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A Reexamination of Real Earnings Management from a Firm-Specific Time-Series
Perspective
E. SCOTT JOHNSON
Virginia Tech University
T. TAYLOR JOO
New Mexico State University
MICHAEL D. STUART
Vanderbilt University
May 2015
Early draft. Please do not cite.
Keywords: Real earnings management, Earnings management, Earnings manipulation, Discretionary expenses,
Firm-specific forecasts, Managerial ability.
Data Availability: Data are publicly available from sources identified in the text.
We thank Brooke Beyer, Bowe Hansen, Jing Huang, James Myers, Mitch Oler, Velina Popova, Sarah Stein, Michael
Wolfe, and participants at the 2014 Arkansas Research Conference for helpful comments and suggestions. We also
thank Peter Demerjian for generously providing access to MA-Score data.
A Reexamination of Real Earnings Management from a Firm-Specific Time-Series
Perspective
ABSTRACT: Managers use real earnings management (REM) to influence reported earnings
through the manipulation of real business activities. Extant literature generally employs cross-
sectional analysis to identify REM, but newer studies suggest that these traditional REM
measures are severely mis-specified. In this study, we employ a new methodology that utilizes
firm-specific time-series characteristics of discretionary expenses to identify REM. Using this
new REM measure, we find that firms engage in REM to meet or just beat zero earnings and to
meet or just beat consensus analyst forecasts, a finding that is not strongly supported by
traditional REM measures. Prior literature also suggests that some managers and researchers
believe that REM is myopic and imposes real costs on firms, but there is mixed empirical
evidence regarding the impact of REM on future operating performance. We find that firms that
opportunistically engage in REM perform, on average, neither better nor worse in the future, a
finding consistent with survey responses suggesting that managers are careful not to sacrifice
future operating performance in order to meet or just beat earnings benchmarks. Additional
analysis shows that firms with high ability managers that engage in REM have better future
operating performance.
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1. Introduction
Prior literature suggests that managers engage in earnings management in order to report
earnings that meet or just beat certain benchmarks. Earnings management is generally classified
as either accruals management (AM) or real earnings management (REM).1 AM refers to the use
of discretionary accruals, either within or outside of generally accepted accounting principles
(GAAP), to opportunistically influence earnings. Common examples of AM include
underestimating bad debt and accelerating the recognition of sales. REM refers to managerial
decisions that influence earnings through real business activities. Roychowdhury (2006, 337)
defines REM as “…departures from normal operational practices, motivated by managers’ desire
to mislead at least some stakeholders into believing certain financial reporting goals have been
met in the normal course of operations.” Examples of REM include opportunistically cutting
discretionary expenses such as advertising, research and development (R&D), and selling,
general and administrative (SG&A). Extant literature generally employs cross-sectional analysis
to identify firms engaging in REM (e.g., Roychowdhury 2006; Cohen, Dey and Lys 2008; Cohen
and Zarowin 2010; McInnis and Collins 2011; Zang 2012; Zhao, Chen, Zhang and Davis 2012),
but newer studies suggest that traditional REM measures are severely mis-specified (Cohen,
Pandit, Wasley and Zach 2014; Siriviriyakul 2014). Additionally, empirical evidence on the
impact of REM on future operating performance is mixed. In this study, we employ a new
methodology to identify REM that utilizes firm-specific time-series characteristics of
discretionary expenses. We then examine the effect of REM on future operating performance.
Emerging research questions the validity of the REM proxies used in the majority of
extant studies. Cohen et al. (2014) find that traditional REM measures used in the literature are
1 Throughout this study, and consistent with prior literature, we use the terms “real earnings management,” “real
activities management,” and “real activities manipulation” interchangeably.
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“severely mis-specified.” Similarly, analysis of current REM models by Siriviriyakul (2014)
suggests the presence of omitted variables. This mis-specification raises the possibility that the
findings of prior research are not robust to alternate specifications. Prior research utilizes cross-
sectional analysis to estimate abnormal discretionary expenses and identify firms engaging in
real earnings management. In practice, however, financial statement analysis relies on firms’
individual characteristics (Stickney, Brown and Wahlen 2003; White, Sondhi and Fried 2003;
Lundholm and Sloan 2009; Penman 2013). Additionally, Francis and Smith (2005) argue that
earnings analysis should be performed using firm-specific time-series estimations because the
persistence of a firm’s earnings components are unlikely to depend on the persistence of other
firms’ similar components.
Anecdotal evidence suggests that managers engage in REM to meet certain benchmarks.
Based on survey results, Graham, Harvey and Rajgopal (2005) find that 80% of chief financial
officers (CFOs) would decrease discretionary spending in order to meet an earnings benchmark
and 55.3% would delay starting a new project to meet an earnings benchmark, even if the delay
entailed a small sacrifice in value. However, respondents indicate that they would take real
actions to boost earnings only if the real sacrifices are not too large. This suggests that managers
understand the potential costs associated with REM, but it also suggests that they attempt to
mitigate those costs even while engaging in REM to meet earnings benchmarks.
Theoretical research suggests that REM imposes value-destroying costs on firms. Ewert
and Wagenhofer (2005, 1102) state that real earnings management imposes real costs on the
firm. Additionally, Jensen (2005, 8) states that “…when numbers are manipulated to tell the
markets what they want to hear (or what managers want them to hear) rather than the true state of
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the firm – it is lying, and when real operating decisions that would maximize value are
compromised to meet market expectations, real long-term value is being destroyed.”
However, prior empirical research provides mixed evidence on the impact of REM on
future performance. On the one hand, some studies find that REM imposes value-destroying
costs. Cohen and Zarowin (2010) find that seasoned equity offering (SEO) firms often engage in
both forms of earnings management (i.e., REM and AM), and of the firms that suffer post-SEO
declines in performance, the declines are more severe for firms engaging in greater levels of
REM than for firms engaging in greater levels of AM. Additionally, Francis, Hasan and Li
(2011b) find that firms engaging in REM are more likely to suffer subsequent stock price crashes
than firms not engaging in REM, and Zhao et al. (2012) find that abnormally low discretionary
expenses and production costs lead to lower future performance in the absence of just meeting
earnings benchmarks. On the other hand, some studies do not find a negative impact from REM
on future performance. For instance, Taylor and Xu (2010) find that REM does not lead to a
decline in subsequent operating performance, and Gunny (2010) provides evidence that firms
that engage in REM to meet certain earnings benchmarks have better future operating
performance than firms that either miss the benchmarks or that meet the benchmarks without
engaging in REM.
This study is dually motivated by (1) the need to develop an alternate method for
identifying REM, given the growing evidence that traditional REM models are mis-specified,
and (2) the mixed empirical evidence regarding the impact of REM on future operating
performance.
To conduct our analyses, we construct a preliminary sample of 31,782 firm-year
observations (representing 4,383 unique firms) between 1995 and 2013. We first employ a firm-
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specific time-series methodology to estimate expected discretionary expenses (i.e., the sum of
advertising, R&D, and SG&A costs). We then calculate the abnormal level of discretionary
expenses as the difference between actual and expected discretionary expenses. Because prior
research suggests that traditional REM measures are severely mis-specified, we examine the
properties of our firm-specific time-series measure of abnormal discretionary expenses (Firm-
Specific REM) and compare these properties to the traditional REM measure (Traditional REM).
Siriviriyakul (2014) states that the Traditional REM measure is highly persistent, which we
confirm through an examination of the sign (positive or negative) of the abnormal discretionary
expenses over time. The Firm-Specific REM measure proves to be much less persistent and
exhibits the variation we would expect to see if managers are making myopic decisions about
discretionary expenses in order to meet certain earnings benchmarks.
Next, we examine whether Firm-Specific REM is associated with the likelihood of
meeting certain benchmarks. Because we compare our measure to the Traditional REM measure
first developed by Roychowdhury (2006), we use the same benchmarks utilized in his study:
zero earnings and the consensus analyst forecast.2 We first use the Firm-Specific REM measure
and find that it is negatively associated with the likelihood of meeting or just beating both zero
earnings and the consensus analyst forecast, at the 5% and 1% level, respectively.3 This result
suggests that the Firm-Specific REM measure is identifying manipulations in real activities to
meet these important benchmarks. Next, we use the Traditional REM measure and find that it is
negatively associated with the likelihood of meeting or just beating zero earnings, but only at the
2 It is important to note that although Roychowdhury (2006) does test for REM to meet or just beat the consensus
analyst forecast, he does not use the same measure (Traditional REM) that he utilizes to test for REM to avoid
losses. Instead, he uses an alternate specification that is based on a performance-matching technique advocated by
Kothari, Leone, and Wasley (2005). 3 Note that lower levels of abnormal discretionary expenses result in higher net income.
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10% level. We do not, however, find an association between Traditional REM and meeting or
just beating the consensus analyst forecasts. Overall, these results suggest that the Firm-Specific
REM measure is more accurately identifying REM than the Traditional REM measure.
In supplemental analyses, we classify firms with negative abnormal discretionary
expenses in year t but positive abnormal discretionary expenses in year t-1 and t+1 as “blip”
suspect firms and examine whether these firms engage in REM to meet or just beat zero earnings
or prior year’s earnings.4 A common criticism of REM research is that negative abnormal
discretionary expenses are simply permanent cost cutting decisions made by management to
improve future profitability or are part of the normal operations of the firm. In other words, it
may not be real earnings management at all, but rather “good’ management, a change in
management philosophy, or inherent firm differences. By identifying firms that have positive
abnormal discretionary expenses in year t-1, followed by negative abnormal discretionary
expenses in year t, and then returning to positive abnormal discretionary expenses in year t+1,
we attempt to decrease the likelihood that our suspect firms are simply cutting costs in an effort
to improve future profitability. We utilize this “blip” in discretionary expenses to identify firms
that strategically manipulate real activities in year t to meet certain benchmarks. As predicted, we
find a positive and statistically significant association, at the 1% level, between “blip” firms,
determined by utilizing the Firm-Specific REM measure, and the likelihood of meeting or just
beating zero earnings or last year’s earnings. However, we do not find a statistically significant
association between “blip” firms, determined using the Traditional REM measure, and the same
benchmarks. This test provides additional evidence of the validity of the Firm-Specific REM
4 Change in earnings is an important benchmark in earnings management literature that is addressed, but not tested,
in Roychowdhury (2006). We add this benchmark to our tests as a robustness check for the validity of our measure
of abnormal discretionary expenses, as we believe that managers may engage in REM to meet or just beat last year’s
earnings.
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measure and introduces a new way of identifying REM firms through outwardly-visible
indicators.
We also replicate Gunny (2010) to examine the impact of REM on future performance. In
contrast to the findings in Gunny (2010), we do not find evidence that firms that engage in REM
to report small positive earnings perform better in the future using the new Firm-Specific REM
measure. However, we also do not find evidence that firms that engage in REM to report small
positive earnings perform worse in the future. These results suggest that, on average, engaging in
REM to meet earnings benchmarks has no discernible impact on future operating performance.
This may be due to the fact that, while managers engage in REM to meet earnings benchmarks,
they are careful not to sacrifice future operating performance. This finding is consistent with the
Graham et al. (2005) survey findings that managers weigh the costs and benefits of engaging in
REM. In additional analyses, we find some evidence that firms with high ability managers that
engage in REM perform better in the future, suggesting that either high ability managers are
better at mitigating the negative impact of REM on future performance or that behavior that
appears to be REM, when engaged in by firms with high ability managers, is actually just “good”
management.
Although there are other studies examining the consequences of REM, there is no
consensus on its effect on future performance, and given recent evidence suggesting that
traditional REM models are severely mis-specified, we believe a re-examination of the topic is
warranted. This study contributes to the REM literature by (1) developing a new time-series
firm-specific empirical method to identify income-increasing REM, and (2) reexamining the
impact of REM on future performance using this new methodology.
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Our results should be of interest to investors, because they reveal that, contrary to prior
research (i.e., Gunny 2010), engaging in REM to meet earnings benchmarks does not appear to
lead to better future operating performance. Additionally, we find that REM firms do perform
better in the future when led by high ability managers. Finally, our results should be of interest to
accounting researchers as they consider the measurement of real earnings management and
evaluate the effect of this behavior on future performance.
Our paper proceeds as follows. Section 2 reviews prior evidence from the REM literature
and develops our hypotheses. Section 3 describes our sample and research design. Section 4
presents our empirical results. Section 5 concludes.
2. Background and Hypothesis Development
Anecdotal evidence suggests that managers engage in real activities manipulation to
influence earnings. Graham et al. (2005) provide evidence, from surveys, that managers are
willing to manipulate real activities, even if the manipulations have the potential to reduce firm
value, as long as the negative impact is only a “…small sacrifice in value.” In fact, Graham et al.
(2005, 29) indicate that several CFOs argue, “…you have to start with the premise that every
company manages earnings.” Of particular importance to our study, Graham et al. (2005) report
that a larger number of managers admit to reducing discretionary expenses than to engaging in
other methods of real activities manipulation.
Extant REM studies utilize the methodology developed by Roychowdhury (2006) to
calculate abnormal discretionary expenses and identify firms engaging in REM. However, Cohen
et al. (2014) find that this Traditional REM measure is severely mis-specified. Specifically, they
find that the Traditional REM measure indicates the presence of REM far too often in randomly
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constructed samples. Similarly, analysis of the Traditional REM model by Siriviriyakul (2014)
suggests the presence of omitted variables. She states that “[b]ecause real earnings management
is a departure from normal activity, if the REM proxies truly capture REM activity, they should
exhibit subsequent reversal” (Siriviriyakul 2014, 3). Contrary to her expectation, she finds that
the REM proxies are highly persistent, which she interprets as evidence of omitted correlated
variables.
Whereas prior research utilizes cross-sectional analysis to identify firms engaged in real
earnings management, in practice, financial statement analysis is conducted using individual firm
characteristics and fundamentals (e.g., Stickney et al. 2003; White et al. 2003; Lundholm and
Sloan 2009; Penman 2013). In addition, Francis and Smith (2005) argue that earnings analysis
should be performed using firm-specific time-series estimations because the persistence of a
firm’s earnings components are unlikely to depend on the persistence of other firms’ similar
components. Therefore, we develop a firm-specific time-series model that does not rely on
information about other firms within an industry and does not make the assumption that the
persistence of earnings components (e.g., discretionary expenses) are the same for all firms.
Thus, our model provides a method for more accurately forecasting a firm’s expected level of
discretionary expenses, which allows us to identify firms that deviate from those expected levels.
Early empirical evidence of REM focuses on the opportunistic reduction of R&D
expenditures to reduce expenses. Baber, Fairfield and Haggard (1991) find evidence suggesting
that R&D investment decisions are influenced by managers’ concerns about reported earnings,
and Bushee (1998) finds that managers reduce R&D expenditures to meet short-term earnings
benchmarks. Dechow and Sloan (1991) provide evidence that CEOs reduce R&D expenditures
to increase current earnings near the end of their tenure. Finally, Bens, Nagar and Wong (2002)
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provide empirical evidence that firms engage in REM for purposes other than meeting a specific
earnings benchmark. They find that firms experiencing significant employee stock option
exercises shift resources away from real investments, including R&D, toward the repurchase of
their own stock. Consistent with Bushee (1998), they also suggest that this imposes real costs on
the firm.
More recent research finds evidence of REM employing multiple types of real activities
manipulation. Roychowdhury (2006) develops empirical methods to detect these manipulations
in large samples and provides evidence that managers reduce discretionary expenses to avoid
reporting annual losses. Using a modified version of his abnormal discretionary expense
measure, Roychowdhury (2006) also finds less robust evidence that firms use REM to meet
consensus analyst forecasts. Cohen et al. (2008) confirm the findings of Roychowdhury (2006)
and document that, while AM declined after the passage of the Sarbanes-Oxley Act (SOX) in
2002, REM increased significantly. This suggests that firms shifted away from using AM to
REM after SOX. Badertscher (2011) finds that managers engage in earnings management to
support overvalued equity and that they generally exhaust AM strategies before moving to REM.
Finally, Zang (2012) finds that managers use real activities management and accrual-based
earnings management as substitutes and engage in real activities management when its relative
cost is low.
Prior research finds mixed results on the consequences of REM. Cohen and Zarowin
(2010) provide evidence that managers engage in earnings management around seasoned equity
offerings (SEOs) and that declines in post-SEO performance due to REM are more severe than
declines due to AM. Their evidence is important, because it shows that post-SEO
underperformance is driven not just by accruals reversal, but also reflects real consequences of
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operational decisions made to manage earnings. Bhojraj, Hribar, Picconi and McInnis (2009)
show that firms that beat analyst forecasts using both REM and AM have worse future operating
performance and returns, in the subsequent three years, than firms that just miss analyst forecasts
but do not engage in any form of earnings management. Their study, however, does not attempt
to disentangle the effect of AM and REM individually, but rather uses a measure that
incorporates both forms of earnings management. Francis et al. (2011b) find that firms engaging
in REM are more likely to suffer subsequent stock price crashes than firms not engaging in
REM. Additionally, Kim and Park (2013) investigate the impact of REM on auditor’s client-
retention decisions and find that auditor resignations are more likely when firms manage
earnings through opportunistic operating decisions. These results illustrate the negative
consequences and risk associated with REM.
Alternatively, Taylor and Xu (2010) find that REM does not lead to a significant decline
in subsequent operating performance. Similarly, Gunny (2010) provides evidence that firms that
engage in REM to meet or just beat earnings benchmarks have better operating performance, in
the subsequent three years, than firms that either miss the earnings benchmarks or meet the
earnings benchmarks but do not engage in REM. She suggests that engaging in REM is not
opportunistic, but rather a way for managers to produce current-period benefits and to signal
superior future earnings. Zhao et al. (2012) find that although abnormal real activities, in general,
are associated with lower future performance, real earnings management intended to just meet
earnings benchmarks is associated with higher future performance, consistent with real earnings
management conveying a signal of superior future performance.
One possible reason for the inconsistent results in prior research is that different
managers could be engaging in real activities manipulation to achieve different goals, or perhaps
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that the association between REM and future performance varies with the ability levels of
managers. It is possible that behavior that appears to be REM is actually “good” management or
similarly that REM may be used to signal strong future performance. This may be particularly
true for firms with high managerial ability. Demerjian, Lev, and McVay (2012) develop a
managerial ability score that is calculated in two-stages: the first stage provides an estimate of
firm-level operational efficiency and the second stage controls for various firm characteristics to
isolate the effects of the manager.5 Utilizing this managerial ability measure allows us to
determine whether managers with higher ability are better able to mitigate the costs of REM on
future performance and signal stronger future performance, or whether they are perhaps not
engaging in REM at all, but rather engaging in “good” management that leads to better future
performance. Demerjian, Lev, Lewis, and McVay (2013) find that better managers are less likely
to engage in earnings management when it reduces financial reporting quality, and Demerjian,
Lewis-Western, and McVay (2015) find that better managers are able to intentionally smooth
earnings in a way that is low-cost to their firms. These findings suggest that managers may also
be able to engage in REM without negatively impacting future operating performance.
The lack of consensus in prior literature regarding the effects of REM on future operating
performance, and the need for a new methodology of identifying firms suspected of engaging in
REM, motivate this study.
3. Research Design
Managers use earnings management to meet certain earnings benchmarks, achieve
internal bonus targets, appear more attractive prior to initial public offerings (IPOs) and SEOs,
5 The MA-Score data developed by Demerjian et al. (2012) is publicly available for download at
https://community.bus.emory.edu/personal/PDEMERJ/Pages/Download-Data.aspx.
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avoid debt covenant violations, meet regulatory requirements, avoid taxes, and for numerous
other reasons (Graham et al. 2005). Examples of REM include cutting discretionary expenses
such as advertising, R&D, and SG&A, in order to achieve desired outcomes. Theoretically, REM
should be an infrequent event that reverses as firms return to normal levels of activity in
subsequent years. REM, however, is difficult to measure because we cannot observe the
intentions of management. Prior research (e.g., Roychowdhury 2006; Gunny 2010) uses cross-
sectional models to estimate the expected level of discretionary expenses and then compares the
estimate to actual discretionary expenses to determine abnormal discretionary expenses. These
cross-sectional models are estimated by year and industry. Cohen et al. (2014) and Siriviriyakul
(2014) express concern that the traditional models used to measure real earnings management are
severely mis-specified and do not accurately identify firms that depart from normal levels of
expenses. Furthermore, because of the importance of firm-specific characteristics in financial
statement analysis, the analysis is performed at the firm level in practice (e.g., Stickney et al.
2003; White et al. 2003; Lundholm and Sloan 2009; Penman 2013). Consistent with this
argument, Francis and Smith (2005) suggest that earnings analysis should be performed using
firm-specific time-series estimations because the persistence of a firm’s earnings components are
unlikely to depend on the persistence of other firms’ earnings components. To address the
concerns raised in prior literature, we develop a new methodology of measuring REM.
Specifically, we use firm-specific time-series estimations.
3.1 Firm-specific persistence of discretionary expenses
Firms that deviate from their normal levels and report abnormally low levels of
discretionary expenses to meet earnings targets are likely engaging in REM. In our analysis, we
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focus on advertising, R&D, and SG&A expenses, which prior research suggests are commonly
manipulated to meet certain earnings benchmarks. We include advertising and R&D because
they are expensed as incurred and because the future benefit of these expenditures may be
uncertain. As a result, firms may cut advertising and R&D if they feel pressure to hit important
earnings benchmarks in a given year, especially if the benefit from the expenses will not be
realized until future periods. We include SG&A because prior research suggests that SG&A
costs are sticky (Anderson et al. 2003),6 and a significant decrease in SG&A costs in a particular
year is potentially suspicious. Furthermore, SG&A frequently includes costs such as employee
training, maintenance and travel, etc., which are highly discretionary in nature. To identify
abnormal changes in a firm’s discretionary expenses, we sum advertising, R&D, and SG&A
expenses and estimate the following firm-specific regression:
DisExpt = α + β1DisExpt-1 + εt, (1)
where:
DisExpt = the sum of advertising, R&D, and SG&A expenses, scaled by total assets at the
beginning of year t.7
We estimate model (1) for each firm-year using a 10-year rolling window. Using the
estimated values of the intercept and β1 from model (1), we derive the expected level of
discretionary expenses in year t. The abnormal level of discretionary expenses in year t
(AbnDisExpt) is calcualted as the difference between the actual level of discretionary expenses in
year t and the predicted level of discretionary expenses in year t. Because our calcualtion of
abnormal discretionary expenditures is performed at the firm level, our measure addresses the
6 Anderson et al. 2003 suggest that SG&A costs are “sticky” because they decrease less when sales decrease than
they increase when sales increase by an equal amount. This is attributed to both the fixity of certain costs and
managerial decisions to avoid cutting slack resources in anticipation of a rebound in sales. 7 In order to maximize the number of sample observations, and following prior research, we set advertisting expense
and R&D expense to zero if missing.
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concern that some firms are inherently different from other firms in their respective industry. It is
important to note that abnormally low levels of discretionary expenditures are income increasing.
3.2 Identification of suspect firms
Prior research suggests that firms face intense pressure to meet certain earnings
benchmarks, and we expect that firms engage in REM to meet these benchmarks. Following
Roychowdhury (2006), we utilize the following benchmarks to identify firms that are more likely
to be engaging in REM: reporting earnings that meet or just beat zero and meeting or just beating
the consensus anlayst forecast. With respect to the positive earnings benchmark, prior literature
(e.g., Burgstahler and Dichev 1997; Roychowdhury 2006) finds a significant spike in the number
of firms reporting earnings in the interval just to the right of zero (i.e., earnings scaled by lagged
total assets between 0.000 and 0.005) when compared with the interval immediately to the left of
zero (i.e., firms that just missed reporting positive earnings). This suggests that the spike in
earnings just to the right of zero is the result of firms managing earnings to report profits.
Therefore, consistent with prior research, we classify observations that fall into the interval
immediately to the right of zero as suspect firm-years (Bench_NIt).8 Prior research finds that
firms that meet or just beat consensus analyst forecasts suffer severe stock price delcines
(Skinner and Sloan 2002), and these penalties may provide ample incentive for firms to engage
8 Focusing on the interval just to the right of zero presents some potential problems. First, firms that meet or just
beat zero earnings are not likely to be the only firms that engage in real earnings management. Specifically, firms
may use real earnings management to report earnings growth, achieve internal bonus targets, appear more attractive
prior to initial public offerings (IPOs) and secondary equity offerings (SEOs), avoid debt covenant violations, meet
regulatory requirements, avoid taxes, and for numerous other reasons. Second, firms with pre-managed earnings
safely to the right of zero have an incentive to use income-decreasing real earnings management to create reserves,
thus making it easier to achieve earnings targets in the future. This income-decreasing REM could place the firms
just to the right of zero. These problems lower the power of the subsequent empirical tests; however, they are
shortcomings of all earnings management studies that utilize these benchmarks to identify suspect firms.
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in REM. Therefore, consistent with prior research, we classify observations that meet or just beat
consensus analyst forecasts by one cent or less as suspect firm-years (Bench_AFt).
3.3 Model testing the association between REM and meeting earnings benchmarks
To examine whether firms use REM to meet or just beat earnings benchmarks, we
estimate the following model from Gunny (2010):
AbnDisExpt = α + β1Sizet + β2MTBt + β3ROAt + β4Bencht + εt (2)
where:
AbnDisExpt = Firm-Specific REM or Traditional REM;
Firm-Specific_REM = the difference between the actual and expected level of discretionary
expenses in year t derived from model (1);
Traditional_REM = the difference between the actual and expected level of discretionary
expenses derived following Roychowdhury (2006);
Sizet = the natural log of total assets at the beginning of year t;
MTBt = the ratio of the market value of equity to the book value of equity at the
beginning of year t;
ROAt = the difference between the firm-specific income before extraordinary
items in year t, scaled by lagged total assets, and the median income
before extraordinary items in year t, scaled by lagged total assets, for the
same industry (two-digit SIC);
Bencht = Bench_NIt or Bench_AFt.
Bench_NIt = an indicator variable equal to 1 if income before extraordinary items in
year t, scaled by lagged total assets, is between 0 and 0.005, 0 otherwise;
and
Bench_AFt = an indicator variable equal to 1 if the firm meets or just beats the final
consensus analyst forecast before the fiscal year end by one cent or less, 0
otherwise.
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Consistent with Roychowdhury (2006) and Gunny (2010), we include Sizet to control for
size effects and MTBt to control for growth opportunities. Additionally, we include ROAt because
real earnings management may be related to performance. We estimate model (2) in a pooled
regression. All variables are winsorized at the top and bottom 1% of the sample distribution to
mitigate the impact of outliers. Bencht is our variable or interest. We expect a negative
coefficient on Bencht, indicating that firms that report small positive earnings or that meet or just
beat consensus analyst forecasts report abnormally low levels of discretionary expenses, which is
consistent with firms engaging in REM to meet earnings benchmarks.
3.4 Model testing the association between “blip” suspect firms and meeting earnings benchmarks
We perform an additional test to validate that the Firm-Specific REM measure is
capturing real earnings management. We create a “blip” variable for both the Firm-Specific
REM and the Traditional REM measures. The “blip” variable is an indicator variable equal to 1
if abnormal discretionary expenses are negative in year t, but positive in both years t-1 and t+1.
A common criticism of REM studies is that abnormally low discretionary expenses may
represent a change in philosophy by managers to lower expenses moving forward, in order to
produce better future operating performance, or may indicate that some firms are inherently
different from other firms in their industry. This measure is intended to mitigate that criticism by
identifying firms that have abnormally low discretionary expenses in a year that is surrounded by
positive abnormal discretionary expenses. To examine the likelihood that firms with a “blip” are
more likely to meet or just beat zero earnings or zero earnings growth benchmarks, we estimate
the following model:
SuspectMBt = α + β1Blipt +β2Sizet + β3MTBt + β4BigNt + β5ROAt + β6Losst + εt (3)
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where:
SuspectMBt = an indicator variable equal to 1 if income before extraordinary items,
scaled by lagged total assets, is between 0 and 0.005 or if the change in
income before extraordinary items, scaled by lagged total assets, between
years t-1 and t is between 0 and 0.005, 0 otherwise;
Blipt = Firm-Specific_Blipt or Traditional_Blipt;
Firm-SpecificBlipt = an indicator variable equal to 1 if Firm-Specific REM is negative in year
t and positive in both years t-1 and t+1, 0 otherwise;
TraditionalBlipt = an indicator variable equal to 1 if Traditional REM is negative in year t
and positive in both years t-1 and t+1, 0 otherwise;
BigNt = an indicator variable equal to 1 if the firm’s auditor is one of the Big N
audit firms, 0 otherwise;
Losst = an indicator variable equal to 1 if the firm reported negative income
available to common shareholders before extraordinary items in year t, 0
otherwise, and
all other variables as previously defined.
We run the model separately using the Firm-Specific_Blipt measure and then the
Traditional_Blipt measure. A positive coefficient on Blipt would indicate that the likelihood of
meeting earnings benchmarks in year t is greater for firms with an unexpected drop in
discretionary expenses in that year. This test also provides evidence on whether the REM
measures are effectively capturing the underlying real earnings management construct.
3.5 Model testing the association between REM and future performance
Prior theoretical research suggests that REM is value-destroying and imposes real costs
on firms (Ewert and Wagenhofer 2005). Furthermore, Jensen (2005) argues that when firms
make non-value maximizing decisions, firm value is destroyed. However, empirical findings are
mixed on the impact of REM on future operating performance. To examine the impact of real
18
earnings management on future operating performance we follow Gunny (2010) and estimate the
following model:
ROAt+1 or CFOt+1 = α + β1Beatt +β2JustMisst + β3Bench_NIt + β4Neg_AbnDisExpt or
Low_AbnDisExpt + β5Bench_NI*(Neg_AbnDisExpt or Low_AbnDisExpt ) + β6ROAt or
CFOt + β7Sizet + β8MTBt + β9Returnt + β10ZScoret-1+ εt, (4)
where:
ROAt+1 = the difference between the firm-specific income before extraordinary
items in year t+1, scaled by lagged total assets, and the median income
before extraordinary items in year t+1, scaled by lagged total assets, for
the same industry (two-digit SIC);
CFOt+1 = the difference between the firm-specific cash flow from operations in
year t+1, scaled by lagged total assets, and the median cash flow from
operations in year t+1, scaled by lagged total assets, for the same industry
(two-digit SIC);
Beatt = an indicator variable equal to 1 if income before extraordinary items,
scaled by lagged total assets, is greater than 0.005, 0 otherwise;
JustMisst = an indicator variable equal to 1 if income before extraordinary items,
scaled by lagged total assets, is greater than or equal to -0.005 and less
than 0.000, 0 otherwise;
Bench_NIt = an indicator variable equal to 1 if income before extraordinary items in
year t, scaled by lagged total assets, is between 0 and 0.005, 0 otherwise;
Neg_AbnDisExpt = an indicator variable equal to 1 if AbnDisExpt is negative, 0 otherwise;
Low_AbnDisExpt = an indicator variable equal to 1 if AbnDisExpt is in the lowest quintile
ranked by industry and year, 0 otherwise;
CFOt = the difference between the firm-specific cash flow from operations in
year t, scaled by lagged total assets, and the median cash flow from
operations in year t, scaled by lagged total assets, for the same industry
(two-digit SIC);
Returnt = the buy and hold return of the firm over the 12 months of year t minus
the buy and hold return of a portfolio of firms within the same CRSP
decile during year t; and
ZScoret-1 = a measure of bankruptcy risk calculated as 3.3*(pretax income/lagged
total assets) + (sales/lagged total assets) + 1.25*(retained earnings/lagged
19
total assets) + 1.2*((current assets – current liabilities)/lagged total assets),
and
all other variables as previously defined.
We include ROAt to control for firm performance and Sizet to control size. We include
MTBt to control for growth opportunities. We include Returnt to control for the association
between stock return performance and future earnings, and we include ZScoret to control for the
financial health of the firm. We estimate model (4) in a pooled regression. We include industry
and year fixed effects to control for time and industry effects in our sample. In addition, we
control for potential time-series correlation by clustering standard errors by firm and year. All
variables are winsorized at the top and bottom 1% of the sample distribution to mitigate the
impact of outliers. Our coefficient of interest is β5, the interaction between Bencht and
AbnDisExpt. Prior research suggests that real earnings management is potentially value-
destroying because firms sacrifice potential value creating activities at the expense of meeting
earnings benchmarks. However, an alternate view exists. Gunny (2010) finds that firms that use
real earnings management to meet earnings benchmarks have better future performance. She
suggests that firms engage in real earnings management in order to attain benefits that allow the
firm to perform better in the future and to signal stronger future performance. Additionally,
Graham et al. (2005, 35) report that more than half of the CFOs they surveyed would delay a
project even if it entailed a “...small sacrifice in value.” This suggests that, although managers
admit to manipulating real activities to meet earnings benchmarks, they are cognizant of the
potential costs and attempt to keep these costs as low as possible. In light of the two competing
arguments provided by prior research, coupled with the survey evidence of Graham et al. (2005),
we do not make a directional prediction on β5 and expect to find that using REM to meet
earnings benchmarks has neither a positive nor negative impact on future operating performance.
20
Prior research also suggests that high ability managers are able to manipulate income,
through intentional smoothing, in a way that imposes a very low cost on their firms (Demerjian
et al. 2015). Additionally, Demerjian et al. (2013) find that high ability managers are less likely
to engage in earnings management when it reduces financial reporting quality. Therefore, we use
the managerial ability measure developed in Demerjian et al. (2012) to examine whether high
ability managers who engage in REM are able to mitigate the potential negative impacts on
future operating performance. We modify Gunny (2010) and estimate the following model:
ROAt+1 or CFOt+1 = α + β1Firm-Specific_REMt +β2HAMt + β3Firm-Specific_REMt*HAMt +
β4ROAt or CFOt + β5Size + β6MTBt + β7Returnt + β8ZScoret-1+ εt,
(5)
where:
HAMt = an indicator variable equal to 1 when the managerial ability score is
above the median for the industry (two-digit SIC) and year, 0 otherwise,
and
all other variables as previously defined.
Our coefficient of interest in this model is β3, the interaction between Firm-
Specific_REMt and HAMt. If high ability managers are able to engage in REM without negatively
impacting future performance, or if they are not engaging in REM at all, but rather prudently
cutting costs as “good” managers, then we expect the coefficient on β3 to be positive.
3.6 Sample Selection
To examine the relation between REM and future operating performance, we identify all
firm-year observations from the Compustat database between 1995 and 2013. Our sample
consists of firms with available data from the Center for Research in Security Prices (CRSP),
firms where the sum of advertising, R&D, and SG&A expenses is non-zero, and firms with
21
enough data to calculate abnormal discretionary expenses using our firm-specific methodology.
We develop our measure of high managerial ability from publicly available managerial ability
score data. We eliminate firms in regulated industries (SIC codes 4400 to 5000) and banks and
financial institutions (SIC codes 6000 to 7000). These restrictions result in a preliminary sample
of 31,782 firm-year observations (representing 4,383 unique firms). We also winsorize all
variables at the top and bottom 1% of the distribution to eliminate the impact of extreme
observations. We perform the multivariate analyses that follow using the maximum number of
observations with complete data available for each test. Because of this, the number of
observations varies across specifications.
4. Results
4.1 Descriptive Statistics
We report sample descriptive statistics in Table 1. The sample mean (median) of Firm-
Specific_REMt is -0.0077 (-0.0042) with a standard deviation of 0.2039, indicating a wide
variance of abnormal discretionary expenses among the sample firms. The sample mean
(median) of Traditional_REM is 0.0288 ((0.0063) with a standard deviation of (0.2008). The
mean (median) of the natural log of total assets (Sizet) is 5.6910 (5.8020) and the mean (median)
of the market to book ratio is 2.2215 (1.6854). The mean (median) of firm industry-adjusted
return on assets (ROAt) is 0.2193 (0.2030), and the mean (median) of firm industry adjusted cash
flow from operations (CFOt) is 0.1242 (0.1122). Approximately 2% of our firm-year
observations report income before extraordinary items, scaled by lagged total assets, between 0
and 0.005 (Bench_NIt), and approximately 12% meet or just beat consensus analyst forecasts by
one cent or less (Bench_AFt). Approximately 6% of our firm-year observations report income
22
before extraordinary items, scaled by lagged total assets, or the change in income before
extraordinary items, scaled by lagged total assets, between 0 and 0.005 (SuspectMBt).
Approximately 6% of our firm-year observations have negative Firm-Specific REM in year t and
positive Firm-Specific REM in years t-1 and t+1 (FirmSpecific_Blipt), and approximately 2%
have negative Traditional REM in year t and positive Traditional REM in years t-1 and t+1
(Traditional_Blipt). Approximately 56% of our firm-year observations are during years where
the firm was audited by a Big N auditor (BigNt), and approximately 30% are years with negative
income (Losst). The mean (median) of one-year ahead return on assets (ROAt+1) is 0.2291
(0.2141) and the mean (median) of one-year ahead cash flow from operations (CFOt+1) is 0.1277
(0.1148)., Approximately 68% of our firm-year observations have income before extraordinary
items, scaled by lagged total assets, greater than 0.005 (Beatt), and approximately 1% have
income before extraordinary items, scaled by lagged total assets, between -0.005 and 0 of lagged
assets (JustMisst). Approximately 56% of our firm-year observations have negative abnormal
discretionary expenses (Neg_AbnDisExpt), and approximately 18% have abnormal discretionary
expenses ranked in the lowest quitile their industry (Low_AbnDisExpt). The mean (median) 12
month buy and hold return (Returnt) is -0.0331 (-0.0465). The mean (median) Z Score (ZScoret)
is 1.0783 (1.9341). Finally, approximately 42% of our firm-year observations are under the
direction of high ability managers.
[Insert Table 1 here]
In Table 2, we present Pearson correlations for select variables of our sample
observations. We find that the correlations between our Firm-Specific measure of abnormal
discretionary expenses (Firm-Specific_REMt) is negatively and significantly associated with
Sizet, MTBt, ROAt, Firm-Specific_Blipt, Traditional_Blipt, Beatt, Neg_AbnDisExpt, and
23
Low_AbnDisExpt, and is positively and significantly associated with HAMt. We find that the
correlations between the Traditional measure of abnormal discretionary expenses
(Traditional_REMt) is negatively and significantly associated with ROAt, Bench_NIt,
Suspect_MBt, Firm-Specific_Blipt, Traditional_Blipt, Beatt, JustMisst, Neg_AbnDisExpt, and
HAMt, and is positively and significantly associated with Sizet, MTBt, Bench_AFt, and
Low_AbnDisExpt.
[Insert Table 2 here]
In Figure 1, we examine and compare the persistence of the Firm-Specific REM measure
with the persistence of the Traditional REM measure. Siriviriyakul (2014) contends that the
Traditional REM measure is highly persistent and mis-specified, which is an indication of
correlated ommitted variables. If a firm engages in REM to achieve certain earnings benchmarks,
we would expect the firm to exhibit abnormally low levels of abnormal discretionary expenses in
the manipulation years and then a return to higher levels of abnormal discretionary expenses as
the expenses are shifted, or return to normal levels, in prior and subsequent periods. We present
the percentage of surviving firms that have abnormal discretionary expenses with the same sign
(either positive or negative) in all years prior, for both the Traditional REM measure and the
Firm-Specific REM measure. We also compare the measures to the likelihood that abnormal
discretionary expenses would exhibit the same sign (either positive or negative) under the
assumption of random chance. The figure illustrates that the Traditional REM measure is highly
persistent, a criticism echoed by Siriviriyakul (2014). In fact, using the traditional methodology,
13 out of the 21 firms (or 61.9%) that survive the entire eighteen year sample period have the
same abnormal discretionary expenses sign. In other words, using the traditional methodology,
nearly two-thirds of the surviving firms have either eighteen consecutive years of positive
24
abnormal discretionary expenses or eighteen consecutive years of negative abnormal
discretionary expenses. By comparison, none of the sample firms have the same sign of
abnormal discretionary expenses for the full eighteen year period using our firm-specific
methodology. This is compared to the 0.00038% chance that a firm would have the same sign of
abnormal discretionary expenses for the full eighteen years under the assumption of random
chance. Thus the Firm-Specific REM measure appears to alleviate the concerns expressed by
Siriviriyakul (2014).
[Insert Figure 1 here]
4.2 Empirical Results
In Table 3 we present results from estimating model (2), where we examine whether
firms that meet or just beat zero earnings or consensus analyst forecast benchmarks report lower
levels of abnormal discretionary expenses. In Columns (1) and (2), we report results where
Bencht is equal to Bench_NIt, and the dependent variable in Column (1) (Column (2)) is Firm-
Specific_REM (Traditional_REM). Consistent with our expectations, in Column (1) we find that
the coefficient on Bench_NIt is negative and significant, at the 5% level, suggesting that firms
use real earnings management to avoid reporting negative earnings. In Column (2), we also find
that the coefficient on Bench_NIt is negative and significant, at the 10% level, confirming the
findings from Roychowdhury (2006) that firms use real earnings management to avoid reporting
negative earnings, albeit at a lower level of statistical significance. In Columns (3) and (4), we
report results where Bencht is equal to Bench_AFt, and the dependent variable Column (3)
(Column (4)) is Firm-Specific_REM (Traditional_REM). Consistent with our expectations, we
find that the coefficient on Bench_AFt in Column (1) is negative and significant, at the 1% level,
25
suggesting that firms use real earnings management to meet or just beat consensus analyst
forecasts. However, in Column (4), we do not find a statistically significant coefficient on
Bench_AFt, suggesting either that firms do not use real earnings management to meet or just beat
consensus analyst forecasts or, alternatively, that the Traditional REM measure is not accurately
identifying abnormal discretionary expenses, and, therefore, is less accurately identifying real
earnings management.
[Insert Table 3 here]
In Table 4, we provide supplemental analysis on the likelihood that firms are using real
earnings management to meet or just beat zero earnings or zero earnings change benchmarks and
whether the Traditional REM measure and the Firm-Specific REM measure are effective at
capturing the construct of real earnings management under an alternate specification. In Column
(1), Firm-Specific_Blipt is the variable of interest and in Column (2), Traditional_Blipt is the
variable of interest. These “blip” variables are indicators equal to one for firms with negative
abnormal discretionary expenses in year t and positive abnormal expenses in years t-1 and t+1,
and 0 otherwise. These measures are intended to capture whether firms that have abnormally
lower discretionary expenses in year t, surrounded by years with positive abnormal discretionary
expenses are, on average, cutting discretionary expenses to meet or just beat zero earnings or
zero earnings change benchmarks. In Column (1) we find that the coefficient on Firm-
Specific_Blipt is positive and significant at the 1% level, suggesting that these “blip” firms are
engaging in REM to meet or just beat these earnings benchmarks. In Column (2) we find that the
coefficient on Traditional_Blipt is not statistically significant, suggesting either that “blip” firms
do not use REM to meet these earnings benchmarks or, alternatively, that the Traditional REM
measure is less accurately identifying REM.
26
[Insert Table 4 here]
In Table 5, we present results from estimating model (4), where we examine the impact
of real earnings management on future firm performance. In Columns (1) and (2), the dependent
variable is ROAt+1 and in Columns (3) and (4), the dependent variable is CFOt+1. In Columns (1)
and (3) we use an indicator variable for negative abnormal discretionary expenses,
Neg_AbnDisExpt. In Columns (2) and (4) we rank AbnDisExpt into quintiles and construct
Low_AbnDisExpt, which is an indicator variable equal to one if AbnDisExpt is in the lowest
quintile, and zero otherwise. In Column (2) we find that the coefficient on Low_AbnDisExpt is
negative and significant, indicating that low abnormal discretionary expenses are negatively
associated with future return on assets. However, we do not find statistically significant
coefficients on the interaction between Bench_NIt and either Neg_AbnDisExpt or
Low_AbnDisExpt, suggesting that firms that report lower abnormal discretionary expenses in
order to meet earnings benchmarks perform neither better nor worse in the future than firms
without abnormally low discretionary expenses. This result supports the survey evidence from
Graham et al. (2005), and our prediction, that managers only use REM to meet earnings
benchmarks when the negative impact on firm value is small.
[Insert Table 5 here]
In Table 6, we present results from estimating model (5), where we examine the impact
of high ability managers on the association between real earnings management and future firm
performance. In Column (1), the dependent variable is ROAt+1 and in Column (2), the dependent
variable is CFOt+1. Our variable of interest is the interaction between Firm-Specific_REMt and
HAMt, which is an indicator variable equal to one if managerial ability is above the median for
the industry and year, and zero otherwise. In Column (1) we find that the coefficient on Firm-
27
Specific_REMt*HAMt is positive and significant, indicating that high ability managers that
engage in REM produce higher ROA in future periods. However, we do not find a statistically
significant coefficient on Firm-Specific_REMt*HAMt in Column (2), suggesting that high ability
managers that engage in REM produce neither higher nor lower CFO in future periods. This
result supports the evidence found in Demerjian et al. (2015) that high ability managers are able
to engage in earnings management (intentional income smoothing) at a low cost to the firm.
5. Conclusion
REM refers to opportunistic operational decisions made by management to influence
reported earnings. Prior research measures REM using cross-sectional analysis, which is used to
identify firms suspected of engaging in REM and to investigate its impact on future firm
performance (e.g., Roychowdhury 2006; Cohen et al. 2008; Cohen and Zarowin 2010; Gunny
2010). Recent papers, however, argue that the traditional measures of REM are severely mis-
specified (Cohen et al. 2014; Siriviriyakul 2014). In response to these papers, and in light of
conflicting evidence on the impact of REM on future performance, we develop a firm-specific
time-series measure of REM.
A firm-specific time-series measure is ideal because it eliminates the concern that the
REM measure is merely capturing changes or differences in management philosophy or inherent
firm differences. We first address concerns that the traditional measure of REM is mis-specified
by providing evidence that our firm-specific REM measure is less persistent than the traditional
REM measure. Second, we find that income-increasing REM, using both the firm-specific and
the time-series measures, is associated with meeting or just beating zero earnings. Importantly,
however, we find that only income-increasing REM is associated with meeting or just beating
28
the consensus analyst forecast only when using the firm-specific measure. In our final analyses,
we examine the impact of REM on future firm performance, the results of which are mixed in
prior research. Using the firm-specific measure, we do not find evidence that firms that
opportunistically engage in REM perform either better or worse in the future. This result is
consistent with survey responses from Graham et al. (2005), who suggest that managers are
careful not to sacrifice future operating performance in order to meet earnings benchmarks. In
additional analyses, we find that high ability managers that engage in REM produce higher
ROAs in the subsequent year, apparently mitigating the hypothesized negative impacts that REM
has on future operating performance and accurately signaling stronger future performance. Our
results should be of interest to investors and researchers as they consider the measurement and
implications of real earnings management.
29
REFERENCES
Anderson, M., R. Banker, and S. Janakiraman. 2003. Are selling, general, and administrative
costs “sticky”? Journal of Accounting Research 41(1): 47-63.
Baber, W.R., P.M. Fairfield, and J.A. Haggard. 1991. The effect of concern about reported
income on discretionary spending decisions: the case of research and development. The
Accounting Review 66: 818-829.
Badertscher, B.A. 2011. Overvaluation and the choice of alternative earnings management
mechanisms. The Accounting Review 86(5): 1491-1518.
Bartov, E. 1993. The timing of asset sales and earnings manipulation. The Accounting Review
68: 840-855.
Bens, D.A., V. Nagar, and F. Wong. 2002. Real ivnestment implications of employee stock
option exercises. Journal of Accounting Research 40(2): 359-393.
Bhojraj, S., P. Hribar, M. Picconi, and J. McInnis. 2009. Making sense of cents: an examination
of firms that marginally miss or beat analyst forecasts. The Journal of Finance 64(5): 2361-2388.
Burgstahler, D., and I. Dichev. 1997. Earnings management to avoid earnings decreases and
losses. Journal of Accounting and Economics 24: 99-126.
Bushee, B. 1998. The influence of institutional investors on myopic R&D investment behavior.
The Accounting Review 73(3): 305-333.
Call, A.C., M. Hewitt, T. Shevlin, and T.L. Yohn. 2013. Using firm-specific characteristics of
accruals and operating cash flows to predict future earnings and stock returns. Working paper,
Arizona State University, Indiana University, University of California – Irvine, and Indiana
University.
Cohen, D.A., A. Dey, and T.Z. Lys. 2008. Real and accrual-based earnings management in the
pre- and post-sarbanes-oxley periods. The Accounting Review 83(3): 757-787.
Cohen, D.A., and P. Zarowin. 2010. Accrual-based and real earnings management activities
around seasoned equity offerings. Journal of Accounting and Economics 50: 2-19.
Cohen, D. A, S. Pandit, C. Wasley, and T. Zach. 2014. Measuring real activity management.
Working paper, University of Texas at Dallas, University of Illinois at Chicago, University of
Rochester, and The Ohio State University.
Dechow, P.M., and R. Sloan. 1991. Executive incentives and the horizon problem: an empirical
investigation. Journal of Accounting and Economics 14: 51-89.
30
Dechow, P.M., S.P. Kothari, and R.L. Watts. 1998. The relation between earnings and cash
flows. Journal of Accounting and Economics 25: 133-168.
Dechow, P.M., and I.D. Dichev. 2002. The quality of accruals and earnings: the role of accrual
estimation errors. The Accounting Review 77(Supplemental): 35-59.
Dechow, P. M., and C. Shakespeare. 2009. Do managers use securitization volume and fair value
estimates to hit earnings targets? The Accounting Review 84: 99-132.
Dechow, P. M., L. A. Myers, and C. Shakespeare. 2010. Fair value accounting and gains from
asset securitizations: A convenient earnings management tool with compensation side-benefits.
Journal of Accounting and Economics 49: 21-25.
Degeorge, F., J. Patel, and R. Zeckhauser. 1999. Earnings management to exceed thresholds.
Journal of Business 72(1): 1-33.
Demerjian, P., B. Lev, and S. McVay. 2012. Quantifying managerial ability: A new measure and
validity tests. Management Science 58(7): 1229-1248.
Demerjian, P., B. Lev, M. Lewis, and S. McVay. 2013. Managerial ability and earnings quality.
The Accounting Review 88(2): 463-498.
Demerjian, P., M. Lewis-Western, and S. McVay. 2015. Earnings smoothing: For good or evil?
Working paper, University of Washington, University of Utah, and University of Washington.
Eldenburg, L.G., K.A. Gunny, K.W. Hee, and N. Soderstrom. 2011. Earnings management using
real activities: evidence from nonprofit hospitals. The Accounting Review 86(5): 1605-1630.
Ewert, R., and A. Wagenhofer. 2005. Economic effects of tightening accounting standards to
restrict earnings management. The Accounting Review 80(4): 1101-1124.
Francis, J., and M. Smith. 2005. A reexamination of the persistence of accruals and cash flows.
Journal of Accounting Research 43(3): 413-51.
Francis, B. B., I. Hasan, and L. Li. 2011a. A cross-country study of legal environment and real
earnings management. Working paper, Rensselaer Polytechnic Institute.
Francis, B. B., I. Hasan, and L. Li. 2011b. Firms’ real earnings management and subsequent
stock price crash risk. Working paper, Rensselaer Polytechnic Institute.
Graham, J.R., C.R. Harvey, and S. Rajgopal. 2005. The economic implications of corporate
financial reporting. Journal of Accounting and Economics 40: 3-73.
Gunny, K. A. 2010. The relation between earnings management using real activities
manipulation and future performance: evidence from meeting earnings benchmarks.
Contemporary Accounting Research 27: 855-88.
31
Herrmann, T., T. Inoue, and W. B. Thomas. 2003. The sale of assets to manage earnings in
Japan. Journal of Accounting Research 41: 89-108.
Jackson, S., and W. Wilcox. 2000. Do managers grant sales price reductions to avoid losses and
declines in earnings and sales? Quarterly Journal of Business and Economics 39(4): 3-20.
Kim, Y., and M.S. Park. 2013. Real activities manipulation and auditors’ client-retention
decisions. The Accounting Review 89(1): 367-401.
Kothari, S. P., A. Leone, and C. Wasley. 2005. Performance matched discretionary accrual
measures. Journal of Accounting and Economics 39: 163-97.
Lundholm, R.J., and R.G. Sloan. 2009. Equity valuation and analysis with eVal. 3rd edition.
Boston, MA: McGraw-Hill Irwin.
McInnis, J., and D.W. Collins. 2011. The effect of cash flow forecasts on accrual quality and
benchmark beating. Journal of Accounting and Economics 51: 219-239.
Penman, S.H. 2013. Financial statement analysis & security valuation. 5th edition. Boston, MA:
McGraw-Hill Irwin.
Roychowdhury, S. 2006. Earnings management through real activities manipulation. Journal of
Accounting and Economics 42: 335-370.
Schipper, K. 1989. Commentary on earnings management. Accounting Horizons 3(4): 91-102.
Siriviriyakul, S. 2014. Re-examining real earnings management to avoid losses. Working paper,
University of California at Berkeley.
Skinner, D., and R. Sloan. 2002. Earnings surprises, growth expectations, and stock returns or
don’t let an earnings torpedo sink your portfolio. Review of Accounting Studies 7 (2–3): 289–
312.
Stickney, C.P., P. Brown, and J.M Wahlen. 2003. Financial reporting and statement analysis: a
strategic approach. 5th edition. Mason, OH: South-Western College Publishing.
Taylor, G. K., and Z. Xu. 2010. Consequences of real earnings management on subsequent
operating performance. Research in Accounting Regulation 22: 128-132.
Thomas, J.K., and H. Zhang. 2002. Inventory changes and future returns. Review of Accounting
Studies 7: 163-187.
White, G., A. Sondhi, and H. Fried. 2003. The analysis and use of financial statements. 3rd
edition. New York, NY: Grace and White, Inc.
32
Zang, A.Y. 2012. Evidence on the trade-off between real activities manipulation and accrual-
based earnings management. The Accounting Review 87(2): 675-703.
Zhao, Y., K.H. Chen, Y. Zhang, and M. Davis. 2012. Takeover protection and managerial
myopia: evidence from real earnings management. Journal of Accounting and Public Policy
31(1): 109-135.
33
FIGURE 1
Percentage of Observations that have Abnormal Discretionary Expenses with the Same
Sign as All Years Prior
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10 t+11 t+12 t+13 t+14 t+15 t+16 t+17
Percentage of Surviving Firms that have Abnormal Discretionary Expenses with the Same Sign in all Years
Traditional Measure Firm-Specific Measure Random Chance
34
TABLE 1
Descriptive Statistics
Variable N Mean Std. Dev.
25th
Percentile Median
75th
Percentile
Firm-Specific_REMt 31,782 -0.0077 0.2039 -0.0358 -0.0042 0.0182
Traditional_REM 31,782 0.0288 0.2008 -0.0792 0.0063 0.1272
Sizet 31,782 5.6910 2.3173 4.0013 5.8020 7.4143
MTBt 31,782 2.2215 2.6443 0.9772 1.6854 2.8400
ROAt 31,693 0.2193 0.2471 0.0680 0.2030 0.3560
CFOt 31,693 0.1242 0.1707 0.0264 0.1122 0.2139
Bench_NIt 31,782 0.0188 0.1361 0.0000 0.0000 0.0000
Bench_AFt 15,039 0.1190 0.3237 0.0000 0.0000 0.0000
Suspect_MBt 31,782 0.0636 0.2440 0.0000 0.0000 0.0000
Firm-Specific_Blipt 31,782 0.0564 0.2307 0.0000 0.0000 0.0000
Traditional_Blipt 31,782 0.0177 0.1318 0.0000 0.0000 0.0000
BigNt 31,782 0.5584 0.4966 0.0000 1.0000 1.0000
Losst 31,782 0.3012 0.4588 0.0000 0.0000 1.0000
ROAt+1 27,893 0.2291 0.2500 0.0731 0.2141 0.3677
CFOt+1 27,893 0.1277 0.1720 0.0285 0.1148 0.2171
Beatt 31,782 0.6799 0.4665 0.0000 1.0000 1.0000
Just_Misst 31,782 0.0137 0.1163 0.0000 0.0000 0.0000
Neg_AbnDisExpt 31,782 0.5646 0.4958 0.0000 1.0000 1.0000
Low_AbnDisExpt 31,782 0.1816 0.3855 0.0000 0.0000 0.0000
Returnt 22,093 -0.0331 0.5452 -0.3379 -0.0465 0.2382
ZScoret 31,201 1.0783 4.2031 0.8638 1.9341 2.8565
HAMt 31,782 0.4215 0.4938 0.0000 0.0000 1.0000
This table presents descriptive statistics for our sample firms from 1995 through 2013. All variables are
winsorized at the 1st and 99th percentiles. Variable definitions are provided below:
Firm-Specific_REM = the difference between the actual and expected level of discretionary expenses
in year t derived from model (1);
Traditional_REM = the actual level of discretionary expenses in year t minus the expected level of
discretionary expenses in year t derived following Roychowdhury (2006);
Sizet = the natural log of total assets at the beginning of year t;
MTBt = the ratio of the market value of equity to the book value of equity at the
beginning of year t;
ROAt = the difference between the firm-specific income before extraordinary items in
year t, scaled by lagged total assets, and the median income before extraordinary
items in year t, scaled by lagged total assets, for the same industry (two-digit
SIC);
35
CFOt = the difference between the firm-specific cash flow from operations in year t,
scaled by lagged total assets, and the median cash flow from operations in year t,
scaled by lagged total assets, for the same industry (two-digit SIC);
Bench_NIt = an indicator variable equal to 1 if NIt is between 0 and 0.005 of lagged
total assets, 0 otherwise;
Bench_AFt = an indicator variable equal to 1 if the firm meets or just beats the final
consensus analyst forecast before the fiscal year end by one cent or less, 0
otherwise;
Suspect_MBt = an indicator variable equal to 1if income before extraordinary items, scaled by
lagged total assets, is between 0 and 0.005 or if the change in income before
extraordinary items, scaled by lagged total assets, between years t-1 and t is
between 0 and 0.005, 0 otherwise;
Firm-Specific_Blipt = an indicator variable equal to 1 if Firm-Specific REM is negative in year t and
positive in both years t-1 and t+1, 0 otherwise;
Traditional_Blipt = an indicator variable equal to 1 if Traditional REM is negative in year t and
positive in both years t-1 and t+1, 0 otherwise;
BigNt = an indicator variable equal to 1 if the firm’s auditor is one of the Big N audit
firms, 0 otherwise;
Losst = an indicator variable equal to 1 if the firm reported negative income available
to common shareholders before extraordinary items during the year t, 0
otherwise;
ROAt+1 = the difference between the firm-specific income before extraordinary items in
year t+1, scaled by lagged total assets, and the median income before
extraordinary items in year t+1, scaled by lagged total assets, for the same
industry (two-digit SIC);
CFOt+1 = the difference between the firm-specific cash flow from operations in year t+1,
scaled by lagged total assets, and the median cash flow from operations in year
t+1, scaled by lagged total assets, for the same industry (two-digit SIC);
Beatt = an indicator variable equal to 1 if net income is greater than 0.005 of lagged
total assets, 0 otherwise;
Just_Misst = an indicator variable equal to 1 if income before extraordinary items, scaled by
lagged total assets, is greater than or equal to -0.005 and less than 0.000, 0
otherwise;
Neg_AbnDisExpt = an indicator variable equal to 1 if AbnDisExpt is negative, 0 otherwise;
Low_AbnDisExpt = an indicator variable equal to 1 if AbnDisExpt is in the lowest quintile ranked
by industry and year, 0 otherwise;
Returnt = the buy and hold return of the firm over the 12 months of year t minus the buy
and hold return of a portfolio of firms within the same CRSP decile during year t;
and
ZScoret = a measure of bankruptcy risk calculated as 3.3*(pretax income/lagged total
assets) + (sales/lagged total assets) + 1.25*(retained earnings/lagged total assets)
+ 1.2*((current assets – current liabilities)/lagged total assets);
HAMt = an indicator variable equal to 1 when the managerial ability score is
above the median for the industry (two-digit SIC) and year, 0 otherwise.
36
TABLE 2
Pearson Correlations
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Firm-Specific_REM (1)
Traditional_REM (2) 0.068
Size (3) -0.038 0.015
MTB (4) -0.014 0.130 0.087
ROA (5) -0.341 -0.079 0.184 0.095
Bench_NI (6) -0.008 -0.015 0.011 -0.021 -0.008
Bench_AF (7) -0.012 0.019 0.025 0.077 0.068 -0.023
Suspect_MB (8) -0.003 -0.027 0.090 -0.003 0.016 0.532 0.010
Firm-Specific_Blip (9) -0.041 -0.029 -0.009 -0.013 -0.005 0.005 -0.006 0.016
Traditional_Blip (10) -0.031 -0.048 -0.016 -0.006 -0.017 0.009 -0.015 -0.005 0.067
Beat (11) -0.025 -0.083 0.317 0.097 0.309 -0.201 0.086 -0.023 -0.023 -0.025
Just_Miss (11) -0.007 -0.017 0.015 -0.027 0.001 -0.016 -0.015 -0.013 0.008 0.005 -0.172
Neg_AbnDisExp (13) -0.338 -0.093 0.052 -0.012 0.058 0.012 -0.002 0.005 0.215 0.054 0.003 0.008
Low_AbnDisExp (14) -0.356 0.080 -0.124 0.017 -0.035 0.001 0.013 -0.033 0.014 0.078 -0.082 -0.006 0.413
HAM 0.016 -0.014 0.011 0.055 0.047 -0.036 0.042 -0.014 0.001 -0.014 0.200 -0.034 -0.024 0.004
This table presets Pearson correlations between select variables for the entire sample of 31,782 firm-year observations over the period
1995-2013. Variable definitions are provided in Table 1.
37
TABLE 3
The Association between Suspect Firms and Abnormal Discretionary Expenses
Dependent Variable:
Firm-
Specific
REM
Traditional
REM
Firm-
Specific
REM
Traditional
REM
(1) (2) (3) (4)
Intercept ? -0.0130*** -0.0347*** -0.0177*** -0.0927***
(<.0001) (<.0001) (<.0001) (<.0001)
Sizet ? 0.0004** 0.0026*** 0.0006** -0.0149***
(0.0394) (<.0001) (0.0268) (<.0001)
MTBt ? 0.0004* -0.0238*** 0.0008*** 0.0251***
(0.0956) (<.0001) (0.0036) (<.0001)
ROAt ? -0.0154*** -0.2642*** 0.0135*** -0.2608***
(<.0001) (<.0001) (0.0087) (<.0001)
Bench_NIt - -0.0051** -0.0108*
(0.0284) (0.0579)
Bench_AFt - -0.0037*** 0.0046
(0.0044) (0.1130)
N 31,782 31,782 15,039 15,039
Adjusted R2 0.0007 0.0705 0.0018 0.0847
This table presents robust regression estimation results for Model (2) in which the dependent variable is
Abnormal Discretionary Expense (AbnDisExpt) using either the Firm-Specific or the Traditional
methodology. For Columns (1) and (2), the variable of interest is Bench_NIt, and for Columns (3) and (4),
the variable of interest is Bench_AFt. P-values are reported below the coefficient estimates and are based
on t-statistics clustered by firm. All variables are winsorized at the 1st and 99th percentiles. Variable
definitions are provided in Table 1. ***, **, and * represent significance at the 1%, 5%, and 10% levels,
respectively, using a two-tailed t-test, except for on Bench_NIt and Bench_AFt, where a directional
prediction is made, and one-tailed t-tests are used.
38
TABLE 4
The Association between Blip Suspect Firms and Meeting or Just Beating Zero Earnings or
Last Year’s Earnings
Firm-Specific Traditional
(1) (2)
Intercept ? -2.4910*** -2.4712***
(<.0001) (<.0001)
Firm-Specific_Blipit ? 0.3082***
(0.0008)
Traditional_Blipit ? -0.0464
(0.8026)
Sizeit ? 0.0358*** 0.0359***
(0.0013) (0.0012)
MTBit ? -0.0125* -0.0128*
(0.0746) (0.0690)
BigNit ? 0.1314*** 0.1301***
(0.0079) (0.0086)
ROAit ? -0.3145*** -0.3136***
(<.0001) (<.0001)
Lossit ? -1.9686*** -1.9642***
(<.0001) (<.0001)
N 31,728 31,728
Adjusted R2 0.0194 0.0190
This table presents robust regression estimation results for Model (3) in which the dependent variable is
Blipt, using either the Firm-Specific or the Traditional methodology. For Column (1), the variable of
interest is Firm-Specific_Blipt, and for Column (2), the variable of interest is Traditional_Blipt. P-values
are reported below the coefficient estimates and are based on t-statistics clustered by firm. All variables
are winsorized at the 1st and 99th percentiles. Variable definitions are provided in Table 1. ***, **, and *
represent significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test.
39
TABLE 5
The Association between Abnormal Discretionary Expenses and Future Performance
Dependent Variable: ROAt+1 CFOt+1
(1) (2) (3) (4)
Intercept - -0.0283** -0.0277** -0.0545*** -0.0545***
(0.0208) (0.0227) (<.0001) (<.0001)
Beatt + 0.0181** 0.0178** 0.0270*** 0.0269***
(0.0123) (0.0133) (<.00001) (<.0001)
Just_Misst ? -0.0075 -0.0079 0.0112 0.0112
(0.5179) (0.4928) (0.2029) (0.2033)
Bench_NIt ? -0.0238* -0.0129 0.0194** 0.0171***
(0.0655) (0.1695) (0.0216) (0.0019)
Neg_AbnDisExpt ? -0.0032 0.0017
(0.2235) (0.2578)
Bench_NIt* Neg_AbnDisExpt ? 0.0204 -0.0056
(0.1348) (0.5762)
Low_AbnDisExpt ? -0.0074*** -0.0015
(0.0095) (0.4588)
Bench_NIt*Low_AbnDisExpt ? 0.0071 -0.0063
(0.6435) (0.5731)
ROAt + 0.5024*** 0.5022***
(<.0001) (<.0001)
CFOt + 0.5249*** 0.5247***
(<.0001) (<.0001)
Sizet ? 0.0071*** 0.0069*** 0.0052*** 0.0052***
(<.0001) (<.0001) (<.0001) (<.0001)
MTBt + 0.0027*** 0.0027*** 0.0020*** 0.0020***
(<.0001) (<.0001) (<.0001) (<.0001)
Returnt + 0.0411*** 0.0410*** 0.0184*** 0.0183***
(<.0001) (<.0001) (<.0001) (<.0001)
ZScoret-1 + 0.0112*** 0.0112*** 0.0062*** 0.0062***
(<.0001) (<.0001) (<.0001) (<.0001)
Industry FE Included Included Included Included
Year FE Included Included Included Included
N 21,026 21,026 21,026 21,026
Adjusted R2 0.6237 0.6237 0.6372 0.6372
40
This table presents robust regression estimation results for Model (4) in which the dependent variable is
ROAt+1 in Columns (1) and (2) and CFOt+1 in Columns (3) and (4). In Columns (1) and (3) the variable of
interest is Bench_NIt*Neg_AbnDisExpt and in Columns (2) and (4) the variable of interest is
Bench_NIt*Low_AbnDisExpt. P-values are reported below the coefficient estimates and are based on t-
statistics clustered by firm. All variables are winsorized at the 1st and 99th percentiles. Variable definitions
are provided in Table 1. Industry fixed-effects (two digit SIC code) and year fixed-effects are included in
the model but are not reported. Variable definitions are provided in Table 1. ***, **, and * represent
significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test, except where a
directional prediction is made, and a one-tailed t-test is used.
41
TABLE 6
The Impact of High Ability Managers on the Association between Abnormal Discretionary
Expenses and Future Performance
Dependent Variable: ROAt+1 CFOt+1
(1) (2)
Intercept ? -0.0140*** -0.0419***
(0.0002) (<.0001)
Firm-Specific_REMt ? -0.0171 -0.0222
(0.3786) (0.1075)
HAMt ? 0.0092*** 0.0530***
(<.0001) (<.0001)
Firm-Specific_REMt*HAMt ? 0.0685*** -0.0229
(0.0044) (0.6281)
ROAt + 0.5430***
(<.0001)
CFOt + 0.5130***
(<.0001)
Sizet ? 0.0059*** 0.0053***
(<.0001) (<.0001)
MTBt + 0.0035*** 0.0030***
(<.0001) (<.0001)
Returnt + 0.0458*** 0.0205***
(<.0001) (<.0001)
ZScoret-1 + 0.0072*** 0.0050***
(<.0001) (<.0001)
Industry FE Included Included
Year FE Included Included
N 21,053 21,026
Adjusted R2 0.6939 0.6375
This table presents robust regression estimation results for Model (5) in which the dependent variable is
ROAt+1 in Columns (1) and (2) and CFOt+1 in Columns (3) and (4). In Columns (1) and (3) the variable of
interest is Firm-Specific_REMt*HAMt. P-values are reported below the coefficient estimates and are based
on t-statistics clustered by firm. All variables are winsorized at the 1st and 99th percentiles. Variable
definitions are provided in Table 1. Industry fixed-effects (two digit SIC code) and year fixed-effects are
included in the model but are not reported. Variable definitions are provided in Table 1. ***, **, and *
represent significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test, except where
a directional prediction is made, and a one-tailed t-test is used.