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Earnings management and the accruals anomaly: The
role of industry-specific discretionary accruals
Atif Ikram
Wayne State University
Ranjan D’Mello
Wayne State University
This Version: September 1, 2013
Keywords: Accruals, earnings management, market efficiency, methodology
JEL Classification: G12, G14, G30, M41
Atif Ikram
School of Business Administration, Wayne State University, Prentis Building, 5210 Cass Avenue, Detroit,
MI 48202, USA.
Email: [email protected]
Telephone: 313-577-7837
Ranjan D’Mello
School of Business Administration, Wayne State University, Prentis Building, 5210 Cass Avenue, Detroit,
MI 48202, USA.
Email: [email protected]
Telephone: 313-577-7828
Abstract
We motivate and implement a methodology that decomposes a firm’s discretionary accruals
into a firm-specific and an industry-specific component. We find that the “accruals anomaly”
(Sloan 1996) – the finding that firms with high discretionary accruals subsequently earn
negative abnormal returns – is driven by the firm-specific component of discretionary accruals.
Moreover, although industry-specific discretionary accruals do not contribute directly towards
the anomaly, we find that it is precisely when industry-specific discretionary accruals are high
that firms with large firm-specific discretionary accruals subsequently earn negative abnormal
returns. The results suggest firms use income-increasing discretionary accruals to manipulate
earnings primarily when other firms in the industry also have high discretionary accruals. A
likely explanation for this finding is that industry-wide use of high discretionary reduces
investors’ incentives to distinguish between manipulative and value-relevant discretionary
accruals, and that firms internalize this reduced probability of detection in deciding to
manipulate earnings. The finding also has an important bearing on the earnings management
literature that uses discretionary accruals to proxy for earnings manipulation.
1
1. Introduction
The information content and potential manipulation of accruals continues to
attract the attention of academics and the investing community. Accruals stem from
mismatch in the timing of cash and economic transactions and can help managers convey
value-relevant information about the firm (Dechow 1994). At the same time, given the
discretion enjoyed by managers in accounting for accruals, managers can potentially also
use accruals to manipulate reported earnings (Jones, 1991; Kasanen, Kinnunen and
Niskanen, 1996). Such discretion and the potential for manipulation imply that it is likely
to be difficult for investors to extract the information content (if any) embedded in
reported accruals.
Accordingly a number of studies have explored the relationship between reported
accruals and capital market outcomes. Several results are considered to be anomalous.
For instance Sloan (1996) finds that firms with high (low) accruals subsequently earn
negative (positive) abnormal returns. Bradshaw, Richardson and Sloan (2001) and
Ahmed, Nainar and Zhou (2005) show that even sophisticated investors like sell-side
analysts tend to be optimistic and do not incorporate the predictable earnings declines
associated with high accruals. Xie (2001) decomposes accruals into a discretionary and
non-discretionary component and finds that investors tend to misprice the discretionary
component of accruals, i.e. the component more susceptible to earnings manipulation.
Subsequent research has shown that this “accruals anomaly” can primarily be attributed
to the mispricing of large, income-increasing discretionary accruals (Beneish and Vargus,
2002; Desai, Rajgopal and Venkatachalam, 2004; Kothari, Loutskina and Nikolaev,
2006; Mashruwala, Rajgopal and Shevlin, 2006; Lev and Nissim, 2006). The finding
2
suggests that firms use (high) discretionary accruals to manipulate earnings and that
market participants fail to detect this earnings manipulation. Indeed a large strand of
earnings management literature has used high discretionary accruals to proxy for earnings
manipulation in a variety of settings (e.g. Rangan, 1998; Teoh, Welch and Wong,
1998a,b; Teoh and Wong, 2002; Bergstresser and Phillipon, 2006; Cornett, McNutt and
Tehranian, 2009).
In this paper we develop and implement a methodology that decomposes a firm’s
discretionary accruals into a firm-specific (FSDA) and an industry-specific (ISDA)
component. We define firm-specific discretionary accruals as the difference between a
firm’s reported accruals and those expected of it at a specific point in time within its
industry. This captures the idea that a part of firm’s discretionary accruals is
idiosyncratic. The industry-specific component, on the other hand, measures those
discretionary accruals that arise when contemporaneous expected level of accruals
deviate from those expected of the firm in the long-run. This captures the idea that
sometimes an entire industry can be experiencing a high/low ‘discretionary accruals
wave’ and thus firms in the same industry could be sharing a common component of
discretionary accruals. A firm’s total discretionary accruals (TDA) are hence defined as
the sum of its firm-specific and industry-specific components.
In most existing related literature, researchers have used some variant of the
cross-sectional Jones (1991) model to calculate discretionary accruals. In these models a
firm’s reported accruals are regressed (cross-sectionally, at the industry level) against a
set of explanatory variables, and the residuals obtained from these models are classified
as discretionary (e.g. Kothari, Leone and Wasley, 2005). Such an approach implicitly
3
assumes that a firm’s time-t expected accruals (measured using contemporaneous Jones
model parameter estimates) do not contain a component of discretionary accruals that is
common to all firms within the industry. Under certain situations such a presumption may
not be reasonable.
For instance, Jeter and Shivakumar (1999) argue that discretionary accruals are
likely to be correlated when an industry is enjoying favorable economic conditions and
firms are trying to ‘smooth’ reported earnings. Kang, Liu and Qi (2010) suggest that
discretionary accruals can be correlated if firm managers time the aggregate equity
market and increase discretionary accruals after sensing an increase in next-period’s
market risk premium. Similarly a decrease in discount rates can result in an industry-wide
increase in discretionary accruals as firms optimally adjust investments in response to
discount rate changes (Wu, Zhang and Zhang, 2010). Cheng (2010) suggests that in
certain situations the use of high discretionary accruals at one firm can “spillover” to
other firms in the industry. Relatedly (Bagnoli and Watts, 2000) argue that managers’
relative performance evaluation concerns within an industry can cause manipulative
discretionary accruals to emerge as Nash equilibrium. Discretionary accruals can also be
correlated during times of industry-wide overvaluation as overvalued firms attempt to
manipulate earnings upwards in order to sustain their overvaluation (Jensen 2005;
Kothari et al. 2006). Residuals from variants of the cross-sectional Jones (1991) model
cannot, by construction, capture such correlated discretionary accruals.
Accordingly, we contend that residuals obtained from cross-sectional Jones model
only measure the firm-specific component of discretionary accruals. In order to measure a
firm’s total discretionary accruals one must also estimate the industry-specific
4
component, i.e. the component shared by all firms in the industry. To that end, We
augment the application of the Jones model: we use the arithmetic average of the
parameters obtained from estimating the model in each year and industry to calculated a
firm’s long-run non-discretionary accruals (LRNDA), and calculate industry-specific
discretionary accruals as the difference between a firm’s short-run non-discretionary
accruals (SRNDA), i.e. firm’s time-t expected accruals (estimated using
contemporaneous Jones model parameter estimates) and its long-run non-discretionary
accruals.1 Defining and decomposing discretionary accruals in this manner can allow for
a more incisive examination of the information content and manipulability of accruals,
their role in price discovery, and the source of the documented accrual anomalies. For the
purpose of this paper we focus the lens of this accruals decomposition on the original
anomaly documented by Sloan.
Using a sample of COMPUSTAT firms from 1970 – 2006, we confirm that
Sloan’s accruals anomaly is driven by the discretionary component of accruals. When we
sort firms into decile portfolios based on fiscal year-end TDA, we find that firm-years in
the top TDA decile earn an average annualized abnormal return of -3.84% the following
year. On the other hand, neither the highest- nor the lowest-LRNDA decile earns
abnormal returns in the year following portfolio formation. Additionally, we find that
mispricing of discretionary accruals is driven by the firm-specific component. When we
sort firms based on fiscal year-end FSDA and ISDA we find that firm-years in the top
1 Effectively, our point of departure from prior studies is with respect to the definition of non-discretionary
accruals. Prior studies define non-discretionary accruals as accruals estimated using contemporaneous Jones model parameter estimates. In the context of this paper, the term is misleading because non-
discretionary accruals (measured this way) can contain a correlated component of discretionary accruals.
Therefore, (for the lack of a better word) we refer to these time-t expected accruals as short-run non-
discretionary accruals and distinguish them from a firm’s long-run non-discretionary accruals. The
difference between the two measures the correlated, industry-specific component of discretionary accruals.
5
FSDA decile subsequently earn an annualized abnormal return of -3.60%; in contrast,
firms in the highest ISDA decile do not earn significant abnormal returns subsequent to
portfolio formation. These results suggest that compared to FSDA, ISDA (on average)
are not manipulative and convey value-relevant information to investors.
Moreover, although industry-specific accruals do not directly contribute towards
the accruals anomaly, we find that it is precisely when industry-specific discretionary
accruals are high that firms with high firm-specific discretionary accruals subsequently
earn negative abnormal returns. Specifically, when we divide the top FSDA decile into
subgroups based on member firms’ respective ISDA decile ranking, we find that the
highest-FSDA-highest-ISDA portfolio (i.e. the set of firm-years both in the top FSDA
and ISDA decile) earns an annualized abnormal return of -8.16% in the year following
portfolio formation. In contrast, the highest-FSDA-lowest-ISDA portfolio (i.e. the set of
firm-years in the top FSDA decile and the bottom ISDA decile) does not earn such
negative abnormal returns. The results suggest the accruals anomaly is not merely driven
by the mispricing of high FSDA, but specifically by the mispricing of those high FSDA
that are associated with high ISDA.
The mispricing of high (discretionary) accruals is anomalous in that it questions
the efficiency of capital markets. Accordingly, academics have offered different
explanations for why this mispricing occurs. Sloan (1996) argues that investors misprice
accruals because they naively fixate on earnings without realizing the lower persistence
of accruals compared to cash flows. Fairfield, Whisenant and Yohn (2003) argue that
firms with high accruals subsequently earn negative returns due to diminishing returns to
new investments. Desai et al. (2004) suggest that accrual anomaly is merely a
6
manifestation of the well-documented glamour stock phenomenon. Recently Wu, Zhang
and Zhang (2010) have put forth a q-theory hypothesis for the anomaly according to
which firms optimally adjust their accruals in response to changes in discount rates. Of
all these explanations, Sloan’s ‘earnings-fixation’ hypothesis has received the most
attention (and support) in the literature.2
The findings of this paper suggests that an alternative (though not necessarily
mutually exclusive) explanation for the accruals anomaly could be that industry-wide use
of high discretionary accruals increases the search costs that investors have to incur in
order to detect earnings manipulation. When most firms in the industry have high (value-
relevant) discretionary accruals, a firm whose high discretionary accruals are
manipulative is able to ‘blend in’ more easily compared to when most firms in the
industry have low discretionary accruals. As a result, industry-wide use of value-relevant,
high discretionary accruals can camouflage firms whose high discretionary accruals are
manipulative in nature. For investors, distinguishing between value-relevant and
manipulative discretionary accruals during such times can be time-consuming and
potentially associated with greater information gathering costs. As long as such search
costs are reasonably high, investors may have an incentive to price all high discretionary
accruals as value-relevant, including those that are manipulative.
Relatedly, the use of high discretionary accruals by most firms in the industry can
also act as a credible signal to investors that firms have high discretionary accruals for
‘good’ (i.e. value-relevant) reasons. As a result, investors can become less likely to
subject a given firm’s high discretionary accruals to scrutiny, and/or to deem them
2 Dechow, Khimich and Sloan (2011) provide a comprehensive overview of the potential explanations for
the accrual anomaly as well as evidence supporting the superiority of earnings-fixation hypothesis.
7
manipulative. In some sense, industry-wide use of high discretionary accruals can give
investors reason to be ‘credulous’ of earnings manipulation (Teoh and Wong, 2002),
causing them to overlook firms whose high discretionary accruals are manipulative.
The paper makes notable contributions to the accounting and finance literature.
First, it develops and implements a methodology that augments a widely-used
econometric technique (namely, the cross-sectional Jones model) to measure a firm’s
discretionary accruals. Several academics (e.g. Jeter and Shivakumar, 1999) have argued
that residuals from cross-sectional estimation of the Jones model can produce biased
estimates of discretionary accruals if discretionary accruals are correlated across firms in
the industry. The (discretionary) accruals decomposition motivated in this paper directly
addresses this concern by explicitly measuring this industry-specific component of
discretionary accruals and hence, at the very least, produces a less biased estimate of
discretionary accruals.
Secondly, the paper advances our understanding of the information content
embedded in discretionary accruals. The finding that neither high nor low ISDA
subsequently earn abnormal returns suggests that correlated discretionary accruals tend
not to be manipulative.3 Moreover, the finding that mispricing of high FSDA is driven
specifically by the subset of firms which also has high ISDA suggests that even high
FSDA are not always manipulative. In particular, the results imply that when most firms
in the industry have low discretionary accruals, firms use high FSDA to convey value-
relevant information to investors. The implication potentially has an important bearing on
the earnings management literature that has used residuals from variants of cross-
3 The evidence hence casts doubt on the possibility of industry-wide earnings manipulation (e.g. Bagnoli
and Watts, 2000).
8
sectional Jones model to measure discretionary accruals and classified all firms with high
discretionary accruals as earnings manipulators. This probably helps explain why some
academics find variants of cross-sectional Jones model prone to misspecifying high
discretionary accruals as manipulative (McNichols 2000; Badertscher, Collins and Lys
2012).4 Relatedly, the paper adds to the growing body of literature that has found industry
and market conditions to be particularly important in studying earnings management
(Park, 1999; Jiao, Mertens and Roosenboom, 2007).
Finally, the paper adds to the growing body of literature looking to explain why
investors overprice firms with high discretionary accruals. The results indicate that only
firms with high FSDA and high ISDA subsequently earn negative abnormal returns. This
suggests that a potential explanation for why investors fail to detect earnings
manipulation could be that industry-wide use of high discretionary accruals makes it
difficult/costly for them to distinguish between high discretionary accruals that are value-
relevant and those that are manipulative.
The remainder of the paper is organized as follows: Section 2 describes the
research methodology used to define and decompose discretionary accruals. Section 3
describes the data used to implement the accruals decomposition. Section 4 presents the
main results of the paper. Section 5 provides details on alternative specifications used to
test the robustness of results. Finally Section 6 provides a brief summary of the main
findings and identifies potential areas for future research.
4 It is worth stressing that this implication is based on results obtained for the general accruals anomaly
documeneted by Sloan (1996) and Xie (2001). It is quite possible that industry-specific discretionary
accruals do not play any role in explaining earnings manipulation prompted by firm-specific events or
factors such as equity issuance (Teoh et al. 1998a,b), high equity compensation of CEOs(Bergstresser and
Phillipon 2006), poor corporate governance (Cornett, McNutt and Tehranian 2009) etc.
9
2. Research Methodology
The methodology we use to decompose discretionary accruals into firm-specific
and industry-specific components is similar to the one used by Rhodes-Kropf, Robinson
and Viswanathan (2005, hereafter RKRV) in assessing the impact of misvaluation on
merger activity. Under the pretext that high market-to-book ratios (M/B) indicate
overvaluation, RKRV propose that a firm’s M/B should be broken into two components:
market value-to-true-value, M/V, and true value-to-book value, V/B. Thus, M/B can be
expressed as:
( ) ( ) [1]
where m is market value, b is book value, and v is some measure of fundamental value,
all expressed in logarithms. The authors argue that captures the part of ln(M/B)
that is associated with misvaluation, positive in times of overvaluation and negative in
times of undervaluation.
RKRV further argue that a firm’s misvaluation should be broken down into two
components: a firm-specific component and an industry-specific component. The authors
contend that the firm-specific component captures part of the misvaluation that is unique
to the firm, while the industry-specific component captures the part that is common to all
firms in the industry ( due to an entire industry being misvalued at a given point in time).
Thus, for a given firm i in industry j at time t, the authors decompose as:
( )⏟
( ) ( )⏟
( ) ⏟
[2]
10
In equation [2], RKRV estimate v by expressing it as a function of firm-specific
accounting information at a point in time, , and a vector of conditional accounting
multiples, . The key difference in the ( ) expressions is that (in a given industry j)
time-t multiples are represented as while long-run multiples are represented by .
Thus, the first term (labeled firm-specific) indicates the difference between a firm’s
market value and fundamental value conditional on time t and industry j valuation effects.
The authors call this firm-specific error/misvaluation because the v term captures all
deviations common to a sector at a point in time. The second component (labeled sector-
specific) measures the firm’s time-t fundamental value relative to its long-run value. The
authors call this time-series sector error because the function ( ) captures sector-
specific valuation that does not vary over time; the parameters in capture the long-run
multiples for industry j. Finally, the last component (labeled long-run) measures the
difference between long-run value and book value.
In a manner analogous to RKRV, we decompose a firm’s total accruals (AC) into
three components: firm-specific discretionary accruals (FSDA), time-series industry-
specific discretionary accruals (ISDA), and long-run non-discretionary accruals
(LRNDA). The firm-specific component captures the difference between a firm’s
observed accruals and its predicted level of accruals based on time-t accounting
fundamentals. We call these short-run non-discretionary accruals (SRNDA). The
industry-specific component measures the difference between a firm’s SRNDA and
LRNDA. The sum of firm-specific and industry-specific components equals total
discretionary accruals (TDA), and measures how much a firm’s total accruals deviate
from its LRNDA.
11
Mathematically, if ( ) denotes the ith
firm’s accruals as a function of firm-
specific accounting information, , and a vector of conditional parameter estimates,
then the accrual decomposition can be expressed as:
{ ( )}⏟
{ ( ) ( )}⏟ ⏟
( )⏟
[3]
In equation [3], the first bracketed term on the right (labeled FSDA) measures
firm-specific discretionary accruals as it reflects the difference between a firm’s accrual
measure and its short-run non-discretionary accruals based on time-t accounting
fundamentals and industry j accrual effects, ( ). If economic conditions are
good and all firms in the industry are smoothing reported earnings then this will get
reflected in and consequently in firm’s SRNDA, ( ). Similarly if most firms
in the industry have high discretionary accruals due to better-than-average industry
performance (Kothari, Leone and Wasley 2005) then this too will get reflected in and
hence in ( ). More generally, any component of discretionary accruals
correlated across firms within the industry will be captured by ( ).
The second bracketed term on the right reflects the difference between time-t
expected accruals ( ) and long-run expected accruals ( ). As in RKRV,
the main difference between the two functions is that short-run parameter estimates are
represented by while long-run parameter estimates are represented by . Thus
( ) ( ) captures the industry-specific component of discretionary
accruals because compared to ( ), ( ) measures that part of a firm’s
12
expected accruals that does not vary with time. Finally, the last term in equation [3]
(labeled LRNDA) measures the firm’s long-run non-discretionary accruals.
It is worth noting that each of these three components varies at the firm-level and
involves parameter estimates that vary across industries and overtime. Thus ( )
varies overtime at the firm level as accounting information changes (i.e. varies over t
holding i constant), and also varies across firms within an industry as their accounting
data differ (i.e. varies over i holding t constant).
Equation [3] can be re-written to express a firm’s (total) discretionary accruals as
the sum of firm-specific and industry-specific discretionary accruals:
( )⏟
{ ( )}⏟
{ ( ) ( )}⏟
[4]
The breakdown of discretionary accruals naturally depends on a reliable measure
of expected accruals, ( ). A large body of literature has used some variant of the
Jones model to estimate these expected accruals. In these models a firm’s accruals are
regressed on accounting variables that are considered important determinants of accruals
(e.g. sales, PP&E etc.) and the resulting parameter estimates – or “accrual multiples” –
are used to predict the firm’s time-t expected accruals. In this paper we use the cross-
sectional, (performance-adjusted) modified Jones model to calculate these expected
accruals. There are two main reasons for using this particular specification. First,
compared to time-series versions of the Jones model the cross-sectional variants usually
result in larger samples and are less likely to suffer from survivorship bias (McNichols,
2000). The cross-sectional specifications also tend to have more explanatory power
13
compared to their time-series counterparts (Bartov, Gul and Tsui, 2000).5 Additionally,
literature suggests that within the class of cross-sectional variants, the performance-
adjusted modified Jones model is preferred (Ronen and Yaari, 2008) because it adjusts
for the effect of credit sales (Dechow, Sloan and Sweeney, 1995) and operating
performance (Subramanyam 1996; Kothari et al. 2005) on accruals.
Second, industry-level cross-sectional estimation of expected accruals is aptly
suited to the purpose of this paper because it explicitly accounts for the possibility that
firm’s expected accruals vary overtime and across industries. Hence the regression
coefficients obtained from this procedure can be interpreted as time-varying accrual
multiples and can be used to calculate time-series industry-specific discretionary accruals
in the manner outlined above.
The exact specification of the modified Jones model we use in this paper can be
expressed as:
( )
In equation [5], AC denotes a firm’s reported total accruals, AT denotes total
assets, SALE denotes sales, REC denotes receivables, PPE denotes gross property, plant
and equipment, and NI denotes net income. To generate estimates of (lag-asset weighted)
( ) and ( ) we follow RKRV’s methodology and use fitted values from
equation [5] above:
5 This is also perhaps why a large strand of accounting and finance literature has used cross-sectional Jones
model to measure discretionary accruals.
14
( )
( )
for each firm. Equation [6] yields a firm’s short-run non-discretionary accruals. To obtain
( ) i.e. a firm’s long-run non-discretionary accruals, we average over time to
obtain ⁄ ∑ for each set of parameters { }, and then calculate:
( )
( )
3. Data and Sample Selection
My initial sample consists of all firms for which data are available on
COMPUSTAT Fundamentals Annual and CRSP Monthly Stock Return files from 1968 –
2006. We define each firm’s industry based on its 2-digit SIC code. We drop financials
(SIC codes 6000 – 6999) due to the difficulty involved in interpreting their accruals, and
utilities (SIC codes 4900 – 4999) due to their different regulatory reporting requirements.
We limit our analysis to firms listed on NASDAQ, NYSE and AMEX and to firms whose
shares correspond to common equity (i.e. closed-end funds, units, ADRs and REITs are
excluded from the sample). We also confine our attention to firms incorporated in the US,
to firms with fiscal year-end in December, and to firms with non-missing values for any
of the modified Jones (1991) model variables shown in equation [5].
15
In order to obtain reliable estimates from the cross-sectional Jones model
(equation [5]), we require all industries to have at least 10 firms in a given fiscal year.6
Moreover, in order to obtain reliable estimates of i =1,…,4 in equation [7], we also
limit our sample to firms which have non-missing values for FSDA in at least eight of the
sample years.7 The final sample with non-missing values for TDA, FSDA and ISDA
consists of 55,208 firm-years between 1970 and 2006. We Winsorize all continuous
variables at 1% and 99% to remove the effect of outliers.
At the end of each fiscal year we rank sample firm-years based on the magnitude
of their accrual components, and use these rankings to construct AC, TDA, FSDA ISDA
and NDA decile portfolios. The assignment of firm-years across their respective deciles
remains fixed throughout the analysis.
The measurement of required variables proceeds as follows: for each firm i at
time t we calculate accruals ( ) using the balance-sheet approach (see, for example,
Koathri et al. 2006) as:
( ) ( ) [6]
where is change in current assets (COMPUSTAT item ACT), is change
in cash and cash equivalents (COMPUSTAT item CHE), is change in current
liabilities (COMPUSTAT item LCT), is the change in debt in in current
6 A minimum number of observations are required to obtain ‘reasonable’ parameter estimates from the
cross-sectional Jones (1991) model. According to Ronen and Yaari (2008), the customary minimum
(median) cutoff number is eight (ten). 7 Thus, we require that each industry has a minimum of 10 firms in at least 8 of the sample years between
1968 and 2006.
16
liabilities (COMPUSTAT item DLC), is the change in income taxes payable
(COMPUSTAT item TXP), and is the depreciation expense (COMPUSTAT item
DP). Where necessary we define earnings as income before extraordinary items
(COMPUSTAT item IB) and cash flows as the difference between earnings and accruals.
All these variables are scaled by beginning-of-year total assets (COMPUSTAT item AT)
to ensure compatibility across firms in the sample.
To estimate equation [5] we measure as the change in sales revenue
(COMPUSTAT item SALE), as change in accounts receivable (COMPUSTAT
item RECT), as gross property, plant and equipment (COMPUSTAT item
PPEGT), and as net income (COMPUSTAT item NI).
As in Kothari et al. (2006) we calculate abnormal portfolio returns by annualizing
monthly Jensen’s alphas obtained from estimating the Fama and French (1993) three
factor model.8 To ensure that there is sufficient time for financial statement data to reflect
in market prices we measure portfolio returns starting in April, four months after the end
of the fiscal year. The monthly portfolio alphas are calculated by regressing monthly
equally-weighted portfolio returns on the three Fama-French factors (market, size and
book-to-market respectively). In the event a firm delists, we replace its returns by its
delisting return in the month of delisting and reinvest the liquidating proceeds in the
value-weighted market portfolio for the remainder of the year (Xie, 2001).
4. Results
4.1 Descriptive Statistics
8 The calculation of alphas requires that sample firms be aligned in calendar time. All firm-years in our
sample have fiscal year-end in December and hence meet this requirement.
17
Conditional on firm-specific accounting information , the magnitude of ISDA
depends on the difference between the contemporaneous and average Jones model
parameter estimates, and , respectively. Therefore as a first step we examine the
summary statistics of the parameter estimates obtained from cross-sectionally estimating
equation [5]. These statistics are reported in Table I. Each row shows the industries in my
final sample (as defined using 2-digit SICs), and the three columns labeled Col 1, Col 2
and Col 3 show the summary statistics of , and .9 The mean values of parameter
estimates in each of these columns correspond to , and used in equation [7].
Table I shows that signs of all parameter estimates are, on average, consistent
with expectations. For instance is negative in all industries since PPE captures the
magnitude of depreciation expense. Likewise almost all industries have a positive
average value for which is consistent with the notion that net working capital accruals
are positive for firms whose sales exceed their expenses.10
Positive average values of
are also consistent with the idea that operating accruals tend to increase with firm
performance (Healy 1996).
Additionally Table I suggests considerable heterogeneity across industries in
terms of firms’ average sensitivity to each of explanatory variables in the Jones model.
For instance the median value of is quite high in the Leather and leather products
industry (0.171) and in the Electronic equipment industry (0.160), but considerably lower
in the Eating and drinking industry (-0.036) Likewise, firms’ average sensitivity to last
9 Table I does not report the summary statistics of , the coefficient on inverse-assets (1/At-a) which
appears as one of the regressors in equation [5]. Inverse-assets are included in the model to control for
hetroscedasticity across firms within the same industry. 10 Nonetheless, sensitivity to net working capital accruals can be negative. For further discussion of this
issue, see Chapter 10 of Ronen and Yaari (2008).
18
year’s return on assets is considerably high in Textile mill products industry ( = 0.303)
and Furniture and fixtures industry ( = 0.240), but much lower in other industries like
Oil and gas extraction ( = -0.008). These inter-industry differences suggest that
industry membership and nature of business operations are an important determinant of
accruals.
More importantly, Table I suggests that values of these parameter estimates vary
considerably overtime within industries. In most industries, each parameter estimate
exhibits a large inter-quartile range as well as a high standard deviation relative to the
mean. This is especially true for estimates of and . For instance in the Furniture and
fixtures industry is 0.038, its interquartile range is 0.235 and its standard deviation is
0.158. Similarly in the Metal Mining industry the standard deviation of (0.262) is
more than three times its mean (median) value of 0.045 (0.045). Although these
parameters fluctuate more in some industries than in others, the fact that they do fluctuate
suggests that at any given point a firm is likely to have non-zero correlated discretionary
accruals.
In Table II we report the summary statistics of accrual components and key firm
characteristics. Consistent with earlier studies (Subramanyam 1996; Xie 2001) Panel A
shows that average accruals are negative with a mean (median) of -3.81% (-4.17%). Both
SRNDA and LRNDA form a significant fraction of these accruals (on average) as
reflected by their means (medians) of -3.34% (-3.19%) and -3.52% (-3.28%) respectively.
This is to be expected since FSDA are residuals from the cross-sectional Jones model
and, statistically, must add up to zero. ISDA must also add up to zero since SRNDA are
distributed around LRNDA in a similar fashion. Indeed Panel A shows that, on average,
19
all components of discretionary accruals are approximately zero.11
Nonetheless, Panel A
indicates that discretionary accruals are more volatile than non-discretionary accruals:
TDA have a standard deviation of 9.76% whereas LRNDA have a standard deviation of
5.14%. This finding is also consistent with prior literature (e.g. Xie 2001) and highlights
the ‘discretionary’ nature of TDA. Finally, Panel A shows that most of the variation in
TDA comes from variation in the firm-specific component of discretionary accruals –
FSDA have a standard deviation of 8.85%, whereas ISDA have a standard deviation of
merely 4.10% in comparison.
Panel B in Table 1 shows the (Pearson) correlations among accrual components.
The Panel shows that total accruals are more strongly correlated with discretionary
accruals compared to non-discretionary accruals: the correlation between AC and TDA is
approximately 88%. The Panel also shows that AC and TDA have a high positive
correlation with FSDA (81.4% and 90.7% respectively), which again suggests that firm-
specific discretionary accruals cause most of the fluctuation in total accruals and
discretionary accruals. Additionally, the correlation between SRNDA and LRNDA is
0.753 which, though positive and statistically significant, is less than 1. This suggests that
about 25% of the variation in a firm’s SRNDA occurs due to correlated discretionary
accruals. Finally, Panel B shows a zero correlation between FSDA and ISDA, implying
that industry-wide use of high/low discretionary accruals doesn’t influence firm-level
discretion over accruals.
In Table III we report means and median of accrual component and key firm
characteristics across discretionary accruals decile portfolios. Panel A reports the means
11 FSDA, ISDA and TDA are not exactly equal to zero because the Jones model (equation [5]) does not
include an intercept term.
20
and medians of these components across TDA decile portfolios. Panel B and Panel C
does the same for FSDA and ISDA decile portfolios respectively. Viewing sample
descriptive statistics in this manner helps highlight the potentially different nature of
firms with extreme discretionary accruals.
Consistent with prior evidence (e.g. Kothari et al. 2006), Table III suggests that
firms with extreme discretionary accruals tend to be relatively small, growth firms with
relatively low accounting performance. For instance Panel A shows that firms in the top
and bottom TDA decile have considerably lower average assets and higher market-to
book ratios compared to firms in other TDA decile portfolios. Additionally, average
earnings (scaled by beginning-of-year assets) in the lowest-TDA and highest-TDA
deciles are -12.06% and -5.80% respectively, both of which are significantly lower than
average earnings in other TDA deciles. Panels B and C suggest that firms with extreme
FSDA and ISDA (respectively) display similar characteristics.
Table III also shows that a large part of firms’ total discretionary accruals are
composed of the firm-specific component. For instance, mean TDA and FSDA in the
lowest-TDA decile are -17.67% and -15.49% respectively, and the mean TDA and FSDA
in the highest-TDA decile are 18.08% and 14.44% respectively. In contrast, mean ISDA
in these lowest- and highest-TDA deciles are only -1.69% in and 3.67% respectively.
Nonetheless, Table III shows that both average FSDA and ISDA increase monotonically
with TDA. Moreover, looking at mean ISDA as a fraction of mean TDA in each decile
suggests that ISDA consistently account for about 10% - 25% of a firm’s total
discretionary accruals.12
In the lowest TDA decile, ISDA account for about 12.28% of
TDA (-2.17/-17.67) while in the highest ISDA decile they account for 20.13% of TDA
12 Computing this ratio using median value of ISDA and TDA reduces the range to about 8% - 15%.
21
(3.64/18.08). Overall these statistics suggest that ISDA, though small in comparison to
FSDA, form a non-trivial component of a firm’s total discretionary accruals.
Interestingly, both Panel B and Panel C hint towards a non-linear relationship
between FSDA and ISDA. For instance, Panel C shows that while average FSDA steadily
decrease from an average of 0.43% in the lowest-ISDA decile to an average of -1.44% in
the second-highest-ISDA decile, they jump up to an average -0.13% going from the
second-highest to the highest-ISDA decile. Similarly Panel B shows that mean ISDA are
0.51% in the top FSDA decile, and that they decrease monotonically to -0.31% till the
second-highest FSDA decile before increasing sharply to an average of 0.60% in the
highest FSDA decile. Both panels seem to suggest that firms’ use of FSDA is not
insensitive to industry-wide use of discretionary accruals.
To gain further insight into this potential non-linear relationship, we examine the
distribution of firm-years across FSDA decile ranks within each ISDA decile portfolio.
That is, we examine how firm-years are distributed across their respective FSDA decile
rankings conditional on their ISDA ranking. The results from this examination are
reported in Table IV. The top half of the Table shows the percentage of firms-year in
each FSDA (and TDA) decile in the bottom five ISDA deciles while the bottom half of
the table shows these percentages in the top five ISDA deciles. By construction, summing
across FSDA and TDA ranks (respectively) within each ISDA decile equals 100%.
Likewise summing across ISDA deciles for a given FSDA (or TDA) rank also equals
100%.13
Each decile contains approximately 5,500 firm-years.
The results in Table IV display a peculiar interplay between FSDA and ISDA
whereby firms’ use of large FSDA – both income-increasing and income-decreasing–
13 Not all sums add up to exactly 100% due to rounding-off errors.
22
tends to be particularly high both when ISDA are extremely low and when ISDA are
extremely high. For instance, Table IV shows that in the lowest ISDA decile 13.74% of
firm-years have the lowest FSDA. As ISDA increase this percentage decreases, dropping
to about 7% in ISDA Decile 5. Thereafter however, the percentage of firms with low
FSDA starts to increases steadily with ISDA, reaching its highest of 16.36% in the
highest ISDA decile. A similar pattern is observed with respect to firms’ use of large
income-increasing FSDA. When ISDA are extremely low (ISDA Decile 1), 16.68% of
firm-years have the highest FSDA. This percentage steadily declines to a low of 6.87% in
ISDA Decile 6 and thereafter begins to increase. The greatest increase occurs moving
from the second-highest- to the highest-ISDA decile where the percentage of firms with
the highest FSDA increases from 9.64% to 18.34%, an increase of almost 100%.
The results in Table IV offer a different perspective to the zero correlation
observed between FSDA and ISDA earlier (in Table II). At first blush, Table II suggested
that firm’s use of FSDA is orthogonal to the industry-wide use of discretionary accruals.
If this were true however, then one would have observed a (more or less) uniform
distribution of firm-years across their respective FSDA ranks within each ISDA decile. In
other words, the percentage of firm-years in each FSDA decile rank would have been
roughly 10% within an ISDA decile.14
The results in Table IV show that this is not the
case, and instead suggest a U-shaped relationship whereby extreme ISDA are associated
with an increased tendency by firms to use extreme FSDA.
In the next section we explore the implications of discretionary accruals and its
components towards market returns. Additionally, in light of the descriptive statistics
14 For the same reason, the percentage of firm-years in a fixed FSDA rank across ISDA deciles should have
been roughly 10% as well.
23
above, we also assess the seemingly differential impact of extreme ISDA on firms’ use of
extreme FSDA in order to gain insight into Sloan’s accrual anomaly.
4.2 The Accruals Anomaly
As a first step, we check whether accruals anomaly exists in our sample. To that
end we compute the abnormal portfolio returns earned by discretionary and (long-run)
non-discretionary accrual decile portfolios. As outlined in Section 3, we calculate these
portfolio returns using the Fama-French three factor model. The results are reported in
Table V. Each column shows the monthly abnormal returns earned by portfolio firms
through years t-3 to t+3 where t represents the time of portfolio formation.15
Table V confirms the presence of accruals anomaly in my sample. Specifically,
Panel A shows that the set of firm-years in the top TDA decile earn an annualized
abnormal return of -3.84% (-0.32% x 12) in year t+1. In contrast, the lowest-TDA decile
does not earn any abnormal returns in the years following portfolio formation. The
finding is consistent with the literature which finds that accruals anomaly is driven by the
mispricing of large, income-increasing discretionary accruals (Beneish and Vargus, 2002)
and suggests that compared to high discretionary accruals, low discretionary accruals are
not manipulative (Kothari et al., 2006). A hedge portfolio short in the highest-TDA
portfolio and long in the lowest-TDA portfolio subsequently earns an annualized
abnormal return of 5.88% (0.49% x 12). This hedge portfolio yields significantly positive
abnormal returns even in the second and third year following portfolio formation.
Moreover, Panel B shows that neither the highest- nor the lowest-LRNDA portfolio earns
15 Accrual anomaly pertains to market returns subsequent to portfolio formation. The reason why we
examine market returns prior to portfolio formation is to consider prior overvaluation as a potential
explanation for the accrual anomaly (Kothari et al. 2006). The details are provided in Section 4.4.
24
significant abnormal returns subsequent to portfolio formation. This suggests that, like
low discretionary accruals, (long-run) non-discretionary accruals also convey value-
relevant information to investors.
To see how firm-specific and industry-specific discretionary accruals individually
contribute towards this accrual mispricing, we next examine the monthly abnormal
returns earned by FSDA and ISDA decile portfolios. The results are shown in Table VI.
Panel A shows the monthly abnormal returns for FSDA decile portfolios while Panel B
shows the monthly abnormal returns for ISDA decile portfolios.
Panel A shows that firms in the top FSDA decile earn a significantly negative
annualized abnormal return of -3.60% (-0.30% x 12) one year subsequent to portfolio
formation. Like the top TDA decile, the highest-FSDA decile continues to earn negative
abnormal returns in the second and third year following portfolio formation.
Additionally, firms with the lowest FSDA do not earn such abnormal returns. A hedge
portfolio short in the highest-FSDA portfolio and long in the lowest-FSDA portfolio
earns significantly positive annualized abnormal returns of 6.72% (0.56% x 12), 4.20%
(0.35% x 12), and 2.76% (0.23% x 12) in years t+1, t+2, and t+3 respectively. In
contrast, Panel B shows that neither the highest- nor the lowest-ISDA decile earns
significant abnormal returns subsequent to portfolio formation. The finding suggests that
the mispricing of discretionary accruals is driven by the firm-specific component of
discretionary accruals; industry-specific discretionary accruals, on average, convey value-
relevant information to investors and are not manipulative.
4.3 The Role of Industry-Specific Discretionary Accruals
25
Results from the previous section suggest that the accruals anomaly occurs as a
result of investors mispricing large, income-increasing FSDA. At the same time however,
summary statistics in Table IV suggested that firms’ use of high FSDA varies with
industry-wide use of discretionary accruals. In order to see whether these variations have
implications towards the information content and/or mispricing of large FSDA, we
examine the abnormal returns earned by the highest-FSDA portfolio at varying levels of
ISDA. To do this we divide firms in the top FSDA decile into ten subgroups based on
member firms’ ISDA decile ranking, and then examine the monthly alphas earned by the
resulting portfolios.16
This way the highest-FSDA decile gets subdivided into ten groups
with each group corresponding to firms with a specific ISDA decile ranking.17
Panel A in
Table VII reports the monthly alphas earned by these ISDA-conditioned portfolios. Each
row represents a portfolio with a specific ISDA decile rank. As before the columns show
the monthly alphas of these portfolios from year t-3 to year t+3, where t represent the
year in which the portfolios are formed.
The results in Panel A suggest that the magnitude of ISDA has a direct impact on
the mispricing of high FSDA. In particular Panel A shows that among firms with the
highest FSDA, the subset which has the highest ISDA subsequently earns an annualized
abnormal return of -8.16% (-0.68% x 12). On the other hand, none of the other subgroups
earn such negative abnormal returns. In particular, firms with the highest FSDA but the
16 Arguably, the lowest-FSDA decile warrants a similar subdivision given that firms’ tendency to use low
FSDA also varies with ISDA. Nonetheless, we don’t focus on the lowest-FSDA decile because evidence
suggests that firm use low FSDA to convey value-relevant information to investors. The motivation behind
subdividing the highest-FSDA decile is to see whether the magnitude of ISDA has a differential impact on
the accruals anomaly. 17 Notice that firm-years are not equally distributed across the resulting portfolios. The exact distribution of
firm-years is determined by percentages shown in Table IV. With approximately 5,500 firm-years in each
FSDA decile, Table IV suggests that there are approximately 1,010 firm-years in the highest-FSDA-
highest-ISDA portfolio (18.34% of 5,500), and about 920 firm-years in the highest-FSDA-lowest-ISDA
portfolio (16.68% of 5,500). There are at least 378 firm-years in each of the remaining portfolios.
26
lowest ISDA do not earn significantly negative abnormal returns the following year.
Moreover, returns earned by the highest-FSDA-highest-ISDA portfolio are considerably
higher than those earned by the unconditioned highest-FSDA portfolio (in Table VI,
Panel A); the difference is both statistically and economically significant. Hence, a hedge
portfolio which shorts firms in the highest-FSDA-highest-ISDA portfolio and takes a
long position in firms with the lowest-FSDA earns an annualized abnormal return of
11.28% (0.92% x 12) one year after portfolio formation. These returns are almost 70%
higher than those earned by the hedge portfolio that shorts the highest-FSDA portfolio
and longs the lowest-FSDA portfolio.
In Panel B, we report the results from repeating the exercise with FSDA and
ISDA quintiles. That is, we first sort firms into quintiles based on their FSDA quintile
ranking, and then subdivide firm-years in the top FSDA quintile based on member firms’
ISDA quintile ranking. We do this to ameliorate the potential concern that subdividing
the highest FSDA decile (i.e. 10% of the entire sample) into ten subgroups leads to “too
few” observations in any one resulting portfolio. Panel B shows that the tenor of results
remains unchanged in response to this modification. In particular, firm-years with the
highest FSDA and highest ISDA (as identified by their respective quintile rankings) still
earn an annualized abnormal return of -4.32% (-0.36% x 12) subsequent to portfolio
formation. In contrast, firms with the highest FSDA do not earn any negative abnormal
returns when their ISDA quintile rankings are lower. Overall, these results suggest that
the accruals anomaly is not merely driven by mispricing of firms with high FSDA but
specifically by the mispricing of those firms whose high FSDA that are accompanied by
high ISDA.
27
A likely explanation for the above finding is that firms use large FSDA to convey
value-relevant information to investors when most similar firms in the industry have low
discretionary accruals, and to manipulate earnings when most similar firms in the
industry have high discretionary accruals. A possible reason for this asymmetry could be
that firms face greater relative performance evaluation (RPE) concerns when ISDA are
high than when they are low. Research indicates that discretionary accruals tend to be
positively correlated with contemporaneous/lagged firm performance (Healy 1996;
Kothari et al. 2005). Hence it is plausible that high ISDA are associated with unusually
high industry performance and thereby increase firms’ incentive to manipulate earnings
due to RPE concerns (Bagnoli and Watts, 2000; Cohen and Zarowin, 2007) and/or to
meet inflated analyst expectations (Burghstaler and Eames, 2003). In contrast, such
perverse incentives to manipulate earnings are unlikely to exist when industry-specific
discretionary accruals (and hence industry performance) are low. As a result, firms are
more likely to have high FSDA due to value-relevant reasons. The U-shaped relationship
observed between ISDA and firms’ use of high FSDA also supports this view.
Specifically, Table IV shows that for high levels of ISDA the percentage of firms with
high FSDA increases as ISDA increase. This pattern is consistent with industry-wide use
of high discretionary accruals inducing some firms to manipulate earnings upwards.
Moreover, the result also suggests that a likely reason for why investors overprice
high FSDA is that industry-wide use of high discretionary accruals makes it difficult for
them to detect earnings manipulation. When most firms in the industry have high
discretionary accruals, firms whose high discretionary accruals are value-relevant can
camouflage those firms whose high discretionary accruals are manipulative. During such
28
times, detecting earnings manipulation may be time-consuming and/or associated with
greater information gathering costs. As long as these search costs are reasonably high,
investors may be inclined to price all high discretionary accruals as value-relevant,
including those that are manipulative. Relatedly, it is also plausible that the systematic,
industry-wide use of high discretionary accruals acts as a credible signal to investors that
all firms in the industry have high discretionary accruals for ‘good’ (i.e. value-relevant)
reasons. As a result, investors can end up overpricing firms whose high discretionary
accruals, though manipulative, are ‘reasonable’ given the high discretionary accruals
wave in the industry.
4.5 Discretionary accruals and earnings management
The above interpretation of results suggests that high ISDA have a differential
impact on both the information content and the mispricing of large FSDA. Nonetheless,
an alternative possibility consistent with the findings is that all large income-increasing
FSDA are actually manipulative but that investors price such high FSDA fairly when
concurrent ISDA are low. In other words, it is possible that industry-wide use of high
discretionary accruals only impacts investors’ ability to price large FSDA fairly, and not
necessarily the information content of high FSDA. This possibility is perfectly consistent
with the (potential) explanation forwarded above for why investors misprice high FSDA.
When most firms in the industry have high discretionary accruals, investors may face
high search costs to distinguish between value-relevant and manipulative large FSDA,
and may also have ‘good’ reason to be credulous of earnings manipulation. In contrast,
firms with high (manipulative) FSDA are likely to be more conspicuous when most firms
29
in the industry have low discretionary accruals, making it easier for investors to detect
earnings manipulation. This notion is consistent with Coles, Hertzel and Kalpathy (2006)
who find that investors are able to see through earnings manipulation in more transparent
settings.18
In order to reflect on this possibility, we examine the abnormal returns earned by
portfolio firms prior to portfolio formation. These pre portfolio formation returns are
reported in Tables V through VII. Kothari et al. (2006) have shown that compared to
firms with low discretionary accruals, firms with high discretionary accruals experience
significantly positive risk-adjusted returns in the year(s) leading up to portfolio
formation. The authors cite this as evidence in support of Jensen’s (2005) agency costs of
overvalued equity as an explanation for the accruals anomaly. Specifically, the authors
argue that firms use high discretionary accruals to manipulate earnings (upwards) in order
to sustain their prior overvaluation, and that the subsequent negative returns experienced
by these firms reflects the correction of this overvaluation.
If Kothari et al.’s argument is true, then to the extent that high FSDA are
manipulative only when concurrent ISDA are high as well, one should expect the highest-
FSDA-highest-ISDA portfolio to exhibit signs of significant overvaluation prior to
portfolio formation. In contrast, firms with high FSDA but relatively low ISDA should
not display such signs of prior overvaluation since their high FSDA convey value
relevant information to investors. On the other hand, if ISDA do not have a differential
18 Revisiting the results in Table IV also offers some perspective to this argument. The table shows that more than one-third (35.61%) of the firms have the highest TDA in the highest ISDA decile. It seems likely
that the set of firms with the highest FSDA (about 18%) is less likely to be scrutinized by investors in such
a case. In contrast, the percentage of firms with the highest TDA is less than 6% in the lowest ISDA decile.
In this case, firms with high FSDA (about 16%) are likely to be relatively more conspicuous and hence
more susceptible to investors’ scrutiny.
30
impact on the information content of large FSDA, then one should expect all firms with
high FSDA to be significantly overvalued prior to portfolio formation, regardless of the
magnitude of concurrent ISDA.
In Table VII, both Panel A and Panel B show that in year t-1 all firms with the
highest FSDA earn significantly positive abnormal returns regardless of the magnitude of
concurrent ISDA. In particular, Panel A shows that the highest-FSDA-highest-ISDA
portfolio earns an annualized return of 19.44% (1.62% x 12) whereas the highest-FSDA-
lowest-ISDA portfolio earns an annualized abnormal return of 11.04% (0.92% x 12) in
the year preceding portfolio formation. Panel B shows very similar results with respect to
FSDA-ISDA quintile portfolios. Notably, the highest-FSDA-highest-ISDA portfolio
exhibits signs of greater overvaluation compared to firms in the highest-FSDA-lowest-
ISDA portfolio: the difference between the two portfolios’ abnormal returns is both
statistically and economically significant in all three years prior to formation.
Nonetheless, to the extent that prior overvaluation is not specific to the highest-FSDA-
highest-ISDA decile, the results seem to suggest that all firms with high FSDA consist of
overvalued firms who have an incentive to manipulate earnings.
Although the above results seem to undermine the possibility that high FSDA are
value-relevant when ISDA are low, it is worth bearing in mind that the above
interpretation of pre portfolio-formation returns strictly depends on the extent to which
prior overvaluation is indicative of earnings manipulation! In other words, the
interpretation assumes that Kothari et al.’s claim is correct and that negative post-
portfolio formation returns are only experienced by firms which are significantly
overvalued and have incentive to manipulate earnings.
31
To assess the validity of this claim we also examine pre-portfolio formation
returns in Tables V and VI. Panel A in Table V shows that compared to firms with the
lowest TDA, firms with the highest TDA experience significantly positive abnormal
returns in the three years leading up to portfolio formation. In the year immediately
preceding portfolio formation, the highest TDA portfolio earns an annualized alpha of
15.85% (1.32% x 12); in contrast, the lowest-TDA portfolio earns no such abnormal
returns. Similarly, Panel A in Table VI shows that where the highest FSDA portfolio
earns an annualized risk-adjusted return of 15.26% (1.28% x 12), the lowest FSDA
portfolio does not earn positive abnormal returns prior to portfolio formation. These
results are similar to those found by Kothari et al. and seem to lend support to their
argument that firms with high discretionary accruals consist of earnings manipulators
looking to sustain their prior overvaluation. By the same token, low discretionary
accruals convey value-relevant information to investors.
Nonetheless, an examination of pre-portfolio formation returns in Panel B of
Table V and Table VI undermine agency costs of overvalued equity as an (only)
explanation for the accruals anomaly. Specifically Panel B in Table V shows that even
firms with the highest LRNDA experience significantly positive abnormal returns in the
three years prior to portfolio formation.19
Interestingly, in terms of magnitude firms with
the highest LRNDA experience higher positive returns prior to portfolio formation
compared to firms with the highest TDA and firms with the highest FSDA.20
Kothari et
al.’s argument would suggest that the highest-LRNDA portfolio comprises of firms that
have incentives to manipulate earnings upwards to sustain this overvaluation. This seems
19 In their paper, Kothari et al. (2006) only report the monthly abnormal returns earned by firms sorted into
total and discretionary accrual portfolios, and not for firms sorted into non-discretionary accrual portfolios. 20 In untabulated results we find the difference to be both statistically and economically significant.
32
unlikely given that LRNDA capture the component of discretionary accruals that is less
susceptible to earnings manipulation and more likely to contain value-relevant
information. Indeed, the fact that highest LRNDA portfolio does not experience negative
abnormal returns subsequent to portfolio formation casts doubt on agency costs of
overvalued equity as the only explanation for the accruals anomaly.2122
Similarly, Panel B in Table VI shows that the highest-ISDA portfolio experiences
significantly positive abnormal returns in the three years leading up to portfolio
formation. The fact that this portfolio also does not experience negative returns
subsequent to portfolio formation suggests that prior overvaluation is not necessarily
suggestive of earnings manipulation. By extension, the evidence suggests that it is likely
that firms use high FSDA to convey value-relevant relevant information to investors
when their concurrent ISDA are low – the fact that these firms show signs of prior
overvaluation is not necessarily indicative of their high FSDA being manipulative.
4.6 Robustness Checks
In this paper we have used the performance-adjusted modified Jones (1991)
model to estimate the various components of discretionary accruals. One potential
problem with this specification is that it causes all the effect of a change in accounts
receivable to be placed in discretionary accruals. As a result, the model is likely to
overestimate discretionary accruals for firms experiencing high sales growth and
21 In their paper, Kothari et al. (2006) use SRNDA as a proxy for non-discretionary accruals. In untabulated results, we find that the highest-SRNDA portfolio exhibits similar signs of prior overvaluation. 22 Of course, the argument assumes that cross-sectional Jones model do a good job at measuring non-
discretionary accruals. In that respect, we take the efficacy of Jones model as a given. Moreover, the fact
that Kothari et al. (2006) use a similar specification to measure discretionary and non-discretionary accruals
allows a meaningful comparison of our results with theirs.
33
underestimate them for firms with poor performance (Coles et al. 2006). Although
including return on assets as an additional regressor in the model is likely to ameliorate
this problem to a certain extent, we nonetheless repeat all our tests using the standard
Jones (1991) model, i.e. one in which change in revenue is not computed net of change in
accounts receivables.23
Additionally, to draw parallel with most prior literature we also
redo all our tests using the modified Jones model without lagged ROA as an additional
regressor (Dechow et al., 1995; Kothari et al., 2006). The tenor of our main results
remains unchanged in response to these specifications. Specifically, Panel A in Table
VIII shows that using the standard Jones model does not influence the finding that
accruals anomaly is driven by the mispricing of firms whose high FSDA are
accompanied by high ISDA. Panel B shows that using Dechow et al.’s modified Jones
model (without adjustment for firm performance) also yields similar results.
Another potential concern with the findings of this paper is that they are based on
the balance sheet measure of current accruals. Collins and Hribar (2002) have argued that
this balance sheet approach is prone to measurement error and instead recommend using
cash flow from operations (as determined under SFAS 95) to calculate accruals. For
robustness we replicate our tests using this alternative measure of accruals and find
qualitatively similar results.24
In particular, mispricing of high FSDA continues to be
23 In the standard Jones model, total accruals are regressed on change in sales and gross PP&E. That is, the
model does not adjust for the effect of performance on discretionary accruals. 24
As in Xie (2001), we measure accruals using the cash flow method as the difference between income
before extraordinary items (COMPUSTAT item IB) and cash flows. We measure cash flow as net cash flow from operating activities (COMPUSTAT item OANCF). For fiscal years prior to 1988, we measure
cash flow as the difference between fund flow from operations (COMPUSTAT item FOPT) and the
difference between change in current assets (COMPUSTAT item ACT) net of cash and cash equivalents
(COMPUSTAT item CHE) and change in current liabilities (COMPUSTAT item LCT) net of debt in
current liabilities (COMPUSTAT item DLC).
34
driven by the subset with high ISDA. These results are not reported for the sake of
brevity.
The mispricing of accruals is a joint test of market efficiency and the
appropriateness of the asset pricing model (Subramanyam, 1996). Hence one may argue
that there is potentially a risk-based explanation for the findings of this paper. While
recent evidence casts doubt on risk-based explanations for the accrual anomaly (e.g.
Hirshleifer, Hou and Teoh 2012), for robustness we replicate our main tests using the
four-factor model which enhances Fama-French three factor model by including
‘momentum’ as an additional risk factor (Jegadeesh and Titman 1993).
The monthly alphas obtained from estimating the four-factor model are shown in
Table IX. Panel A shows these alphas for bottom, middle, and bottom FSDA deciles.
Panel B reports alphas for the set of top-FSDA decile firms which lie in the bottom,
middle, and top ISDA deciles respectively. Finally Panel C reports alphas for the set of
firms in the top-FSDA quintile which lie in the bottom, middle, and top ISDA quintiles
respectively. In the spirit of Mashruwala et al. (2006), we estimate the four factor model
using firm-years with fiscal year-end prior to 1997.25
The results in Table IX suggest that the main conclusions of the paper remain
unchanged even after controlling for momentum. Specifically, while Panel A shows that
firms in the top FSDA decile earn significantly negative abnormal returns subsequent to
portfolio formation, Panel B shows that these negative returns are primarily driven by the
25 Mashruwala et al., (2006) show that for the sample of firms between 1976 and 2001, the momentum factor considerably reduces the magnitude and significance of Jensen’s alphas of firms with high
discretionary accruals. The authors attribute this result (in part) to the technology-stocks related bull market
of the late 1990s. Upon confining their sample to firms between 1976 and 1997, the authors find that the
statistical significance of negative alphas following high discretionary accruals is restored. The authors thus
conclude that accrual anomaly is robust to momentum.
35
mispricing of those firms whose high FSDA are accompanied by high ISDA. Panel C
reports similar results with respect to FSDA and ISDA quintiles.26
5. Conclusion
In this paper we augment the cross-sectional Jones (1991) model to define and
decompose discretionary accruals into a firm-specific and an industry-specific
component. Many studies have employed variants of the Jones model to measure
discretionary accruals, particularly in the study of earnings management. Nonetheless,
several academics have suggested that there are industry trends beyond those captured by
these models which can influence the use and information content of discretionary
accruals broadly across all firm in the industry. The accruals decomposition motivated in
this paper specifically addresses this concern by explicitly measuring the industry-
specific component of discretionary accruals. This allows for a more incisive examination
of the information content and manipulability of accruals, their role in price discovery,
and the source of accruals anomaly.
My findings indicate that the accruals anomaly is driven by the firm-specific
component of discretionary accruals, and that industry-specific discretionary accruals, on
average, convey value-relevant information to investors. More importantly, we find that
firms with high firm-specific discretionary accruals are overpriced specifically when
industry-specific discretionary accruals are high as well. The evidence suggests that firms
use high (firm-specific) discretionary accruals to manipulate earnings primarily when
26 Interestingly, both Panel B and Panel C show that while momentum reduces the significance of pre-
portfolio formation returns earned by the highest-FSDA-lowest-ISDA portfolio, it does not do reduce the
significance of pre portfolio-formation returns earned by the high-FSDA-high-ISDA firms. The evidence
bolsters the argument that high FSDA accompanied by low ISDA are likely to be value-relevant.
36
most firms in the industry also have high discretionary accruals and that investors
overlook or fail to detect this earnings manipulation.
Prior literature has offered several explanations for why the accruals anomaly
occurs. The findings of this paper suggest that an additional (though not necessarily
mutually exclusive) explanation could be that industry-wide use of high discretionary
accruals increases the search costs that investors have to incur in order to detect earning
manipulation. When most firms in the industry have high discretionary accruals due to
value-relevant reason, they can camouflage those firms whose high discretionary accruals
are manipulative. As a result investors can face difficulty distinguishing between the two
types of firms. At the same time, the systematic use of high discretionary accruals can act
as a credible signal to investors that firms have high discretionary accruals for value-
relevant reasons. This can increase investors’ tolerance for high discretionary accruals
and can hence cause them to be credulous of those discretionary accruals that are
manipulative.
The results of the paper are at odds with most prior earnings management
literature which has classified all firms with high discretionary accruals as potential
earnings manipulators. The finding that firms with the highest-FSDA earn negative
abnormal returns only when concurrent ISDA are high suggests that when most firms in
the industry have low discretionary accruals, firms use high (firm-specific) discretionary
accruals to convey value-relevant information to investors. If true, the implication
potentially has an important bearing on the earnings management literature which has
unconditionally used high (firm-specific) discretionary accruals to proxy for earnings
manipulation. The fact that some academics have found large discretionary accruals to be
37
a poor predictor of actual cases of fraud and earnings manipulation (e.g. Badertscher et
al. 2012) also suggests that at least some firms use such discretionary accruals for value-
relevant purposes.
Overall, the paper suggests that industry-specific discretionary accruals
(indirectly) help the accruals anomaly. In doing so, the paper identifies several lines of
inquiry for future research. For instance, although the results are consistent with industry-
wide use of high discretionary accruals having a differential impact on the information
content of high (firm-specific) discretionary accruals, an alternative possibility is that all
high FSDA are manipulative but that investors are able to detect this manipulation when
most firms in the industry have low discretionary accruals. we find some evidence which
suggests that the latter possibility is unlikely. Nonetheless, the evidence is not conclusive
and warrants further investigation. Relatedly, it is also not clear why firms would have an
increased incentive to manipulate earnings when most firms in the industry have high
discretionary accruals. One possibility is that managers’ RPE concerns are higher during
such times. Another possibility is that firms internalize investors’ (potentially) decreased
ability and/or incentives to detect earnings manipulation when industry-specific
discretionary accruals are high, and hence manipulate earnings because of a greater
likelihood that such manipulation will go unnoticed by investors. It is also possible that
high industry-specific discretionary accruals are correlated with other variable(s) that
induce earnings manipulation. More research is needed to understand why, if at all, firms
use manipulative discretionary accruals specifically when most firms in the industry also
have high discretionary accruals.
38
Moreover, a deeper understanding is required of factors that result in investors’
failure to detect earnings manipulation. Regardless of whether or not high ISDA have a
differential impact on the information content of high FSDA, results suggest that they do
have a differential impact on the pricing of high FSDA. While a likely explanation for
this mispricing is that investors face increased search costs to detect manipulation during
these times, further research is required to establish the validity of this claim and also to
shed light on the nature of these search costs. Additionally, a related possibility is that
high industry-specific discretionary accruals increase investors’ tendency to be credulous
of earnings manipulation. In this respect, future research can look into whether industry-
wide use of high discretionary accruals increases investors’ tendency to ‘fixate’ on
earnings. High industry-specific discretionary accruals could also be related to low
industry-wide discount rates (Wu et al., 2010), in which case the differential impact of
high ISDA on the mispricing of high FSDA could have a more ‘rational’ explanation.
Finally, the accruals decomposition developed in this paper can be used to revisit
and reflect on prior literature on the accruals anomaly and earnings management. The
scope of this paper is limited in that it only applies the accruals decomposition to address
the original anomaly documented by Sloan. We leave it for future research to examine
the role of industry-specific discretionary accruals (if any) in identifying earnings
manipulation and in explaining anomalous capital market outcomes in other settings.
39
References
Anwer S. Ahmed, S. M. Khalid Nainar, and Jian Zhou. 2005. Do analysts’ earnings
forecasts fully reflect the information in accruals? Canadian Journal of
Administrative Science, 22 (4), 329-342.
Badertscher, B., Collins, D., Lys, T., 2012. Discretionary accounting choices and the
predictive ability of accruals with respect to future cash flows. Journal of Accounting
and Economics, 53(1-2), 330-352
Bagnoli, M., Watts, S., 2000. The effect of relative performance evaluation on earnings
management: a game-theoretic approach. Journal of Accounting and Public Policy, 19,
377-397.
Bartov, E., Gul, F., Tsui, J., 2000. Discretionary accruals models and audit qualifications.
Journal of Accounting and Economics, 30, 421-452.
Beneish, M., Vargus, M., 2002. Insider trading, earnings quality and accrual mispricing.
The Accounting Review, 77 (4), 755-791.
Bergstresser, D., Phillipon, T., 2006. CEO incentives and earnings management. Journal
of Financial Economics, 80 (3), 511-529.
Bradshaw, M., Richardson, S., Sloan, R., 2001. Do analysts and auditors use information
in accruals? Journal of Accounting Research, 39, 45-74.
Burgstahler, D., Eames, M.J., 2003. Earnings management to avoid losses and earnings
decreases: Are analysts fooled? Journal of Accounting and Economics, 24(1), 99-126.
Cheng, I., 2011. Corporate governance spillovers. Working paper, University of
Michigan, Ross School of Business. Available at SSRN:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1299652
Cohen, D.A., Zarowin, P., 2007. Earnings management over the business cycle. Working
paper, New York University. Available online:
http://w4.stern.nyu.edu/emplibrary/EM_08_23_07FINAL.pdf
Coles, J., Hertzel, M., Kalpathy, S., 2006. Earnings management around employee stock
option reissues. Journal of Accounting and Economics, 41(1-2), 173-200.
Collins, D., Hribar, P., 2002. Errors in estimating accruals: Implications for empirical
research. Journal of Accounting Research, 40 (1), 105-134.
Cornett, M., Marcus, A., Tehranian, H., 2008. Corporate governance and pay-for-
performance. The impact of earnings management. Journal of Financial Economics,
87 (2), 357-373.
Dechow, P., 1994. Accounting earnings and cash flows as measures of firm performance:
The role of accounting accruals. Journal of Accounting and Economics, 18, 3-42.
Dechow, P., Khimich, N., Sloan, R., 2011. The accrual anomaly. Working Paper.
Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1793364
Dechow, P., Sloan, R., Sweeney, A., 1995. Detecting Earnings Management. The
Accounting Review, 70, 193-225.
Desai, H., Rajgopal, S., Venkatachalam, M., 2004. Value-glamour and accrual
mispricing: One anomaly or two. The Accounting Review, 79, 355-385.
Fairfield, P., Whisenant, S., Yohn, T., 2003. Accrued earnings and growth: Implication
for future profitability and market mispricing. The Accounting Review, 78, 353-371.
Fama, E., French, K., 1993. Common risk factors in returns on stocks and bonds. Journal
of Financial Economics, 33 (1), 3-56.
40
Healy, P., 1996. Discussion of market-based evaluation of discretionary accrual models.
Journal of Accounting Research, 34 (3), 107-115.
Hirshleifer, D., Hou, K., Teoh, S., 2012. The Accrual Anomaly: Risk or Mispricing?
Management Science, 58(2), 320-335.
Jegadeesh, N., Titman, S., 1993. Returns to buying winners and selling losers:
Implications for stock market efficiency. Journal of Finance, 48, 65-91.
Jensen, M., 2005. Agency costs of overvalued equity. Financial Management, 34, 5-19.
Jeter, D., Shivakumar, L., 1999. Cross-sectional estimation of abnormal accruals using
quarterly and annual data. Effectiveness in detecting event-specific earnings
management. Accounting and Business Research, 29 (4), 299-319.
Jiao, T., Mertens, G., Roosenboom, P., 2007. Industry-valuation driven earnings
management. Working paper, ERASMUS University, ERASMUS Research Institute
of Management.
Jones, J., 1991. Earnings management during import relief investigations. Journal of
Accounting Research, 29, 193-228.
Kang, Q., Liu. Q., Qi, R., 2010. Predicting stock market returns with aggregate
discretionary accruals. Journal of Accounting Research, 48(4), 815-858.
Kasanen, E., Kinnunen, J., Niskanen, J., 1996. Dividend-based earnings management:
Empirical evidence from Finland. Journal of Accounting and Economics, 22, 283-312.
Kothari, S., Leone, A., Wasley, C., 2005. Performance-matched discretionary accrual
measures. Journal of Accounting and Economics, 39, 163-197.
Kothari, S., Loutskina, E., Nikolaev, V., 2006. Agency theory of overvalued equity as an
explanation for the accrual anomaly. CentER Discussion Paper Series No. 2006-103.
Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=871750
Lev, B., and Nissim, D., 2006. The persistence of the accruals anomaly. Contemporary
Accounting Research, 23(1), 193-226.
Mashruwala, C., Rajgopal, S., Shevlin, T., 2006. Why is the accrual anomaly not
arbitraged away? The role of idiosyncratic risk and transaction costs. Journal of
Accounting and Economics, 42 (1-2), 3-33.
McNichols, M.F., 2000. Research design issues in earnings management studies. Journal
of Accounting and Public Policy, 19, 313-345
Park, M., 1999. Industry earnings performance and firms’ accrual decisions. Working
paper, Purdue University.
Rangan. S., 1998. Earnings management and the performance of seasoned equity
offerings. Journal of Financial Economics, 50 (1), 101-122.
Rhodes-Kropf, M., Robinson, D., Viswanathan. S., 2005.Valuation waves and merger
activity: The empirical evidence. Journal of Financial Economics, 77 (3), 561-603.
Ronen, J., Yaari, V., 2008. Earnings management: Emerging insights in theory, practice
and research. Springer Series in Accounting Scholarship.
Sloan, R., 1996. Do stock prices fully reflect the information in accruals and cash flows
about future earnings? Accounting Review, 71, 289-316.
Subramanyam, K.., 1996. The pricing of discretionary accruals. Journal of Accounting
and Economics, 22 (1-3), 249-281.
Teoh, S., Welch, I., Wong, T., 1998a. Earnings management and the long-run
underperformance of seasoned equity offerings. Journal of Financial Economics, 50,
63-100.
41
Teoh, S., Welch, I., Wong, T., 1998b. Earnings management and the long-run
underperformance of initial public offerings. Journal of Finance, 53, 1935-1974.
Teoh, S., Wong, T., 2002. Why new issues and high-accrual firms underperform: The
role of analysts’ credulity. Review of Financial Studies, 15 (3), 869-900.
Wu, J., Zhang, L., Zhang, X., 2010. The q-theory approach to understanding the accrual
anomaly. Journal of Accounting Research, 48(1), 177-222.
Xie, H., 2001. The mispricing of abnormal accruals. The Accounting Review, 76, 357-
373.
42
Table I
Descriptive Statistics of Jones Model Parameter Estimates across Industries The table presents the descriptive statistics of parameter estimates obtained from estimating the performance-adjusted cross-sectional modified Jones (1991) model expressed in equation
[5]. The model is estimated at the industry level using 2-digit SIC codes to classify firm-years into industries. Col 1 shows the descriptive statistics of (coefficient on
), Col 2 shows the summary statistics of (coefficient on PPE) and Col 3 shows the summary statistics of (coefficient on NI). P25 and P75 refer to the 25th percentile and 75th
percentile respectively. Std. refers to the standard deviation. The regression is estimated yearly for all US firms that are present on the COMPUSTAT and CRSP monthly returns file between 1970 and 2006 that do not correspond to financials or utilities, have fiscal year-end in December (FYR = 12), have shares corresponding to common equity, and have non-
missing data for accruals, and its discretionary and non-discretionary accrual components. All continuous variables are Winsorized at 1% and 99%.
Industry
Col 1
Change in Sales - Change in Receivables
Col 2
Gross Property, Plant and Equipment
Col 3
Lag Net Income
Mean Median P25 P75 Std. Mean Median P25 P75 Std. Mean Median P25 P75 Std.
Amuse. & Recreation -0.129 -0.112 -0.269 0.010 0.204 -0.059 -0.055 -0.067 -0.043 0.018 0.076 0.024 -0.087 0.278 0.234
Apparel & Other Text. 0.113 0.124 0.018 0.209 0.150 -0.070 -0.091 -0.131 -0.004 0.103 0.191 0.309 -0.100 0.487 0.382
Business Services 0.079 0.073 0.026 0.114 0.086 -0.107 -0.095 -0.123 -0.078 0.043 0.070 0.069 0.009 0.123 0.134
Chemicals 0.075 0.080 0.015 0.119 0.077 -0.059 -0.060 -0.070 -0.049 0.017 0.109 0.074 0.026 0.148 0.133
Communication -0.020 -0.045 -0.081 0.042 0.116 -0.078 -0.079 -0.094 -0.059 0.022 0.059 0.073 -0.039 0.128 0.147
Eating & Drinking -0.036 -0.039 -0.080 -0.015 0.075 -0.067 -0.067 -0.079 -0.052 0.018 0.017 0.049 -0.115 0.104 0.153
Electronic Equipment 0.160 0.164 0.129 0.199 0.073 -0.086 -0.088 -0.110 -0.068 0.034 0.155 0.113 0.075 0.201 0.139
Engineering Services 0.026 0.034 -0.020 0.073 0.077 -0.080 -0.079 -0.113 -0.044 0.044 0.080 0.050 -0.031 0.166 0.145
Fabricated Metal Prod. 0.133 0.138 0.093 0.173 0.071 -0.069 -0.070 -0.086 -0.047 0.026 0.208 0.208 0.046 0.329 0.228
Food & Kindred Prod. 0.075 0.055 0.042 0.129 0.075 -0.057 -0.062 -0.072 -0.048 0.020 0.041 0.036 -0.058 0.191 0.199
Food Stores 0.022 0.005 0.021 0.041 0.075 -0.074 -0.074 -0.086 -0.062 0.027 0.080 0.106 -0.040 0.214 0.190
Furniture & Fixtures 0.038 0.040 -0.071 0.164 0.158 -0.082 -0.087 -0.122 -0.029 0.067 0.240 0.253 0.022 0.530 0.439
Gen. Build. Contractors 0.048 0.042 0.014 0.071 0.140 -0.070 -0.060 -0.092 -0.033 0.054 0.238 0.276 -0.071 0.555 0.583
Health Services 0.044 0.009 -0.058 0.102 0.151 -0.080 -0.083 -0.107 -0.047 0.039 0.087 0.076 0.008 0.128 0.137
Hotels& Other Lodging -0.054 -0.020 -0.140 0.097 0.217 -0.052 -0.054 -0.061 -0.042 0.022 0.085 0.046 -0.084 0.289 0.312
Industrial Machinery 0.132 0.137 0.096 0.174 0.060 -0.069 -0.071 -0.088 -0.055 0.033 0.174 0.147 0.071 0.234 0.141
Instruments 0.142 0.155 0.095 0.189 0.063 -0.072 -0.075 -0.088 -0.059 0.026 0.214 0.118 0.044 0.335 0.240
Leather & Leather Prod. 0.171 0.196 0.046 0.279 0.129 -0.054 -0.132 -0.171 0.042 0.216 -0.078 -0.108 -0.338 0.326 0.495
Lumber Products 0.018 0.033 -0.066 0.115 0.163 -0.070 -0.073 -0.096 -0.049 0.045 0.231 0.232 -0.039 0.485 0.481
Metal Mining 0.002 0.030 -0.116 0.135 0.227 -0.042 -0.052 -0.070 -0.015 0.045 0.045 0.045 -0.146 0.193 0.262
Misc. Retail 0.044 0.082 -0.051 0.126 0.133 -0.068 -0.073 -0.113 -0.043 0.090 0.126 0.101 -0.033 0.365 0.349
Misc. Manufacturing 0.167 0.162 0.097 0.252 0.171 -0.075 -0.078 -0.125 -0.047 0.062 0.181 0.156 0.051 0.316 0.251
43
Table I (cont.)
Industry
Col 1
Change in Sales - Change in Receivables
Col 2
Gross Property, Plant and Equipment
Col 3
Lagged Net Income
Mean Median P25 P75 Std. Mean Median P25 P75 Std. Mean Median P25 P75 Std.
Oil and Gas Extract 0.006 0.018 -0.093 0.094 0.121 -0.069 -0.072 -0.076 -0.059 0.016 -0.008 0.001 -0.058 0.084 0.134
Paper and Allied Prod. 0.077 0.069 0.023 0.164 0.133 -0.053 -0.053 -0.063 -0.042 0.017 0.043 0.041 -0.093 0.145 0.250
Petroleum and Coal 0.034 0.038 -0.005 0.088 0.083 -0.059 -0.062 -0.069 -0.047 0.015 0.104 0.090 -0.000 0.212 0.293
Primary Metal 0.134 0.128 0.081 0.201 0.085 -0.043 -0.042 -0.051 -0.034 0.014 0.074 0.080 -0.062 0.195 0.222
Printing & Publishing 0.106 0.103 0.034 0.188 0.128 -0.083 -0.077 -0.111 -0.060 0.037 0.071 0.109 -0.085 0.220 0.203
Railroad Transportation -0.105 -0.089 -0.202 0.040 0.240 -0.030 -0.030 -0.041 -0.022 0.019 0.160 0.070 -0.103 0.317 0.527
Rubber and Misc. Prod. 0.114 0.122 0.024 0.204 0.122 -0.066 -0.067 -0.081 -0.050 0.027 0.059 0.025 -0.097 0.139 0.213
Stone, Clay & Glass 0.116 0.099 0.031 0.187 0.134 -0.050 -0.049 -0.062 -0.042 0.029 0.060 0.055 -0.073 0.186 0.217
Textile Mill Prod. 0.099 0.153 0.005 0.174 0.123 -0.056 -0.065 -0.081 -0.037 0.056 0.303 0.178 0.084 0.349 0.471
Transportation Equip. 0.113 0.126 0.064 0.168 0.073 -0.073 -0.072 -0.088 -0.053 0.030 0.169 0.168 -0.006 0.283 0.221
Transportation By Air -0.077 -0.068 -0.142 0.001 0.127 -0.070 -0.069 -0.087 -0.060 0.024 0.150 0.093 -0.015 0.363 0.276
Trucking & Warehouse 0.058 0.054 0.012 0.108 0.088 -0.095 -0.103 -0.110 -0.079 0.020 0.004 -0.007 -0.096 0.103 0.195
Water Transportation 0.039 0.004 -0.109 0.141 0.214 -0.052 -0.048 -0.063 -0.037 0.021 0.093 -0.023 -0.150 0.127 0.426
Wholesale – Durable 0.145 0.131 0.103 0.168 0.065 -0.081 -0.083 -0.117 -0.051 0.048 0.230 0.199 0.097 0.375 0.247
Wholesale – Nondurable 0.062 0.069 0.028 0.098 0.053 -0.071 -0.068 -0.093 -0.044 0.037 0.110 0.129 -0.022 0.277 0.284
44
Table II
Descriptive Statistics and Correlations of Accrual Components and Key Firm Characteristics The table presents the descriptive statistics [Panel A] and Pearson correlations [Panel B] of accrual components and key firm characteristics
for the sample of firms between 1970 and 2006. Accruals (AC) are calculated using the balance sheet approach as difference between non-
cash current assets and change in current liabilities (exclusive of short-term debt and taxes payable), less depreciation expense, all divided by
lagged assets. Firm-specific discretionary accruals (FSDA) are residuals obtained from estimating the performance-adjusted cross-sectional
Jones model presented in equation [5]. Short-run non-discretionary accruals (SRNDA) are estimated using equation [6] and long-run non-
discretionary accruals (LRNDA) are calculated using equation [7]. Total discretionary accruals (TDA) are calculated as the difference between accruals AC and LRNDA. Industry-specific discretionary accruals (ISDA) are calculated as the difference between SRNDA and
LRNDA. Earnings (EARN) are defined as operating income after depreciation, divided by lagged assets. Cash flow (CFO) is the difference
between EARN and AC. In Panel A the descriptive statistics of AC, SRNDA, LRNDA, TDA, FSDA, ISDA, CFO and EARN are all reported
as percentage of lagged assets. Market-Book ratio is calculated as the sum of assets and fiscal year-end market capitalization, less common
equity and deferred taxes, divided by assets. The sample consists of all US firms that are present on the COMPUSTAT and CRSP monthly
returns file between 1970 and 2006 that do not correspond to financials or utilities, have fiscal year-end in December (FYR = 12), have shares
corresponding to common equity, and have non-missing data for accruals, and its discretionary and non-discretionary accrual components.
All continuous variables are Winsorized at 1% and 99%.
Panel A: Descriptive Statistics
Obs. Mean Median Min. Max. Deviation
AC 55,208 -3.81 -4.17 -38.45 35.23 10.65
SRNDA 55,208 -3.34 -3.19 -41.13 41.16 6.19
LRNDA 55,208 -3.52 -3.28 -48.18 25.51 5.14
TDA 55,208 -0.29 -0.34 -57.87 68.69 9.76
FSDA 55,208 -0.47 -0.37 -51.54 56.66 8.85
ISDA 55,208 0.18 -0.04 -39.17 59.82 4.10
CFO 55,208 2.63 7.29 -107.20 39.08 21.66
EARN 55,208 -1.07 4.21 -111.88 30.31 21.30
Assets (000s) 55,208 1,182.86 127.14 2.12 25,199 3,553.68
Market-Book 53,309 1.77 1.26 0.53 9.57 1.49
Panel B: Pearson Correlation Coefficients
AC SRNDA LRNDA TDA FSDA ISDA CFO EARN
AC 0.557*** 0.408*** 0.877*** 0.814*** 0.331*** -0.254*** 0.261***
SRNDA
0.753*** 0.212*** -0.029*** 0.567*** -0.036*** 0.261***
LRNDA
-0.082*** -0.037*** -0.115*** 0.181*** 0.399***
TDA
0.907*** 0.422*** -0.373*** 0.074***
FSDA
0.002 -0.281*** 0.131***
ISDA
-0.281*** -0.105***
CFO
0.843***
EARN
45
Table III
Summary Statistics of Accrual Components and Key Firm Characteristics Across Discretionary Accrual
Decile Portfolios The table presents the mean (median) values of discretionary accrual components and key firm characteristics for the sample of firms
between 1970 and 2006. Panel A reports statistics across total discretionary accruals (TDA) decile portfolios, while Panel B and Panel C
reports the statistics for firm-specific discretionary accruals (FSDA) and industry-specific discretionary accruals decile (ISDA) portfolios
respectively. Firm-specific discretionary accruals (FSDA) are residuals obtained from estimating the performance-adjusted cross-sectional
Jones model presented in equation [5]. Short-run non-discretionary accruals (SRNDA) are estimated using equation [6] and long-run non-
discretionary accruals (LRNDA) are calculated using equation [7]. Total discretionary accruals (TDA) are calculated as the difference
between accruals AC and LRNDA. Industry-specific discretionary accruals (ISDA) are calculated as the difference between SRNDA and
LRNDA. Earnings (EARN) are defined as operating income after depreciation, divided by lagged assets. Cash flow (CFO) is the difference
between EARN and accruals. Accruals (AC) are calculated using the balance sheet approach as difference between non-cash current assets
and change in current liabilities (exclusive of short-term debt and taxes payable), less depreciation expense, all divided by lagged assets. All
descriptive statistics are reported as percentage of lagged assets. Market-Book ratio is calculated as the sum of assets and fiscal year-end
market capitalization, less common equity and deferred taxes, divided by assets. The sample consists of all US firms that are present on the COMPUSTAT and CRSP monthly returns file between 1970 and 2006 that do not correspond to financials or utilities, have fiscal year-end in
December (FYR = 12), have shares corresponding to common equity, and have non-missing data for accruals, and its discretionary and non-
discretionary accrual components. All continuous variables are Winsorized at 1% and 99%.
Panel A: Total Discretionary Accrual (TDA) Decile Portfolios
Lowest
Decile 2 3 4 5 6 7 8 9
Highest
Decile
TDA
-17.67 -7.74 -4.56 -2.62 -1.10 0.29 1.78 3.69 6.91 18.08
(-15.74) (-7.62) (-4.53) (-2.65) (-1.11) (0.26) (1.75) (3.64) (6.74) (15.13)
FSDA -15.49 -7.00 -4.05 -2.27 -0.93 0.22 1.44 3.11 5.78 14.44
(-14.20) (-6.90) (-4.01) (-2.23) (-0.89) (0.28) (1.57) (3.22) (5.99) (12.87)
ISDA -2.17 -0.74 -0.51 -0.35 -0.17 0.07 0.34 0.58 1.12 3.64
(-1.17) (-0.60) (-0.40) (-0.34) (-0.19) (-0.02) (0.13) (0.37) (0.72) (2.10)
LRNDA -2.69 -2.67 -2.98 -3.33 -3.57 -3.85 -4.16 -4.17 -4.17 -3.63
(-2.66) (-2.42) (-2.78) (-3.08) (-3.37) (-3.65) (4.00) (-4.00) (-3.86) (-3.03)
EARN -12.06 -1.68 1.25 1.81 2.04 2.36 1.68 0.80 -1.18 -5.80
(-1.13) (3.43) (4.40) (4.78) (4.84) (4.86) (4.58) (4.44) (4.34) (4.38)
CFO 8.18 8.66 8.79 7.76 6.70 5.94 4.04 1.33 -3.88 -21.33
(15.18) (12.47) (11.37) (10.26) (9.21) (8.22) (6.83) (4.57) (0.80) (-10.96)
ASSETS 462.01 814.08 1204.97 1735.84 1898.29 1951.40 1662.24 1196.31 587.47 310.62
(45.25) (98.33) (159.64) (227.79) (286.90) (274.40) (214.78) (145.37) (85.95) (47.15)
MB 2.00 1.72 1.66 1.66 1.61 1.57 1.59 1.68 1.83 2.31
(1.31) (1.24) (1.23) (1.25) (1.22) (1.21) (1.21) (1.22) (1.27) (1.49)
46
Table III (Continued)
Panel B: Firm-Specific Discretionary Accrual (FSDA) Decile Portfolios
Lowest
Decile 2 3 4 5 6 7 8 9
Highest
Decile
TDA
-16.18 -7.14 -4.09 -2.23 -0.95 0.28 1.53 3.32 6.15 16.38
(-14.68) (-7.04) (-4.21) (-2.39) (-1.08) (0.15) (1.41) (3.08) (5.87) (13.91)
FSDA -16.69 -7.57 -4.52 -2.58 -1.09 0.27 1.70 3.51 6.46 15.78
(-14.69) (-7.40) (-4.44) (-2.53) (-1.06) (0.26) (1.67) (3.44) (6.28) (13.37)
ISDA 0.51 0.43 0.43 0.36 0.14 0.02 -0.16 -0.20 -0.31 0.60
(0.3) (0.30) (0.21) (0.15) (0.00) (-0.10) (-0.29) (-0.46) (-0.47) (0.00)
LRNDA -2.99 -2.96 -3.03 -3.43 -3.72 -3.90 -4.15 -4.09 -3.83 -3.12
(-2.81) (-2.68) (-2.83) (-3.17) (-3.46) (-3.65) (-4.01) (-3.81) (-3.54) (-2.74)
EARN -12.47 -2.77 -0.27 0.78 1.80 1.83 1.53 0.75 0.42 -2.38
(-1.27) (3.26) (4.09) (4.61) (4.63) (4.69) (4.61) (4.64) (4.63) (4.79)
CFO 6.49 7.33 6.85 6.43 6.45 5.39 4.18 1.51 -1.95 -16.48
(13.93) (11.82) (10.41) (9.78) (9.12) (8.27) (7.02) (5.15) (1.97) (-8.00)
ASSETS 441.90 773.80 1,111.59 1,666.74 1,910.23 1,830.08 1,680.26 1,274.97 747.67 386.12
(47.91) (94.94) (148.62) (206.16) (254.89) (265.08) (214.95) (145.13) (96.05) (54.74)
MB 2.02 1.75 1.70 1.71 1.59 1.62 1.63 1.71 1.77 2.15
(1.32) (1.27) (1.24) (1.26) (1.20) (1.22) (1.21) (1.25) (1.27) (1.40)
Panel C: Industry-Specific Discretionary Accrual (ISDA) Decile Portfolios
Lowest
Decile 2 3 4 5 6 7 8 9
Highest
Decile
TDA
-5.81 -2.30 -1.46 -1.15 -0.87 -0.54 -0.30 0.49 1.45 7.55
(-4.19) (-1.83) (-1.13) (-0.83) (-0.63) (-0.43) (-0.03) (0.37) (1.38) (5.55)
FSDA 0.43 0.24 0.05 -0.31 -0.58 -0.77 -1.11 -1.11 -1.44 -0.13
(1.12) (0.57) (0.29) (-0.10) (-0.41) (-0.69) (-0.79) (-1.17) (-1.55) (-1.00)
ISDA -6.24 -2.54 -1.51 -0.83 -0.29 0.23 0.81 1.60 2.89 7.68
(-5.08) (-2.46) (-1.47) (-0.82) (-0.31) (0.17) (0.74) (1.54) (2.82) (6.15)
LRNDA -3.05 -3.32 -3.26 -3.22 -3.21 -3.19 -3.19 -3.55 -3.84 -5.40
(-3.44) (-3.30) (-3.01) (-2.96) (-2.81) (-2.86) (-2.94) (-3.47) (-4.14) (-5.62)
EARN -3.71 1.49 2.79 2.50 2.15 1.71 0.60 -0.99 -4.00 -13.31
(3.29) (4.79) (5.12) (4.73) (4.53) (4.37) (4.17) (4.07) (3.66) (1.80)
CFO 5.26 7.12 7.49 6.87 6.20 5.34 4.01 1.95 -1.75 -16.29
(9.44) (9.47) (9.06) (8.56) (7.85) (7.72) (7.06) (6.38) (5.16) (-3.32)
ASSETS 709.40 1,200.44 1,510.04 1,152.14 1,637.62 1,549.18 1,387.50 1,156.54 798.78 362.79
(54.75) (139.20) (185.06) (196.05) (222.55) 211.92 (163.77) (131.71) (90.12) (34.68)
MB 1.96 1.72 1.65 1.58 1.55 1.55 1.58 1.66 1.90 2.48
(1.34) (1.24) (1.23) (1.20) (1.21) (1.21) (1.20) (1.23) (1.31) (1.55)
47
Table IV
Distribution of Firm-Years across FSDA and TDA Ranks within ISDA Decile Portfolios The table shows the distribution of firm-years across firm-specific (FSDA) and total discretionary accrual (TDA) decile ranks within each
industry-specific discretionary accrual (ISDA) decile portfolio for the sample of firms between 1970 and 2006. The top half shows the
percentage of firm-years in each of the ten FSDA and TDA rankings for the bottom five ISDA decile portfolios, while the bottom half
shows the percentage of firm-years in each of the ten FSDA and TDA rankings for the top five ISDA decile portfolios. FSDA are
residuals obtained from estimating the performance-adjusted cross-sectional Jones model presented in equation [5]. TDA are calculated as
the difference between accruals and LRNDA. Accruals are calculated using the balance sheet approach as difference between non-cash current assets and change in current liabilities (exclusive of short-term debt and taxes payable), less depreciation expense. ISDA are
calculated as the difference between SRNDA (equation [6]) and LRNDA (equation [7]). TDA, FSDA and ISDA decile ranks are
determined based on their respective fiscal year-end values. The sample consists of all US firms that are present on the COMPUSTAT
and CRSP monthly returns file between 1970 and 2006 that do not correspond to financials or utilities, have fiscal year-end in December
(FYR = 12), have shares corresponding to common equity, and have non-missing data for accruals, and its discretionary and non-
discretionary accrual components. All continuous variables are Winsorized at 1% and 99% respectively.
Decile
ISDA Decile 1
(Lowest) ISDA Decile 2
ISDA Decile 3
ISDA Decile 4
ISDA Decile 5
Rankings FSDA% TDA%
FSDA% TDA%
FSDA% TDA%
FSDA% TDA%
FSDA% TDA%
Lowest 13.74 28.53
9.23 13.15
6.90 8.60
7.55 8.27
6.92 6.81
2 8.63 14.59
8.29 13.31
8.56 12.05
8.32 10.55
9.75 10.69
3 7.12 11.28
7.91 12.62
8.63 12.00
10.17 12.20
10.44 10.82
4 5.87 8.69
8.15 11.64
9.52 12.49
10.39 12.29
11.31 12.12
5 6.21 7.31
8.94 10.68
10.75 11.78
11.20 11.20
11.94 12.57
6 7.25 6.16
10.12 9.45
11.06 10.24
11.24 11.56
11.34 11.60
7 8.92 5.58
10.83 7.73
11.75 10.53
12.43 10.55
12.25 11.32
8 10.87 5.96
13.11 7.97
13.18 9.48
11.33 9.16
9.24 9.19
9 14.70 6.11
12.80 7.44
11.60 7.58
9.54 8.27
9.73 9.24
Highest 16.68 5.80
10.61 6.01
8.05 5.23
7.84 5.95
7.08 5.64
100.00 100.00
100.00 100.00
100.00 100.00
100.00 100.00
100.00 100.00
Decile ISDA Decile 6
ISDA Decile 7
ISDA Decile 8
ISDA Decile 9
ISDA Decile 10
(Highest)
Rankings FSDA% TDA%
FSDA% TDA%
FSDA% TDA%
FSDA% TDA%
FSDA% TDA%
Lowest 7.69 6.82
9.02 7.19
9.69 6.90
12.61 7.58
16.36 5.88
2 9.85 9.68
10.39 9.04
11.67 7.95
12.50 7.47
12.08 4.70
3 11.15 10.52
11.04 9.47
11.87 9.01
11.86 7.29
9.86 4.87
4 12.00 11.76
11.59 10.07
11.51 9.37
11.83 7.27
7.92 4.38
5 12.42 12.05
11.51 10.85
11.45 10.53
8.77 8.27
6.74 4.70
6 11.84 12.96
11.88 12.15
10.64 10.62
8.70 9.66
5.99 5.65
7 9.99 10.50
10.41 13.03
9.34 12.88
7.85 10.96
6.39 7.08
8 9.94 10.66
8.88 11.10
7.39 12.54
8.45 13.64
7.59 10.26
9 8.40 8.98
8.46 10.03
8.41 10.85
7.80 14.78
8.73 16.87
Highest 6.73 6.06
6.81 7.19
8.01 9.35
9.64 13.09
18.34 35.61
100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
48
Table V
Monthly Alphas from Fama and French Three-Factor Model for TDA and NDA Decile Portfolios The table presents the monthly alphas for total discretionary accrual (TDA) and long-run non-discretionary accrual (LRNDA) decile
portfolios, Panels A and B respectively. The portfolios are constructed by ranking firms in each year t based on their magnitude of TDA
and LRNDA, respectively. The monthly alphas are estimated from calendar time regression based on Fama-French’s three factor model
using monthly returns: ttHtSftmtmftpt HMLSMBRRRR )( where Rpt is the return on the accrual portfolio in month
t; Rmt is the return on the CRSP value-weighted index in month t; Rft is the 3-month T-bill yield in month t; SMBt is the return on small
firms minus the return on large firms in month t; and HMLt is the return on high book-to-market stocks minus the return on low book-to-
market stocks in month t. For companies in each accrual decile in year t, we report monthly returns earned three years prior and three
years subsequent to portfolio formation. Monthly returns are included starting 4 months after the beginning and 4 months after the end of
each year. LRNDA are calculated using equation [7] and TDA are calculated as the difference between total accruals and LRNDA.
Accruals are calculated using the balance sheet approach as difference between non-cash current assets and change in current liabilities
(exclusive of short-term debt and taxes payable), less depreciation expense. ***, ** and * denote significance at 1%, 5% and 10% respectively.
Deciles
Panel A: Monthly Alphas (%) for TDA Decile Portfolios
Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.43*** 0.29* -0.04 0.02 0.17 0.01 0.17
2 0.25** 0.05 -0.01 0.02 0.40*** 0.25** 0.28**
3 0.23** 0.11 -0.14 0.09 0.24*** 0.12 0.17
4 0.17*** 0.00 -0.08 0.08 0.25*** 0.19** 0.27
5 0.07 0.11 -0.08 0.13 0.15** 0.20** 0.14
6 0.15** 0.12 0.06 -0.04 0.17** 0.09 0.1
7 0.08 0.15* 0.12 0.03 0.08 0.13 0.08
8 0.23*** 0.31** 0.25*** 0.07 0.01 0.22** 0.15*
9 0.37** 0.57*** 0.41*** 0.23** -0.02 0.06 0.25**
10 (Highest) 0.60*** 0.72*** 1.32*** 0.72*** -0.32** -0.22* -0.12
Highest – Lowest 0.17*** 0.22*** 1.35*** 0.70*** -0.49*** -0.23** -0.33**
Deciles
Panel B: Monthly Alphas (%) for LRNDA Decile Portfolios
Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.25 0.28 0.05 0.13 -0.02 -0.04 0.06
2 -0.05 -0.04 -0.17 -0.02 0.07 0.22* 0.14
3 0.06 -0.17 -0.36*** -0.06 0.13 0.11 0.18
4 0.04 -0.21** -0.42*** -0.10 0.13 0.17* 0.10
5 0.01 -0.16 -0.28** -0.13 0.17* 0.10 0.17
6 0.17* -0.04 -0.19** -0.04 0.23*** 0.17** 0.14
7 0.18** 0.08 0.01 0.04 0.20** 0.21** 0.24***
8 0.38*** 0.45*** 0.28** 0.07 0.20** 0.35 0.21**
9 0.60*** 0.78*** 0.83*** 0.44*** 0.17* 0.13 0.17*
10 (Highest) 0.97*** 1.48*** 2.09*** 1.02*** -0.10 -0.10 0.09
49
Highest – Lowest 0.72*** 1.20*** 2.04*** 0.89*** -0.08 -0.06 0.03
Table VI
Monthly Alphas from Fama-French’s Three-Factor Model for Firm-Specific Discretionary Accrual (FSDA)
and Industry-Specific Discretionary Accrual Decile (ISDA) Portfolios The table reports the monthly alphas for firm-specific discretionary accrual (FSDA) decile portfolios [Panel A], and industry-specific
discretionary accrual (ISDA) decile portfolios [Panel B]. The portfolios are constructed by ranking firms in each year t based on their
magnitude of FSDA and ISDA, respectively. The alphas are estimated from calendar time regression based on Fama-French’s three factor
model using monthly returns: ttHtSftmtmftpt HMLSMBRRRR )( where Rpt is the return on the accrual portfolio in
month t; Rmt is the return on the CRSP value-weighted index in month t; Rft is the 3-month T-bill yield in month t; SMBt is the return on
small firms minus the return on large firms in month t; and HMLt is the return on high book-to-market stocks minus the return on low book-to-market stocks in month t. Monthly returns are included starting 4 months after the beginning and 4 months after the end of each
year. FSDA are residuals obtained from estimating the performance-adjusted cross-sectional Jones model presented in equation [5]. ISDA
are calculated as the difference between SRNDA (equation [6]) and LRNDA (equation [7]). ***, ** and * denote significance at 1%, 5%
and 10% respectively.
Panel A: Monthly Alphas (%) for FSDA Decile Portfolios
Deciles Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.33** 0.24* -0.05 0.05 0.26 0.10 0.15
2 0.38*** 0.10 -0.03 0.10 0.36*** 0.20* 0.27**
3 0.26*** 0.08 -0.03 0.03 0.32*** 0.02 0.21**
4 0.27*** 0.21** -0.02 0.11 0.17** 0.19** 0.18**
5 0.07 0.08 0.01 0.07 0.20** 0.13 0.25***
6 0.11 0.11 0.04 0.09 0.09 -0.20** 0.08
7 0.09 0.25*** 0.09 -0.03 0.06 0.11 0.10
8 0.26*** 0.27*** 0.12 0.17* 0.03 0.29*** 0.18*
9 0.32*** 0.39*** 0.44*** 0.12 -0.01 0.07 0.20**
10 (Highest) 0.44*** 0.70*** 1.28*** 0.64*** -0.30** -0.25* -0.09
Highest – Lowest 0.11** 0.46*** 1.33*** 0.59*** -0.56*** -0.35** -0.23**
Panel B: Monthly Alphas (%) for ISDA Decile Portfolios
Deciles Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.25* 0.28* 0.37** 0.20 -0.11 -0.06 -0.07
2 0.18* 0.44*** 0.20* 0.07 0.09 0.09 0.22**
3 0.24** 0.24** 0.11 0.00 0.22** 0.22** 0.28***
4 0.20** 0.10 -0.10 -0.04 0.20** 0.06 0.25***
5 0.20** 0.07 -0.02 0.06 0.23*** 0.20** 0.13
6 0.21** 0.23*** 0.05 0.05 0.16 0.26*** 0.16
7 0.12 0.05 0.07 0.00 0.24*** 0.18** 0.12
8 0.28*** 0.14 0.16 0.02 0.04 0.09 0.16*
9 0.31*** 0.22 0.35*** 0.37*** 0.18 0.12 0.25**
10 (Highest) 0.58*** 0.66*** 0.62*** 0.61*** -0.09 -0.11 -0.04
Highest – Lowest 0.33*** 0.38*** 0.25*** 0.41*** 0.02 -0.05 0.03
50
Table VII
Monthly Alphas from Fama-French’s Three-Factor Model for Firms with the Highest FSDA The table reports the monthly alphas for the portfolio of firms with the highest firm-specific discretionary accrual (FSDA). Panel A
reports these alphas for firms in top FSDA decile across ISDA deciles, while Panel B reports these results for firms in top FSDA quintile
across ISDA quintiles. The portfolios are constructed by ranking firms based on the magnitude of their FSDA and ISDA decile and
quintile rankings respectively at the end of each year t. The alphas are estimated from calendar time regression based on Fama-French’s
three factor model using monthly returns: ttHtSftmtmftpt HMLSMBRRRR )( where Rpt is the return on the accrual
portfolio in month t; Rmt is the return on the CRSP value-weighted index in month t; Rft is the 3-month T-bill yield in month t; SMBt is the
return on small firms minus the return on large firms in month t; and HMLt is the return on high book-to-market stocks minus the return
on low book-to-market stocks in month t. Monthly returns are included starting 4 months after the beginning and 4 months after the end of
each year. Firm-specific discretionary accruals (FSDA) are residuals obtained from estimating the performance-adjusted cross-sectional
Jones model presented in equation [5]. Industry-specific discretionary accruals (ISDA) are calculated as the difference between SRNDA
(equation [6]) and LRNDA (equation [7]). ***, ** and * denote significance at 1%, 5% and 10% respectively.
ISDA Deciles
Panel A: Monthly Alphas (%) for Decile Portfolios
Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.34 0.32 0.92*** 0.77*** -0.39 -0.29 -0.30
2 0.21 0.93*** 0.88*** 0.56** -0.24 -0.36 0.06
3 0.01 0.82*** 1.10*** 0.41* 0.11 -0.22 0.38
4 0.40 0.58** 1.08*** 0.12 0.05 -0.08 -0.25
5 0.22 0.88*** 0.82*** 0.23 -0.19 -0.19 -0.38
6 -0.14 0.96*** 1.31*** 0.41 -0.23 -0.03 -0.39
7 0.88** 0.62** 0.86*** -0.15 -0.46 0.26 -0.01
8 0.81** 0.36* 1.79*** 0.92*** -0.33 -0.21 0.19
9 0.81** 0.61** 1.84*** 0.69*** -0.15 -0.49* 0.23
10 (Highest) 0.71** 1.03*** 1.62*** 1.14*** -0.68*** -0.48* -0.20
Highest – Lowest 0.37*** 0.71*** 0.72*** 0.37*** -0.29** -0.19 0.10
ISDA Quintiles
Panel B: Monthly Alphas (%) for Quintile Portfolios
Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.20 0.39*** 0.66*** 0.31** -0.14 -0.07 0.04
2 1.03*** 0.55*** 0.60*** 0.12 -0.01 -0.06 0.21
3 0.22 0.61*** 0.71*** 0.20 0.00 0.03 -0.18
4 0.51*** 0.46*** 0.92*** 0.27* -0.22 0.12 0.34**
10 (Highest) 0.70*** 0.80*** 1.34*** 0.88*** -0.36** -0.32* -0.03
Highest – Lowest 0.50*** 0.41*** 0.68*** 0.57*** -0.22** -0.25* -0.07
51
Table VIII
Monthly Alphas from Fama-French’s Three-Factor Model for Highest-FSDA Decile Using Alternative
Jones Model Specifications The table reports the monthly alphas for the portfolio of firms with in the top decile of firm-specific discretionary accruals (FSDA) which
lie in the bottom, middle, and top deciles of industry-specific discretionary accruals (ISDA) respectively. In Panel A, FSDA and ISDA are
measured using the standard Jones model which regresses accruals on change in sales and gross property, plant and equipment. In Panel
B, FSDA and ISDA are measured using the modified Jones model which regresses accruals on change in sales net of change in
receivables and gross property, plant and equipment without lag ROA as an additional regressor in the model. The portfolios are
constructed by ranking firms based on the magnitude of their FSDA and ISDA decile rankings respectively at the end of each year t. The
alphas are estimated from calendar time regression based on Fama-French’s three factor model using monthly returns:
ttHtSftmtmftpt HMLSMBRRRR )( where Rpt is the return on the accrual portfolio in month t; Rmt is the return on
the CRSP value-weighted index in month t; Rft is the 3-month T-bill yield in month t; SMBt is the return on small firms minus the return
on large firms in month t; and HMLt is the return on high book-to-market stocks minus the return on low book-to-market stocks in month
t. Monthly returns are included starting 4 months after the beginning and 4 months after the end of each year. ***, ** and * denote significance at 1%, 5% and 10% respectively.
Panel A: Monhtly Alphas (%) for Highest-FSDA Decile Using Standard Jones
ISDA Deciles Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.18 0.56** 0.97*** 0.51** -0.29 -0.33 -0.34
5 0.53* 0.91*** 1.30**** -0.37 -0.34 0.38 -0.08
10 (Highest) 0.66** 0.68** 1.97*** 0.90*** -0.69*** -0.87*** -0.28
Highest – Lowest 0.48* 0.12** 1.00*** 0.39*** -0.40** -0.55*** 0.06
Panel B: Monthly Alphas (%) for Highest-FSDA Decile Using Modified Jones Without Lag ROA
ISDA Deciles Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.25 0.63*** 1.01*** 0.59** -0.13 -0.34 -0.39*
5 0.13 1.09*** 1.53*** -0.05 -0.37 -0.38 0.05
10 (Highest) 0.61* 0.89*** 2.14*** 1.09*** -0.93*** -0.74*** -0.59**
Highest – Lowest 0.36 0.26*** 1.13*** 0.60*** -0.80*** -0.40*** -0.20*
52
Table IX
Monthly Alphas from Fama-French’s Four-Factor Model The table reports monthly Jensen’s alphas using the Fama-French four factor model for the period 1970 – 1996. Panel A reports these
alphas for firms in the bottom, middle, and top FSDA deciles, Panel B reports these alphas for firms in the top FSDA decile in the lowest,
middle, and top ISDA deciles, and Panel C reports these alphas for firms in the top FSDA quintile in the lowest, middle, and top ISDA
quintiles respectively. The portfolios are constructed by ranking firms in each year t based on their magnitude of FSDA and ISDA decile
and quintile rankings respectively. The alphas are estimated from calendar time regression based on Fama-French’s four factor model
using monthly returns: ttUtHtSftmtmftpt UMDHMLSMBRRRR )( where Rpt is the return on the accrual portfolio
in month t; Rmt is the return on the CRSP value-weighted index in month t; Rft is the 3-month T-bill yield in month t; SMBt is the return on
small firms minus the return on large firms in month t; HMLt is the return on high book-to-market stocks minus the return on low book-to-
market stocks in month t; and UMDt is the difference between returns on portfolios of past winners and losers. Monthly returns are
included starting 4 months after the beginning and 4 months after the end of each year. Firm-specific discretionary accruals (FSDA) are
residuals obtained from estimating the performance-adjusted cross-sectional Jones model presented in equation [5]. Industry-specific
discretionary accruals (ISDA) are calculated as the difference between SRNDA (equation [6]) and LRNDA (equation [7]). ***, ** and *
denote significance at 1%, 5% and 10% respectively.
FSDA Deciles
Panel A: Monthly Alphas (%) for FSDA Decile Portfolio
Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.05 -0.03 -0.36** -0.10 0.35** 0.17 0.25*
5 -0.03 0.14* 0.07 0.17** 0.24*** 0.20** 0.23**
10 (Highest) 0.27* 0.49*** 0.95*** 0.40*** -0.32** -0.21* -0.04
Highest – Lowest 0.22* 0.52*** 1.31*** 0.50*** -0.67*** -0.38** -0.29*
ISDA Deciles
Panel B: Monthly Alphas (%) for Highest FSDA Decile Portfolio
Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.01 -0.19 0.23 0.60** -0.42 -0.33 -0.10
5 0.25 0.82** 0.88*** 0.29 0.03 -0.03 0.18
10 (Highest) -0.70** 0.74*** 1.51*** 0.66** -0.89*** -0.38 -0.26
Highest – Lowest -0.71** 0.93*** 1.28*** 0.06 -0.47*** -0.05 -0.16
ISDA Quintiles
Panel C: Monthly Alphas (%) for Highest FSDA Quintile Portfolio
Year With Respect to Portfolio Formation
t = -3 t = -2 t = -1 t = 0 t = +1 t = +2 t = +3
1 (Lowest) 0.07 -0.00 0.13 0.24 -0.04 -0.11 0.19
3 0.40*** 0.58*** 0.63*** -0.00 -0.00 0.04 0.15
5 (Highest) 0.56*** 0.64*** 1.32*** 0.65*** -0.46*** -0.28 -0.09
Highest – Lowest 0.49*** 0.64*** 1.19*** 0.41*** -0.42*** -0.17 -0.28*