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Evidence on the Tradeoff between Real Manipulation and Accrual Manipulation
Amy Y. Zang*
I study whether managers use real and accrual manipulations as substitutes in managing earnings, and I
study the order that managers make these decisions. I find that managers determine real manipulation
before accrual manipulation. Based on this result, I use an empirical model that captures the sequentiality
of real and accrual manipulations to test the tradeoffs between the two. The results of the broad sample
tests are consistent with managers using real and accrual manipulations as substitutes. In a small sample
test examining firms subject to securities class action lawsuits, I examine whether real and accrual
manipulations change over time with changes in litigation risk. Consistent with managers using real and
accrual manipulations as substitutes, I find that managers switch from accrual manipulation to real
manipulation after lawsuit filings.
Draft: December 2005
Comments welcome.
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1. Introduction
I study earnings management through both real activities management (RM) and discretionary
accruals management (AM) for a broad sample of firms over 1992-2003 and for a small sample of lawsuit
firms over 1995-2004. By real activities management, I mean a purposeful action to alter reported
earnings in a particular direction, which is achieved by changing the timing or structuring of an operation,
investment or financing transaction, and which has sub-optimal business consequences. The idea that
firms engage in RM is supported by Graham, Harvey, and Rajgopal’s [2005] survey evidence
documenting the widespread use of earnings management, especially as it concerns real manipulation.1
They report that 80% of surveyed CFOs state that, in order to deliver earnings, they would decrease R&D,
advertising, and maintenance expenditures, while 55% said they would postpone a new project. Despite
survey and anecdotal evidence concerning real manipulation, most empirical earnings management
research focuses on accruals management.2
The focus of my paper is on how managers trade off real and accrual manipulations based on their
relative costs. This question is important for two reasons. First, as mentioned by Fields, Lys and Vincent
[2001], examining only one earnings management technique at a time cannot explain the overall effect of
earnings management activities. In particular, if managers use RM and AM as substitutes, examining
either type of manipulation in isolation cannot lead to definitive conclusions. Second, by studying how
managers trade off RM and AM, my paper sheds light on the economic implications of accounting
choices; that is, whether the costs that managers bear for manipulating accruals affect their decisions
about real manipulations. As such, the question has implications about whether enhancing SEC scrutiny
or reducing accounting flexibility in GAAP might, for example, increase managers’ levels of real
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manipulation.
I start with a cost-benefit analysis that provides a structure for empirical tests of the relation between
RM and AM. This analysis produces different models depending on whether RM and AM are determined
simultaneously or sequentially. My empirical tests show that RM and AM are determined sequentially:
specifically, the RM decision precedes the AM decision. Based on this result, I further test the
substitutive relation between RM and AM using an empirical model that controls for this sequentiality. In
testing managers’ tradeoff of RM and AM, I first identify cost determinants of each. I then use a broad
sample of firms to show that the relations between RM, AM and their cost determinants are consistent
with managers using both types of manipulations as substitutes. The substitutive relation is further
supported by a small sample test examining firms experiencing securities class action lawsuits. I find that
subsequent to the filing of the lawsuit, sued firms’ AM levels drop abruptly, while their RM levels (via
cutting R&D expenditure and overproduction) increase significantly. These results are consistent with my
prediction that managers turn to RM as a response to increased litigation risk and outside scrutiny.
My paper contributes to the earnings management literature in several ways. First, it is one of a few
studies (Hunt, Moyer and Shevlin [1996], Beatty, Chamberlain and Magliolo [1995], Gaver and Paterson
[1999], Barton [2001], Pincus and Rajgopal [2001]) to examine how managers use multiple tools to
manage earnings. It is also one of the first to specifically examine how managers trade off RM and AM.
Second, I extend the prior literature by incorporating cost factors for RM and AM in the empirical tests. I
argue that the main costs of RM are the economic consequences of deviating from optimal business
operations and therefore, jeopardizing the firm’s competitive advantage; in contrast, AM is costly
primarily due to auditor and regulators’ scrutiny and litigation risk. Although many prior studies of
multiple accounting choices recognize the importance of the relative costs of different accounting (and/or
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substitutive relation between RM and AM.
Third, by empirically testing the simultaneity/sequentiality of real and accrual manipulations, my
paper expands our understanding of managers’ decision processes. Most prior studies on multiple
accounting (and/or economic) choices implicitly assume that managers use multiple choices
simultaneously and treat all of the choice variables as endogenous, without testing the sequential decision
process as an alternative hypothesis (one exception is Pincus and Rajgopal [2001]).4 In contrast, I
explicitly model and test the sequentiality versus simultaneity of the two manipulations, and show
evidence consistent with managers deciding the RM decision before the AM decision.
Finally, my paper is among the first to validate RM proxies measured by cross-sectional estimation
models. In the first validity test, I find that real transaction manipulators manipulate more in the fourth
fiscal quarter than in other fiscal quarters. This evidence is consistent with managers having, in the fourth
quarter, both more information about the total amount of earnings management needed and more
incentive to manipulate. In the second validity test, I use a performance matching procedure and show
that real transaction manipulators are associated with negative abnormal performance in subsequent years,
consistent with RM proxies capturing suboptimal business decisions.
The rest of the paper is organized as follows. In section 2, I develop a simple model to show how
managers trade off real and accrual manipulations; I then use this model to develop hypotheses. Section 3
constructs the proxies for real and accrual manipulations, and section 4 conducts validity tests of the RM
proxies. Section 5 contains the research design, measurement of independent variables, sample selection,
and empirical results for the broad sample test. The small sample test is presented in section 6. Section 7
concludes and discusses implication of my results.
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benefits of earnings management, the levels of RM and AM, and their cost determinants. Some stylized
simplifying assumptions are made, but I expect the basic results to hold in a more general setting where
empirical tests are conducted.5
I assume that managers obtain benefits (denoted B) from earnings management, with the benefit
function being concave and twice continuously differentiable. A concave benefit function means that the
marginal benefit is decreasing in the amount of earnings being managed. For simplicity and without loss
of generality, I assume a quadratic function for B, B = a·K – β·K 2/2, where K is the total amount of
earnings being managed, and α and β > 0. Therefore, the marginal benefit of earnings management at K
can be expressed as MBK = ∂B/∂K = α – β·K. I assume that managers have two techniques, AM and RM,
to achieve an expected level of K. Let the costs of implementing AM and RM be denoted as CA and CR ,
respectively. Again, for simplicity and without loss of generality, I assume a quadratic cost function for
both CA and CR : CA = a·AM2/2 and CR = b·RM2/2, with a > 0 and b > 0. Therefore, the marginal cost of
AM is MCA = a·AM, and the marginal cost of RM is MCR = b·RM.
To capture the possibility that realized earnings may differ from managers’ choices of AM or RM due
to exogenous shocks, I assume that realized earnings management K is the sum of managers’ choice of
RM and AM, and their disturbance terms u and v : K = RM +u + AM + v , where u is the exogenous
shock to RM and v is the exogenous shock to AM.
Managers’ decisions with respect to the optimal levels of RM and AM depend on whether managers
can observe these exogenous shocks when they make their decisions. Prior studies investigating multiple
earnings management tools (Hunt, Moyer and Shevlin [1996], Beatty, Chamberlain and Magliolo [1995],
Gaver and Paterson [1999], Barton [2001], Pincus and Rajgopal [2001]) implicitly assume that managers
make the manipulation decisions simultaneously without observing any exogenous shocks However if
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simultaneously and, separately, under the assumption they are determined sequentially. As I will show,
the assumption of simultaneity or sequentiality leads to different empirical models for how managers
trade off RM and AM. I leave the test of this assumption to section 5.
In the first scenario, assuming that managers cannot observe any shocks before their AM aqnd RM
decisions, managers will choose their optimal levels of RM and AM simultaneously, as follows:
R K
A K
MC MB
MC MB
=⎧⎨
=⎩
**
**
α-β AMRM =
b+β
α-β RMAM =
a+β
⎧ ⋅⎪⎪
⇔ ⎨⋅⎪
⎪⎩
. (I)
Note that the marginal cost of the optimal level of AM (AM*) and the marginal cost of the optimal level
of RM (RM*) simultaneously equal the marginal benefit of earnings management (i.e., MCA = MCR =
MBK ), since managers want to be cost efficient.
In the second scenario, I assume that managers make RM and AM decisions sequentially because it is
likely that real transactions are performed during the fiscal year but accrual manipulation is conducted
close to or after the fiscal year end. If this is the case, a shock that affects RM (u ) during the fiscal year
can affect AM* by changing the marginal benefit of earnings management (MBK ). In contrast, a shock
that affects AM ( v ) after the fiscal year end cannot change RM*, since RM is already realized. In other
words, there is no feedback from AM to RM*, since RM is the lower level of the causal chain.
Assuming managers can observe the exogenous shocks to RM (u, the realized value of u that
managers can observe when making the AM decision) before deciding the optimal level of AM (AM*),
we can obtain the following:
R K MC MB=⎧⎨
( )
( )
aRM
a b a b
α ⋅⎧ =⎪ ⋅ + + ⋅β⎪⇔ ⎨
*
(II)
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shocks to RM (u) for their AM decisions. Thus, AM* will depend on realized RM (RM* + u) and other
exogenous variables including benefit factors (α and β), and its own cost factor (a).
The above cost-benefit analysis has the following implications. First, simple comparative analysis
shows that RM and AM are negatively correlated with their own cost factors and positively correlated
with earnings management incentives. Second, the substitutive relation between RM and AM is manifest
by a positive relation between the amount of one manipulation technique and the cost of the other because
managers trade off the two until the marginal costs are equal (i.e., MCA = MCR ). Note that, for testing the
tradeoff between RM and AM, it is important to separate the benefits of earnings management from the
costs of these manipulations. The reason is that managers trade off RM and AM according to their
marginal costs (which differ) but not their marginal benefit (which are the same, i.e., a $1 increase in RM
and $1 increase in AM both result in $1 increase in reported earnings). Third, the fact that real and
accrual manipulations are jointly determined does not necessarily imply they are simultaneous. In
particular, the two can be joint but managers can determine RM first (based on the cost functions of both
manipulation techniques and the benefit function of an earnings increase) and then determine AM (based
on realized RM, AM’s cost and incentives). Fourth, irrespective of whether the two decisions are made
sequentially or simultaneously, AM is negatively correlated with RM as indicated by the negative sign on
RM in the AM function. These findings lead to the following hypotheses:
H1: Both RM and AM are negatively correlated with their own cost determinants.
H2: The level of RM is positively correlated with the cost determinants of AM.
H3: The level of AM is negatively correlated with the level of RM, controlling for the cost determinants of
AM and the incentives to increase earnings.
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general and administrative expenditures, overproducing inventory to reduce the cost of goods sold, and
selling fixed assets with a market value greater than book value to report a gain.
Following Berger [1993] and Gunny [2005], I estimate the normal level of R&D expenditure using
equation (1). The regression is estimated cross-sectionally for each industry-year with at least 15
observations, where industry is defined following Fama and French [1997]:6
t t 1 t t0 1 2 3 t 4 t
t 1 t 1 t 1 t 1
RD RD Funds CapitalExpTobinsQ
A A A A
−
− − − −
= α + α + α + α + α + ε (1) where, RDt = R&D expense = Data 46;
At = Total assets = Data 6;
Fundst = Internal funds = IBEI + R&D + Depreciation = Data 18 + Data 46 + Data14;
TobinsQt = (MVE + Book value of preferred stock + Long-term debt + Short-term debt)/ Total assets = (Data 199μData 25 + Data 130 + Data 9 + Data 34)/Data 6;
CapitalExpt = Capital expenditure = Data 128.
Lag R&D expense (RDt-1) proxies for the firm’s innovation opportunity; the coefficient on this
variable is expected to be positive. Internal fund (Fundst) is included based on the argument that
expanding R&D investment is cheaper for firms with more internal funds since external funds are more
expensive for R&D projects than internal funds. Therefore, I expect a positive coefficient on this variable.
Tobin’s Q (TobinsQt) is measured as average Q, which captures the firm’s growth potential. Similarly,
capital expenditure (CapitalExpt) represents the firm’s investing activities in the current year. I expect the
coefficients on both Tobin’s Q and capital expenditure to be positive.
Following Anderson, Banker, and Janakiraman [2003], I estimate the normal level of selling, general
and administrative (excluding R&D expense) expenses using equation (2):
t t t t-1 t-11 2 3 t 4 5 t-1 t
t-1 t-1 t-1 t-2 t-2
SG&A S S S SLog =α +α Log +α Log DS +α Log +α Log DS +ε
SG&A S S S S
⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞× ×⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ (2)
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SG&A expenditures when sales decrease. They also find that this stickiness in SG&A costs reverses in
subsequent periods. Following their work, I use the ratio form and log specification to improve the
comparability of the variables across firms and to mitigate heteroskedasticity. DSt, a dummy variable
equal to one when sales are deceasing and zero otherwise, is included to capture the asymmetric relation
between SG&A costs and sales levels when sales increase and decrease. I expect the coefficient on
Log(St/St-1) to be positive since the change in SG&A is generally positively correlated with the change in
sales. The coefficient on Log(St/St-1)·DSt is expected to be negative because of the stickiness of SG&A
costs. The coefficient on Log(St-1/St-2), which captures the lagged adjustment to SG&A for the change in
sales, is expected to be positive. I expect the coefficient on Log(St-1/St-2)·DSt-1 to be positive, reflecting
the reversal of SG&A stickiness.
I use Roychowdhury’s [2004] model to estimate the normal level of production costs:
t t t t-112 3 4 t
t-1 t-1 t-1 t-1 t-1
Prod S ΔS ΔSα= +α +α +α
A A A A A+ ε (3)
where, Prodt = COGSt + ΔInventoryt = Data 41 + ΔData 3;
St = Net sales = Data 12;ΔSt = St – St-1.
Roychowdhury [2004] develops equation (3) based on Dechow, Kothari, and Watts [1998], who model
COGS and change in inventory as a linear function of sales and change in sales. Again, the regression is
estimated for each industry-year with at least 15 observations. According to Dechow et al. [1998]’s
model, all of the coefficients, except that onΔSt-1/At-1, are expected to be positive.
I follow Gunny [2005]’s model to estimate the normal level of gains on asset sales:
t 0 t t t1 2 3 t
t 1 t 1 t 1 t 1 t 1
GLA PPESales ISales S
A A A A A− − − − −
α Δ= + α + α + α + ε (4)
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investments (GLAt) is modeled as a linear function of sales of PPE and investment, both of which are
expected to have positive coefficients. Change in sales is included to control for firm growth, because
growth firms tend to be younger firms who are less likely to recognize gains from selling assets.
Table 1, Panel A reports the estimation results for equations (1) to (4). All of the equations are
estimated cross-sectionally for each industry-year over 1988-2004. The mean coefficients, number of
observations, and adjusted R 2 across all the industry-years are reported. The t-statistics are calculated
based on the standard errors of the mean coefficients. All of the mean coefficients (except for the
coefficient on Log(St-1/St-2)·DSt-1) are significant and have the expected signs. The mean adjusted R 2
ranges from 26% for the gain on asset sales model to 94% for the production cost model, indicating
reasonable to substantial explanatory power for all models.
I measure the abnormal level of each transaction (R&D, SG&A, Prod, and GLA) as the residual from
the relevant estimation model.8 I multiply the residuals from the estimation models of R&D, SG&A, and
GLA by negative one, such that higher values (denoted as Ab_RD, Ab_SGA, and Ab_GLA) indicate a
higher probability that firms cut R&D, SG&A expenses and incur abnormally lower gains from asset
sales to increase reported earnings. Additionally, the higher the abnormal production cost (Ab_Prod,
measured as the residual from the production cost model), the more likely that managers overproduce
inventories to reduce reported cost of goods sold. In measuring real manipulation with assets sales
(Ab_GLA), I impose an additional restriction that the gains from asset sales (GLA) themselves be
positive. Therefore, firms with lower abnormal gains from asset sales (i.e. higher Ab_GLA) and positive
total gains from asset sales (GLA) are more likely to have engaged in real manipulation.9
8 Note that the residual of equation (2) is in log form The following transformation derives the Ab SGA from the
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I use Dechow, Richardson and Tuna’s [2003] (hereafter, DRT) model to estimate the normal level of
total accruals (i.e., nondiscretionary accruals):10
( ) j t j t j t j t j t 1 j t 101 2 3 4 j t
j t 1 j t 1 j t 1 j t 1 j t 2 j t
TAC 1 k S REC PPE TAC S
A A A A A S
, , , , , ,
,
, , , , , ,
− +
− − − − −
+ Δ − Δ Δα= + β + β + β + β + ε (5)
where, TACt = Total accruals = Data 123 – Data 308;
ΔSt = Salest – Salest-1;
ΔRECt = Change in account receivable = ΔData 2;
k = Estimated slope coefficient from a regression of ΔREC on ΔSales for eachFama-French industry-year grouping, i.e., ΔREC = a + k·ΔS + ε;
PPEt = Property, plant and equipment = Data 8.
Similar to the estimation models for the normal level of real transactions, equation (5) is estimated
cross-sectionally for all Fama-French industry-years with at least 15 observations. DRT improve the
cross-sectional modified Jones model (Dechow, Sloan and Sweeney [1995], hereafter DSS) in several
ways. First, DRT’s model does not assume that all credit sales are discretionary. Instead, it estimates k as
the slope coefficient ofΔREC on ΔSales for each industry-year; k captures the expected change in credit
sales for a given change in sales. Second, lag total accruals (TACt-1/At-2) is included to capture the
predictable portion of total accruals. Third, the model is forward-looking since it includes next-year’s
sales growth (ΔSt+1/St) to incorporate the increase in inventory that is due to growth prospects.
Panel B, Table 1 reports the estimation results for equation (5). Results of DSS’s modified Jones
model are also provided for comparison purposes. Consistent with DRT, I find that the average adjusted
R 2 of the DRT model (of 44.77%) is higher than that of the DSS model (of 35.50%). All of the estimated
coefficients are significant with the same signs as found by DRT. I use the residuals from equation (5) to
measure the abnormal level of accruals (Ab_Accr). I assume that the higher the abnormal accrual, the
more likely managers have managed accruals.
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skewness and kurtosis of Ab_GLA are due to the requirement that raw GLA must be positive. The
Pearson correlations among the variables are shown in Panel D, Table 1. Recall that the larger the
Ab_RD, Ab_SGA, and Ab_Prod, the higher the probability of RM. Therefore, the significant positive
correlations between Ab_Accr and Ab_RD, between Ab_Accr and Ab_SGA, and between Ab_Accr and
Ab_Prod, indicating positive relations between AM and RM achieved with R&D, SG&A and
overproduction. The positive correlation between RM and AM is explained by both RM and AM being
positively correlated with incentives to manage earnings.
4. Validity Tests of Real Manipulation Proxies
Before I test the main hypotheses of the paper, I provide two validity tests for the RM proxies
(Ab_Rd, Ab_SGA, Ab_Prod, Ab_GLA) developed in the previous section. Validity tests of these
measures are important both because of the conceptual difficulty in defining RM and because of the
paucity of prior research on RM. The first validity test considers the timing of managers’ RM decisions.
Note that RM is expected of managers during the fiscal year. When a manager is making the RM
decision, presumably two conditions should be met. The first is that the manager has strong incentives to
manipulate earnings for the current quarter; the second is that he has gathered adequate information about
both the true earnings performance and the market’s expectation to estimate how far unmanipulated
earnings are from the earnings target – in order to determine the amount of RM needed. Given these
requirements, I argue that managers are more likely to perform RM during the fourth fiscal quarter than in
the other fiscal quarters.
Along the same lines, several prior studies use quarterly patterns in profit margins to detect potential
earnings management For example Thomas and Zhang [2002] use unusual patterns of COGS scaled by
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Taking firms in the top quintile of the RM measures (Ab_RD, Ab_SGA, Ab_Prod, and Ab_GLA) as
“suspect RM firms,” I predict they have less R&D, SG&A expenses, and gains from asset sales, and more
production costs, in the fourth fiscal quarter than the other fiscal quarters, compared with other firms in
the same industry-year. I construct the following model to estimate the quarterly pattern of these real
transactions:4 4 12
f k f c
q,t q q t q q m m t
q=1 q=1 m=1
X = γ D TopRM + f D + c D +ε× × × ×∑ ∑ ∑ (6) where, Xq,t œ {QRDq,t/At-1, QSGAq,t/At-1, QProdq,t/At-1, QGLAq,t/At-1};
QRD = Quarterly R&D expenses = Data 4;
QSGA = Quarterly SG&A expenses = Data 1 – Data 4;
QProd = Quarterly production costs = Data 30 + change in Data 3 from last quarter;QGLA = Quarterly gains from asset sales = – Data 102;
Df q = Dummy variable equals 1 if it is fiscal quarter q (q=1 to 4), zero otherwise;
Dcm = Dummy variable equals 1 if it is calendar month m (m = 1 to 12), zero otherwise;
TopRMk t = Dummy variable equals 1 if the firm’s Ab_RD (Ab_SGA, Ab_Prod, or Ab_GLA) is
in the top quintiles in its industry-year. For example, if the independent variable isQRDqt/At-1 from fiscal quarter 2, TopRM is a dummy variable equals one if this
firm’s annual Ab_RD is in the top quintile of Ab_RD of the same industry-year.Here, k = RD, SGA, Prod, or GLA.
Equation (6) is estimated cross-sectionally for each Fama-French industry-year that satisfies the
following requirements: (1) at least 15 observations; (2) fewer than 80% of the firms have a December
fiscal year end; (3) there are at least three different fiscal year ends among the firms.11 The dependent
variable is the quarterly amount of real transactions scaled by lagged annual assets. Dummy variables for
calendar months (Dcm) are used to control for calendar seasonality; dummy variables capturing fiscal
quarters (Df q) are used to proxy for average quarterly patterns in real transactions. TopRMk
t equals one if
the firm’s RM proxy (Ab_RD, Ab_SGA, Ab_Prod, and Ab_GLA) is in the top quintile in its industry-year.
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these firms have abnormally low annual R&D, SG&A expenses and gains from asset sales. For
overproducing firms, I expect to find positive values of γ1 to γ4, since they have abnormally high annual
production costs. Under the view that managers are more likely to use RM in the fourth fiscal quarter
than in the other fiscal quarters, the focus of the tests is on the difference between γ4 and the mean of γ1 ,
γ2 and γ3. If suspect RM firms cut R&D, SG&A expenses, or sell assets at abnormally lower prices
(overproduce inventories) in the fourth fiscal quarter, I expect to find that γ4 is smaller (larger) than the
mean of γ1 , γ2 and γ3. Table 2, Panel A reports the results of this test. For quarterly R&D expense
(production costs), the results are consistent with the prediction that γ1 to γ4 are negative (positive), and
that γ4 is smaller (larger) than the mean of γ1, γ2 and γ3 (significant at the 0.001 level). These results
indicate that not only do the suspect RM firms have lower (higher) R&D expense (production costs)
throughout the year than the industry average, they cut R&D expense (overproduce inventories)
significantly more in the fourth fiscal quarter than in the first three fiscal quarters. I do not, however, find
significant results for the SG&A or asset selling manipulation proxies.
My second validity test examines whether suspect RM firms are associated with adverse subsequent
performance. Gunny [2005] also studies the consequence of RM and finds negative performance-
matched operating performance for firms she identifies as real manipulators for years subsequent to the
RM behavior. She identifies real manipulators as firms with abnormal real transactions but little accrual
manipulation flexibility. My tests are similar to hers, but our definitions of suspect RM firms differ,
because I argue that firms that engage in RM can conduct AM at the same time, as evidenced by the
positive correlation between RM and AM proxies in Table 1, Panel D. In particular, I identify a
performance-matched control firm for each suspect RM firm using the following procedure: (1) the
control firm is in the same industry year as the suspect firm; (2) it is selected from the bottom three
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measured as the difference between the performance measure of the suspect firm and that of the control
firm. Two performance measures are examined, return on assets (ROA) and cash flow from operation
scaled by assets (CFO/At-1). I expect to find subsequent negative abnormal performance as the
consequences of suboptimal decisions are revealed.
Table 2, Panel B reports the results of this second validity test. Note that all of the abnormal
performance measures in year t are around zero, indicating the success of the matching procedure.
Consistent with RM having subsequent adverse economic consequences, most of the subsequent
abnormal ROA and scaled CFO in year t+1 to t+3 for firms in the top Ab_SGA and Ab_Prod quintiles are
negative (significance levels of 0.005 or better). Also consistent with predictions, the subsequent
abnormal ROAs from year t+1 to t+3 are significantly negative for firms in the top Ab_RD quintile.
In sum, the first validity test provides support for Ab_RD and Ab_Prod as capturing RM; the second
validity test provides support for Ab_RD, Ab_SGA and Ab_Prod as capturing RM. These tests increase
confidence in the results reported in the next section which focus on the tradeoffs between RM and AM.
However, neither validity test provides support for Ab_GLA as capturing RM. The latter finding is
consistent with Graham et al. [2005] who report little evidence that managers use timing of asset sales to
manage earnings. For these reasons, I do not include Ab_GLA in subsequent empirical tests.
5. Empirical Tests – Broad Sample Analysis
5.1. Research design
As discussed in section 2, whether managers determine RM and AM simultaneously or sequentially
leads to different empirical models for testing the tradeoffs between them. In order to identify the correct
empirical model for testing my hypotheses I first test the assumption of the simultaneity/sequentiality of
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of simultaneity of RM and AM decisions. Since RM and AM are assumed to be endogenous, I estimate
(7) using two-stage least squares. Equation (7) can be identified because the cost determinants in the two
equations differ. I test the simultaneity/sequentiality of RM and AM with the Hausman test. As discussed
in section 2, if RM and AM are simultaneous, RM should be endogenous in the AM equation, i.e.,
correlated with the error term of the AM equation; hence, the Hausman test should reject exogeneity of
RM in the AM equation. On the other hand, if RM is determined before AM, there is no feedback from
AM to RM, which means RM should be orthogonal to the error term of the AM equation; that is, the
Hausman test should fail to reject the exogeneity of RM in the AM equation. Moreover, if managers
make AM decisions based on realized RM, the Hausman test should reject the exogeneity of AM in RM
equation since AM will be correlated with the disturbance term of RM.
If the Hausman test rejects simultaneity, I further test H1-H4 using the following recursive
simultaneous equation system which captures the sequentiality of RM and AM:
i,t 0 1,j j,i,t 2,p p,i,t 3,k k,i,t 4,l l,i,t i,t
j p k l
RM = + Cost of RM + Cost of AM + Incentives + Controls +uλ λ λ λ λ∑ ∑ ∑ ∑ ,
i,t 0 1 i,t 2,p p,i,t 3,k k,i,t 4,l l,i,t i,t
p k l
AM = + RM + Cost of AM + Incentives + Controls +vδ δ δ δ δ∑ ∑ ∑ .(8)
Note that in this recursive equation system, RM is predetermined by the costs of RM and AM, as well
as incentives. On the other hand, the AM equation in equation system (8) has RM as an independent
variable. Note that, under the assumption of sequentiality, when managers determine the level of AM,
they observe the realized RM. Hence, the RM variable in the AM equation is exogenous. Since equation
system (8) is a recursive equation system, it can be estimated consistently with OLS.
H1 predicts that in system (8), both RM and AM are negatively correlated with their own cost
determinants. H2 predicts that in the RM equation, RM variable is positively correlated with the cost
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the cost determinants of manipulations and for earnings management incentives. For RM, I identify five
such proxies. The first proxy, the total benefit of R&D expenditure (TotalBenefit_RD), is based on Lev
and Sougiannis [1996], who study the relation between R&D expenditure and future earnings. They
develop a procedure to measure the total effect of $1 invested R&D on current and future operating
income. When managers cut R&D expenditure to increase current earnings, they forgo the current and
future benefits from the R&D investment that is cut. Hence, the forgone benefit is a cost determinant for
R&D manipulation. Following Lev and Sougiannis [1996], I measure the total benefits of R&D
expenditure to current and future earnings using the following model:13
0 1 2,k 3 i,t
i,t i,t-1 i,t-k i,t-1k
OI TA RD AD= α +α + α +α +e
S S S S
⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠
∑ , (9) where, OI = Annual operating income, before depreciation, advertising and R&D expenses;
TA = The value of plant and equity, inventory and investment in unconsolicated subsidiariesand goodwill, in current dollars;
RD = Annual R&D expenditures, in current dollars;
AD = Annual advertising expenses, in current dollars.
To address the potentially severe collinearity problem in equation (9) due to the inclusion of lagged R&D
expenses as independent variables, an Almon lag procedure is used to impose a lag structure for the
coefficients of lagged R&D expenses. The procedure also reduces the parameters needed to be estimated.
The total benefit of current R&D to current and future earnings (TotalBenefit_RD) is measured as the sum
of significant 2,∑ k
k α .
The second proxy for RM cost is level of competition, proxied by the Herfindahl index and calculated
as the sum of the squared share of each company’s sales in total sales of the industry.14 This index ranges
from one in the case of pure monopoly to zero in the case of perfect competition. In order to reduce
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French industries, based on 3-digit SIC codes.
Within an industry, different firms likely face different levels of competition and therefore, different
pressure when deviating from optimal business strategy. Management research (as reviewed by Woo
[1983]) has shown that market leaders enjoy more competitive advantages than market followers due to
leaders’ greater cumulative experience, economic of scale, bargaining power with suppliers and customers,
attention of investors, as well as influence on their competitors. Therefore, managers in market leader
firms may perceive RM as less costly since the erosion to their firms’ competitive advantage is relatively
small. I use market share (MarketShare), my third proxy for RM cost, to capture companies’ leadership in
the industry, measured as the percentage of the company’s sales to the total sales of its industry.
For a firm close to bankruptcy, the marginal cost of deviating from optimal business strategy is likely
to be high. In this case, managers might perceive RM as quite costly since their primary goal is the
survival of the firm.15 Following previous research, I use a modified version of Altman’s Z-score (Altman
[1968]) to proxy for a firm’s financial health:
j,t j,t j,t j,t
j,t
j,t j,t j,t j,t
j,t
j,t
NI Sales Retained Earnings Working Capital
Z-score = 3.3 +1.0 +1.4 +1.2Assets Assets Assets Assets
Stock Price Shares Outstanding +0.6
Total Liabilities
×.
Altman [2000] examines three samples of subsequent distressed firms from 1969-1975, 1976-1995, and
1997-1999, and finds that a cutoff score of 2.675 yields prediction accuracy of 82% to 94%. Following
Altman [2000], my fourth proxy for RM cost is a dummy variable (Distressed) that equals one if the
firm’s Z-score is smaller than 2.675, and zero otherwise.
My last proxy concerns cost of overproduction. Managers can reduce the cost of goods sold through
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Note that some of the cost determinants of RM are costs of deviating from optimal business strategy
(assumed to be the industry norm) in both directions. For example, in a highly competitive industry both
under- and over-investment in R&D may be costly for the firm. To distinguish between positive and
negative deviations, I create a dummy variable (D_Neg) that equals -1 when the independent variable is
negative, and +1 otherwise. I interact D_Neg with the three cost determinants (Herfindahl, MarketShare,
Distressed) that constrain firms to deviate from optimal level in both directions.
Unlike RM, AM does not have a cash flow effect. Instead, managers are constrained by the flexibility
within GAAP and the scrutiny from outsiders. I identify the following five cost determinants for AM.
The first, auditor reputation, is based on research showing that Big Eight audit firms constrain earnings
management through discretionary accruals (Becker, DeFond, Jiambalvo, and Subramanyam [1998];
Francis, Maydew, and Sparks [1999]).16 The reason is that Big Eight audit firms are likely to be more
experienced, invest more resources in auditing, and have more reputation at risk. To proxy for auditors’
reputation, I use a dummy variable (Big8) that equals one if the firm’s auditor is one of the Big Eight,
zero otherwise. My second AM cost proxy is auditor tenure. Beck, Frecka, and Solomon [1988] and Lys
and Watts [1994] find that auditor independence decreases with auditor tenure. However, Stice [1991]
finds the opposite relation and argues that the risk of not detecting errors due to unfamiliarity decreases
with tenure. Therefore, I consider auditor tenure as a proxy for auditor scrutiny without predicting its
sign. I measure auditor tenure (AuditorTenure) as the number of years the auditor has audited the client.
One obvious cost of manipulating accruals upward is that abnormal accruals will mechanically
reverse in the short-run, reducing earnings in the next period. Follow Hunt et al. [1996], I use firm-
specific estimation of current accruals’ first-order autocorrelation over the sample period as a proxy for
the reversal rate of accruals (ReversalRate) my third AM cost proxy 17 In some growing firms the
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Due to the limited flexibility within GAAP and the reversal of previous accruals, managers’ ability to
manipulate accruals upward in the current period is constrained by AM activities in previous periods. To
capture the level of abnormal accruals in previous periods (my fourth proxy for AM cost), I use Barton
and Simko’s [2002] balance sheet measure of previous accounting choices (NOAt-1/Salest-1), which equals
net operating assets at the beginning of the year divided by lagged sales (net operating assetst-1 =
shareholders’ equity t-1 – cash and marketable securities t-1 + total debt t-1). The rationale for this proxy is
that because of the articulation between the income statement and the balance sheet, previous abnormal
accruals reflected in past earnings are also reflected in net assets; hence, the latter are overstated.19
My last AM cost captures the level of scrutiny from investors and regulators. Prior research (Francis,
Philbrick, and Schipper [1994]) identifies four industries with particularly high litigation risk:
biotechnology, computer, electronics, and retailing industry. I use a dummy variable to indicate firms in
these four industries (HighLitigation) and use it to proxy for litigation risk.20
Prior earnings management work examines numerous managerial incentives to manage earnings.
Recent studies (reviewed by Dechow and Skinner [2000], Fields et al. [2001], Healy and Wahlen [1999])
have shown that capital market incentives dominate other incentives. The first capital market incentive I
consider is equity issuance. In particular, managers have incentives to boost stock price when they are
issuing equity, in the belief that inventors cannot “see through” earnings management at the time of
equity issuance. Consistent with this argument, Teoh, Welch, and Wong [1998] and Rangan [1998] find
that managers manage earnings at the time of seasoned equity offerings. Following Choudhary, Rajgopal,
and Venkatachalam [2005], I measure this incentive using a dummy variable (StockIssuance) that equals
one if the firm has issued equity in the last three fiscal years, and zero otherwise.
Recent research documents that analysts’ forecast consensus is an important earnings threshold that
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analysts’ forecast consensus at the end of the third fiscal quarter as the earnings target. Following Hunt et
al. [1996], I calculate earnings per share excluding the effects of manipulation as EPS before
extraordinary items reduced by the sum of the per share effect of unscaled abnormal real transactions and
abnormal accruals. Ex ante distance to earnings threshold at the end of third quarter (ExAnteDistance) is
the difference between analysts’ forecast consensus at the end of the third quarter and EPS excluding the
effects of manipulations. If managers have incentives to meet analyst forecast with manipulation, they
need to exert more effort if ExAnteDistance is large.
Since the earnings target (i.e., analysts’ forecast consensus) is a per share number, one penny short in
EPS translates into more dollars of actual earnings for firms with more shares outstanding than for firms
with fewer shares outstanding. Therefore, I predict that controlling for the distance to earnings threshold
at the end of third quarter, the amounts of RM and AM are positively correlated with firm’s number of
shares outstanding (Shares).
Furthermore, Bartov, Givoly, and Hayn [2002] find that firms that meet or beat analysts’ earnings
forecasts enjoy a higher return than firms that fail to meet the target. Both Bartov et al. [2002] and
Kasznik and McNichols [2002] find that this premium is greater for “habitual beaters” than for “sporadic
beaters.” Therefore, managers of firms who repeatedly beat earnings targets have stronger incentives to
keep beating targets. I use the percentage of beating/meeting analysts’ forecast consensus in the past four
quarters (Beater) to measure a firm’s history of beating earnings thresholds.
The next capital market incentive proxy relates to stock compensation where I argue that managers
with more stock compensation have greater incentives to increase stock price. I measure the earnings
management incentives from equity compensation in two ways. The first is the top five managers’ total
stock wealth (StkWealth) calculated as the sum of their stock option value under Black Scholes method
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total stock compensation for a 1% change in the stock price. In order to aggregate the individual-level
measure to firm-level, I average the values of Sensitivity for the top five executives.
There are two views in the literature about the analysts’ role in managers’ earnings management
decision. One view is that analysts play a monitoring role (Jensen and Meckling [1976], Healy and
Palepu [2001]), the other is that analysts pressure managers to beat forecast targets. A recent paper by Yu
[2005] finds that the monitoring role of analysts dominates the pressure they exert on managers to manage
earnings. Therefore, I include analyst following (LogAnalystFoll), measured as the total number of
analysts who issue at least one forecast for the firm’s current fiscal year, as a proxy for managerial
incentive to manage earnings, and predict a negative sign.22
It is well recognized that Jones’ model estimates of abnormal accruals are correlated with firm
performance (Dechow et al. [1995], Kasznik [1999], McNichols [2000]). Specifically, research shows
that estimated abnormal accruals are negative for firms with low return on assets (ROA) and positive for
firms with high ROA, even in the absence of earnings management. Therefore, I include ROA in the AM
equations and expect it to have a positive sign. Since there is no prior research about the relation between
RM proxies and firm performance, I include ROA in the RM equations to control for firm performance
without predicting its sign. Other control variables include firm size, measured as the log value of net
sales (LogSales), and firms’ growth prospect, proxied by ratio of market value to book value (MtoB).
5.3. Sample selection
I start from the population of CRSP/COMPUSTAT Merged Database from 1988 to 2003, when
statement of cash flow data are available (Collins and Hribar [2001]). I exclude financial institutions
(SIC 6000-6999) and regulated industries (SIC 4400-5000), and obtain a sample of 84,178 firm-year
observations Of this sample 54 105 firm year observations have at least one RM proxy and the AM
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24,401 firm-year observations remain. The sample size declines to 10,980 firm-year observations after
obtaining independent variables calculated from Execucomp Database (StkWealth and Sensitivity). Note
that I do not require all firms to have all dependent and independent variables. Therefore, the number of
observations varies across the regressions. Table 3 reports summary statistics for the variables.
5.4. Tests of Hypotheses
To test whether managers make RM and AM decisions simultaneously or sequentially, I conduct the
Hausman test by regressing Ab_Accr on the exogenous variables (i.e., cost determinants of AM,
incentives, and control variables), the instrument for Ab_RM (the predicted value from the first-stage
regression), and the actual Ab_RM. If RM is determined after AM, then the coefficient on the
instrumental variable of Ab_RM should equal zero. Table 4 reports the results of Hausman tests for
equation (7). Consistent with sequentiality, all of the Hausman tests fail to reject the exogeneity of
Ab_RD, Ab_SGA, and Ab_Prod, in the AM regressions (with p-values ranging from 0.1244 to 0.1758).
In contrast, all of the Hausman tests reject the exogeneity of Ab_Accr in the RM equations, which means
Ab_Accr is correlated with RM’s error term. These results indicate that RM and AM are determined
sequentially, with RM preceding AM.
Given the finding of the sequentiality of RM and AM, I use the recursive equation system (8) to test
H1-H4. Since the equation system is triangular, it can be estimated consistently with OLS; results are
reported in Table 5. The adjusted R 2’s for the RM equations of Ab_RD and Ab_Prod are 13.57% and
57.10%, respectively; for the AM equations, the adjusted R 2
s range from 23.90% to 54.32%. These
results indicate reasonable explanatory power for Ab_RD, Ab_Prod, and Ab_Accr. However, the adjusted
R 2 for the Ab_SGA equation is 2.97%, indicating that this regression has limited explanatory power.
H1 predicts that in both equations RM and AM are negatively related with their own cost
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likely to deviate from optimal levels of R&D expenditures (i.e., manipulate R&D expenditures).
In the AM equation of Panel A, I find significant negative coefficient on HighLitigation and significant
positive coefficient on AuditorTenure, consistent with H1 which argues that AM is negatively correlated
with litigation risk and auditor independence. However, the significant positive coefficient on NOA/Sales
in the AM equation is not consistent with H1. This result may be due to the measurement error in this
proxy (discussed by DeFond [2002]) or to the possibility that firms with greater accounting flexibility in
the past are likely to have greater accounting flexibility in the current period. Panel B reports the results
of SG&A manipulation. The significant positive (negative) coefficient on D_Neg*MarketShare
(D_Neg*Distressed) suggests that firms are reluctant to deviate from the optimal SG&A decision if they
are market followers or closer to bankruptcy, consistent with H1. In the AM equation of Panel B,
abnormal accruals are negatively related with cost determinants, litigation risk (HighLigtigation) and
auditor reputation (Big8), consistent with H1. However, the significant negative coefficient of
NOA/Sales is inconsistent with predictions, potentially due to the reasons discussed above. Finally, Panel
C also shows evidence consistent with H1. Here, results for the Ab_Prod equation show that distressed
firms are less willing to overproduce inventories, and that firms with higher fixed cost (PPE/Sales) are
more likely to overproduce inventories. Results for the AM equation show that firms with higher auditor
independence (i.e. smaller AuditorTenure), higher auditor reputation (Big8), higher accrual reversal rate
(ReversalRate), and higher litigation risk (HighLitigation) are less likely to use AM.
If managers trade off RM with AM, the level of RM should increase with the cost of AM (H2). Tests
of H2 are indicated by the coefficient estimates for the cost determinants of AM (i.e., AuditorTenure, Big8,
NOA/Sales, ReversalRate, and HighLitigation) in the RM equations. In Panel A of Table 5, the cost
determinants of AM ReversalRate and HighLitigation are significantly positively correlated with Ab RD
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incentives to cut SG&A when accruals reverse faster and when auditors’ scrutiny is lower. In Panel C of
Table 5, H2 is supported by the significant positive coefficients on ReversalRate and HighLitigation in
Ab_Prod equation, and by the significant negative coefficient on AuditorTenure, suggesting that firms
with higher accrual reversal rate, higher litigation risk, and higher auditor independence are more likely to
use overproduction to increase reported income.
H3 predicts a negative relation between AM and RM in the AM equation. Consistent with H3, all
three panels of Table 5 show that RM levels are significantly negatively correlated with AM (p-values <
0.05), providing strong evidence for the substitutive relation between RM and AM.
A positive relation between earnings management incentives and manipulation level is predicted by
H4. In Panel A of Table 5, the significant coefficients on Beater, LogAnalystFoll, Shares, and
ExAnteDistance in both Ab_RD and Ab_Accr equations suggest that firms which consistently beat
earnings, have less analyst monitoring, more shares outstanding, and greater ex ante distance to analysts’
forecast consensus, are more likely to manipulate both R&D expenditures and discretionary accruals,
consistent with H4. Panel B of Table 5 shows that firms that consistently beat earnings and have more
shares outstanding are more likely to manipulate SG&A expenditures, consistent with H4. In the AM
equation, Panel B shows that the relation between AM (Ab_Accr and most of the earnings management
proxies (except for StkWealth) are significant and consistent with the H4. Panel C of Table 5 reports a
significant positive relation between incentives captured by Beater, LogAnalystFoll, EquityIssuance,
Sensitivity, and ExAnteDistance and both RM and AM. Overall, the results reported in Table 5 support
H4, which predicts that RM and AM are positively correlated with earnings management incentives.
Taken together, the broad sample tests generally show results consistent with my models’ predictions.
That is both RM and AM are negatively related with their own cost determinants (H1); RM is negatively
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sample tests. There are two distinguishing features of the small sample test. First, I focus on the over-
time effects of the change in cost determinants (firm-specific litigation risk) on RM and AM behaviors.
Prior research (Dechow et al. [1996]; Lu [2004]) shows that firms subject to SEC enforcement or
securities class actions have distinct patterns of abnormal accruals: abnormal accruals increase over the
alleged years of earnings management, and decrease precipitously after the alleged years of earnings
management. Based on H1 and the results of the broad sample tests, I assume that the documented
decrease in abnormal accruals after the class period is due to the increase in litigation risk and the
negative relation between AM and its cost determinants. Assuming a substitutive relation between RM
and AM, I expect that sued firms engage in more RM after security lawsuits are filed, as implied by H2.
Therefore, lawsuit firms offer an opportunity for comparing over-time behaviors in RM and AM due to
the change in cost determinants before and after the lawsuit filing.23
Second, I adopt a conservative matching procedure to control for the time-series properties of
abnormal real transactions and abnormal accruals. Such a procedure is necessary because abnormal
levels of real transactions and accruals are likely to be mean-reverting. Without controlling for mean-
reversion, the decrease in abnormal accruals after SEC enforcement or securities class actions
documented by the prior research cannot be solely attributable to the change in cost determinants of AM.
I use the following procedure to match lawsuit firms with control firms:24 (1) the control firm is in the
same industry-year as the lawsuit firm; (2) it does not experience any securities class action lawsuits in
the sample period; (3) it has the closest abnormal real transactions or accruals (depending on the type of
manipulations being examined) to that of the lawsuit firm in the year prior to the lawsuit filling; (4) a firm
cannot serve as control firm more than once. I calculate the difference between the abnormal levels of
real transactions and accruals of the lawsuit firm and its matched control firm and study the change in
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maintained by Stanford Law School in cooperation with Cornerstone Research. The Clearinghouse
maintains an index of filings of 1,880 issuers that have been named in federal class action securities
lawsuits since 1995. For each lawsuit, I code information about the name of the sued company, the
company’s ticker, the starting and ending dates of the class period, and the date of the class action filing.
I merge the lawsuit sample into CRSP/ COMPUSTAT Merged Database by ticker and company name.
After removing financial institutions, firms in regulated industries, and firms with multiple lawsuits
during the sample period, I obtain a sample size of 823 lawsuit firms over 1995-2004 which have both the
AM proxy and at least on of the RM proxies.
Table 6 reports descriptive statistics for the 823 lawsuit firms. Panel A shows that lawsuit firms are
larger than their control group in terms of market value of equity and total assets (both p-values < 0.01).
However, there are no significant differences in market to book ratios or ROA between lawsuit firms and
control firms. Panel B of Table 6 lists the number of lawsuit firms by industry. Note that the lawsuit
sample is concentrated in the computer industry (38.3%). Panel C reports the lawsuit filings by year.
Panel D lists the number of lawsuit firms by the length of their class periods.
Table 7 compares the performance-matched abnormal real transactions and abnormal accruals for the
lawsuit sample before and after the lawsuit filing year. To better illustrate the over-time changes in
performance-matched RM and AM measures, I plot the mean values in Figure 1. As illustrated there, the
mean performance-matched RM and AM measures in year -1 (one year prior to lawsuit filing) are close to
zero, indicating the success of the matching procedure. Consistent with H1 and prior research, the
performance-matched abnormal accruals drop abruptly in the year of lawsuit filing (year 0) and in the
following two years (years 1 and 2).25 Note that this for the lawsuit firms is not due to potential reversal
of abnormal accruals because the matching procedure ensures that the matched firms have the same
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If managers trade off the relative costs of RM and AM when making RM decisions (as posited by H2)
then the increase in litigation risk and outside scrutiny due to the lawsuit filing will lead to higher levels
of RM following the lawsuit. Consistent with H2, Figure 1 shows that the performance-matched Ab_RD
increases substantially in year 1 and in year 3. Similarly, the performance-matched abnormal production
costs increase dramatically in the year of lawsuit filing. However, I do not find evidence of managers
increasing RM by cutting SG&A expenditure after the filing of lawsuit. Overall, I believe the small
sample results support the broad sample evidence concerning H2. Specifically, both indicate a
substitutive relation between RM and AM activities, consistent with H2.
7. Conclusion
My paper provides evidence on how managers trade off real manipulation and accrual manipulation.
The real manipulations I examine include cutting R&D and SG&A expenditures, overproducing inventory,
and timing of asset sales. I use cross-sectional models to estimate abnormal levels of real transactions as
proxies for real manipulation, and I provide two validity tests of these proxies. The first validity test
shows that suspect manipulator firms conduct more real manipulation in the fourth fiscal quarter than in
the other fiscal quarters, consistent with managers engaging in more manipulation in periods when they
have both more information about the total amount of earnings management needed and greater
incentives to manage earnings to achieve annual targets. The second validity test shows that suspect
manipulator firms have negative abnormal performance in years after real manipulation is detected,
consistent with my real manipulation proxies capturing suboptimal business decisions.
My main (broad sample) tests begin by establishing the simultaneity or sequentiality of managers’
real manipulation and accrual manipulation decisions Hausman tests reject simultaneity showing instead
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with earnings management incentives. The hypothesized substitutive relation between real manipulation
and accrual manipulation is further supported by the results of a small sample test examining over-time
patterns of real manipulation and accrual manipulation for firms subject to securities class action lawsuits
during 1995-2004. I find that accrual manipulation decreases and real manipulation increases after
lawsuit filings, consistent with managers changing their earnings management strategies in response to
the increase in litigation risk and outside scrutiny.
My finding that managers treating real manipulation and accrual manipulation as substitutes has
implications for both researchers and regulators. For researchers, this substitutive relation suggests that
focusing on accrual manipulation exclusively may not fully explain earnings management activities. For
regulators, this result implies that increasing scrutiny or constraints over accounting discretion may not
eliminate earnings management activities, but only change managers’ priority of earnings management
strategies, some of which (real manipulation for example) may be more costly to investors.
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Appendix I: Variable DefinitionsVariable Measurement
Variable used in the estimation models for normal levels of real transactions and accruals
RDt R&D expense = Data 46
At Total assets = Data 6
Fundst Internal funds = IBEI + R&D + Depreciation = Data 18 + Data 46 + Data 14
TobinsQt (MVE + Book value of preferred stock + Long-term debt + Short-term debt) / Total assets =(Data 199 * Data 25 + Data 130 + Data 9 + Data 34)/Data 6
CapitalExpt Capital expenditure = Data 128
SG&At SG&A expenses, excluding R&D expense = Data 189 – Data 46St Net sales = Data 12
DSt Dummy for decreasing sales that equals 1 if St < St-1, 0 otherwise
Prodt COGSt + ΔInventoryt = Data 41 + ΔData 3
ΔSt St – St-1
GLAt Gain or loss from sale of PPE and investment = – Data 123. A negative sign is needed sinceCOMPUSTAT presents losses as positive numbers for Gain or loss from sale of assets.
PPESalest Sale of PPE = Data 107
ISalest Sale of investment = Data 109
TACt Total accruals = Data 123 – Data 308ΔRECt Change in account receivables = ΔData 2
k Estimated slope coefficient from a regression of ΔREC on ΔSales for each Fama-Frenchindustry-year grouping, i.e.,ΔREC = a + k ΔS + ε.
PPEt Property, plant and equipment = Data 8
Proxies for real and accrual manipulations
Ab_RD Residuals from the following regression multiplied by -1, estimated cross-sectionally for Fama-French industry years with at least 15 observations:
RD RD Funds CapitalExpt t 1 t tTobinsQ
0 1 2 3 t 4 tA A A At 1 t 1 t 1 t 1
−= α + α + α + α + α + ε
− − − −.
Ab_SGA Ab_SGA = -1*{Exp[Log(SGAt/SGAt-1)] – Exp[Log(SGAt/SGAt-1) – residual ofLog(SGAt/SGAt-1)]}*SGAt-1, where Log(SGAt/SGAt-1)] is the residuals from the followingmodel estimated cross-sectionally for Fama-French industry years with at least 15 observations:
SG&A S S S St t t t-1 t-1Log =α +α Log +α Log *DS +α Log +α Log *DS +ε1 2 3 t 4 5 t-1 tSG&A S S S St-1 t-1 t-1 t-2 t-2
⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠
.
Ab_Prod Residuals from the following regression, estimated cross-sectionally for Fama-French industryyears with at least 15 observations:Prod S ΔS ΔSαt t t t-11
= +α +α +α2 3 4 tA A A A At-1 t-1 t-1 t-1 t-1 +ε .
Ab_GLA If the firm has positive GLA, Ab_GLA is measured as residuals from the following regressionmultiplied by -1, estimated cross-sectionally for Fama-French industry years with at least 15observations:GLA PPESales ISales St 0 t t t
1 2 3 tA A A A At 1 t 1 t 1 t 1 t 1
α Δ= +α +α +α +ε .
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TopRMk t Dummy variable equals 1 if the firm’s abnormal transaction is in the top quintiles of RM in its
industry-year. For example, if the independent variable is QRDt/At-1 from fiscal quarter 2,TopRM is a dummy variable equals 1 if this firm’s annual Ab_RDt is in the top quintile ofAb_RDt of the same industry-year. Here, k = RD, SGA, Prod, or GLA.
Independent variables in the main test
OI Annual operating income, before depreciation, advertising and R&D expenses.
TA The value of equipment and equity, inventory and investment in unconsolidated subsidiariesand goodwill, in current dollars.
AD Annual advertising expenses.
IRD Industry average R&D expense using four-digit SIC industry grouping.TotalBenefit_RD The total benefit of current R&D to current and future earnings (TotalBenefit_RD) is measured
as the sum of significant 2,k α , estimated with the following model, which is run cross-
sectionally for each industry-year using Almon lag procedure:
0 1 2, 3, , 1 , , 1
OI TA RD ADe
k it S S S S k i t i t i t k i t
α α α α
⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞∑= + + + +⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠− − −, where RD/S is replaced by its
instrumental variable estimated with the following regression: ,, ,
RD IRDa b e
i t S S i t i t
⎛ ⎞ ⎛ ⎞= + +⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠.
Herfindahl The sum of the squared share of each company in total sales of the industry.MarketShare The percentage of the company’s sales to the total sales of its industry
Distressed Dummy variable that equals 1 if the firm’s Z-score is less than 2.675, and 0 otherwise, where NI Sales Retained Earnings Working Capital Stock Price SharesOutstanding
jt jt jt jt jtZ-score = 3.3 +1.0 +1.4 +1.2 +0.6 jt Assets Assets Assets Assets Total Liabilities
jt jt jt jt jt
×.
PPE/Sales Data 8/Data 12
D_Neg Dummy variable equals -1 if the independent variable is negative, 0 otherwise.
Big8 Dummy variable equals 1 if the firm’s auditor is a Big Eight, 0 otherwise.
AuditorTenure Number of years the auditor has worked for the client.
ReversalRate Firm-specific estimation of current accruals’ first-order autocorrelation over the sample period.I require the time series of firm-specific current accruals to be at least 7 years. I set theaccruals’ autocorrelation coefficient to 0 if the estimated value is positive.
NOAt-1/Salest-1 (Shareholders’ equity – cash and marketable securities + total debt)t-1/Salest-1
HighLitigation Dummy variable equals 1 if the firm belongs to one of the following industries: biotechnology(SIC 2833-2836), computer (SIC 3570-3577, 7370-7374), electronics (SIC 3600-3674), andretailing industry (SIC 5200-5961).
StockIssuance Dummy variable equals 1 if the firm has issued equity in the last three fiscal years and 0otherwise.
Beater Percentage of beating/meeting analysts’ forecast consensus in the past 4 quarters.StkWealth The sum of the top five executives’ stock option, valued under Black-Scholes method, and thevalue of their other stock holdings. The Black-Scholes value of top executives’ stock option iscalculated with “one-year approximation” procedure developed by Core and Guay [2002].
Sensitivity The average sensitivity of stock compensation to stock price for top five executives. Thesensitivity of stock compensation to stock price is calculated as the sum of the changes inBlack Scholes value of the stock options and in other stock holding value for a 1% change in
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Table 1: Estimation of the Normal Levels of R&D Transactions and Accruals Panel A: Estimation of normal level of real transactions
The following regressions are estimated cross-sectionally within each industry-year over 1988-2004. Fama-French
48 industry grouping is used. The model is estimated for an industry-year with at least 15 observations. The
reported coefficients are the mean value of the coefficients across the industry-years. T-statistics are calculated
using the standard error of the mean coefficients across the industry-years. The adjusted R 2 (number of observations)
is the mean adjusted R 2 (number of observations) across the industry-years.
t t 1 t t0 1 2 3 t 4 t
t 1 t 1 t 1 t 1
RD RD Funds CapitalExpTobinsQA A A A
−
− − − −
= α + α + α + α + α + ε (1) t t t t-1 t-1
1 2 3 t 4 5 t-1 t
t-1 t-1 t-1 t-2 t-2
SG&A S S S SLog =α +α Log +α Log *DS +α Log +α Log *DS +ε
SG&A S S S S
⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠
(2) t t t t-11
2 3 4 t
t-1 t-1 t-1 t-1 t-1
Prod S ΔS ΔSα= +α +α +α
A A A A A+ ε (3)
t 0 t t t
1 2 3 t
t 1 t 1 t 1 t 1 t 1
GLA PPESales ISales S
A A A A A− − − − −
α Δ
= + α + α + α + ε (4)
t
t 1
RD
A −
t
t-1
SG&ALog
SG&A
⎛ ⎞⎜ ⎟⎝ ⎠
t
t-1
Prod
A t
t 1
GLA
A −
Intercept0.0011(0.91)
Intercept0.0179(7.49)
t 1
1
A −
0.0446(0.51)
t 1
1
A −
0.0059(0.88)
t 1
t 1
RD
A
−
−
0.9263(47.47)
t
t-1
SlogS
⎛ ⎞⎜ ⎟⎝ ⎠
0.6220(39.90)
t
t-1
S
A 0.7067
(167.45)t
t 1
PPESales
A −
0.2679(7.94)
t
t 1
Funds
A −
0.0008(0.26)
tt
t-1
SLog *DS
S
⎛ ⎞⎜ ⎟⎝ ⎠
-0.2396(-1.97)
t
t-1
ΔS
A 0.0638
(6.79)t
t 1
ISales
A −
0.0591(2.52)
TobinsQt 0.0027(6.54)
t-1
t-2
SLog
S
⎛ ⎞⎜ ⎟⎝ ⎠
0.0716(6.24)
t-1
t-1
ΔS
A -0.0174
(-2.26)t
t 1
S
A −
Δ -0.0174
(-1.11)
t
t 1
CapitalExp
A − 0.1089(8.06)
t-1 t-1
t-2
SLog *DSS
⎛ ⎞⎜ ⎟⎝ ⎠
0.1993(1.77)
Adj. R 2(%) 82.08 49.58 94.08 26.00# of obs. 102.37 100.13 111.00 98.97# of ind-yrs 448 603 620 577
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Panel B: Estimation of normal level of total accrualsThe following regressions are estimated cross-sectionally within each industry-year over 1988-2004. Fama-French
48 industry grouping is used. The model is estimated for an industry-year with at least 15 observations. The
reported coefficients are the mean value of the coefficients across the industry-years. T-statistics are calculated
using the standard error of the mean coefficients across the industry-years. The adjusted R 2 (number of observations)
is the mean adjusted R 2 (number of observations) across the industry-years.
DRT [2003] model: ( ) j t j t j t j t j t 1 j t 101 2 3 4 j t
j t 1 j t 1 j t 1 j t 1 j t 2 j t
TAC 1 k S REC PPE TAC S
A A A A A S
, , , , , ,
,
, , , , , ,
− +
− − − − −
+ Δ − Δ Δα= + β + β + β + β + ε (5)
DSS [1995] model: j t j t j t j t0
1 2 j t
j t 1 j t 1 j t 1 j t 1
TAC S REC PPE
A A A A
, , , ,
,
, , , ,− − − −
Δ − Δα= + β + β + ε
DRT model DSS model
a0/A j,t-1-0.2228(-4.35)
a0/A j,t-1 -0.4354(-6.74)
( ) j t j t
j t 1
1 k S REC
A
, ,
, −
+ Δ − Δ 0.0196
(2.99)
j t j t
j t 1
S REC
A
, ,
, −
Δ − Δ 0.0401
(5.90)
PPE j,t/A j,t-1-0.1172(-22.74)
PPE j,t/A j,t-1 -0.1505(-23.29)
TAC j,t-1/A j,t-20.2160(15.30)
ΔS j,t+1/S j,t 0.0243(3.86)
Adj. R 2(%) 44.77 35.50# of obs. 99.71 117.66# of ind-yrs 543 636
Panel C: Summary statistics for abnormal real and accrual levels
Ab_RD Ab_SGA Ab_Prod Ab_GLA Ab_Accr
Mean 0.0015 -0.0001 -0.0285 -0.0130 -0.0079Median -0.0008 0.0000 -0.0319 0.0020 -0.0018
Std. Dev. 0.0645 0.0216 0.2680 0.0409 0.139325% -0.0075 -0.0007 -0.1582 -0.0098 -0.059875% 0.0170 0.0007 0.0908 0.0001 0.0519Skewness -0.9698 -0.2829 0.2895 -4.5216 -0.4484Kurtosis 8.0336 16.4449 2.1356 23.8872 3.5897# of obs. 45,860 60,378 68,820 12,937 54,142
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Table 2: Validity Tests of Real Manipulation Proxies
Panel A: Timing test
The following regressions are estimated cross-sectionally for each Fama-French industry-year over 1988-2003. The
model is estimated for an industry-year that satisfies the following requirements: (1) ≥ 15 observations; (2) ≤ 80%
of the firms with a December fiscal year end; 3) ≥ three different fiscal year ends among the firms. The reported
coefficients are the mean of the coefficients across the industry-years. The significance of the mean coefficients is
based on the standard errors across the industry-years.4 4 12
f k f c
qt q q t q q m m t
q=1 q=1 m=1
X = γ D TopRM + f D + c D +ε× × × ×∑ ∑ ∑ (6) Xqt QRDt/At-1 QSGAt/At-1 QProdt/At-1 QGLAt/At-1
Pred.sign
ResultsPred.sign
ResultsPred.sign
ResultsPred.sign
Results
Df 1μTopRM (γ1) – -0.0104 – -0.0788 + 0.1681*** – 0.0000
Df 2μTopRM (γ2) – -0.0058 – -0.0280 + 0.1897*** – 0.0001
Df 3μTopRM (γ3) – -0.0023 – 0.0231 + 0.1963*** – -0.0005
Df 4μTopRM (γ4) – -0.0095** – 0.0232* + 0.2264*** – -0.0011
γ4 – mean (γ1, γ2, γ3) – -0.0034*** – 0.0511 + 0.0417*** – -0.0010
# of industry-years 383 567 1,072 348
Panel B: Subsequent firm performance
A performance-matched control firm (control firm) is identified for each firm in the top abnormal real transaction
quintiles (target firm). The abnormal performance in year t+1 to t+3 of is measured as the difference between the
performance measures of the target firms and those of the control firm.
Performance-matched ROA Performance-matched CFO/At-1
N Mean Median N Mean Median
Top Ab_RD quintile:
t 4,894 0.0000 0.0006*** 2,793 0.0003 0.0009***t+1 4,894 -0.0100* 0.0012 2,793 0.0015 0.0059*t+2 4,894 -0.0187* 0.0009 2,793 -0.0051 0.0086***t+3 4,894 -0.0215*** 0.0022 2,793 -0.0145 0.0072
Top Ab_SGA quintile: t 10,128 0.0001 0.0000*** 3,416 0.0005*** 0.0007***
t+1 10,128 -0.0104*** 0.0002 3,416 -0.0221*** -0.0162***t+2 10,128 -0.0140*** -0.0035*** 3,416 -0.0072*** -0.0180***t+3 10,128 -0.0289** -0.0086*** 3,416 -0.0366 -0.0198***
Top Ab_Prod quintile: t 9 079 -0 0002 0 0002 3 957 0 0000 0 0000
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Table 3: Descriptive Information About Independent Variables
Variable N Mean Median Std. Dev. 25% 75%
Cost determinants of real manipulation:
TotalBenefit_RD 6827 10.2154 3.5329 49.8448 0.0000 17.3446Herfindahl 11129 0.1440 0.1124 0.0965 0.0777 0.1830MarketShare 11129 0.0669 0.0225 0.1083 0.0051 0.0762
Distressed 11129 0.2805 0.0000 0.4493 0.0000 1.0000PPE/Sales 11129 0.4090 0.2277 0.6930 0.1295 0.4116
Cost determinants of accrual manipulation:
Big8 11129 0.9825 1.0000 0.1312 1.0000 1.0000AuditorTenure 11129 11.3527 9.0000 8.1872 5.0000 19.0000ReversalRate 11129 -0.1747 -0.0573 0.2533 -0.2829 0.0000 NOA/Sales 11129 0.7777 0.5868 0.7397 0.3711 0.8841HighLitigation 11129 0.3600 0.0000 0.4800 0.0000 1.0000
Incentives of earnings management:Beater 11129 0.3860 0.2500 0.3175 0.0000 0.6667LogAnalystFoll 11129 2.4073 2.4849 0.7414 1.9459 2.9444StockIssuance 11129 0.8934 1.0000 0.3086 1.0000 1.0000Sensitivity 11129 0.3398 0.0768 3.2071 0.0314 0.1960StkWealth 11129 35.0971 6.7581 346.5280 2.7121 17.7762Shares 11129 114.0876 40.4190 231.1821 21.2280 98.8000ExAnteDistance_RD 7445 0.3858 0.2453 1.7429 -0.3997 1.0263ExAnteDistance_SGA 10328 0.2659 0.1849 1.7724 -0.4574 0.9207
ExAnteDistance_Prod 10932 -0.3380 -0.6623 5.3202 -2.7909 1.4070ExAnteDistance_GLA 7372 0.2318 0.1673 1.6831 -0.4417 0.8433
Controls:
ROA 11129 0.0556 0.0617 0.1143 0.0207 0.1074LogSales 11129 6.8006 6.7266 1.5986 5.7665 7.8286MtoB 11129 3.3064 2.4350 3.2413 1.5862 3.8889
For variable definitions, please refer to Appendix I.
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Table 4: Hausman Test for Simultaneity versus Sequentiality of Real and Accrual Manipulations
The following regressions are estimated for the pooled sample over 1992-2003. Costs of RM are TotalBenefit_RD,
D_Neg*Herfindahl, D_Neg*MarketShare, D_Neg*Distressed PPE/Sales; costs of AM are AuditorTenure, Big8,
NOA/Sales, ReversalRate, HighLitigation; incentives are Beater, LogAnalystFoll, EquityIssuance, Sensitivity,
StkWealth, Shares, ExAnteDistance; other controls are ROA, LogSales, MtoB. The Hausman test is conducted by
regressing Ab_Accr on the exogenous variables (i.e., cost determinants of AM, incentives, and control variables), the
instrument for Ab_RM (the predicted value from the first-stage regression), and the actual Ab_RM. The coefficient
on the predicted value of Ab_RM (P_Ab_RM) is tested against zero.
i,t 0 1 i,t 2,j j,i,t 3,k k,i,t 4,l l,i,t i t
j k l
RM = γ +γ AM + γ Cost of RM + γ Incentives + γ Other Controls u ,
+∑ ∑ ∑ ,
i,t 0 1 i,t 2,j' j',i,t 3,k k,i,t 4,l l,i,t i,t
j' k l
AM = φ +φ RM + φ Cost of AM + φ Incentives + φ Other Controls +v∑ ∑ ∑ ,(7)
Panel A: Panel B:
Ab_RD equation(N = 5,104)
Ab_Accr equation(N = 5,104)
Ab_SGA equation(N = 10,276)
Ab_Accr equation(N = 10,276)
Coef. p-value Coef. p-value Coef. p-value Coef. p-value
Endogenous
variables:
Ab_Accr -0.0370 <.0001 0.0000 0.4333Ab_RM -0.1044 <.0001 0.1051 0.4332
P_Ab_Accr -0.2083 0.0057 -0.0138 <.0001P_Ab_RM -1.0877 0.1758 -19.3241 0.1244 Hausman test:
1st-stage adj. R 2 (%) 13.57 45.59 2.97 54.202nd-stage adj. R 2 (%) 13.69 45.73 2.79 54.18
p-value for
Hausman stat.0.0057 0.1758 <.0001 0.1244
Panel C:
Ab_Prod equation(N = 10,453)
Ab_Accr equation(N = 10,453)
Coef. p-value Coef. p-value
Endogenous
variables:
Ab Accr -0.1749 <.0001
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Table 5: Tests of the Tradeoff between Real and Accrual Manipulations
The following regressions are estimated for the pooled sample over 1992-2003. Costs of RM are TotalBenefit_RD,
D_Neg*Herfindahl, D_Neg*MarketShare, D_Neg*Distressed PPE/Sales; costs of AM are AuditorTenure, Big8,
NOA/Sales, ReversalRate, HighLitigation; incentives are Beater, LogAnalystFoll, EquityIssuance, Sensitivity,
StkWealth, Shares, ExAnteDistance; other controls are ROA, LogSales, MtoB.
i,t 0 1,j j,i,t 2,p p,i,t 3,k k,i,t 4,l l,i,t i,t
j p k l
RM = + Cost of RM + Cost of AM + Incentives + Other Controls +uλ λ λ λ λ∑ ∑ ∑ ∑ ,
i,t 0 1 i,t 2,p p,i,t 3,k k,i,t 4,l l,i,t i,t
p k l
AM = + RM + Cost of AM + Incentives + Other Controls +vδ δ δ δ δ∑ ∑ ∑ ,(8)
Panel A: R&D manipulation
Ab_RD equation(N = 5,104)
Ab_Accr equation(N = 5,104)
Pred. sign Coef. p-valuePred.sign
Coef. p-value
Intercept ? -0.0256 0.0002 ? 0.0155 0.0886Ab_RD – -0.1166 <.0001Cost determinants of RM:TotalBenefit_RD – -0.0001 0.0211D_Neg*Herfindahl + 0.0256 0.0001
D_Neg*MarketShare + 0.0356 <.0001D_Neg*Distressed – -0.0033 0.0022Cost determinants of AM:
AuditorTenure +/– 0.0001 0.1723 +/– 0.0002 0.0956Big8 + -0.0147 0.2600 – -0.0027 0.2785 NOA/Sales + 0.0008 0.2282 – 0.0068 <.0001ReversalRate + 0.0027 0.0472 – -0.0029 0.2475HighLitigation + 0.0055 <.0001 – -0.0116 <.0001
Incentives:
Beater + 0.0034 0.0437 + 0.0203 <.0001LogAnalystFoll – -0.0061 <.0001 – -0.0044 0.0083EquityIssuance + 0.0005 0.4140 + 0.0037 0.1353Sensitivity + -0.0001 0.4547 + 0.0016 0.2124StkWealth + 0.0000 0.4389 + -0.0000 0.0580Shares + 0.0000 0.0178 + 0.0000 <.0001
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Panel B: SGA manipulation
Ab_SGA equation(N = 10,276)
Ab_Accr equation(N = 10,276)
Pred. sign Coef. p-value Pred. sign Coef. p-value
Intercept ? 0.0005 <.0001 ? -0.0335 <.0001Ab_SGA – -0.0266 0.0483
Cost determinants of RM:D_Neg*Herfindahl + 0.0001 0.2578
D_Neg*MarketShare + 0.0004 <.0001D_Neg*Distressed – -0.0001 <.0001
Cost determinants of AM:AuditorTenure +/– -0.0000 0.0055 +/– 0.0000 0.3297Big8 + -0.0000 0.3904 – -0.0081 0.0348 NOA/Sales + 0.0001 <.0001 – 0.0052 <.0001ReversalRate + 0.0001 0.0065 – -0.0021 0.2240HighLitigation + -0.0000 0.4857 – -0.0023 0.0519
Incentives:
Beater + 0.0001 <.0001 + 0.0068 0.0005LogAnalystFoll – -0.0000 0.1582 – -0.0058 <.0001EquityIssuance + -0.0000 0.2267 + 0.0007 0.3729Sensitivity + 0.0000 0.4518 + 0.0020 0.0232StkWealth + -0.0000 0.4651 + -0.0000 0.0009Shares + -0.0000 0.0212 + 0.0000 0.0738ExAnteDistance + 0.0000 0.4790 + 0.0339 <.0001Control variables:ROA ? -0.0001 0.3206 + 0.3700 <.0001
LogSales ? 0.0001 <.0001 ? 0.0007 0.1315MtoB ? -0.0000 0.4282 ? -0.0030 <.0001
Adj. R 2 (%) 2.97 54.32
For variable definitions, please refer to Appendix I.
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Panel C: Production cost manipulation
Ab_Prod equation(N = 10,453)
Ab_Accr equation(N = 10,453)
Pred. sign Coef. p-value Pred. sign Coef. p-value
Intercept ? -0.1940 <.0001 ? -0.0184 0.0120Ab_Prod – -0.0517 <.0001
Cost determinants of RM:D_Neg*Herfindahl + -0.0748 <.0001
D_Neg*MarketShare + -0.0153 0.1123D_Neg*Distressed – -0.0305 <.0001PPE/Sales + 0.0070 0.0474Cost determinants of AM:
AuditorTenure +/– -0.0003 0.0576 +/– 0.0004 <.0001Big8 + -0.0328 0.0017 – -0.0105 0.0079 NOA/Sales + 0.0208 <.0001 – 0.0130 <.0001ReversalRate + 0.0049 0.0521 – -0.0047 0.0613HighLitigation + 0.0195 0.0754 – -0.0014 0.0859
Incentives:Beater + 0.0483 <.0001 + 0.0041 0.0589LogAnalystFoll – -0.0298 <.0001 – -0.0035 0.0055EquityIssuance + 0.0178 0.0001 + 0.0038 0.0781Sensitivity + 0.0042 0.0482 + 0.0018 0.0856StkWealth + -0.0000 0.0268 + -0.0000 0.0087Shares + -0.0001 <.0001 + 0.0000 <.0001ExAnteDistance + 0.0282 <.0001 + 0.0074 <.0001Control variables:
ROA ? -0.5044 <.0001 + 0.3180 <.0001LogSales ? 0.0299 <.0001 ? -0.0035 <.0001MtoB ? -0.0060 <.0001 ? -0.0025 <.0001
Adj. R 2 (%) 57.10 23.90
For variable definitions, please refer to Appendix I.
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Table 6: Summary Statistics for 823 Lawsuit Firms (1995-2004)
Panel A: Selective descriptive statistics:
Lawsuit firms(N=823)
Control firms(N=2,577)
Difference
Variable Mean Median Mean Median Mean Median
MVE ($million) 5,036.23 485.41 2,343.99 108.06 2,692.24*** 377.35***
MtoB 3.49 1.90 3.05 1.89 0.44 0.01
Assets ($million) 2,432.80 273.29 1,431.35 95.22 1,001.45*** 178.07***
ROA (%) -0.18 -0.06 -0.14 0.01 -0.04 -0.07
***, **, and * represents significance at 1%, 5%, and 10% respectively.
Panel B: Number of lawsuit firms by industry:
Industry SIC code Number of suits
1. Mining and construction 1000-1299, 1400-1999 132. Food 2000-2111 123. Textile, printing and publishing 2200-2799 264. Chemicals 2800-2824, 2840-2899 155. Pharmaceuticals 2830-2836 666. Extractive industries 2900-2999, 1300-1399 13
7. Durable manufacturers 3000-3569, 3580-3669, 3680-3999 1578. Computers 7370-7379, 3570-3579, 3670-3679 3159. Retail 5000-5999 9210. Services 7000-7369, 7380-8999 10111. Other 13
Total 823
Panel C: Number of filings by year:
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Total
3 45 101 108 107 88 198 105 67 1 823
Table 7: Performance Matched Analysis For Small Sample
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Table 7: Performance-Matched Analysis For Small SampleThe following procedure is used to match lawsuit sample with control firms: (1) the control firm is in the same industry-year as lawsuit firm; (2) it does notexperience any securities class action lawsuits over 1995-2004; (3) it has the closest abnormal real transactions or accruals (depending on the type ofmanipulation being examined) to that of the lawsuit firm in the year prior to the lawsuit filling year; (4) one firm cannot serve as control firm more than once.
The difference between the abnormal level of real transactions or accruals of a lawsuit firm and that of its control firm is reported.
Ab_Accr Ab_RD Ab_SGA Ab_Prod
Year Obs Mean p-value Obs Mean p-value Obs Mean p-value Obs Mean p-value
-3 322 -0.0097 0.2072 228 -0.0121 0.0567 317 -0.0014 0.0924 362 -0.0033 0.4193-2 426 0.0025 0.4031 319 -0.0121 0.0330 395 -0.0017 0.0657 466 -0.0077 0.2765-1 576 -0.0004 0.3188 564 -0.0002 0.2275 534 -0.0001 0.2086 600 -0.0045 0.00820 435 -0.0590 0.0000 459 -0.0042 0.1887 452 -0.0021 0.0088 488 0.0296 0.0092+1 310 -0.0251 0.0159 341 0.0119 0.0039 342 0.0013 0.0688 387 0.0042 0.3840
+2 202 -0.0124 0.1909 241 0.0073 0.1456 247 0.0003 0.4005 264 -0.0101 0.2831+3 131 0.0056 0.3502 152 0.0146 0.0334 165 -0.0031 0.0120 180 -0.0429 0.0301
Figure 1. Mean difference of RM/AM between litigation firms and control firms*
-0.07
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
-3 -2 -1 0 1 2 3
Year relative to lawsuit filing year
A s s e t - s c a
l e d V a l u e f o r A b_
A c c r , A b_
P r o d
,
A b_
R D ( % )
-0.007
-0.006
-0.005
-0.004
-0.003
-0.002
-0.001
0
0.001
0.002
0.003
0.004
A s s e t - s c a l e d V a l u e f o r A b_ S
G A ( % )
Ab_Accr
Ab_RD
Ab_Prod
Ab_SGA
For variable definitions, please refer to Appendix I.