Beneish n Nichols.2005

Earnings Quality and Future Returns:

The Relation between Accruals and the Probability of Earnings Manipulation

M. D. Beneish and D.C. Nichols

Indiana University

Current Draft: May 17, 2005

Corresponding Author M..D. Beneish Indiana University Kelley School of Business 1309 E. 10th Street Bloomington, Indiana 47405 [email protected]

Earnings Quality and Future Returns:

The Relation between Accruals and the Probability of Earnings Manipulation

Abstract:

The paper examines the relation between the probability of manipulation, accruals, and future returns. We show that firms that have a high likelihood of earnings manipulation (as measured by the Beneish (1999)’s M-Score) experience lower future earnings, but that investors expect these firms to have higher future earnings. Indeed, we find that investors overestimate next-period return on assets by 490 to 690 basis points (this is significant as the median ROA in the sample 4.6%). We also show that the probability of manipulation is a correlated omitted variable for the earnings forecasting models used in prior research on accrual mispricing and that including the probability of manipulation greatly attenuates the mispricing of accrual persistence. Finally, we show that the probability of earnings manipulation predicts economically significant abnormal returns of approximately 15% per year after controlling for accruals and various controls for risk factors, including a factor compensating for earnings quality differences (Easley and O’Hara (2004), Francis et al. (2005)). We interpret our results that the predictive ability of accruals for returns is greatly diminished in the presence of the M-Score as indicating that accrual mispricing arises because investors are misled by managers’ opportunistic management of earnings. Keywords: Earnings Manipulation; Accrual Mispricing; Future Returns. JEL Classification: M4, G11

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1. Introduction

In this paper, we investigate the relation between accruals and the probability of

earnings manipulation. Specifically, we rely on work by Beneish (1997, 1999) to estimate

the probability of manipulation and examine whether this assessment alters the persistence

and pricing implications of current earnings and its components. We conjecture that current

earnings that have a high likelihood of income-increasing manipulation lead to poor future

earnings and returns performance. Because the model for assessing the probability of

manipulation relies on publicly available information, and includes accruals as a predictive

variable, we assess (1) whether the likelihood of manipulation predicts future returns, and

(2) whether this relation is distinct from accrual mispricing.

Our paper has the potential to contribute to a large body of research that has

confirmed Sloan (1996)’s seminal findings and frequently proposed an earnings

management explanation for investors’ failure to recognize until later periods that

accruals are less persistent than operating cash flows. 1 We further examine the role of

earnings management in accrual mispricing by introducing a construct that ranks firms

according to the likelihood that they have manipulated earnings. This is important

because accruals and abnormal accruals measure earnings management with error, and

recent research suggests that the mispricing may be due to investors’ inability to forecast

the effects of growth rather than earnings management (Tarpley (2000) and Fairfield et

1These studies provide (1) evidence of mispricing for alternative measurements of accruals, abnormal accruals, and components of accruals (Xie (2001); Collins and Hribar (2002); Hribar (2002); Thomas and Zhang (2002); Richardson et al. (2004)), (2) evidence that accrual mispricing appears to be distinct from post-earnings announcement drift (Collins and Hribar (2001)), and from the tendency of stock prices to drift in the direction of analysts’ forecast revisions Barth and Hutton (2004); (3) evidence that sophisticated investors such as analysts, auditors, and institutional investors also fail to fully understand the implications of accruals for future earnings (Bradshaw et al. (2001); Collins et al. (2003), Barth and Hutton (2004), Lev and Nissim (2004); (4) evidence that top executives understand the implication of accruals for future earnings and trade their equity contingent wealth accordingly (Beneish and Vargus (2002)).

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al. (2003)).2 The M-Score (Beneish (1997, 1999)) is a composite of eight ratios that, in

addition to total accruals, includes specific accruals intended to capture the financial

statement distortions that can result from earnings manipulation as well as incentives to

engage in earnings manipulation. Beneish (1997, 1999) validates the M-Score as a

measure of earnings management by showing the M-Score’s ability to identify firms

subject to SEC accounting enforcement actions.3 As McNichols (2000, p. 335) suggests,

the M-Score approach to exploiting information about specific accruals and allowing

variation in the exercise of discretion across accruals has the potential to lead to more

powerful methods of detecting earnings management.

Our findings include the following. First, we show that firms that have a high

likelihood of earnings manipulation experience lower future earnings. In contrast, we find

that the market acts as if it expects these firms to have higher future earnings. Consistent

with earnings manipulation misleading investors, we show that, for firms with a high

probability of manipulation, investors overestimate return on assets by 490 to 690 basis

points. Second, we show that the probability of manipulation is a correlated omitted

variable for the earnings forecasting models used in prior research on accrual mispricing.

We document that including the probability of manipulation greatly attenuates the

mispricing of accrual persistence. Third, we document that trading strategies based on M-

Score rankings earn economically significant abnormal returns ranging from 20.1 percent

2 The former reflects not only the impact of deliberate earnings management, but also changes in firms’ economic performance, and the latter relies on accrual expectation models’ whose ability to disentangle the earnings management component in accruals from the performance component have been widely questioned (e.g., see McNichols (2000) and Beneish (2001) for reviews and evidence). 3 Specifically, Beneish (1997) shows that the M-Score correctly classifies 41 out of 64 firms charged with GAAP violation whereas aggregate accrual models identify between 15 and 19 of the 64 firms. Beneish also shows in holdout sample tests that a strategy that sells short (buys) firms classified as violators (non-violators) yields systematically higher returns for a classification based on the M-Score rather than based on accruals.

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(size-adjusted) to 21.6 percent (Fama-French three-factor model). These hedge returns

are 45 and 480 basis points higher than their accrual ranking counterparts (19.6 and 16.8

percent).

We conduct several tests to (1) distinguish the M-Score from the accrual

strategy,4 and (2) rule out omitted risk factors as an explanation for the evidence.5

Specifically, we show that the hedge returns for neither the accrual nor the M-Score

rankings are simply manifestations of price-to-book, price-to-earnings, size, return

momentum, cash flow from operations to price, or earnings surprise effects. When we

consider the accrual and M-Score strategies jointly, we find that the explanatory power of

accruals for future returns declines in the presence of the M-Score alone, and that the

explanatory power disappears in the presence of the M-Score and control variables. We

interpret these results as indicating that accrual mispricing arises because investors are

misled by managers’ opportunistic management of earnings.

Third, we consider whether augmenting the Fama-French three-factor model with

a fourth factor proxying for the premium required by investors to hold stocks with greater

uncertainty about accruals/earnings persistence accounts for the abnormal returns we

observe.6 We find that the returns to the trading strategies remain economically

4 These two strategies have substantial overlap. First, fifty percent of the low M-Score decile firms also appear in the low accrual decile while 30 percent of the high M-Score decile firms appear in the high accrual decile. Second, both partitions reveal a pattern of increasing earnings and decreasing cash flows. 5 Although prior research (cf. fn 1) has been careful to address risk-based explanations, recent studies have suggested that the accrual anomaly is subsumed by risk explanations based on growth and cash-flow-from operations to price, and information risk in earnings (Francis et al. (2003), Desai et al. (2004), Khan (2005)). 6 The expanded return generating model is motivated by recent research developments. Specifically, analytical studies by Easley et al. (2002) and Easley and O’Hara (2004) show that uncertainty about valuation parameters can affect firms’ costs of capital and, that such information uncertainty may be a non-diversifiable risk factor priced by investors. Empirically, evidence consistent with these predictions appears in Francis et al. (2005) and Ecker et al. (2005) who show that a factor proxying for earnings quality explains variation in future stock returns incrementally to the three-factors proposed by Fama and French (1992).

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significant when we use the expanded model proposed by Ecker et al. (2005). However,

we find that the long side of the hedge no longer yields a significant return, while the

returns to the short side of the hedge become larger in magnitude. Because short selling

strategies have high collateral transaction costs we conduct two additional analyses. We

show that limiting the trading strategy to either (1) firms with market capitalization

greater than $500 million, or (2) firms in which institutional investors take investment

positions, yields hedge returns that are similar to that of the whole sample. Indeed, we

show that institutional investors also appear to be misled by earnings management as, on

average, they increase their holdings independently of whether firms are in the high or

low manipulation decile.

We interpret our results that the predictive ability of accruals for returns is greatly

diminished in the presence of the M-Score as indicating that accrual mispricing arises

because investors are misled by managers’ opportunistic management of earnings. Our

results also suggest that accrual mispricing and the relation between the M-Score and

future returns both belong to the class of phenomena indicating that investors do not fully

exploit publicly available financial statement information (e.g., Ou and Penman (1989),

Bernard and Thomas (1989), Abarbanell and Bernard (1992), Lev and Thiagarajan

(1993), Abarbanell and Bushee (1997), Beneish (1997), Piotroski (2000), Beneish et al.

(2001)). Our results on the persistence of current earnings conditional on the probability

of earnings manipulation have the potential to be useful for academics and professionals

interested in forecasting future earnings.

We present the remainder of the paper in four parts. The next section discusses

the data and method. Section 3 presents the empirical results. Section 4 reports several

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robustness checks, and Section 5 concludes.

2. Method

2.1. Sample

We select the initial sample from the Compustat Industrial, Research, and Full

Coverage files for the period 1993 to 2003.7 We eliminate (1) financial services firms

(SIC codes 6000 – 6899), (2) firms with less than $100,000 in sales (Compustat #12) or

in total assets (Compustat #6), and (3) firms without sufficient data to compute accruals

and the M-Score. To ensure that the trading strategies that we examine are

implementable, we (1) require all firms used in accruals and M-Score rankings to have

stock return data available at the time rankings are made, and (2) use prior year cut-offs

to assign firms to accruals or M-Score deciles in the current year. The final sample

consists of 25,285 firm-year observations from 1993 to 2003.

We winsorize financial statement variables at the 1% and 99% levels each year in

our sample period to control for the effect of potential outliers. Our trading strategy

return computations are based on taking positions at the beginning of the month

following the annual earnings announcement, and in case of delisting, we include

delisting returns in the buy-and hold return. We next describe our partitioning variables

and the characteristics of our sample.

2.2. Partitioning Variables

2.2.1 Accruals

Following Collins and Hribar (2002), we measure accruals deflated by total assets as

follows:

Accruals = ( )DEPOTHTAXAPINVAR +∆+∆+∆+∆+∆− (1)

7 Because the Beneish (1999) model was tested on data through 1992, we begin the sample period in 1993.

6

Current Accruals = Accruals+DEP (2)

In the Appendix, we discuss several measures of abnormal accruals derived from

aggregate accrual expectation models based on modifications of the Jones (1991) model.

As well, because recent work suggests that such constructs measure earnings management

with error (e.g., see McNichols (2000) among others), we follow Kothari et al. (2005) and

adjust both accruals and current accruals from such models by computing performance-

matched abnormal accruals.

2.2.2 Beneish’s M-Score

We use the M-Score developed by Beneish (1999) to rank firms according to the

likelihood that they have manipulated earnings. The M-Score is composed of eight ratios

that capture either financial statement distortions that can result from earnings

manipulation or indicate a predisposition to engage in earnings manipulation. Beneish

(1999) estimates the model using firms that admit to accounting manipulations and firms

targeted by the SEC for earnings manipulation.

There are two main differences between the M-Score and accrual-based proxies

for earnings management. First, the M-Score captures not only the possible financial

statement consequences of manipulation, but also incentives for manipulating earnings.

Second, in addition to considering the information in aggregate accruals, the M-Score

exploits information about specific accruals. As McNichols (2000) points out, the M-

Score approach to exploiting information about specific accruals and allowing variation

in the exercise of discretion across accruals has the potential to lead to more powerful

methods of detecting earnings management.

The M-Score has been applied on a limited basis in the literature as it does not

measure the magnitude of earnings management. Beneish (1997) found this approach

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performed better than abnormal accruals from aggregate accrual expectation models

based on Jones (1991) in distinguishing firms with financial statement fraud from firms

with extreme accruals. Teoh et al. (1998) applied the M-Score as an alternative proxy for

the occurrence of earnings management in the context of initial public offerings. Both

studies documented that firms with higher probabilities of manipulation subsequently

experienced poor stock market performance. The model we use to estimate the

probability of earnings manipulation is:

M= -4.84+.920*DSR+.528*GMI+.404*AQI+.892*SGI+.115*DEPI (3) -.172*SGAI)+4.679*ACCRUALS-.327*LEVI

Where: DSR = (Receivablest[2]/Salest[12]/(Receivablest-1/Salest-1)

GMI= Sales [12]- Costs of Goods Sold [41]

Sales [12]t-1 t-1

t-1

/

Sales [12]- Costs of Goods Sold [41]Sales [12]

t t

t

AQI= 1−

Current Assets [4]+ PPE [8]Total Assets [6]

t t

t

/ 1−

Current Assets + PPETotal Assets

t-1 t-1

-1t

SGI= Salest[12]/Salest-1

DEPI= Depreciation [14 less 65]Depreciation PPE [8]

t-1

t-1 + t-1

/

Depreciation Depreciation PPE

t

t + t

SGAI=SGA Expense [189]

Sales [12]t

t

/

SGA Expense Sales

t-1

t-1

LEV= LTD [9]+ Current Liabilities [5]

Total Assets [6]t t

t

/

LTD + Current LiabilitiesTotal Assets

t-1 t-1

t-1

ACCRUALS= (IBX [18]-CFO[308])/ TAt[6] 2.3 Sample Characteristics

In Table 1, we describe the characteristics of our sample. Accrual and M-Score

decile ranks are highly correlated (correlation = 0.618, p < 0.001, untabulated), and we

report the descriptive statistics by both accrual decile (Panel A) and by M-Score decile

(Panel B) to highlight similarities and differences across the rankings.

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Both partitions reveal a pattern of increasing earnings and decreasing cash flows.

However, the accrual rankings generate larger spreads in earnings (Accruals: 0.177, M-

Score: 0.074, which are significantly different at p < 0.001, untabulated), and cash flows

from operations (Accruals: 0.156, M-Score: 0.140, which are significantly different at p <

0.001, untabulated). If the mapping between earnings and returns is linear, these

statistics suggest that we should observe larger returns to strategy based on accruals.

We also report descriptive statistics for other financial characteristics across

accrual and M-Score deciles. Extreme accrual deciles do not significantly differ on price-

to-book, while the extreme M-Score deciles do not display significant differences with

respect to return volatility. However, the extreme deciles of both accruals and the M-

Score significantly differ on other characteristics such as price-to-earnings, cash flow-to-

price, earnings surprises (measured as the change in the quarterly earnings for the quarter

of the annual earnings announcement, scaled by market value at the end of the month

before the annual earnings announcement), and size. Because prior research shows that

these characteristics are related to future returns, we control for these variables in

subsequent tests.

3. Empirical Results

3.1 Persistence and Valuation Tests

Following Sloan (1996), we use the framework proposed by Mishkin (1983) to

investigate whether the market rationally prices the implications for one-year-ahead

earnings of the likelihood that current earnings are manipulated. We estimate the

following system

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[ ] 1tt5t4t3t2t101t11t

1tt5t4t3t2t101t

SMSENegMEPosMENegEPosECSARSMSENegMEPosMENegEPosE

+++

++

υ+β′−β′−β′−β′−β′−α−φ=ε+β+β+β+β+β+α=

(4)

E = (CFO + Acc); CFO = Cash flows from operations (#308) divided by average total assets; Acc = ( ) assets totalaverage/DEPOTHTAXAPINVAR +∆+∆+∆+∆+∆− ; CSAR = Twelve-month buy and hold size-adjusted return from the beginning of

the month following the annual earnings announcement; EPos = E if E > 0; 0 otherwise; ENeg = E if E < 0; 0 otherwise; SMS = Scaled M-Score. M-Scores are computed using the model in Beneish

(1999). M-Scores are ranked annually using the prior year decile rank cutoffs. Ranked M-Scores are scaled to have a zero mean and range from -1 (lowest M-Score) to +1 (highest M-Score);

EPosM =EPos*SMS; ENegM =ENeg*SMS.

The first equation in this system estimates the forecasting coefficients (βi) of

earnings and the M-Score for predicting one-year-ahead earnings. We disaggregate

earnings into positive and negative because our predictions about persistence conditional

on the M-Score differ according to the sign of earnings. Thus, we expect that positive

earnings associated with a high probability of manipulation will be less persistent (β3<0)

and negative earnings associated with a high probability of manipulation will be more

persistent (β4>0). With respect to the influence of the probability of manipulation on the

level of one-year-ahead earnings, we test the hypothesis in the forecasting equation that

high probability of manipulation leads to lower earnings (β5<0).

The second equation in this system estimates the valuation coefficients (β’i) that

the market assigns to earnings components and the M-Score. The Mishkin framework

provides a statistical comparison between the forecasting coefficients (measures of the

predictive ability of current earnings and the M-Score for one-year-ahead earnings) and

the valuation coefficients (measures of the market’s pricing of current earnings and of the

M-Score).

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We implement the tests of rational pricing by estimating this system of equations

jointly using a two-stage iterative generalized nonlinear least squares procedure. We first

estimate the unconstrained system without imposing any constraints on the coefficients.

In the second stage, we test whether the valuation coefficients differ from the forecasting

coefficients obtained in the first stage by imposing rational pricing constraints β’i= βi, for

all q). Under the null hypothesis that the market rationally prices current earnings and the

M-Score with respect to their association with one-year-ahead earnings, Mishkin shows

that the following likelihood ratio statistic is asymptotically χ2(q) distributed:

2 × N × Ln(SSRc/SSRu),

where q = the number of rational pricing constraints imposed; N = the number of observations in the sample; SSRc = the sum of squared residuals from the constrained regressions in the second stage; SSRu = the sum of squared residuals from the unconstrained regressions in the first stage.

We report the results of this analysis in Table 2, Panel A. The results for the

persistence tests from the forecasting equation are consistent with our predictions. The

estimate for EPosM is negative and significant (-0.056, t-statistic=-2.52) suggesting that

positive earnings are less persistent when the probability is high that these earnings are

managed. Second, the estimate for ENegM is positive and significant (0.097, t-

statistic=10.01) suggesting that negative earnings are more persistent when the

probability is high that current earnings are managed. The estimate for SMS is negative

and significant (-0.021, t-statistic= -9.57) suggesting that one-year-ahead earnings are

lower when the probability is high that current earnings are managed.

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Our tests of mispricing reveal, consistent with Sloan (1996), that the persistence

of earnings is not mispriced. However, the market perception of the effect of the

probability of manipulation of current earnings implicit in the pricing regression (0.048)

is significantly different from that implied by the earnings forecasting regression (-0.021)

(likelihood ratio=38.97, p-value<0.001). This result suggests that investors not only fail

to discount current earnings for the probability that such earnings have been manipulated,

but that on average they expect return on assets to be 690 basis points higher than that

implied by the forecasting equation.8

We next investigate the relation between accruals and the M-Score, because the

partitions on these variables described in Table 1 display a similar (though not identical)

pattern. In Figure 1, we display the distribution of total accrual decile rankings for firms

in the highest and lowest probability of manipulation decile. Over half the firms in the

lowest probability of manipulation decile (54 percent) are firms that are in the Low

accrual decile, and a further 21 percent are from the next lowest accrual decile (decile 2).

The rankings on the low end are thus similar, suggesting that firms with low probability

of manipulation have income-decreasing accruals. This is consistent with the M-Score

being derived from studying firms that overstate earnings. A different picture emerges in

the highest probability of manipulation decile. Only 30 percent of the firms in the highest

probability of manipulation decile are in the highest accrual decile and a further 14

percent are in next highest accrual decile (decile 9). These results suggest that the

rankings are not substitutes, and that their intersection is less pronounced on the short

side of the hedge.

8 This effect is economically significant. The median firm in the sample has ROA of 4.6 percent (460 basis points).

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To investigate whether a relation exists between the M-Score and the persistence

and valuation implication of accruals, we estimate three different versions of the

following system:

[] 1tt76t5

t4t3t2t101t11t

1tt7t6t5

t4t3t2t101t

SMSDepCAccNegM

CAccPosMCAccNegCAccPosCFOECSARSMSDepCAccNegM

CAccPosMCAccNegCAccPosCFOE

+

++

+

+

υ+β′−β′−β′−

β′−β′−β′−β′−α−φ=ε+β+β+β+

β+β+β+β+α=

(5)

where:

E = (CFO + CAcc + Dep); CSAR = Twelve-month buy and hold size-adjusted return from the beginning of

the month following the annual earnings announcement. CFO = Cash flows from operations (#308) divided by average total assets; CAcc = ( ) assets totalaverage/OTHTAXAPINVAR ∆+∆+∆+∆+∆− ; Dep = Depreciation and amortization (#125) divided by average total assets; CAccPos = CAcc if CAcc > 0; 0 otherwise; CAccNeg = CAcc if CAcc < 0; 0 otherwise; SMS = Scaled M-Score. M-Scores are computed using the model in Beneish

(1999). M-Scores are ranked annually using the prior year decile rank cutoffs. Ranked M-Scores are scaled to have a zero mean and range from -1 (lowest M-Score) to +1 (highest M-Score);

CAccPosM = CAccPos x SMS; CAccNegM = CAccNeg x SMS.

In Panel B, we reproduce Sloan’s test after disaggregating accruals into working

capital accruals and depreciation following evidence in prior work suggesting that the

accrual mispricing phenomenon is due to working capital accruals (Bradshaw et al. 2001;

Thomas and Zhang 2002). We also disaggregate current accruals into positive and

negative accruals because our later predictions about persistence conditional on the M-

Score differ according to the sign of accruals. In general, our results are consistent with

Sloan as they suggest that accruals are mispriced.

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In Panel C, we augment the specification by conditioning signed accruals on the

M-Score. We expect that positive accruals associated with a high probability of

manipulation will be less persistent (β4<0) and negative accruals associated with a high

probability of manipulation will be more persistent (β5>0). Our results of our persistence

tests from the forecasting equation are consistent with our predictions. The estimate for

CAccPosM is negative and significant (-0.124, t-statistic=-4.23) suggesting that positive

accruals are less persistent when the probability is high that current earnings are

managed. Second, the estimate for CAccNegM is positive and significant (0.225, t-

statistic=7.95) suggesting that negative accruals are more persistent when the probability

is high that current earnings are managed. The pricing tests reveal substantial accrual

mispricing for AccPos (likelihood ratio = 5.58, p < 0.001), CAccNegM (likelihood ratio

= 7.71, p < 0.001), and CAccPos + CAccPosM (likelihood ratio = 21.89, p < 0.001).

Our final specification includes SMS as a stand-alone variable, and, similar to

Panel , we predict β7<0. We find that the market overprices positive accruals (likelihood

ratio = 5.47, p < 0.05), but that the market does not appear to misprice CAccNegM or

(CAccPos + CAccPosM). As in Panel A, the market perception of the effect of the

probability of manipulation of current earnings implicit in the pricing regression (0.039)

is significantly greater than that implied by the earnings forecasting regression (-0.010)

(likelihood ratio=24.64, p-value<0.001). This result also suggests that investors not only

fail to discount current earnings for the probability that such earnings have been

manipulated, but that on average they expect return on assets to be 490 basis points

higher than that implied by the forecasting equation.

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A striking picture emerges in comparing results across Panels B, C, and D. Panel

B reveals mispricing of positive accruals. In Panel C, we condition positive accruals on

the M-Score, but omit SMS as a stand-alone variable. We find that the mispricing of the

positive accruals is concentrated in the interaction between positive accruals and the M-

Score. In the final panel, we include SMS in the specification. Here we find that the

mispricing of the positive accruals conditioned on the M-Score disappears and is replaced

by mispricing of the M-Score. We interpret these findings to indicate that much of the

predictive content of accruals for future returns arises from the correlation of accruals

with the M-Score. To the extent that the M-Score captures earnings management, this

suggests that managers’ attempts to opportunistically manipulate earnings successfully

mislead investors.

The pricing tests reported in Table 2 are subject to several limitations. The

Mishkin test is a joint test of market rationality and the ability of our model to correctly

capture the market’s expectation of earnings. Thus, if the test rejects market rationality,

one interpretation is that the earnings expectation model is misspecified. The expectation

model is likely misspecified because it assumes that the ability to forecast one-year-ahead

earnings is the only value-relevant dimension of current earnings. To the extent that our

earnings expectation model is incomplete, the Mishkin (1983) framework assumes that

stock prices are efficient with respect to all omitted variables that are correlated with

earnings or with the M-Score. To ensure that our market mispricing results are not due to

errors in model specification, we next investigate (1) whether investment strategies based

on accrual and M-Score earn economically significant returns, and (2) the extent to which

the M-Score and accruals measures capture the same underlying event.

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3.2 Returns to Trading Strategies on Accruals and the M-Score

We investigate the returns to investment strategies based on accruals as well as

the M-Score in Table 3.9 The table presents mean size-adjusted returns for deciles

formed on accruals, current accruals and the M-Score. The one-year holding period

begins on the first day of the month following a firm’s annual earnings announcement.

Because fiscal year ends and earnings announcement dates vary across sample firms, we

assign firms into deciles based on the prior years’ decile cut-offs.

The return to the hedge portfolio formed by taking a long position in the lowest

accrual decile and a short position in the highest total accrual decile earns a hedge return

of 19.6 percent. This hedge return is higher than the 10.4 percent documented in Sloan

(1996, p. 307), a fact that we attribute to a combination of (1) taking an investment

position on average two months earlier (beginning of the month following the earnings

announcement rather than four months after the end of the fiscal year), (2) our use of

accruals measured using the statement of cash flows rather than the balance sheet

(Collins and Hribar (2002)), and (3) our more recent sample period (Lev and Nissim

(2004)). The partition based on the M-Score rankings yields a hedge return of 20.1

percent that is similar (higher by 45 basis points) to the total accrual hedge. Figure 2

demonstrates the similarity in returns across the two strategies. This similarity leads us to

investigate the incremental returns to these strategies.

In addition to examining how the strategies perform in relation to each other, we

investigate whether the hedge returns to these strategies can be explained by omitted

variables associated with future returns. Specifically, we rely on prior research that has

shown that the following six characteristics are correlated with subsequent returns: (1) the 9 We reproduce these tests for abnormal accruals in the Appendix.

16

price-to-book ratio, following evidence in Chan et al. (1996), Davis (1994), and Haugen

and Baker (1996), who document that firms with high market-to-book ratios (P/B)

subsequently earn lower returns; (2) returns in the prior year (RETt-1), following evidence

in Jegadeesh (1990), and Jegadeesh and Titman (1993) that short-run returns tend to

continue in the subsequent year; (3) price-to-earnings (P/E), following evidence that

firms with low P/E ratios outperform firms with high P/E ratios on a risk-adjusted basis

(among others, Haugen and Baker 1996); (4) firm size (ln(MVE)), following evidence in,

among others, Fama and French (1992), that size explains future returns; (5) unexpected

earnings (UE), to capture the post-announcement drift documented by (among others)

Freeman and Tse (1989) and Bernard and Thomas (1989); and (6) cash flow from

operations to price (CFO/P) following evidence in Desai et al. (2004) that strategies

based on CFO/P explain away the returns to returns to accrual-based strategies. We

estimate several versions of the regression below for each year in our sample as well as

by pooling observations across years:

CSARt+1 = a0 + a1HEDGEMSCORt + a2HEDGEACCRt + a3P/Bt + a4P/Et +a5RETt-1

+ a6ln(MVEt) + a7UEt + a8CFO/Pt + et+1 (6)

where CSAR is the one-year ahead size-adjusted return from the beginning of the month

following the annual earnings announcement, HEDGEMSCOR and HEDGEACCR are

the scaled M-Score and accrual ranks, and the control variables (described above) are

recast as deviations from the mean, scaled by the in-sample standard deviation of the

variable. Under the assumption of linearity in the relation between rankings and returns,

the coefficient on HEDGEMSCOR (HEDGEACCR) is the return to the M-Score

(accrual) hedge portfolio after controlling for the effect of all the other variables.

17

Table 4 presents results of six different versions of equation (7), which we

estimate as a pooled cross-sectional regression (Panel A) as well as year-by-year (Panel

B). The first version we estimate only has HEDGEMSCOR as an explanatory variable

for returns, and the coefficient estimate is 0.159 (t-statistic 8.91). This suggests a 15.9

percent return to a hedge portfolio based on extreme M-Score decile rankings, and is

lower than the 20.1 percent reported in Table 3. This is because the coefficient’s

interpretation as a hedge portfolio return depends on linearity, and Figure 2 reveals non-

linearity in the relation between decile ranks and returns. The second version we

estimate only has HEDGEACCR as an explanatory variable for returns. The coefficient

estimate of 0.133 (t-statistic 7.40) suggests a 13.3 percent return to a hedge portfolio

based on extreme accrual decile rankings and is similarly lower than the 19.6 percent

reported in Table 3. Indeed, the spread between estimates of hedge portfolio returns for

the M-Score of 4.2 percent (0.201-0.159) is lower than the corresponding measure for

accruals (6.3 percent or 0.196-0.133), and we interpret the lower spread as an indication

that M-Score rankings better delineate one-year-ahead returns.

The third estimation includes both HEDGEMSCOR and HEDGEACCR and

reveals statistically positive coefficients of 0.124 and 0.059, respectively. These results

suggest commonality in the two investment strategies: using both strategies yields lower

hedge returns than using each strategy separately. Though both strategies in combination

yield significant returns, the effect of controlling for the other strategy is less pronounced

for the M-Score. That is, controlling for accrual decile rankings, the hedge return to M-

Score rankings is 12.4 percent (or 350 basis points lower than previously reported), but

18

controlling for M-Score decile rankings, the hedge return to accrual rankings is 5.9

percent (or 740 basis points lower than previously reported).

The remaining estimations reproduce the results above while incorporating the

control variables that have been documented in prior work to be correlated with future

returns.10 When we incorporate these controls for the M-Score and the accruals strategies

separately, we obtain qualitatively similar results, suggesting that controlling for market-

to-book ratio, prior year returns, earnings yield, firm size, earnings surprise, and cash

flow from operations to price does not diminish the profitability of hedge strategies based

on M-Score and accruals. However, in the full model that includes both the M-Score and

the accrual strategies, we only obtain an economically and statistically significant return

on the M-Score hedge. These results are corroborated by the year-by-year estimations in

Panel B.

In sum, our results suggest that the probability of manipulation is useful in

predicting future returns and that it acts as a correlated omitted variable in that it greatly

attenuates the predictive ability of accruals for future returns. We conjecture that the

incremental predictive ability of the M-Score stems from using incentive variables in

addition to accruals and that the M-Score provides an index of how reliable earnings

are.11 We thus present tests based the Fama-French three factors, and on the augmented

version that incorporates a factor capturing the compensation for earnings uncertainty

(Ecker et al. (2005)).

3.3 Asset pricing tests

10 We do not include both earnings and cash flow yield variables in regressions that also include accruals. 11 In fact, our finding that the M-Score rankings generate a lower spread in terms of earnings components than accrual rankings, suggests that the spread in accruals is not the sole source of predictive ability for future returns.

19

In this section, we report results of asset pricing tests for portfolio returns formed

on accrual deciles and M-Score deciles. These tests provide additional evidence on the

extent to which the returns we report in previous tables reflect mispricing or risk. We

estimate the following model for each of the ten decile portfolios for both the accrual

strategy and the M-Score strategy:

( ) ( ) t,itititiftt,Mii

ftt,i EQFeHMLhSMBsRRRR ε++++−β+α=− (7)

where t,iR denotes the return to decile i for month t, ftR denotes the return on the 30-day

T-bill for month t, t,MR denotes the return on the value-weighted CRSP index for month

t, SMB denotes the Fama-French size factor mimicking portfolio, HML denotes the

Fama-French book-to-market factor mimicking portfolio, and EQF denotes the Ecker et

al. (2005) earnings quality factor mimicking portfolio.

Francis et al. (2005) and a companion paper by Ecker et al. (2005) identify EQF

as an important determinant of returns. These papers argue that EQF factor loadings, or

“e-loadings,” capture a firm’s sensitivity to poor earnings quality. Neither paper provides

intuition for what shocks to the EQF represent, but the theoretical motivation for the EQF

might shed light on this issue. Easley and O’Hara (2004) argue that investors who are at

an information disadvantage face an adverse selection problem in trading securities in the

capital market. The greater the information asymmetry the greater is the adverse selection

problem and hence the greater is the premium these uninformed investors demand to hold

securities. High information quality “levels the playing field” by reducing information

asymmetry. As a result, high earnings quality can reduce the adverse selection problem

and the premium required by investors to hold securities.

20

Under this line of thought, shocks to the EQF reflect whether the amount of

private information that arrives to sophisticated or “informed” investors is greater than or

less than expected. When relatively less (more) private information arrives, the EQF is

relatively high (low) because the adverse selection costs faced by uninformed investors

are relatively low (high). The e-loadings that Francis et al. (2005) and Ecker et al. (2005)

argue as capturing firms’ exposure to poor earnings quality reflect firms’ sensitivities to

shocks to the adverse selection costs associated with the arrival of private information.

Whether this, or an alternative, interpretation of the EQF and the e-loadings is

valid can only be resolved by additional research. However, we include the EQF in our

regressions for two related reasons. First, Francis et al. (2005) document that EQF

provides incremental explanatory power relative to the Fama-French factors in firm-

specific regressions and Francis et al. (2005) and Ecker et al. (2005) document that e-

loadings are correlated with widely-used measures of earnings quality. Second, earnings

quality could differ across portfolios of stocks sorted on accruals and the M-Score. By

controlling for the differential asset pricing implications of earnings quality across the

two strategies, we can more crisply compare any remaining abnormal returns.

We generally follow the procedures in Ecker et al. (2005) for constructing the

EQF. First, we estimate the following model for every industry-year (using Fama and

French (1997) industry definitions) for which we have a minimum of 20 observations in

Compustat:

tt5t41t3t21t10t PPEvReCFOCFOCFOAcc ε+α+∆α+α+α+α+α= +− , (8)

where Acc =-(CHGAR [#302]+ CHGINV [#303]+ CHGAP [#304]+ CHGTAX [#305]

+ CHGOTH [#307]+ DEP [#125]) / average total assets;

21

CFO = Cash from operations (#308) / average total assets;

∆Rev = Change in Sales (#12) from t-1 to t / average total assets;

PPE = Property, plant, and equipment, gross (#7) / average total assets.

This estimation results in firm-year-specific residuals. For each firm-year, we

estimate the standard deviation of these residual accruals ( RAσ ) using the residuals for the

past five years. We then sort firms into deciles at the beginning of each month based on

the firms’ most recent RAσ . We compute the average returns for each decile-month, and

form the EQF by taking a long position in the highest RAσ decile and a short position in

the lowest RAσ decile each month. The EQF averages 1.7% per month over the sample

period, which is similar to the average EQF reported by Ecker et al. (2005).

We report the results for accruals-based rankings in Table 5. In Panel A, we report

the results for the Fama-French three factor model. The alpha for the lowest accrual

decile is 1% per month, while the alpha for the highest decile is -0.4% per month. Thus,

the hedge portfolio generates a 1.4% abnormal return per month, or a 16.8% annualized

risk-adjusted return. The top and bottom deciles do not significantly differ along

dimensions of risk captured by the market, SMB, and HML factors.

Panel B reports the results from augmenting the Fama-French model with the

EQF. The EQF loads with a positive coefficient in all deciles. The e-loading is largest for

the top and bottom deciles, forming a U-pattern across the portfolios. This finding

indicates that firms with extreme accruals have poor earnings quality. Also consistent

with Francis et al. (2005), we find that the abnormal returns to the accrual strategy are

largely unaffected by inclusion of EQF. However, we do find that including EQF reduces

the abnormal returns across all the portfolios. While the spread declines slightly to 1.3%

22

per month, the majority of those returns are generated by the short side (–2.0% per

month) of the strategy – where transactions costs and limits to arbitrage are likely most

severe.

The results for the M-Score portfolios are reported in Table 6. Panel A reports the

results of regressing the portfolio excess returns on the Fama-French factors. The results

indicate that, while the Fama-French factors explain a significant proportion of the

variation in each of the decile portfolios, the strategy nevertheless generates significant

positive abnormal returns. The hedge portfolio generates a 1.8% return per month, or an

annualized 21.6% risk-adjusted return. The difference between the M-Score hedge

portfolio and the accrual hedge portfolio, depicted in Figure 3, is 480 basis points on an

annualized basis and is statistically significant (p < 0.001 , not tabulated).

In Panel B, we report results from including EQF in the asset pricing regressions.

As for the accrual strategy, the EQF loads with a significant positive coefficient for each

decile and the e-loadings exhibit a U-pattern. Unlike the accrual strategy, the risk-

adjusted returns for the M-Score actually increase when including the EQF in the

regressions. The abnormal returns to the strategy rise to 2.1% per month for an

annualized risk-adjusted return of 25.2%. The returns to the M-Score strategy are

significantly greater than the returns to the accrual strategy (p < 0.001, not tabulated), as

depicted in Figure 4.

3.4 Sensitivity Analyses

3.4.1 Transaction Costs

We measure hedge returns before transaction costs, yet a large portion of the

hedge return arises from the short side of the hedge. While collateral transaction costs

23

seem unlikely to explain the large returns to our short position, we are not able to

estimate them, and we thus do not know whether these returns are sufficient to

compensate investors for the costs and risks associated with short sales. To assess the

reasonableness of these seemingly large hedge returns, we examine the hedge returns that

would obtain to limiting the investment strategy to larger firms. Specifically, because

transaction costs are lower for larger firms, and short sellers typically focus on such firms

to reduce the risk that a lender demands the return of a stock (Staley 1997), we focus on

firms with market capitalization greater than $500 million. As we describe in Table 7,

approximately one quarter of the firms in the extreme probability of manipulation decile

have market capitalization greater than this cutoff. Although this potentially limits the

number of firms over which the strategy can be implemented, the hedge return of 18.4

percent seems unlikely to be explained away by collateral transaction costs on the short

side. In addition, the short side of the hedge is similar for each of the size-based sub-

samples (-9.77 v. -9.04 percent).

3.4.2 Institutional Investors

Sophisticated investors are a priori less likely to be misled by managers’ exercise

of accounting discretion and potentially better able to effect (at lower cost) a short selling

transaction. As a result, we examine changes in institutional holdings in the quarters

surrounding the earnings announcement (quarter 0) that results in the firms being ranked

in the extreme deciles. Table 7 reports that institutional investors hold 70 and 73 percent

of the firms in the Low and High M-Score decile rankings. If we limit the trading

strategy to those firms, we obtain a hedge return (21.88 percent) similar to that observed

for the sample as a whole. We also document that the percentage of shares held by

24

institutions display a similar pattern of increases whether the firms are on the long or

short side of the hedge. Indeed, the increases in the percentage of shares held by

institutions in quarters 0 and +1 are greater for firms in the high probability of

manipulation decile (1.71% and 4.03%) than in the low probability of manipulation

decile (0.67% and 2.92%). This preliminary evidence suggests that institutional investors

are also misled by managers’ discretionary accounting actions.

4. Conclusion

In this paper, we investigate the relation between accruals and the probability of

earnings manipulation and document the following results. First, we provide evidence

that firms that have a high likelihood of earnings manipulation experience lower future

earnings, but that investors expect these firms to have higher future earnings, consistent

with earnings manipulation misleading investors. Second, we show that the probability

of manipulation is a correlated omitted variable in earnings forecasting models as

including the probability of manipulation greatly attenuates the mispricing of accrual

persistence. Third, we show that trading strategies based on M-Score rankings earn

economically significant abnormal returns that are 45 basis points higher (size-adjusted

returns) and 480 basis points higher (Fama-French three-factor model) than their accrual

ranking counterparts. Fourth, we show that the hedge returns for either the accrual or the

M-Score rankings are not simply manifestations of market-to-book, earnings yield, size,

price momentum, cash flow yield (cash flow from operations to price), or earnings

surprise effects. Fifth, when we consider the accrual and M-Score strategies jointly, we

find that the explanatory power of accruals for future returns declines in the presence of

the M-Score; the explanatory power disappears in the presence of the M-Score and

25

control variables. Sixth, we show that economically significant returns remain when we

allow the return-generating model to include a factor that proxies for the compensation

demanded by investors for uncertainty about the quality of earnings.

We interpret these results as indicating that accrual mispricing arises because

investors are misled by managers’ opportunistic management of earnings. In addition,

our results suggest that accrual mispricing and the relation between the M-Score and

future returns both belong to the same class of phenomena characterized by investors’

failure to fully exploit publicly available financial statement information. Our evidence

on the persistence of current earnings conditioned on the probability of earnings

manipulation has the potential to be useful for academics and professionals interested in

forecasting future earnings.

26

Appendix

Abnormal Accruals and Performance-Matched Abnormal Accruals

We consider several alternative proxies for earnings management based on measures of abnormal accruals and performance-matched abnormal accruals (e.g., Healy, 1985, Jones 1991, Dechow et al. 1995, Beneish 1997, 1998,, and Kothari et al. 2005).

We estimate normal or expected accruals using the Jones (1991) model and two of its variants. The model proposed by Jones follows Kaplan's (1985) suggestion that accruals likely result from the exercise of managerial discretion and from changes in the firm's economic conditions. Jones’ model relates total accruals (Accruals) to the change in sales (ДSales) and the level of gross property, plant and equipment (PPE):12

Accruals it = a1i + b1i ДSalesit + c1i PPEit + u1it (A.1) The second version uses current accruals (CAcc=Accruals +DEP) as the

dependent variable and only the change in sales as an explanatory variable: CAccit = a2i + b2i ДSalesit + u2it (A.2) The first modification we consider is the modification attributed to Dechow et al.

(1995). The modification only pertains to the computation of predicted accruals. That is, Dechow et al. (1995) use the Jones model in the estimation period, and make a receivable adjustment in the prediction period to recognize that sales growth may be partly due to management exercising discretion over sales. Thus, for example, predicted total accruals under the Dechow et al. (1995) modification are given by:

Predicted Accrualsit = ậ1i + b 1i (ДSalesit - ДReceivablesit) + c 1i PPEit + u3i (A.3)

The second modification attributed to Beneish (1998) replaces change in sales by change in cash sales. Cash sales growth is an alternative construct that deals with the endogeneity problem and plays the same role as sales growth in explaining changes in working capital accounts. In their total accrual form, the models are given by:

Accrualsit = a2i + b2i ДCash Salesit + c2i PPEit + u3it (A.4) We estimate these models in cross-section, using all firms in a given two-digit

industry and year. Cross-sectional estimation has the advantage of not restricting the analysis only to firms with long time series of data. Each year-estimation is used to make one-year-ahead forecasts of expected accruals which, subtracted from the dependent variable, yield abnormal accruals.

Recent work examining the properties of abnormal accruals generated with models

12 The model is based on two assumptions. First, working capital accruals resulting from changes in the firm's economic environment are related to changes in sales, or sales growth, since equation (1) is typically estimated with all variables deflated by either lagged assets or lagged sales. Second, gross property, plant and equipment controls for the portion of total accruals related to non-discretionary depreciation expense.

27

based on Jones (1991) concludes that such proxies measure earnings management with error (e.g., see McNichols (2000) among others). As a result, we follow Kothari et al. (2005) and adjust both accruals and abnormal accruals from the above three models by computing performance-matched abnormal accruals. Specifically, for each sample firm, we identify a performance-matched firm based on industry membership, period, and lagged ROA. We estimate performance-matched abnormal accruals as the difference between various accrual metrics for treatment and control firms.

We report in Table A.1 hedge returns that are comparable to those in Table 3. Two

features are noteworthy. We find that the hedge returns for abnormal total accruals and abnormal current accruals are in line with their raw total and current accruals counterparts. For example, the hedge return for abnormal total accruals using the DSS and Beneish modifications of the Jones’ model are 17.2 percent of 18.2 percent while the total accruals hedge in Table 3 (19.6 percent). The similarity in hedge returns likely reflects similar rankings as these variables are highly correlated in our sample, and suggest that little is gained from partitioning on abnormal accruals. Second, we find that performance-matched accruals generate hedge returns that are systematically lower than their raw total and current accruals counterparts. For example, the hedge return for performance-matched total accruals of 13.0 percent is lower than the total accruals hedge in Table 3 (19.6 percent). A decrease is apparent on both sides of the hedge: the long position yields one-year ahead abnormal returns of 7.9 percent (vs. 10.2 percent for total accruals) and the short position yields one-year ahead abnormal returns of -5.1 percent (vs. -9.4 for total accruals). These findings are subject to two possible interpretations. One possibility is that the abnormal accruals measures do not yield more precise estimates of earnings management than raw accruals. Alternatively, the abnormal accruals are a more precise measure of earnings management, and these results suggest that earnings management is a partial explanation for the accruals anomaly.

28

TABLE A.1 Average One-Year-Ahead Size-Adjusted Returns for Portfolios of Firms Ranked by Decile

Based on Abnormal Accruals 25,283 Firm-years between 1993 and 2003.

Panel A: Abnormal Accruals (Total)

Perf. Matched Perf. Matched Perf. Matched Abnormal Total Abnormal Total Abnormal Total Abnormal Total

Decile N Total Accruals N Accruals (DSS) N Accruals (DSS) N Accruals (B) N Accruals (B) Low 2537 7.88% 2601 10.04% 2592 8.83% 2559 10.39% 2594 8.31%

2 2493 2.17% 2459 5.77% 2542 2.16% 2491 4.95% 2517 2.95% 3 2537 1.32% 2532 2.83% 2537 1.14% 2494 2.72% 2516 1.19% 4 2481 3.19% 2471 3.03% 2459 3.90% 2526 2.27% 2505 2.06% 5 2537 0.46% 2482 2.25% 2483 1.67% 2491 1.44% 2419 2.60% 6 2480 2.38% 2487 -0.44% 2445 1.37% 2421 1.56% 2517 -0.14% 7 2592 3.65% 2488 0.10% 2576 1.22% 2463 1.39% 2537 3.15% 8 2509 1.63% 2489 -1.17% 2510 0.22% 2552 -2.32% 2529 1.13% 9 2540 -5.05% 2580 -2.43% 2522 -2.78% 2560 -1.47% 2561 -3.24%

High 2579 -5.14% 2696 -7.15% 2619 -5.28% 2728 -7.83% 2590 -5.57% Hedge 13.02% 17.19% 14.11% 18.22% 13.87%

Panel B: Abnormal Accruals (Current)

Perf. Matched N Abnormal Curr.

Perf. Matched Abnormal Curr. Abnormal Curr.

Perf. Matched Abnormal curr.

Decile N Curr. Accruals 2559 Accruals (DSS) N Accruals (DSS) N Accruals (B) N Accruals (B) Low 2529 6.89% 2585 7.22% 2554 6.71% 2551 7.85% 2545 5.79%

2 2503 3.61% 2494 9.11% 2498 3.16% 2565 5.72% 2521 2.73% 3 2512 1.82% 2562 -0.66% 2509 2.15% 2605 0.00% 2534 1.51% 4 2568 0.41% 2504 -1.29% 2583 2.03% 2510 -0.05% 2500 1.61% 5 2508 0.91% 2569 3.16% 2455 -0.45% 2481 0.17% 2486 0.99% 6 2449 0.71% 2498 -0.02% 2488 1.03% 2545 2.97% 2492 2.55% 7 2525 3.73% 2499 3.27% 2521 3.13% 2592 2.80% 2507 2.70% 8 2576 1.30% 2516 0.49% 2572 1.67% 2437 1.68% 2580 2.51% 9 2548 -3.37% 2499 -3.66% 2547 -2.46% 2495 -4.22% 2565 -3.32%

High 2567 -3.53% -5.56% 2558 -4.64% 2504 -4.87% 2555 -4.64% Hedge 10.42% 12.78% 11.36% 12.72% 10.43%

29

Holding-Period: The one-year holding period begins on the first day of the month following a firm’s annual earnings announcement. Because fiscal year ends and earnings announcement dates vary across sample firms, we assign firms into deciles based on the prior years’ decile cut-offs. Accruals: We compute accruals as using COMPUSTAT data (numbers in parentheses) as Total Accruals = - (∆AR[#302] + ∆INV[#303]+ ∆AP[#304] + ∆TAX [#305] + ∆OTH [#307]+ DEP [#125])/Total Assets-1[#6], and Current Accruals = Total Accruals + DEP[#125]/ Total Assets-

1[#6]. ,We use the difference between total accruals for a firm and total accruals for its closest lagged-ROA-match from other firms in the same two-digit industry and time period as Performance-Matched Total Accruals. We similarly compute Performance-Matched Current Accruals. Abnormal Accruals: We estimate abnormal total and current accruals using three models: 1 - The Jones (1991) model relates total accruals (defined above) to the change in sales (#12) and the level of gross property, plant and equipment (#8) and is written as Total accrualsit = ai + bi ∆Salesit + ciPPEit + uit (all variables deflated by lagged total assets). We estimate the model cross-sectionally using all firms in a given two-digit SIC code industry and year. We make one-year ahead forecasts of expected accruals as Expected accrualsit+1 = αi + βi (∆Salesit+1)+γi PPEit+1, where the Greek letters reflect estimates of the model coefficients in the prior year. We estimate abnormal total accruals as the difference between total accruals and expected accruals. We estimate abnormal current accruals by estimating the model after dropping the PPE variable. 2 - The DSS modification: Jones’ model as modified by Dechow, Sloan and Sweeney (1995) is the same as the Jones model in the estimation period. The modification introduced by DSS is the subtraction of the change in receivables (#2) from the change in sales in the prediction period so that expected accruals are given by: Expected accrualsit+1 = αi + βi (∆Salesit+1 - ∆Receivables it+1)+ γi PPEit+1. We estimate unexpected total accruals as the difference between total accruals and expected accruals, and use the difference between abnormal accruals for a firm and abnormal accruals for its closest lagged ROA match from other firms in the same two-digit industry and time period as Performance-Matched Abnormal Total Accruals. (DSS). We estimate abnormal current accruals by estimating the model after dropping the PPE variable. 3 - The Beneish (1998) model relates total accruals (defined above) to the change in cash sales and the level of gross property, plant and equipment (#8) and is written as Total accrualsit = ai + bi ∆CashSalesit + ciPPEit + uit (all variables deflated by lagged total assets). We estimate the model cross-sectionally using all firms in a given two-digit SIC code industry and year. We make one-year ahead forecasts of expected accruals as Expected accrualsit+1 = αi + βi (∆Cash Salesit+1)+γi PPEit+1, where the Greek letters reflect estimates of the model coefficients in the prior year. We estimate unexpected total accruals as the difference between total accruals and expected accruals, and use the difference between abnormal accruals for a firm and abnormal accruals for its closest lagged ROA match from other firms in the same two-digit industry and time period as Performance-Matched Abnormal Total Accruals. (B). We estimate abnormal current accruals by estimating the model after dropping the PPE variable.

30

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Freeman, R., and S. Tse. 1989. The multi-period information content of earnings announcements: confirmations and contradictions of previous earnings reports. Journal of Accounting Research 27 (Supplement): 49-79. Haugen, R. A., and N. L. Baker. 1996. Commonality in the determinants of expected stock returns. Journal of Financial Economics 41 (July): 401-439. Hribar, P. 2002. The market pricing of components of accruals. Working paper, Cornell University, Ithaca, NY. Jegadeesh, N. 1990. Evidence of predictable behavior of security returns. Journal of Finance 45 (July): 881-898. Jegadeesh, N., and S. Titman. 1993 Returns to buying winners and selling losers. Journal of Finance 48 (March): 65-91. Jones, J. 1991. Earnings management during import relief investigations. Journal of Accounting Research 29 (Autumn): 193-228. Khan, M. 2005. Are accruals really mispriced? University of Toronto working paper. Kothari, S.P., A. Leone, and C. Wasley. 2004. Performance matched discretionary accruals. Working paper no. FR 01-04. University of Rochester, Rochester, N.Y. Lev, B and D. Nissim. 2004. The Persistence of the Accruals Anomaly. New York University working paper. Lev, B., and R. Thiagarajan. 1993. Fundamental information analysis. Journal of Accounting Research 31 (Autumn): 190-215 McNichols, M. 2000. Research design issues in earnings management studies. Journal of Accounting and Public Policy 19 (Winter): 313-345. Mishkin, F. 1983. A Rational Expectations Approach to Macroeconomics, Chicago: University of Chicago Press. Ou, J. A. and S. H. Penman. 1989. Accounting Measurement, Price-Earnings Ratio, and the Information Content of Security Prices. Journal of Accounting Research :111-44. Piotroski, J. 2000. Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research 38 (1-40). Richardson, S., R. Sloan, M. Soliman, and I. Tuna. 2004. Accrual reliability, earnings persistence, and stock prices. University of Pennsylvania working paper.

33

Sloan, R.G. 1996. Do stock prices fully reflect information in accruals and cash flows about future earnings? The Accounting Review 71 (July): 289-315. Staley, K. F. 1997. The Art of Short Selling. New York: Wiley. Tarpley, R. 2000. Profitability of information contained in the accrual component of earnings. Working paper, Cornell University, Ithaca, N.Y. Teoh, S.H., T.J. Wong, and G.R. Rao. 1998. Are accruals during initial public offerings opportunistic? Review of Accounting Studies 3 (1-2): 209-221. Thomas, J.K. and P.H. Zhang. 2002. Inventory changes and future returns. Review of Accounting Studies 7 (Jun-Sep): 163-187.

Xie, H. 2001. The Mispricing of Abnormal Accruals. The Accounting Review 76 (July): 357-373.

34

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

Low 2 3 4 5 6 7 8 9 HighAccrual Decile Ranks

Figure 1: Distribution of Total Accrual Decile Rankings for Firms in the Highest Probability of Manipulation Decile (2655 firm-years, light) and Lowest probability of Manipulation (2498 firm

years, dark) in 1993-2003

Low probability of Manipulation High Probability of Manipulation

35

Figure 2Accrual and M-Score Decile Size-Adjusted Portfolio Returns

1993 - 2003

-15.00%

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

Highest 9 8 7 6 5 4 3 2 Lowest

Decile Rankings

Ave

rage

Buy

-and

-Hol

d Si

ze-A

djus

ted

Ret

urns

AccrualsM-Score

36

Figure 3 Accrual and M-Score Decile Risk-Adjusted Portfolio Returns

Based on Fama-French Factors 1993 - 2003

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%


Decile Rankings

Average Annualized Risk-Adjusted Returns

AccrualsM-Score

37

Figure 4 Accrual and M-Score Decile Risk-Adjusted Portfolio Returns Risk Adjustment

Based on Fama-French and Earnings Quality Factors 1993 - 2003

-35.00%

-30.00%

-25.00%

-20.00%

-15.00%

-10.00%

-5.00%

0.00%


Decile Rankings

Ave

rage

Ann

ualiz

ed R

isk-

Adj

uste

d R

etur

ns

AccrualsM-Score

38

TABLE 1

Mean (Median) Values of Selected Characteristics for Ten Portfolios of Firms Formed Annually by Assigning Firms To Deciles Based on Total Accruals and the M-Score a

Sample Consists of 25,285 Firm-years between 1993 and 2003 Panel A. Accrual Deciles Lowest Highest Accruals

2 3 4 5 6 7 8 9 Accruals

N 2515 2501 2543 2536 2506 2638 2534 2483 2555 2474 Earnings -0.137 -0.001 0.016 0.023 0.026 0.027 0.035 0.035 0.043 0.040*** (-0.045) (0.040) (0.043) (0.043) (0.044) (0.046) (0.051) (0.054) (0.060) (0.065) Accruals -0.189 -0.096 -0.070 -0.051 -0.036 -0.022 -0.003 0.021 0.059 0.169*** (-0.156) (-0.092) (-0.067) (-0.049) (-0.034) (-0.020) (-0.001) (0.022) (0.057) (0.142) CFO 0.057 0.108 0.097 0.083 0.074 0.060 0.050 0.030 -0.001 -0.099*** (0.114) (0.136) (0.114) (0.097) (0.085) (0.073) (0.062) (0.043) (0.012) (-0.067) M-Score Rank 3.006 3.615 4.149 4.622 5.112 5.685 6.246 6.878 7.655 8.686*** (1.000) (3.000) (3.000) (4.000) (5.000) (5.000) (6.000) (7.000) (8.000) (9.000) Price-to-Book 2.733 2.569 2.509 2.518 2.349 2.403 3.089 2.866 2.641 2.621 (1.486) (1.896) (1.863) (1.752) (1.805) (1.842) (1.916) (1.775) (1.696) (1.485) Price-to-Earnings -21.370 -2.690 2.773 1.294 4.615 3.417 6.170 6.474 7.641 4.849*** (-1.649) (11.924) (13.511) (13.092) (14.180) (13.787) (14.184) (13.953) (13.642) (12.077) Cash Flow-to-Price 0.042 0.135 0.174 0.198 0.142 0.134 0.037 -0.018 -0.087 -0.206*** (0.058) (0.100) (0.099) (0.095) (0.082) (0.064) (0.045) (0.025) (0.005) (-0.052) Ret(t-1) 0.229 0.240 0.245 0.206 0.188 0.201 0.275 0.253 0.309 0.300* (-0.026) (0.069) (0.072) (0.063) (0.066) (0.049) (0.064) (0.058) (0.075) (0.021) UE -0.210 -0.110 -0.092 -0.073 -0.073 -0.075 -0.067 -0.088 -0.084 -0.081*** (-0.003) (0.001) (0.001) (0.001) (0.001) (0.002) (0.002) (0.002) (0.002) (0.003) Ret Vol 0.189 0.150 0.137 0.130 0.130 0.133 0.138 0.145 0.156 0.171*** (0.166) (0.130) (0.118) (0.110) (0.112) (0.114) (0.120) (0.130) (0.137) (0.155) ln(MVE) 4.574 5.322 5.518 5.618 5.507 5.417 5.209 4.955 4.600 4.113***

(4.493) (5.332) (5.610) (5.728) (5.598) (5.515) (5.167) (4.977) (4.628) (4.131)

39

TABLE 1 (continued)

Panel B. M-Score Deciles Lowest Highest M-Score

2 3 4 5 6 7 8 9 M-Score

N 2498 2432 2489 2442 2515 2584 2528 2571 2572 2655 Earnings -0.120 0.028 0.034 0.045 0.038 0.039 0.039 0.036 0.018 -0.046*** (-0.024) (0.045) (0.046) (0.052) (0.052) (0.053) (0.052) (0.054) (0.050) (0.027) Accruals -0.135 -0.074 -0.053 -0.040 -0.026 -0.014 0.002 0.020 0.042 0.049*** (-0.113) (-0.070) (-0.049) (-0.036) (-0.022) (-0.012) (0.003) (0.020) (0.037) (0.023) CFO 0.064 0.114 0.097 0.092 0.072 0.061 0.044 0.022 -0.015 -0.076*** (0.116) (0.123) (0.100) (0.093) (0.080) (0.068) (0.052) (0.037) (0.008) (-0.035) Accrual Rank 2.170 3.086 4.093 4.822 5.617 6.159 6.818 7.256 7.498 7.116*** (1.000) (3.000) (4.000) (5.000) (6.000) (7.000) (7.000) (8.000) (8.000) (8.000) Price-to-Book 2.145 2.219 2.041 2.515 2.339 2.448 2.787 2.660 3.248 3.785*** (1.571) (1.784) (1.810) (1.841) (1.874) (1.780) (1.745) (1.762) (1.767) (1.597) Price-to-Earnings -16.811 -1.008 4.114 5.014 3.141 2.784 5.643 1.502 7.850 0.855*** (-1.092) (13.725) (13.722) (15.008) (14.216) (14.120) (13.803) (13.317) (12.381) (5.945) Cash Flow-to-Price 0.053 0.180 0.235 0.185 0.092 0.037 0.035 0.007 -0.074 -0.163*** (0.071) (0.111) (0.099) (0.080) (0.065) (0.049) (0.031) (0.015) (0.001) (-0.020) Ret(t-1) 0.144 0.217 0.180 0.181 0.198 0.216 0.272 0.305 0.334 0.383*** (-0.040) (0.057) (0.045) (0.080) (0.063) (0.077) (0.062) (0.063) (0.051) (0.043) UE -0.195 -0.109 -0.077 -0.063 -0.077 -0.071 -0.072 -0.102 -0.067 -0.120*** (-0.002) (0.002) (0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.000) Ret Vol 0.180 0.135 0.128 0.126 0.130 0.136 0.145 0.155 0.162 0.179 (0.157) (0.117) (0.112) (0.110) (0.113) (0.119) (0.127) (0.137) (0.146) (0.157) ln(MVE) 4.677 5.428 5.564 5.583 5.380 5.188 5.030 4.826 4.692 4.568* (4.589) (5.492) (5.614) (5.672) (5.504) (5.201) (5.004) (4.869) (4.690) (4.554)

40

Construction of Accrual and M-Score Deciles Because fiscal year ends and earnings announcement dates vary across sample firms, we assign firms into deciles based on the prior years’ decile cut-offs. Accruals: We compute accruals as using COMPUSTAT data (numbers in parentheses) as Total Accruals = - (∆AR[#302] + ∆INV[#303]+ ∆AP[#304] + ∆TAX [#305] + ∆OTH [#307]+ DEP [#125])/Total Assets-1[#6], and Current Accruals = Total Accruals + DEP[#125]/ Total Assets-1[#6]. M-Score: We rely Beneish (1999) model to estimate the probability that the firm has engaged in earnings manipulation and calculate the M-Score as: M= -4.84+.920*DSR+.528*GMI+.404*AQI+.892*SGI+.115*DEPI

-.172*SGAI)+4.679*ACCRUALS-.327*LEVI Where: DSR = (Receivablest[2]/Salest[12]/(Receivablest-1/Salest-1)


Sales [12]t-1 t-1

t-1

/


t t

t

AQI= 1−


t t

t

/ 1−


t-1 t-1

-1t



t-1

t-1 + t-1

/


t

t + t


Sales [12]t

t

/

SGA Expense Sales

t-1

t-1


Total Assets [6]t t

t

/


t-1 t-1

t-1

ACCRUALS= (IBX [18]-CFO[308])/ TAt[6]

41

Variable Definitions: Earnings = Earnings before extraordinary items (#123) divided by average total assets; CFO = Cash flows from operations (#308) divided by average total assets; Accruals =-(CHGAR [#302]+ CHGINV [#303]+ CHGAP [#304]+ CHGTAX [#305]

+ CHGOTH [#307]+ DEP [#125])/average total assets; LMVE = Natural logarithm of the market value (in millions) of common equity at the end of the month preceding the annual

earnings announcement; RET(t-1) =12 month size-adjusted return ending at the end of the month preceding the annual earnings announcement; P/E = The market value (in millions) of common equity at the end of the month preceding the annual earnings

announcement divided by income before extraordinary items (#18); P/B = The market value (in millions) of common equity at the end of the month preceding the annual earnings

announcement divided by book value of equity (#60); Ret Vol = Standard deviation of returns for the prior 24 months; UE = Unexpected earnings, computed as the change in income before extraordinary items divided by the market value (in

millions) of common equity at the end of the month preceding the annual earnings announcement; CFO/P = Cash flows from operations (#308) divided by MVE. *,**,*** denote that the difference in means across extreme deciles is significance at the 10%, 5%, and 1% levels, respectively.

42

TABLE 2 Results from Nonlinear Least Squares Estimation of the Stock Price

Reaction to the Differential Information in Earnings and Earnings Components by M-Score for Future Earnings

Sample Includes 20,416 Firm-Year Observations Panel A. The Differential stock price reaction to Earnings by M-Score

[ ] 1tt5t4t3t2t101t11t

1tt5t4t3t2t101t

SMSENegMEPosMENegEPosECSARSMSENegMEPosMENegEPosE

+++

++

υ+β′−β′−β′−β′−β′−α−φ=ε+β+β+β+β+β+α=

Forecasting Expectation Approx. Approx. Estimate t-statistic Estimate t-statistic ERC 1.461 28.91*** Constant -0.007 -5.07*** -0.026 -3.74*** EPos 0.747 50.28*** 0.750 10.22*** ENeg 0.793 102.71*** 0.844 22.11*** EPosM -0.056 -2.52** -0.200 -1.82* ENegM 0.097 10.01*** 0.035 0.73 SMS -0.021 -9.57*** 0.048 4.33*** Test of selected restrictions: Variable Restriction LR statistica EPos 11 β′=β 0.00 ENeg 22 β′=β 1.62 EPosM 33 β′=β 1.63 ENegM 44 β′=β 1.61 SMS 55 β′=β 38.97*** EPos + EPosM 3131 β′+β′=β+β 1.25 ENeg + ENegM 4242 β′+β′=β+β 0.02 All i,ii ∀β′=β 85.53***

43

TABLE 2 (continued)

Panel B. The stock price reaction to earnings components

[ ] 1t4t3t2t101t11t

1tt4t3t2t101t

DepCAccNegCAccPosCFOECSARDepCAccNegCAccPosCFOE

+++

++

υ+β′−β′−β′−β′−α−φ=ε+β+β+β+β+α=

Forecasting Expectation Approx. Approx. Estimate t-statistic Estimate t-statistic ERC 1.490 29.23*** Constant 0.000 -0.02 -0.009 -1.00 CFO 0.834 129.40*** 0.806 25.59*** CAccPos 0.539 34.78*** 0.906 11.81*** CAccNeg 0.433 19.83*** 0.649 6.06*** Dep -0.901 -36.41*** -1.332 -10.93*** Test of selected restrictions: Variable Restriction LR statistica CFO 11 β′=β 0.73 CAccPos 22 β′=β 22.59*** CAccNeg 33 β′=β 3.91* Dep 44 β′=β 12.21*** All i,ii ∀β′=β 59.13***

44

TABLE 2 (continued)

Panel C. The stock price reaction to accruals conditioned on M-Score, but omitting the direct effect of M-Score

[] 1t6t5

t4t3t2t101t11t

1tt6t5

t4t3t2t101t

DepCAccNegM

CAccPosMCAccNegCAccPosCFOECSARDepCAccNegM


+

++

+

+

υ+β′−β′−

β′−β′−β′−β′−α−φ=ε+β+β+

β+β+β+β+α=

Forecasting Expectation Approx. Approx. Estimate t-statistic Estimate t-statistic ERC 1.487 29.11*** Constant 0.001 0.26 -0.011 -1.13 CFO 0.830 128.69*** 0.808 25.54*** CAccPos 0.625 23.21*** 0.944 7.11*** CAccNeg 0.541 20.02*** 0.552 4.16*** CAccPosM -0.124 -4.23*** -0.042 -0.29 CAccNegM 0.225 7.95*** -0.168 -1.20 Dep -0.931 -37.03*** -1.331 -10.72*** Test of selected restrictions: Variable Restriction LR statistica CFO 11 β′=β 0.43 CAccPos 22 β′=β 5.58*** CAccNeg 33 β′=β 0.01 CAccPosM 44 β′=β 0.31 CAccNegM 55 β′=β 7.71*** Dep 66 β′=β 10.11*** CAccPos + CAccPosM 4242 β′+β′=β+β 21.89*** CAccNeg + CAccNegM 5353 β′+β′=β+β 2.42 All i,ii ∀β′=β 67.18***

45

(TABLE 2, continued) Panel D. The Stock Price Reaction to Positive and Negative Accruals by M-Score

[] 1tt76t5

t4t3t2t101t11t

1tt7t6t5

t4t3t2t101t

SMSDepCAccNegM

CAccPosMCAccNegCAccPosCFOECSARSMSDepCAccNegM


+

++

+

+

υ+β′−β′−β′−

β′−β′−β′−β′−α−φ=ε+β+β+β+

β+β+β+β+α=

Forecasting Expectation Approx. Approx. Estimate t-statistic Estimate t-statistic ERC 1.480 28.95*** Constant 0.001 0.58 -0.013 -1.37 CFO 0.825 127.12*** 0.824 25.73*** CAccPos 0.626 23.25*** 0.943 7.07*** CAccNeg 0.534 19.72*** 0.580 4.34*** CAccPosM -0.068 -2.18** -0.252 -1.64 CAccNegM 0.166 5.44*** 0.053 0.35 Dep -0.955 -37.40*** -1.242 -9.82*** SMS -0.010 -5.26*** 0.039 3.94*** Test of selected restrictions: Variable Restriction LR statistica CFO 11 β′=β 0.01 CAccPos 22 β′=β 5.47** CAccNeg 33 β′=β 0.12 CAccPosM 44 β′=β 1.38 CAccNegM 55 β′=β 0.54 Dep 66 β′=β 4.97** SMS 77 β′=β 24.64*** CAccPos + CAccPosM 4242 β′+β′=β+β 1.70 CAccNeg + CaccNegM 5353 β′+β′=β+β 0.70 All i,ii ∀β′=β 91.90***

46

Variable Definitions: CSAR = The 12 month -month buy and hold size-adjusted return from the beginning of

the month following the annual earnings announcement; CFO = Cash flows from operations (#308); CAcc = - (∆AR[#302] + ∆INV[#303]+ ∆AP[#304] + ∆TAX [#305] + ∆OTH [#307]); E = CFO + CAcc + Dep (#125); CAccPos = CAcc if CAcc > 0; 0 otherwise; CAccNeg = CAcc if CAcc < 0; 0 otherwise; SMS = Scaled M-Score. M-Scores are computed using the model in Beneish (1999).

M-Scores are ranked annually using the prior year decile rank cutoffs. Ranked M-Scores are scaled to have a zero mean and range from -1 (lowest M-Score) to +1 (highest M-Score);

CAccPosM = CAccPos x SMS; CAccNegM = CAccNeg x SMS; All variables are scaled by average total assets. a Mishkin (1983) shows that the test statistic ( )uc SSR/SSRlogN2 ×× is asymptotically ( )q2χ distributed where: q = the number of rational pricing constraints imposed; N = the number of observations in the sample;

cSSR = the sum of squared residuals from the constrained regressions in the second stage; uSSR = the sum of squared residuals from the unconstrained regressions in the first stage;

*,**,*** denote significance at the 10%, 5%, and 1% levels, respectively.

47

TABLE 3 Average One-Year-Ahead Size-Adjusted Returns for Portfolios of Firms Ranked by Decile

Based on Accruals and the M-Score 25,285 Firm-years between 1993 and 2003.

Total Current M-Score Decile N Accruals N Accruals N Ranks Low 2515 10.23% 2573 10.03% 2498 10.93%

2 2501 6.23% 2640 4.71% 2432 5.83% 3 2543 3.14% 2502 1.08% 2489 3.45% 4 2536 1.42% 2622 0.18% 2442 5.53% 5 2506 0.03% 2450 0.06% 2515 1.33% 6 2638 1.64% 2505 1.63% 2584 1.08% 7 2534 -1.18% 2574 2.63% 2528 -0.37% 8 2483 1.17% 2431 0.26% 2571 -1.74% 9 2555 -1.06% 2502 -2.35% 2572 -3.41%

High 2474 -9.42% 2486 -6.42% 2655 -9.18% Hedge 19.65% 16.45% 20.11%

Holding-Period: The one-year holding period begins on the first day of the month following a firm’s annual earnings announcement. Because fiscal year ends and earnings announcement dates vary across sample firms, we assign firms into deciles based on the prior years’ decile cut-offs. Accruals: We compute accruals as using COMPUSTAT data (numbers in parentheses) as Total Accruals = - (∆AR[#302] + ∆INV[#303]+ ∆AP[#304] + ∆TAX [#305] + ∆OTH [#307]+ DEP [#125])/Total Assets-1[#6], and Current Accruals = Total Accruals + DEP[#125]/ Total Assets-1[#6]. M-Score: We rely Beneish (1999) model to estimate the probability that the firm has engaged in earnings manipulation and calculate the M-Score as: M= -4.84+.920*DSR+.528*GMI+.404*AQI+.892*SGI+.115*DEPI

-.172*SGAI)+4.679*ACCRUALS-.327*LEVI Where: DSR = (Receivablest[2]/Salest[12]/(Receivablest-1/Salest-1)


Sales [12]t-1 t-1

t-1

/


t t

t

AQI= 1−


t t

t

/ 1−


t-1 t-1

-1t



t-1

t-1 + t-1

/


t

t + t


Sales [12]t

t

/

SGA Expense Sales

t-1

t-1


Total Assets [6]t t

t

/


t-1 t-1

t-1

ACCRUALS= (IBX [18]-CFO[308])/ TAt[6]

48

TABLE 4 Regression of Annual Buy-and-Hold Returns on Scaled Decile Ranks of M-Score,

Scaled Decile Ranks of Accruals, and Control Variables Sample includes 25,285 Firm-Year Observations

Panel A. Results from Pooled Regressions

Scaled Decile

Ranksa Controls Return

Constant M-score Accruals B/P E/P Momentum ln(MVE) UE Ret Vol CFO/P Adj. R-sq. Estimate 0.093 0.159 0.31%t-statistic (8.69) (8.91) Estimate 0.079 0.133 0.21%t-statistic (7.41) (7.40) Estimate 0.104 0.124 0.059 0.33%t-statistic (9.04) (5.61) (2.64) Estimate 0.086 0.146 0.221 -0.050 -0.082 0.083 -0.026 0.017 0.020 7.07%t-statistic (8.28) (8.36) (35.14) (-7.89) (-14.15) (12.47) (-4.07) (2.92) (3.43) Estimate 0.062 0.098 0.210 -0.083 0.073 -0.040 0.014 0.017 6.68%t-statistic (5.93) (5.60) (33.93) (-14.26) (11.17) (-6.43) (2.39) (2.94) Estimate 0.093 0.146 0.012 0.212 -0.082 0.071 -0.039 0.017 0.016 6.85%t-statistic (8.17) (6.78) (0.57) (34.24) (-14.01) (10.91) (-6.29) (2.86) (2.71)

49

(TABLE 4, continued) Panel B. Time-series Means of Annual Cross-sectional Regressions

Scaled Decile

Ranksa Controls Return Average

Constant M-score Accruals B/P E/P Momentum ln(MVE) UE Ret Vol CFO/P Adj. R-sq. Estimate 0.126 0.192 0.88%t-statistic (3.36) (3.83) Z-statistic (7.23) (5.05) Estimate 0.098 0.136 0.31%t-statistic (3.02) (4.42) Z-statistic (3.54) (4.04) Estimate 0.132 0.159 0.049 1.11%t-statistic (4.23) (1.82) (0.70) Z-statistic (9.78) (2.48) (0.76) Estimate 0.135 0.223 0.213 -0.004 -0.104 0.073 -0.103 0.045 -0.144 9.76%t-statistic (3.43) (4.42) (6.25) (-0.15) (-2.71) (2.27) (-3.17) (1.10) (-2.52) Z-statistic (6.79) (7.11) (6.17) (0.25) (-3.94) (4.30) (-3.56) (0.06) (-1.25) Estimate 0.091 0.133 0.208 -0.103 0.074 -0.093 0.043 -0.139 8.80%t-statistic (3.92) (4.60) (6.39) (-2.72) (2.40) (-2.69) (1.06) (-2.42)Z-statistic (5.66) (3.55) (6.19) (-3.83) (4.72) (-3.17) (-0.04) (-1.17) Estimate 0.134 0.211 0.014 0.210 -0.102 0.072 -0.088 0.046 -0.150 9.51%t-statistic (4.36) (2.80) (0.29) (6.41) (-2.69) (2.27) (-2.51) (1.15) (-2.55)Z-statistic (9.07) (4.23) (0.54) (6.27) (-3.83) (4.50) (-2.92) (0.11) (-1.34)

50

a See Table 1 for construction of M-Score and accrual deciles. The M-Score and accrual decile ranks are scaled to range from 0 to 1. Variable Definitions: LMVE = Natural logarithm of the market value (in millions) of common equity at the end of the month preceding the annual

earnings announcement. RET(t-1) =12 month size-adjusted return ending at the end of the month preceding the annual earnings announcement. P/E = The market value (in millions) of common equity at the end of the month preceding the annual earnings

announcement divided by income before extraordinary items (#18). P/B = The market value (in millions) of common equity at the end of the month preceding the annual earnings

announcement divided by book value of equity (#60). Ret Vol = Standard deviation of returns for the prior 24 months. UE = Unexpected earnings, computed as the change in income before extraordinary items divided by the market value (in

millions) of common equity at the end of the month preceding the annual earnings announcement. CFO/P = Cash flows from operations (#308) divided by MVE. Control variables are recast as deviations from the mean, scaled by the in-sample standard deviation of the variable. The Z-statistic is a test that the time-series mean of the t-values from the cross-sectional estimations is significantly different from zero.

51

TABLE 5 Regression of Excess Returns on Market, Small-Minus-Big, High-Minus-Low, and

Earnings Quality Asset Pricing Factors by Accrual Decilea Sample Includes 126 Monthly Observations per Accrual Decile

( ) ( ) t,itititi

ftt,Mii

ftt,i EQFeHMLhSMBsRRRR ε++++−β+α=−

Panel A. Regression of excess returns on MKT, SMB, HML

Portfolio α β s h Adj. R-sq. Low Accruals Estimate 0.010 1.132 0.822 -0.092 75.70%

t-statistic (2.78) (11.98) (8.34) (-0.73) 2 Estimate 0.005 1.034 0.660 0.111 77.84% t-statistic (1.92) (14.47) (8.85) (1.16) 3 Estimate 0.002 1.012 0.592 0.264 83.45% t-statistic (1.02) (19.08) (10.70) (3.72) 4 Estimate 0.000 1.020 0.555 0.345 85.95% t-statistic (0.19) (22.18) (11.57) (5.60) 5 Estimate 0.002 0.985 0.609 0.302 86.34% t-statistic (1.01) (21.44) (12.72) (4.92) 6 Estimate 0.001 0.991 0.606 0.187 85.69% t-statistic (0.65) (19.85) (11.64) (2.81) 7 Estimate 0.000 0.998 0.738 0.162 83.36% t-statistic (0.11) (16.85) (11.95) (2.05) 8 Estimate 0.001 1.070 0.835 0.109 84.82% t-statistic (0.27) (16.98) (12.69) (1.29) 9 Estimate 0.000 1.121 0.898 0.160 84.13% t-statistic (0.06) (16.67) (12.80) (1.78)

High Accruals Estimate -0.004 1.148 0.866 -0.006 72.49% t-statistic (-1.01) (11.27) (8.15) (-0.04)

Difference (High - Low) 0.014 -0.016 -0.043 -0.087 t-statistic (2.63) (-0.11) (-0.30) (-0.47)

52

a See Table 1 for construction of accrual deciles. Variable Definitions:

t,iR = Raw return for decile i in month t; firms are sorted each month into deciles based on the accruals for the firms’ most recent year;

ftR = The return on the 30-day T-bill for month t;

t,MR = The return on the CRSP value-weighted index for month t;

tSMB = The return to the small-minus-big factor mimicking portfolio for size for month t;

tHML = The return to the high-minus-low factor mimicking portfolio for book-to-market equity for month t;

(TABLE 5, continued) Panel B. Regression of excess returns on MKT, SMB, HML, and EQF

Portfolio α β s h e Adj. R-sq. Low Accruals Estimate -0.007 0.996 0.388 0.432 0.997 87.35%

t-statistic (-2.27) (14.36) (4.72) (4.17) (10.61) 2 Estimate -0.006 0.946 0.376 0.453 0.651 85.70% t-statistic (-2.26) (16.19) (5.44) (5.19) (8.22) 3 Estimate -0.005 0.954 0.406 0.489 0.428 88.03% t-statistic (-2.57) (20.79) (7.47) (7.13) (6.88) 4 Estimate -0.004 0.985 0.444 0.479 0.256 87.74% t-statistic (-2.09) (22.53) (8.57) (7.33) (4.32) 5 Estimate -0.004 0.939 0.465 0.476 0.332 89.35% t-statistic (-2.13) (22.77) (9.52) (7.73) (5.94) 6 Estimate -0.006 0.937 0.432 0.398 0.400 89.60% t-statistic (-2.91) (21.63) (8.43) (6.14) (6.82) 7 Estimate -0.010 0.916 0.478 0.476 0.597 90.64% t-statistic (-4.98) (20.29) (8.94) (7.05) (9.75) 8 Estimate -0.010 0.990 0.576 0.421 0.594 90.60% t-statistic (-4.26) (19.61) (9.63) (5.58) (8.69) 9 Estimate -0.012 1.026 0.594 0.526 0.696 91.45% t-statistic (-5.28) (20.44) (10.00) (7.01) (10.23)

High Accruals Estimate -0.020 1.019 0.454 0.490 0.944 82.62% t-statistic (-5.51) (12.38) (4.66) (3.98) (8.46)

Difference (High - Low) 0.013 -0.023 -0.067 -0.058 0.054 t-statistic (2.75) (-0.21) (-0.52) (-0.36) (0.37)

53

tEQF = The return to the earnings quality factor mimicking portfolio. The following model is estimated for every industry-year for which at least twenty observations exist:

tt5t41t3t21t10t PPEvReCFOCFOCFOAcc ε+α+∆α+α+α+α+α= +− , where where,

Acc =-(CHGAR [#302]+ CHGINV [#303]+ CHGAP [#304]+ CHGTAX [#305] + CHGOTH [#307]+ DEP [#125]);

CFO = Cash from operations (#308); Rev = Sales (#12); PPE = Property, plant, and equipment, gross (#7). All variables are scaled by average total assets.

Each firm-year, the standard deviation of residual accruals from the above model is estimated over the prior five years. Firms are ranked each month into deciles based on each firm’s most recent estimate of the standard deviation of the residual accruals. EQF is formed as the return to the highest residual accrual decile less the return to the lowest residual accrual decile.

54

TABLE 6 Regression of Excess Returns on Market, Small-Minus-Big, High-Minus-Low, and

Earnings Quality Asset Pricing Factors by M-Score Decilea Sample Includes 126 Monthly Observations per M-Score Decile

( ) ( ) t,itititi

ftt,Mii

ftt,i EQFeHMLhSMBsRRRR ε++++−β+α=−

Panel A. Regression of excess returns on MKT, SMB, HML

Portfolio α β s h Adj. R-sq. Low M-Score Estimate 0.011 1.044 0.756 0.095 75.05%

t-statistic (3.41) (12.69) (8.83) (0.86) 2 Estimate 0.007 0.931 0.613 0.342 74.20% t-statistic (2.65) (14.59) (9.21) (4.00) 3 Estimate 0.004 0.921 0.546 0.353 81.89% t-statistic (2.39) (19.01) (10.80) (5.45) 4 Estimate 0.004 0.963 0.561 0.405 83.61% t-statistic (1.99) (20.60) (11.50) (6.48) 5 Estimate 0.002 1.005 0.593 0.361 82.87% t-statistic (1.22) (19.48) (11.03) (5.24) 6 Estimate 0.001 1.021 0.670 0.269 86.51% t-statistic (0.49) (20.80) (13.09) (4.10) 7 Estimate 0.001 1.037 0.694 0.146 82.09% t-statistic (0.40) (16.53) (10.61) (1.74) 8 Estimate 0.000 1.070 0.814 0.048 82.29% t-statistic (-0.11) (15.23) (11.10) (0.51) 9 Estimate -0.004 1.210 0.957 0.031 81.79% t-statistic (-1.34) (14.64) (11.09) (0.28)

High M-Score Estimate -0.007 1.285 0.955 -0.400 82.46% t-statistic (-1.94) (13.14) (9.36) (-3.06)

Difference (High - Low) 0.018 -0.241 -0.199 0.494 t-statistic (3.68) (-1.89) (-1.49) (2.90)

55

a See Table 1 for construction of M-Score deciles. Variable Definitions:

t,iR = Raw return for decile i in month t; firms are sorted each month into deciles based on the accruals for the firms’ most recent year;

ftR = The return on the 30-day T-bill for month t;

t,MR = The return on the CRSP value-weighted index for month t;

tSMB = The return to the small-minus-big factor mimicking portfolio for size for month t;

tHML = The return to the high-minus-low factor mimicking portfolio for book-to-market equity for month t;

(TABLE 6, continued) Panel B. Regression of excess returns on MKT, SMB, HML, and EQF

Portfolio α β s h e Adj. R-sq. Low Accruals Estimate -0.004 0.927 0.382 0.545 0.857 86.87%

t-statistic (-1.44) (15.27) (5.33) (6.02) (10.44) 2 Estimate -0.001 0.872 0.423 0.570 0.435 79.25% t-statistic (-0.36) (14.97) (6.14) (6.55) (5.51) 3 Estimate -0.002 0.874 0.394 0.536 0.349 85.85% t-statistic (-0.79) (20.05) (7.64) (8.23) (5.90) 4 Estimate -0.002 0.919 0.421 0.574 0.321 86.85% t-statistic (-1.01) (21.58) (8.35) (9.01) (5.55) 5 Estimate -0.004 0.952 0.424 0.565 0.389 87.00% t-statistic (-2.09) (20.82) (7.83) (8.27) (6.27) 6 Estimate -0.007 0.959 0.472 0.509 0.456 91.50% t-statistic (-3.92) (24.19) (10.05) (8.59) (8.49) 7 Estimate -0.008 0.965 0.462 0.427 0.534 87.63% t-statistic (-3.48) (18.18) (7.35) (5.39) (7.43) 8 Estimate -0.013 0.971 0.496 0.431 0.729 90.52% t-statistic (-5.51) (18.56) (8.01) (5.52) (10.29) 9 Estimate -0.019 1.097 0.594 0.468 0.832 89.72% t-statistic (-6.60) (17.36) (7.94) (4.96) (9.71)

High Accruals Estimate -0.025 1.147 0.512 0.134 1.015 90.61% t-statistic (-7.64) (15.76) (5.94) (1.23) (10.29)

Difference (High - Low) 0.021 -0.220 -0.130 0.411 -0.158 t-statistic (4.94) (-2.32) (-1.16) (2.90) (-1.23)

56

tEQF = The return to the earnings quality factor mimicking portfolio. The following model is estimated for every industry-year for which at least twenty observations exist:

tt5t41t3t21t10t PPEvReCFOCFOCFOAcc ε+α+∆α+α+α+α+α= +− , where where,

Acc =-(CHGAR [#302]+ CHGINV [#303]+ CHGAP [#304]+ CHGTAX [#305] + CHGOTH [#307]+ DEP [#125]);

CFO = Cash from operations (#308); Rev = Sales (#12); PPE = Property, plant, and equipment, gross (#7). All variables are scaled by average total assets.

Each firm-year, the standard deviation of residual accruals from the above model is estimated over the prior five years. Firms are ranked each month into deciles based on each firm’s most recent estimate of the standard deviation of the residual accruals. EQF is formed as the return to the highest residual accrual decile less the return to the lowest residual accrual decile.

57

Table 7 Sensitivity Analysis

Transaction Costs and Institutional Holdings on the Returns to the M-Score Strategy Panel A: Returns by Size

MVE<$500 million MVE>$500 million

N Mean CSAR N Mean CSAR Low Probabilty of Manipulation 1881 11.66% 617 8.71% High Probability of Manipulation 2059 -9.04% 596 -9.67% Panel B: Institutional Ownership Firms Not Held By Institutions Firms Held By Institutions N Mean CSAR N Mean CSAR Low Probability of Manipulation 654 10.17% 1844 11.20% High Probability of Manipulation 792 -5.70% 1863 -10.66% Panel C: Percent Institutional Ownership Relative to Quarter of Earnings Annoucement (Quarter 0) Quarter-3 Quarter-2 Quarter-1 Quarter 0 Quarter+1 Quarter+2 Quarter+3 Low Probability of Manipulation Mean 35.39% 35.43% 36.19% 36.44% 37.50% 38.87% 39.56% (N=1844) Median 32.32% 32.66% 33.30% 32.27% 33.72% 35.79% 37.38% High Probability of Manipulation Mean 31.35% 31.43% 31.67% 32.21% 33.50% 33.36% 33.86% (N=1863) Median 25.88% 25.67% 26.19% 26.95% 27.84% 28.23% 28.99%

58

a. Market value of equity is measured at the end of the month prior to the earnings announcement. b. We determine that firms are not held by institutions in the quarter containing the earnings announcement by reference to Form 13-F filings. c. The percentage held by institutions is the ratio of shares held by all institutions reporting their holdings on Form 13-F to shares outstanding at the end of

that quarter.

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