
24
5.1.
5.2.
5.2.1.
5.2.2.
Industry Comparisons ............................................................................................................
25
5.2.3.
Fama-French Three-Factor Analysis....................................................................................
27
6.
Co n clu sion ...................................................................................................................................................
30
7.
Tables and Figures .......................................................................................................................................
32
8.
A p p en d ix .....................................................................................................................................................
52
3
9.
8.1.
Appendix 1: Beneish M -Score Variables ...............................................................................
52
8.2.
Appendix 2: Dechow F-Score Variables ................................................................................
53
8.3.
Appendix 3: Sensitivity of Dechow F-Score .........................................................................
54
8.4.
Appendix 4: SIC Industry Codes ...........................................................................................
55
References ...................................................................................................................................................
4
56
1.
Introduction
In an ideal world, financial reporting helps the best-performing firms in the economy to distinguish themselves from
poor performers, and it also facilitates efficient resource allocation (Healy and Wahlen 1999). However, firms have
substantial discretion in determining the level of earnings, as they have considerable flexibility in recognizing
inventory, allowance for losses, sales, leases, etc. In fact, there is evidence that about 20% of firms manage earnings
to misrepresent economic performance (Dichev et al. 2013). The nature and extent of earnings manipulation by
firms is of broad interest, as misstated earnings offers inaccurate information about firms and potentially causes
significant costs to lenders, investors, regulators, auditors, suppliers, and customers. Thus, finding out the motives
for and consequences of earnings management is of critical importance to the efficient functioning of capital
markets. Existing research extensively examines different kinds of short-term incentives for managers to misstate
earnings upward or downward, but it offers little evidence on the differences in earnings manipulation practices
across industries and times. Moreover, while many previous papers investigate stock returns of firms with high
accruals (Sloan 1996; Teoh et al. 1998), this is the first one to study the medium- to long-term valuation
consequences of potential earnings management using two popular statistical scoring models that not only reflect
accruals but also many other financial variables.'
Healy and Wahlen (1999) say that earnings management occurs when managers alter financial reports to either
mislead some stakeholders or to influence contractual outcomes. In this paper, earnings management is defined as
intentionally increasing or decreasing reported earnings within or outside the generally accepted accounting
principles (GAAP) with the intent of obtaining some private gain. This paper extends previous research by
documenting the changes in the frequency at which firms manage their earnings between 2004 and 2012 in each
industry. Furthermore, it examines the changes in stock prices of potential manipulators one, two, and five years
after the firms are believed to have manipulated their earnings. Two widely-used earnings management scoring
models are used in this study: the Beneish M-Score and the Dechow F-Score. These models distinguish the firms
that are likely to have overstated earnings from those that aren't.
'Throughout the paper, I use the terms -earnings misstatements," "earnings management," and "earnings manipulation"
interchangeably.
5
According to previous literature on earnings management, firms misstate their earnings for various reasons: (1) for
stock market incentives, (2) to avoid debt covenant violations, (3) to maximize earnings-based bonus compensation,
(4) to reduce career risk, (5) to smooth income, and (6) to reduce political costs or increase political benefits. The
following three paragraphs discuss each of these motives in detail. Understanding these incentives for managers to
misstate earnings is an important first step to constructing models that detect signs of earnings management.
First, it is widely believed that earnings are manipulated for stock-related reasons. Some managers overstate
earnings to meet analysts' forecasts (Burgstahler and Dichev,1997; Burgstahler and Eames 2006). Kasznik (1999)
and Degeorge et al. (1999) also find evidence that firms use positive discretionary accruals to manage earnings
toward specific targets. In addition, managers exercise their accounting discretion to overstate earnings prior to
equity offers (Teoh et al. 1998b) or stock-financed acquisitions (Erickson and Wang 1999). Firms not only overstate
but also understate earnings. They decrease discretionary accruals to increase the value of management stock
options (Balsam et al. 2003).
Second, some firms have incentives to manage their earnings in order to mitigate potential violation of debt
covenants. DeFond and Jiambalvo (1994) show that debt covenant restrictions cause managers to engage in accruals
manipulation. There's considerable evidence that banks that are close to minimum capital requirements artificially
increase earnings by overstating loan loss provisions, understating loan write-offs, and recognizing abnormal
realized gains on securities portfolios (Collins et al. 1995). Third, Healy (1985) and Guidry et al. (1999) suggest that
earnings-based bonuses are likely to be a part of managers' incentives when overstating earnings. They give support
to the fixed-target hypothesis, where managers select accounting procedures and accruals that maximize the present
value of their awards.
Fourth, DeAngelo (1988) reports evidence that incumbent managers, during an election campagin, report abnormal
income-increasing accruals to draw a favorable picture of their own performance. He further shows that newlyelected managers take an immediate earnings "bath" to blame prior mangement. Fifth, it is also shown that
companies engage in upward and downward management of earnings to smooth income because income smoothing
generates steady and predictable earnings streams, which investors prefer (DeFond and Park 1997). Finally, another
reason managers understate earnings is to reduce political costs or increase political benefits (Watts and Zimmerman
6
1986; Wong 1988). Watts and Zimmerman (1986) propose the political cost hypothesis that firms manage earnings
downward so as not to attract public attention and to reduce costly regulations. Jones (1991) extended the political
cost hypothesis. She finds that firms that were likely to benefit from government-sponsored import relief managed
earnings downward to extract first-order benefits from tax payers. Mills, Nutter, and Schwab (2013) corroborate this
theory by providing further evidence that federal contractors report higher effective tax rates to avoid regulative
scrutiny.
These various incentives are not mutually exclusive. Reported earnings are the weighted result of the combination of
the motivations mentioned above. This paper focuses on the overstatement, not understatement, of earnings, and it is
specifically related to the stock-related incentives for earnings management. More specifically, I examine how the
frequency of earnings management varies across differcnt times and industries and how the stock market responds to
earnings management, using the Beneish M-Score and the Dechow F-Score as proxies for earnings overstatements.
I first collect financial statements of companies publicly traded on the New York Stock Exchange (NYSE), the
National Association of Securities Dealers Automated Quotations (NASDAQ), and the American Stock Exchange
(AMEX) from FY 2002 to FY 2012 and obtain stock data from the Center for Research in Security Prices (CRSP).
Using the data, I calculate each company's M-Score and F-Score for every fiscal year from 2004 to 2012. Then I
compare the number of potential misstating firms flagged by the M-Score and the F-Score in each industry over time.
Furthermore, I compute the stock returns of these potential manipulators for one, two, and five years after they are
believed to have overstated earnings, and I investigate whether they earn returns significantly different from the
benchmark by running t-tests for difference and Fama-French regressions. I expect the stock returns of these
potential misstating firms to be significantly below the benchmark (or the market index) because investors naively
fixate on earnings and over-value stocks with low-quality (or managed) earnings; the mispricing will be corrected
when they realize that future earnings are lower than expected.
The reason I use the M-Score and the F-Score to proxy for earnings management is that it is infeasible to distinguish
actual misstating firms from non-misstating firms. The U.S. Securities and Exchange Commission (SEC), with a
limited budget and little private information, investigates and charges only a small subset of all misstating firms.
Therefore, I assume that the M-Score and the F-Score--given their high statistical power in determining the
7
possibility of earnings overstatements-are good proxies for earnings manipulation. Much to my surprise, my
analyses show that the firms that are flagged by the M-Score are quite different from the firms that are flagged by
the F-Score, and consequently, the stock returns of the two groups are significantly different. In particular, I find that
the M-Score has substantial investment value-in terms of short selling-because those flagged by the M-Score
generate returns significantly lower than the return of the benchmark. This evidence of a systematic association
between an accounting-based model for earnings misstatements and stock returns is of interest to both accounting
researchers and financial analysts because it suggests that accounting data not only provide information about firms
but also offer ideas for trading strategies.
My findings contribute to several research areas. First, this study broadly explores the two popular accounting-based
models for earnings overstatements and attempts to draw a better picture of earnings management practices across
both times and industries. To my knowledge, this study is the first to compare the frequency of potential earnings
misstatement by industry and by time using the Beneish M-Score and the Dechow F-Score. Second, this paper
provides evidence in support of the existence of market arbitrage. Both the M-Score and the F-Score are computed
only based on the data that are available on public annual financial statements, and the companies flagged by the MScore generate negative and significant excess stock returns. My evidence shows that public information is
imperfectly reflected in stock prices in a timely fashion and highlights the investment value of forensic accounting.
Furthermore, it underlines the value added by fundamental investors by demonstrating that careful analysis of
financial statements (i.e., fundamental analysis) can provide investment value.
The study continues as follows: Section 2 provides a literature review on the models for earnings management and
the stock return predictability of earnings management. Section 3 describes the Beneish M-Score and the Dechow FScore. My sample selection and score estimation procedures are reported in Section 4. Section 5 presents the
empirical results of the time-series analysis of the number of potential misstating firms as well as the results of the
stock-return analysis. This paper ends with a discussion on implications of my study and future possible research
areas in Section 6.
8
2.
Literature Review
This paper is related to two bodies of work: (1)
earnings management and (2) the predictive power of earnings
management over stock return.
2.1. EarningsManagementModels
Jensen and Heckling (1976) define an agency relationship as "a contract under which one or more persons
(principal[s]) engage another person (the agent) to perform some service on their behalf which involves delegating
some decision making authority to the agent." In the corporate world, shareholders are principals and managers are
agents. Although corporate financial statements are supposed to provide useful information for investors, financial
statements are often manipulated by managers whose incentives do not align with those of investors. In relation to
this, Watts (1977) and Watts and Zimmerman (1978) propose a positive accounting theory and suggest that
managers act on their self-interests by adopting accounting procedures that allow them to make a (private) gain.
Healy (1985) provides evidence in support of the theory by showing that CEOs increase or decrease discretionary
accruals to maximize the value of their future bonuses. He uses total accruals as a proxy for discretionary (or
abnormal) accruals. However, his implicit assumption that there are no non-discretionary accruals is very unrealistic.
Moreover, this creates a bias toward finding the results because both reported earnings and total accruals include
non-discretionary accruals. While Healy (1985)
deserves substantial credit for developing the first model for
accruals management and spawning a considerable amount of following research on earnings management, his
simplistic model is subject to significant measurement errors.
Jones (1991)
later proposes a more sophisticated model that estimates discretionary accruals. Total accruals are
regressed on factors reflecting a firm's economic condition (e.g., the change in sales and the gross level of PP&E),
and then the estimated coefficients from the regression are combined with total accruals as well as the firm's
economic factors to determine discretionary (or abnormal) accruals. By using this two-stage approach to estimate
discretionary accruals of firms subject to import relief, she concludes that managers make income-decreasing
accruals during import relief investigations. Dechow et al. (1995) modify the Jones model to allow the possibility of
managers exercising discretion over credit sales. They further show that the modified Jones model is powerful in
9
detecting earnings management in a sample of firms identified as earnings manipulators. Nonetheless, Bernard and
Skinner (1996) suggest that discretionary accruals estimated using the (modified) Jones model reflect measurement
errors partially because of the systematic misclassification of normal accruals as abnormal accruals. Similarly, Guay
et al. (1996) claim that five commonly-used models for discretionary accruals, including the Jones and modified
Jones models, do not generate a reliable measure by showing that the coefficients of non-discretionary and
discretionary accruals in the regression of stock returns are not significantly different from each other. However, it
should be noted that evaluations of discretionary accruals using stock returns depend on assumptions about the
relationship between earnings components and stock prices (e.g., market efficiency).
While the papers mentioned so far use the total discretionary accruals as the evidence of earnings management,
some other research uses specific components of an accruals measure-such as depreciation, bad debt allowances,
loan loss provisions, and deferred tax assets-and nonfinancial measures. For example, Teoh et al. (1998) find that
firms are more likely to have income-increasing depreciation policies and bad debt allowances in the IPO year and
in several subsequent years. Also, Wahlen (1994), Beatty et al. (1995), and Liu et al. (1997) conduct studies on bank
loan loss provisions. Wahlen (1994) develops and estimates expectation models for the bank-level values of the
primary loan loss indicators and regresses abnormal stock returns on the unexpected changes in these indicators. He
shows that bank managers, when the expected cash flow improves, increase current loan loss provisions to smooth
earnings and to consequently have positive stock reactions. Liu et al. (1997) also offer evidence suggesting that an
unexpected increase in loan loss privisions is followed by a positive market reaction but only for banks with low
regulatory capital levels.
Other discretionary-accrual models use deferred tax assets (Miller and Skinner 1998; Phillips and Rego 2003;
Ettredge et al. 2006). In particular, Ettredge et al. (2006) report that deferred taxes-along with auditor change,
market-to-book, revenue growth, and whether the firm is an over-the-counter one-have the statistical power to
predict misstatements. Several pieces of research provide additional insights into other variables that are useful for
detecting earnings misstatements. For example, Brazel et al. (2009) predict and find that several nonfinancial
measures (e.g., number of patents, employees, and products) combined with financial measures (e.g., revenue
growth) can be effectively used to assess the likelihood of an earnings misstatement. They show that fraudulent
10
firms have greater differences in the change in revenue and the change in nonfinancial measures than non-fraudulent
ones.
Because managing accruals is less observable and less costly than switching accounting methods or altering
operating cash flows, earnings management is largely done by manipulating accruals. Therefore, researchers have
increasingly used accruals variables to detect earnings management. The Beneish M-Score and the Dechow F-Score
are unique in that they use a broad range of financial variables including sales, gross margin, leverage, accruals, and
many others to detect potential earnings manipulators (Beneish 1999; Dechow et al. 2011). These two models are
constructed by analyzing the common characteristics of misstating firms, and the variables of the models are
designed to capture either the accounting distortions that can result from manipulation or preconditions that might
motivate companies to engage in such activities. Similar to the Altman Z-Score for predicting corporate bankruptcy,
the two models each yield a manipulation score of a firm, which can be used as a red flag for earnings management.
The computation of both scores can be easily done as it only requires the data on a firm's annual financial
statements; the M-Score requires two years of financial data, and the F-Score requires three years to evaluate the
likelihood of manipulation. So the inexpensively-applied M-Score and F-Score are widely used in many industries
to screen a large number of firms and identify potential manipulators. Moreover, the Beneish M-Score earned
popularity after flagging Enron well in advance of its notorious bankruptcy. It has also been featured in financial
statements analysis textbooks and in articles (Beneish et al. 2012).
Academics in the fields of accounting and finance have actively studied earnings management. Most of the literature
relies on accruals or other accounting-based measures as proxies for earnings management and finds the conditions
under which firms are more likely to commit manipulation. In this paper, I make a further step and look for an
extended use of the M-Score and the F-Score. Using these two scores, I explore the distribution of potential
misstating firms across both industries and times and test whether these firms generate negative abnormal returns. A
strand of research that focuses on the stock return predictability of earnings management is summarized in the next
section.
11
2.2. PredictingStock Returns Based on the Signs of EarningsManipulation
Investors' ability to identify earnings manipulation is fundamental in financial reporting research. There exist two
contrasting views on whether investors can differentiate earnings that are arbitrarily managed from earnings that
truly reflect the economic conditions of a firm. Beaver and McNichols (1998) find that, in the property and casualty
insurance industry, investors can at least partially identify manipulation of loss reserves and adjust firm values
accordingly. Similar results emerge from the banking industry. A number of studies conclude that investors suspect
that firms with abnormally low loan loss provisions are managing earnings upward and that they discount the firm
values for that reason (Wahlen 1994; Petroni et al. 2000). These findings suggest that investors indeed view
abnormal accruals as more likely to reflect earnings management.
On the other hand, extensive evidence exists that investors fail to process all the available information, and so
earnings management prevents efficient resource allocation. Dechow et al. (1996) find that firms earn negative and
significant abnormal returns (i.e., the average of -9%) upon the first announcement of potential earnings
management. This impies that investors do not fully see through earnings management reflected in abnormal
accruals and consider high earnings driven by high accruals attractive. In relation to this, Sloan (1994) provides
pioneering documentation of the "accruals anomaly" and shows that stock prices do not reflect earnings
management. He claims that investors "fixate" on earnings, failing to reflect the information contained in the
accruals and cash flows. Richardson et al. (2005) corroborate Sloan (1996)'s evidence by showing that investors are
not aware that accruals lead to lower persistence of earnings and thus they misprice stocks.
Another study reports that managers inflate reported earnings prior to public equity offers (i.e., IPO), and investors
are deceived by the high abnormal accruals, resulting in subsequent negative stock returns (Teoh, Welch and Wong
1998b). In particular, the study shows that public equity issuers in the most "aggressive" quartile of earnings
management have a three-year market-adjusted stock return of approximately 20% less than those in the most
"conservative" quartile. Recently, Hirshleifer et al. (2012) confirm again that firms with low accruals outperform
firms with high accruals and further show that the excess return is due to mispricing, not risk factors. All of these
papers provide evidence for the predictive power of accruals for stock returns and against the traditional efficient
market's view. Lastly, the paper by Beneish et al. (2012)-most similar to this paper-report that the firms that are
12
flagged by the Beneish M-Score as potential misstaing firms generate negative annual size-adjusted returns in every
decile sorted by size, book-to-market, momentum, accruals, and short interest. They also show that the excess return
on a long-short portfolio with a long position in the firms with the lowest M-Score (i.e., the lowest probability of
earnings manipulation) and a short position in the firms with the highest M-Score (i.e, the highet probability of
earnings manipulation) survive the usual risk controls. In summary, although investors may adjust for earnings
management in certain industries such as banking or insurance where changes in accruals are more closely
monitored, investors fail to detect managed earnings in general. Motivated by this idea, I attempt to find out whether
the likelihood of earnings management can be used as a stock-trading strategy.
The primary motivation for this research is to identify mispriced securities based on the M-Score and the F-Score.
While this paper extends previous literature on market arbitrage using the probability of earnings management as a
tool, it differs from many of the above studies in the following six aspects. First, I explore both the M-Score and the
F-Score and examine whether there is any systematic difference between the negative abnormal returns generated by
the firms flagged by the M-Score and the F-Score. These two scoring models are distinct from many previous
models for earnings management, because they combine not only some components of accruals but also a large set
of other financial statement items (e.g., changes in inventory, depreciation, and asset turnover) into one measure.
Second, I compare the results from different investment horizons (i.e., one-, two-, and five-year returns). This can
help find when the earnings management effect fades away as the market starts to receive more value-relevant
information. Third, I conduct industry-by-industry analyses to make sure the results are still found at the industry
level and to find industries with more significant results. Fourth, I do not construct a long-short investment strategy.
Instead of computing the returns on long-short portfolios, I simply test whether a long-only portfolio of firms
flagged by the M-Score or the F-Score underperforms the market. This is more useful in achieving an intuitive grasp
of the performance of potential manipulators. Fifth, I set the return on an equal-weighted portfolio of the entire
sample firms as a benchmark. This is more accurate than using the average return on the firms of similar size as a
benchmark because there are many cases where firms of similar size generate substantially different returns. Lastly,
the sample period of this paper-unlike all of the papers cited above-includes the recent financial crisis 2007-2008.
Therefore, the results of this paper can be important to many investors and researchers as it tests whether the socalled earnings management investment strategy worked in the recent recession.
13
3.
Two Scoring Models
3.1. The Beneish M-Score
Beneish (1999) profiles firms identified as earnings manipulators (i.e., firms that are charged with manipulation or
firms that admit to manipulation in the public press) from 1982 to 1988 and develops a statistical model (i.e., the
Beneish M-Score) to discriminate manipulators from non-manipulators. The Beneish M-Score does not apply to the
finance industry (i.e., Division H), so I exclude the finance industry in my analysis of the M-Score throughout the
paper. Also, when the two scores are compared with each other, the finance industry is eliminated from the F-Score
sample for a fair comparison.
The model relies exclusively on financial statement data. Specifically, the model consists of variables that seek to
capture the effects of manipulation or the conditions that incentivize the managers to misstate earnings. The MScore increases with (1)
increasing receivables (DSR), (2) deteriorating gross margin (GMI), (3) increasing
expenditure capitalization (AQI), (4) increasing sales (SGI), (5) declining depreciation rates (DEPI), (6) decreasing
administrative and marketing efficiency (SGAI), (7) increasing leverage (LEVI), and (8) increasing accruals to total
assets (TATA). The M-Score is different from other accruals-based models because only DSR, DEPI, and TATA are
aligned with accrual measures. The following shows the eight variables and the loading on these variables. The
detailed description of each of these eight variables can be found in Appendix 1.
MScore = -4.84 + 0.920 * DSR + 0.528 * GMI + 0.404 * AQI + 0.892 * SGI + 0.115 * DEPI - 0.172 * SGAI
-
0.327 * LEVI + 4.679 * TATA
Two types of error can arise from this model. Type I classification error (i.e., the probability of missing a
manipulator) needs to be traded off against Type
11 classification error (i.e., the probability of flagging an innocent
firm). Beneish (1999) measures the relative cost of the two error types and sets a threshold (i.e., cutoff) value of 1.78. That is, the firms with M-Scores greater than -1.78 are flagged as potential manipulators. Using this cut-off
value, Beneish (1999) validates the model's efficacy using a holdout sample from 1989 to1992. Surprisingly, 13%
of the sample firms that were flagged by the M-Score during the holdout period include approximately half of all
14
firms identified as earnings manipulators. Moreover, according to Beneish et al. (2012), the model correctly
identifies 71% of firms with earnings frauds in 1998-2002 in advance of their public revelation.2 The model flags
17.7% of all firms in 1998-2002, and therefore the detection rate of 71% is highly statistically significant. It is also
worth noting that the power of this test may be even higher, given that not all manipulations are caught and led to
indictment.
3.2. The Dechow F-Score
Dechow et al. (2011) construct a statistical model for earnings management by conducting a thorough analysis of the
2,190 Accounting and Auditing Enforcement Releases (AAERs) by the SEC from 1982 to 2005.' They select
variables based on the idea that the likelihood of earnings management increases with (1)
increasing accruals, (2)
decreasing performance, and (3) increasing market-related incentives. They conduct tests of pairwise difference in
mean values of the variables for misstatement firm-years and non-misstatement firm-years. They then estimate
logistic regressions to determine whether the variables are jointly significant in predicting misstatement firm-years
and use a backward elimination technique to arrive at their prediction model. The predicted value is obtained by
plugging each firm's financial data into the following model. The detail description of each variable can be found in
Appendix 2.
Predicted Value
=
-7.90 + 0.790 * rsstacc + 2.518 * chrec + 1.191 * chinv + 1.979 * softassets
+ 0.171 * chcs - 0.932 * chroa + 1.029 * issue
The predicted value does not represent the probability of earnings misstatements, but it can be turned into the
predicted probability using the logistic transformation. Dividing the predicted probability by the unconditional
expectation of a misstatement (i.e., the number of misstatement firms divided by the total number of firms) gives the
F-Score. Therefore, the F-Score is defined to be
epredicted value
1
F+epredicted value * 0.0037
2 These
firms include Cendant, Enron, Global Crossing. Qwest, etc.
3 AAERs report fraudulent firms engaged in illegal activities such as earnings management or bribery.
15
Similar to the M-Score, the F-Score can make two types of errors. It can misclassify a fraudulent firm as a nonmanipulator (i.e., Type I error) and it can misclassify an innocent firm as a manipulator (i.e., Type 11 error). The
table in Appendix 3 evaluates the sensitivity of the F-Score for different cutoff values. The F-Score of 1.00 indicates
that the firm has the same probability of a misstatement as the unconditional expectation. The F-Score greater than
1.00 indicates a statistically higher likelihood of a misstatement. The cutoff values of 1.85 and 2.45 are used in this
paper given their high statistical power relative to Type
II error.
Finally, it should be noted that both the M-Score and the F-Score detect overstatements, not understatements, of
earnings. So the interpretation of the results in this paper should be limited to earnings overstatements. The higher
the M-Score or the F-Score is, the higher the likelihood that the firm in question has overstated its earnings.
16
4.
Data and Sample Formation
4.1. Calculations of M-Scores and F-Scores
I first download the financial data of firms publicly traded on three major US exchanges (i.e., NYSE, NASDAQ, and
AMEX) between 2002 and 2012.4 For calculations of the M-Scores, I eliminate (1)
financial services firms (i.e.,
Division H; SIC codes 6000-6899) to which the M-Score does not apply and (2) firms without sufficient data to
compute the M-Score. Similarly, for calculations of the F-Scores, I drop firms without enough data to compute the
F-Score. Because the M-Score and the F-Score require financial data for two and three years, respectively, the
sample period covers the period FY 2004-2012. Furthermore, I assign a Standard Industrial Classification (SIC)
industry code to each sample firm. Appendix 4 describes the SIC industry codes and Table I and Table 2
respectively show the details of each sample year for each score. According to Table I and Table 2, Division D
(Manufacturing), Division H (Finance, Insurance, and Real Estate), and Division I (Services) were the three largest
industries-in terms of the number of constituents-from FY 2004 to FY 2012.
For both the M-Score and the F-Score, I winsorize each factor at the 1%
and 99% levels to prevent the variables
from being extremely too large or too small. This will not change the results of my analysis, because whether a
firm's M-Score or F-Score exceeds the cutoff value is not changed. Additionally, if the denominator of a variable is
zero, I set the value of the variable to (neutral) one in computing the M-Score and to (neutral) zero in computing the
F-Score. Hereafter, I call the firms flagged by the M-Score in a given year "M-Score Flag" and those flagged by the
F-Score "F-Score Flag." Table 3 and Table 4 list the mean values of each component (i.e., the average value of a
variable*coefficient specified in the model) of the M-Score and the F-Score, respectively. It is observed that changes
in the M-Score are largely due to changes in sales growth index (SGI), and changes in the F-Score are mainly due to
changes in cash sales (ch-cs) and in return on assets (chroa). This is interesting because, excluding financial
companies (i.e., Division H), the SGI component (i.e., 0.892*SGI) of the M-Score and the ch roa component (i.e., -
4 Because the screening function of Capital IQ only goes through "restated" financial statements, the dataset straight from the
screening process may include restated figures, not the original figures. Therefore, for a restated financial statement, I manually
find the original 10-K and update the figures accordingly.
17
0.932*ch roa) of the F-Score have a significant negative correlation coefficient of -0.41. This implies that there are
cases in which one firm is flagged by the M-Score but not by the F-Score. In fact, a further analysis of the two
scores in Section 5 shows that the group of firms flagged by the M-Score is noticeably different from the group of
firms flagged by the F-Score.
4.2. Stock Returns of PotentialOverstatingFirms
To test for the return predictability of the M-Score and the F-Score, I download monthly stock data from CRSP for
each firm-year observation obtained through the process outlined in Section 4.1. The annual equal-weighted buyand-hold return of the "M-Score Flag" portfolio or the "F-Score Flag" portfolio is computed from the last trading
day of the corresponding calendar year to the last trading day of the following calendar year. That is, at the end of
every calendar year, firms that are flagged by each score in their last fiscal year are selected, and their average oneyear return is computed. Two- and five-year returns are computed in similar ways.
I make sure that the returns on the "M/F-Score Flag" portfolio have no survivorship bias or
look-ahead bias. If a
firm is delisted during the holding period, I include returns until the delisting date and assume the proceeds from
delisting are equally re-invested in the rest of the firms in the portfolio. If the delisting return is coded as missing by
CRSP, I assume a delisting return of -100%. This way, I ensure that I do not know which firms will be delisted or go
bankrupt on the portfolio formation date. For benchmarks, I compute one-, two-, and five-year stock returns on an
equal-weighted portfolio of the entire sample firms (hereafter "Total Sample") for each year between 2004 and 2012.
Finally, it is worth noting that this buy-and-hold trading strategy does not require a portfolio rebalance throughout
the holding period of one year, two years, or five years, mitigating concerns about transaction costs.
18
5.
Empirical Results
5.1. The Number of PotentialOverstating Firms
5.1.1.
In this section,
Aggregate Percentage Changes over Time
I test the following null hypothesis (HI).
Hl: The number of potential overstatements by nonfinancial firms during the
recent financial crisis is not higher than other years.
Because most firms suffer economically during recessions, I conjecture that more firms overstate earnings during
the crisis to make their performance less disappointing. Therefore, I expect to reject Hi.' Figure 1 illustrates the
changes in the relative size of "M-Score Flag" and "F-Score Flag". In the top graph of Figure 1, Division H (Finance,
Insurance, and Real Estate) is excluded not only from the M-Score sample but also from the F-Score sample for an
unbiased comparison of the two scores. It can be seen that the changes in the percentages of firms flagged by the MScore and the F-Score are dissimilar, especially in FY 2007-2008 and in FY 2010- 2011. From FY 2007 to FY 2008,
the relative size of "M-Score Flag" substantially decreased while that of "F-Score Flag" noticeably increased. This
implies that the F-Score predicted more earnings overstatements in the recent financial crisis (or rejects HI) and the
M-Score did not (or accepts HI). The bottom graph of Figure
1 includes Division H (Finance, Insurance, and Real
Estate) and thus only illustrates the F-Score. It shows that the inclusion of Division H (Finance, Insurance, and Real
Estate) dramatically increases the percentage of the firms flagged by the F-Score in FY 2009, suggesting that many
financial companies that were severely affected by the depressed market conditions managed earnings upward in
2009.
The main reason for the difference between the two scores in FY 2007-2008 and FY 2010-2011 is that, as shown in
Section 4.1 (or Table 3 and Table 4), variable selections for the M-Score and the F-Score are somewhat conflicting.
5 Because
the M-Score does not apply to financial companies, testing of H I only covers non-financial firms.
19
Specifically, the M-Score specifies that, if the sales (i.e., SGI) of a company have decreased, the company is less
likely to have overstated its earnings. On the other hand, the F-Score suggests that, if a company's return on assets
(i.e. ch roa) has decreased, the company is more likely to have overstated its earnings. Because firms' average sales
and return on assets both dramatically decreased in FY 2007-2008, the two scores gave contradicting results during
this period. Additionally, the M-Score expects firms with increasing accruals (i.e., TATA) to more likely have
overstated earnings, and the F-Score expects firms with decreasing cash sales (i.e., ch-cs) to less likely have
overstated earnings.6 From FY 2010 to FY 2011, firms' average cash sales (i.e., ch cs) significantly decreased and,
not surprisingly, accruals (i.e., TATA) increased. The lower chcs decreased the F-Score, and the higher TATA
increased the M-Score, causing the relative sizes of "M-Score Flag" and "F-Score Flag" to change in opposite
directions during FY 2010-201 1.7
5.1.2.
Industry PercentageChanges over Time
It is constructive to see whether the results survive a separate analysis for each industry. So, I test the following
hypothesis in the section.
H2: The discrepanciesof MScore and FScore in FY 2007 - 2008 also exist at the industry level.
Table 5 and Table 6 present the number and the percentage of firms flagged by the M-Score and F-Score,
respectively, per industry from FY 2004 to FY 2012, and Figure 2 illustrates the changes in the percentage of
flagged firms per industry. The solid line represents the F-Score, and the dashed line represents the M-Score.' While
the analysis at the aggregate level indicates that the M-Score (F-Score) predicts less (more) overstatements from FY
2007 to FY 2008, a detailed investigation at the industry level implies that this does not hold in all industries and
Interestingly, the F-Score predicts firms with increasing cash sales to be more likely to have manipulated earnings. because
many misstating firms front-load their sales by engaging in unusual transactions (e.g., Sunbeam and Computer Associates).
6
that the firms flagged by the M-Score in a given year are called "M-Score Flag" and those flagged by the F-Score are
called "F-Score Flag."
7 Recall
8 One should be restrained from making a comparison in Division J (Public Administration), which consists of only 4 to 9 firms
(i.e.. less than 0.5% of Total Sample).
20
thus H2 should be rejected. Only Division D (Manufacturing), Division E (Transportation, Communications,
Electric, Gas, and Sanitary Services), Division F (Wholesale Trade), and Division I (Services) had decreasing
(increasing) percentages of the firms flagged by the M-Score (F-Score); Division B (Mining) and Division G (Retail
Trade) had increasing percentages of the firms flagged by the M-Score and the F-Score, respectively. Additionally,
while the examination at the aggregate level implies that the M-Score (F-Score) predicts more (less) overstatements
from FY 2010 to FY 2011, the industry-by-industry analysis concludes that this story only holds in Divisions A
(Agriculture, Forestry, and Fishing), E (Transportation, Communications, Electric, Gas, and Sanitary Services), and
F (Wholesale Trade).
Decreasing size of "M-Score Flag" in Division D (Manufacturing), Division E (Transportation, Communications,
Electric, Gas, And Sanitary Services), Division F (Wholesale Trade), and Division I (Services) and decreasing size
of "F-Score Flag" in Division A (Agriculture, Forestry, and Fishing) in FY 2007-2008 may suggest that more
managers instead take a "big bath" (or engage in earnings understatements) during difficult times to report superior
performance in the following fiscal year. While this is unlikely given that most firms already expected a prolonged
and severe recession in the early stage of the crisis, addressing this possibility is outside the scope of this paper
because both the M-Score and the F-Score only detect overstatements, not understatements.
Finally, unless there exists an unrealistic way to figure out which firms manipulate earnings within or outside the
GAAP, I cannot conclude which score gives the correct answer to how non-financial firms behaved during the
financial crisis on 2007-2008. However, what I can conclude is that the M-Score (F-Score) indicates that fewer
9
(more) non-financial firms at the aggregate level overstated earnings in FY2008 than in other bullish years. From
this point forward, regardless of whether each score accurately flags potential earnings manipulators, I examine
whether there can be any trading strategy based on the two scores.
5.2. One-, Two-, and Five-year Stock Return Analysis
9 The F-Score indicates that there were more overstatements during 2007-2008 in the finance industry (i.e., Division H) but we
cannot compare it with the M-Score which does not apply to the finance industry.
21
5.2.1.
Aggregate Comparisonof M-Score and F-Score
5.2. 1.1.
Stock Returns of Firms Flaggedin a Given Year
I expect stock returns of potential earnings manipulators to be significantly lower than the benchmark (or the market
index) because investors tend to naively focus on earnings and subsequently invest in stocks with low-quality
(managed-upward) earnings; it is speculated that, as firms that have overstated earnings eventually report
disappointing performance or reverse accounting distortions, the stock prices of these companies will go down and
the mispricing will be corrected. The opposite view is that the stock market is efficient and investors see through
earnings manipulation (see Section 2.2). The economic significance of a deviation from market efficiency can
therefore be assessed by examining the returns of a trading strategy based on these two scores that find potential
earnings manipulators. Thus, the following null hypothesis is tested in this section.
H3: Stock returnsof potential earnings manipulatorsare not significantly lower than the entire sample.
Testing this hypothesis can also help conclude whether these two scoring models can be used as a trading strategy.
Following the steps specified in Section 4.2, 1 compute buy-and-hold returns on the "M-Score Flag" and "F-Score
Flag" portfolios for one, two, and five years after the portfolios are formed. Table 7 compares one-, two-, and five0
year returns on the "M-Score Flag" portfolio (i.e., Sub-sample) to those on Total Sample each sample year. I
observe that the firms flagged by the M-Score generate negative annual abnormal returns in six out of nine years. I
run t-tests to account for possible event clustering, and four out of six negative excess returns are statistically
significant and all the positive excess returns are statistically insignificant. So the M-Score suggests that flagged
firms are associated with lower expected stock returns. This is consistent with the results found by Beneish et al.,
(2012). They, using an earlier sample period, show that firms flagged by the M-Score tend to generate negative sizeadjusted returns.
0 These two- and five-year returns may not provide entirely independent tests, because each fiscal year appears in multiple twoyear or five-year periods.
22
Similarly, Table 8 compares one-, two-, and five-year returns on the "F-Score Flag" portfolio (i.e., Sub-sample) to
those on Total Sample each sample year using the cutoff values of 1.85 and 2.45." The findings suggest that, in
contrast to the M-Score's return predictability, the F-Score does not give steady results; the firms flagged by the FScore give both significantly positive and negative abnormal returns. For instance, the "F-Score Flag" portfolio
using the cutoff value of 1.85 yields an annual return that is statistically -11% below Total Sample from 2007 to
2008 but 5.7% above Total Sample from 2011 to 2012. However, it is worth noting that the firms flagged by the FScore underperformed Total Sample during the financial crisis on 2007-2008. This suggests that potential earnings
manipulators were more severely affected by the bad economic conditions. I also include the annually-balanced
CRSP equal-weighted market returns in Panel A of Table 8 as a reference. The comparison between the CRSP
market returns and the "F-Score Flag" portfolio returns gives similar results.
Both Table 7 and Table 8 show that excess returns fade away as the time horizon of investment increases. Only one
five-year period (i.e., from 2007 to 2012) gives negative and statistically significant abnormal returns. This is
expected because there can be more systematic noise in longer time horizons. Noting that the M-Score sample does
not include Division H (Finance, Insurance, and Real Estate; SIC codes 6000-6899) and that some previous
literature demonstrates investors' ability to see through earnings manipulation in the finance industry (see Section
4.2), 1 exclude Division H from the F-Score sample and run the same stock return analysis (see Table 9). However,
there is still no consistency in the results; some years have negative and significant excess returns, and some other
years have positive and significant excess returns. So it can be concluded that it is not Division H that aggravates the
F-Score's stock return predictability.
In summary, the M-Score rejects H3 and the F-Score fails to reject H3. The evidence from the M-Score suggests that
public availability of financial statements alone does not ensure that all the elements of financial statements are fully
integrated into prices in a timely manner, and that the M-Score may be incorporated into a short-selling trading
strategy. On the other hand, the reason the F-Score does not give persistent negative abnormal returns may be
" The cutoff of 1.85 gives more statistically significant excess returns than the cutoff of 2.45 because it uses a bigger subset of
Total Sample.
23
because people indeed see through earnings management or that the score does not accurately flag potential earnings
management (e.g., flag too many innocent firms or too few culprits). A more detailed industry-level analysis in
Section 5.2.2 will give more insights in to this.
5.2.1.2.
In this section,
Stock Returns of Firms Flaggedby Both Scores in a Given Year
I compute stock returns of the firms flagged by both the M-Score and the F-Score in a given fiscal
year (hereafter "Both Flag"). Figure 3 shows that these firms represent a very small portion (i.e., less than 3%) of the
entire sample. This is not surprising given that the two scoring models are structurally different, as highlighted in
Section 5.1.1.12 The percentage of firms that are flagged by both the M-Score and the F-Score--equivalently, the
relative size of "Both Flag"-increases from FY 2007 to FY 2008. This indicates that the firms as a percentage of
Total Sample that both the F-Score and the M-Score suspect to be earnings manipulators decreased during the recent
financial crisis 2007-2008. This is interesting, given that the F-Score predicted more earnings overstatements in the
recent financial crisis and the M-Score did not (see Section 5.1.1).
Table 10 summarizes one-, two-, and five-year stock returns of the "Both Flag" portfolios. Similar to the stock
return analysis of the F-Score in in the earlier section, the cutoff of 1.85 gives more significant results. However, the
firms flagged by both the M-Score and the F-Score with the cutoff of 1.85 still do not give lower excess returns than
the firms just flagged by the M-Score; the frequency and the magnitude of significantly or insignificantly negative
abnormal returns are smaller. Therefore, even accounting for the fact that it is harder to reject the null hypothesisthat there is no difference between "Both Flag" and Total Sample return-with smaller sample sizes, the F-Score
does not add much value to the (short-selling) trading strategy based on the M-Score.
5.2. 1.3.
Stock Returns of Firms Flaggedin Two Consecutive Years
Another extension of the suggested trading strategy of short selling the stocks flagged by the M-Score or the F-Score
in a given fiscal year is to short sell the stocks flagged in two consecutive fiscal years. I tabulate one-year stock
Because the M-Score sample does not contain Division H (Finance, Insurance, and Real Estate), no company in "Both Flag" is
a financial company.
12
24
returns of the firms flagged by the M-Score and the F-Score in two consecutive years in Table I1 and Table 12,
respectively. The results are qualitatively similar with Table 7 and Table 8 (or Table 9): the M-Score gives negative
and significant excess returns, the F-Score yields both significantly positive and negative excess returns. However,
the firms flagged by the M-Score both in the last two fiscal years do not yield better one-year negative excess
returns than the firms flagged in one fiscal year (see Section 5.2. 1.1). Nonetheless, it should be emphasized that,
considering transaction costs and short-selling costs, such as borrowing fees and margin restrictions, this strategy of
short selling a smaller group of firms (i.e., 3%-4% of Total Sample) flagged in two consecutive years as opposed to
a relatively large group firms (i.e., 12%-16% of Total Sample) flagged in one year may be better to implement.
5.2.2.
Industty Comparisons
I compare the stock return results at the industry level because it is possible that the pooled t-test results are affected
by cross-sectionally varying parameters. Industry-specific t-tests can ensure that the results are robust to the problem.
Moreover, comparing stock returns of the "M/F-Score Flag" portfolio with those of Total Sample in each industry
can help find some industries with more significant differences. This can contribute to finding a better trading
strategy that is more implementable and profitable. Table 13 and Table 14 present one- and two-year stock returns of
the "M-Score Flag" portfolios, respectively. Similarly, Table 15 and Table 16 show one-year and two-year stock
returns of the "F-Score Flag" portfolios, respectively. Divisions A (Agriculture, Forestry, And Fishing) and J (Public
Administration)-consisting of less than 20 firms-rarely have flagged companies, and so the stock return analysis
of these industries is very limited.
According to Table 13, Divisions B (Mining), C (Construction), D (Manufacturing), and G (Retail Trade) have six
sample years, out of nine, with negative abnormal returns; Divisions E (Transportation, Communications, Electric,
Gas, and Sanitary Services) and F (Wholesale Trade) have seven and eight years, respectively, with negative excess
returns. Also, Division D (Manufacturing) has five years with negative and significant abnormal returns, and
Divisions F (Wholesale Trade) and G (Retail Trade) each have three years with negative and significant abnormal
returns. Thus, Division D (Manufacturing)-with six (five) years of negative (negative and significant) excess
returns-seems to be suited to the trading strategy based on the M-Score. Additionally, taking into account that
small sizes of some industries make it harder to reject the null hypothesis of no difference in returns (e.g., Division
25
C is 2% of Total Sample), Divisions B (Mining), C (Construction), E (Transportation, Communications, Electric,
Gas, and Sanitary Services), F (Wholesale Trade), and G (Retail Trade) may be good target industries to implement
the trading strategy for one year. For similar reasons, based on Table 14, Divisions B (Mining), C (Construction), D
(Manufacturing), E (Transportation, Communications, Electric, Gas, and Sanitary Services), and F (Wholesale
Trade) are potentially attractive industries for short selling the "M-Score Flag" portfolio in the investment horizon of
two years.
The F-Score seems to not give consistent excess stock returns even at the industry level, and many industries have
both significantly positive and negative excess returns (e.g. Divisions E, F, G, and H in Table
15)." However, Table
15 suggests that Division B (Mining)-with seven (one) negative (negative and significant) excess returns-and
Division D (Manufacturing)-with
four (one) negative (negative and significant) abnormal returns-may be
appropriate industries to use the one-year trading strategy based on the F-Score with the cutoff score of 1.85. For
similar reasons, based on Table 16, Divisions B (Mining), F (Wholesale Trade), and I (Services) can potentially be
good areas to use the trading strategy in the investment horizon of two years. The results are interesting because the
investment strategy based on the F-Score does seem attractive in my earlier analysis at the aggregate level (see
Section 5.2. 1). Therefore, it may be that, while the F-Score does not accurately flag potential earnings manipulators
at the aggregate level, it does so in some industries. I believe the constant negative excess returns of theses earnings
manipulators are observed because investors naively fixate on reported earnings.
The differences between the M-Score and the F-Score can also be observed here. For example, in 2005, the
companies in Division C (Construction) flagged by the M-Score significantly underperformed, while those flagged
by the F-Score significantly outperformed. Also, similarly, the companies in Division G (Retail Trade) flagged by
the M-Score significantly underperformed in 2012, but those flagged by the by F Score (using the cutoff score of
1.85) significantly outperformed during the same period. In conclusion, although the firms flagged by the M-Score
" Note that the observation that flagged companies in Division H do not persistently give negative abnormal returns aligns with
previous literature that investors likely see through earnings manipulation in the closely-watched finance industry (see Section
2.2).
26
perform differently from the firms flagged by the F-Score, a careful analysis at the industry level gives evidence that
some industries may be profitable areas to implement the trading strategy of short selling the firms flagged by the
M-Score or the F-Score.' 4
5.2.3.
Fana-FrenchThree-FactorAnalysis
Using the monthly stock data of the "M-Score Flag" portfolios and the "F-Score Flag" portfolios and monthly riskfree returns and market returns (adjusted for dividends) downloaded from CRSP, I run Fama-French three-factor
regressions to obtain risk-adjusted returns generated by the firms flagged by the M-Score or the F-Score (Fama and
French
1993)." More specifically, I assess the possibility that systematic variation in the subsequent stock returns
6
between the "M-Score Flag" portfolio and Total Sample is attributable to incomplete adjustment for risk.' The
Fama-French three-factor model is the following.
Monthly excess return = alpha + beta * EMR + b * SMB + c * HML
where EMR = excess market return, SMB=return on small firm minus return on big firms, and HML=return on high
book-to-market minus return on low book-to-market firms.
Running the regressions specified above, I test the following null hypothesis.
H4: The firms f lagged by the M(F)Score do not generate lower returns controlling
for market risk,size risk, and value risk
Given the limited sample period, one may not be tempted to draw a definite conclusion that this strategy will always generate
negative excess returns in some industries.
'"
'" I also run Carhart (1997) four-factor regressions including the momentum factor (untabulated). The results are qualitatively
similar as the coefficients on the momentum factor are insignificant.
It is debatable whether size and value factors are true risk factors or market anomalies. We are simply testing whether the
negative excess returns generated by the firms flagged by the M-Score are attributable to the factors that are already known to
generate inferior returns (i.e., market exposure., big size, growth).
16
27
The results in Table 17 demonstrate that the M-Score's negative excess return predictability is generally robust to
three Fama-French factors (Fama and French
1993). Alphas (i.e., factors-adjusted returns) are either not
significantly different from zero or significantly negative. This indicates that, even adjusting for size and value
effects, the companies flagged by the M-Score still underperform. '7 Betas (i.e., the coefficients on EMR) are
significant and greater than 1, indicating that the firms flagged by the M-Score are volatile stocks. The coefficients
on SML are generally positive and statistically significant, and this suggests that flagged firms are small in general.
Also, the coefficients on HML are, on average, not significantly different from zero. So it can be said that the "MScore Flag" portfolios are not overrepresented by either value or growth companies. I conclude that the M-Score
rejects H4 and that the average negative excess returns generated by the "M-Score Flag" portfolio is not driven by
the factors that are already known to cause inferior returns (i.e., big size and growth). In fact, Beneish et al. (2012)
show that taking a short position in the firms in the highest M-Score decile and long position in the firms in the
lowest M-Score decile will generate a positive alpha (i.e., risk-adjusted returns).
Table 18 presents the results of the regressions on returns of the firms flagged by the F-Score. The results from oneand two-year returns imply that the firms flagged by the F-Score, in contrast to the firms flagged by the M-Score, do
not persistently generate positive or negative alphas (i.e., risk-adjusted returns). Betas (i.e., the coefficients on EMR)
are significant and greater than I. However, they tend to be smaller than the betas of the "M-Score Flag" portfolios,
suggesting that the firms flagged by the F-Score are less volatile stocks. The coefficients on SML are positive and
statistically significant on average; this implies that the firms flagged by the F-Score, similar to those flagged by the
M-Score, are small in general. The coefficients on HML of the "F-Score Flag" portfolios are mostly positive and
higher than those of the "M-Score Flag" portfolios, indicating that the firms flagged by the F-Score tend to have
higher book-to-market ratios (i.e., lower growth).
In summary, while the t-test results simply indicate that the firms flagged by the M-Score tend to underperform and
those flagged by the F-Score do not, the regression results imply that those flagged by the M-Score yield negative
17 Note that t-tests examine whether the returns on flagged portfolios are statistically different from the benchmark and FamaFrench regressions examine whether the factors-adjusted returns (i.e., alphas) on flagged portfolios are positive, negative, or zero.
28
abnormal returns even controlling for the factors known to cause underperformance and those flagged by the FScore do not. The results give support to the idea that the stock market does not reflect all public information in a
timely fashion and thus that the trading strategy based on the M-Score can have its edge."
18 Recall that the firms flagged by the F-Score in some industries yield negative excess returns (i.e., Divisions B, D, F, and 1).
One could run Fama-French three-factor regressions at the industry level to find whether these industries also generate negative
risk-adjusted returns.
29
6.
Conclusion
The idea that the contents of financial statements are influenced by managers' maximizing their own self-interests
has produced a large body of studies on earnings manipulation, because fraudulent financial reporting imposes large
costs on financial markets by misdirecting capital allocation. The Beneish M-Score and the Dehow F-Score, among
many models for detecting earnings management, are unique in that they each incorporate a variety of public
financial data-not just accruals-into one measure that gives the likelihood of an earnings overstatement. Using
these two statistically-driven models, I perform two sets of analyses. Firstly, I conduct a time-series analysis on the
changes in the percentages of potential earnings manipulators flagged by the M-Score and the F-Score, respectively.
At the aggregate level, the M-Score (F-Score) indicates that fewer (more) non-financial firms overstated earnings
during the recent financial crisis on 2007-2008 than other sample years. However, a detailed analysis by industry
suggests that this disagreement of the two scores does not always hold at the industry level: while some industries
have conflicting results from the M-Score and the F-Score (e.g. Divisions D, E, F, and
I), some others have
consistent results from the two scores (e.g. Divisions B and G).
Secondly, I compute subsequent stock returns of the firms flagged by the M-Score or the F-Score and demonstrate
their respective performances in predicting stock returns. Specifically, I document that, at the aggregate level, the
firms flagged by the M-Score tend to underperform the market index (or the benchmark) and those flagged by the FScore do not. Nonetheless, I do find that, in some industries, the firms flagged by the F-Score generally yield
negative excess returns (e.g., Divisions B, D, F, and
I). Overall, my analyses provide substantial support for the use
of forensic accounting in equity trading. They imply that the information available on public financial statements is
imperfectly reflected in prices. This trading strategy based on the M-Score or the F-Score gives negative abnormal
returns because investors fail to see through earnings manipulation; they naively invest in stocks with low-quality
earnings that are managed upward, and are disappointed by subsequent earnings realizations. Further analysis using
Fama-French three-factor regressions indicates that the firms flagged by the M-Score generate negative risk-adjusted
returns. This finding confirms the advantage of the trading strategy of short selling the flagged firms.
This paper raises several questions for future investigation. Firstly, the sample period (i.e., FY 2004-2012) adopted
in this paper is very limited. To strengthen my claim that the analysis of earnings manipulation can provide
30
investment value, and to better predict the performance of the suggested trading strategy, there needs to be extensive
back-testing on broader historical data. Secondly, I divide the entire sample into ten Divisions, but one could break
down the entire sample into smaller industries or sectors. For example, within Division D (Manufacturing), there are
the food products industry, petroleum refining industry, computer equipment industry, etc., each of which has its
unique characteristics and risk factors. Conducting a detailed analysis in each of these distinct groups will contribute
to finding evidence that is robust to cross-sectional noise and to identifying the sectors or industries with more
significant results. Lastly, I speculate that the predictive power of the M-Score or the F-Score for stock returns is due
to a delayed response to current information about future earnings. This speculation can be tested by examining
whether abnormal stock returns are concentrated around subsequent earnings announcements (or the release of other
value-relevant information about the firm). Moreover, further research could investigate how different types of
information and different delivery paths affect investors' reactions. Such knowledge could give us a richer
understanding of how investors process information and price stocks.
31
7.
Tables and Figures
Table 1: M-Score Sample Summary
FY 2004
%
FY2005
%
FY2006
%
FY2007
%
FY2008
Division A
8
0%
9
0%
10
0%
9
0%
10
Division B
132
6%
159
6%
172
7%
180
7%
186
%
FY2009
%
FY2010
%
FY2011
%
FY2012
%
0%
16
1%
16
1%
16
1%
16
0%
7%
196
7%
213
7%
224
7%
235
7%
Division C
34
2%
39
2%
41
2%
42
2%
43
2%
43
49
1%
Division D
1087
51%
1250
50%
1331
50%
1366
50%
1403
50%
1450
50%
1503
49%
1552
49%
1635
49%
Division E
241
11%
284
11%
303
11%
317
12%
321
12%
336
12%
344
11%
353
11%
371
11%
Division F
81
4%
95
4%
98
4%
100
4%
102
4%
106
4%
112
4%
117
4%
121
4%
Division G
161
8%
184
7%
191
7%
193
7%
194
7%
203
7%
213
7%
223
7%
235
7%
1
386
18%
453
18%
487
18%
511
19%
526
19%
563
19%
605
20%
636
20%
676
20%
Division J
4
0%
4
0%
4
0%
4
0%
4
0%
4
0%
5
0%
5
0%
9
0%
Division
2134 100%
Total
24771100%
2637 100%
27221100%1
27891100%
1%
2917 100%
42
1%
3053 100%
45
1%
3171 100%
3347 100%
*Division H (Finance, Insurance, and Real Estate) is dropped.
Table 2: F-Score Sample Summary
FY 2004
%
FY 2005
%
FY 2006
%
FY 2007
%
FY 2008
%
FY 2009
%
FY 2010
%
FY 2011
%
FY 2012
%
Division A
7
0%
8
0%
8
0%
9
0%
9
0%
10
0%
16
0%
16
0%
16
0%
Division B
127
5%
140
5%
159
5%
169
5%
179
5%
186
5%
196
5%
211
5%
221
5%
Division C
34
37
1%
39
1%
41
1%
42
1%
41
1%
42
1%
42
Division D
1,073
39%
1,130
38%
1,248
38%
1,323
38%
1,363
37%
Division E
235
8%
252
8%
280
8%
297
8%
315
Division F
78
3%
84
3%
93
3%
94
3%
97
Division G
161
6%
169
6%
186
6%
189
5%
Division H
686
25%
739
25%
835
25%
893
I
381
14%
405
14%
460
14%
Division J
4
0%
4
0%
4
0%
2,786
100%
2,968
100%
3,312
100%
Division
Total
1%
1%
45
1%
1,404
37%
1,453
36%
1,503
36%
1,554
36%
9%
317
8%
331
8%
342
8%
350
8%
3%
100
3%
103
3%
110
3%
116
3%
195
5%
194
5%
204
5%
214
5%
225
5%
25%
950
26%
1,010
27%
1,070
27%
1,119
27%
1,180
27%
493
14%
518
14%
536
14%
574
14%
612
15%
645
15%
4
0%
4
0%
4
0%
4
0%
5
0%
5
0%
3,512
100%
3,672
100%
3,802
100%
3,993
100%
4,174
100%
4,357
100%
32
Table 3: Average Values of M-Score Components
0.920*DSR
0.528*GMI
0.404*AQI
0.892*SGI
0.115*DEPI
-0.172*SGAI
-0.327*LEVI
4.679*TATA
FY 2012 Max-Min
FY 2004
FY 2005 FY 2006 FY 2007 FY 2008 FY 2009 FY 2010 FY 2011
0.59
1.34
1.71
1.46
1.12
1.56
1.34
1.28
1.63
1.44
0.30
0.59
0.46
0.73
0.56
0.43
0.55
0.63
0.49
0.52
0.63
0.26
0.69
0.68
0.52
0.68
0.78
0.64
0.69
0.59
2.21***
1.37
1.52
1.85
1.14
1.27
1.19
1.69
3.35
3.06
0.04
0.12
0.12
0.12
0.12
0.16
0.12
0.12
0.12
0.15
0.49
-0.20
-0.20
-0.23
-0.20
-0.22
0.22
-0.19
-0.26
-0.19
-0.47
0.63
-0.46
-0.42
-0.57
-0.39
-0.99
-0.47
-0.41
-0.36
-0.30
0.59
-0.36
-0.44
-0.81
-0.26
-0.50
-0.22
-0.27
-0.27
The differences that are statistically significant at 1%, 5%, and 10% levels are denoted by ***, ** and *,
respectively.
Table 4: Average Values of F-Score Components
1)
Excluding Division H (Finance, Insurance, and Real Estate):
FY 2004
0.790*rsst acc
FY 2005
FY 2006
FY 2008
FY 2007
0.01
0.01
0.01
0.05
0.05
0.01
FY 2009
-0.02
-0.01
-0.01
2.518*ch rec
1.191*ch nv
0.06
0.06
0.02
0.04
0.05
0.02
0.06
0.05
0.02
1.979*soft assets
1.19
1.20
1.20
1.18
1.15
1.12
0.171*ch cs
0.11
1.23
0.02
0.11
-0.01
0.03
-0.90
-0.86
-0.04
-0.64
1.47
-0.45
0.98
0.99
0.99
0.98
0.95
0.92
-0.932*ch roa
1.029*issue
2)
FY 2010
FY 2011
FY 2012
Max-Min
0.08
0.06
0.03
0.01
0.04
0.02
0.03
0.02
0.01
1.12
1.13
1.14
0.09
0.10
-2.09
0.02
3.32***
-1.36
-1.42
-0.96
2.89***
0.93
0.94
0.93
0.07
0.04
0.04
0.01
Including Division H (Finance, Insurance, and Real Estate):
FY 2004
FY 2005
FY 2006
FY 2008
FY 2007
FY 2009
FY 2010
FY 2011
FY 2012
0.03
0.02
Max-Min
0.06
0.06
0.02
0.790*rsst acc
2.518*ch rec
1.191*ch inv
0.05
0.05
0.01
0.03
0.05
0.01
0.05
0.05
0.01
0.04
0.04
0.01
0.00
0.00
0.00
-0.01
-0.01
-0.01
0.03
0.03
0.01
0.01
0.03
0.01
1.979*soft assets
0.171*ch Cs
-0.932*ch roa
1.029*issue
1.29
1.30
1.30
1.29
1.27
1.25
1.26
1.26
1.27
0.05
0.10
1.05
0.01
0.01
0.00
0.02
0.06
-1.55
0.01
2.60***
-0.91
0.98
-0.92
0.98
-0.27
0.97
-0.61
0.97
1.15
0.94
-0.45
0.89
-1.28
0.90
-1.25
0.91
-1.95
0.89
3.10***
0.09
0.01
5%, and 10% levels are denoted by ***, **, and *,
The differences that are statistically significant at 1%,
respectively.
33
Figure 1: Changes in Percentages of Flagged Firms
1) Excluding Division H (Finance, Insurance, and Real Estate):
18
%%
16
14 %
Percentage of Firms with
M-Score> -1.78
/
12 %
%
%
Percentage of Firms with
F-Score> 2.45
10 %
...........
.........
.......
..
..
4.............
.............
8
Percentage of Firms with
F-Score>1.85
6%
4%
FY
FY
FY
FY
FY
FY
FY
FY
FY
2004 2005 2006 2007 2008 2009 2010 2011 2012
2)
F-Score, including Division H (Finance, Insurance, and Real Estate):
-
20%-18%
16%
14%
12%-
-..
10%
Percentage of Firms
with F-Score> 2.45
8%
4%
Percentage of Firms
with F-Score>1.85
-
6%
-
2%
FY
FY
FY
FY
FY
FY
FY
FY
FY
2004 2005 2006 2007 2008 2009 2010 2011 2012
*These are as percentages of the total number of the sample firms with the sufficient available data to compute the
corresponding score (i.e., the M-Score or the F-Score).
34
Table 5: Firms Flagged by M-Score per Industry
%
FY 2004
FY 2005
FY 2006
%
FY 2007
%
%
FY 2008
%
%
FY 2009
FY 2011
%
FY 2010
1%
FY 2012
%
5
1%
%
2
0%
Division A
1
0%
1
0%
2
0%
2
0%
3
1%
1
0%
4
Division B
25
8%
33
8%
34
7%
38
9%
40
10%
31
8%
49
11%
50
11%
34
8%
Division C
11
4%
14
3%
7
2%
5
1%
6
2%
7
2%
3
1%
6
1%
14
3%
Division D
168
55%
211
52%
254
55%
247
55%
206
52%
214
58%
258
56%
247
52%
233
54%
Division E
16
5%
31
8%
33
7%
43
10%
35
9%
30
8%
32
7%
43
9%
28
6%
Division F
11
4%
19
5%
26
6%
22
5%
13
3%
6
2%
16
3%
19
4%
14
3%
Division G
15
5%
22
5%
27
6%
22
5%
28
7%
17
5%
18
4%
28
6%
24
6%
1
58
19%
72
18%
82
18%
67
15%
67
17%
65
18%
84
18%
77
16%
85
20%
1
0%
1
0%
Division
0%
0%
0%
0%
0%
-
0%
-
-
0%
DivisionJ
-
Total
305
403
466
447
398
371
464
475
434
140%
14.9%
16.5%
154%
132%
116%
139%
14,4%
12 2%
as percentage of total number of firms
-
-
-
Table 6: Firms Flagged by F-Score per Industry
1)
Using the Cutoff Score of 1.85:
FY 2004
Division A
-
Division B
8
%
0%
2
3%
12
4%
13
18
FY 2009
%
FY 2010
%
1
0%
3
0%
2
0%
4
5%
33
5%
23
4%
34
5%
23
4%
28
2%
12
3%
35%
176
37%
4%
25
5%
27
6%
2%
14
3%
7
1%
15
2%
20
4%
16
3%
192
30%
154
29%
134
28%
76
12%
99
18%
75
16%
33%
187
29%
270
42%
6%
19
6%
13
4%
19
5%
32
5%
28
4%
25
3%
10
3%
11
3%
15
4%
18
3%
11
2%
15
7
2%
11
4%
14
4%
9
2%
26
4%
24
4%
32
11%
47
15%
50
14%
80
20%
246
38%
278
44%
54
19%
58
19%
69
20%
57
14%
70
11%
78
12%
0%
285
100%
0%
-
100%
310
346
100%
0%
-
100%
396
0%
-
645
100%
0%
-
634
100%
0%
-
638
100%
0%
-
536
100%
0%
-
479
100%
11.0%
12 8%
160%
16 7%
176%
11 3%
104%
104%
102%
0%
-
1%
6%
10
5
214
-
%
189
1%
4%
45%
Division H
4
8
15
180
Division G
1%
1%
2%
9
FY 2012
0%
48%
18
%
1
8
1%
FY 2011
FY 2008
1%
166
Division F
2)
4%
%
%
3%
Division E
as percentage of total number of firms
3
46%
54%
Total
FY 2007
1%
9
154
1
%
143
3
Division D
Division J
FY 2006
1
Division C
Division
%
FY 2005
0%
Using the Cutoff Score of 2.45:
FY 2004
Division A
-
8
Division B
%
FY 2005
%
FY 2006
%
FY 2007
0%
1
0%
2
1%
3
3%
12
5%
10
4%
18
%
%
FY 2008
1%
1
0%
6%
30
5%
FY 2009
-
21
%
FY 2010
%
FY 2011
%
FY 2012
0%
1
0%
2
0%
3
4%
30
6%
23
5%
25
%
1%
6%
8
3%
6
2%
13
4%
5
8
2%
9
2%
54%
113
46%
134
49%
148
46%
189
34%
153
29%
225
43%
152
36%
145
37%
16
7%
15
6%
12
4%
15
5%
31
6%
23
4%
23
4%
22
5%
24
6%
8
3%
8
3%
5
2%
12
4%
15
3%
9
2%
13
3%
10
2%
5
1%
Division G
4
2%
11
4%
14
5%
7
2%
22
4%
20
4%
12
2%
16
4%
9
2%
Division H
26
11%
34
14%
37
14%
63
20%
212
38%
240
46%
153
29%
1ff
26%
114
29%
Division 1
43
18%
46
19%
53
19%
42
13%
57
10%
57
11%
59
83
19%
60
15%
DivisionJ
-
394
100%
Division C
2
Division D
128
Division E
Division F
Total
235
as percentage of total number of firms
84%
1%
100%
0%
0%
0%
248
84%
100%
273
82%
100%
%
321
91%
35
100%
-
562
153%
4
1%
0%
100%
527
13.9%
4
1%
0%
100%
520
130%
1%
11%
0%
100%
427
102%
0%
100%
90%
Figure 2: Changes in Flagged Firms by Industry
..................
Division B
Division A
25%
35%
30VO
20%
25%
01
15%
/
-
0
10%
5%
.. ...
.............. ..
.................
0%
0%
0%
2005
2004
2006
2010
2009
2008
2007
2011
2004
2012
2005
2007
2006
2008
2009
2010
2011
20 12
Division D
Division C
25%
40%
---
-
-
-
20%
-
10%
-
15%
10%
5%
5%'
0%
0%
2004
2005
2006
2007
2008
2009
2011
2010
2004
2012
2005
2006
2007
2008
2009
2010
2011
2012
2011
2012
Division F
Division E
30%
16%
14%
25%
12%
5
9
20%
I
8
S
.1.
.
9
-
15%
-
-- -
-9
'K
9
10%
5%
0%
0%
4
2004
2005
2006
2007
2008
2009
2010
2011
2004
2012
36
2005
2006
2007
2008
2009
2010
Division H
Division G
14%
-
2-
12%
4%0%
2004
2006
2005
2008
2007
2009
2010
2011
2005
2004
2012
2006
2008
2009
2010
2011
2012
Division J
Division I
--
30%-----25% .-.
S
18%
2007
.....
209
- --- -2%
-
-
---
--
5%
0%
8%
2004
2005
2006
2007
2008
2009
2010
2011
-5%
2012
Solid line: F-Score; dashed line: M-Score.
37
2004
.--
20iS
2006
- --
2007
-- -
2009
2008
-- -
2010
2011
2012
Table 7: 1-year, 2-year, and 5-year Stock Returns (%) on Companies Flagged by M-Score in Fiscal Year t
Panel A: 1-year Returns
to t+1
Item
Total Sample
Average [1]
12.8
22.1
11
-42.7
89.1
30.9
12/30/2011- 12/31/201212/31/2012 12/31/2013
50.9
13.1
-8.9
1.4
13.1
-2.1
-47.8
40.1
19.9
-9.7
7.7
32.8
Sub-sample>-1.78
Average [2]
Median
[2]-[1]
11.8
-3.1
-1.0
17.4
5.2
-4.7
20.6
-5.2
9.6
-51.7
-58.1
-9.0***
108.4
46.4
19.3
20.9
7.6
-20
-25.1
-11.1***
4
55.1
-3.3
29.4
4.2
Excess Return
Median
12/31/201012/31/2011
12/31/200912/30/2010
12/31/2004- 12/30/2005- 12/29/2006- 12/31/2007- 12/31/200812/30/2005 12/29/2006 12/31/2007 12/31/2008 12/31/2009
-10.0**
-9.1***
Panel B: 2-year Returns
to t+2
Item
Total Sample
Average [1]
36.6
34.4
-38.3
12/31/2008- 12/31/2009- 12/31/2010- 12/30/201112/31/2010 12/30/2011 12/31/2012 12/31/2013
65.8
3.4
21.3
147.3
-13.3
Median
17.4
11.2
-46.4
-22.4
73.0
12.1
-2.8
41.9
Sub-sample>-1.78
Average [2]
Median
[2]-[1]
28.8
3.8
-7.8
29.1
-4.7
-5.2
-44.6
-59.1
-6.2*
-24.2
-35.1
-10.9***
190.9
79.2
43.6
5.2
-13.9
-16.1***
-11.9
-29.3
-15.3***
59.6
29.5
-6.2
Excess Return
12/31/2004- 12/30/2005- 12/29/2006- 12/31/200712/29/2006 12/31/2007 12/31/2008 12/31/2009
Panel C: 5-year Returns
12/31/200912/31/200812/31/2004- 12/30/2005- 12/29/2006- 12/31/200712/31/2009 12/31/2010 12/30/2011 12/31/2012 12/31/2013 (4yer/etrn
t to t+5
Item
Total Sample
Average [1]
Median
17.6
-8.7
34.6
5.9
6.8
-15.4
Sub-sample>-1.78
Average [2]
Median
[2]-[1]
19.6
-16.3
2.0
32.8
-10.7
-1.8
1.6
-33.2
-5.2
Excess Return
15.4
-8.6
-8.2
-38.0
-23.6***
262.7
122.9
260.3
103.9
-2.4
95.1
57.5
77.5
5.8
-17.6
The tables compare the returns of the firms flagged by the M-Score (i.e., the "M-Score Flag" portfolio) with those of
Total Sample. The returns are buy-and-hold returns on an equal-weighted portfolio of flagged firms formed in the
last trading day of the calendar year. Any proceeds upon delisting of a company are assumed to be reinvested in the
"M-Score Flag" portfolio. t-tests for difference in average returns are conducted, and the differences that are
statistically significant at 1%, 5%, and 10% levels are denoted by ***, **, and *, respectively.
38
Table 8: 1-year, 2-year, and 5-year Stock Returns (%) on Companies Flagged by F-Score in Fiscal Year t
Panel A: I-year Returns
t to t+1
Item
Market Index
CRSP
Equal-weighted
12/31/200412/30/2005
5.6
12/30/200512/29/2006
18.8
12/29/200612/31/2007
12/31/200712/31/2008
-43.2
-3.2
12/31/200912/30/2010
12/31/200812/31/2009
-9.0
25.2
64.3
12/30/201112/31/2012
12/31/201012/31/2011
12/31/201212/31/2013
16.8
30.9
43.8
Total Sample
Average
Median
11.0
0.0
20.5
14.0
3.9
-8.3
-40.1
-44.3
70.4
31.2
27.3
16.8
-8.3
-8.0
15.7
9.9
Sub-sample>2.45
Average
Median
11.6
-2.6
26.1
15.4
19.5
-0.8
-51.2
-56.7
83.4
43.8
25.3
13.6
-11.2
-13.1
20.6
9.9
31.7
Sub-sample>1.85
Average
Median
10.7
-2.2
22.9
15.0
16.2
-3.3
-51.1
-57.2
78.9
40.1
24.9
-12.3
21.5
51.1
13.6
-14.4
11.6
32.9
2.45
1.85
0.7
-0.2
5.6
2.4
15.6*
12.4*
-11.1***
13.0
8.5
-2.0
-2.4
-2.9
4.9
6.4
7.3
12/29/200612/31/2008
12/31/200712/31/2009
12/31/200812/31/2010
Excess Return
1
-11.0*
5.7*
-4.0*
29.0
50.3
Panel B: 2-year Returns
tto t+2
Item
12/31/200412/29/2006
12/30/200512/31/2007
12/31/200912/30/2011
12/31/201012/31/2012
12/30/201112/31/2013
Total Sample
Average
Median
35.2
14.8
24.7
2.8
-39.1
-45.7
-18.9
-23.6
119.1
55.5
18.4
9.8
5.7
1.7
63.2
40.5
Sub-sample>2.45
Average
Median
46.1
18.7
36.4
7.6
-44.1
-55.0
-28.6
-34.5
142.0
62.6
17.1
9.2
4.0
-2.1
86.3
50.9
Sub-sample>1.85
Average
Median
37.9
17.9
33.7
S.4
-45.1
-56.2
-28.2
-34.7
133.3
62.2
18.0
8.9
2.3
-2.6
85.2
47.5
Excess Return
2.45
1.85
10.9
2.6
11.7
9.0
-5.0
-5.9**
-9.7***
-9.3***
22.8
14.2
-1.3
-0.4
-1.7
-3.4
23.0
21.9***
Panel C: 5-year Returns
12/31/200412/31/2009
12/30/2005- 12/29/200612/31/2010 12/30/2011
t to t+5
Ite m
Total Sample
Average
Median
11.3
-17.0
21.2
-3.6
-2.9
-23.1
Sub-sample>2.45
Average
Median
7.3
-25.6
40.6
-1.3
-3.7
-30.6
Sub-sample>1.85
Average
Median
6.2
-24.1
28.8
-1.4
-3.0
-31.0
Excess Return
Excess Return
2.45
1.85
-3.9
-5.1
19.4
7.6
-0.8
-0.1
12/31/200912/31/2007- 12/31/200812/31/2013
12/31/2012 12/31/2013 (4yer/etrn
(4-year return)
88.5
221.0
7.2
52.8
-10.6
94.8
89.6
254.5
-3.5
53.7
105.5
-23.3
89.9
238.7
-5.2
50.3
102.8
-25.9
-10.6
-12.3**
33.5
17.7
1.1
1.4
The tables compare the returns of the firms flagged by the F-Score (i.e., the "F-Score Flag" portfolio) with those of
Total Sample. The returns are buy-and-hold returns on an equal-weighted portfolio of flagged firms formed in the
last trading day of the calendar year. Any proceeds upon delisting of a company are assumed to be reinvested in the
"F-Score Flag" portfolio. t-tests for difference in average returns are conducted, and the differences that are
statistically significant at 1%, 5%, and 10% levels are denoted by ***, **, and *, respectively.
39
Table 9: 1-year, 2-year, and 5-year Stock Returns (%) on Companies (excluding Division H) Flagged by FScore in Fiscal Year t
Panel A: I-year Returns
Item
ttot
12/31/2010- 12/30/2011- 12/31/201212/31/200912/31/2012
12/31/2013
12/31/2011
12/30/2010
50.6
13.3
-8.9
31.0
32.9
7.7
-9.6
20.0
59.3
14.7
-12.6
36.1
12/31/200812/31/2009
87.9
-42.8
40.1
-47.8
118.9
-51.0
12/31/200712/31/2008
12/29/200612/31/2007
12/30/200512/29/2006
12/31/200412/30/2005
Total Sample excluding Division H
Average
Median
12.7
1.1
21.4
13.1
10.6
-2.0
Sub-sample excluding Division H>2.45
Average
12.4
_ubsmpeexddingDivsinH>2.45
Median
-3.6
25.2
15.5
23.4
0.7
-56.1
62.8
24.6
-18.0
4.3
37.8
Sub-sample excluding Division H>1.85
Average
11.7
22.0
21.0
-51.1
114.0
34.9
-13.6
15.9
59.5
_ub-smpeexdudngDivis__nH>_.85
Median
-2.4
15.2
-2.0
-57.5
62.6
24.8
-18.3
5.6
37.9
2.45
1.85
-0.3
-0.9
3.7
12.9
10.4
-8.2**
-8.3-*
31.0**
26.2**
5.1
3.9
-3.7
-4.7"*
1.4
2.7
8.7
8.9
12/31/200712/31/2009
12/31/200812/31/2010
12/31/200912/30/2011
12/31/201012/31/2012
Excess Return
0.5
Panel B: 2-year Returns
Item
to t+2
12/31/200412/29/2006
12/30/200512/31/2007
12/29/200612/31/2008
54.4**
21.4
11.6
24.0
7.8
25.5
10.2
2.6
3.4
-2.7
-0.2
-9.7
-1.8
-10.4
-3.6
12/30/201112/31/2013
65.6
42.3
87.4
41.6
88.2
42.4
21.8*
31.9*
4.2
-5.2
22.5**
Total Sample excluding Division H
Average
Median
36.1
17.3
32.9
10.4
-38.2
-46.3
-15.6
-23.1
145.6
72.9
Sub-sample excluding Division H>2.45
Average
Median
45.2
17.4
36.4
7.6
-44.9
-55.7
-26.3
-33.9
199.9
112.2
Sub-sample excluding Division H>1.85
Average
Median
65.6
18.9
35.4
32.9
-45.1
-57.4
-26.5
-15.6
177.4
145.6
Excess Return
2.45
1.85
9.0
3.4
-6.7*
-10.7***
29.4
2.4
-10.9***
12/31/200412/31/2009
12/30/200512/31/2010
1
Excess Return
-6.9'
Panel C: 5-year Returns
12/31/2009tIto +5
Item
Total Sample excluding Division H
Average
Median
16.8
-8.9
32.3
5.8
7.0
-15.25
12.6
-8.61
Sub-sample excluding Division H>2.45
Average
Median
15.7
-18.9
31.8
-1.3
-0.2
-31.7
-1.0
-21.5
Sub-sample excluding Division H>1.85
Average
Median
9.3
-19.7
-1.1
-7.5
30.5
-1.4
-0.6
-1.8
1.8
-31.7
-7.3
-5.2
-3.1
-25.5
-13.6"'
-15.7*
_______________________
2.45
Excess Return
E
1.85
12/29/200612/30/2011
12/31/200712/31/2012
329.0
12/31/2013
(4-year return)
95.3
58
100.5
62.3
110.1
171.1
91.3**
67.8*
68.4
5.2
14.8
12/31/200812/31/2013
261.2
127.5
352.5
179.7
The tables compare the returns of the firms (excluding financial firms) flagged by the F-Score (i.e., the "F-Score
Flag" portfolio excluding Division H) with those of Total Sample. The returns are buy-and-hold returns on an equalweighted portfolio of flagged firms formed in the last trading day of the calendar year. Any proceeds upon delisting
of a company are assumed to be reinvested in the "F-Score Flag" portfolio. t-tests for difference in average returns
are conducted, and the differences that are statistically significant at 1%, 5%, and 10% levels are denoted by ***, **,
and *, respectively.
40
Figure 3: Changes in Percentages of Flagged Firms
18%
16%
14%
4.,
- - - Percentage of Firms with
M-Scre>
-1.78
M-Score> -1.7
12%
Percentage of Firms with
F-Score> 2.45
10%
8%
8%
=Percentage of Firms with
F-Score>1.85
6%
-
2%
ago
-Percentage
of Firms with
F-Score>2.45 and MScore>-1.78
ft
0%
FY
2004
FY
2005
FY
2006
FY
2007
FY
2008
FY
2009
FY
2010
FY
2011
FY
2012
These are as percentages of the total number of the sample firms with the sufficient available data to compute the
corresponding score(s) (i.e., the M-Score, the F-Score, or both).
41
Table 10: 1-year, 2-year, and 5-year Stock Returns (%) on Companies Flagged by Both Scores in Fiscal Year
t
Panel A:
1-year Returns
12/31/2004- 12/30/200512/30/2005 12/29/2006
12/31/2008- 12/31/200912/31/2009 12/30/2010
12/29/2006- 12/31/200712/31/2007 12/31/2008
12/31/2010- 12/30/201112/31/2011 12/31/2012
t to t+1
Item
Total Sample
Average
Median
12.8
1.4
22.1
13.1
11.0
-2.1
-42.7
-47.8
89.1
40.1
30.9
19.85
-8.9
-9.7
Sub-sample>2.45
Average
Median
8.1
-6.3
21.8
13.0
47.2
16.5
-49.9
-55.5
94.6
45.4
33.3
11.0
-14.5
-20.9
Sub-sample>1.85
Average
Median
14.6
-4.4
15.9
6.3
42.9
8.2
-51.7
-57.3
103.0
52.3
33.3
11.0
Excess Return
2.45
1.85
-4.7
1.8
-0.3
36.2
31.9
-7.1*
-9.0***
5.5
13.9
2.4
2.4
-17.7
-26.2
-5.6
-8.8*
-6.1
13.1
7.7
2.1
-10.4
2.2
-6.9
-11.0
-10.9*
12/31/201212/31/2013
50.9
32.75
48.9
51.6
50.9
50.2
-2.0
0.0
Panel B: 2-year Returns
Item
to t+2
12/31/2004- 12/30/200512/29/2006 12/31/2007
12/31/2009- 12/31/201012/30/2011 12/31/2012
12/29/2006- 12/31/2007- 12/31/200812/31/2008 12/31/2009 12/31/2010
Total Sample
Average
Median
36.6
17.4
34.4
11.2
-38.3
-46.4
-13.3
-22.4
147.3
73.0
21.3
12.1
Sub-sample>2.45
Average
Median
24.5
1.8
6.8
-12.6
-40.4
-59.3
-27.8
-29.6
154.8
63.1
10.7
0.6
Sub-sample>1.85
Average
Median
30.0
3.1
8.5
-12.6
-43.1
-59.8
-29.7
-29.5
152.2
84.5
13.9
-0.4
Excess Return
Excess Return
2.45
1.85
-12.2
-6.6
-34.4**
-25.9**
-2.1
-4.8
-14.5***
-16.4***
7.5
4.8
-10.7
-7.4
3.4
-2.8
-1.3
-22.5
-6.4
-28.9
-4.7
-9.8
12/30/201112/31/2013
65.8
41.9
59.9
11.9
66.9
16.3
-6.0
1.1
Panel C: 5-year Returns
12/31/200412/31/2009
12/30/200512/31/2010
12/29/200612/30/2011
12/31/200712/31/2012
12/31/2008-
12/31/2009-
12/31/2013
(4-year reurn)
t to t+5
Item
Total Sample
Average
Median
17.6
-8.7
34.6
5.9
6.8
-15.35
15.4
-8.64
262.7
122.9
Sub-sample>2.45
Average
Median
-1.5
-33
22.0
-23.8
21.9
-36
11.3
-21.6
201.8
105.7
Sub-sample>1.85
Average
Median
1.7
-40
21.1
-11.7
15.4
-37.1
3.5
-26.8
180.6
96.1
-19.0
-15.9
-12.6
-13.5
15.1
8.6
-4.1
-11.9
-60.9
-82.1*
Excess Return
1
2.45
1.85
95.1
57.5
77.9
27.3
95.8
20.85
-17.2
0.7
The number of firms that are flagged by both the M-Score and the F-Score with the cutoff of 2.45 are 31, 45, 67, 77,
55, 41, 97, 80, and 58, in FY 2004-2012, respectively. The number of firms that are flagged by both the M-Score
and the F-Score with the cutoff of 1.85 are 59, 57, 88, 95, 67, 54, 119, 94, and 71 in FY 2004-2012, respectively.
The changes in these firms as a percentage of the entire sample firms can be seen in Figure 1. t-tests for difference in
average returns are conducted, and the differences that are statistically significant at 1%, 5%, and 10% levels are
denoted by ***, **, and *, respectively.
42
Table
11: Stock Price Change (%) from t+1 to t+2 of Companies Flagged by M-Score Consecutively in Year t
and t+I
Firm
FY 2005-2006
FY 2004-2005
Flagged Years (t to t+1)
s and t + 1
aedatat
Excess 1-year Return
(from t+1 to t+2) over
Total Sample
3.4%
4.1%
12.3
3.4
FY 2008-2009
FY 2007-2008
FY 2006-2007
4.3%
-11.1***
FY 2009-2010
FY 2010-2011
FY 2011-2012
3.7%
3.0%
3.0%
3.9%
3.4%
12.4
-16.0**
-10.3
-12.7**
11.7
The table compares one-year returns of the firms flagged by the M-Score in the last two fiscal years with those of
Total Sample. The returns are buy-and-hold returns on an equal-weighted portfolio of flagged firms formed in the
last trading day of the calendar year. Any proceeds upon delisting of a company are assumed to be reinvested in the
"M-Score Flag" portfolio. t-tests for difference in average returns are conducted, and the differences that are
statistically significant at 1%, 5%, and 10% levels are denoted by ***, **, and *, respectively.
Table 12: Stock Price Change (%) from t+I to t+2 of Companies Flagged by F-Score Consecutively in Year t
and t+1
5.8%
s t and t + 1
Firm Flagd at
Excess 1-year Return
(from t+1 to t+2) over
Total Sample
FY 2005-2006
FY 2004-2005
Flagged Years (t to t+1)
8.9**
I
I
2.2%
2.5%
-5.2
-13.5***
FY 2008-2009
FY 2007-2008
FY 2006-2007
4.7%
2.6%
-1.1
I
-12.9***
FY 2009-2010
FY 2010-2011
FY 2011-2012
3.4%
2.6%
2.6%
-1.1
13.7*
-2.1
I
*the cutoff score of 1.85 is used.
The table compares one-year returns of the firms flagged by the F-Score in the last two fiscal years with those of
Total Sample. The returns are buy-and-hold returns on an equal-weighted portfolio of flagged firms formed in the
last trading day of the calendar year. Any proceeds upon delisting of a company are assumed to be reinvested in the
"F-Score Flag" portfolio. t-tests for difference in average returns are conducted, and the differences that are
statistically significant at 1%, 5%, and 10% levels are denoted by ***, **, and *, respectively.
43
Table 13:
Industry
1-year Stock Returns (%) on Companies Flagged by M-Score in Each Industry
Item
12/31/200412/30/2005
12/30/200512/29/2006
12/29/200612/31/2007
12/31/200712/31/2008
12/31/200812/31/2009
12/31/2009-
12/31/2010-
12/30/2011-
12/31/2012-
12/30/2010
12/31/2011
-22.3
3.6
12/31/2012
11.2
12/31/2013
26.7
Total Sample
7.5
7.7
50.4
-39.6
72.1
Division B
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
93.1
85.6
55.4
33.8
26.4
18.7
16.5
23.2
-1.1
-51.5
38.5
13.4
-37.5
2.2
-41.6
-58.6
50.6
-21.5
92.7
112.5
-7.3
-10.9
56.2
77.4
-30.7
-8.3
-7.8
-14.6
-16.8
-28.0
-10.3
-11.8
46.7
20.0
15.0
12.3
Division C
Excess Return
Total Sample
Sub-sample >-1.78
-21.6
27.6
7.7
6.7
7.7
-15.9
-25.1
-0.8
-39.5
-17.0**
-37.4
-31.3
19.7
41.0
16.5
21.2
9.3
-0.8
-6.9
-18.7
-19.0
-19.9*
-23.5***
-38.7
6.0
-24.5
-10.0
Division D
Total Sample
Sub-sample >-1.78
8.1
7.3
21.8
13.8
11.3
24.1
-44.7
-52.2
102.6
131.1
32.6
13.0
Excess Return
-0.8
-8.0*
12.8
-7.5***
28.5
-19.6***
-0.3
-11.4
-20.4
-9.0**
-1.6
67.6
73.3
5.6
15.6
6.4
-9.2**
-2.7
27.9
22.7
-5.2
58.3
62.3
3.9
Division E
Total Sample
Sub-sample >-1.78
6.4
-14.9
18.2
19.4
5.5
-3.4
-37.8
-53.4
68.8
52.2
20.7
18.4
-6.1
-17.1
6.0
0.2
45.8
56.4
-21.3**
1.2
-8.9
-15.5***
-16.6
-2.3
-11.0
-5.7
10.5
17.3
-7.2
21.7
20.1
4.5
1.1
-39.6
-49.8
70.2
81.5
27.8
18.7
-1.0
-26.3
13.7
0.3
46.3
41.5
Division A
Excess Return
Excess Return
Division F
Total Sample
Sub-sample >-1.78
-24.4**
-1.6
-3.4
-10.2*
11.3
-9.1
-25.3***
-13.4
-4.8
19.5
21.9
23.5
2.1
-11.5
-9.0
-40.2
-52.7
100.2
167.8
32.5
5.7
1.2
-11.9
13.7
0.4
45.4
34.9
Excess Return
2.4
-21.4**
2.4
-12.5
67.6
-26.9**
-13.1
-13.3*
-10.5
Total Sample
11.7
28.1
15.0
-43.1
68.3
28.2
-8.5
15.0
53.0
Sub-sample >-1.78
Excess Return
23.7
12.0
35.6
7.5
44,0
29.0
-47.6
-4.5
48.8
-19.5
26.0
-2.2
-22.8
-14.2***
7.2
-7.8
66.8
13.7
Total Sample
Sub-sample >-1.78
13.7
N/A
15.1
N/A
1.5
-5.7
-37.8
-18.8
18.6
N/A
26.5
N/A
1.7
22.7
28.0
N/A
N/A
N/A
Excess Return
N/A
N/A
-7.2
19.0
N/A
N/A
N/A
N/A
N/A
Excess Return
Division G
Division I
Division J
Total Sample
Sub-sample > -1.78
t-tests for difference in average returns are conducted, and the differences that are statistically significant at 1%, 5%,
and 10% levels are denoted by ***, **, and *, respectively.
44
Table 14: 2-year Stock Returns (%) on Companies Flagged by M-Score in Each Industry
Industry
Division A
Division B
Division C
Division D
Division E
Division F
Division G
Division I
Division J
Item
Industry Item
Total Sample
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
Excess Return
Total Sample
Sub-sample >-1.78
Excess Return
12/31/2004- 12/30/2005- 12/29/2006- 12/31/2007- 12/31/2008- 12/31/2009- 12/31/2010- 12/30/201112/29/2006
12/31/2007 12/31/2008 12/31/2009 12/31/2010 12/30/2011 12/31/2012 12/31/2013
-4.6
40.7
-15.6
70.0
-28.2
11.8
63.0
10.5
7.3
-5.5
20.8
41.0
-34.6
302.4
-48.7
65.0
-19.9
-1.0
22.9
-29.0
-6.4
-60.5
239.4
54.5
3.7
-17.1
38.1
214.5
25.0
51.9
-24.2
87.1
1.1
-25.1
258.0
42.5
-56.9
-36.2
47.7
47.3
-2.5
-8.0
4.4
43.5
-32.7**
-61.3***
-4.5
-39.4
104.8
30.0
-6.2
56.2
-23.2
19.2
-45.1
45.4
34.6
120.3
7.0
-22.7
2.4
-75.1
-1.9
-57.2
15.5
4.6
13.2
0.5
-53.8***
-29.9**
-76.3***
-47.3**
74.2
3.4
19.8
161.5
-16.7
-40.3
33.3
30.4
64.4
-1.6
-10.5
-23.7
234.9
23.2
-42.9
24.6
-9.8
-13.8**
-21.4***
73.4
-7.1
-2.6
-10.1
-5.8
53.3
1.2
16.0
111.1
-19.0
-34.3
24.0
25.8
60.4
-25.0
13.6
92.9
-29.6
-59.3
-8.6
36.0
7.0
-26.2***
-2.4
-18.2
-10.6
-25.1***
12.0
-34.4***
65.0
10.6
27.3
110.6
-9.7
-38.9
27.2
41.1
26.0
76.5
-38.6
-18.6
10.8
-49.3
-13.3
11.8
-39.0**
-29.2**
-65.9***
-34.1
-3.6
-10.4
-16.4
-29.3
62.3
15.6
41.5
166.8
1.3
-48.8
6.6
48.3
40.1
19.3
-3.7
227.7
-26.0
-58.2
-12.5
39.6
-22.2
3.7
-45.2**
60.9
-27.2
-9.4
-19.1
-8.7
73.5
4.0
16.7
117.2
-19.1
-36.4
52.1
39.2
96.8
11.6
-11.2
82.3
-18.9
-30.5
48.3
68.4
23.4
-15.2*
-5.1
-35.0*
0.3
5.9
16.2
9.1
25.3
57.0
28.6
49.1
-26.2
-37.1
18.3
31.2
N/A
N/A
N/A
-8.2
N/A
N/A
-47.3
N/A
N/A
N/A
N/A
N/A
18.0
-10.2
N/A
N/A
t-tests for difference in average returns are conducted, and the differences that are statistically significant at 1%, 5%,
and 10% levels are denoted by ***, **, and *, respectively.
45
Table 15:
1-year Stock Returns (%) on Companies Flagged by F-Score in Each Industry
Industry
Item
Division A
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Division B
Division C
Division D
Division E
Division F
Division G
Division H
Division I
Division J
12/31/2004- 12/30/2005- 12/29/2006- 12/31/2007- 12/31/2008- 12/31/2009- 12/31/2010- 12/30/2011- 12/31/201212/31/2013
12/31/2012
12/31/2011
12/30/2010
12/31/2009
12/31/2007 12/31/2008
12/30/2005 12/29/2006
26.7
11.2
-22.3
5.5
27.1
-39.6
58.7
4.2
7.5
28.4
27.8
16.8
N/A
110.1
-21.5
49.2
39.9
N/A
18.8
27.8
-24.0
-22.3
110.1
-21.5
49.2
39.9
N/A
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
N/A
N/A
55.3
37.2
37.2
-18.1
-18.1
35.7
35.7
17.0
8.7
8.7
-8.3
-8.3
-9.5
-9.5
39.7
-12.3
20.3
-52.1
-19.4*
18.2
18.2
-43.4
-54.6
-54.6
-11.2
-11.2
83.0
83.0
91.9
93.7
87.3
1.8
-4.6
N/A
-27.8
56.2
69.9
71.5
13.7
15.3
39.1
-1.7
-7.9
-12.4
-10.2
-4.5
-2.3
16.5
16.5
-10.2
-12.9
-12.9
-2.6
-2.6
1.7
-7.9
16.9
23.2
18.2
6.3
1.3
Total Sample
20.8
9.8
-0.8
-37.4
41.0
9.3
-18.7
68.8
27.9
Sub-sample > 2.45
19.8
36.2
15.6
-38.4
36.3
31.3
-36.7
140.9
33.5
Sub-sample > 1.85
21.0
35.2
2.2
-41.3
36.3
11.1
-23.0
129.9
36.6
Excess Return (2.45)
Excess Return (1.85)
-1.0
0.1
26.5**
25.5***
16.4
2.9
-1.0
-3.9
-4.7
-4.7
22.0
1.9
-18.0
-4.3
72.2
61.1
5.6
8.7
Total Sample
8.5
20.6
10.6
-44.7
101.7
32.8
-11.3
15.8
57.3
Sub-sample > 2.45
Sub-sample > 1.85
12.5
11.4
24.1
18.9
18.3
17.5
-51.2
-50.8
116.2
115.4
33.2
35.2
-12.8
-14.3
12.3
14.1
60.3
59.9
Excess Return (2.45)
Excess Return (1.85)
Total Sample
4.0
2.9
6.6
3.5
-1.7
17.8
7.7
6.9
3.1
-6.5**
-6.1**
-37.4
14.6
13.7
69.1
0.4
2.4
20.6
-1.6
-3.1
-6.1
-3.5
-1.7
6.1
3.0
2.6
46.2
Sub-sample > 2.45
2.9
26.5
-16.3
-51.1
193.7
41.0
-24.0
-13.9
96.4
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
5.6
-3.7
-0.9
24.2
8.7
6.4
-16.8
-19.3**
-19.9**
-54.4
-13.7
-17.0
186.5
124.6*
117.4*
31.9
20.4
11.3
-22.3
-17.9**
-16.3**
-8.1
-20.1**
-14.2
109.3
50.2*
63.1**
Total Sample
16.2
22.1
5.4
-39.6
70.6
28.3
-0.8
13.7
46.4
Sub-sample > 2.45
5.2
16.0
44.0
-33.7
125.4
27.1
6.3
-6.0
79.6
Sub-sample > 1.85
13.1
13.5
5.1
-38.8
116.3
23.8
5.4
2.9
48.8
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
-11.0
-3.1
18.7
17.6
4.3
-1.2
-6.1
-8.6
23.4
36.1
36.1
12.6
38.7**
-0.2
-11.7
12.3
12.3
24.0
6.0
0.8
-39.4
-67.3
-69.7
-27.9***
54.8
45.6
97.9
170.4
161.4
72.5
-1.2
-4.5
32.7
40.5
40.7
7.8
7.1
6.2
1.7
-2.2
4.3
-3.8
-19.7*
-10.9
13.1
37.6
43.8
24.5
33.2
2.4
45.8
45.1
66.5
-0.7
Excess Return (1.85)
Total Sample
-14.5
5.8
12.6
24.0
8.0
-15.7
-30.3***
-32.1
63.5
17.8
20.5
17.1
2.6
-6.8
30.7*
22.5
20.7
25.2
Sub-sample > 2.45
5.2
32.3
-4.7
-51.9
28.3
12.7
-7.9
37.4
28.2
Sub-sample > 1.85
3.1
28.3
-11.1
-51.1
24.5
12.4
-9.3
35.2
29.6
Excess Return (2.45)
-0.6
14.5
11.0*
14.9*
2.9
10.5
4.7
4.1
-4.4
0.1*
-1.1
-2.7
-19.8***
-19.0***
7.8
Excess Return (1.85)
Total Sample
-2.5
12.7**
4.3
11.1
27.8
15.2
-43.1
67.0
26.5
-8.7
15.0
53.5
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
Total Sample
13.2
13.1
2.1
2.0
4.1
27.9
27.9
0.2
0.1
15.1
49.9
39.9
34.7
24.7
1.5
-56.8
-54.0
-13.7***
-10.9**
-37.8
89.1
78.9
22.1
11.9
18.6
30.4
26.7
3.9
0.3
26.5
-12.9
-15.2
-4.3
-6.5
1.7
18.6
15.8
3.6
0.8
22.7
63.7
61.2
10.2
7.7
28.0
Sub-sample > 2.45
Sub-sample > 1.85
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Excess Return (2.45)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Excess Return (1.85)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
t-tests for difference in average returns are conducted, and the differences that are statistically significant at 1%, 5%,
and 10% levels are denoted by ***, **, and *, respectively.
46
Table 16: 2-year Stock Returns (%) on Companies Flagged by F-Score in Each Industry
Industry
Division A
Division B
Division C
Division D
Division E
Division F
Division G
Division H
Division I
Division J
Item
12/31/200412/29/2006
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
14.0
N/A
N/A
N/A
N/A
85.8
23.9
69.8
-61.9
-16.0
35.9
64.6
82.2
28.7**
46.3**
30.4
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
-0.8
-3.4
39.8
12.7
20.8
-27.0
-19.0
49.2
91.9
48.6
42.7
-0.6
32.6
52.3
40.1
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
Excess Return (2.45)
Excess Return (1.85)
Total Sample
Sub-sample > 2.45
Sub-sample > 1.85
19.7
7.6**
Excess Return (2.45)
Excess Return (1.85)
12/30/200512/31/2007
57.2
39.9
26.9*
9.5
26.4
25.6
23.1
37.0
24.1
32.1
-12.9
-4.9
31.2
N/A
N/A
N/A
N/Al
12/29/200612/31/2008
44.9
2.3
2.3
-42.6
-42.6
48.4
-2.3
-2.3
-50.7***
-50.7***
22.0
86.8
85.1
64.8
63.1
31.3
37.6
30.9
6.3
-0.4
22.2
19.7
20.7
-2.5
-1.5
30.4
8.5
20.6
-21.9
-9.8
7.0
24.5
24.5
17.5
17.5
-0.1
29.2
20.0
19.6
-2.7
-2.7
-22.2
-22.2
-22.6
-48.5
-56.2
-25.9
-33.5**
-45.1
-47.4
-54.2
-2.2
-9.0
-40.0
-46.6
-44.3
-6.5
-4.2
-34.3
-63.9
-63.0
-29.7***
-28.8***
-38.5
-22.4
-45.0
16.1
-6.6
-48.9
-50.1
-50.1
-1.2
-1.2
-41.9
-39.4
-44.9
29.3
20.1
51.2
51.3
58.4
0.1
7.2
17.5
N/A
N/A
N/A
N/A
2.5
-3.0
-36.6
-38.7
-41.9
-2.1
-5.3
-37.1
N/A
N/A
N/A
N/A
12/31/200712/31/2009
-28.2
-11.4
-11.4
16.8
16.8
-6.6
-27.5
-27.5
-20.9
-20.9
-23.2
-22.3
-23.7
0.9
-0.6
-17.2
-26.0
-24.2
-8.8**
-7.0*
-18.9
-18.7
-25.5
0.3
-6.6
-9.6
7.3
-2.6
16.9
7.0
1.8
-55.9
-57.7
-57.7***
-59.5***
-28.5
-37.5
-34.6
-9.0**
-6.2
-19.0
-37.0
-36.5
-18.0***
-17.5***
-26.2
N/A
N/A
N/A
N/A
12/31/200812/31/2010
12/31/200912/30/2011
12/31/201012/31/2012
30.8
28.9
28.9
-1.9
-14.0
N/A
-53.8
N/A
-1.9
51.3
-11.2
53.2***
-1.9
208.4
187.6
175.2
-20.8
-33.2
58.7
-39.8***
38.6
67.9
62.5
29.2
23.8
-6.2
36.8
36.8
43.1
43.1
20.2
15.0
19.3
-5.3
-0.9
15.3
-9.3
-14.7
-18.9
-19.3
-4.2
-4.6
30.0
-13.3
12.6
26.5
26.5
-32.1
-32.1
160.0
203.6
195.6
43.5
35.6
111.6
314.1
302.4
202.6
190.9
111.3
149.1
140.9
37.8
29.6
165.7
219.7
214.9
54.0
49.2
43.6
51.8
47.6
8.2
4.0
116.9
156.6
136.9
39.8
20.0
49.1
N/A
N/A
N/A
N/A
63.1
52.9
47.8
37.6
27.7
23.8
30.6
-3.9
2.9
42.2
29.8
36.6
-12.4
-5.6
-43.3
-17.3
3.6
-2.0
-2.7
-5.6
-6.3
1.3
-19.8
-11.3
-21.0*
-12.5
11.0
28.6
31.4
17.6
20.4
15.4
49.9
44.5
34.5
29.1
12/30/201112/31/2013
40.7
71.0
71.0
30.3
30.3
3.4
-10.2
-10.2
-13.6
-13.6
103.5
163.5
156.5
60.0
53.1
73.6
101.8
96.3
28.1
22.7
54.4
57.6
71.7
3.3
17.3
63.8
4.6
20.4
-59.2***
10.1
8.8
8.5
-1.3
-1.6
16.6
13.8
16.5
11.8
13.9
11.7
2.1
-0.1
3.7
-43.5**
63.3
106.2
156.7
42.9
93.4***
56.7
83.1
77.8
26.4***
21.1***
73.0
2.2
-3.2
95.7
88.3
-2.8
-0.1
28.6
N/A
N/A
N/A
N/A
-1.5
-6.9
25.3
N/A
N/A
22.6
15.3
57.0
N/A
N/A
N/A
N/A
N/A
N/A
t-tests for difference in average returns are conducted, and the differences that are statistically significant at 1%, 5%,
and 10% levels are denoted by ***, **, and *, respectively.
47
Table 17: Fama-French Three-Factor Regressions on 1-year, 2-year, and 5-year Returns of Firms Flagged by
M-Score
Panel A: I-year Returns
t to t+1
12/31/200412/30/2005
12/30/200512/29/2006
12/29/200612/31/2007
12/31/200712/31/2008
12/31/200812/31/2009
12/31/2009-
Intercept
(Alpha, %)
Coefficient for
Small-Minus-Big Return
Coefficient for
Excess Market Return
(Beta)
-0.28
1.41***
0.75**
0.34
-0.08*
1.15***
1.30***
0.32
0.88
1.28***
1.07
-0.03
-0.85**
1.26**
0.71*
-0.30
1.30
1.68***
0.60
-0.72
-0.45**
1.08***
0.55***
0.18
-2.14***
1.47***
-0.06
0.15
-0.97**
1.30***
1.24***
-0.20
0.25
0.92***
1.26***
-0.28
12/30/2010
12/31/201012/31/2011
12/30/201112/31/2012
12/31/201212/31/2013
Coefficient for
High-Minus-Low Return
Panel B: 2-year Returns
t to t+2
Intercept
(Alpha, %)
Coefficie nt for
Small-Minus-Big Return
Coefficie nt for
Excess Market Return
(Beta)
Coefficient for
High-Minus-Low Return
12/31/200412/29/2006
-0.3*
1.27***
1.04***
0.26
12/30/200512/31/2007
0.24
1.19**
1.13***
-0.05
12/29/200612/31/2008
-0.29*
1.42***
0.50
-0.09
12/31/200712/31/2009
1.97
1.84***
1.03
-0.68
12/31/2008-
1.65
1.26***
0.50
-0.21
-1.22**
1.14***
0.53***
0.15
12/31/201012/31/2012
-1.51***
1.39***
0.48
-0.06
12/30/201112/31/2013
-0.75*
1.23***
1.19***
-0.37
12/31/2010
12/31/2009-
12/30/2011
1
1
48
Panel C: 5-year Returns
t to t+5
Intercept
(Alpha, %)
Coefficient for
Excess Market Return
Coefficie nt for
Small-Minus-Big Return
Coefficient for
High-Minus-Low Return
12/31/200412/31/2009
0.50
1.27***
0.89***
-0.19
12/30/200512/31/2010
-0.08
1.23***
0.83***
-0.10
12/29/2006-
0.17
1.31***
0.68***
-0.16
0.80
1.38***
0.96
-0.21
-0.6*
1.29***
0.70***
-0.30
-1.32**
1.23***
0.61***
-0.09
12/30/2011
12/31/200712/31/2012
12/31/200812/31/2013
12/31/200912/31/2013
(4-year return)
Monthly risk-free returns and market returns (adjusted for dividends) are obtained from CRSP. The adjusted Rsquareds range from 79.3 % to 99.8%. The numbers are statistically significant at 1%, 5%, and 10% levels are
denoted by ***, **, and *, respectively.
49
Table 18: Fama-French Three-Factor Regressions on 1-year, 2-year, and 5-year Returns of Firms Flagged by
F-Score
Panel A: ]-year Returns
t to t+1
Cutoff Value
12/31/200412/30/2005
2.45
1.85
12/30/200512/29/2006
Intercept
(Alpha, %)
Coefficient for
Excess Market Return
(Beta)
Coefficient for
Small-Minus-Big Return
Coefficient for
High-Minus-Low Return
0.21
1.09***
0.73*
0.50
-0.16
1.35***
0.64*
0.38
2.45
1.85
0.93*
1.08***
1.09***
0.17
0.77
1.02***
1.06***
0.23
12/29/200612/31/2007
2.45
1.85
2.20**
1.37***
0.65
0.57
1.86**
1.34***
0.84
0.32
12/31/200712/31/2008
2.45
1.85
0.31
12/31/200812/31/2009
-1.56
1.12***
0.95**
-1.57*
1.14***
0.92**
2.45
1.85
2.53
1.18**
2.25
1.20***
0.66
0.26
12/31/200912/30/2010
2.45
0.31
0.84***
0.67***
0.4 ***
1.85
0.25
0.85***
0.69***
0.36***
12/31/201012/31/2011
2.45
-0.89*
1.26***
0.20
1.85
-0.96**
1.25***
0.19
0.28
12/30/201112/31/2012
2.45
-0.22
1.29***
1.36***
0.00
1.85
-0.02
1.24***
1.27***
-0.02
12/31/201212/31/2013
2.45
1.85
0.20
1.05***
0.75**
-0.07
0.28
1.02***
0.73***
-9.60
0.67
0.26
0.30
4
0.22
Panel B: 2-year Returns
t to t+2
Cutoff Valu
Intercept
(Alpha, %)
Coefficientfor
Excess Market Return
(Beta)
Coefficient for
Small-Minus-Big Return
Coefficient for
High-Minus-Low Return
12/31/200412/29/2006
2.45
1.85
0.78**
0.95***
0.87***
0.36*
0.23
1.28***
0.82***
0.47***
12/30/200512/31/2007
2.45
1.01***
1.13***
1.07***
0.18
1.85
0.98***
1.10***
1.00***
0.17
12/29/200612/31/2008
2.45
1.85
0.83
1.35***
0.29
0.55
1.31***
0.43
0.14
12/31/200712/31/2009
2.45
1.85
1.13
1.37***
0.82***
-0.03
1.03
1.41***
0.73**
-0.01
1.53**
0.98***
0.59*
0.53*
1.48**
0.98***
0.58*
0.50*
12/31/200812/31/2010
2.45
1.85
0.18
12/31/200912/30/2011
2.45
0.15
0.91***
0.55***
0.42***
1.85
0.14
0.91***
0.57***
0.36***
12/31/201012/31/2012
2.45
-0.66*
1.21***
0.43**
1.85
-0.69**
1.20***
0.46**
0.10
12/30/201112/31/2013
2.45
-0.24*
1.24***
1.09***
-0.05
1.85
-0.13
1.19***
0.98***
-0.04
50
0.03
Panel C: 5-year Returns
Cutoff Value
Intercept
(Alpha, %)
Coefficient for
Excess Market Return
(Beta)
Coefficient for
Small-Minus-Big Return
Coefficient for
High-Minus-Low Return
12/31/200412/31/2009
2.45
0.77**
1.27***
0.70**
0.05
1.85
0.66**
1.30***
0.77***
0.02
12/30/200512/31/2010
2.45
1.85
0.62**
1.27***
0.78***
-0.04
0.58**
1.25***
0.77***
-0.03
12/29/200612/30/2011
2.45
1.85
0.48*
1.20**
0.64***
0.02
0.48*
1.22***
0.65***
0.06
12/31/200712/31/2012
2.45
1.85
0.41
1.26***
0.66***
0.18
0.28
1.28***
0.65***
0.17
12/31/200812/31/2013
2.45
0.36
1.06***
0.69***
0.40***
1.85
0.35
1.06***
0.68***
0.37***
12/31/200912/31/2013
(4-year return)
2.45
-0.05*
1.01***
0.49***
0.22**
1.85
-0.07
1.00***
0.53***
0.17*
Monthly risk-free returns and market returns (adjusted for dividends) are obtained from CRSP. The adjusted Rsquareds range from 83.8 % to 99.8%. The numbers are statistically significant at 1%, 5%, and 10% levels are
denoted by ***, **, and *, respectively.
51
8.
Appendix
8. 1. Appendix
1: Beneish M-Score Variables
The Beneish M-Score is based on a combination of the following eight different indices.
Variable
Description
(Recales
Days' Sales in Receivables Index (DSR)
aes1
(Gross Margin_1
(
Gross Margin Index (GMI)
Gross Margin
A
1 - (PPEt + CurrentAssetst)/Total Assets
Asset Quality Index (AQI)
G-
(PPEt-_ + Current Asse tst_1) ITotal Assetst- 1
(Salese
Salest )
_1
Sales Growth Index (SGI)
(Depreciation Ratet_
1
Depreciation Index (DEPI)
DepreciationRater
SGA -_
(SGA
1
\Salest /'kSalest_
1
Sales, General and Administrative Expenses Index
(SGAI)
( Leveraget
Leverage Index (LEVI)
(TATA)
Assets
Total Accruals to Total
Tota
AsetAcruas
(TTA)Totalt
R(
Leveraget_1
Income Before ExtraordinaryItemt
Totl
Assetst
52
-
CFOt
I
8.2. Appendix 2: Dechow F-Score Variables
The Dechow F-Score consists of the following seven variables.
Variable
Description
(ANoncash net operatingassets
Change in Non-cash Net Operating
Assets (rsst-acc)
\
Average Total Assets
I
(AAccounts Receivable
Change in Receivables (chrec)
R
Average TotalAssets
AInventory
Change in Inventory (chinv)
Average Total Assets)
Soft Assets (soft-assets)
Total Assets - PPE- Cash&CashEquivalents
Average Total Assets
Change in Cash Sales (chcs)
% Change in (Sales - AAccounts Receivables)
Change in
Change in Return on Assets (ch-roa)
Actual Issuance (issue)
Earnings
)
(AeaeTotal Assets)
An indicatorvariablecoded 1 if the firm issued securities
during the year
53
8.3. Appendix 3: Sensitivity of Dechow F-Score
I error
Correct Classification Rate
Cutoff Value
Type
> 2.45
5% non-misstating firms
18.8% of misstating firms
> 1.85
10% of non-misstating firms
32.6% of misstating firms
> 1
36.3% of non-misstating firms
68.6% of misstating firms
< 1
63.7% of non-misstating firms
31. 4 % of misstating firms
Type I errors = misclassified non-misstating firm; Type
II errors = misclassified misstating firms.
54
8.4. Appendix 4: SIC Industty Codes
Division
Constituents
Division A: Agriculture, Forestry, and Fishing
agricultural production, agricultural services, forestry, fishing,
hunting, and trapping
Division B: Mining
metal mining, coal mining, mineral mining, and
oil and gas extraction
Division C: Construction
building construction, heavy construction, and
special trade contractors
Division D: Manufacturing
mechanical, physical, and chemical manufacturing
Division E: Transportation, Communications,
Electric, Gas, and Sanitary Services
railroads, highway passenger transportation,
water transportation, transportation by air
Division F: Wholesale Trade
wholesale trade of durable and nondurable goods
Division G: Retail Trade
general merchandise stores, food stores, restaurants, equipment
stores, apparel stores, automotive dealers, and gasoline service
stations
Division H: Finance, Insurance, and Real Estate
(non-)depository institutions, brokers, dealers, exchanges,
insurance carriers, real estate, and other investment offices
Division 1: Services
hotels, personal or business services, health services, repair
services, and educational services
Division J: Public Administration
justice, public order and safety, public finance, taxation and
monetary policy, administration economic programs, and
national security
55
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