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A Diagnostic for Earnings Management Using Changes
in Asset Turnover and Prot Margin*
IVO PH. JANSEN, Rutgers UniversityCamden
SUNDARESH RAMNATH, University of Miami
TERI LOMBARDI YOHN, Indiana University
1. Introduction
Identifying earnings management is important for nancial
statement users to assesscurrent economic performance, to predict
future protability, and to determine rm value.However, it is often
difcult and time-consuming to identify earnings management,
espe-cially in generic settings where an obvious incentive to
manage earnings is absent. Whileacademic research has used numerous
proxies for (or diagnostics of) earnings manage-ment, most recent
studies use accruals models to decompose total accruals into a
normal,economics-driven component and an abnormal, earnings
management component.1
McNichols (2000) points out, however, that there is limited
theory about how accrualsshould behave in the absence of
discretion, and Fields, Lys, and Vincent (2001) argue thatthe use
of existing accruals models may lead to serious inference
problems.
In DuPont analysis, a rms return on assets is decomposed into
asset turnover (ATO,the ratio of sales to net operating assets) and
prot margin (PM, the ratio of operatingincome to sales), and
nancial statement analysis textbooks broadly advocate making
thisdecomposition when investigating protability and changes in
protability (see, e.g.,White, Sondhi, and Fried 2003; Palepu,
Bernard, and Healy 2004; Penman 2007; Stickney,Brown, and Wahlen
2004; Lundholm and Sloan 2004). In this study, we propose a
simplediagnostic of earnings management that relies on the widely
held notion underlyingDuPont analysis that sales is a fundamental
driver of a rms investment and income, andthat net operating assets
on the balance sheet and net operating income on the
incomestatement should vary directly with sales. In other words,
changes in ATO or PM warrantfurther investigation in quality of
earnings analyses. Moreover, we note that changes inATO and PM in
opposite directions could signal earnings management. We base this
obser-vation on the articulation of the income statement and
balance sheet, which ensures thatearnings management affects
operating income and net operating assets in the same direc-tion,
and thus causes ATO and PM to move in opposite directions. For
example, for agiven level of sales, if a rm manages earnings upward
by understating bad debt expense,both net income relative to sales
and the net realizable value of accounts receivable relative
* Accepted by K.R. Subramanyam. We thank Patricia Faireld for
her contributions to the paper, as well as
Bill Baber, Walt Blacconiere, Bill Brown, Dave Burgstahler, Prem
Jain, Chris Jones, Bin Ke, Jim Ohlson,
Scott Richardson, D. Shores, and seminar participants at George
Washington University, Georgetown Uni-
versity, Michigan State University, University of Washington,
Morgan State University, University of Min-
nesota, Rutgers UniversityCamden, Suffolk University, Loyola
Marymount University, University of New
Hampshire, Villanova University, the Financial Economics and
Accounting Conference, and the University
of Utah Winter Accounting Conference. We also thank Glass Lewis
& Co. for the restatement data. Teri
Yohn acknowledges the generous support of the
PricewaterhouseCoopers Fellowship.
1. See, for example, Healy 1985; DeAngelo 1986; Jones 1991;
Dechow, Richardson, and Tuna 2003; and
Kothari, Leone, and Wasley 2005.
Contemporary Accounting Research Vol. 29 No. 1 (Spring 2012) pp.
221251 CAAAdoi:10.1111/j.1911-3846.2011.01093.x
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to sales will be overstated. The increase in net income relative
to sales will lead to anincrease in PM, while the increase in net
accounts receivable relative to sales will lead to adecrease in
ATO. In general, upward earnings management causes PM to increase
andATO to decrease, while downward earnings management causes PM to
decrease and ATOto increase.
We rely on this observation to argue that contemporaneous,
directionally oppositechanges in a rms ATO and PM can serve as a
signal of potential earnings management.Specically, we propose and
investigate the usefulness of contemporaneous increases inPM and
decreases in ATO as a diagnostic for upward earnings management,
and ofcontemporaneous decreases in PM and increases in ATO as a
diagnostic for downwardearnings management.
The above relations between earnings management and ATO PM hold
when the rela-tion between net operating assets and sales is stable
and when earnings are managedthrough expenses. When earnings are
managed through sales, the relations will hold if theprot margin on
the managed sales is greater than the prot margin on unmanaged
salesand if the asset turnover of the managed sales is less than
the asset turnover of unmanagedsales. The ATO PM diagnostic further
assumes that a company has not changed its strat-egy and has
constant growth rates in investment. A Type I error could occur if
a companychanges its strategy or experiences unexpected growth. The
ATO PM diagnostic alsoassumes that a company does not manage
earnings through cash ows. A Type II errorcould occur if a rm
manages earnings upward by delaying, for example, advertising
orresearch and development expenditures.
Because earnings management is not directly observable, we
cannot perform directtests to validate the ATO PM earnings
management diagnostic. Instead, we rely on priorresearch which
documents situations and outcomes indicative of earnings
managementand show that the ATO PM diagnostic is associated with
these earnings managementscenarios. We also suggest that
directionally opposite changes in ATO and PM can be auseful
complement to abnormal accruals in detecting earnings management in
academicresearch. Therefore, in all tests, we compare the relative
and incremental informationcontent of the ATO PM diagnostic to
performance-adjusted abnormal accruals, a widelyaccepted proxy for
earnings management. (For recent studies that use abnormal
accrualssee, e.g., Cohen, Dey, and Lys 2008; Gong, Louis, and Sun
2008; Zhao and Chen2008.)2
Relying on prior research which suggests that rms manage
earnings upward to meetor beat analyst forecasts (e.g., Burgstahler
and Eames 2006; Matsumoto 2002), we rstexamine the association
between the ATO PM diagnostic and rms propensity to meetor beat
earnings expectations. We nd that the ATO PM diagnostic provides
informa-tion about the likelihood of a rm meeting or beating
expectations, even after control-ling for performance-adjusted
abnormal accruals. Moreover, we nd that the ATO PMmeasure has
signicantly greater discriminating ability than
performance-adjusted abnor-mal accruals in identifying rms that
meet or beat earnings expectations.
Second, we argue that when rms beat or miss earnings
expectations by a wide mar-gin, they are more likely to manage
earnings downward (i.e., smooth earnings or take abath). We nd that
the ATO PM diagnostic provides information about the likelihoodof a
rm experiencing an extreme earnings surprise. Once again the ATO PM
diagnostic
2. We estimate performance-adjusted abnormal accruals using the
abnormal accruals model from Dechow et
al. 2003 (259, model 3), augmented with protability as an
independent variable to control for perfor-
mance. All results reported in the paper are qualitatively
similar if we omit the performance adjustment,
or if we estimate more basic versions of the abnormal accruals
model.
222 Contemporary Accounting Research
CAR Vol. 29 No. 1 (Spring 2012)
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has signicantly greater discriminating ability than
performance-adjusted abnormal accru-als in identifying rms that
report extreme earnings surprises.
Third, a growing body of research uses subsequent earnings
restatements as an indica-tor of earnings management (Richardson,
Tuna, and Wu 2002; Kedia 2003). We nd thatthe ATO PM diagnostic
provides information about the likelihood of a rm
subsequentlyrestating earnings. In comparison, performance-adjusted
abnormal accruals are unrelatedto future earnings restatements.
Finally, earnings management temporarily inates or deates
earnings articially andshould therefore lead to a reversal in
future protability (Penman 2007: 633). In addition,failure of the
stock market to see through the earnings management will lead to
predictablefuture returns (e.g., Xie 2001). We nd that both the ATO
PM diagnostic and performance-adjusted abnormal accruals are
useful, and incrementally informative, for identifying
futureearnings reversals and future abnormal returns.
Based on these ndings, we conclude that contemporaneous,
directionally oppositechanges in ATO and PM are informative about
earnings management, even after control-ling for
performance-adjusted abnormal accruals, a widely accepted earnings
managementproxy. In addition, the ATO PM diagnostic has signicantly
greater discriminating abilitythan performance-adjusted abnormal
accruals in identifying rms that meet or beat expec-tations, report
extreme earnings surprises, and subsequently restate earnings.
As a diagnostic of earnings management, the ATO PM measure has
several appeal-ing features. First, the measure relies on
fundamental relations in the accounting model,as opposed to
estimated relations typically used in abnormal accruals models.
Second,ATO and PM are primary ratios in nancial statement analysis
that are likely to beinvestigated by many users of nancial
statements, even when they are not explicitlyconsidering earnings
management. In addition, unlike abnormal accruals measures
whichrequire nancial statement data from a substantial time series
or even an entire industry,the ATO PM diagnostic can be computed
for any rm using very few years of rm-leveldata. In practical
terms, our ndings suggest that nancial statement users will
benetfrom investigating the possibility that a rm has managed its
earnings upward (down-ward) when there is a contemporaneous
increase (decrease) in the rms PM anddecrease (increase) in its
ATO. In short, we believe that the ATO PM diagnostic can beused in
academic and investment research as a (complementary) diagnostic of
earningsmanagement.
The paper proceeds as follows. The next section provides the
background and motiva-tion for our earnings management diagnostic.
In section 3, we discuss our sample, variablemeasurement, and
descriptive statistics. In section 4, we describe our analyses and
reportour ndings. In section 5, we summarize and conclude the
paper.
2. Background and motivation
Prior literature
Healy and Wahlen (1999) argue that earnings management occurs
when managers usejudgment in nancial reporting to alter nancial
reports to mislead stakeholders about theunderlying economic
performance of the company. Dechow and Skinner (2000) argue
thatearnings management could arise from accounting choices that
are fraudulent or fromchoices that are aggressive, but acceptable,
uses of accounting discretion. Identifying bothforms of earnings
management is important to investors for assessing rm value;
however,it is often difcult to do so, in part because many
discretionary earnings components arenot separately observable.
This task is further complicated when earnings managementoccurs in
the absence of an obvious incentive to manage earnings, such as
preceding anequity offering or a leveraged buyout. Thus, it is
important for nancial statement users
A Diagnostic for Earnings Management 223
CAR Vol. 29 No. 1 (Spring 2012)
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and academic researchers to have diagnostics for earnings
management that are informa-tive even when no obvious incentive to
manage income exists.
In quality of earnings analyses, one is generally concerned when
growth in net oper-ating assets exceeds growth in sales. Such a
scenario could suggest that a company isinappropriately recording
costs on the balance sheet instead of the income statement.The most
popular proxy for earnings management abnormal accruals, estimated
usingsome version of the Jones 1991 model builds on this idea and
adjusts total accruals(i.e., growth in working capital less
depreciation expense) for the amount of accrualsexplained by
changes in sales and property, plant, and equipment. There are
severalvariations of this model. For example, the modied Jones
model (see Dechow, Sloan,and Sweeney 1995) subtracts growth in
accounts receivable from growth in sales whencalculating
nondiscretionary accruals, to avoid the assumption, implicit in the
Jones 1991model, that earnings are not managed through sales.
Dechow et al. (2003) furtherenhance the model by including an
estimation of the relation between the change inreceivables and the
change in sales to avoid the assumption implicit in the modiedJones
model that the entire change in accounts receivable stems from
revenue-based earn-ings management and by including prior total
accruals. Kothari et al. (2005) suggestthat to isolate abnormal
accruals researchers should control for rm performance in
theestimation model as well. Regardless of the specic model, the
model parameters are gen-erally estimated using annual,
cross-sectional regressions within two-digit SIC codes, orusing
time-series regressions by rm. The estimates are then used to
calculate nondiscre-tionary accruals as the predicted value of
total accruals, and the difference between totalaccruals and
nondiscretionary accruals is deemed discretionary and is used as a
proxy formanaged earnings.
Bernard and Skinner (1996: 31617) argue that there are likely to
be important omit-ted variables in explaining working capital
accruals, and that any nonlinearity in the rela-tion between growth
in working capital and the explanatory variables will
createmeasurement error in estimating discretionary accruals. In
addition, when the model isestimated cross-sectionally in an
industry, it is assumed that all rms in that industry havethe same
strategy and relation between accruals and the explanatory
variables. Alterna-tively, when the model is estimated by rm, one
needs a sufcient estimation period to cal-culate the parameter
estimates. Bernard and Skinner (1996) argue that the
cross-sectionalestimates are very imprecise, even when estimated
within two-digit industry codes, andthat the time-series estimates
are even less precise. The abnormal accruals models, there-fore,
make signicant assumptions that may or may not hold. Moreover, the
choice ofexplanatory variables to capture the drivers of accruals
is ad hoc, and the models lacktheoretical support.
Barton and Simko (2002) develop a measure of past earnings
management that ismore grounded in the accounting model. They argue
that rms that have aggressivelycapitalized expenditures will have
high net operating assets relative to sales and, there-fore, rms
with bloated balance sheets are more likely to have managed
earnings upwardin the past. However, their metric assumes that all
bloat in the balance sheet is due toearnings management and does
not consider other plausible reasons why some rmsmay have a higher
ratio of net operating assets to sales than others, such as
differencesin strategy or protability. In addition, while the
metric is potentially useful for identify-ing past earnings
management, it is less useful for identifying earnings management
inthe current period.
In short, existing proxies for earnings management build on the
intuition that growthin net operating assets should be accompanied
by growth in sales. As discussed above,however, these proxies have
several limitations. In this study, we propose a new diagnosticfor
earnings management that builds on similar intuition, but exploits
the accounting
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model to gain insight into when changes in the ratio of sales to
net operating assets thatis, changes in the asset turnover ratio
are likely due to earnings management.3
Hypotheses
It is well accepted that sales is the fundamental driver of net
operating income on theincome statement and net operating assets on
the balance sheet. Indeed, most nancialstatement analysis textbooks
advocate forecasting income statement and balance sheet lineitems
based on sales forecasts. For example, Penman (2007: 559) provides
a frameworkfor forecasting and states that sales forecasting is the
starting point. This intuition,which also underlies the widely used
DuPont analysis, is that there should be a stable rela-tion between
sales and both operating income on the income statement and net
operatingassets on the balance sheet. These relations are captured
by the ATO and PM ratios:ATO = Sales Net operating assets;PM =
Operating income Sales.
We argue that ATO and PM should remain relatively constant in a
stable operatingenvironment and that, in quality of earnings
analysis, changes in ATO and or PM warrantfurther investigation. We
also argue that one should be particularly concerned when ATOand PM
change in opposite directions as this could signal earnings
management. This isbased on the fact that the articulation of the
income statement and balance sheet forcesearnings management to
affect operating income and net operating assets in the
samedirection. This is apparent from the denition of net operating
assets:
Net operating assetst Net operating assetst1 DWorking
capitaltDepreciation expenset DLong-term net operating assetst
Net operating assetst1 Operating incomet Cash from operationst
DLong-term net operating assetst
Thus, assuming that earnings are not managed through cash ows
(e.g., real earningsmanagement), any upward management of operating
income will also overstate netoperating assets. Because operating
income is the numerator of PM and net operatingassets is the
denominator of ATO, upward earnings management increases PM
anddecreases ATO, while downward earnings management decreases PM
and increases ATO.We therefore propose that directionally opposite
changes in the PM and ATO ratios canbe used as a diagnostic for
earnings management. We state our rst set of hypotheses
asfollows:4
Hypothesis 1a. Contemporaneous increases in PM and decreases in
ATO signal upwardearnings management.
Hypothesis 1b. Contemporaneous decreases in PM and increases in
ATO signal down-ward earnings management.
3. McNichols (2000) suggests three methods to identify earnings
management: (i) using aggregate accrual
models such as the Jones 1991 model, (ii) examining the behavior
of specic accruals, and (iii) examining
the distribution of earnings after management. Our diagnostic is
in the spirit of the aggregate accrual
models, in that the aim is to identify general earnings
management. We do not examine specic accruals
because earnings management is likely to occur in accounts that
may not be separately reported in the
nancial statement and footnotes. We use the distribution of
realized earnings to test whether our diagnos-
tic is informative about earnings management.
4. All hypotheses are stated in the alternative form.
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CAR Vol. 29 No. 1 (Spring 2012)
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The ATO PM earnings management diagnostic is in the same spirit
as abnormal accrualsmodels, in that it also attempts to identify
discretionary growth on the balance sheet; how-ever, the ATO PM
diagnostic exploits the accounting model to produce additional
insightsabout earnings management over those obtained from abnormal
accruals models. Forexample, consider a rm that invests in current
operating assets in anticipation of futuresales growth. In this
case, abnormal accruals would likely be positive, even in the
absenceof upward earnings management, because additional
investments in working capital (i.e.,additional accruals) would not
necessarily be accompanied by current sales growth. TheATO PM
diagnostic, on the other hand, would not suggest upward earnings
managementfor this scenario, because even though ATO would
decrease, investments in operatingassets in anticipation of future
sales growth will not affect current PM. We therefore pro-pose that
directionally opposite changes in ATO and PM can be a useful
complement toabnormal accruals in detecting earnings management in
academic research. This leads toour second hypothesis:
Hypothesis 2. The ATO PM diagnostic provides incremental and
greater relativeinformation content over abnormal accruals in
identifying earnings management.
More insights into the ATO PM earnings management diagnosticThe
argument made above, which underlies the ATO PM diagnostic, is that
under fairlygeneral conditions upward earnings management increases
PM and decreases ATO anddownward earnings management decreases PM
and increases ATO. These relations holdfor (noncash) expense
management when there is no change in business strategy and
whenthere is neutral accounting or aggressive conservative
accounting with constant growth innet operating assets.5 The
relations do not hold for all revenue management cases, how-ever,
because the numerators and denominators of both ATO and PM would
change. Thediagnostic will correctly classify (upward) earnings
management if the revenue manage-ment causes PM to increase and ATO
to decrease. In other words, the diagnostic willcorrectly signal
earnings management if: (i) the prot margin on the managed revenues
isgreater than the prot margin on unmanaged revenue and (ii) the
asset turnover of themanaged revenue is less than the unmanaged
asset turnover. This rst condition is verylikely because prot
margin on normal revenue will be reduced by both product and
per-iod costs, whereas managed revenue is likely accompanied by
only additional productcosts, not additional period costs.
The second condition is less clear-cut. If the company does not
accrue any expensesrelated to the managed component of sales, the
ATO of managed sales will be equal toone because the numerator
(sales) will equal the denominator (receivables). In our sam-ple,
82 percent of the rm-year observations have ATO greater than one,
which makesit likely that, in the absence of related expenses being
accrued, the second condition willalso generally be satised.
However, sales management will most likely be accompaniedby some
accrued expenses which will decrease net operating assets (e.g.,
decreases ininventory, increases in payables) resulting in the ATO
of managed sales being greaterthan one. In these instances, the
second condition may or may not be satised. Expensesaccrued on
managed sales are not observable, and therefore we are unable to
provideempirical estimates of how frequently rms in our sample meet
or do not meet the sec-ond condition. In short, the ATO PM
diagnostic will identify most expense-based earn-ings management,
but can only capture sales management in certain situations, which
is
5. See Penman 2007 (593603) and Rajan, Reichelstein, and Soliman
2007 for discussions of the interaction
between growth and accounting methods and their effects on
nancial ratios.
226 Contemporary Accounting Research
CAR Vol. 29 No. 1 (Spring 2012)
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a limitation of the diagnostic. As a result, the diagnostic is
more prone to Type II errors(failure to identify earnings
management) than Type I errors (falsely agging
earningsmanagement).
The ATO PM diagnostic assumes that a company has not changed its
strategy andhas constant growth rates in investment. A Type I error
could occur if a company changesits strategy or experiences
unexpected growth. For example, if a rm changes its strategyfrom
low-margin high-turnover to high-margin low-turnover, the ratios
would most likelymove in opposite directions even in the absence of
earnings management. Under this sce-nario, however, it is also
likely that a rms abnormal accruals would be signicantly non-zero,
because the change in strategy would probably be accompanied by
signicantaccruals (i.e., a signicant change in working capital)
that would be characterized byaccruals models as abnormal. In
addition, it seems more reasonable to assume that a rmwill continue
the same strategy over time than to assume that all rms in the same
indus-try have the same strategy, which is the implicit assumption
in cross-sectional estimates ofthe Jones model.6
The ATO PM diagnostic also assumes that a company does not
manage earningsthrough cash ows. A Type II error could occur if a
rm manages earnings upward bydelaying advertising or research and
development expenditures. In this case, PM wouldincrease because of
higher operating income, but ATO would be unaffected because of
theabsence of an accrual on the balance sheet. Of course, given the
latter, accruals modelswould also likely fail to detect such
earnings management.
3. Sample, variable denitions, and descriptive statistics
Sample
We obtain nancial statement data from the 2006 COMPUSTAT Annual
Industrial, FullCoverage, and Research tapes. Because funds from
operations (COMPUSTAT data item#110) which which we need to compute
cash from operating activities in years before1988 is not available
prior to 1971, and because we need year-ahead data for
severalvariables, our sample spans the years 1971 through 2005 (we
use the COMPUSTAT yearconvention). The nancial statement variables
we use in our study are available for118,679 rm-year observations.
We eliminate observations in which net operating assetsare negative
in year t)1 or year t, because ATO is undened for negative net
operatingassets (5,578 observations). We also eliminate all nancial
rms from our analyses (SIC60006999) because it is difcult to
distinguish between operating and nancial activitiesfor these rms
(2,931 of the remaining observations). After applying the above
screens,our primary sample consists of 110,170 rm-year
observations.7 Missing stock returnsaround the earnings
announcement date of the rst scal quarter of the next year
reducethe sample size to 67,075 observations for our abnormal
returns tests. When we intersectthe primary sample with nonmissing
analyst forecast data from I B E S, the sample sizedecreases to
46,522 observations. For the restatement analysis, the sample size
decreases to
6. Consider, for example, Walmart and Macys, which are in the
same four digit SIC code but have very dif-
ferent strategies. It seems more realistic to assume that each
rm continues its strategy over time than to
assume that the rms have similar strategies (i.e., forcing the
abnormal accruals model parameters to be
equal across all rms in the industry).
7. As a robustness test, we also ran the analyses after
eliminating rms that were involved in any divestitures
and or mergers or acquisitions, because these transactions can
cause the articulation between balancesheet changes and the income
statement to break down. Specically, we eliminated rm-year
observations
in which a rm discontinued operations or was involved in a
merger or acquisition (COMPUSTAT annual
footnote code #1) in year t)1, year t, or year t + 1. We also
deleted rm-years with increases in goodwillin year t)1, year t, or
year t + 1. The results are qualitatively similar to those reported
in the tables.
A Diagnostic for Earnings Management 227
CAR Vol. 29 No. 1 (Spring 2012)
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22,160 because we only have restatement data available for the
20002005 period. Weobtain our base sample of 2,319 restatements
from Glass Lewis.8
Variable measurement
We provide a detailed description of the denition and
measurement of all variables inTable 1. We dene the change in PM
(DPMt) and the change in ATO (DATOt) as follows:
DPMt operating incomet=salest operating incomet1=salest1;
andDATOt salest=net operating assetst salest1=net operating
assetst1:
We argue that a contemporaneous increase in PM and decrease in
ATO signals poten-tial upward earnings management, and that a
contemporaneous decrease in PM andincrease in ATO signals potential
downward earnings management. We dene two corre-sponding indicator
variables that represent our diagnostic for earnings
management:EM_UP for upward earnings management and EM_DN for
downward earnings manage-ment. They are dened as follows:
EM UPt one if DPMt>0;DATOt
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TABLE 1
Variable denitions
DPMt = change in PM = (operating incomet salest (COMPUSTAT data
item #12);where operating incomet = salest (#12) (cost of goods
sold (#41) + selling,
general and administrative expenses (#189) + depreciation and
amortization
expense (#14))t;
DATOt = change in ATO = (salest (COMPUSTAT data item #12) net
operating assetst) (salest)1 net operating assetst)1); where net
operating assetst = net assetst(#216) net nancial assetst; and net
nancial assetst = cash and short term
investments (#1) interest-bearing liabilitiest (#34 + #9).
EM_UPt = 1 if DPMt > 0, DATOt < 0, and EM_DNt)1 1, and 0
otherwise.EM_DNt = 1 if DPMt < 0, and DATOt > 0, and EM_UPt)1
1, and 0 otherwise.PABNACt = performance adjusted abnormal accruals
= the tted residual from the follow-
ing model:
TACt TAt)1 = a1(1 TAt)1) + a2((1 + k)(DREVt )DRECt) TAt)1)+
a3(PPEt TAt)1) + a4(TACt)1 TAt)1) + a5(RNOAt TAt)1)
+ et,
where:
TACt = income before extraordinary itemst (#18) cash from
opera-
tions (CFO)t;
TAt)1 = total assetst)1 (#6),
DREVt = changes in salest (#12),DRECt = change in receivablest
(#2);PPEt = gross property, plant, and equipmentt (#7);
RNOAt = return on net operating assets;
CFOt = net cash ow from operating activities (#308) in 1988
and
thereafter;
CFOt = funds from operationst (#110) change in current assetst
(#4)
+ change in cash and short-term investmentst (#1) + change
in current liabilitiest (#5) change in short term debtt
(#34)
prior to 1988;
where all variables above are deated by total assetst)1 (#6);
and k = the
slope 1 coefcient from the following model:
DRECt = b0 + kDREVt + u.PAB_UP = 1 if PABNAC is positive, and 0
otherwise;
PAB_DN = 1 if PABNAC is negative, and 0 otherwise.
SURP = actual earnings less the median analyst forecast of
earnings closest to the earn-
ings announcement date. MBE = 1 if SURP is between 0 and 2
cents, and 0
otherwise;
EES = 1 if SURP is in the top or bottom decile of SURP, and 0
otherwise;
RESTATE = the difference (in $ millions) between restated and
the originally reported oper-
ating income for year t;
DN_RESTATEt = 1 if the rm subsequently restates its operating
earnings down for year t, and 0
otherwise;
UP_RESTATEt = 1 if the rm subsequently restates its operating
earnings up for year t, and 0
otherwise;
NO_RESTATEt = 1 if the rm does not subsequently restated its
operating earnings for year t,
and 0 otherwise;
(The table is continued on the next page.)
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our diagnostic is intended to ag all potential earnings
management, we do not use perfor-mance-matched abnormal accruals in
our analyses, but instead use the alternativeapproach suggested in
Kothari et al. 2005, in which rm performance is included in
theaccruals estimation.
Accordingly, we add the rms return on net operating assets
(RNOA) for the currentperiod to the enhanced version of the Jones
1991 model suggested in Dechow et al. 2003(259, model 3) and
estimate abnormal accruals.11 Specically, we estimate
performance-adjusted abnormal accruals (PABNACt) as the tted
residual from the following model:
12
TACtTAt1
a1 1TAt1
a2 1 kDREVt DRECt1
TAt1
a3 PPEt
TAt1
a4 TACt1TAt1
a5 RNOAt
TAt1
et 1
where:
TACt = income before extraordinary itemst cash from operations
(CFO)t;TAt)1 = total assetst)1,;DREVt = changes in salest;DRECt =
change in receivablest;PPEt = gross property, plant and
equipmentt;RNOAt = return on net operating assets;CFOt = net cash
ow from operating activities (for rm-years from 1988);
= funds from operationst change in current assetst + change in
cash andshort-term investmentst + change in current liabilitiest
change in shortterm debtt (for rm-years prior to 1988); and
k = the slope coefcient from the following model:DRECt = b0 +
kDREVt + u.
TABLE 1 (Continued)
ABRETt+1 = the size-adjusted cumulative abnormal return over the
three-day period centered on
next years rst scal quarter earnings announcement;
IRNOAt = average of RNOAt for the 2-digit SIC code to which the
rm belongs (excluding the
rm);
MTBt = the market-to-book ratio dened as the ratio of the rms
market value of equity
(#25 * #199) divided by its book value (#60) at the end of the
scal year;
MVEt = the rms market value of equity (#25 * #199);
RNOAt = return on net operating assets = operating incomet
average net operating assetst;where average net operating assetst =
(net operating assetst + net operating
assetst)1) 2;DRNOAt = change in return on net operating assets =
RNOAt RNOAt)1;NOAt = net operating assetst salest; andDNOAt =
change in net operating assets = (net operating assetst net
operating assets
t)1) net operating assetst)1.
11. Dechow et al. (2003) also modify the Jones model by
including forward-looking sales growth. We do not
use this variation because our purpose is to identify earnings
management using current, not future, nan-
cial statement information. In addition, Dechow and Dichev
(2002) suggest an alternative model that cap-
tures earnings quality over an extended time period. We do not
examine this measure because our focus is
on short-horizon earnings management behavior, not long-run
earnings quality.
12. We suppress rm subscripts.
230 Contemporary Accounting Research
CAR Vol. 29 No. 1 (Spring 2012)
-
Following DeFond and Jiambalvo 1994, Subramanyam 1996, and Xie
2001, weestimate the model in cross-section, for each two-digit SIC
code and year combination.13
While PABNAC is a continuous measure in our primary analyses, we
also examine anindicator variable, PAB_UP (PAB_DN), which equals
one if PABNAC is positive (nega-tive), and zero otherwise, as
indicators of upward (downward) earnings management.
We examine ve variables that are intended to capture the
consequences of earningsmanagement, or earnings management
outcomes, to determine if the ATO PM diagnos-tic is predictive of
these outcomes. Specically, we examine if the diagnostic is useful
inidentifying rms that meet or just beat analyst forecasts (MBE),
report extreme earningssurprises (EES), subsequently restate
earnings upward (UP_RESTATE) or downward(DN_RESTATE), experience a
reversal in year-ahead protability (DRNOAt+1) or pro-duce
predictable year-ahead abnormal returns (ABRETt+1). We dene the
variables asfollows:MBE = one if SURP is greater than or equal to
zero and less than $0.02, and
zero otherwise;whereSURP = actual earnings per share less the
consensus median analyst forecast
of earnings per share closest to the earnings announcement
date;14
EES = one if SURP is in the top or bottom decile of SURP, and
zero other-wise;
DN_RESTATEt = one if the rm subsequently restates its operating
earnings down foryear t, and zero otherwise;
UP_RESTATEt = one if the rm subsequently restates its operating
earnings up for yeart, and zero otherwise;
DRNOAt = RNOAt+1 RNOAt; andABRETt+1 = the size-adjusted
cumulative abnormal return over the three-day per-
iod centered on next years rst scal quarter earnings
announcement.We dene control variables as follows:15
IRNOAt = average RNOAt for two-digit SIC code to which the rm
belongs(excluding rm);
MVE = market value of equity;MTB = MVE book value of equity at
the end of the scal year;RNOAt = operating incomet average net
operating assetst;DRNOAt = RNOAt RNOAt)1;NOAt = net operating
assetst salest;16 andDNOAt = (NOAt NOAt)1) NOAt)1.
13. Consistent with these studies, we require at least six
observations for a given combination to be included
in the sample. We also performed the analyses using the Jones
model and the modied Jones model to cal-
culate abnormal accruals. The results (untabulated) using these
alternative abnormal accrual models are
similar to those reported in the tables.
14. We also dened MBE as analyst forecast errors greater than or
equal to zero but less than $0.01. The
results are similar to those reported in the tables.
15. Note that we use ending net operating assets in computing
ATO as well as for the change in net operating
assets so as to better capture earnings management that occurs
at the end of the year. On the other hand,
we compute return on net operating assets using average net
operating assets in the denominator because
the variable is included to control for the overall protability
of the company for the year. Redening
RNOA using ending net operating assets in the denominator does
not alter our conclusions.
16. We use the term NOA to describe this variable to be
consistent with Barton and Simko 2002 but note that
since net operating assets is scaled by sales, the ratio reects
the inverse of the ATO ratio. Excluding this
variable from the analyses does not alter our conclusions.
A Diagnostic for Earnings Management 231
CAR Vol. 29 No. 1 (Spring 2012)
-
Descriptive statistics
Table 2 presents descriptive statistics, sample correlations,
and univariate analyses. Allvariables are winsorized at the 1st and
99th percentiles to mitigate the inuence of outliers.The
descriptive statistics in panel A suggest that the ATO PM
diagnostic identies poten-tial upward earnings management in 14.8
percent of the sample observations and down-ward earnings
management in 17.3 percent of the sample observations. EM_UP
and
TABLE 2
Descriptive statistics
Panel A: Descriptive statisticsa
Variable Mean Std Dev 25% Median 75%
EM_UPt 0.148 0.355 0.000 0.000 0.000
EM_DNt 0.173 0.378 0.000 0.000 0.000
PABNACt )0.008 0.386 )0.071 )0.002 0.065PAB_UPt 0.495 0.499
0.000 0.000 1.000
DPMt 0.005 0.275 )0.027 0.000 0.025DATOt 0.052 1.369 )0.225
0.023 0.265IRNOAt )0.038 0.092 )0.080 )0.022 0.034MTBt 2.38 3.41
0.87 1.55 2.78
MVEt 969.28 3291.29 14.33 67.16 388.33
RNOAt 0.000 0.508 )0.030 0.095 0.194DRNOAt )0.008 0.316 )0.070
)0.002 0.056NOAt 0.795 1.083 0.333 0.510 0.796
DNOAt 0.207 0.658 )0.056 0.071 0.259MBEt 0.243 0.429 0.000 0.000
0.000
EESt 0.200 0.399 0.000 0.000 0.000
DN_RESTATEt 0.016 0.126 0.000 0.000 0.000
UP_RESTATEt 0.011 0.105 0.000 0.000 0.000
DRNOAt+1 )0.018 0.352 )0.070 )0.002 0.055ABRETt+1 0.006 0.093
)0.034 0.000 0.039
Panel B: Sample Pearson correlations
Variable DPMt DATOt IRNOAt MTBt MVEt RNOAt DRNOAt NOAt DNOAt
PABNACt EM_UPt EM_DNt
DPMt 1.00 0.08 )0.01 0.08 0.00 0.01 0.48 )0.02 0.06 0.10 0.10
)0.15
DATOt 1.00 )0.01 )0.01 )0.01 )0.11 0.05 )0.07 )0.56 )0.08 )0.22
0.20
IRNOAt 1.00 )0.17 )0.12 0.30 )0.02 )0.14 )0.07 )0.05 0.01
0.00
MTBt 1.00 0.22 0.01 0.11 )0.03 0.15 0.03 0.09 )0.07
MVEt 1.00 0.10 0.02 0.01 )0.01 )0.03 0.03 )0.03RNOAt 1.00 0.24
)0.10 0.02 0.01
0.10 )0.08DRNOAt 1.00 0.03 0.14 0.07 0.16 )0.18NOAt 1.00 0.15
)0.01 )0.10
)0.05DNOAt 1.00 0.14 0.25 )0.20PABNACt 1.00 0.09 )0.09EM_UPt
1.00 )0.19EM_DNt 1.00
(The table is continued on the next page.)
232 Contemporary Accounting Research
CAR Vol. 29 No. 1 (Spring 2012)
-
TABLE 2 (Continued)
Panel C: EM_UP, EM_DN, and PAB_UP by industry
Industry by rst digit of SIC code N EM_UP EM_DN PAB_UP
0 Agriculture and shing 338 14% 17% 44%
1 Extraction and construction 8,583 13% 16% 44%
2 Commodity production 19,812 15% 18% 46%
3 Manufacturing 42,815 14% 16% 50%
4 Utilities and transportation 5,949 15% 18% 49%
5 Wholesale and retail 15,696 17% 19% 50%
7 Business services and entertainment 12,128 16% 18% 55%
8 Health and other services 3,853 17% 19% 54%
9 Public administration 996 12% 19% 5%
Panel D: EM_UP, EM_DN, and PAB_UP by year
Year n EM_UP EM_DN PAB_UP
1971 1,929 12% 20% 50%
1972 1,894 12% 18% 50%
1973 1,908 14% 15% 50%
1974 1,991 12% 20% 50%
1975 2,215 9% 18% 48%
1976 2,913 12% 17% 49%
1977 2,767 15% 17% 51%
1978 2,697 17% 17% 49%
1979 2,589 13% 19% 50%
1980 2,510 11% 21% 51%
1981 2,548 13% 18% 47%
1982 2,673 11% 17% 44%
1983 2,788 16% 13% 45%
1984 2,953 15% 14% 52%
1985 3,020 14% 19% 47%
1986 3,031 16% 17% 48%
1987 2,665 15% 16% 45%
1988 3,003 12% 21% 50%
1989 3,283 14% 19% 45%
1990 3,318 14% 21% 46%
1991 3,404 13% 20% 46%
1992 3,507 14% 18% 46%
1993 3,596 17% 17% 49%
1994 3,791 18% 14% 49%
1995 3,935 19% 15% 50%
1996 4,096 19% 15% 52%
1997 4,453 21% 14% 49%
1998 4,390 19% 14% 50%
1999 4,137 18% 15% 58%
(The table is continued on the next page.)
A Diagnostic for Earnings Management 233
CAR Vol. 29 No. 1 (Spring 2012)
-
EM_DN thus identify approximately 32 percent of rms as having
managed earnings,which is in contrast to PABNAC which, by
construction, identies all rms as havingmanaged earnings either up
or down. The distributions of the control variables are consis-tent
with prior research (e.g., Faireld and Yohn 2001). The descriptive
statistics on theearnings management outcome variables suggest that
24.3 percent of the observations meetor beat analyst expectations
(MBE) while, by construction, 20 percent of the observationsare
classied as extreme earnings surprises (EES). Consistent with
restatements being anunusual event, only 1.6 (1.1) percent of the
observations experiences downward (upward)restatements of reported
earnings. The mean year-ahead change in return on net
operatingassets is )1.8 percent and size-adjusted abnormal returns
around the earnings announce-ment date of the rst scal quarter of
the subsequent year are 0.60 percent.
Panel B of Table 2 reports Pearson correlation coefcients
between the explanatoryvariables used in the study. Consistent with
information overlap in both diagnostics, thereis a signicant
positive correlation of 0.09 between EM_UP and PABNAC and a
signi-cant negative correlation of )0.09 between EM_DN and PABNAC.
EM_UP and EM_DNare signicantly correlated with most of the control
variables. None of the correlationsexceed 30 percent, however,
suggesting that EM_UP and EM_DN capture a substantialamount of
unique information relative to the other variables. All reported
correlations aresufciently low to mitigate concerns about
multicollinearity when estimating multivariatemodels.
In panel C of Table 2, we report the percentage of observations
with EM_UP,EM_DN and PAB_UP (the observations for which PABNAC is
positive) by industry.17
We dene industries by the rst digit of the rms primary SIC code.
We note that there issome variation in the frequency of EM_UP and
EM_DN across industries. Although wedo not perform formal tests for
differences across industries, we include industry xedeffects in
all of our tests to control for industry clustering.
In panel D of Table 2, we report the percentage of observations
with EM_UP,EM_DN, and PAB_UP by year. We note that the frequency of
EM_DN observations isgreater than that of EM_UP in 24 of the 35
years. The years in which EM_DN is lessfrequent are concentrated in
the period 1993 through 1999 when the EM_UP percentage is
TABLE 2 (Continued)
Year n EM_UP EM_DN PAB_UP
2000 4,109 14% 19% 58%
2001 4,018 9% 23% 46%
2002 3,832 14% 17% 60%
2003 3,715 15% 19% 53%
2004 3,573 16% 16% 52%
2005 2,932 15% 18% 47%
Notes:
a ABRETt+1 includes 67,075 observations; MBE EES includes 46,522
observations; RESTATEincludes 22,160 observations; all other
variables include 110,170 observations.
indicates that the correlation coefcient estimate is not
signicantly different from zero at the
10 percent level (two-tailed). All remaining correlation
coefcient estimates are signicantly
different from zero at the 10 percent level (two-tailed).
17. We do not report PAB_DN because the abnormal accruals model
classies all observations as either man-
aging earnings up or down, and therefore, PAB_DN = 1 )
PAB_UP.
234 Contemporary Accounting Research
CAR Vol. 29 No. 1 (Spring 2012)
-
markedly higher relative to other years. It is possible that rms
were under greater pres-sure to manage earnings up during the bull
market of the late 1990s. Alternatively, thesurge in EM_UP
observations may be related to the rise of Internet and high-growth
rmsduring this period. We also note that the EM_UP percentage drops
markedly and theEM_DN percentage increases in 2000 and 2001, likely
due to rms reporting conserva-tively subsequent to the accounting
scandals that surfaced during this period. We also notethat the
PAB_UP percentage is greater than 50 percent in only nine of the 35
years sug-gesting that, similar to the ATO PM diagnostic, PABNAC is
more often negative thanpositive. However, in contrast to the ATO
PM diagnostic, the PAB_UP percentage tendsto be greater than 50
percent in the later years of the sample.
4. Results
We present and discuss our results in two sections. First, we
present contingency tables toprovide insight into the univariate
relations between EM_UP, EM_DN, PAB_UP,PAB_DN, and the earnings
management outcome variables. Next, we present results
frommultivariate analyses that investigate the relation between the
earnings management out-come variables and the ATO PM
diagnostic.
Univariate analyses and ndings
Panel A in Table 3 shows the relation between EM_UP and PAB_UP,
and betweenEM_DN and PAB_DN. In the rst contingency table, we
report the percentage of obser-vations for which EM_UP is equal to
zero or one and PAB_UP is equal to zero or one.When PAB_UP is equal
to zero, EM_UP is equal to zero in 89.8 percent of observationsand
equal to one in 10.2 percent of observations. When PAB_UP is equal
to one, EM_UPis equal to zero in 80.6 percent of observations and
equal to one in 19.4 percent of obser-vations. This suggests that
the ATO PM diagnostic ags upward earnings managementalmost twice as
often when PABNAC is positive than when it is negative (19.4
percent vs.10.2 percent). Similarly in the EM_DN and PAB_DN
contingency table in panel A, theATO PM diagnostic ags downward
earnings management almost twice as often whenPABNAC is negative
than when it is positive (22.6 percent vs. 11.9 percent). A test of
pro-portions shows that these ratios are highly signicant, which
indicates that EM_UP agsupward earnings management much more often
when PABNAC is positive than when it isnegative, and that EM_DN ags
downward earnings management much more often whenPABNAC is negative
than when it is positive.
Panel B in Table 3 shows the relation between EM_UP, PAB_UP, and
rms that meetor beat expectations (MBE). We nd that PAB_UP
correctly ags more of the meet orbeat observations than EM_UP (51.2
percent versus 21.3 percent); however, PAB_UP alsoincorrectly ags
more of the non-MBE observations than EM_UP as having
managedearnings (48.9 percent versus 14.7 percent). This is not
surprising because there are morePAB_UP than EM_UP observations
with a value of one. To make a more meaningfulcomparison between
the two diagnostics, we rely on the following observation: if a
diag-nostic is useful in identifying a particular event, we expect
the diagnostic to ag the eventmore often when the event occurs (=
1) than when the event does not occur (= 0). Inother words, the
ratio of proportions, the former proportion over the latter
proportion,should be signicantly greater than one. The ratio of
proportions for PAB_UP is 1.05(51.2 percent over 48.9 percent),
which is signicantly greater than one (Z-statistic = 4.08;p-value
< 0.001) based on a test of proportions (Hildebrand, Ott, and
Gray 2005). Thecorresponding ratio for EM_UP is 1.45 (21.3 percent
over 14.7 percent), which is also sig-nicantly greater than one
(Z-statistic = 15.48; p < 0.001). Thus, both diagnostics
areuseful for identifying MBE, but EM_UP appears to be more
discriminating than PAB_UPbased on a comparison of the two ratios.
It is also evident from the contingency tables
A Diagnostic for Earnings Management 235
CAR Vol. 29 No. 1 (Spring 2012)
-
TABLE 3
Contingency tablesa
Panel A: The association between EM_UP and PAB_UP and between
EM_DN and PAB_DN
EM_UP
N
EM_DN
N0 1 0 1
PAB_UP 0 89.8% 10.2% 55,351 PAB_DN 0 88.1% 11.9% 54,819
1 80.6% 19.4% 54,819 1 77.4% 22.6% 55,351
Ratio of proportions 1.90 Ratio of proportions 1.90
Z-statistic 43.16 Z-statistic 47.59
p-value
-
that PAB_UP is more prone to Type I errors (agging observations
as having managedearnings when they have not), while EM_UP is more
prone to Type II errors (not identify-ing rms that may have managed
earnings). Indeed, all panels in this table conrm thehigher
propensity for Type I errors for the PABNAC diagnostic, and for
Type II errorsfor the ATO PM diagnostic.18
Panel C in Table 3 shows the relation between EM_DN, PAB_DN, and
rms thathave extreme earnings surprises (EES). As discussed
earlier, rms with EES are morelikely to manage earnings down;
consequently, we expect PAB_DN and EM_DN to bemore frequently equal
to one for EES rms than for other rms. The contingency tablesconrm
our expectation: the ratio of proportions where EM_DN is equal to
one whenEES is equal to one, compared to when EES is equal to zero,
is 1.40 (Z-statistic = 13.12;p < 0.0001). This indicates that
EM_DN ags downward earnings management muchmore frequently when a
rm reports extreme earnings surprises than when it does
not.Similarly, the corresponding ratio of proportions for PAB_DN
equals 1.10 (Z-statis-tic = 8.76; p < 0.001), conrming the
usefulness of PABNAC for identifying rms withextreme earnings
surprises as well. The evidence is consistent with the ability of
PAB_DNand EM_DN to identify possible downward earnings
management.
TABLE 3 (Continued)
EM_DN
N
PAB_DN
N0 1 0 1
Ratio of
proportions
1.18 Ratio of
proportions
1.05
Z-statistic 1.31 Z-statistic 1.01
p-value
-
Finally, panels D and E in Table 3 provide similar details with
respect to downward andupward earnings restatements. The proportion
of observations classied as having managedearnings up is not
signicantly higher for either EM_UP or PAB_UP when rms
restateearnings downward as opposed to when they do not. In other
words, neither EM_UP norPAB_UP appears to discriminate between rms
that may have initially managed earningsup from rms that do not
manage earnings. When rms subsequently restate earnings up,on the
other hand, EM_DN is signicantly more likely to ag these
observations as havinginitially managed earnings down (ratio =
1.58; Z-statistic = 3.73; p < 0.001). However,the proportion of
rms that PAB_DN identies as having managed earnings down is not
sig-nicantly higher when rms subsequently restate earnings up than
when they do not.
Taken together, Table 3 provides preliminary evidence of the
usefulness of theATO PM diagnostic for identifying earnings
management associated with rms meetingor beating analyst forecasts,
reporting extreme earnings surprises, and subsequently restat-ing
earnings upward. In addition, it appears that the ATO PM diagnostic
is at least asreliable in identifying earnings management as
PABNAC. We next investigate the relativeand incremental information
content of the two diagnostics in multivariate analyses, wherewe
also control for other variables known to be related to the
earnings management out-come variables.
Multivariate analyses and ndings
Meet or beat expectations
Prior research (e.g., Burgstahler and Eames 2006; Matsumoto
2002; Moehrle 2002; Chengand Wareld 2005) provides evidence that
rms manage earnings upward to avoid missinganalyst forecasts of
earnings. This suggests that rms that meet or just beat analyst
fore-casts are more (less) likely to have managed their earnings
upward (downward) than otherrms. We therefore predict that EM_UP
(EM_DN) is positively (negatively) associatedwith the incidence of
rms meeting or just beating analyst forecasts.19
Descriptive statistics for analyst forecast errors (SURP),
EM_UP, EM_DN andPABNAC by MBE are reported in panel A of Table 4.
We note that 11,286 rm-yearsmeet or just beat the analyst forecast.
We nd a mean EM_UP of 21.34 percent for MBErms compared to a mean
of only 14.69 percent for all other rms. This difference is
sta-tistically signicant at the one percent level. Our results also
show a mean EM_DN of13.61 percent for MBE rms compared to a mean of
17.52 percent for all other rms. Thisdifference is also signicant
at the one percent level. These univariate results are
consistentwith EM_UP and EM_DN identifying rms that meet or just
beat analyst forecasts. PanelA also provides evidence that PABNAC
is signicantly higher for MBE rm-years relativeto non-MBE
rm-years.
In panel B of Table 4, we use multivariate analyses to evaluate
the ability of EM_UPand EM_DN to identify rms that meet or just
beat analyst forecasts. Specically, we runlogistic regressions with
MBE as the dependent variable and EM_UP, EM_DN, andPABNAC as the
primary explanatory variables.20 We also include industry
RNOA(IRNOAt), market to book ratio (MTBt),
21 and market value of equity (MVEt) as control
19. Barton and Simko (2002) point out that the pressure to meet
analyst forecasts appears to be a relatively
recent phenomenon and that I B E S changed the formulae to
calculate actual earnings per share in1993. Given this, we also
performed the analysis after excluding observations prior to 1993.
The results
are similar to those reported in the tables.
20. It is possible that rm-years in which the rm missed the
analyst forecast by a large amount reect signi-
cant economic changes, which could confound our analyses.
Therefore, we repeated the tests including
only those rm-year observations with earnings surprises between
)$0.02 and +$0.02. The results are sim-ilar to those reported in
the tables.
21. We repeated all the multivariate tests after including the
price to earnings ratio and sales growth as addi-
tional control variables. The results are similar to those
reported.
238 Contemporary Accounting Research
CAR Vol. 29 No. 1 (Spring 2012)
-
TABLE4
AssociationbetweenEM_UP,EM_DN,andmeetingorjustbeatingexpectations(M
BE)
Panel
A:Characteristics
ofMBErm
srelativeto
other
rm
sa
MBE
Allother
rm
sTestofdifference
#observations
11,286
35,236
MeanSURP
0.0063
)0.1108
48.793***
MedianSURP
0.0051
)0.0100
71.79***
MeanEM_UP
0.2134
0.1469
15.48***
MeanEM_DN
0.1361
0.1752
)10.29***
MeanPABNAC
0.0120
0.0007
3.52***
MedianPABNAC
0.0021
)0.0018
4.08***
Panel
B:MultivariateanalysisoftheassociationbetweenEM_UP,EM_DN,andMBEb
MBEtc 0c 1IRNOAtc 2MTBtc 3MVEtc 4DATOtc 5DPM
tc 6RNOAtc 7DRNOAtc 8NOAtc 9DNOAtc 1
0PABNACtc 1
1EM
UPtc 1
2EM
DNtn t1
IRNOA
MTB
MVE
DATO
DPM
RNOA
DRNOA
NOA
DNOA
PABNAC
EM_UP
EM_DN
PseudoR2
1.3244***
0.0560***
0.0001***
)0.0365***
0.2583***
0.0588
6.03%
(29.42)
(258.66)
(106.29)
(16.81)
(28.74)
(2.25)
1.2586***
0.0526***
0.0001***
)0.0021
0.1592***
0.3354***
)0.1834***
6.57%
(25.60)
(225.86)
(98.95)
(0.05)
(10.57)
(127.39)
(31.15)
1.2717***
0.0525***
0.0001***
)0.0016
0.1560***
0.0266
0.3345***
)0.1825***
6.57%
(26.99)
(225.41)
(99.22)
(0.03)
(10.05)
(0.46)
(126.47)
(30.79)
0.5606**
0.0468***
0.0001***
0.0344***
0.2505***
0.67808***
)0.0044
)0.0295*
0.0754***
0.0073
0.2701***
)0.1330***
8.33%
(5.09)
(158.79)
(48.94)
(8.30)
(13.79)
(431.59)
(0.01)
(3.08)
(12.20)
(0.03)
(78.39)
(16.04)
Notes:
aTestofdifference
presentsat-statistic(Z-statistic)from
at-test(W
ilcoxonsigned
ranktest)ofdifference
inmeans(m
edians).bModelsare
estimatedusing
44,237rm
-yearobservations,controllingforxed
effectsbyyearandindustry
(two-digitSIC
code).v2statisticsare
reported
inparentheses.See
Table1
forvariabledenitions.
***,**and
*indicatethatvalueissignicantlydifferentfrom
zero
atthe1,5,and10percentlevel,respectively(two-tailed).
A Diagnostic for Earnings Management 239
CAR Vol. 29 No. 1 (Spring 2012)
-
variables because prior research (e.g., Koh, Matsumoto, and
Rajgopal 2008; Barton andSimko 2002) shows that these variables are
informative for identifying rms that meet orjust beat
expectations.22 We further include the change in asset turnover
(DATOt) and thechange in prot margin (DPMt) as control variables to
test whether EM_UP and EM_DNprovide incremental information over
these fundamental ratios on which the diagnostic isbased. Finally,
we include xed effects dummy variables by year and two-digit SIC
codeto control for cross-sectional and time-series correlation in
the errors.
In the rst model in panel B, we include the control variables
and PABNAC. We ndthat rms with higher IRNOA, higher MTB, and higher
MVE are more likely to meet orjust beat analyst forecasts than
other rms. These results are consistent with prior research(e.g.,
Barton and Simko 2002). The signicant positive coefcient on DPM and
signicantnegative coefcient on DATO are consistent with the notion
that rms that manageearnings upward experience increases in PM and
decreases in ATO. We also note thatPABNAC is not signicant at the
10 percent level, suggesting that abnormal accruals donot provide
incremental information in identifying rms that are more likely to
meet orbeat analyst forecasts.
In the second model, we include the control variables and EM_UP
and EM_DN. Wedocument a positive and signicant coefcient on EM_UP
and a negative and signicantcoefcient on EM_DN (both at the 1
percent level). The results suggest that the ATO PMdiagnostic for
earnings management is successful in identifying rm-years that meet
or justbeat analyst forecasts. We also note that the pseudo R2 is
higher for the EM_UP EM_DNmodel than for the PABNAC model. We
formally compare the relative predictive powerof models 1 and 2 by
comparing the area under the Receiver Operating Characteristic(ROC)
curves for the two models (DeLong, DeLong, and Clarke-Pearson
1988). We nd(untabulated) that the area under the ROC curve for the
PABNAC model is 0.6377 com-pared to 0.6415 for the ATO PM
diagnostic model, and the difference is signicant at lessthan the
one percent level (v2 = 15.08). Consistent with the univariate
ndings in the con-tingency tables presented in Table 3, this
evidence conrms that, compared to the PAB-NAC model, the
multivariate ATO PM model better discriminates between rms thatmeet
or just beat expectations versus rms that do not.
In the third model, we include PABNAC and EM_UP and EM_DN to
assess whetherEM_UP and EM_DN provide incremental information over
this widely used proxy forearnings management. We nd that both
EM_UP and EM_DN are still highly signicantwith the expected signs,
whereas the coefcient on PABNAC is not signicant at conven-tional
levels. The odds ratio for EM_UP in this specication is 1.40, which
indicatesthat, after controlling for the effect of other variables,
the odds of meeting or just beatinganalyst forecasts are 40 percent
higher for observations where EM_UP ags earnings man-agement than
when it does not. In the nal model, we follow Barton and Simko 2002
andinclude the current return on net operating assets (RNOAt),
current change in return onnet operating assets (DRNOAt), current
net operating assets (NOAt), and the currentchange in net operating
assets (DNOAt) as additional control variables, to determine
ifEM_UP and EM_DN provide incremental information over these
nancial statementratios. Again, we nd an insignicant coefcient on
PABNAC and a signicant positive(negative) coefcient on EM_UP
(EM_DN). The results suggest, therefore, that the
22. We note that prior research (e.g., Barton and Simko 2002)
has included additional control variables, such
as the rms litigation risk, whether the rm previously met or
beat analyst forecasts, and auditor type, in
the analysis of rms that meet or beat analyst forecasts. We do
not include these variables as our interest
is not in developing a comprehensive model to predict rms that
meet or beat analyst forecasts but rather
to determine the nancial statement variables that are useful in
predicting whether a rm meets or beats
analyst forecasts (i.e., managed earnings).
240 Contemporary Accounting Research
CAR Vol. 29 No. 1 (Spring 2012)
-
ATO PM diagnostic is useful for identifying rms that meet or
beat expectations whilePABNAC has no association with rms that meet
or just beat expectations.23
Extreme earnings surprises
In the previous section, we based our analyses on the notion
that rms manage earningsupward to meet or just beat analyst
forecasts. In this section, we argue that rms that missor beat the
analyst forecast by a large amount are more likely to have managed
earningsdownward. That is, rms with surprisingly low earnings have
an incentive to take a bath,while rms with surprisingly high
earnings have an incentive to save earnings for thefuture (i.e.,
create cookie-jar reserves). Further, given that they have already
missed (orbeaten) expectations by a wide margin, these rms are
unlikely to manage earningsupward. We therefore predict that EM_DN
(EM_UP) is positively (negatively) associatedwith the incidence of
extreme earnings surprises (EES).
Results of this test are reported in Table 5. We rst sort all
earnings surprises intodeciles and then label the observations in
the top and bottom deciles as extreme earningssurprises (EES).
Panel A provides descriptive statistics. The mean (median) absolute
earn-ings surprise in the extreme deciles is $6.82 ($0.40).24 The
mean (median) absolute earningssurprise for the remaining deciles
is $0.05 ($0.02). The mean EM_UP is 11.56 percent forEES rms
compared to a mean of 17.48 percent for other rms. On the other
hand, themean EM_DN is 21.46 percent for EES rms compared to a mean
of 15.35 percent for allother rms. The mean and median PABNAC are
also signicantly lower for EES rms rel-ative to the other rms.
In panel B of Table 5, we estimate logistic regressions with the
indicator variable EESas the dependent variable. As in the previous
analysis, we include EM_UP, EM_DN, con-trol variables, PABNAC, and
year and industry xed effects. We expect a positive (nega-tive)
coefcient on EM_DN (EM_UP) because rms are more (less) likely to
havemanaged earnings downward (upward) when they miss or beat
analyst forecasts by a largeamount.
The rst model includes the control variables and PABNAC. The
coefcient signs ofthe control variables are as expected, and have
the opposite association with EES com-pared to MBE. Consistent with
PABNAC being useful for identifying earnings manage-ment, we nd a
signicantly negative coefcient on PABNAC. In the second model, wend
a signicantly positive (negative) coefcient on EM_DN (EM_UP),
suggesting thatthe ATO PM diagnostic provides information for
identifying rms with EES, consistentwith our expectation. The
pseudo R2 is 4.77 percent for the PABNAC model comparedto a pseudo
R2 of 5.25 percent for the EM_UP EM_DN model, providing evidence
thatthe ATO PM diagnostic provides more information for EES than
PABNAC. Consistentwith this, we nd (untabulated) that the area
under the ROC curve is larger for theATO PM model (0.6335) than for
the PABNAC model (0.6305), and the difference is sig-nicant at less
than the ve percent level (v2 = 5.75). Moreover, when we include
allthree variables, EM_UP, EM_DN and PABNAC (model 3), we nd that
the ATO PMdiagnostic is incrementally informative to PABNAC. The
(untabulated) odds ratio of 1.30for EM_DN in this specication
suggests that after controlling for the effect of othervariables,
the odds of a rm having EES is 30 percent higher when EM_DN equals
one.
23. We also estimated all regression models in this paper with
the indicator PAB_UP in place of the continu-
ous variable PABNAC, as well as with indicator variables (for
observations above and below the median)
instead of continuous variables for each of the explanatory
variables. The (untabulated) results are similar
to those reported in the tables.
24. We do not scale the earnings surprise by price because it is
difcult to argue that investors assess the sur-
prise relative to price. In addition, Cheong and Thomas (2009)
show that the magnitude of earnings sur-
prises do not seem to vary with scale.
A Diagnostic for Earnings Management 241
CAR Vol. 29 No. 1 (Spring 2012)
-
TABLE5
AssociationbetweenEM_UP,EM_DN,andextrem
eearningssurprises(EES)
Panel
A:Characteristics
ofEESrm
srelativeto
other
rm
sa
EES
Other
rm
sTestofdifference
#observations
9,272
37,250
Mean|SURP|b
6.8192
0.0478
3.55***
Median|SURP|
0.4000
0.0200
107.62***
MeanEM_UP
0.1156
0.1748
)15.34***
MeanEM_DN
0.2146
0.1535
13.12***
MeanPABNAC
)0.0124
0.0074
)5.51***
MedianPABNAC
)0.0099
0.0008
)8.75***
Panel
B:MultivariateanalysisoftheassociationbetweenEM_UP,EM_DN,andEESc
EEStc 0c 1IRNOAtc 2MTBtc 3MVEtc 4DATOtc 5DPM
tc 6RNOAtc 7DRNOAtc 8NOAtc 9DNOAtc 1
0PABNACtc 1
1EM
UPtc 1
2EM
DNtn t1
IRNOA
MTB
MVE
DATO
DPM
RNOA
DRNOA
NOA
DNOA
PABNAC
EM_UP
EM_DN
PseudoR2
)1.6405***
)0.0735***
)0.0001***
0.1000***
)0.3344***
)0.1970***
4.77%
(40.78)
(221.50)
(79.19)
(103.56)
(37.82)
(21.24)
)1.5310***
)0.0687***)0.0001***
0.0682***
)0.2297***
)0.3060***
0.2696***
5.25%
(35.79)
(195.58)
(74.99)
(44.23)
(17.69)
(67.63)
(75.12)
)1.6053***
)0.0683***
)0.0001***
0.0652***
)0.2087***
)0.1654***
)0.3014***
0.2642***
5.30%
(39.01)
(193.36)
(75.70)
(40.18)
(14.56)
(14.83)
(65.56)
(71.97)
)0.5615**
)0.0633***
)0.0001***
)0.0121
)0.0604
)0.7126***
)0.0640
0.0911***
)0.1845***
)0.1387***
)0.2176***
0.2216***
8.22%
(4.52)
(171.90)
(31.49)
(0.94)
(1.26)
(770.04)
(1.83)
(41.99)
(50.90)
(9.90)
(32.86)
(48.63)
Notes:
aTestofdifference
presentsthet-statistic(Z-statistic)from
at-test(W
ilcoxonsigned
ranktest)ofdifference
inmeans(m
edians).
b|SURP|istheabsolutevalueofSURP.
cModelsare
estimatedusing44,237rm
-yearobservations,controllingforxed
effectsbyyearandtwo-digitSIC
code.v2
statisticsare
reported
inparentheses.
See
Table1forvariabledenitions.
***,**and
*indicatethatvalueissignicantlydifferentfrom
zero
atthe1,5and10percentlevel,respectively(two-tailed).
242 Contemporary Accounting Research
CAR Vol. 29 No. 1 (Spring 2012)
-
In the nal model, when additional nancial statement ratios are
included in the regres-sion, we continue to nd that EM_UP and EM_DN
provide incremental informationcontent for EES.
Restatements
Dechow and Skinner (2000) note that earnings management can
occur through accountingchoices that are acceptable within U.S.
generally accepted accounting principles (GAAP)or through
accounting choices that violate U.S. GAAP. The previous analyses on
MBEand EES likely capture earnings management that falls within the
acceptable use of discre-tion available in U.S. GAAP. In order to
test whether EM_UP and EM_DN are informa-tive about earnings
management that violates U.S. GAAP, we examine whether EM_UPand
EM_DN are predictive of earnings restatements.
We argue that rms that subsequently restate their earnings
downward (upward) aremore likely to have initially managed their
earnings up (down). We therefore expectEM_UP to be more (less)
frequent in a sample of rms that subsequently restate theirearnings
downward (upward). Similarly, we expect EM_DN to be more (less)
frequent ina sample of rms that restate their earnings upward
(downward). We control for allpreviously identied variables
including year and industry-xed effects and examinethe association
between the ATO PM diagnostic and the probability of
subsequentrestatements.
We obtain restatement announcements from the Glass Lewis
database and then deter-mine the income effect of the restatements
by comparing originally reported and restatednumbers in the
COMPUSTAT Point-in-Time database. We sum the quarterly data overthe
scal year for both the originally reported and the restated numbers
and identifyrestatements as observations where the originally
reported operating income25 differs fromthe restated income in the
periods around the restatement ling.26 The nal sampleincludes 604
rm-year observations involving operating income restatements (355
down-ward and 249 upward) and 21,556 nonrestatement rm-year
observations from 2000through 2005.
We report the restatement analysis results in Table 6. The
descriptive statistics in panelA show that the mean (median)
downward restatement is $90.52 million ($2.4 million),whereas the
mean (median) upward restatement is $97.22 million ($1.44 million).
We nd,consistent with expectations, that the mean EM_UP is
signicantly higher (t-statistic of2.30) for rm-year observations
that resulted in a downward restatement than in anupward
restatement, and mean EM_DN is signicantly higher in the upward
restatementsample than the downward restatement sample (t-statistic
of )3.02). We do not nd a sim-ilar, signicant difference for
PABNAC. We also nd that EM_UP (EM_DN) is signi-cantly lower
(higher) for UP_RESTATE rms relative to rms that do not
subsequentlyrestate earnings (t-statistics of )1.96 and 3.72,
respectively). We document a similar meandifference for PABNAC.
Finally, we do not nd signicant differences in EM_UP,EM_DN or
PABNAC, in comparing DN_RESTATE rms relative to rms that do
notrestate earnings (NO_RESTATE).
In panel B, we report results of logistic regression analyses
with DN_RESTATE,which is set equal to one (zero) if the rm
subsequently restates (does not restate) earnings
25. Consistent with the rest of the paper, we dene operating
income as sales (cost of goods sold + selling,
general and administrative expenses + depreciation and
amortization expense).
26. The Point-in-Time database provides nancial information as
originally reported in the announcement
month as well as for each month thereafter. We identify the
impact of restatements by capturing changes
in the nancial variables reported within four months surrounding
the month of the restatement ling.
This methodology was used to ensure that we capture the effect
of the restatement of interest rather than
the effect of other events that led to restatements.
CAR Vol. 29 No. 1 (Spring 2012)
A Diagnostic for Earnings Management 243
-
TABLE6
AssociationbetweenEM_UP,EM_DN,andrestatementsofoperatingincome
Panel
A:Characteristics
ofDN_RESTATEandUP_RESTATErm
srelativeto
other
rm
sa
DN_RESTATE
UP_RESTATE
NO_RESTATE
Testofdifferences
DN_RESTATEvs.
UP_RESTATE
DN_RESTATEvs.
NO_RESTATE
UP_RESTATEvs.
NO_RESTATE
#ofobservations
355
249
21,556
MeanRESTATE($
millions)
)90.52
97.22
0.00
4.70***
3.84***
3.02***
MedianRESTATE($
millions)
)2.40
1.44
0.00
20.94***
)120.96***
112.83***
MeanEM_UP
0.1634
0.1004
0.1380
2.30**
1.28
)1.96*
MeanEM_DN
0.1859
0.2932
0.1852
)3.02***
0.03
3.72***
MeanPABNAC
0.0197
)0.0444
0.0095
1.64
0.41
)1.77*
MedianPABNAC
0.0119
0.0025
0.0076
1.20
0.43
)1.08
Panel
B:MultivariateanalysisoftheassociationbetweenEM_UP,EM_DNandDN_RESTATEb
DNRESTATEtc 0c 1IRNOAtc 2MTBtc 3MVEtc 4DATOtc 5DPM
tc 6RNOAtc 7DRNOAtc 8NOAtc 9DNOAtc 1
0PABNACtc 1
1EM
UPt
c 1
2EM
DNtn t
IRNOA
MTB
MVE
DATO
DPM
RNOA
DRNOA
NOA
DNOA
PABNAC
EM_UP
EM_DN
PseudoR2
)0.0441
)0.0102
0.0000
0.0183
)0.4450**
0.0335
10.16%
(0.00)
(0.39)
(0.13)
(0.33)
(5.39)
(0.08)
)0.1740
)0.0115
0.0000
0.0325
)0.4909**
0.1783
)0.1098
10.22%
(0.01)
(0.49)
(0.11)
(0.97)
(6.34)
(1.26)
(0.55)
)0.1545
)0.0116
0.0000
0.0325
)0.4940**
0.0234
0.1770
)0.1088
10.22%
(0.01)
(0.50)
(0.11)
(0.98)
(6.38)
(0.04)
(1.24)
(0.54)
(Thetableiscontinued
onthenextpage.)
CAR Vol. 29 No. 1 (Spring 2012)
244 Contemporary Accounting Research
-
TABLE6(Continued)
IRNOA
MTB
MVE
DATO
DPM
RNOA
DRNOA
NOA
DNOA
PABNAC
EM_UP
EM_DN
PseudoR2
)0.2798
)0.0161
0.0000
0.0953**
)0.5233**
0.3742***
)0.2962
)0.0164
0.2031**
0.0274
0.1353
)0.0975
10.64%
(0.02)
(0.88)
(0.01)
(6.06)
(4.22)
(9.02)
(2.27)
(0.07)
(5.18)
(0.05)
(0.70)
(0.42)
Panel
C:MultivariateanalysisoftheassociationbetweenEM_UP,EM_DNandUP_RESTATEb
UPRESTATEtc 0c 1IRNOAtc 2MTBtc 3MVEtc 4DATOtc 5DPM
tc 6RNOAtc 7DRNOAtc 8NOAtc 9DNOAtc 1
0PABNACtc 1
1EM
UPt
c 1
2EM
DNtn t
IRNOA
MTB
MVE
DATO
DPM
RNOA
DRNOA
NOA
DNOA
PABNAC
EM_UP
EM_DN
PseudoR2
3.5931
0.0076
)0.0000*
0.0175
)0.6389***
)0.2187
10.79%
(2.40)
(0.18)
(3.29)
(0.21)
(8.34)
(2.26)
4.0816*
0.0113
)0.0000*
)0.0225
)0.5109**
)0.2396
0.5335***
11.26%
(3.15)
(0.39)
(2.98)
(0.30)
(4.97)
(1.12)
(12.34)
3.7852
0.0119
)0.0000*
)0.0229
)0.4852**
)0.1942
)0.2291
0.5267***
11.32%
(2.66)
(0.44)
(3.05)
(0.58)
(4.44)
(1.76)
(1.03)
(12.01)
3.5755
0.0132
)0.0000**
)0.0374
)0.7891**
0.3082**
0.1628
)0.0617
)0.1230
)0.1873
)0.2300
0.5196***
11.63%
(2.34)
(0.50)
(4.07)
(0.49)
(6.23)
(4.04)
(0.42)
(0.63)
(0.72)
(1.60)
(1.00)
(11.46)
Notes:
aTestofdifference
presentsthet-statistic(Z-statistic)from
at-test(W
ilcoxonsigned
ranktest)ofdifference
inmeans(m
edians).
bModelsare
estimatedusing22,160rm
-yearobservations,controllingforxed
effectsbyyearandindustry
(two-digitSIC
code).v2
statisticsare
reported
inparentheses.See
Table1forvariabledenitions.
***,**and
*indicatethatthevalueissignicantlydifferentfrom
zero
atthe1,5,and10percentlevel,respectively(two-tailed).
CAR Vol. 29 No. 1 (Spring 2012)
A Diagnostic for Earnings Management 245
-
downward, as the dependent variable. As explained earlier, we
assume that rms thatsubsequently restate earnings down must have
initially managed earnings up for that year.The only control
variable that is consistently signicant in all variations of the
logisticregression is DPM which has a negative coefcient. This
suggests that rms with decreas-ing prot margins are more likely to
subsequently restate earnings downward, which isconsistent with
poorly performing rms attempting to manage earnings upward. The
rstmodel also shows that the coefcient on PABNAC is positive but
not signicant. In thesecond model, we nd that EM_UP (EM_DN) is
positively (negatively) related toDN_RESTATE, but the coefcients
are not signicant at conventional levels. The resultssuggest,
somewhat surprisingly but consistent with our ndings from the
univariateanalysis, that neither PABNAC nor the ATO PM diagnostic
is useful in identifying rmswith subsequent downward restatements.
The lack of association between the earningsmanagement proxies and
the binary restatement variable may in part be due to
earningsrestatement being a noisy partition of rms that manage or
do not manage earnings. Spe-cically, some of the NO_RESTATE
observations may in fact have managed earnings up,but managed to go
undetected during our sample period.
In panel C, we report results of logistic regression analyses
with UP_RESTATE as thedependent variable. With respect to the
control variables, MVE has a signicantly negativecoefcient,
suggesting that smaller rms are more likely to subsequently restate
earningsupward. In addition, the coefcient on DPM is signicantly
negative, which is consistentwith poorly performing rms managing
earnings down (i.e., big bath). The negative rela-tion between DPM
and both DN_RESTATE and UP_RESTATE suggests that rms withincreasing
protability are less likely to subsequently restate earnings upward
or down-ward. The rst model further shows that the coefcient on
PABNAC is negative but notsignicant at conventional levels. In the
second model, we nd that the coefcient onEM_UP is also negative but
not signicant at conventional levels. However, EM_DN ispositively
related to the probability that rms subsequently restate upward and
highly sig-nicant (p < 0.001). The pseudo R2 for the ATO PM
model (11.26 percent) is higher thanthat of the PABNAC model (10.79
percent) suggesting that the ATO PM diagnostic hasgreater
explanatory power for rms that subsequently restate earnings
upward. A formaltest (untabulated) of the area under the ROC curve
for the two logistic models conrmsthat the ATO PM model better
discriminates UP_RESTATE rms from rms that do notsubsequently
restate earnings upwards (v2 = 3.93; p < 0.05). In the third
model, wherewe include PABNAC, EM_UP and EM_DN, the coefcient on
EM_DN continues to besignicantly positive. The (untabulated) odds
ratio of 1.69 for EM_DN in this specicationsuggests that after
controlling for other variables, the probability of upward
restatement isabout 69 percent higher for rms that are classied as
EM_DN than when they are not.Overall, both EM_UP and EM_DN are
associated with restatements in the expected direc-tion, although
the effect is much stronger for subsequent upward restatements. We
nd nosignicant association between earnings restatements and
PABNAC.
Future performance
Penman (2007: 634) states that if a rm manipulates earnings
upward (downward), futureprotability should fall (rise) as the
income contribution from earnings management in thecurrent period
reverses. Consistent with this, Dechow et al. (2003) use future
reversals inprotability and stock returns as measures of earnings
management. Relying on theseobservations, we predict that rms with
EM_UP (EM_DN) will report lower (higher)year-ahead changes in
performance than other rms. To test our prediction, we use
regres-sion analysis with the year-ahead change in return on net
operating assets (DRNOAt+1)and year-ahead abnormal returns
(ABRETt+1) as dependent variables. We expect a nega-tive coefcient
on EM_UP and a positive coefcient on EM_DN. We include the
following
CAR Vol. 29 No. 1 (Spring 2012)
246 Contemporary Accounting Research
-
control variables that have been identied in previous research
(e.g., Faireld and Yohn2001) to predict year-ahead performance:
RNOA, DRNOA, NOA, DNOA, DATO, andDPM.27
We examine the information content of EM_UP and EM_DN for
explaining year-ahead DRNOA in panel A of Table 7. In the rst
model, we include the control variablesas well as PABNAC. The
results for the control variables are consistent with mean
rever-sion in RNOA (Freeman, Ohlson, and Penman 1982; Faireld,
Sweeney, and Yohn 1996),positive serial correlation in DRNOA
(Faireld and Yohn 2001), rms with bloated bal-ance sheets (NOA)
being more likely to have engaged in upward earnings management
inprior years (Barton and Simko 2002), and DATO being informative
about future prot-ability changes (Faireld and Yohn 2001). We nd a
signicant negative coefcient onPABNAC, which suggests that PABNAC
identies earnings components that will likelyreverse in the next
year, and is consistent with reversal in abnormal accruals
documentedin prior research (Dechow et al. 2003).
In the second model, we include the control variables and EM_UP
and EM_DN(without PABNAC). Consistent with our predictions, we
document a signicant negativecoefcient on EM_UP and a signicant
positive coefcient on EM_DN. Our ndings sug-gest, therefore, that
the ATO PM diagnostic is successful in identifying protability
rever-sals. We note, however, that the adjusted R2 for the PABNAC
model is 4.08 percent whilethe adjusted R2 for the EM_UP EM_DN
model is 4.05 percent, indicating that PABNAChas greater relative
information content than the ATO PM diagnostic for explaining
year-ahead DRNOA.28 Finally, in the third model, we include EM_UP,
EM_DN and PABNAC,and nd that the coefcients on all variables remain
signicant. These results suggest thatthe ATO PM diagnostic has
incremental information content relative to PABNAC foridentifying
future earnings reversals.
In panel B, we estimate the same three models as in panel A,
except that we use as thedependent variable the size-adjusted
abnormal return for the three-day window surround-ing the
subsequent rst quarter earnings announcement (ABRETt+1). With
respect to thecontrol variables, we nd that the coefcient estimates
on RNOA, NOA, DNOA andDATO are signicantly different from zero,
suggesting that the market does not correctlyprice the information
in these variables. We nd a signicant negative coefcient onPABNAC,
which is consistent with prior research (Xie 2001) and with the
notion that thestock market overprices abnormal accruals.
Consistent with our predictions, we also nd asignicant negative
coefcient on EM_UP and a signicant positive coefcient onEM_DN.
These results suggest that EM_UP and EM_DN predict year-ahead
abnormalreturns. We note that the PABNAC model yields an adjusted
R2 of 0.44 percent while theEM_UP EM_DN model yields an adjusted R2
of 0.39 percent, indicating that PABNAChas greater relative
information content than the ATO PM diagnostic for
explainingyear-ahead returns.29 Finally, in the third model, we nd
that the ATO PM diagnostic hasincremental information content
relative to PABNAC for identifying future abnormalreturns. Overall,
the ATO PM diagnostic identies future protability reversals as well
as
27. We also ran the analyses excluding NOA from the model, since
NOA is dened as net operating assets
scaled by sales and is, therefore, the inverse of ATO. The
conclusions regarding the relative and incremen-
tal information content of PABNAC and EM_UP and EM_DN are
unchanged when NOA is excluded
from the model.
28. A Vuong 1989 test (untabulated) conrms the greater
explanatory power of the PABNAC model over the
EM_UP EM_DN model. The Vuong statistic for comparison of these
two models is 16.79, which is signif-icant at the 1 percent
level.
29. A Vuong 1989 test (untabulated) conrms the greater
explanatory power of the PABNAC model over the
EM_UP/EM_DN model. The Vuong statistic for a comparison of these
two models is 13.41, which is sig-
nicant at the 1 percent level.
CAR Vol. 29 No. 1 (Spring 2012)
A Diagnostic for Earnings Management 247
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future abnormal stock returns, two commonly documented
consequences of earningsmanagement.
Additional analyses
Controlling for the effect of contemporaneous sales on net
operating assets and operatingincome is crucial in the computation
of the ATO PM diagnostic we propose. To ensurethat contemporaneous
sales is not merely acting as a deator, we repeat all the
analysesreported in the paper after computing ATO and PM with two
alternative variables lagged sales and market value of equity in
place of contemporaneous sales. We ndthat the ATO PM diagnostic
derived with these alternative deators is not associated withthe
earnings management scenarios we examine in the paper (results not
tabulated).
We also note that changes in ATO and PM could be driven by a
change in rm per-formance. However, if changes in ATO and PM are
driven by rm performance, then they
TABLE 7
Association between EM_UP and EM_DN and future protability and
returnsa
Panel A: Multivariate analysis of the association between EM_UP,
EM_DN and year
DRNOAt1 c0 c1RNOAt c2DRNOAt c3NOAt c4DNOAt c5DATOt c6DPMt
c7PABNACt c8EM UPt c9EM DNt nt1
RNOA DRNOA NOA DNOA DATO DPM PABNAC EM_UP EM_DN Adj.R2
)0.1381*** 0.0132** )0.0034*** )0.0031 0.0039*** )0.0001
)0.0359*** 4.08%()61.07) (3.37) ()3.03) ()1.56) (4.21) ()0.33)
()8.95))0.1374*** 0.0156*** )0.0034*** )0.0025 0.0035*** )0.0018
)0.0167*** 0.0086*** 4.05%()60.66) (3.94) ()2.97) ()1.29) (3.78)
()0.40) ()5.40) (2.97))0.1782*** 0.0160*** )0.0034*** )0.0014
0.0030*** 0.0000 )0.0346*** )0.0159*** 0.0076*** 4.11%()60.59)
(4.06) ()3.05) ()0.74) (3.23) (0.09) ()8.63) ()5.13) (2.61)
Panel B: Multivariate analysis of the association between EM_UP,
EM_DN and year-ahead returns
ABRETt1 c0 c1RNOAt c2DRNOAt c3NOAt c4DNOAt c5DATOt c6DPMt
c7PABNACt c8EM UPt c9EM DNt nt1
RNOA DRNOA NOA DNOA DATO DPM PABNAC EM_UP EM_DN Adj.R2
0.0021*** )0.0025* )0.0016*** )0.0021*** 0.0021*** )0.0025
)0.0096*** 0.44%(2.61) ()1.79) ()3.46) ()3.06) (6.05) ()1.42)
()7.00)0.0022*** )0.0019 )0.0015*** )0.0020*** 0.0020*** )0.0027
)0.0022** 0.0034*** 0.39%(2.80) ()1.39) ()3.33) ()2.94) (5.72)
()1.48) ()2.10) (3.40)0.0023*** )0.0019 )0.0016*** )0.0018***
0.0019***)0.0019 )0.0093*** )0.0020** 0.0032*** 0.46%(2.87) ()1.34)
()3.42) ()2.58) (5.34) ()1.07) ()6.79) ()1.99) (3.14)
Notes:
a DRNOAt+1 (ABRETt+1) models are estimated using pooled
regressions, on 110,170 (67,075)rm-year observations, controlling
for xed effects by year and industry (two-digit SIC
code). t-statistics are reported in parentheses. See Table 1 for
variable denitions ***, **
and * indicate coefcients signicantly different from zero at the
1, 5, and 10 percent level,
respectively (two-tailed).
CAR Vol. 29 No. 1 (Spring 2012)
248 Contemporary Accounting Research
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are likely to move in the same direction. For example, if a rm
has an increase in sales,ATO will likely go up because sales is in
its numerator, and PM will likely go up because,in the presence of
period costs, an additional dollar of sales increases the ratio of
operat-ing income to sales. We nd, indeed, that rms with a
signicant increase in sales (denedas rms in the top quintile of
sales growth), experience an increase in ATO 6