Evaluating the ‘accrual-fixation’ hypothesis as an explanation for the
accrual anomaly
Tzachi Zach*
Olin Business School
Washington University in St. Louis
St. Louis, MO 63130
Tel: (314)-9354528
zach@wustl.edu
First version: August 2004
This version (2.0): September 2007
Abstract. In this study, I directly examine whether the accrual-fixation hypothesis explains
the accrual anomaly, first documented in Sloan (1996). The accrual-fixation hypothesis
posits that investors fixate on earnings without taking into account accruals’ tendency to
reverse. Thus, the returns to an accrual-based strategy are related to accruals’ reversals. I
use the reversal property of the accrual-fixation hypothesis to generate testable empirical
predictions. I find that extreme accrual firms are sticky and tend to remain in extreme
deciles in two consecutive years. Sticky firms are associated with future abnormal returns
only in the low accruals decile. In contrast, in the high accruals decile, abnormal returns
are related to accruals’ reversals. This asymmetry between low and high accrual deciles
suggests that the causes of the accrual anomaly differ across the two groups. Evidence on
firm characteristics reveals that in the high accruals decile future abnormal returns are
related to high abnormal accruals, while in the low accruals decile future abnormal returns
are related to high bankruptcy risk. Overall, the evidence implies that the accrual-fixation
hypothesis is descriptive of the behavior of high accrual firms but not of low accrual firms.
*
This paper is based on parts of my dissertation completed at the University of Rochester. I would like to
thank the members of my dissertation committee: Bill Schwert, Jerry Warner, Ross Watts (chair) and Jerry
Zimmerman for their valuable comments and suggestions. I also benefited greatly from the insights of an
anonymous referee, Sudipta Basu, Daniel Cohen, Nick Dopuch, Philip Joos, Ron King, S.P. Kothari, Andy
Leone, Thomas Lys (the editor), Richard Sloan, Michela Verardo and Charles Wasley. I also thank seminar
participants at University of California at Berkeley, University of Chicago, Columbia University, Emory
University, MIT, Northwestern University, Washington University in St. Louis and University of
Washington. Finally, I thank the Deloitte Foundation for generous financial support. All errors are my own.
1
1.
Introduction
Sloan (1996) and a number of subsequent studies report that a trading strategy based
on publicly available information about operating accruals earns abnormal returns of
approximately 10% in the year following its initiation. The strategy is based on firms in the
extreme high and low deciles of accruals’ cross-sectional distribution. This empirical
regularity has been termed the ‘accrual anomaly.’ Sloan (1996) also shows that accruals and
cash flows exhibit differential persistence with respect to future earnings. He attributes the
return predictability to the market’s inability to take the differential persistence into account.
More specifically, he argues that “stock prices act as if investors ‘fixate’ on earnings, failing
to distinguish fully between the differential properties of the accrual and cash flow
components of earnings.”
In this study I directly examine Sloan’s (1996) explanation for the causes of the
accrual anomaly. I label this explanation “the accrual-fixation hypothesis.” According to this
hypothesis, return predictability arises because market participants treat accruals and cash
flows similarly, without taking into account accruals’ tendency to reverse in the following
year. Investors’ misprocessing of the reversal properties of accounting information is central
to the accrual-fixation hypothesis.
Under the accrual-fixation hypothesis, and based on the results of Mishkin (1983) tests
of rational pricing, investors overprice (underprice) firms with high (low) accruals. As a
result, firms with extreme accruals are associated with abnormal returns in the following year.
Since abnormal returns appear in extreme accrual deciles and because under the accrualfixation hypothesis returns arise from accruals’ reversals, I expect that firms in these deciles
2
will exhibit reversal patterns similar to those of accruals. Under this scenario accrual
extremeness is expected to be transitory. Thus, the accrual-fixation hypothesis predicts that
extreme accrual deciles are likely to be composed of different firms in adjacent years. In
addition, the accrual-fixation hypothesis predicts that no abnormal returns will be associated
with firms that do not exhibit reversals because reversals are the primary cause of abnormal
returns. If reversals are associated with future returns, the accrual-fixation hypothesis does not
specify the degree or type of reversals that are more likely to result in predicable return
patterns.
In my analysis, I use the reversal feature of the accrual-fixation hypothesis to generate
testable empirical predictions for it. I then perform several empirical tests to evaluate whether
the data is consistent with these predictions. First, I examine the time-series pattern of firms
with extreme accruals to see whether these firms tend to leave the top and bottom accrual
deciles in the following period or whether they are sticky.1 In other words, do these firms
habitually reside in extreme deciles in successive years? The accrual-fixation hypothesis,
together with the fact that the top and bottom accrual deciles are associated with abnormal
returns, predicts that extreme accrual firms will leave the extreme accrual deciles in the
following year.
Second, I investigate the sources of abnormal returns earned by extreme accrual firms.
Are these returns related to habitual extremes or to firms that exhibit reversal patterns?
Because under the accrual-fixation hypothesis the returns are a result of accrual reversals, it
predicts that no returns will be associated with sticky firms.
1
Throughout this paper, sticky refers to firms that belong to an extreme accrual decile (high or low) in two
consecutive years.
3
Summary of results. In the time-series analysis, I find that extreme accrual firms are
habitual extremes. About 25% of firms in portfolios of extreme high (low) accruals were in
the same portfolios in the previous year. Such stickiness is observed across various measures
of accruals, including working capital accruals and investing accruals. This frequency is
higher than 10%, a benchmark which assumes independence across years and ignores the
tendency of accruals to reverse. After considering accruals’ reversals, which are an important
element in the accrual-fixation hypothesis, the expected frequency is even lower than 10%.
One shortcoming of this analysis is that the specification of the transition matrix under the
null hypothesis is unclear. Since I also find that stickiness is related to past growth rates in
sales and to the length of firms’ operating cycles, it is possible that under the accrual-fixation
hypothesis the expected stickiness frequency is higher than 10%. To address this, the second
set of tests investigates the future abnormal returns of sticky and non-sticky firms.
When I examine the sources of returns to an accrual-based strategy, I find an
asymmetry between high and low accrual firms. In high accrual firms, future returns largely
arise from accrual reversals. The degree of reversals varies by sample. In the nonNYSE/AMEX sample, some of these reversals are from the highest accrual decile in one year
to the lowest accrual decile in the following year. In the NYSE/AMEX sample the reversals
are less dramatic, from the highest accrual decile in one year to the second and third lowest
deciles in the following year. Since the accrual-fixation hypothesis does not specify the
degree of reversals that are expected to generate abnormal returns, the NYSE/AMEX
evidence is important in highlighting the various sources of returns to an accrual strategy. In
4
both samples, abnormal returns do not arise to sticky high accrual firms. Overall, the evidence
regarding high accrual firms is consistent with the accrual-fixation hypothesis.
In low accrual firms, abnormal returns are not associated with strong reversals from
the lowest decile in one year to any one of the three highest deciles in the following year.
There is some evidence of abnormal returns related to weaker reversal patterns, from the
bottom decile in one year to the second and third lowest deciles in the following year. Further,
I find some evidence that abnormal returns are associated with sticky firms, inconsistent with
the accrual-fixation hypothesis.
The documented asymmetry in the sources of abnormal returns to low and high
accrual firms suggests that the causes of the anomaly in each extreme decile are different. To
shed light on the potential different sources of the anomaly, I report some characteristics of
firms that are the drivers of returns to an accrual strategy. In the NYSE/AMEX sample,
abnormal returns to high accrual firms are not associated with the fastest-growing firms. The
high accrual firms that drive the returns have the highest book-to-market ratios but not the
lowest market capitalizations or the highest rates of growth in sales. These firms also do not
have the highest abnormal accruals. In the non-NYSE/AMEX sample, a successful accrual
strategy is exposed to smaller stocks with higher bankruptcy risk. These firms have
significantly larger abnormal accruals suggesting that at least some of the returns are related
to reversals of accrual manipulations (Xie, 2001).
The low accrual firms that drive the returns in the NYSE/AMEX sample are larger and
do not have the lowest book-to-market ratios. Moreover, they have the lowest (in absolute
value) abnormal accruals, suggesting that the causes of the accrual anomaly in this set are not
5
related to accounting discretion. Finally, low accrual firms that drive the strategy’s returns
have higher bankruptcy risk. This raises the possibility that an alternative explanation for the
accrual anomaly in low accrual firms is bankruptcy risk. Similar findings regarding abnormal
accruals and bankruptcy risk also emerge in the non-NYSE/AMEX sample.
This study contributes to the debate regarding the potential explanations for the
accrual anomaly. I extend the literature by providing direct evidence on the sources of returns
to an accrual strategy, and on whether these returns are related to accruals’ reversals. I show
that abnormal returns are associated with firms that exhibit varying degrees of reversals, and
not necessarily strong reversals from one extreme decile to the other extreme decile. This is
important because the accrual-fixation hypothesis does not specify the degree or type of
reversals that are more likely to result in predicable return patterns. My results support the
accrual-fixation hypothesis in high accrual firms but not in low accrual firms. By highlighting
the asymmetry between high and low accrual firms, I conclude that the sources of anomaly
differ across the two groups. In particular, there is evidence that high accrual firms that
reverse have higher abnormal accruals than other high accrual firms, suggesting that
accounting discretion may explain the accrual anomaly in these firms. Low accrual firms that
are associated with future returns have higher bankruptcy risk than other low accrual firms,
suggesting that bankruptcy risk may explain the anomaly in these firms. This asymmetry is
important and is related to other recent studies that examine the accrual anomaly (for
example, Dechow and Ge, 2006).
The rest of this paper is organized as follows. Section 2 provides a brief overview of
the relevant literature and a discussion of the reversal property of the accrual-fixation
6
hypothesis used in this study. The data are described in Section 3. I present the results in
Section 4 and conclude in Section 5.
2.
2.1
The accrual-fixation hypothesis
Literature background
Sloan (1996) reports two results. First, he shows that abnormal returns are predictable
based on current information about accruals. Second, he shows that accruals and cash flows
exhibit differential persistence with respect to future earnings. To link the return predictability
result with the persistence result, Sloan (1996) performs Mishkin (1983) tests. They reveal
that the persistence coefficients of accruals and cash flows that are implied in market prices
are different from the ‘true’ coefficients obtained from a predictive regression of future
earnings on current accruals and cash flows. More specifically, the market assigns a higher
persistence to the accrual component of earnings than the one implied by the predictive
regression and, as a result, over (under)-prices firms whose earnings contain high (low)
accrual components. The conclusion is that return predictability is due to investors processing
accounting information in a particular, yet incorrect, way.
The conclusion is essentially Sloan’s (1996) original interpretation for the accrual
anomaly, which has become the most widely accepted explanation for the phenomenon. I call
this explanation the ‘accrual-fixation’ hypothesis. According to the accrual-fixation
hypothesis, return predictability is due to mispricing driven by investors mis-interpreting
accrual information. When pricing securities investors do not consider the future reversals of
accruals. Sloan views future reversals to be a result of aggressive or “bad” accounting that
originally inflated or deflated accruals. Investors, for some reason, do not process the
7
information correctly leading to mispricing. An important tenet underlying this hypothesis is
the link between the reversals of accruals and the reversals of prices. It is also important to
note that the accrual-fixation hypothesis originated from the results of the Mishkin tests. In
that sense, the hypothesis is descriptive in nature and does not stem from predictions of an
information misprocessing theory.
A large body of literature has explored the anomaly since Sloan (1996). However,
there is still no consensus about the causes of the anomaly. In my discussion below I survey
the literature from the perspective of the three potential explanations for the anomaly.
The first explanation argues that the anomaly does not stem from true mispricing.
Instead, the anomaly is a manifestation of problems in asset pricing models such as
inappropriate controls for risk (Zach, 2003; Khan, 2007). Zach (2003) finds that the accrual
strategy still produces abnormal returns, even after controlling for size, book-to-market and
momentum. Khan (2007), on the other hand, implements a different asset pricing model and
argues that the strategy’s returns can be explained by the use of a naïve asset pricing model.2
Alternatively, the anomaly can be a result of selection biases and lack of control for outlying
observations (Kraft, Leone and Wasley, 2006).
The second explanation for the accrual anomaly is the accrual-fixation hypothesis. It
argues that the anomaly represents true mispricing which is caused by investors’
misprocessing of accrual information. The existing evidence supporting the accrual-fixation
2
Related studies in this category examine whether the calculated abnormal returns are realizable (Lev and
Nissim, 2006; Mashruwala, Rajgopal and Shevlin, 2006). Both studies argue that implementing an accrual
strategy is too costly for arbitrageurs. As a result, predictability remains observable. However, these studies do
not comment on the causes of the anomaly. Rather, they examine the factors responsible for the existence and
persistence of abnormal returns.
8
hypothesis is indirect. Several studies have looked for variation in the accrual strategy’s
returns across accrual components. The association of mispricing with abnormal accruals
(Xie, 2001), with inventory accruals (Thomas and Zhang, 2002), and with less reliable
accruals (Richardson, Sloan, Soliman and Tuna, 2005), led researchers to conclude that
investors are misled by accrual manipulation. This provides indirect evidence supporting the
accrual-fixation hypothesis.
Other studies examine the behavior of analysts and institutional investors (Bradshaw,
Richardson and Sloan, 2001; Ali, Hwang and Trombley, 2000; Collins, Gong and Hribar,
2003; Barth and Hutton, 2004). These investors are presumably less subject to any processing
biases implied by the accrual-fixation hypothesis. The studies seek to exploit cross-sectional
variation in the presumed processing bias to generate predictions about the nature of the
anomaly across groups of investors. The evidence is mixed. While Ali, Hwang and Trombley
(2000) do not find evidence consistent with naïve fixation, Collins, Gong and Hribar (2003)
show that returns to an accrual strategy vary with the level of institutional (or sophisticated)
investors in a manner consistent with naïve fixation. Bradshaw, Richardson and Sloan (2001)
find evidence that analysts are subject to the same misprocessing bias as the market.
Francis and Smith (2005) question the accrual-fixation hypothesis indirectly. Their
results suggest that accruals and cash flows do not possess differential persistence. By
extension, if that is the case, then the accrual-fixation hypothesis, which relies on the different
persistence properties of accruals and cash flows, no longer has merit.
The third explanation for the accrual anomaly can be characterized generally as a
correlated omitted variable problem. That is, accruals are correlated with a variable that is
9
associated with abnormal returns. The generation of abnormal returns could be due to a
misspecification in existing asset pricing models with respect to the omitted variable. This
explanation, although related to the first explanation, is distinct from it because the associated
variable is not known to be a priced-risk factor. In that sense, its discovery is important to the
asset pricing literature. The association of the omitted variable with abnormal returns could
also be due to investors mispricing securities with respect to that variable. This is related to
the second explanation because both argue true mispricing. However, the important difference
is that the information set over which investors make mistakes is different. The accrualfixation hypothesis is very specific in positing that investors make mistakes in interpreting
accrual information. The third explanation does not limit the information set exclusively to
accruals, but rather to any variable correlated with accruals. This is an important difference
that has implications on how researchers and practitioners interpret the anomaly.
Desai, Rajgopal and Venkatachalam (2004) find that the accrual anomaly is related to
the value-glamour anomaly. Since accruals are correlated with growth, Fairfield, Whisenant
and Yohn (2003) argue that the accrual anomaly is a special case of a more general “growth”
anomaly. Ng (2005) suggests that some of the anomaly’s returns are associated with increased
exposure to distress risk. The correlation of accruals with corporate financing events is
discussed in Zach (2003) and Bradshaw, Richardson and Sloan (2006).
In summary, there is no agreement in the literature about any of the three
explanations.3 Moreover, there is no direct evidence supporting or refuting the accrual-
3
There are other studies that pertain to the accrual anomaly but whose conclusions do not sway readers to one
particular explanation. For example, Collins and Hribar (2002) argue that the anomaly is distinct from the postearnings announcement drift. Other examples include studies that examine the anomaly in an international
10
fixation hypothesis. In this paper I take a more direct approach in investigating the validity of
this hypothesis. Direct evidence supporting or refuting it is an important step in understanding
the accrual anomaly.
2.2
The accrual-fixation hypothesis and reversals
Under the accrual-fixation hypothesis, and stemming from the Mishkin (1983) tests’
results, market participants treat accruals and cash flows similarly ignoring the transitory
nature of accruals (i.e. accruals’ tendency to reverse). As a result, firms with extreme high
(low) accruals are over- (under-) priced and future abnormal returns are generated. Since
abnormal returns appear in extreme accrual deciles and because abnormal returns under the
accrual-fixation hypothesis are driven by accruals’ reversals, I expect that firms in extreme
deciles will exhibit reversal patterns similar to those of accruals. Thus, under this scenario
accrual extremeness is expected to be transitory. Further, if aggressive accounting is related to
accrual-fixation then, for a given firm, accruals are not likely to remain extreme in the
following period because of disciplinary mechanisms such as auditor oversight. Thus, my first
testable prediction is that under the accrual-fixation hypothesis, extreme accrual deciles are
likely to be composed of different firms across periods.
To test my first prediction, I examine the time-series properties of extreme accrual
firms. I look at whether extreme accrual firms tend to leave the extreme accrual deciles in the
following year as implied by the accrual-fixation hypothesis. Alternatively, these firms could
reside in extreme deciles in successive years. I label such firms as sticky.
context (Pincus, Rajgopal and Venkatachalam, 2007; LaFond, 2005) and those that examine the anomaly as a
function of the disclosure of accrual information at the time of an earnings announcement (Levi, 2006).
11
A second implication of the accrual-fixation hypothesis is that the returns associated
with an accrual strategy are a result of accruals’ reversals. Thus, not only does the accrualfixation hypothesis predict reversals of firms from extreme deciles, but it also predicts that no
abnormal returns will be associated with sticky firms that do exist. The accrual-fixation
hypothesis does not stipulate the degree of reversals that would be associated with abnormal
returns. To explore this property of the accrual-fixation hypothesis, I identify the firms that
are the drivers of abnormal returns to an accrual strategy. I investigate whether these firms are
sticky or whether they experience accrual reversals. If so, I examine what is the degree of
these reversals.
3.
Data
3.1
Sample selection and variable measurement
For my main analysis, I use two samples: (1) NYSE and AMEX firms and (2)
NASDAQ firms. My main sample period is 1988-1999. In cases, where I calculate accruals
using the balance sheet approach, I use the 1970-1999 sample period.
Accounting variables. Accounting variables are drawn from the COMPUSTAT
primary, secondary and tertiary files. First, I use differences between successive balance sheet
accounts according to the following formula (COMPUSTAT item numbers in parentheses, Δ
stands for annual changes in the corresponding items):
•
Balance-sheet accruals: Δcurrent assets(#4)-Δcash(#1)-Δcurrent liabilities(#5)+
Δdebt in current liabilities(#34)-depreciation(#14).
I also calculate accruals directly from the statement of cash flows, according to the following
formula:
12
•
Cash-flow-statement accruals: Earnings before extraordinary items from the Cash
Flow Statement (#123) – Cash flows from operations (#308).
All accounting variables are deflated by average total assets (at year t-1 and year t).
Stock returns. I obtain stock returns from the Center for Research in Security Prices
(CRSP) tapes as of 2001. I choose equal-weighted, size- and book-to-market adjusted buyand-hold returns as my reference normal return benchmark. I use NYSE cutoffs to assign
firms to non-equal-sized size and book-to-market portfolios at the end of fiscal year. I
calculate abnormal returns after compounding monthly returns of both firms’ raw returns and
benchmark portfolio returns. Benchmark portfolios are implicitly rebalanced every month and
their composition may change after the ranking period.
Accrual strategy. To apply the accrual strategy, at the end of each year firms are
ranked into deciles based on the magnitude of their deflated accruals. Once the rankings are
determined, abnormal returns for each accrual decile are calculated by averaging the annual
average abnormal returns of all firms in a particular accrual decile. Like other studies in the
literature, I start the calculation period four full months after the end of the fiscal year. This
assures that all the information in the financial statements is available to implement the
strategy.
3.2
Descriptive statistics
Table 1 reports medians and means of several variables of interest by deciles of
accruals calculated from the cash flow statement and deflated by average total assets. In
general, the descriptive statistics are similar to those reported in previous studies. First, the
negative correlation between accruals and cash flows is immediately apparent. Cash flows
13
from operations decrease monotonically as we move from the low accrual decile (median of
0.22) to the high accrual decile (median of 0.02).
Second, it is evident that firms with extreme accruals, those that occupy the top and
bottom accrual deciles, are smaller in terms of market capitalization. A similar pattern is also
present for total assets or total revenues (not reported). All these variables exhibit an inverted
U-shaped pattern with respect to the accrual deciles. Moreover, the median size of low accrual
firms is smaller than that of high accrual firms (p-value smaller than 1%), but the mean size of
low accrual firms is larger than the mean size of high accrual firms.
Third, high accrual firms have lower book-to-market ratios. Starting with the eighth
decile, there is a decrease in the median book-to-market ratio. It falls from around 0.55 to 0.51
in the eighth decile, to 0.48 in the ninth decile and to 0.45 in the high accrual decile. The
difference in median book-to-market ratios between the low and high accrual deciles is
significant at conventional levels. Fourth, firm performance measured in terms of both
contemporaneous stock performance and accounting rates of return and growth, is increasing
monotonically with accruals. For example, the median raw return in the fiscal year for which
accruals are measured increases from 3.2% in the low accrual decile to 16.6% in the high
accrual decile. The median return on assets starts at 5.5% in the low accrual decile and
increases to 10.5% in the high accrual decile. The median growth in sales is 1.9% for the low
accrual decile and 23.4% for the high accrual decile. This difference may be slightly
misleading because of the larger preponderance of mergers in the high accrual deciles and of
divestitures in the low accrual deciles. After controlling for these factors, however, the
median sales growth remains different across the extreme accrual deciles (1.4% for the low
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decile versus 20.2% for the high decile). Fifth, compared to firms in the middle accrual
deciles, analysts’ forecasts of long-term growth are slightly larger for low accrual firms
(13.5% vs. 12%), whereas they are much larger for high accrual firms (18% vs. 12%).
Finally, I report summary statistics on Altman’s (1968) Z-score, which measures a
company’s financial strength. Altman’s Z is negatively correlated with the probability of
bankruptcy. Table 1 reveals that the ex-ante probability of bankruptcy is monotonically
decreasing as we move from the low to the high accrual deciles. That is, low accrual firms
have a higher bankruptcy probability than high accrual firms. The median Z-score of low
accrual firms is 2.7 and it reaches 3.9 in the high accrual decile. All the patterns mentioned
above (size, book-to-market, performance, bankruptcy risk) are present in the unreported
summary statistics of non-NYSE/AMEX firms.
4.
Empirical results
In section 4.1 I test whether extreme accrual firms are habitual extremes (i.e. sticky) or
whether reversal patterns of firms across deciles are observed. In section 4.2 I investigate
whether there is a relation between stickiness and future abnormal returns. I explore
characteristics that distinguish firms that drive the anomaly’s returns from firms that do not
drive the returns in section 4.3.
4.1
Are extreme-accrual firms sticky?
Recall from section 2.2 that the accrual-fixation hypothesis predicts that extreme
accrual firms will tend to move away from the extreme accrual portfolios in the following
year. Table 2 reports transition matrices containing frequencies of firms that occupy decile i in
15
year t-1 and decile j in year t. I use the traditional definition of working capital accruals as in
Sloan (1996) and many subsequent studies. I measure these working capital accruals using
both the balance-sheet approach (Panel A) and the cash-flow-statement approach (Panel B).4
The results in Table 2 are for NYSE firms. Similar (unreported) results apply for non-NYSE
firms.5
Panel A of Table 2 indicates that there is a tendency for extreme accrual firms in year
t-1 to remain in the extreme accrual deciles in year t. For example, 26.4% of firms in the
bottom balance-sheet accrual decile remain in that decile in the following year. Similarly,
27.1% of firms in the top accrual decile in year t-1 remain in that same decile in year t. Based
on chi-squared tests, these frequencies are significantly higher than a frequency of 10%,
which assumes independence across years and ignores the tendency of accruals to reverse.6
Taking accrual reversals into account yields an expected frequency that is even lower than
10%. In fact, inventory accruals, which are part of working capital accruals, are likely to be
the most transitory, reinforcing this lower expected frequency. However, the null hypothesis
of expected frequency under accrual reversals is not readily and easily specified.
As Hribar and Collins (2002) argue, measuring accruals using the cash-flow approach
is preferable in the context of the accrual anomaly. Thus, it is worth noting that when accruals
are measured using this approach, they exhibit an even stronger stickiness. Panel B shows that
in the bottom (top) accrual deciles, 31.7% (30.7%) of firms remain in their respective accrual
deciles in the subsequent year compared to 26.4% (27.1%) in panel A, wherein accruals are
4
In similar unreported results (available from the author upon request) I also use the more comprehensive
measure of total accruals that includes working capital accruals, investing accruals and financing accruals (see
Richardson, Sloan, Soliman and Tuna, 2005).
5
In my discussions of the results, the NYSE sample includes firms from both NYSE and AMEX.
6
P-values from chi-squared tests were lower than 0.01.
16
measured using the balance-sheet approach. Part of this increased frequency, compared to
balance-sheet accruals, is because many firms are in the bottom (top) balance-sheet accrual
decile as a result of divestitures (mergers) which are presumably transitory events (Zach,
2003).
In Table 3, I examine two characteristics that can contribute to accrual stickiness:
growth and the length of the operating cycle. I report medians of these variables, measured at
the end of year t across different cells of accrual rankings in years t-1 and t. Consistent with
the analyses in the next section, I construct five uneven groups: The High and Low groups
correspond to firms in extreme accrual deciles. The non-extreme firms are divided into three
groups as follows: (i) firms in the 2nd or 3rd accrual deciles (Mid1), (ii) firms in accrual deciles
4 through 7 (Mid2), and (iii) firms in accrual deciles 8 and 9 (Mid3). For brevity, I do not
report results of differences-in-medians tests. All the comparisons I discuss below are
significant at conventional levels.
It is evident from Table 3 that sticky low firms in the NYSE sample (as well as LowMid1 firms) have a significantly shorter operating cycle than Low-High firms, which fully
reverse (84.3 days compared to 118.2 days). Sticky low firms have lower growth rates than
Low-High firms (6.5% versus 9.7%). The difference in growth rates is much starker in the
non-NYSE sample (1.7% versus 12.7%). Sticky high accrual firms do not have a significantly
larger operating cycle than High-Low in both samples. However, growth rates of sticky high
accrual firms are significantly higher in both the NYSE and non-NYSE samples (22.5% and
38.3%, respectively). This is consistent with the evidence in Fairfield, Whisenant and Yohn
(2003). Thus, stickiness of high accrual firms is more related to high growth rates and less
17
related to the length of the operating cycle. Stickiness of low accrual firms is related to the
length of the operating cycle (especially in NYSE firms), and, to a lesser extent to growth
rates (stronger in the non-NYSE sample).
In summary, Table 2 shows that extreme accrual firms, regardless of the type of
accruals, are sticky. Recall that, under the accrual-fixation hypothesis, extreme accrual firms
are expected to be transitory. Thus, the results reported in Table 2 are not consistent with this
hypothesis. One shortcoming of this set of tests is that the specification of the transition
matrix under the null hypothesis is unclear. This makes the interpretation of the transition
frequencies somewhat subjective. While I benchmark my results against a naïve expectation
of an even distribution of firms (i.e. 10%) from year t-1 across the accrual deciles in year t,
and argue that under the accrual-fixation hypothesis accruals are expected to reverse, this
interpretation of the results can still be construed as subjective. It can be argued that the
evidence is still consistent with the accrual-fixation hypothesis because the majority of firms
do not reside in the same decile in two consecutive years. Moreover, as Table 3 shows, this
stickiness is related to actual growth rates, especially in high accrual firms. Thus, it is possible
that under the accrual-fixation hypothesis the expected stickiness frequency is higher than
10%. To address this, the second set of tests in the next section investigates another prediction
of the accrual-fixation hypothesis regarding the future abnormal return patterns of sticky and
non-sticky firms.
4.2
Extreme accrual stickiness and abnormal returns
Given the documented evidence of accruals’ stickiness in section 4.1, I now explore a
second implication of the accrual-fixation hypothesis. I examine whether abnormal returns
18
earned during year t to portfolios of extreme accrual firms formed at the end of year t-1, vary
depending on whether an extreme accrual firm is sticky or not in year t. Under the accrualfixation-hypothesis, sticky firms are not expected to be associated with abnormal returns.
Rather, the returns are expected to result from the reversals of accruals of extreme accrual
firms, although the degree of reversals is not stipulated by the accrual-fixation hypothesis.
Table 4 reports the strategy’s abnormal returns stratified by a five-by-five partition of
accrual groups in both year t-1 and year t.7 The table allows us to observe the sources of the
returns to an accrual strategy that is implemented at the end of year t-1. The High and Low
groups correspond to firms in extreme accrual deciles in either year t-1 or year t. The nonextreme firms are divided into three groups as follows: (i) firms in the 2nd or 3rd accrual
deciles (Mid1), (ii) firms in accrual deciles 4 through 7 (Mid2), and (iii) firms in accrual
deciles 8 or 9 (Mid3). The abnormal returns in the table are adjusted for both size and bookto-market and the t-statistics are calculated based on the standard deviation of the time-series
average of annual abnormal returns. I base my analysis on the traditional measures of working
capital accruals, calculated using the cash-flow-statement approach and corresponding to
panel B in Table 2. Similar (unreported) results emerge for working capital accruals
calculated using the balance-sheet approach. The table reports results separately for the NYSE
sample in Panel A and for the non-NYSE sample in Panel B.
7
In unreported results I construct three-by-three partitions, where extreme high and low accrual groups are
defined as in Lys and Sabino (1992). According to this classification scheme, the extreme high and low groups
each contain 27% of firms and the remaining middle group contains 46% of firms. This scheme maximizes the
power of the tests to detect abnormal stock return performance. The results using this alterative classification
scheme are similar. I chose to report five-by-five partitions because they provide more information on the
reversal patterns of accruals.
19
It is important to emphasize that the returns reported in Table 4 are not returns to
implementable trading strategies. Notice that as of the end of year t-1, investors do not have
information about the future accrual grouping to which a firm will belong at the end of year t.
Without such information, investors cannot earn the returns that are reported in Table 4.
Instead, Table 4 helps researchers identify the firms that drive the returns to an accrual
strategy. To the extent that at the end of year t-1 it is possible to predict which firms will
reverse and which firms will remain sticky in year t, the returns reported in Table 4 can be
used to potentially enhance the returns to an accrual trading strategy (See the analysis in
section 4.3).
4.2.1
High accrual firms in year t-1
I first discuss firms with high accruals in year t-1. Past studies (e.g., Sloan, 1996)
show that these firms earn negative abnormal stock returns in the following year. Table 4
investigates whether these returns differ depending on the future path that high accrual firms
take.
High-Low firms. Consider first firms that are ranked in the highest accrual group in
year t-1 and experience a complete reversal to the lowest accrual decile in year t (High-Low
firms).8 High-Low firms earn significantly negative abnormal returns of -9.53% (t-statistic of
-2.08) in the NYSE sample and -35.5% (t-statistic of -8.32) in the non-NYSE sample.9
However, firms in the lowest decile of accruals in year t may experience poor stock price
8
Throughout my discussion, I first refer to the accrual ranking in year t-1 and then to the ranking in year t. Thus,
High-Low firms are those with high accruals in year t-1 and low accruals in year t.
9
Zach (2003) shows that the returns to high accrual firms in the non-NYSE sample are higher (in absolute
values) than in the NYSE sample. The significantly higher returns earned by non-NYSE firms may be related to
the greater difficulty of short selling firms in this subsample (e.g. Mashruwala, Rajgopal and Shevlin 2006) or to
a greater difficulty of asset pricing models to capture expected returns in this group (e.g. Khan, 2007).
20
performance in that year because of true sub-par economic performance, and not because of
accruals’ reversals due to, for example, past manipulations. The reason is that accruals,
earnings and stock returns are all positively correlated (e.g. Dechow, 1994). Thus, when
evaluating whether accruals’ reversals are the source of returns to High-Low firms, it is
important to control for firm performance in year t. In effect, there needs to be a control for
the look-ahead bias that is present in Table 4, because of the foreknowledge of the accrual
rankings at time t.
To separate the effects of true poor performance from the effects of accrual reversals,
and control for the look-ahead bias, I compare the returns of High-Low firms with the returns
of two groups: Mid3-Low and Mid2-Low. Firms in both Mid3-Low and Mid2-Low have low
accruals in year t (i.e. they perform as poorly as High-Low firms in year t), and moderate
levels of accruals in year t-1, which are closest to the accrual levels of High-Low firms in year
t-1. Mid3-Low (Mid2-Low) firms earn in year t an insignificant abnormal return of -5.16%
(-4.10%) in the NYSE group and a marginally significant (significant) abnormal return of
-11.36% (-15.75%) in the non-NYSE group.
The difference in returns between the High-Low group and the two comparison groups
is reported in the right side of Table 4, under the columns titled “Hi-Mid3” and “Hi-Mid2.”
These differences (4.36% and 5.42%) are insignificant (with t-statistics of 0.56 and 1.27) in
the NYSE sample. Thus, in this sample, there is no evidence that the returns to an accrual
strategy are a result of complete reversals of high accrual firms in one year to the lowest
accrual decile in the following year. Note that the earnings performance at time t of High-Low
firms (reported in brackets) is lower than the comparison groups (0.6% vs. 2.2% and 2.0%).
21
Thus, it is possible that the benchmark return performance of Mid3-Low and Mid2-Low is too
high.
The results in the non-NYSE are different. The return differential between the HighLow group and both comparison groups is significant (24.15% and 19.76%). This difference
represents the returns to High-Low firms that are not attributable to year t’s performance.10
Instead, these returns are attributed to the high levels of accruals in year t-1 that completely
reversed to the other extreme in year t, consistent with the accrual-fixation hypothesis.
High-Mid1 and High-Mid2 firms. Consider next firms that are ranked in the high
accrual group in year t-1 and experience partial reversals to non-extreme deciles (Mid1 or
Mid2) in year t. High-Mid1 firms earn a negative and significant abnormal return of -8.36%
(t-statistic of -2.76) in the NYSE sample. Compared to firms with similar accrual levels in
year t, the differential return of High-Mid1 firms is positive and significant (9.16% relative to
Mid3-Mid1 firms and 8.53% relative to Mid2-Mid1 firms). Note that the earnings
performance in year t of High-Mid1 firms is similar (4.1%) to that of the benchmark firms
(4.1% and 3.9%), indicating that the control for year t’s performance is effective. This
evidence suggests that the returns to an accrual strategy taking short positions in high accrual
NYSE firms are mostly attributed to partial reversals from the top accrual decile in one year
to the second or third lowest deciles in the following year. The evidence in the non-NYSE
sample is slightly weaker. High-Mid1 firms earn a negative return of -14.67% (with a tstatistic of -2.46) in the non-NYSE sample. The differential return with respect to the Mid3-
10
While I try to control for the performance in year t, the earnings performance of High-Low firms (-21.7%) is
inferior to the earnings performance of Mid3-Low (-18.2%) and Mid2-Low (-15.5%). This could be a source of
the differential return that may not be fully attributed to accruals’ reversals.
22
Mid1 groups is an insignificant 3.33%, but it is significant with respect to the Mid2-Mid1
group (15.36%). Note, however that the earnings performance at time t of High-Mid1 firms is
-3.8% compares with a superior 0.1% of Mid2-Mid1 firms. Thus, the control for
contemporaneous performance may not be as effective in this group.
I next examine abnormal returns associated with reversal patterns of high accrual
firms to the middle deciles (4 through 7). High-Mid2 NYSE firms earn a negative and
significant abnormal return of -9.07% (with a t-statistic of -3.66). Controlling for the general
performance of Mid2 firms in year t, we observe that the differential return of High-Mid2
firms is insignificant (4.27% and a t-statistic of 1.31) relative to Mid3-Mid2 firms. It is
significant (7.85% and t-statistic of 2.53) relative to Mid2-Mid2 firms. Thus, there is some
evidence that part of the returns to the high-accrual firms is attributed to mild reversals to the
middle deciles of accruals in year t. This evidence is weaker in the non-NYSE sample.
High-Mid3 and High-High (sticky) firms. Finally, I look at the returns to the high
accrual decile associated with firms that either remain in the top decile (sticky firms) or move
slightly down to the 8th or 9th deciles (Mid3) in the following year. In the NYSE sample these
firms do not earn significant returns in year t (-1.62% for High-Mid3 and 0.13% for HighHigh). Further, there is no evidence that the differential returns of these firms, relative to other
firms with similar accrual performance in year t, are significantly different from zero. Thus,
there appears to be no evidence that firms with very mild reversal patterns or sticky firms are
driving the returns of the short positions of the accrual strategy in the NYSE sample. The
evidence is slightly different in the non-NYSE sample. High-Mid3 firms earn a significant
abnormal return of -9.29% (with a t-statistic of -3.25) and the differential returns between
23
them and Mid3-Mid3 or Mid2-Mid3 firms are significant (13.02% and 10.16%). This
suggests that some of the returns to an accrual strategy in the non-NYSE sample are a result
of very mild reversals from the highest accrual decile to the second and third highest deciles
of accruals. With respect to sticky firms, there is no evidence of abnormal returns in the nonNYSE sample as well.
In summary, the sources of returns to an accrual strategy that takes short positions in
high accrual firms vary by sample. In the NYSE sample, most of the returns are driven by
firms with high accruals in year t-1 that reverse to the second and third lowest deciles in year
t. In the non-NYSE sample, there is evidence of abnormal returns associated with both
complete reversals (from the highest decile to the lowest one) as well as with very mild
reversals (from the highest decile to the two next-to-highest deciles). In both samples, there is
no evidence that abnormal returns are due to sticky firms, consistent with the accrual-fixation
hypothesis.
4.2.2
Low accrual firms in year t-1
In this section, I examine firms that belong to the lowest accrual decile in year t-1.
Based on prior studies, these firms earn positive abnormal stock returns in the following year.
Low-High firms. Consider low accrual firms in year t-1 that experience a complete
reversal to the highest accrual decile in year t (Low-High firms). Low-High firms earn
insignificant abnormal returns in both the NYSE sample (2.82%) and the non-NYSE sample
(33.99%). Note that the number of firms in this group is quite small (111 over a 12-year
period in the NYSE sample). However, these returns are also insignificant in unreported
24
results, where the groupings are based Lys and Sabino’s (1992) most powerful classification
scheme, wherein the extreme portfolios contain 27% of firms.
Firms in the high decile of accruals in year t may experience good stock price
performance in year t because of true above-par economic performance. To control for this
issue, and as I did in the case of High-Low firms, I examine the differential returns between
Low-High firms and other high accrual firms in year t. In both samples, the differential
returns reported in Table 4 under the columns titled “Low-Mid1” and “Low-Mid2” are
insignificant (-4.68% and 2.15% in the NYSE sample and 24.10% and 15.99% in the nonNYSE sample). Thus, in both samples, and unlike in the case of high accruals, there is no
evidence that the returns to low accrual firms are related to complete reversals from the lowest
accrual decile in year t-1 to the highest accrual decile in year t.
Low-Mid3 and Low-Mid2 firms. Consider low accrual firms in year t-1 that experience
partial reversals to non-extreme deciles (Mid3 or Mid2) in year t. In both the NYSE and nonNYSE samples, Low-Mid3 firms earn an insignificant abnormal return (5.56% and 1.59%,
respectively). When compared to firms with similar accrual levels in year t, the differential
returns of Low-Mid3 firms are also insignificant in both samples.
I next examine abnormal returns associated with mild reversals of low accrual firms to
the middle deciles (4 through 7). Low-Mid2 NYSE firms exhibit a positive and significant
abnormal return of 8.39% (with a t-statistic of 1.94). Controlling for the general performance
of Mid2 firms in year t, we observe that the differential return of Low-Mid2 firms is positive
and marginally significant (8.05% and a t-statistic of 1.70) relative to Mid1-Mid2 firms. The
differential return is a significant 9.61% (t-statistic of 2.29) relative to Mid2-Mid2 firms. This
25
suggests that the positive abnormal returns to low accrual firms are a result of mild reversals
to the middle deciles of accruals in year t. Similar evidence is observed in the non-NYSE
sample, in which Low-Mid2 firms earn a significant abnormal return of 27.4% in year t.
Low-Mid1 and Low-Low (sticky) firms. Finally, I examine the returns of firms in the
low accrual decile that either remain in the low accrual decile in the following year (sticky
firms) or move to the second or third lowest deciles (Mid1). In the NYSE sample, Low-Mid1
firms earn a significant return of 7.50% and sticky firms earn an insignificant return of 6.02%.
The differential returns of Low-Mid1 firms, after controlling for the performance of Mid1
firms in year t, is positive and significant (6.27% with a t-statistic of 1.78 relative to Mid1Mid1 firms, and 7.32% with a t-statistic of 2.43 relative to Mid2-Mid1 firms). While sticky
firms do not earn statistically significant returns, the difference between their returns and the
returns of similar low accrual firms is positive and significant (10.83% with a t-statistic of
2.08 relative to Mid1-Low firms, and 10.13% with a t-statistic of 2.15 relative to Mid2-Low
firms). This suggests that the returns to the accrual strategy’s long positions in low accrual
firms are partially due to sticky firms and partially due to very mild reversals of low accrual
firms to the second and third lowest deciles of accruals. The evidence is similar (albeit
slightly weaker) in the non-NYSE sample. Low-Mid1 firms earn significant abnormal returns
of 8.51% (t-statistic of 1.96). The difference between this return and the returns to similar
Mid1 firms is also positive and significant (7.81% and 6.31%). Sticky Low-Low firms do not
earn a significant abnormal return, but their differential return relative to one of the
benchmark groups, Mid2-Low firms, is positive (15.17%) and significant, providing some
evidence of returns associated with sticky firms in the non-NYSE sample as well.
26
In summary, the returns to the long side of an accrual strategy that takes positions in
low accrual firms do not arise from reversals of low accrual firms to the top three accrual
deciles in the following year. This is in contrast to the evidence for high accrual firms. There
is evidence that abnormal returns arise from mild reversals to the middle accrual deciles (4
through 7). In addition, some of the returns arise from very mild reversals to the second and
third lowest deciles. Sticky low accrual firms are also associated with abnormal returns,
especially in the NYSE sample. This is inconsistent with the accrual-fixation hypothesis.
Further, unlike the evidence regarding high accrual firms, the return patterns of low accrual
firms do not vary by the sample of firms (NYSE versus non-NYSE).
4.2.3
Summary
Overall, my examination of the sources to an accrual strategy, based on the path that
extreme accrual firms take in the year following the strategy’s formation, reveals an
asymmetry in the return patterns of low and high accrual firms. In high accrual firms, the
returns to the short positions of an accrual strategy arise from accruals’ reversals. In the nonNYSE sample, some of these reversals are complete, from the highest accrual decile in one
year to the lowest accrual decile in the following year. In the NYSE sample the reversals are
less dramatic, from the high accrual decile in one year to the second and third highest deciles
in the following year. In both samples sticky firms are not associated with future abnormal
returns. Thus, the evidence for high accrual firms supports the accrual-fixation hypothesis.
In the low accrual firms, abnormal returns are not associated with either complete or
partial reversals, from the lowest decile in one year to one of the three highest deciles in the
following year. Abnormal returns are related to mild reversals and to sticky firms, inconsistent
27
with the accrual-fixation hypothesis. The documented asymmetry in the abnormal returns to
low and high accrual firms is important and may suggest that the causes of the anomaly are
different across these two extreme deciles (for example, Dechow and Ge, 2006; Kothari,
Loutskina and Nikolaev, 2007). I explore some characteristics of sticky and non-sticky firms
in the next section, to help shed light on possible causes of the accrual anomaly.
4.3
Characteristics of sticky and non-sticky extreme-accrual firms
In this section, I evaluate the characteristics of firms that are largely responsible for
the returns to the accrual strategy, based on the results in the previous section. The motivation
for this analysis is mostly exploratory. Although the main purpose of this paper is to shed
light on the accrual-fixation hypothesis, given the findings in the previous section, it is
interesting to identify, ex-ante, the types of firms that are or are not responsible for the accrual
strategy’s returns. This can help enhance the strategy’s returns. In addition, this analysis has
potential to shed light on the asymmetry between low and high accrual firms observed in the
return analysis.
Table 5 reports medians of several variables for sticky and non-sticky firms at the end
of year t-1, i.e. just before the implementation of an accrual strategy. The reported medians
are stratified by accrual groups in both year t-1 and year t. The accrual groupings are identical
to those in Table 4. For brevity, I do not report p-values of Wilcoxon differences-in-medians
tests. All the differences I discuss below are statistically significant at conventional levels.
4.3.1
High accrual firms in year t-1
I first look at high accrual firms as of year t-1. Recall that high accrual firms moving
to the Mid1 and Mid2 groups are driving most of the returns to the short positions of an
28
accrual strategy in the NYSE sample. Table 5 assesses to what extent are these firms different
from other high accrual firms, at time t-1. In terms of market capitalizations, the smallest high
accrual firms are those that completely reverse in year t (High-Low firms), with a median
market capitalization of $77.8 million. The High-Mid1 and High-Mid2 are significantly
larger, with High-Mid2 being the largest of all high accrual firms, with a median market
capitalization of $197.3 million. High-Mid1 firms have the largest book-to-market ratio (0.58)
of all high accrual firms. The sticky firms (High-High) and High-Mid3 firms have the lowest
book-to-market ratios. Sticky and High-Mid3 firms also have higher rates of growth in sales
(23% and 17%, respectively) than the firms that drive the accrual strategy returns (13.2% for
High-Mid1 and 14.3% for High-Mid2). Abnormal accruals of high accrual firms tend to
monotonically decline with year t’s accruals (with the exception of High-High firms). HighMid1 and High-Mid2 firms are somewhere in the middle of the group. Similar pattern
emerges with the Altman Z-score, although the sticky firms tend to have a higher score (lower
risk of bankruptcy) than the rest (4.74).
In the non-NYSE sample the main drivers of the strategy’s returns are High-Low and
High-Mid1 firms, although all firms except sticky firms are, to some extent, associated with
future abnormal returns. Sticky firms are the largest, with a median market capitalization of
$50.3 million, twice that of High-Low firms. This suggests that success in the accrual strategy
in this sample requires exposure to the smaller high accrual firms. In terms of book-to-market
ratio, there is no clear indication of a difference between the firms that drive the returns and
those that do not. Clear differences also do not emerge when examining the rates of growth in
sales. Sticky high accrual firms, though, have the highest growth rates (42.9%). The abnormal
29
accruals of firms that completely reverse are significantly larger than the rest of the groups
(21.3%) suggesting that at least some of the returns to high accrual firms are related to
reversals of accrual manipulations (Xie, 2001). The High-Low firms also have the lowest Zscore (4.33), indicating that they possess the highest probability of bankruptcy.
In summary, in the NYSE sample, the firms that are driving the returns to high accrual
firms (High-Mid1 and High-Mid2) do not have the lowest market capitalizations, have the
highest book-to-market ratios but not the highest rates of growth in sales. Firms that are not
associated with returns to an accrual strategy (firms in the sticky and High-Mid3 groups) have
the lowest book-to-market ratios, the highest rates of growth in sales and the highest Z-scores.
These are presumably the firms to avoid when trying to implement a successful accrual-based
strategy. In the non-NYSE sample, a successful accrual strategy is exposed to smaller firms
with higher bankruptcy risk. In addition, the drivers of returns among non-NYSE firms have
significantly higher abnormal accruals, but not the highest rates of growth in sales. This
suggests that some of the returns to the anomaly in this sample are related to reversals of past
aggressive accruals. Further, and consistent with the characteristics in the NYSE sample,
abnormal returns are associated with firms that do not possess the highest growth rates or the
lowest book-to-market ratios. This suggests that extreme growth may not be related to the
accrual anomaly in high accrual firms (Fairfield, Whisenant and Yohn, 2003).
4.3.2
Low accrual firms in year t-1
I now examine the characteristics of low accrual firms, and whether they differ for
firms that are largely responsible for the returns to an accrual strategy. Recall that the low
accrual firms that are sticky (Low-Low) or that have mild reversal patters (Low-Mid1 and
30
Low-Mid2) are the drivers of abnormal returns to an accrual strategy. Examining first the
NYSE sample in Panel A of Table 5, these three groups of firms are significantly larger than
the Low-High firms and the Low-Mid3 firms (median capitalizations of $175.4, $238.1 and
$208.0 million compared to $131.9 and $65.3 million). Thus, the strategy’s returns in this
sample are not arising from the smaller firms. In terms of book-to-market ratio, there does not
seem to be a pattern, although these firms have larger book-to-market ratios (0.53) than the
Low-High group (0.48). The drivers of returns have higher rates of growth in sales (7.1% and
5.7%). Also, they have lower (in absolute value) levels of abnormal accruals (-6.1% and
-5.7%). This is different from high accrual firms and suggests that the causes of the accrual
anomaly in the low accrual groups are different than in the high accrual group. Interestingly,
the drivers of the accrual returns in the low accrual decile have lower Z-scores (2.15 and
2.49), and, thus, higher probability of bankruptcy, than other low accrual firms.
In the non-NYSE sample (panel B of Table 5) the patterns of characteristics are not as
clear. In this sample, too, the main drivers of strategy’s returns are sticky (Low-Low) or
mildly reversing firms (Low-Mid1 and Low-Mid2). With respect to market capitalizations,
sticky Low-Low firms are the smallest (median size of $16.3 million), although the LowMid1 firms are the largest ($25.0 million). The book-to-market ratio does not reveal a pattern
that distinguishes the drivers of returns from other firms, and neither do the rates of sales
growth. Abnormal accruals are lower for these firms (-11.4% and -12.9%) than for the
reversing firms (-14.8% and -18.6%), suggesting that, similar to the NYSE firms, accrual
discretion may not be related to the causes of the accrual anomaly in low accrual firms. The
Z-score, too, does not reveal a clear pattern that distinguishes the drivers of the abnormal
31
returns from other firms, with the exception of sticky low accrual firms that possess the
lowest scores (1.45), similar to the NYSE sample.
In summary, in the NYSE sample, the low accrual firms that are driving the returns to
an accrual strategy are larger, do not have the lowest book-to-market ratios and have the
highest rates of growth in sales. These firms have the lowest (in absolute value) abnormal
accruals, suggesting that the causes for the accrual anomaly in this set may not be related to
accounting discretion, unlike in the case of high accrual firms. This result is also apparent in
the non-NYSE group. Interestingly, the low accrual firms that drive the strategy’s returns
have a higher bankruptcy risk in the NYSE sample (and to some extent in the non-NYSE
sample as well). This raises the possibility that an alternative explanation for the accrual
anomaly in low accrual firms is bankruptcy risk. While a link between bankruptcy risk and
future returns has not been established in Dichev (1998), Vassalou and Xing (2004) do find
such relation. Furthermore, Ng (2005) shows that the returns to the accrual anomaly are
related to distress risk.
5.
Conclusion
This paper directly examines one explanation for the accrual anomaly: the accrual-
fixation hypothesis. The hypothesis, first proposed in Sloan (1996), posits that the
predictability of future abnormal returns based on accrual information is a result of investors
not appropriately anticipating accruals’ reversals. In this paper, I show that firms with extreme
accruals tend to remain in extreme accrual deciles in the following year. While this fact is not
consistent with the accrual-fixation hypothesis, I show that abnormal returns to firms in the
high accrual deciles are related to various degrees of accrual reversals. Reversing firms have
32
higher abnormal accruals (especially in the non-NYSE sample), suggesting that some of the
reversals are related to prior accounting discretion. This is consistent with the accrual-fixation
hypothesis. That is, the correlation between accruals and price reversals could be a result of
investors misinterpreting, and thus over-pricing, firms with past high accruals. However, this
pattern is also consistent with an alternative story in which overvaluation creates incentives
for accounting manipulations, and thus, precedes them (Kothari, Loutskina and Nikolaev
2007). Distinguishing between the timing of upward accrual manipulations and the timing of
overvaluation is an interesting area for future research.
In contrast, I show that future abnormal returns to low accrual firms are not related to
strong degrees of reversals. Instead, sticky low accrual firms and extreme low accrual firms
that move to the second and third lowest deciles earn future positive abnormal returns. This
evidence is inconsistent with the accrual-fixation hypothesis. The characteristics of low
accrual firms that drive the returns to an accrual strategy reveal that they have a high
probability of bankruptcy. This raises the possibility that abnormal returns to low accrual
firms are related to bankruptcy risk (Khan, 2007; Ng, 2005).
33
References
Ali, A., Hwang, L., Trombley, M., 2000, Accruals and future stock returns: Tests of the naïve
investor hypothesis, Journal of Accounting, Auditing and Finance, 161-181.
Altman, E., 1968, Financial ratios, discriminant analysis and the prediction of corporate
bankruptcy, Journal of Finance, 23, 589-609.
Barth, M., Hutton, A., 2004, Analyst earnings forecast revisions and the pricing of accruals,
Review of Accounting Studies 9, 59-96.
Bradshaw, M., Richardson, S., Sloan, R., 2001, Do analysts and auditors use information in
accruals, Journal of Accounting Research 39(1), 45-74 .
Bradshaw, M., Richardson, S., Sloan, R., 2006, The relation between corporate financing
activities, analysts’ forecasts and stock returns, Journal of Accounting & Economics
42, 53-85.
Collins, D., Hribar, P., 2000, Earnings-based and accrual-based market anomalies: one effect
or two? Journal of Accounting & Economics 29, 101-123.
Collins, D., Gong, G., Hribar, P., 2003, Investor sophistication and the mispricing of accruals,
Review of Accounting Studies 8, 251-276.
Dechow, P., 1994, Accounting earnings and cash flows as measures of firm performance: The
role of accounting accruals, Journal of Accounting & Economics 18(1), 3-42.
Dechow, P., Ge, W., 2006, The persistence of earnings and cash flows and the role of special
items: Implications for the accrual anomaly, Review of Accounting Studies 11, 253296.
Desai, H., Rajgopal, S., Venkatachalam, M., 2004, Value-glamour and accrual mispricing:
One anomaly or two?, The Accounting Review 79(2), 355-385.
Dichev, I., 1998, Is the risk of bankruptcy a systematic risk?, Journal of Finance 53(3), 11311147.
Fairfield, P., Whisenant S., Yohn, T., 2003, Accrued earnings and growth: Implications for
future profitability and market mispricing, The Accounting Review 78(1), 355-371.
Francis, J., Smith, M., 2005, A reexamination of the persistence of accruals and cash flows,
Journal of Accounting Research 43(3), 413-451.
Hribar, P., Collins, D., 2002, Errors in estimating accruals: Implications for empirical
research, Journal of Accounting Research 40(1), 105-134.
Khan, M., 2007, Are Accruals Really Mispriced? Evidence from Tests of An Intertemporal
Capital Asset Pricing Model, Journal of Accounting & Economics, Forthcoming.
Kothari, S.P., Loutskina, E., Nikolaev, V., 2007, Agency Theory of Overvalued Equity as an
Explanation for the Accrual Anomaly, working paper, Massachusetts Institute of
Technology.
34
Kraft, A., Leone, A., Wasley, C., 2006, An analysis of the theories and explanations offered
for the mispricing of accruals and accrual components, Journal of Accounting
Research 44(2), 297-339.
LaFond, R., 2005, Is the accrual anomaly a global anomaly?, working paper, MIT.
Lev, B., Nissim, D., 2006, The persistence of the accrual anomaly, Contemporary Accounting
Research 23(1), 193-226.
Levi, S., 2006, Voluntary disclosure of accruals in preliminary earnings announcements and
the pricing of accruals, Review of Accounting Studies, Forthcoming.
Lys, T., Sabino, J., 1992, Research Design Issues in Grouping-Based Tests Journal of
Financial Economics 32(3), 355-387.
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 &
Economics 42, 3-33.
Mishkin, F., 1983, A rational expectations approach to macroeconomics, Chicago, IL:
University of Chicago Press.
Ng, J., 2005, Distress risk information in accruals, working paper, University of Pennsylvania.
Pincus, M., Rajgopal, S., Venkatachalam, M., 2007, The accrual anomaly: International
evidence, The Accounting Review 82(1), 169-203.
Richardson, S., Sloan, R., Soliman, M., Tuna, I., 2005, Accrual reliability, earnings
persistence and stock prices, Journal of Accounting & Economics 39(3), 437-485.
Sloan, R., 1996, Do stock prices fully reflect information in accruals and cash flows about
future earnings? The Accounting Review 71, 289-315.
Thomas, J., Zhang, H., 2002, Inventory changes and future returns, Review of Accounting
Studies, 7, 163-187.
Vassalou, M., Xing, Y., 2004, Default risk in equity returns, Journal of Finance 59(2), 831868.
Xie, H., 2001, The mispricing of abnormal accruals, Accounting Review, 76(3) 357-373.
Zach, T., 2003, Inside the accrual anomaly, doctoral dissertation, University of Rochester.
35
Table 1. Summary Statistics. The table reports medians and (means) of various variables partitioned
by deciles of total accruals calculated using the cash flow statement approach. The sample consists of
all NYSE/AMEX firms with available data during the 1988-1999 period.
NI
CFO
ACC
BM
SIZE
RET12
ROA
Sales
growth
Low
2
3
4
5
6
7
8
9
High
0.02
(-0.00)
0.22
(0.23)
-0.15
(-0.18)
0.04
(0.03)
0.18
(0.17)
-0.10
(-0.10)
0.05
(0.04)
0.16
(0.17)
-0.07
(-0.07)
0.05
(0.04)
0.15
(0.15)
-0.06
(-0.06)
0.05
(0.05)
0.14
(0.15)
-0.04
(-0.04)
0.05
(0.05)
0.13
(0.13)
-0.03
(-0.03)
0.05
(0.06)
0.12
(0.12)
-0.02
(-0.02)
0.05
(0.05)
0.10
(0.11)
0.00
(-0.00)
0.06
(0.07)
0.09
(0.09)
0.02
(0.03)
0.07
(0.08)
0.02
(0.01)
0.10
(0.13)
0.55
0.54
0.53
0.55
0.55
0.55
0.55
0.51
0.48
0.45
(0.70) (0.67) (0.66) (0.67) (0.62) (0.63) (0.63) (0.62) (0.59) (0.54)
153.02 388.41 494.77 621.20 726.15 607.40 513.39 354.73 304.01 166.80
(1,684) (2,877) (3,707) (3,102) (3,862) (3,259) (3,281) (2,267) (1,881) (968)
3.2%
7.5%
7.7%
8.3% 10.8% 10.5% 11.5% 10.4% 9.3% 16.6%
(11.4%) (15.7%) (13.5%) (13.1%) (16.0%) (16.8%) (16.6%) (17.9%) (18.5%) (35.4%)
5.5%
8.2%
8.7%
8.9%
9.2%
9.0%
9.3%
9.6% 10.5% 10.5%
(4.0%) (7.2%) (8.9%) (8.9%) (9.6%) (9.1%) (9.9%) (9.8%) (10.5%) (12.3%)
1.9%
4.9%
5.4%
5.3%
6.5%
7.0%
8.0% 10.3% 13.4% 23.4%
(52.0%) (11.8%) (20.5%) (10.2%) (12.3%) (13.0%) (15.4%) (21.1%) (20.6%) (90.5%)
LTG
13.50
12.00
12.00
12.00
12.00
12.00
12.50
14.00
15.00
18.00
(14.61) (13.78) (13.32) (12.73) (12.51) (12.45) (13.19) (14.88) (16.34) (19.30)
Z
2.72
(3.60)
2.81
(3.80)
2.90
(3.72)
2.83
(3.54)
2.84
(3.66)
2.81
(3.55)
3.04
(4.13)
3.32
(5.08)
3.90
(6.05)
3.89
(6.26)
Variable definitions (NI, CFO and ACC are scaled by total assets):
NI: Income before extraordinary items (Compustat # 18) scaled by total assets.
CFO: Operating income after depreciation (#178) – ACC.
ACC (Δcurrent assets(#4)-Δcash(#1)-Δcurrent liabilities(#5)+ Δdebt in current liabilities(#34)-depreciation(#14)).
BM: Book-to-market ratio (#60/Size).
SIZE: Market capitalization at end of fiscal-year (#25*#199).
RET12: Annual raw stock returns over the firm’s fiscal year.
ROA: return on assets (#178 / #6).
Growth in Sales (#12 / lag(#12) – 1).
LTG: Long-term sales growth forecasts from IBES (in percentage points).
Z: Altman’s (1968) Z-score. 1.2*(#179/ #6)+1.4*(#36/ #6)+3.3*(#18+#16+#15)/ #6+0.6*size/ #181+#12/ #6.
36
Table 2. Transition matrices of accrual deciles. This table presents transition matrices of accrual deciles in year t-1 to accrual deciles in year t. The columns
correspond to past accrual rankings and the rows correspond to current accrual rankings. The table also reports the delisting frequencies of firms based on the
accrual decile in year t-1. Delisting is based on CRSP classification. Panels A and B report working capital accruals measured based on the balance sheet and the
cash flow statement, respectively. All accruals are deflated by average total assets. Firms belong to the NYSE/AMEX universe. The sample period is 1970-1999
in Panel A and 1988-1999 in Panel B.
Past (t-1)
Current (t)
Bottom
2
Bottom
2
3
4
5
6
7
8
9
Top
26.4%
14.6%
10.0%
6.9%
5.8%
4.8%
5.9%
7.4%
7.9%
10.3%
100%
6.1%
14.5%
18.3%
14.9%
10.8%
8.5%
7.4%
7.4%
6.3%
6.1%
5.8%
100%
4.0%
31.7%
17.1%
9.7%
6.5%
5.1%
5.1%
5.4%
5.3%
6.3%
7.8%
100%
5.7%
15.2%
20.1%
15.6%
9.6%
9.0%
6.5%
7.0%
5.5%
6.6%
4.9%
100%
3.0%
Delist
Bottom
2
3
4
5
6
7
8
9
Top
Delist
3
4
5
6
7
8
Panel A: Working capital accruals based on balance-sheet
9.1%
6.2%
5.0%
4.9%
5.6%
6.5%
14.9%
10.3%
8.1%
7.3%
6.5%
6.4%
15.6%
13.8%
10.2%
9.2%
8.2%
7.3%
13.3%
15.9%
14.1%
10.9%
9.2%
7.8%
11.3%
13.9%
14.6%
13.8%
11.7%
9.5%
8.5%
11.8%
13.9%
16.1%
14.1%
10.9%
8.4%
9.6%
11.9%
13.0%
14.4%
13.4%
7.6%
8.2%
9.1%
10.8%
13.3%
14.3%
6.1%
6.6%
7.6%
8.4%
10.4%
14.3%
5.1%
3.6%
5.5%
5.7%
6.5%
9.8%
100%
100%
100%
100%
100%
100%
3.4%
3.3%
2.6%
2.6%
2.9%
3.5%
Panel B: Working capital accruals based on cash-flow statement
10.1%
8.0%
4.7%
4.4%
4.0%
4.3%
13.9%
9.2%
8.5%
7.0%
4.9%
6.1%
16.1%
13.5%
11.0%
9.2%
8.3%
7.4%
16.3%
15.9%
15.4%
9.4%
8.8%
7.6%
10.7%
13.3%
15.9%
14.9%
10.4%
8.6%
7.8%
12.8%
12.3%
16.2%
15.6%
10.3%
8.0%
9.7%
11.3%
13.6%
15.5%
15.2%
7.4%
7.2%
9.0%
10.5%
13.8%
17.9%
5.6%
6.7%
7.4%
8.4%
11.7%
13.0%
4.2%
3.5%
4.6%
6.3%
7.1%
9.7%
100%
100%
100%
100%
100%
100%
3.0%
3.9%
3.4%
4.0%
3.8%
4.0%
9
Top
Missing
8.9%
7.3%
7.1%
6.3%
7.3%
7.4%
10.6%
14.2%
16.7%
14.3%
100%
2.9%
12.8%
6.9%
5.3%
5.4%
4.9%
6.2%
6.9%
9.4%
15.1%
27.1%
100%
2.5%
10.6%
9.9%
8.6%
9.2%
8.3%
9.0%
8.2%
9.7%
11.0%
15.6%
100%
6.7%
7.6%
5.7%
7.1%
6.9%
8.7%
9.6%
14.3%
19.0%
14.6%
100%
2.9%
9.7%
6.2%
5.0%
5.4%
5.8%
5.8%
5.6%
9.7%
16.0%
30.7%
100%
2.3%
11.0%
10.0%
8.9%
8.4%
9.6%
9.2%
9.5%
9.9%
10.0%
13.6%
100%
37
Table 3. Factors associated with stickiness. This table reports the medians, as of the end of
year t, of the length (in days) of the operating cycle and the actual rates of growth in sales.
These are reported for each accrual cell constructed based on a two-way partition of accruals
in year t-1 and year t. Each year, firms are assigned to accrual deciles based on their deflated
accruals. Low firms belong to the lowest accrual decile, High firms belong to the highest
accrual decile and middle firms belong to the eight remaining deciles as follows: Mid1 - firms
in the 2nd or 3rd accrual deciles, Mid2 - firms in accrual deciles 4 through 7, and Mid3 - firms
in accrual deciles 8 and 9. Thus, the Low-Low cell contains firms that belong to the low
accrual decile in both year t-1 and year t. Accruals are calculated using the statement of cash
flows. Sample period is 1988-1999. Results are reported separately for NYSE/AMEX firms
(Panel A) and non-NYSE/AMEX firms (Panel B).
t-1
t
Low
Mid1
Mid2
Mid3
High
Panel A: NYSE-AMEX firms
Low
Mid1
Mid2
Mid3
High
84.3
78.2
106.5
114.5
118.2
Low
Mid1
Mid2
Mid3
High
6.5%
7.6%
5.2%
7.4%
9.7%
Operating cycle (days)
92.2
110.7
129.3
82.1
99.9
125.0
95.9
97.2
128.7
115.6
126.2
137.6
120.8
137.3
143.6
Year-over-year growth in sales
4.2%
2.5%
2.7%
6.7%
5.6%
5.6%
6.5%
6.3%
8.1%
9.2%
8.4%
10.6%
9.8%
12.4%
16.3%
138.1
151.3
137.6
144.6
147.9
-0.1%
8.1%
8.3%
13.6%
22.5%
Panel B: non- NYSE-AMEX firms
Low
Mid1
Mid2
Mid3
High
107.6
101.3
121.0
109.2
150.2
Low
Mid1
Mid2
Mid3
High
1.7%
2.3%
7.9%
12.0%
12.7%
Operating cycle (days)
110.2
117.9
150.5
80.6
108.2
139.8
100.2
107.6
141.4
130.5
136.2
155.5
135.4
147.5
148.9
Year-over-year growth in sales
-2.0%
1.3%
-2.7%
8.7%
7.3%
3.7%
9.1%
9.0%
12.1%
11.7%
14.8%
20.2%
20.3%
24.4%
29.0%
185.6
153.7
152.8
156.9
161.6
-3.6%
8.9%
14.6%
24.7%
38.3%
38
Table 4. Panel A. Origins of returns to an accrual strategy. This table reports average
abnormal returns, adjusted for both size and book-to-market, earned during year t (starting
four months after the end of the fiscal year) for firms belonging to each accrual cell based on
the two-way partitions of accruals in year t-1 and year t. Each year, firms are assigned to
accrual deciles based on their deflated accruals. Low firms belong to the lowest accrual
decile, High firms belong to the highest accrual decile and middle firms belong to the eight
remaining deciles as follows: Mid1 - firms in the 2nd or 3rd accrual deciles, Mid2 - firms in
accrual deciles 4 through 7, and Mid3 - firms in accrual deciles 8 or 9. On the right side, the
table reports the difference in returns between groups of firms across the columns in each
row, i.e. holding constant the accrual performance in year t. T-statistics are in parentheses and
are calculated based on the standard deviation of the time-series average of annual abnormal
returns. The number of observations (N) is reported in italics and bold characters. In brackets,
the table reports earnings before extraordinary items in year t (data#18) divided by assets.
Accruals are calculated using the statement of cash flows. Sample period is 1988-1999.
Results are reported separately for NYSE/AMEX firms (Panel A) and non-NYSE/AMEX
firms (Panel B).
Difference in returns across columns
t-1
Low
Mid1
Mid2
Mid3
High
t
LowMid1
LowMid2
HiMid2
HiMid3
Panel A: NYSE-AMEX firms
Low
6.02%
(1.50)
436
[2.5%]
-4.81%
(-1.18)
374
[1.6%]
-4.10%
(-1.01)
309
[2.0%]
-5.16%
(-0.93)
161
[2.2%]
-9.53%
(-2.08)
142
[0.6%]
10.83%
(2.08)
10.13%
(2.15)
5.42%
(1.27)
4.36%
(0.56)
Mid1
7.50%
(2.77)
382
[4.1%]
1.23%
(0.68)
974
[4.7%]
0.18%
(0.11)
1,060
[3.9%]
0.80%
(0.35)
397
[4.1%]
-8.36%
(-2.76)
163
[4.1%]
6.27%
(1.78)
7.32%
(2.43)
8.53%
(2.01)
9.16%
(1.87)
Mid2
8.39%
(1.94)
305
[4.8%]
0.34%
(0.20)
1,102
[5.1%]
-1.22%
(-0.86)
3,144
[4.6%]
-4.80%
(-2.20)
1,086
[5.1%]
-9.07%
(-3.66)
331
[5.4%]
8.05%
(1.70)
9.61%
(2.29)
7.85%
(2.53)
4.27%
(1.31)
Mid3
5.56%
(0.87)
156
[3.6%]
2.39%
(0.75)
360
[5.3%]
-1.60%
(-1.23)
1,093
[5.6%]
-4.24%
(-1.39)
937
[6.6%]
-1.62%
(-0.62)
378
[6.7%]
3.18%
(0.54)
7.17%
(1.22)
0.01%
(0.00)
-2.62%
(-0.57)
High
2.82%
(0.39)
111
[5.1%]
7.51%
(1.69)
136
[5.5%]
0.68%
(0.18)
319
[6.8%]
-1.81%
(-0.35)
355
[7.3%]
0.13%
(0.03)
445
[7.4%]
-4.68%
(-0.56)
2.15%
(0.33)
0.55%
(0.11)
-1.94%
(-0.35)
39
Table 4. Panel B.
Panel B: Non NYSE-AMEX firms
Difference in returns across columns
t-1
Low
Mid1
Mid2
Mid3
High
t
Mid1Low
Mid2Low
HiMid2
HiMid3
Low
-0.57% -5.93% -15.75% -11.36% -35.50%
(-0.10) (-0.77) (-3.21) (-1.73) (-8.32)
458
480
519
303
219
[-16.7%] [-12.5%] [-15.5%] [-18.2%] [-21.7%]
5.35%
(0.84)
15.17%
(2.10)
19.76%
(2.59)
24.15%
(3.25)
Mid1
8.51%
(1.96)
458
[-3.4%]
2.20%
(0.62)
1,305
[1.1%]
0.69%
(0.18)
1,555
[0.1%]
-11.33% -14.67%
(-2.77) (-2.46)
621
283
[-2.3%] [-3.8%]
6.31%
(1.71)
7.81%
(2.08)
15.36%
(2.14)
3.33%
(0.48)
Mid2
27.40%
(2.74)
423
[-1.3%]
12.09%
(2.23)
1,514
[2.7%]
-1.00%
(-0.92)
4,589
[3.3%]
-8.87% -11.42%
(-3.37) (-1.91)
1,649
486
[3.5%] [2.3%]
15.30%
(1.29)
28.40%
(2.98)
10.42%
(1.77)
2.56%
(0.58)
Mid3
1.59%
(0.20)
220
[0.7%]
12.95%
(1.43)
533
[2.8%]
0.88%
(0.36)
1,567
[4.6%]
3.73%
(1.21)
1,217
[6.4%]
-9.29%
(-3.25)
568
[5.4%]
-11.36%
(-1.33)
0.71%
(0.09)
10.16%
(2.79)
13.02%
(2.74)
High
33.99%
(1.48)
160
[1.4%]
9.89%
(1.98)
201
[3.3%]
18.00%
(3.94)
373
[5.2%]
11.48%
(2.80)
476
[6.2%]
7.22%
(1.30)
497
[7.9%]
24.10%
(1.18)
15.99%
(0.67)
10.78%
(1.33)
4.26%
(0.69)
40
Table 5. Panel A. Characteristics of sticky and non-sticky firms at the end of year t-1.
This table reports the medians, as of the end of year t-1, of several variables for each accrual
cell constructed based on a two-way partition of accruals in year t-1 and year t. Each year,
firms are assigned to accrual deciles based on their deflated accruals. Low firms belong to the
lowest accrual decile, High firms belong to the highest accrual decile and middle firms belong
to the eight remaining deciles as follows: Mid1 - firms in the 2nd or 3rd accrual deciles, Mid2 firms in accrual deciles 4 through 7, and Mid3 - firms in accrual deciles 8 or 9. Thus, the
Low-Low cell contains firms that belong to the low accrual decile in both year t-1 and year t.
Accruals are calculated using the statement of cash flows. Sample period is 1988-1999.
Statistics are reported separately for NYSE/AMEX firms (Panel A) and non-NYSE/AMEX
firms (Panel B).
t-1
t
Low
Mid1
Mid2
Mid3
High
Panel A: NYSE-AMEX firms
Low
Mid1
Mid2
Mid3
High
175.5
238.1
208.0
131.9
65.3
Low
Mid1
Mid2
Mid3
High
0.53
0.53
0.54
0.53
0.48
Low
Mid1
Mid2
Mid3
High
7.1%
5.7%
2.6%
2.9%
3.2%
Low
Mid1
Mid2
Mid3
High
-6.1%
-5.7%
-7.0%
-8.4%
-12.9%
Low
Mid1
Mid2
Mid3
High
2.15
2.49
2.83
3.13
3.54
Market Capitalization
226.9
110.8
553.8
262.9
789.8
495.2
477.2
407.2
128.2
178.8
Book-to-market ratio
0.57
0.57
0.66
0.50
0.55
0.54
0.52
0.55
0.50
0.43
0.50
0.48
0.53
0.53
0.49
Year-over-year growth in sales
6.6%
7.8%
7.0%
7.8%
6.4%
10.1%
6.3%
6.7%
9.3%
6.2%
8.6%
10.6%
7.6%
10.2%
14.1%
Abnormal accruals (Modified Jones)
-2.0%
0.0%
3.3%
-1.7%
0.7%
3.5%
-2.5%
0.4%
2.8%
-4.0%
-0.5%
2.8%
-5.3%
-0.8%
2.7%
Altman’s (1968) Z score
2.72
3.08
3.28
2.88
3.00
3.62
2.91
2.74
3.45
3.53
3.43
3.79
4.07
3.91
4.34
278.3
558.7
565.9
208.0
90.6
77.8
109.3
197.3
172.8
133.4
0.53
0.58
0.49
0.44
0.41
14.5%
13.2%
14.3%
17.0%
23.0%
11.8%
10.3%
8.2%
7.3%
9.3%
3.90
4.11
4.02
4.16
4.74
41
Table 5. Panel B.
t-1
t
Low
Mid1
Mid2
Mid3
High
Panel B: Non NYSE-AMEX firms
Low
Mid1
Mid2
Mid3
High
16.6
25.0
23.6
20.3
20.6
Low
Mid1
Mid2
Mid3
High
0.35
0.43
0.44
0.36
0.30
Low
Mid1
Mid2
Mid3
High
2.3%
1.1%
3.8%
2.0%
1.5%
Low
Mid1
Mid2
Mid3
High
-11.4%
-12.9%
-12.9%
-14.8%
-18.6%
Low
Mid1
Mid2
Mid3
High
1.45
2.15
2.15
2.52
1.82
Market Capitalization
24.0
29.2
41.4
31.1
71.3
61.6
52.6
57.8
31.8
50.0
Book-to-market ratio
0.52
0.57
0.52
0.59
0.59
0.54
0.59
0.58
0.50
0.50
0.52
0.43
0.38
0.44
0.39
Year-over-year growth in sales
4.7%
10.0%
19.2%
10.2%
10.7%
15.3%
8.5%
10.6%
17.8%
10.4%
12.8%
21.7%
10.1%
14.6%
29.4%
Abnormal accruals (Modified Jones)
-4.9%
1.6%
7.4%
-6.1%
0.4%
6.1%
-6.1%
0.4%
6.1%
-7.9%
-0.6%
6.2%
-9.0%
-1.8%
4.9%
Altman’s (1968) Z score
2.71
3.07
3.79
3.40
3.61
4.40
3.40
3.61
4.40
3.54
4.38
4.780
3.98
4.59
4.86
22.5
39.7
44.1
30.1
19.5
25.7
31.8
42.3
49.0
50.3
0.34
0.42
0.42
0.36
0.33
30.3%
28.6%
29.9%
34.1%
42.9%
21.3%
16.1%
16.1%
16.0%
17.9%
4.33
4.91
4.91
5.21
5.73