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Last Updated 28 August 2014 CRICOS Code 00098G

School of Accounting Seminar Series Semester 1, 2015

Earnings Co-Movements and Earnings Manipulation

Andrew Jackson

The University of New South Wales 

Date: Friday April 24 2015 Time: 3.00pm – 4.00pm Venue: UNSW Business School building 216

Business School

School of Accounting

Earnings Co-Movements and Earnings Manipulation∗

Andrew B. Jackson†

UNSW Business SchoolThe University of New South Wales

Brian R. RountreeJones Graduate School of Business

Rice University

April 2015

∗We acknowledge comments from Jeremy Bertomeu, Philip Brown, Greg Clinch, Jeff Coulton, Asher Cur-tis, Peter Easton, Weili Ge, Phil Quinn, Shiva Sivaramakrishnan, Phil Stocken, Gunter Strobl, Dan Taylor,Steve Taylor, Irene Tuttici, Terry Walter, Anne Wyatt, Teri Yohn and seminar participants at Universityof Melbourne, University of Otago, Rice University, University of Queensland and Victoria University ofWellington, 2013 AAA Annual Conference, 2014 AAA FARS Section Meeting, 2014 AFAANZ Annual Con-ference, and 2014 University of Technology Sydney Summer Research Symposium. All errors remain ourown responsibility. This paper was previously titled “Sentiment, Earnings Co-Movements and EarningsManipulation”.†Contact author: a.b.jackson@unsw.edu.au

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 bi-

ased 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 en-

forcement 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 co-

movements 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 industry-

quarter as a proxy for the markets earnings.1 We then estimate earnings betas on a firm

1All earnings are deflated on a per share basis for our estimation.

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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 un-

certain 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 in-

fluence earnings management including growth, firm size, bid-ask spread, the presence of

institutional investors, stock return volatility, firm age, and performance. Results are fur-

ther 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

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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 re-

lated 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 ac-

counting 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

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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 cor-

relations 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 circum-

stances 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 demon-

strate 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 cor-

relation 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 co-

movements and earnings management in our primary tests, but also investigate the extent

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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 con-

struct 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. Fur-

thermore, 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 liter-

ature.

III Earnings Co-Movements

Brown and Ball (1967), Ball and Brown (1968), and Beaver et al. (1970) developed mea-

sures 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 ba-

sis 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 re-

quirement 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 de-

viation 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 mar-

ket 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

2We re-estimate our analysis based on value weighted portfolios of the entire market to capture earningsco-movements. We also employ equal weighted measures in robustness tests. All reported inferences remainunchanged.

3The 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 overtime and there is nothing systematically biasing the results by using the entire sample period in the reportedtables.

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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 (FScore1), 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:

FScore1 = −7.893 + 0.790rsst+ 2.518∆rec+ 1.191∆inv + 1.979soft 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; soft 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 (FScore2) includes non-financial measures and off-

balance-sheet activities. Specifically, it includes ∆emp, the change in the number of em-

ployees; and leasedum an indicator variable equal to 1 if future operating lease obligations

4Heinle and Verrecchia (2011) actually explicitly limit their theory to positive asset correlations to makethe analysis more tractable.

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are greater than 0, and 0 otherwise.

FScore2 = −8.252 + 0.665rsst+ 2.457∆rec+ 1.393∆inv + 2.011soft assets+ (2)

0.159∆cs− 1.029∆roa+ 0.983issue− 0.150∆emp+ 0.419leasedum

The third level of the F -Score (FScore3) 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

FScore3 = −7.966 + 0.909rsst+ 1.731∆rec+ 1.447∆inv + 2.265soft 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

5Market-adjusted returns are the annual buy-and-hold return inclusive of delisting returns minus theannual buy-and-hold value-weighted market return.

9

previous five years; σCFO is the standard deviation of cash flows from operations over the

previous five years; NegEarn 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) correla-

tions 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

6Dechow et al. (2011) illustrate in robustness tests that the book to market ratio is negatively related toF -Score.

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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 indi-

cating as earnings co-movements increase there is a lower probability of manipulation as mea-

sured 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 ma-

nipulation 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 devi-

ation 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

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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

(CFBeta). 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 CFBeta, 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 co-

movement variables to explain the probability of being caught by the SEC for manipulation

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of financial statements, as expressed in model (4) for the equivalent of FScore3:7

AAER = α0 + α1rsst+ α2∆rec+ α3∆inv + α4soft assets+ (4)

α5∆cs+ α6∆roa+ α7issue+ α8∆emp+ α9leasedum+

α10rett + α11rett−1 + α12EBeta+ α13Beta+ ε

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.

7We perform this analysis for the three levels of F -Score, but for brevity only formally state the fullmodel.

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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

8Across our sample, only 0.1% of observations just meet/beat one metric while simultaneously just missinganother benchmark.

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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 esti-

mated in equation (5). All continuous independent variables have been standardized, and

standard errors are two-way clustered by firm and year.

Suspecti,t = β0 + β1EBetai,t + β2Betai,t + β3OperCyci,t + (5)

β4σSALESi,t + β5σCFOi,t + β6Levi,t + β7NegEarni,t +

β8Sizei,t + β9Growthi,t + β10σEarni,t + εi,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 co-

move 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

9A countervailing force is that firms that co-move more with the market might be easier to forecast, thusthey might have a higher probability of just meeting/beating given the benchmark can be more accuratelyset. 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 vari-

ation 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 Manip is the measure of

earnings manipulation:

Manipi,t = γ0 + γ1EBetai,t + γ2Betai,t + γ3OperCyci,t + (6)

γ4σSALESi,t + γ5σCFOi,t + γ6Levi,t + γ7NegEarni,t +

γ8Sizei,t + γ9Growthi,t + γ10σEarni,t + εi,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 co-

move with other firms, again consistent with theory. We base our classifications on the

FScore3 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 FScore3, which translates to a 1.3% increase in

FScore3 relative to its mean (the results for the other FScore 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 manipu-

lation. 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 FScore

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 (σCFO),

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 (NegEarn) 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 (CFBeta) 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 CFBeta 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 FScore 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 re-

sources 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 Institu-

tion (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 FScore, 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 impor-

tantly, 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. Neverthe-

less, 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. Specifi-

cally, 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

10The sentiment beta proposed by Glushkov (2006) is essentially the Carhart four-factor model including asentiment factor from Baker and Wurgler (2006) estimated over rolling 60-month periods, with the coefficienton 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 co-

movement 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 in-

sights 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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TABLE 1: Descriptive Statistics

Mean Std Dev p1 Q1 Median Q3 p99FScore1 0.9672 0.6539 0.1218 0.4881 0.8281 1.2621 3.8738FScore2 0.9413 0.6750 0.1031 0.4513 0.7860 1.2434 3.9115FScore3 1.0361 0.7623 0.1308 0.4897 0.8475 1.3547 4.4628ABSACC 0.0070 0.0145 0.0000 0.0003 0.0015 0.0060 0.0852EBeta 1.0449 3.7717 -10.6918 -0.1751 0.4480 1.6203 19.3733CFBeta 1.7975 7.8449 -20.3895 -0.9043 0.6344 3.1283 41.0428Beta 0.9952 0.6745 -0.7963 0.5825 0.9591 1.3639 3.1052OperCyc 4.6878 0.7300 2.2514 4.2932 4.7643 5.1621 6.3995σSALES 0.1972 0.1850 0.0120 0.0769 0.1403 0.2506 1.0530σCFO 0.1087 0.1402 0.0085 0.0373 0.0658 0.1186 0.9497Lev 0.5006 0.2335 0.0625 0.3319 0.5011 0.6446 1.3002NegEarn 0.2935 0.4124 0.0000 0.0000 0.0000 0.9000 1.0000Size 4.9460 2.2550 0.3060 3.2492 4.8543 6.5384 10.5081Growth 0.7731 0.7332 -1.0660 0.3295 0.5992 1.0191 3.9113σEarn 0.1742 8.4320 0.0031 0.0203 0.0423 0.0945 0.9723

Notes: This table presents the descriptive statistics of the full sample (N = 82,467) over the period 1970-2011. FScore1, FScore2 (N = 79,999), and FScore3 (N = 70,355) are the three levels of the F-Scoredeveloped 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 weightedco-movement of earnings; CFBeta is the value weighted co-movements of cash flows (N = 62,218); Beta isfirm beta; OperCyc is the natural logarithm of the firm’s operating cycle, defined as the sum of the daysof receivables and the days of inventory; σSALES is the standard deviation of sales over the previous fiveyears; σCFO is the standard deviation of cash flows from operations over the previous five years; Lev is theratio of total liabilities total assets; NegEarn 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 σEarnis the standard deviation of earnings scaled by lagged total assets over the previous five years.

27

TABLE 2: Correlation MatrixFScore1 FScore2 FScore3 ABSACC EBeta CFBeta Beta OperCyc σSALES σCFO Lev NegEarn Size Growth σEarn

FScore1 0.964 0.943 0.127 -0.034 0.039 0.028 0.205 0.157 0.046 0.044 -0.086 0.009 -0.077 -0.006FScore2 0.962 0.9712 0.130 -0.030 0.042 0.037 0.193 0.167 0.056 0.054 -0.068 0.021 -0.089 -0.006FScore3 0.949 0.979 0.141 -0.022 0.056 0.035 0.205 0.176 0.0645 0.041 -0.084 0.008 -0.091 -0.004ABSACC 0.103 0.119 0.133 0.0141 0.026 -0.001 0.118 0.202 0.308 0.085 0.208 -0.215 -0.071 0.019EBeta -0.041 -0.045 -0.043 -0.018 0.121 0.035 -0.016 0.026 0.064 0.104 0.089 -0.023 0.040 -0.009CFBeta 0.046 0.056 0.065 0.023 0.091 0.043 0.019 0.041 0.035 0.077 0.058 -0.073 0.040 0.016Beta 0.059 0.076 0.066 0.026 0.037 0.065 0.034 0.024 0.047 0.018 0.077 0.114 -0.048 0.007OperCyc 0.251 0.240 0.270 0.148 -0.009 0.039 0.045 -0.128 0.041 -0.144 0.049 -0.149 0.087 -0.002σSALES 0.160 0.184 0.197 0.241 -0.004 0.033 0.054 -0.065 0.305 0.056 0.188 -0.262 -0.032 0.024σCFO 0.047 0.076 0.093 0.397 0.006 0.049 0.082 0.157 0.442 0.022 0.370 -0.225 -0.128 0.055Lev 0.057 0.060 0.049 -0.011 0.099 0.045 0.008 -0.169 0.018 -0.073 0.107 -0.004 -0.138 0.019NegEarn -0.116 -0.077 -0.095 0.210 0.019 0.037 0.081 0.064 0.234 0.463 0.056 -0.288 -0.053 0.019Size 0.053 0.060 0.031 -0.247 -0.001 -0.033 0.125 -0.183 -0.317 -0.377 0.012 -0.308 -0.391 -0.009Growth -0.079 -0.103 -0.094 -0.071 0.107 0.035 -0.057 0.087 -0.046 -0.130 -0.071 -0.099 -0.399 -0.016σEarn -0.015 0.032 0.028 0.293 0.020 0.055 0.139 0.103 0.417 0.669 -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%, exceptfor those significant at a 5% level (not significant) where they are presented in italic (bold) typeface. All variables are defined as in Table 1.

28

TABLE 3: AAERs and Earnings Co-Movements

EBeta abs(EBeta) EBeta rank

Panel A: Actual AAER YearsAAER -0.4899*** -0.6424*** -0.0306***

(-3.41) (-5.22) (-3.17)

Panel B: Randomly Assigned AAER YearsAAER 0.0148 0.0408 -0.0023

(0.11) (0.43) (-0.26)

CFBeta abs(CFBeta) CFBeta rank

Panel C: Actual AAER YearsAAER -1.1303*** -0.5872** -0.0341***

(-3.65) (-2.21) (-3.32)

Panel D: Randomly Assigned AAER YearsAAER -0.6498 -0.9658 -0.0882

(-0.16) (-0.27) (-0.60)

Notes: The table presents the results of estimating a firm-fixed effects model where thedependent variable is earnings co-movements, taken as the signed value (EBeta), the absolutevalue (abs(EBeta), and the rank value (EBeta rank) for firms subject to an AAER over theperiod 1982 - 2011. Panel A reports the results where AAER is the actual AAER year, whilePanel B reports the results where Random is where the AAER is randomly assigned. PanelsC and D repeat the analysis using cash flow betas (CFBeta).

29

TABLE 4: Logistic Regression of Determinants of AAERs

(1) (2) (3) (4) (5) (6)

Intercept -8.1166*** -8.5588*** -8.5077*** -7.7698*** -8.1476*** -8.1087***(812.20) (755.86) (608.06) (925.39) (918.58) (666.29)

rsst 0.8648*** 0.7216** 0.3991 0.7018*** 0.4846** 0.1017(6.90) (5.92) (1.41) (7.64) (4.82) (0.11)

∆rec 0.3635 0.4789 0.3888 0.3452 0.4100 0.3463(0.46) (0.89) (0.34) (0.32) (0.50) (0.25)

∆inv -1.0234 -0.9220 -1.3575* -0.2681 -0.2584 -0.7507(2.36) (1.85) (3.50) (0.14) (0.11) (0.79)

soft assets 2.1380*** 1.9603*** 2.1619*** 2.2987*** 2.1164*** 2.3098***(130.94) (117.42) (111.73) (192.07) (164.09) (203.70)

∆cs 0.0878*** 0.1002*** 0.0887*** 0.0462** 0.0536*** 0.0679***(11.12) (11.61) (10.99) (6.13) (7.15) (7.52)

∆roa -0.5354 -0.6299 -10.6426 -0.3187 -0.3833 -0.4327(1.72) (2.70) (1.61) (0.74) (1.26) (0.83)

issue 1.3896*** 1.2682*** 1.1812*** 1.1370*** 1.0381*** 0.9363***(32.39) (27.02) (19.52) (22.77) (18.75) (12.82)

∆emp -0.1262 -0.1609 -0.2158** -0.2437**(1.17) (1.97) (4.15) (4.47)

leasedum 0.8180*** 0.7343*** 0.6890*** 0.6274***(31.48) (26.20) (25.36) (19.44)

rett 0.0015* 0.1653*(3.34) (3.29)

rett−1 0.2383*** 0.2157***(12.98) (9.65)

EBeta -0.0140* -0.0159** -0.0232**(3.20) (4.27) (4.76)

CFBeta -0.0170*** -0.0173*** -0.0162***(12.76) (11.36) (6.95)

Beta 0.4263*** 0.3950*** 0.3690*** 0.3617*** 0.3374*** 0.3004(26.36) (23.33) (14.14) (21.60) (19.50) (10.17)

LogLikelihood 235.20 265.45 251.44 190.98 204.81 192.14N 82,742 80,263 70,491 62,539 60,819 54,097

Notes: The table presents the results of equation (4) estimated over the period 1982 - 2011. The threemodels represent the F -Score factors of Dechow et al. (2011) as expressed in equations (1) to (3) with theinclusion of earnings co-movements (EBeta) in models (1) to (3) and cash flow co-movements (CFBeta) inmodels (4) to (6) and beta (Beta). Standard errors are clustered by firm and year, with Wald χ2 statisticsare presented in parentheses. The three models presented represent the three F -Score measures.

30

TABLE 5: Suspect Firms

Panel A: Mean and Median EBetaMean Median

Suspect 0.7732 0.3039Just Miss 0.8945 0.3528Non-Suspect 1.0910 0.4820

Panel B: Logistic RegressionFull Sample Just Miss/Meet

Intercept -1.8645*** 0.7952***(-47.66) (23.29)

EBeta -0.0599*** -0.0280*(-3.11) (-1.68)

Beta 0.0151 0.0121(0.96) (0.68)

OperCyc -0.0218 -0.0210(-1.33) (-1.11)

σSales 0.0797*** 0.0778***(5.41) (4.98)

σCFO -0.0759*** -0.0130(-4.71) (-1.07)

Lev -0.1922*** -0.0139(-10.57) (-0.67)

NegEarn -0.0574** -0.0767***(-2.09) (-3.39)

Size 0.3622*** 0.1543***(10.70) (5.08)

Growth -0.1410*** 0.0180(-5.72) (0.73)

σEarn -1.0358 -0.0194*(-1.46) (-1.75)

LogLikelihood 2,361.50 139.08N 82,467 17,483

Notes: The table presents the results of estimating equation (5). Panel A presents the meanand median values of the Suspect firms, where a suspect firm is identified as a firm that justmeets either an analyst forecast, small earnings levels, or small earnings changes; Just Missfirms, 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-suspectfirm is any firm that is no classified as a Suspect firm (Just Miss and Non-Suspect are notmutually exclusive). Panel B presents the logistic regression, with the Full sample includingall 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 withz-statistics presented in parentheses. All variables are defined as in Table 1.

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TABLE 6: General Analysis

Panel A: Mean (Median) EBeta by F-Score Risk Classification“High” “Substantial” “Above Normal” “Normal”

F -Score Level > 2.45 > 1.85 > 1 < 1Mean 0.8040 0.8088 0.9586 1.0565(Median) (0.3216) (0.2670) (0.3903) (0.4717)

Panel B: Regression ResultsFScore1 FScore2 FScore3 ABSACC

Intercept 0.9672*** 0.9413*** 1.0361*** 0.0070***(64.52) (57.02) (57.72) (140.47)

EBeta -0.0187*** -0.0188*** -0.0139** -0.0002***(-3.65) (-3.50) (-2.02) (-3.21)

Beta 0.0111* 0.0142* 0.0158** -0.0001**(1.72) (1.94) (1.92) (-2.22)

OperCyc 0.1702*** 0.1701*** 0.2008*** 0.0017***(20.67) (20.05) (20.26) (32.41)

σSALES 0.1404*** 0.1510*** 0.1759*** 0.0014***(19.47) (21.39) (22.92) (25.76)

σCFO 0.0153** 0.0183*** 0.0280*** 0.0030***(2.40) (2.71) (3.63) (53.52)

Lev 0.0512*** 0.0578*** 0.0580*** 0.0011***(9.34) (9.55) (8.32) (20.84)

NegEarn -0.0968*** -0.0859*** -0.1159*** 0.0007***(-15.60) (-12.71) (-14.29) (12.64)

Size 0.0240*** 0.0374*** 0.0239** -0.0021***(2.88) (4.09) (1.99) (-34.86)

Growth -0.0460*** -0.0480*** -0.0668*** -0.0014***(-5.69) (-5.35) (-6.21) (-23.66)

σEarn -0.0077*** -0.0084*** -0.0080*** -0.0000(-11.82) (-16.41) (-9.18) (-0.08)

AdjR2 0.1114 0.1098 0.1221 0.1525N 82,467 79,999 70,355 71,943

Notes: Panel A reports the mean and median values of EBeta by the FScore3 risk classifications asprovided by Dechow et al. (2011, Figure 2, p. 63). “High” risk firms are those with a F -Score greaterthan 2.45, “Substantial” risk firms are those with a F -Score greater than 1.85, “above normal” riskfirms are those with a F -Score greater than 1, and “normal” risk firms are those with a F -Scoreless 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 variablesare defined as in Table 1.

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