Business School
School of Accounting
School of Accounting Seminar Series
Semester 1, 2015
Earnings Co-Movements and Earnings
Manipulation
Andrew Jackson
The University of New South Wales Date:
Time:
Venue:
Friday April 24 2015
3.00pm – 4.00pm
UNSW Business School building 216
business.unsw.edu.au
Last Updated 28 August 2014
CRICOS Code 00098G
Earnings Co-Movements and Earnings Manipulation∗
Andrew B. Jackson†
UNSW Business School
The University of New South Wales
Brian R. Rountree
Jones Graduate School of Business
Rice University
April 2015
∗
We acknowledge comments from Jeremy Bertomeu, Philip Brown, Greg Clinch, Jeff Coulton, Asher Curtis, Peter Easton, Weili Ge, Phil Quinn, Shiva Sivaramakrishnan, Phil Stocken, Günter Strobl, Dan Taylor,
Steve Taylor, Irene Tuttici, Terry Walter, Anne Wyatt, Teri Yohn and seminar participants at University
of Melbourne, University of Otago, Rice University, University of Queensland and Victoria University of
Wellington, 2013 AAA Annual Conference, 2014 AAA FARS Section Meeting, 2014 AFAANZ Annual Conference, and 2014 University of Technology Sydney Summer Research Symposium. All errors remain our
own responsibility. This paper was previously titled “Sentiment, Earnings Co-Movements and Earnings
Manipulation”.
†
Contact author: [email protected]
Earnings Co-Movements and Earnings Manipulation
Abstract
Several theories use variation in the degree to which firms’ earnings are correlated
with the market to make predictions about the probability a firm will issue a biased signal of firm performance. Given the popularity of the theoretical construct, we
investigate the empirical validity of the assumption. First, using a sample of SEC enforcement actions we show a marked decline during the manipulation period in earnings
co-movements for firms that clearly manipulated their financial statements. We extend
this analysis to a sample of firms that just meet/beat a benchmark where the earnings
manipulation is less clear but the incentive to manipulate clearly exists, with results
consistent with theory. Finally, in the most general setting we find co-movements are
helpful in explaining variation in the earnings management construct of Dechow et al.
(2011) and Jones (1991) discretionary accruals. Overall, we provide strong support for
the use of earnings co-movements as a theoretical construct of earnings management,
which critically captures the ability to manage earnings thus helping to isolate the
sample of firms most likely to be biasing their signals of firm performance.
Keywords: Accounting and Auditing Enforcement Releases, Accounting Theory, Earnings
Co-Movements, Earnings Management, Market Earnings.
Data Availability: Data used in this study are available from public sources identified
in the study.
I
Introduction
A number of theoretical models imply that the greater the degree a firm’s earnings is
correlated with the market, the less likely that firm will be to issue a biased signal of firm
performance (Fischer and Verrecchia 2000; Dye and Sridhar 2004; Strobl 2013; Jorgensen and
Kirschenheiter 2012; Heinle and Verrecchia 2011, among others). The intuition is when a
firm’s earnings exhibit low correlations with the market, investors learn relatively little about
the firm from reports issued by other firms. On the other hand, when the firm’s earnings are
highly correlated with the market, investors are able to learn more from other firms, reducing
the importance of the earnings figure and thus providing relatively lower incentives and
opportunities for managers to engage in manipulation. Although numerous theories use this
construct, we are not aware of any empirical evidence documenting the relationship between
earnings co-movements and earnings management, which is the focus of the current study.
Using a sample of Accounting and Auditing Enforcement Releases (AAERs) over the period
1970-2011, we document significant relationships between ex-post realizations of earnings
management (AAERs) and both cross-sectional and within firm variation in earnings comovements with the market that are consistent with existing theories. We further illustrate
that the probability of just meeting various thresholds (i.e., small profits, small increases
in earnings, analysts’ forecasts) is also higher the lower the co-movement of earnings with
the market. Overall, our results help to empirically validate a popular theoretical construct
determining the conditions under which firms are more likely to manipulate earnings.
We begin our analysis by examining within firm variation in earnings co-movements and
the timing of AAERs. We adapt the earnings beta measure from Brown and Ball (1967),
Ball and Brown (1968), and Beaver, Kettler, and Scholes (1970) by calculating firm specific
earnings betas over a rolling 20 quarter period. We use both equal weighted and value
weighted market measures of earnings by summing all firms earnings in a given industryquarter as a proxy for the markets earnings.1 We then estimate earnings betas on a firm
1
All earnings are deflated on a per share basis for our estimation.
1
specific basis using up to 20 quarters of prior earnings information. In a simple firm fixed
effect regression of earnings betas regressed on an indicator for periods in which firms were
forced to restate earnings (AAER periods), the coefficient on the time period indicator is
negative and significant meaning during manipulation periods there is a structural shift in
the correlation of firm’s earnings with the market earnings. Specifically, firm’s earnings are
less correlated with market earnings meaning there is less information to be learned from
the market signal when firm’s manipulate their earnings.
Although the firm fixed effect regression results are consistent with those implied by
theory, we examine the degree to which earnings co-movements help in determining the
probability of manipulation by extending the Dechow, Ge, Larson, and Sloan (2011) F score to include earnings betas. Regardless of the F -score model used, earnings betas are
a significant incremental variable in the models, with the strongest results occurring in
FScore3, which contains return based measures. This is interesting in that accounting based
measures of co-movements are helpful in explaining earnings manipulations above and beyond
return based measures. A one unit increase (which is approximately 1/4 of a standard
deviation) in earnings co-movement with the market results in a 2 percent decrease in the
probability of an AAER. This result is both economically and statistically significant once
again providing support for its use as a theoretical construct concerning the ability and
likelihood to manipulate financial statements. It is important to note, we are not suggesting
researchers add earnings co-movements to the F -score model since the stated purpose in
Dechow et al. (2011) is to provide a relatively simple model, whereas earnings betas require
significant time-series calculations. Instead, the purpose of these tests is to verify the extent
to which the theoretical construct is informative concerning earnings management activity
above and beyond other variables already used in the empirical literature.
We further extend our analysis to situations in which earnings manipulations are uncertain by examining the probability of just meeting/beating three earnings benchmarks:
1) analysts’ forecasts, 2) small profits, and 3) small increases in earnings. Earnings co-
2
movements are negatively related to the probability of meeting each of these benchmarks,
meaning as earnings co-move more with the market firms are less likely to just meet/beat
a particular benchmark. Here a one standard deviation increase in earnings co-movements
results in a decrease of 6 percent in the probability of just meeting/beating a target. Alone,
these tests are not necessarily indicative of earnings management, but when coupled with the
earlier AAER results they provide greater assurance that this measure accurately captures
the intent of the theoretical literature.
In our final set of tests, we examine the ability of earnings co-movements to explain
variation in two popular measures of earnings quality: 1) Jones model discretionary accruals,
and 2) Dechow et al. (2011) F -Score measures. Following Francis, LaFond, Olsson, and
Schipper (2005), we control for innate factors known to drive variation in accruals: operating
cycle, variation in sales and cash flows, the incidence of negative earnings, and firm size. We
also control for growth since growth firms are expected to have greater accrual activity. The
results reveal that earnings co-movements provide stronger results on the F -Score measures,
which is consistent with earnings co-movements providing a more parsimonious measure of
the ability and desire to manipulate earnings relative to discretionary accruals. This may
not be surprising given discretionary accrual models do not necessarily capture the ability
to manipulate earnings thus it performs worse in this particular context. Nevertheless, the
evidence provides clear support for the theoretical literature using earnings co-movements
instead of items like discretionary accruals in demarking earnings manipulation behavior.
Our results are robust to the inclusion of a number of factors previously found to influence earnings management including growth, firm size, bid-ask spread, the presence of
institutional investors, stock return volatility, firm age, and performance. Results are further unaffected using market-wide level measures of earnings co-movements, as well as the
adjusted R2 from these regressions as an alternative measure of market co-movements. Our
results are also robust to the inclusion of the firm comparability measure developed by
De Franco, Kothari, and Verdi (2011), which uses both returns and earnings, but is missing
3
for roughly 40 percent of our sample. The inclusion of earnings volatility does not alter
inferences. Finally, the results are robust to the inclusion of return co-movement measures
like CAPM betas, providing assurance that earnings co-movements are fundamentally related to earnings manipulation as suggested by theory. Overall, our study investigates the
importance of a measure used extensively in theory to develop both the ability and need to
manipulate financial statement signals.
Given many criticisms of earnings management studies involve the fact that they lack a
direct tie to theory, this study is helpful in bridging this gap by providing empirical validation
of a popular earnings management construct used in the theoretical literature. Furthermore,
the use of earnings betas as an earnings management metric makes it explicit that firms
must consider the actions of other firms in making earnings management decisions. A firm’s
co-movement with the market determines its ability to manipulate earnings, whereas much
of the prior empirical research on earnings management effectively ignores other market
participants, essentially assuming each firm operates within a vacuum without consideration
of other firms. This is an important distinction in that it more closely aligns with the
information set available to managers when making decisions about accrual estimates.
Finally, our paper further contributes to the literature that examines the ability of accounting measures to compete with market measures (Beaver et al. 1970; Shumway 2001;
Skinner and Sloan 2002; Nekrasov and Shroff 2009, among others). In the context of earnings
management, accounting betas are clearly more informative than return betas.
II
Theory and Correlations with the Market
There are many theories that invoke the use of a construct similar to the notion of earnings
co-movements to determine the costs/benefits of manipulating various kinds of earnings
reports. For instance, in Fischer and Verrecchia (2000), Dye and Sridhar (2004), and Beyer
(2009) the cost of biasing a report is essentially conditional on idiosyncratic volatility of
4
the firm’s earnings, with the greater the volatility the lower the cost of bias/detection (of
course these models also incorporate the notion that the market identifies the bias and will
price protect accordingly). Many models in the disclosure literature invoke similar notions
of bias where a firm decides whether or not to disclose, along with whether or not to bias
the disclosure based on the degree of information obtainable from other sources (see Beyer,
Cohen, Lys, and Walther 2010, for a review of this literature).
More recently, Strobl (2013) and Heinle and Verrecchia (2011) formally model asset correlations and indicate the greater the cross-correlations the higher the cost of manipulation
since stakeholders can assess the firm specific signal using non-firm specific information.
Strobl (2013) introduces a theoretical model that uses an agency framework with multiple
firms whose earnings are correlated to demonstrate the extent of earnings manipulation.
He illustrates that the probability of manipulation is decreasing in the extent that a firm’s
earnings co-move with the market. Strobl (2013) extends the model to illustrate the circumstances under which earnings management can influence a firms cost of capital (see Bertomeu
(2013) for a discussion of Strobl (2013)).
Heinle and Verrecchia (2011) introduce the notion of accounting co-movements to demonstrate the extent of bias in management earnings forecasts in a multi-firm setting. They
demonstrate that the extent of bias in management earnings forecasts decreases as the correlation across firms’ cash flows increases. Again, the intuition is the more information that
is available to investors, the less they rely on a firm’s own forecast to estimate the firm’s
future prospects. As the weight investors assign to a firm’s forecast to determine the stock
price decreases, the benefit of managers biasing their report decreases, and hence there is the
expectation of a decrease in the degree of bias. Heinle and Verrecchia (2011) use cash flow
co-movements, as opposed to our earnings co-movements, as they are interested in pricing
effects, which theoretically is the sum of all future cash flows. Given managers have more
discretion in terms of accrual estimates, we investigate the relation between earnings comovements and earnings management in our primary tests, but also investigate the extent
5
to which cash flow co-movements help explain the phenomenon.
These theoretical arguments are consistent with the literature on intra-industry transfers
of information. Prior research demonstrates that information releases of one firm has an effect
on the share price of other firms in the same industry (see Firth 1976; Foster 1981; Clinch
and Sinclair 1987; Han, Wild, and Ramesh 1989; Han and Wild 1990, among others). These
results are not explicitly related to the notion that managers use the degree of co-movement
in a firm’s earnings with the market to determine the extent of bias in their information
signals. However, it is consistent with the notion that investors use earnings releases, along
with other disclosures, of firm i to infer expectations of firm j ’s future performance. The
degree to which investors are able to infer expectations of future performance from other
firms’ earnings signals regulates the ability of managers to bias their own information signals.
In general, the theoretical literature involving the biasing of signals invokes the notion
that as correlations increase with other sources (or alternatively as idiosyncratic volatility
decreases) the lower the likelihood of manipulation. Ultimately, whether this notion holds in
practice is an empirical question which we examine in this paper. Of course, our empirical
validation requires that we are able to create a measure that captures the theoretical construct with some accuracy. Thus, failure to detect earnings management is not a sufficient
condition to conclude the theoretical literature is incorrect in employing co-movements. Furthermore, theoretical models have built in mechanisms to measure earnings management,
whereas empirically we are forced to use proxies. If the proxies measure the construct with
error then we are biasing against finding an association consistent with the theoretical literature.
III
Earnings Co-Movements
Brown and Ball (1967), Ball and Brown (1968), and Beaver et al. (1970) developed measures of earnings betas or co-movements, in order to assess the market reaction to earnings
6
related news or whether earnings risk is informative about market prices above and beyond
market risk measures like return betas. In calculating earnings co-movements or betas, we
most closely follow Beaver et al. (1970) and create a value weighted earnings portfolio, where
the weights are the beginning of calendar quarter market values of equity on an industry basis to capture earnings co-movements within industries.2 We then use this market portfolio
to calculate an earnings beta, which in essence captures the sensitivity of firm i’s earnings
to the market earnings. These earnings betas are calculated over 20 quarters, with a requirement of at least 10 quarters of earnings data needed to be included in the sample.
Table 1 provides descriptive statistics on earnings betas over the 1970-2011 sample period.3
Accounting and price data is sourced from the Compustat Annual Fundamentals and CRSP
files, respectively. Our final sample is made up of 82,467 firm-year observations. All variables
are winsorized at the 1st and 99th percentiles.
- - - INSERT TABLE 1 ABOUT HERE - - The mean (median) earnings co-movement (EBeta) is 1.05 (0.45) with a standard deviation of 3.78. Given the large amount of variation of this variable, in robustness tests
we use various rank measures (simple rank, decile ranks, and quintile ranks) without any
changes to the inferences. The variation indicates many firms are quite sensitive to market movements, while other firms’ earnings are effectively uncorrelated with the market. A
number of firms’ earnings negatively co-move with the market meaning they are effectively
contrarian earnings firms. Given the relatively smaller proportion of these firms (31 percent)
and the fact that they have generally lower absolute correlations than the corresponding
firms in the opposite tail, we maintain the sign on the variable. However, we also estimate
tests using absolute values, eliminating negative co-movement observations, as well as the
2
We re-estimate our analysis based on value weighted portfolios of the entire market to capture earnings
co-movements. We also employ equal weighted measures in robustness tests. All reported inferences remain
unchanged.
3
The sample is restricted to this period because of the need for AAER data obtained from Dechow et al.
(2011) for later tests. We conduct sub-period analyses as robustness to ensure the results are consistent over
time and there is nothing systematically biasing the results by using the entire sample period in the reported
tables.
7
previously mentioned ranks with no changes to inferences indicating that a majority of the
results come from cross-sectional variation within the positive co-movement firms, which is
most consistent with the theoretical literature.4
Table 1 also contains a number of other measures used in the prior literature related
to earnings management including the three F -Score measures from Dechow et al. (2011).
In the first estimation of the F -Score (F Score1), Dechow et al. (2011) considers accrual
quality and financial performance, and obtain the following fitted values from a logistic
model estimated over the period 1982 to 2005:
F Score1 = −7.893 + 0.790rsst + 2.518∆rec + 1.191∆inv + 1.979sof t assets + (1)
0.171∆cs − 0.932∆roa + 1.029issue
where rsst is the accruals from Richardson, Sloan, Soliman, and Tuna (2005), defined as the
sum of changes in non-cash working capital plus the change in net non-current operating
assets plus the change in net financial assets (scaled by average total assets); ∆rec is the
change in receivables; ∆inv is the change in inventories; sof t assets is a measure of soft
assets, defined as total assets less the sum of PP&E and cash and cash equivalents (scaled
by total assets); ∆cs is the change in cash sales; ∆roa is the change in the return on assets;
and issue is an indicator variable equal to 1 if the firm issued securities during the year, 0
otherwise.
The second level of the F -Score (F Score2) includes non-financial measures and offbalance-sheet activities. Specifically, it includes ∆emp, the change in the number of employees; and leasedum an indicator variable equal to 1 if future operating lease obligations
4
Heinle and Verrecchia (2011) actually explicitly limit their theory to positive asset correlations to make
the analysis more tractable.
8
are greater than 0, and 0 otherwise.
F Score2 = −8.252 + 0.665rsst + 2.457∆rec + 1.393∆inv + 2.011sof t assets + (2)
0.159∆cs − 1.029∆roa + 0.983issue − 0.150∆emp + 0.419leasedum
The third level of the F -Score (F Score3) includes market-based measures to capture
incentives to manage earnings and includes the current market-adjusted stock return (rett )
and lagged market-adjusted stock returns (rett−1 ).5
F Score3 = −7.966 + 0.909rsst + 1.731∆rec + 1.447∆inv + 2.265sof t assets +
(3)
0.160∆cs − 1.455∆roa + 0.651issue − 0.121∆emp + 0.345leasedum +
0.082rett + 0.098rett−1
To obtain the F -Score we perform a log transformation of the predicted values from
equations (1) to (3), divided by the unconditional probability (0.0037 from Dechow et al.
2011). All three measures are close to 1, which as explained by Dechow et al. (2011), an F Score of 1.00 indicates the firm has the same probability of misstatement as the unconditional
expectation, with values above 1.00 indicating higher probabilities of misstatement than the
unconditional expectation.
We also calculate the squared value of Jones (1991) discretionary accruals, adjusted for
performance as suggested by Kothari, Leone, and Wasley (2005), on an annual industry
basis for industries with at least 20 observations. The mean (median) discretionary accruals
(ABSACC) for the sample is 0.0070 (0.0015). Following Francis et al. (2005), OperCyc
is the natural logarithm of the firm’s operating cycle, defined as the sum of the days of
receivables and the days of inventory; σSALES is the standard deviation of sales over the
5
Market-adjusted returns are the annual buy-and-hold return inclusive of delisting returns minus the
annual buy-and-hold value-weighted market return.
9
previous five years; σCF O is the standard deviation of cash flows from operations over the
previous five years; N egEarn is the incidence of negative earnings over the past 10 years;
and Size is the natural logarithm of the market value of equity. The variation in the innate
factors is expected to bias against finding results on the earnings co-movement variable since
these factors all lead to lower sensitivities to market wide movements. Thus any results on
the earnings co-movement variable can be thought of as baseline estimates. We include the
book to market ratio as a proxy for growth, Growth, consistent with the findings in Skinner
and Sloan (2002), which illustrate firms with lower book to market ratios (i.e., growth firms)
are more sensitive to negative earnings news. Given this, we expect firms with lower book
to market ratios to have higher probabilities of manipulation (meaning a negative coefficient
on Growth).6 Finally, we include leverage, Lev, using the ratio of total liabilities to total
assets, because it has been shown to be associated with discretionary accruals (Becker,
DeFond, Jiambalvo, and Subramanyam 1998; DeFond and Park 1997). All these variables
have values consistent with prior research.
Table 1 further reports statistics on returns betas, which on average are approximately
1, consistent with a long history of finance related studies. Given our tests use both earnings
and returns based co-movements, our tests are similar in nature to studies that examine the
incremental information content of earnings variables relative to market based measures (see
Beaver et al. 1970; Shumway 2001; Skinner and Sloan 2002, among others). In this particular
instance, we expect the earnings beta to potentially provide more information concerning
the probability of manipulation given earnings is the key variable of interest. Ultimately,
this is an empirical question, which we address.
Table 2 reports the correlations between the variables with Pearson (Spearman) correlations above (below) the diagonal. The earnings co-movement variable (EBeta) is statistically
correlated with many of the variables, but all the correlations are quite small with the largest
being 0.104 with Lev, meaning more highly leveraged firms generally have earnings that are
6
Dechow et al. (2011) illustrate in robustness tests that the book to market ratio is negatively related to
F -Score.
10
more sensitive to market-wide earnings. Although the correlations are relatively low, it is
interesting to note that EBeta is negatively correlated with all three F -Score measures indicating as earnings co-movements increase there is a lower probability of manipulation as measured by the various F -Scores. In contrast, EBeta is positively correlated with ABSACC
indicating greater co-movements are related to higher absolute discretionary accruals (or in
other words what researchers typically think of as greater earnings management), however
as previously mentioned the correlations in general are quite small.
- - - INSERT TABLE 2 ABOUT HERE - - -
IV
Results
Earnings Co-Movements and AAERS
In our first set of tests, we investigate whether or not firms that are subject to SEC
enforcement actions have systematically different earnings co-movements during their manipulation periods relative to non-manipulation periods. We estimate a simple firm fixed
effect regression using EBeta as the dependent variable regressed on an indicator for whether
the observation occurred during an AAER period. The results from column 1 in Table 3
indicate the coefficient on AAER period is −0.4899 (t-value -3.41), which means earnings
co-movements are significantly lower during manipulation periods. In terms of economic
magnitude, earnings co-movements fall by approximately 13 percent of one standard deviation in EBeta. Column 2 uses the absolute value of EBeta, while column 3 uses ranks.
Regardless of the form of the dependent variable all the regressions provide similar results,
namely earnings co-movements are lower during periods of manipulation. This is consistent
with the theoretical literature that states firms are more likely to bias financial reports when
there is less information available from outside sources. However, it is unclear from these
tests whether the earnings manipulation has simply caused the firm’s earnings to co-move less
11
with the market rather than necessarily documenting that firms with lower co-movements
are more likely to manipulate earnings.
- - - INSERT TABLE 3 ABOUT HERE - - To further strengthen our findings, we randomly assign AAER years in our analysis
and repeat the firm fixed effects regressions. In doing so, we retain the same number of
AAER years for each firm. Results in Panel B of Table 3 indicate that there is no difference
in earnings co-movements between randomly assigned AAER and non-AAER years. This
randomization procedure insures the validity of the finding that the actual AAER years
are indeed different from non-manipulation years. The lack of results here confirm that
manipulation periods truly do have lower earnings betas.
In Panel C (Panel D) we repeat the analyses of Panel A (Panel B) using a cash flow beta
(CF Beta). We calculate a cash flow co-movement measure in the same manner as for the
EBeta, however, due to limitations of availability in Compustat of all quarterly cash flows,
we acknowledge this measure contains significant noise. Our results on CF Beta, however,
are consistent with the results reported fro earnings co-movements. Specifically, cash flow
co-movements are lower during periods of manipulation.
In Table 4, we use instances of AAERs similar to Dechow et al. (2011) to estimate a
logistic regression with both violators and non-violators in the sample. Our sample is limited
to the period ending in 2003 as this is when our data on AAERs ends. We adapt each of the
F -Score models above to include EBeta, as well as Beta to investigate the ability of both comovement variables to explain the probability of being caught by the SEC for manipulation
12
of financial statements, as expressed in model (4) for the equivalent of F Score3:7
AAER = α0 + α1 rsst + α2 ∆rec + α3 ∆inv + α4 sof t assets +
(4)
α5 ∆cs + α6 ∆roa + α7 issue + α8 ∆emp + α9 leasedum +
α10 rett + α11 rett−1 + α12 EBeta + α13 Beta + The first thing to note is that the results on all the variables from Dechow et al. (2011)
are consistent with their results with the exception of the change in inventory, which is
insignificant in all models. Focusing on the coefficient on EBeta, the results indicate that
the greater the co-movement of earnings the lower the probability of being subject to an
AAER. In other words, firms with greater earnings co-movements do not have the flexibility
to manipulate earnings since detection is relatively easy because stakeholders can simply
look to the market to determine the firm’s earnings in the extreme scenario. The economic
magnitude indicates that a one unit change in EBeta results in a 1.4 - 2.3 percent change in
the odds of having an AAER. Alternatively, a one standard deviation change would result in
5-9 percent change in the odds of being detected for manipulating earnings depending on the
model. Regardless of the metric used, the economic magnitude is large providing a strong
justification for using earnings co-movements in both theoretical and empirical investigations
of earnings management.
- - - INSERT TABLE 4 ABOUT HERE - - In the final three columns we repeat out analysis usign cash flow co-movements instead of
earnings co-movements and obtain qualitatively similar results. Given the consistent results
across both earning and cash flow co-movements reported in Tables 3 and 4 we focus on
earnings co-movements, but consider the effect of cash flow co-movements in subsequent
testing in sensitivity analysis.
7
We perform this analysis for the three levels of F -Score, but for brevity only formally state the full
model.
13
Suspect Firms
In the previous section we document that greater earnings co-movement with the market
leads to a decreased ability to manipulate earnings and thus lower SEC accounting and
enforcement actions. The strength of the tests lies in the fact that a clear manipulation has
occurred and thus there is little argument about whether or not the financial statements were
biased. However, the downside is that SEC enforcement actions are relatively infrequent and
involve egregious violations in which the SEC knows it will be successful in prosecuting. It is
unclear the extent to which earnings co-movements are capable of distinguishing firms that
use more subtle forms of earnings management. In an effort to provide some evidence on
this topic, we adopt the benchmark beating research designs made popular by Burgstahler
and Dichev (1997) and Degeorge, Patel, and Zeckhauser (1999). In particular, we use the
firms that just meet/beat the following three benchmarks: 1) analysts’ forecasts, defined
where actual EPS less the latest consensus mean forecast EPS are between 0 and 1 cent;
2) small profits, defined as between 0 and 0.5% of total assets; and 3) small changes in
earnings, defined as a change in EPS of between 0 and 1 cent. Results are consistent across
all three measures, and thus we report results for firms categorized as just meeting/beating
any of the three benchmarks, but inferences are unchanged if we exclude firms that are
simultaneously in just miss and just meet/beat classifications across the three metrics.8 For
firms not covered in the I/B/E/S population, we assume there is no analyst coverage, and
therefore will not have an analyst EPS forecast to meet.
In Table 5, Panel A, we perform simple mean/median comparisons between firms that
just meet/beat one of the three benchmarks versus everyone else, as well as those firms
that just miss one of the benchmarks. The results reveal in each instance, firms that just
meet/beat always have lower earnings co-movements (all significant at less than the 1% level),
confirming the earlier AAER results and providing evidence that earnings co-movements are
8
Across our sample, only 0.1% of observations just meet/beat one metric while simultaneously just missing
another benchmark.
14
important even in situations where earnings management is not clearly defined, but rather
suspected.
- - - INSERT TABLE 5 ABOUT HERE - - Panel B of Table 5 reports the results from logistic regressions of just meet/beat on
EBeta, Beta, the innate accrual factors, growth, leverage and earnings volatility, as estimated in equation (5). All continuous independent variables have been standardized, and
standard errors are two-way clustered by firm and year.
Suspecti,t = β0 + β1 EBetai,t + β2 Betai,t + β3 OperCyci,t +
(5)
β4 σSALESi,t + β5 σCF Oi,t + β6 Levi,t + β7 N egEarni,t +
β8 Sizei,t + β9 Growthi,t + β10 σEarni,t + i,t
In Column 1, we report the full sample results, while Column 2 reports results for only
the just meet/beat versus just miss category, which is defined analogously to just meet/beat,
but on the negative side of zero. Larger firms, with less leverage, and greater sales volatility
are more likely to just meet/beat one of the three benchmarks. Growth firms and firms with
greater cash flow volatilities are less likely to just meet/beat the benchmark. Turning to the
variable of interest, EBeta is negative and significant indicating firms whose earnings comove more with the market are less likely to just meet/beat the benchmark. This is consistent
with the AAER results in that these firms do not have the flexibility to manipulate earnings
since the market learns more about them from outside sources. On the other hand, if earnings
co-move less with the market there is a higher probability that the firm can use its discretion
to meet/beat the benchmark.9 The Column 2 results are similar, but generally statistically
weaker because of the reduced sample size. EBeta has the same sign and is marginally
significant indicating that firms with higher co-movements are less likely to just meet/beat
9
A countervailing force is that firms that co-move more with the market might be easier to forecast, thus
they might have a higher probability of just meeting/beating given the benchmark can be more accurately
set. This biases against finding a negative relation between co-movements and benchmark beating.
15
versus just miss a benchmark. Given the lower power of these tests, this provides further
evidence of the importance of earnings co-movements in determining potential manipulation
probabilities.
General Analysis
Our last set of analyses look at the ability of EBeta to explain cross-sectional variation
in two common earnings management proxies used in the literature: 1) F -Score, and 2) a
measure of discretionary accruals (ABSACC). Note, the tests concerning the F -Score are
distinguished from our earlier tests where we examined whether EBeta is an incremental
factor in determining the probability of an AAER. Here we are investigating whether variation in the F -Score is associated with earnings co-movements. We also use the popular
Jones (1991) model of discretionary accruals in an effort to understand its association with
earnings co-movements. We estimate the following model, where M anip is the measure of
earnings manipulation:
M anipi,t = γ0 + γ1 EBetai,t + γ2 Betai,t + γ3 OperCyci,t +
(6)
γ4 σSALESi,t + γ5 σCF Oi,t + γ6 Levi,t + γ7 N egEarni,t +
γ8 Sizei,t + γ9 Growthi,t + γ10 σEarni,t + i,t
We present the results of estimating equation (6) in Panel B of Table 6. All continuous
independent variables have been standardized, and standard errors are two-way clustered
by firm and year. Note, the intercept roughly captures the sample mean of the dependent
variable and the associated t-value is in relation to whether this is different from 0. Given
the F -Score has an unconditional expectation of 1 and ABSACC are bound at the lower
tail by 0, the t-statistics on the intercepts are expected to be highly significant.
- - - INSERT TABLE 6 ABOUT HERE - - -
16
First, in Panel A of Table 6 we classify our sample into the F -Score risk classifications
as outlined by Dechow et al. (2011, Figure 2, p. 63) and report the mean and median
EBeta. We find that the higher the risk level, the lower the degree to which earnings comove with other firms, again consistent with theory. We base our classifications on the
F Score3 measure, but the results are consistent across the other levels. We do not find any
significant difference in the mean and median between “high” risk firms (F -Score greater
than 2.45) and “substantial” risk firms (F -Score greater than 1.85). The differences between
substantial risk and “above normal” risk firms (F -Score greater than 1), and above normal
and “normal” risk firms (F -Score less than 1) are significant at less than the 1% level.
Turning to the regression results in Panel B, the negative and significant coefficient on
EBeta indicates a one standard deviation decrease in a firms earnings sensitivity to the
market results in a 0.0139 increase in the F Score3, which translates to a 1.3% increase in
F Score3 relative to its mean (the results for the other F Score measures are slightly stronger
in terms of economic magnitude). This means that firms for which the market learns less
about its performance via other firms, there is significantly greater probability of manipulation. This is consistent with the accounting theories discussed earlier and represents an
economically significant shift in the probability of earnings management. It is also important
to note that the significance of our earnings co-movement, or accounting beta, measure is
over and above a CAPM beta, indicating that accounting risk measures provide incremental
information beyond market risk measures. Furthermore, the results are in addition to innate
accrual factors, which clearly capture a significant proportion of variation in the F Score
reducing the power of our tests. In other words, the results presented in Panel B are rough
baseline estimates of the influence of EBeta on the probability of manipulation.
All other control variables act in the manner expected, and are generally consistent with
prior studies on earnings manipulation, even if not used in the specific context of the F Score. Specifically, firms with greater variation in sales (σSALES) and cash flows (σCF O),
as well as those firms with more debt (Lev) have higher probabilities of manipulation. On
17
the other hand, firms with a greater incidence of negative earnings (N egEarn) and lower
growth (Growth) have lower probabilities of manipulation. Size is the only coefficient that is
potentially contrary to other earnings management studies, which generally find smaller firms
are more likely to manipulate earnings. A potential explanation for this finding is that the
F -Score is based upon SEC enforcement actions, which generally target larger corporations
which clearly violated GAAP (e.g., Enron and Worldcom).
In column 4, we present the results from estimating equation (6) using the squared value
of discretionary accruals from the Jones (1991) model. Our results indicate that the degree
to which a firm co-moves with the market is associated with the magnitude of discretionary
accruals. The results indicate a one standard deviation decrease in a firm’s sensitivity to
market earnigns results in a 0.0002 increase in ABSACC, which corresponds to a 2 percent
increase relatvie to its mean.
In summary, our EBeta captures the ability to manipulate and thus provides a better
measure to be used theoretically to determine when/if a manager will manipulate earnings.
In fact, any measure that effectively ignores the movements of other firms in the market is
limiting its ability to detect earnings management, since managers clearly do not operate
in a vacuum when making accrual value decisions. Earnings co-movement is one variable
that succinctly captures this concept, but researchers should consider alternative measures
that aim to capture the same construct (i.e., accrual co-movements, separations of earnings
versus cash flow co-movements, etc.).
Sensitivity Analysis
We perform a variety of robustness tests related to our results. First, we consider whether
it is only the co-movement in earnings which is driving the results, or whether cash-flow
co-movements also affect the probability of managers biasing their performance signals. We
calculate a cash-flow co-movement measure (CF Beta) in the same manner as for the EBeta,
however, due to limitations in the availability in Compustat of all quarterly cash flows, we
18
acknowledge this measure contains significant noise. The results are also subject to the caveat
expressed in Hribar and Collins (2002) in that we use changes in balance sheet accounts to
determine cash flows, thus inducing known measurement error in the variable. With these
caveats in mind, we repeat all our analyses using the CF Beta in place of our EBeta and
determine that the degree of co-movement in cash flows is consistent with our main analysis.
Next, we consider whether there are any components of the F -Score that are driving our
results in the general analysis. We repeat our analysis from equation (6) with the individual
components of F Score as the dependent variables. Our untabulated analysis reveals that
the variables of interest are largely consistent across the individual components, and that no
single item is responsible for our inferences (i.e., it is not just the correlation with receivables
that is driving the findings).
We next partition our sample on high and low institutional ownership. Prior research
suggests that institutional shareholders are more sophisticated investors who have the resources and opportunities to perform better analysis because of the access to more timely and
relevant information. Institutional investors can provide active monitoring that is difficult
for smaller, more passive or less-informed investors (Almazan, Hartzell, and Starks 2005).
As such, we predict that greater institutional ownership should lead to lower probabilities
of manipulation. Institutional ownership data is obtained from Thomson Reuters Institution (13F) Holdings. We are limited to data from 1980 thus reducing the sample to 37,160
firm-year observations. In general, we find that higher levels of institutional ownership are
associated with a higher F Score, which is consistent with institutional investors not playing
an active role in monitoring management activity (Porter 1992; Duggal and Miller 1999),
which could be the result of focusing on short-term results (Bushee 1998). More importantly, inferences on the remaining variables remain unchanged, and there appears to be no
systematic difference in firm’s EBeta contingent on the level of institutional ownership.
We also consider whether F -Score is picking up aspects of information asymmetry. To
control for this we include in our analysis the average bid-ask spread for the year. Our results
19
are robust to this specification. Our results are further robust to alternative measures of
growth beyond the book to market ratio included in our tabulated findings. Specifically,
inferences are unaltered using sales growth (Collins, Pungaliya, and Vijh 2012), asset growth
(Lee and Mande 2003), and growth in book value of equity (Francis et al. 2005).
We also include the financial statement comparability measure developed in De Franco
et al. (2011), which uses both returns and earnings to determine firm comparability. This
is clearly related to our earnings co-movement variable, but is different in the sense that
it estimates whether the earnings report is comparable based on the information provided
in returns. This measure might actually fail to detect earnings management if a firm is
considered comparable as a result of manipulating its financial statements. Furthermore,
because of the stringent data requirements it reduces our sample by 40 percent. Nevertheless, the coefficient on earnings co-movements is slightly stronger while the coefficient on
the comparability measure varies across all our analysis from positive and insignificant to
negative and significant. Given these varying results, we simply conclude that the results on
earnings co-movements are robust to the inclusion of other measures of financial statement
comparability.
We also replicate our results with a number of different returns-based betas. Specifically, we include a measure of downside risk (Ang, Chen, and Xing 2006) and a sentiment
beta which captures a firm’s sensitivity to market wide sentiment (Glushkov 2006).10 The
inclusion of both these measures does not alter our main inferences.
Prior research has often used factors such as firm age and volatility to proxy for firm’s
sensitivity to market sentiment. As such, we partition our sample on young (old) firms, and
firms with high (low) stock return volatility to assess the robustness of our main analysis. In
general, we find that younger and more volatile firms are associated with a higher probability
of material misstatement. We also find EBeta is more important for younger and more
10
The sentiment beta proposed by Glushkov (2006) is essentially the Carhart four-factor model including a
sentiment factor from Baker and Wurgler (2006) estimated over rolling 60-month periods, with the coefficient
on the sentiment factor capturing how sensitive a firm’s returns are to market wide sentiment.
20
volatile firms. This result is not unexpected, as investors must rely heavily on firm-specific
signals for firms with greater uncertainty in their operating environments and thus the comovement of earnings plays an even more critical role in determining the probability of
manipulation.
As explained earlier, the results are robust to using ranks for our key variables, as well
as using measures of earnings co-movements based on the entire market and adjusted R2
measures of the sensitivity of firm’s earnings to the market earnings factor. Given the
large variation in the EBeta measure we also limit our analysis to observations with only
positive earnings betas and observations within the range of ±3, with no change in our
inferences. Furthermore, our results are robust to firm, industry, and year fixed effects
providing even greater assurance that our results are not somehow mechanically related
to expected variations in the manipulation proxies. Overall, the results are robust to the
inclusion of a variety of control variables, partitions of the data, and measurement concerns.
V
Conclusions
Consistent with accounting theory, we find firms whose earnings co-move more with the
market have lower probabilities of manipulation. Overall, these results help to empirically
validate a popular theoretical construct determining the conditions under which firms are
more likely to manipulate earnings. Our study is notable in that we explicitly consider
competing information from other firms in the market in documenting the likelihood of
earnings manipulations. Most other empirical studies on earnings management implicitly
assume firms operate in isolation and do not consider other firms when making their earnings
management decisions. Our study provides a sort of calibration of how much and when firms
consider other information in making decisions, but the literature needs much more work
on this subject. By appealing to theory, the empirical results in this study provide for clear
insights that are often lacking in the earnings management literature using discretionary
21
accrual models. Finally, our results help to identify an area in which accounting based
measures actually provide greater information than market based measures.
Developing more refined measures of earnings co-movements will lead to greater insights representing a fruitful area for future research. For instance, determining whether
co-movements are primarily related to cash flows versus accruals could help academics,
regulators, and auditors better isolate earnings management activities. In general, using
information available from resources outside the firm will be helpful in understanding and
motivating when managers might choose to bias their financial performance. The current
study provides the necessary first step in this process opening the door for a variety of future
research paths.
22
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26
TABLE 1: Descriptive Statistics
F Score1
F Score2
F Score3
ABSACC
EBeta
CF Beta
Beta
OperCyc
σSALES
σCF O
Lev
N egEarn
Size
Growth
σEarn
Mean
0.9672
0.9413
1.0361
0.0070
1.0449
1.7975
0.9952
4.6878
0.1972
0.1087
0.5006
0.2935
4.9460
0.7731
0.1742
Std Dev
0.6539
0.6750
0.7623
0.0145
3.7717
7.8449
0.6745
0.7300
0.1850
0.1402
0.2335
0.4124
2.2550
0.7332
8.4320
p1
0.1218
0.1031
0.1308
0.0000
-10.6918
-20.3895
-0.7963
2.2514
0.0120
0.0085
0.0625
0.0000
0.3060
-1.0660
0.0031
Q1
0.4881
0.4513
0.4897
0.0003
-0.1751
-0.9043
0.5825
4.2932
0.0769
0.0373
0.3319
0.0000
3.2492
0.3295
0.0203
Median
0.8281
0.7860
0.8475
0.0015
0.4480
0.6344
0.9591
4.7643
0.1403
0.0658
0.5011
0.0000
4.8543
0.5992
0.0423
Q3
p99
1.2621 3.8738
1.2434 3.9115
1.3547 4.4628
0.0060 0.0852
1.6203 19.3733
3.1283 41.0428
1.3639 3.1052
5.1621 6.3995
0.2506 1.0530
0.1186 0.9497
0.6446 1.3002
0.9000 1.0000
6.5384 10.5081
1.0191 3.9113
0.0945 0.9723
Notes: This table presents the descriptive statistics of the full sample (N = 82,467) over the period 19702011. F Score1, F Score2 (N = 79,999), and F Score3 (N = 70,355) are the three levels of the F-Score
developed byDechow et al. (2011) to measure the probability of material misstatement; ABSACC (N =
71,943) is the squared value of discretionary accruals from a Jones model; EBeta is the value weighted
co-movement of earnings; CF Beta is the value weighted co-movements of cash flows (N = 62,218); Beta is
firm beta; OperCyc is the natural logarithm of the firm’s operating cycle, defined as the sum of the days
of receivables and the days of inventory; σSALES is the standard deviation of sales over the previous five
years; σCF O is the standard deviation of cash flows from operations over the previous five years; Lev is the
ratio of total liabilities total assets; N egEarn is the incidence of negative earnings over the past 10 years;
Size is the natural logarithm of the market value of equity; Growth is the book to market ratio; and σEarn
is the standard deviation of earnings scaled by lagged total assets over the previous five years.
27
TABLE 2: Correlation Matrix
F Score1
28
F Score1
F Score2
F Score3
ABSACC
EBeta
CF Beta
Beta
OperCyc
σSALES
σCF O
Lev
N egEarn
Size
Growth
σEarn
0.962
0.949
0.103
-0.041
0.046
0.059
0.251
0.160
0.047
0.057
-0.116
0.053
-0.079
-0.015
F Score2 F Score3 ABSACC
0.964
0.943
0.127
0.9712
0.130
0.979
0.141
0.119
0.133
-0.045
-0.043
-0.018
0.056
0.065
0.023
0.076
0.066
0.026
0.240
0.270
0.148
0.184
0.197
0.241
0.076
0.093
0.397
0.060
0.049
-0.011
-0.077
-0.095
0.210
0.060
0.031
-0.247
-0.103
-0.094
-0.071
0.032
0.028
0.293
EBeta
-0.034
-0.030
-0.022
0.0141
0.091
0.037
-0.009
-0.004
0.006
0.099
0.019
-0.001
0.107
0.020
CF Beta
0.039
0.042
0.056
0.026
0.121
0.065
0.039
0.033
0.049
0.045
0.037
-0.033
0.035
0.055
Beta OperCyc
0.028
0.205
0.037
0.193
0.035
0.205
-0.001
0.118
0.035
-0.016
0.043
0.019
0.034
0.045
0.054
-0.065
0.082
0.157
0.008
-0.169
0.081
0.064
0.125
-0.183
-0.057
0.087
0.139
0.103
σSALES
0.157
0.167
0.176
0.202
0.026
0.041
0.024
-0.128
0.442
0.018
0.234
-0.317
-0.046
0.417
σCF O
0.046
0.056
0.0645
0.308
0.064
0.035
0.047
0.041
0.305
-0.073
0.463
-0.377
-0.130
0.669
Lev
0.044
0.054
0.041
0.085
0.104
0.077
0.018
-0.144
0.056
0.022
N egEarn Size Growth
-0.086
0.009
-0.077
-0.068
0.021
-0.089
-0.084
0.008
-0.091
0.208
-0.215
-0.071
0.089
-0.023
0.040
0.058
-0.073
0.040
0.077
0.114
-0.048
0.049
-0.149
0.087
0.188
-0.262
-0.032
0.370
-0.225
-0.128
0.107
-0.004 -0.138
0.056
-0.288
-0.053
0.012
-0.308
-0.391
-0.071
-0.099
-0.399
-0.144
0.568
-0.269
-0.251
Notes: This table presents the Pearson (Spearman) correlations above (below) the diagonal for the full sample. All correlations are significant at the 1%, except
for those significant at a 5% level (not significant) where they are presented in italic (bold) typeface. All variables are defined as in Table 1.
σEarn
-0.006
-0.006
-0.004
0.019
-0.009
0.016
0.007
-0.002
0.024
0.055
0.019
0.019
-0.009
-0.016
TABLE 3: AAERs and Earnings Co-Movements
EBeta
abs(EBeta)
Panel A: Actual AAER Years
AAER -0.4899***
-0.6424***
(-3.41)
(-5.22)
Panel B: Randomly Assigned AAER Years
AAER
0.0148
0.0408
(0.11)
(0.43)
CFBeta
abs(CFBeta)
Panel C: Actual AAER Years
AAER -1.1303***
-0.5872**
(-3.65)
(-2.21)
Panel D: Randomly Assigned AAER Years
AAER
-0.6498
-0.9658
(-0.16)
(-0.27)
EBeta rank
-0.0306***
(-3.17)
-0.0023
(-0.26)
CFBeta rank
-0.0341***
(-3.32)
-0.0882
(-0.60)
Notes: The table presents the results of estimating a firm-fixed effects model where the
dependent variable is earnings co-movements, taken as the signed value (EBeta), the absolute
value (abs(EBeta), and the rank value (EBeta rank) for firms subject to an AAER over the
period 1982 - 2011. Panel A reports the results where AAER is the actual AAER year, while
Panel B reports the results where Random is where the AAER is randomly assigned. Panels
C and D repeat the analysis using cash flow betas (CF Beta).
29
TABLE 4: Logistic Regression of Determinants of AAERs
Intercept
rsst
∆rec
∆inv
sof t assets
∆cs
∆roa
issue
(1)
(2)
(3)
(4)
(5)
(6)
-8.1166***
(812.20)
0.8648***
(6.90)
0.3635
(0.46)
-1.0234
(2.36)
2.1380***
(130.94)
0.0878***
(11.12)
-0.5354
(1.72)
1.3896***
(32.39)
-8.5588***
(755.86)
0.7216**
(5.92)
0.4789
(0.89)
-0.9220
(1.85)
1.9603***
(117.42)
0.1002***
(11.61)
-0.6299
(2.70)
1.2682***
(27.02)
-0.1262
(1.17)
0.8180***
(31.48)
-7.7698***
(925.39)
0.7018***
(7.64)
0.3452
(0.32)
-0.2681
(0.14)
2.2987***
(192.07)
0.0462**
(6.13)
-0.3187
(0.74)
1.1370***
(22.77)
-8.1476***
(918.58)
0.4846**
(4.82)
0.4100
(0.50)
-0.2584
(0.11)
2.1164***
(164.09)
0.0536***
(7.15)
-0.3833
(1.26)
1.0381***
(18.75)
-0.2158**
(4.15)
0.6890***
(25.36)
-8.1087***
(666.29)
0.1017
(0.11)
0.3463
(0.25)
-0.7507
(0.79)
2.3098***
(203.70)
0.0679***
(7.52)
-0.4327
(0.83)
0.9363***
(12.82)
-0.2437**
(4.47)
0.6274***
(19.44)
0.1653*
(3.29)
0.2157***
(9.65)
-0.0140*
(3.20)
-0.0159**
(4.27)
-8.5077***
(608.06)
0.3991
(1.41)
0.3888
(0.34)
-1.3575*
(3.50)
2.1619***
(111.73)
0.0887***
(10.99)
-10.6426
(1.61)
1.1812***
(19.52)
-0.1609
(1.97)
0.7343***
(26.20)
0.0015*
(3.34)
0.2383***
(12.98)
-0.0232**
(4.76)
-0.0173***
(11.36)
0.3374***
(19.50)
-0.0162***
(6.95)
0.3004
(10.17)
204.81
60,819
192.14
54,097
∆emp
leasedum
rett
rett−1
EBeta
CF Beta
Beta
LogLikelihood
N
0.4263***
(26.36)
0.3950***
(23.33)
0.3690***
(14.14)
-0.0170***
(12.76)
0.3617***
(21.60)
235.20
82,742
265.45
80,263
251.44
70,491
190.98
62,539
Notes: The table presents the results of equation (4) estimated over the period 1982 - 2011. The three
models represent the F -Score factors of Dechow et al. (2011) as expressed in equations (1) to (3) with the
inclusion of earnings co-movements (EBeta) in models (1) to (3) and cash flow co-movements (CF Beta) in
models (4) to (6) and beta (Beta). Standard errors are clustered by firm and year, with Wald χ2 statistics
are presented in parentheses. The three models presented represent the three F -Score measures.
30
TABLE 5: Suspect Firms
Panel A: Mean and Median EBeta
Mean
Suspect
Just Miss
Non-Suspect
0.7732
0.8945
1.0910
0.3039
0.3528
0.4820
Panel B: Logistic Regression
Full Sample
Intercept
EBeta
Beta
OperCyc
σSales
σCF O
Lev
N egEarn
Size
Growth
σEarn
LogLikelihood
N
Median
Just Miss/Meet
-1.8645***
(-47.66)
-0.0599***
(-3.11)
0.0151
(0.96)
-0.0218
(-1.33)
0.0797***
(5.41)
-0.0759***
(-4.71)
-0.1922***
(-10.57)
-0.0574**
(-2.09)
0.3622***
(10.70)
-0.1410***
(-5.72)
-1.0358
(-1.46)
0.7952***
(23.29)
-0.0280*
(-1.68)
0.0121
(0.68)
-0.0210
(-1.11)
0.0778***
(4.98)
-0.0130
(-1.07)
-0.0139
(-0.67)
-0.0767***
(-3.39)
0.1543***
(5.08)
0.0180
(0.73)
-0.0194*
(-1.75)
2,361.50
82,467
139.08
17,483
Notes: The table presents the results of estimating equation (5). Panel A presents the mean
and median values of the Suspect firms, where a suspect firm is identified as a firm that just
meets either an analyst forecast, small earnings levels, or small earnings changes; Just Miss
firms, where a just miss firm is identified as a firm that just misses either an analyst forecast,
small earnings levels, or small earnings changes; and Non-Suspect firms, where a non-suspect
firm is any firm that is no classified as a Suspect firm (Just Miss and Non-Suspect are not
mutually exclusive). Panel B presents the logistic regression, with the Full sample including
all firms, and Just Miss/Meet is limited to firms in the Suspect and Just Miss classifications.
All variables have been standardized, standard errors are clustered by firm and year with
z-statistics presented in parentheses. All variables are defined as in Table 1.
31
TABLE 6: General Analysis
Panel A: Mean (Median) EBeta by F-Score Risk Classification
“High” “Substantial” “Above Normal”
F -Score Level
Mean
(Median)
> 2.45
0.8040
(0.3216)
Panel B: Regression Results
FScore1
Intercept
0.9672***
(64.52)
EBeta
-0.0187***
(-3.65)
Beta
0.0111*
(1.72)
OperCyc
0.1702***
(20.67)
σSALES
0.1404***
(19.47)
σCF O
0.0153**
(2.40)
Lev
0.0512***
(9.34)
N egEarn
-0.0968***
(-15.60)
Size
0.0240***
(2.88)
Growth
-0.0460***
(-5.69)
σEarn
-0.0077***
(-11.82)
AdjR2
N
0.1114
82,467
“Normal”
> 1.85
0.8088
(0.2670)
>1
0.9586
(0.3903)
<1
1.0565
(0.4717)
FScore2
0.9413***
(57.02)
-0.0188***
(-3.50)
0.0142*
(1.94)
0.1701***
(20.05)
0.1510***
(21.39)
0.0183***
(2.71)
0.0578***
(9.55)
-0.0859***
(-12.71)
0.0374***
(4.09)
-0.0480***
(-5.35)
-0.0084***
(-16.41)
FScore3
1.0361***
(57.72)
-0.0139**
(-2.02)
0.0158**
(1.92)
0.2008***
(20.26)
0.1759***
(22.92)
0.0280***
(3.63)
0.0580***
(8.32)
-0.1159***
(-14.29)
0.0239**
(1.99)
-0.0668***
(-6.21)
-0.0080***
(-9.18)
ABSACC
0.0070***
(140.47)
-0.0002***
(-3.21)
-0.0001**
(-2.22)
0.0017***
(32.41)
0.0014***
(25.76)
0.0030***
(53.52)
0.0011***
(20.84)
0.0007***
(12.64)
-0.0021***
(-34.86)
-0.0014***
(-23.66)
-0.0000
(-0.08)
0.1098
79,999
0.1221
70,355
0.1525
71,943
Notes: Panel A reports the mean and median values of EBeta by the F Score3 risk classifications as
provided by Dechow et al. (2011, Figure 2, p. 63). “High” risk firms are those with a F -Score greater
than 2.45, “Substantial” risk firms are those with a F -Score greater than 1.85, “above normal” risk
firms are those with a F -Score greater than 1, and “normal” risk firms are those with a F -Score
less than 1. Panel B presents the results of estimating equation (6). All variables are standardized,
standard errors are clustered by firm and year, with t-stats presented in parentheses. All variables
are defined as in Table 1.
32