Business Model Shocks and Abnormal Accrual Models1
Edward Owens
Joanna Shuang Wu
Jerold Zimmerman
Simon School of Business, University of Rochester, Rochester, NY 14627
June 2013
Abstract
Insights from economics challenge the development of better specified accrual models. Since
rent seeking firms pursue differentiated business strategies, firms in the same industry have
heterogeneous accrual generating processes. Moreover, technological innovation, regulatory
changes, and entry of new firms (i.e., business model shocks) frequently force existing firms to
revise their extant business models. We present evidence that such business model shocks are
widespread, propagate through several years of financial statements, reduce accrual models’
goodness of fit, and result in unrealistically large unsigned “abnormal” accruals. There is a
spillover effect among firms in the same industry in that one firm’s abnormal accrual is affected
by business model shocks experienced by the other firms in the industry. We show that business
model shocks not only add noise to abnormal accruals, but can also introduce biases into both
unsigned and signed discretionary accruals. The effect of business model shocks on estimating
abnormal accruals cannot be eliminated by excluding a few major corporate events, such as
mergers and acquisitions, as is common in the literature. We conclude that further refinements of
accrual models likely will prove difficult due to the random, diverse, and persistent affects
business model shocks have on firms’ accrual generating processes.
1
We gratefully acknowledge the financial support provided by the Simon School at the University of Rochester and
the comments from Dan Amiram, Dan Collins, Mark Evans, Shane Heitzman, Jerry Warner, Charles Wasley and
seminar participants at the University of Rochester and the Penn State Accounting Research Conference.
1.
Introduction
Since Healy (1985), the accounting literature has pursued an accruals-based measure of
accounting discretion. Jones (1991) introduced a methodology to extract “discretionary accruals”
from total accruals by modeling “non-discretionary accruals” as a function of firm
characteristics, with the residuals then interpreted as "discretionary" or "abnormal" accruals.
Despite significant effort seeking better specified accrual models, many concerns regarding
accrual model misspecification, the implausibly large magnitude of discretionary accruals,
power, and bias remain. A couple of representative quotations illustrate the lack of progress:1
“The (positive correlation between measures of abnormal accruals and total accruals)
raises concerns about whether abnormal accruals reflect accounting distortions or
whether they instead are the result of poorly specified accruals models and include a
component that measures fundamental performance.” (Dechow et al. 2010 p. 358)
“A powerful cocktail of authors’ strong priors, strong ethical and moral views, limited
knowledge of the determinants of accruals in the absence of manipulation, and
willingness to ignore correlated omitted variables in order to report a result, seems to
have fostered a research culture that tolerates grossly inadequate research designs and
publishes blatantly false positives.” (Ball 2013).
As a profession, we have very limited theory of the accrual generating process and how
fundamental firm performance maps into discretionary accruals (McNichols 2000; Dechow et al.
2010). In the face of these problems, several authors encourage research to develop improved
discretionary accrual models (e.g., Holthausen et al. 1995; Guay et al. 1996; Dechow et al.
2010), and there is a general belief that such improvements can indeed be discovered. In this
study, based on research in economics we offer more texture as to the theoretical challenges
accounting researchers face in trying to develop better specified discretionary accruals models.2
We argue that certain key insights from economics challenge the fundamental assumptions
underlying “normal” (non-discretionary) accruals, and hence innately thwart the ability of
researchers to parse discretionary accruals from total accruals.
Estimation of either cross-sectional or firm-specific accrual models relies on two
assumptions: firm stationarity (i.e., firms should have reasonably stable accrual generating
1
Also see Bernard and Skinner (1995), Dechow et al. (1994), Subramanyam (1996), Guay, et al. (1996), Ball and
Shivakumar (2008), and Dichev et al. (2012).
2
We rely on research from industrial organization, finance, and strategy. For parsimony, we refer to these literatures
broadly as “economics,” and review these literatures in section 2.1.
1 processes) and intra-industry homogeneity (i.e., industry peers should have similar accrual
generating processes). 3 However, a key economic principle dictates that a firm should seek rents
by developing a unique, sustainable market niche that differentiates its business strategy from
other firms (Brickley and Zimmerman 2010). Profit maximization via innovation and strategy
differentiation presents an inherent challenge to the intra-industry homogeneity assumption.
Further, technological innovations, regulatory changes, and entry by new (or existing) firms
(what we call “business model shocks”) cause firms to frequently alter their existing business
strategies. These frequent business model revisions/shocks present an inherent challenge to the
firm stationarity assumption, and likewise decrease intra-industry homogeneity if the shock
causes further strategy differentiation.4
The central issue we examine in this study is the extent to which business model shocks
affect accrual models and the residuals from these models (discretionary accruals). To illustrate
the complexities involved, consider that firms respond differently to a given random shock to
their business strategies and it can take several years for firms to adjust to the shock. A given
shock will cause some firms’ total accruals to increase and other firms’ total accruals to decrease
as managers change their business strategies, cash operating cycles, and their accrual generating
processes. Since firms in the same industry react differently to common business model shocks,
including an instrument for a shock that occurs in year t in estimating a cross-sectional accrual
model in year t only captures the average effect of the shock for all firms in the industry-year t.
Moreover, these shocks likely impact several years of operations as managers react to the shock
by implementing new strategies (e.g., Gerakos and Kovrijnykh 2012). Including an instrument
for the business model shock fails to capture how the shock in year t affects accruals in years t+1
and t+2 for all firms in the industry. If firm-specific accrual models are estimated, a business
model shock in year t can affect accruals in years t, t+1, and t+2 in different and unpredictable
ways as this firm and other firms in the same market dynamically adjust their business strategies.
3
Dopuch et al. (2011) conclude that substantial heterogeneity exists in accrual generating processes in some
industries and that this heterogeneity generates “a large noise component in abnormal accruals.”
4
Cross-sectional accrual models rely not only on intra-industry homogeneity but also the firm stationarity
assumption. For example, if large business model shocks last year caused large accruals last year and these accruals
reverse, then the cross-sectional model this year will be estimated with less precision. Likewise, firm-specific timeseries accrual models rely on intra-industry homogeneity in addition to firm stationarity. Large business model
shocks reduce intra-industry homogeneity, which affects the nature of competition in the industry. Firm-specific
accrual models will be estimated less precisely because the accrual generating process is affected by the changing
industry competition.
2 For example, suppose a new technological innovation causes one firm (either with a patent or
complementary technology) to introduce a new product with a different accrual generating
process than its existing products (i.e., Amazon enters the e-reader market with its Kindle).
Unless the researcher can specify ex ante the evolution of this firm’s accrual generating process,
the time-series regression for this firm’s normal accruals fails to correctly capture how the
accrual generating process changes dynamically. Absent such knowledge of the evolution, some
“non-discretionary” accruals will be misclassified as “discretionary” accruals.
Because the same business model shock can increase some firms’ value and decrease
others’ and firms react to the same shock with different strategy responses, we focus on unsigned
abnormal accruals in this study, although in supplemental tests (Section 4.6.3) we also
investigate the implications for signed abnormal accruals. Unsigned abnormal accruals have been
used as instruments for earnings management (when the research study does not have a signed
prediction about earnings management) and as an instrument for “earnings quality.” Firms
generally do not disclose intentional changes to their business models as shocks occur (likely due
to competitive reasons) and rarely disclose immediately shocks that adversely impact their
business strategies. The effect of these adverse shocks on firms’ strategies usually gets reflected
in the financial statements with a lag. Hence, we use a measure of large, firm-specific abnormal
returns as our primary indicator for the presence of business model shocks.
We begin by documenting that 55% of all firm-years from 1988 to 2010 have at least one
monthly unsigned abnormal stock return of 20% or more. Large abnormal returns reflect both
realizations of past activities and changes in expectations of future activities (including changes
in the firm’s business model). Moreover, large abnormal returns provide signals to managers
about the efficacy of their and their competitors’ business strategies. Hence, large abnormal
returns capture both changes in growth rates of the firm’s current business model as well as
expected revisions to the firm’s business model. To help validate this return-based shock proxy,
we next identify firm-years with large acquisitions, four-digit SIC code changes, and large
restructuring charges or special items. Not surprisingly, the incidence of these large operational
shocks is statistically and economically associated with large contemporaneous and lagged
unsigned abnormal returns. Large unsigned abnormal returns appear to be capturing large
business model shocks, not just changes in growth rates of firms with stable business models.
Moreover, our finding that one- and two-year lagged unsigned abnormal returns are correlated
3 with contemporaneous operational shocks suggests that business model shocks can affect several
years of financial statements, including accruals.
To address the primary focus of this paper, i.e., the impact of business model shocks on
estimation of abnormal accruals, we compute traditional abnormal accruals using the Jones
(1991) model via annual industry cross-sectional estimation, along with modifications to the
original Jones model suggested by Ball and Shivakumar (2006) (i.e., a nonlinear accrual model)
and Kothari et al. (2005) (i.e., with performance control). Consistent with prior literature, we find
that for the median firm in our sample, the Jones model produces unsigned abnormal accruals
that are 67% of unsigned net income, which implies roughly one part management discretion and
12 parts “noise” assuming auditors apply a materiality threshold of a 5% net income (62%/5%).
We document that the existence of business model shocks materially affects the
estimation of abnormal accruals for all three accrual models. Specifically, contemporaneous and
lagged business model shocks are positively correlated with large unsigned abnormal accrual
estimates from cross-sectional accrual model estimations. These results suggest that unsigned
abnormal accruals capture shocks to firms’ business models, and therefore likely overstate the
existence of unsigned discretionary earnings management. Further, we document a spillover
effect on a firm’s unsigned abnormal accruals from business model shocks experienced by the
other firms in the same industry-year due to the effects of these shocks on the overall fit of the
accrual model. We also show that the variability in firms’ cash operating cycles is positively
correlated with large operational shocks and large unsigned abnormal returns. This finding
establishes a direct causal link between our proxy for business model shocks and firms’ accruals
generating processes.
Next, we examine how the inclusion of firm-year observations identified as having large
business model shocks (proxied by the existence of a large unsigned monthly abnormal stock
return during the year or the previous two years) impacts the magnitude of unsigned abnormal
accruals. As more “contaminated” observations (i.e., those with large business model shocks) are
included in the industry-year Jones model estimation, the mean unsigned abnormal accrual
increases from 3.3% of total assets when all contaminated observations are removed to 8.1% of
total assets when all contaminated observations are included. Moreover, including only a
relatively small number of contaminated observations significantly affects the outputs of the
4 abnormal accrual model. In other words, the magnitude of unsigned abnormal accruals remains
large unless a substantial number of contaminated observations are removed.
To directly investigate the effect of business model shocks on empirical inferences we
conduct simulation tests. Our results suggest that when studying the relationship between
“earnings quality” (proxied by unsigned abnormal accruals) and a partitioning variable, the null
of no relation between the two variables is over rejected even when modest correlation exists
between the partitioning variable and business model shocks. The results hold after controlling
for various firm characteristics including the operating volatility measures in Hribar and Nichols
(2007). Furthermore, the above results hold for both firms’ own business model shocks and the
shocks experienced by the other firms in the same industry-year, again confirming a spillover
effect. We further utilize the setting examined by Bharath et al. (2008) to demonstrate that
relations between abnormal accruals and dependent variables of interest (e.g., cost of debt) may
capture effects of operational risk, rather than effects of discretionary accounting choices.
Specifically, we show that a statistically significant association between unsigned abnormal
accruals and cost of private debt only obtains for observations where business model shocks
exist. Finally, we examine how business model shocks affect inferences regarding earnings
management based on signed abnormal accruals. There, our simulation tests show that business
model shocks can exacerbate the problem of over-rejecting the null of no earnings management
in various subsamples based on firm characteristics such as market-to-book, firm size and sales
growth.5
Our paper makes several contributions. First, extant literature recognizes that normal
accruals and earnings quality depend on both the firm’s fundamental performance and on the
accounting system that measures that performance. Dechow et al. (2010 p. 345) conclude that
“we have relatively little evidence about how fundamental performance affects earnings quality.”
Our analysis provides an economics-based framework and associated evidence on how one
driver of fundamental performance, business model shocks, affects abnormal accruals and hence
those earnings quality proxies that rely on reported earnings. Second, researchers point to several
methods for improving discretionary accrual models, such as using the balance sheet approach to
5
In our primary empirical specifications we estimate accrual models using total accruals, but our inferences remain
intact when using working capital accruals. Further, although we do not impose any explicit size filters in our
sample, our results continue hold when we exclude smaller firms (for example, the bottom 10% of firms with market
capitalization of less than $10 million, or the bottom 50% of firms with market cap of less than $250 million,
alternatively).
5 compute accruals (Collins and Hribar, 2002) and reducing various other forms of model
misspecification (e.g., Kothari et al., 2005, Ball and Shivakumar, 2006). Our analysis suggests
that such attempts are likely to prove ineffective, as they do not purge abnormal accruals of nondiscretionary components that arise from pervasive business model shocks. The reason for our
pessimism is that these shocks occur randomly, their effects on accruals can persist over several
years, observable proxies for these shocks are very noisy, and firms in the same industry respond
to shocks in different ways and with different implications for abnormal accruals. Better
understanding of the limitations of accrual models will allow researchers to construct more
compelling tests involving earnings management and earnings quality.
The remainder of the paper proceeds as follows: in Section 2 we discuss related literature
and our empirical conjectures. Section 3 describes our sample selection. Section 4 presents our
research design and main empirical findings. Section 5 concludes.
2.
Related literature and empirical conjectures
This section first summarizes the economics, strategy and finance literatures that question
the firm stationarity and intra-industry homogeneity assumptions underlying the estimation of
accrual models. Then we summarize related accounting studies that have recognized and tried to
address the misspecification problems in accrual models. Finally, we derive the empirical
conjectures that follow from these literature reviews
2.1.
Firm stationarity and intra-industry homogeneity assumptions - economic insights
An early, influential concept in economics was Schumpeter’s (1942) "creative
destruction," whereby entry by entrepreneurs capitalizing on new technology or regulatory
changes disrupts existing markets and destroys the value of established companies (e.g., digital
imaging destroying traditional photography). Schumpeter’s “creative destruction” forces both
incumbent firms and new entrants to modify their business strategies in response to entry and to
adjustments in business models by competitors. If the rate of technological and/or regulatory
change is high, creative destruction casts doubt on the firm stationarity assumption.
Successful firms search for unique market niches that differentiate and shield themselves
from competition by other firms in the industry. Porter (1979) describes the various competitive
forces affecting business strategies (i.e., threat of entry, bargaining power, and current
contestants revising their strategies). So, changes in any of these competitive forces, not just new
technologies, generate shocks to firms’ extant business models. The shocks include (i) managers
6 intentionally innovating in response to new technologies or changes they observe in their
customers’, suppliers’, or competitors’ strategies, and (ii) unintentional shocks to their business
as competitors enter or alter their strategies (e.g., Schumpeter’s “creative destruction”). Porter
and other business strategists emphasize establishing differentiated products, solidifying
customer relationships, vertical integration, and technological leadership. Prahalad and Hamel
(1990) argue that firms differentiate themselves by developing and exploiting their unique core
competencies (coordinating diverse production skills and integrating multiple technologies that
are difficult to imitate). The strategic management literature predicts that firms competing within
the same product markets will exhibit heterogeneous firm characteristics, thereby challenging the
intra-industry homogeneity assumption (Brickley and Zimmerman 2010).
The following empirical regularities further challenge the validity of the firm-stationarity
and intra-industry-homogeneity assumptions:

considerable heterogeneity exists across industries in firm survival, entry and exit
rates, and market structures (Berry and Reiss 2007);

firm size distributions within industries are highly skewed (Schmalensee 1989);

as new markets develop, the number of firms tends to rise and later falls and these
trends vary widely across product markets (Sutton 2007);

firm-specific growth rates have become more volatile (Comin and Mulani, 2006);

various operating characteristics of firms such as idiosyncratic risk and survival rates
of newly listed firms have changed over time (Fama and French, 2004, Irvine and
Pontiff, 2006, and Brown and Kapadia, 2007).
Another general view from both economics (Sutton 2007) and strategic management that
questions both the firm stationarity and intra-industry homogeneity assumptions involves the
dynamic nature of business models. Business models are “likely over time to be replaced by an
improved model that takes advantage of further technological or organizational innovations. The
right business model is rarely apparent early on in emerging industries. Of course, once a
business model is successfully established, changing technology and enhanced competition will
require more than defenses against imitation. It is also likely that even successful business
models will at some point need to be revamped, and possibly even abandoned” (Teece, 2010).
Successful implementation of a new business model often requires changes in real
investment and operating policies and an organizational architecture (decision rights partition,
7 performance evaluation, and compensation schemes) that provides managers with incentives to
execute the strategy (Brickley et al. 2009). Likewise, firms experiencing shocks to their business
model from competitors also alter their real investment, operating, and organizational structures
to survive. Milgrom and Roberts (1995) argue that complementarities exist between strategy and
organizational design variables. Because business model shocks usually involve changing the
firm’s core competencies, location of the firm in the value chain, its asset base, capital structure,
and organizational architecture, these shocks usually impact the firm’s operating cycle and hence
its accrual generating process.6 In fact, Dichev et al. (2012) report that CFOs list their firm’s
business model as being the most important factor affecting their company’s earnings quality.
As further illustration of the questionable validity of the firm stationarity and intraindustry homogeneity assumptions, in Figure 1 we plot the annual operating cycles of seven
major airlines (a relatively homogenous industry) over the period 1990-2011. As revealed in the
figure, there is significant variation in operating cycle not only over time within a single airline,
but also in the industry cross-section. Specifically, the average pair-wise correlation between any
two airlines is only 0.18. Further, Fama and French (2004) report that the ten-year survival rate
for seasoned firms falls from 61% for the 1973 cohort to 47% for the 1991 cohort, and the
likelihood that a newly listed firm survives its first ten years falls from 61% for the 1973 cohort
to 37% for the 1991 cohort. Hence, firms do not appear very stationary, and firm stationarity
appears to have declined.
2.2.
Related accounting studies of abnormal accruals
Various accrual regression models exist to parse “discretionary accruals” from total
accruals. The regression residuals usually represent “discretionary accruals” and are used as
proxies for earnings management (signed residuals) or earnings quality (unsigned residuals).
Jones (1991) pioneered the accruals regression framework by modeling “normal” accruals as a
linear function of current period change in sales and PPE.7 Dechow and Dichev (2002, hereafter
DD) represents another significant model innovation, where working capital accruals are
regressed on past, current, and future periods’ operating cash flows and the standard deviation of
6
As an example of how business model revisions affect the accrual generating process consider Amazon.com. It
started with few accounts receivables because customers paid with third-party credit cards, which amount to cash
sales to Amazon. As it introduced its own credit card and began selling services to other merchants, Amazon’s
receivables increased, affecting its operating cycle. In addition, as Amazon entered into the hardware business
(Kindle devices), inventory turnover slowed, impacting its operating cycle.
7
Dechow et al. (1995) develop the modified Jones model where credit sales growth are backed out of sales changes
due to concerns about potential manipulations of credit sales. 8 the regression residuals is interpreted as a measure of “earnings quality.” 8 While accrual models
play an important role in earnings management/earnings quality studies, many researchers voice
concerns about accrual model misspecifications. Some studies point out that the magnitudes of
the accrual model residuals are implausibly large and hence difficult to attribute solely, or even
mostly, to management discretion (e.g., Ball and Shivakumar, 2008, Dopuch et al., 2011). The
large residuals suggest poor fit of the models and are consistent with significant violations of the
firm stationarity and intra-industry homogeneity assumptions due to business model shocks.
Further, accrual model misspecifications not only introduce noise but can also lead to
biased inferences. Dechow et al. (1995) find excessive rejection rates in favor of the existence of
earnings management in firms with extreme financial performance. McNichols (2002)
conjectures that both the Jones and DD models are likely misspecified due to inadequate
considerations of firm economic fundamentals such as uncertain economic environments and
sales growth. In a recent survey, Dechow et al. (2010) similarly emphasize the importance of
firm fundamentals by stating that “(t)he literature often inadequately distinguishes the impact of
fundamental performance on (earnings quality) from the impact of the measurement system.”
Efforts have been made to better incorporate the effects of firm fundamentals, especially
firm performance, into the estimation of “normal” accruals. Kothari et al. (2005) develop a
performance matching procedure where discretionary accruals (estimated by industry-year) of
treatment firms are measured relative to those of control firms in the same industry and with
similar current or lagged ROA. However, as recognized by the authors, the success of their
matched-firm approach depends on “the homogeneity in the relation between performance and
accruals for the matched and the sample firm.” In other words, Kothari et al. (2005) employ the
standard assumption of intra-industry homogeneity to estimate normal accruals and to choose
their matched control firms. Collins et al. (2012) further match firms based on sales growth in
addition to ROA. Zhang and Zhuang (2012) add current period signed stock returns to the
standard accrual models to capture anticipated future firm performance. Again, these recent
papers continue to rely on the standard accrual model assumptions of firm stationarity and intraindustry homogeneity, which are likely violated when business model shocks are present.
8
DD estimate firm-specific accrual models and require at least eight annual observations per firm. Besides imposing
a survivorship bias on their results, their approach assumes business model stationarity, and data restrictions reduce
their final sample by roughly 75%.
9 Ball and Shivakumar (2006) argue that the standard linear accrual models are
misspecified because accruals likely reflect economic losses faster than economic gains due to
accounting conservatism. They introduce a nonlinear accrual model to allow asymmetric
associations of accruals with gains relative to losses. Their model explicitly accounts for one
important aspect of firm performance, i.e., economic gains versus losses, in accrual estimations.
However, within the subsample experiencing either gains or losses their model still relies on the
assumption of intra-industry homogeneity, which is likely violated due the presence of business
shocks within that subsample. While the prior literature’s attempts to incorporate fundamental
performance in the accrual models have resulted in improved model specifications and
regression goodness-of-fit, these models continue to suffer from the potential effects of business
model shocks. Adding performance controls and/or performance interaction terms to the accrual
models are unlikely to be effective because business shocks are unpredictable and have different
effects on firms’ accrual generating processes. The same shock likely elicits different strategy
reactions (and hence different accounting accrual effects) from firms in the same industry, and
this shock will impact several years of firms’ financial statements.
The poor fit of the accrual models due to business model shocks has implications for
research on “earnings quality,” which relies on unsigned accrual model residuals to measure
accounting quality. Hribar and Nichols (2007) point out that the mean of the unsigned accrual
model residual is a positive function of the standard deviation of the signed residual and is
associated with firm operating volatility. They show that when researchers associate “earnings
quality” based on the unsigned residual with a partitioning variable, biased inferences result
when the partitioning variable is correlated with firm operating volatility. Similar problems of
biased inferences also arise for signed abnormal accruals. Prior studies such Kothari et al. (2005)
and Collins et al. (2012) show that the null of no earnings management tends to be over-rejected
in samples with extreme firm characteristics, such as market-to-book, size, and sales growth.
However, none of these studies considers how the prevalence of business model shocks affects
these biases, which we examine in our subsequent analyses.
Finally, our paper is related to Collins and Hribar (2002), who provide evidence that
accrual models are better specified using accrual data from the cash flow statement than from the
balance sheet because of the existence of certain "non-articulation" events (e.g., mergers and
acquisitions and discontinued operations). Many subsequent studies using cash flow statement
10 data do not exclude major events such as M&A because the common perception is that these
events are problematic only for balance sheet data.9 A key message from our study is that major
events remain problematic even after using cash flow statement data to compute accrual models
because strategy shocks violate the firm stationarity and intra-industry homogeneity assumptions
implicit in these regressions. Further, those studies that remove certain events do not go far
enough, because as we show later, business model shocks are much more pervasive than those
few events that are often excluded from studies.10
2.3.
Empirical predictions
We start with the following generic cross-sectional accrual model:
N
TAijt   0 jt    njt X nijt   ijt
(1)
n 1
where TAijt is total accruals for firm i in industry j in year t, Xnijt is the nth firm-specific variable
thought to explain the accrual generating process, βnjt is the estimated coefficient on the nth
variable in industry j in year t, and  ijt is the residual. In the literature the signed residual (the
prediction error),  ijt , is often used as a proxy for earnings management and the unsigned
residual,  ijt , a measure of “earnings quality” or sometimes a measure of earnings management
if the researcher predicts earnings management but not its direction. However, due to model
misspecifications,  ijt likely contains both non-discretionary and discretionary accruals:
 ijt  DAijt  NDAijt  ijt
(2)
where DAijt is discretionary accruals for firm i in industry j in year t, NDAijt is non-discretionary
accruals for firm i in industry j in year t, and  ijt is white noise arising from accounting errors. In
this study we focus on the implications of business models shocks on the properties of the
unsigned residual  ijt .
9
For example, papers that use the cash flow statement data and do not make adjustments for major events include
Dechow and Dichev (2002), Wysocki (2008), Ecker et al. (2011), and Dechow et al. (2012). 10
McNichols (2002) uses the cash flow statement data and excludes M&A and discontinued operations because, as
she points out, accruals in one period and cash flows in another period may be for different economic entities. Ball
and Shivakumar (2006) use the cash flow statement data and exclude acquisitions.
11 We expect business models shocks to negatively affect the fit of the accrual models for
two reasons. First, business model shocks are associated with changes in firm strategies,
operations, and accrual generation processes. Liu and Zhang (2008) report that firms with large
lagged returns (either positive or negative), our proxy for business model shocks, are more
sensitive to industrial production growth in the following period. This suggests that these firms
are more likely to make investments and divestitures and other major business decisions.11 These
real changes cause firms to restructure their long-term assets and working capital, which can then
alter their operating cash flow cycles and generate large non-discretionary abnormal accruals
(NDA ≠ 0 in Eq. 2) as GAAP requires write-offs, write-downs and the recognition/de-recognition
of deferred tax assets and so forth. Furthermore, extreme stock returns in year t signal additional
business model uncertainty that manifests itself in real investment and operating changes in the
following years. The Liu and Zhang (2008) findings provide evidence consistent with the
dynamic nature of business model shocks.12
Second, because firms in the same industry have unique core competencies, each will
react to the same shock with a different strategy response. The same shock can cause some
firms’ total accruals to increase and other firms’ total accruals to decrease, which makes it
difficult to control for the effect of the shocks in the accrual models. For example, a new
scientific advance will provide new opportunities for those firms with complementary
technologies to the new advance and reduce the value of firms with similar products without
complementary technologies. Large coal mines were made better off by tighter mine-safety
regulations because the increased regulation shut down small, marginal mines (Henderson 1977).
The invention of the automobile opened vast new markets for Standard Oil because it had the
gasoline cracking technology necessary to produce large quantities of gasoline (Chernow 2007).
Ball (2013) argues that two firms experiencing a similar negative demand shock could respond
very differently with one firm seeing an increase in inventory and the other a decrease. This
suggests that business model shocks can affect firms’ accrual generating processes in
11
Liu and Zhang (2008) suggest that the growth in industrial production is a priced risk factor and firms with
extreme stock performances are more sensitive to this risk. 12
Strategy changes can take several years to implement. For example, some firms adapt to a shock by adding new
competencies via acquisitions. If the strategy fails, the acquirer may sell or close the business. Mitchell and Lehn
(1990) document that firms are more likely to become subsequent takeover targets if they made prior valuedestroying acquisitions; and later, these firms divested or restructured to thwart their own takeover. 12 unpredictable ways that are difficult to model and control for ex ante, reducing the fit of the
accrual regression. The preceding discussion leads to the following predictions:
Prediction 1: The unsigned residual  ijt in Eq. (1) for firm i in year t is positively
associated with business model shocks experienced by firm i in years t, t-1, and t-2.
Because business model shocks experienced by a particular firm affect the overall fit of
the accrual model in the same and subsequent industry-years, we expect to observe a spillover
effect of the shocks on other firms’ accrual residuals in the same and subsequent industry-years.
Prediction 2: The unsigned residual  ijt in Eq. (1) for firm i in industry j and year t is
positively associated with business model shocks experienced by firm i’(i’ i) in industry
j and years t, t-1, and t-2.
We expect the goodness of fit of the accrual model to be more negatively affected when
more firms in a given industry-year experience business model shocks.
Prediction 3: The goodness of fit (R2s) of the regressions estimated in Eq. (1) in industry j
year t is negatively associated with the percentage of firms experiencing business model
shocks in industry j and years t, t-1, and t-2.
Business model shocks can inflate the magnitude of Jones-type abnormal accruals by
increasing the volatility in firms’ operating cycles. Dechow et al. (1998) develop an algebraic
model of working capital accruals, where the accrual is approximated by the product of a firm’s
operating cycle and the sales shock. This implies that a firm’s operating cycle affects the strength
of the relation between accruals and sales changes (assuming sales follow a random walk).
Cross-sectional accrual models impose the same slope coefficient (i.e., the same operating cycle)
on all firm-year observations in that regression. However, if operating cycles are affected by
business model shocks and vary by firm and over time, such variation can be one mechanism
that leads to the large absolute abnormal accruals that we predict earlier.
Prediction 4: The change in firm i’s operating cycle volatility between years t-1 and t is
positively associated with business model shocks to firm i in year t.
Empirical evidence on the above predictions benchmarks the pervasiveness of the
potential accrual model misspecifications that arise from violating the firm stationarity and intraindustry homogeneity assumptions. We next investigate whether business model shocks can bias
13 researchers’ inferences regarding the determinants and effects of “earnings quality” when  ijt is
used to proxy for DAijt as a measure of earnings quality.
From Hribar and Nichols (2007) (hereafter HN) we know (i) the mean and variance of
 ijt increase in the variance of  ijt in Eq. (1), and (ii) operating volatility is associated with the
variance of  ijt . To the extent business model shocks in year t cause firms to change real
investment and operating decisions in years t, t+1, and t+2, and these real changes result in large
unsigned non-discretionary accruals ( NDAijt ), then business model shocks create bias in
unsigned abnormal accruals (  ijt ) as an instrument for unsigned discretionary accruals ( DAijt ).
HN document that  ijt in year t are correlated with the variances of cash flow from operations
and revenue, where the later variances are computed over years t-5 to t-1. While the HN
variances will capture some business model shocks, implicit in their methodology is the
assumption of firm stationarity (past cash flow and revenue volatility predict future abnormal
accrual volatility). While the likelihood of business model shocks in period t is correlated with
cash flow and revenue volatilities from t-5 to t-1, business model shocks in year t will cause
large unsigned non-discretionary accruals ( NDAijt ) in years t, t+1, and t+2. So, controlling for
past cash flow and revenue volatility in computing unsigned abnormal accruals in year t does not
undo all the bias caused by business model shocks that occur in years t-2, t-1, and t that have not
yet manifested in higher cash flow or revenue volatility computed over t-5 to t-1.13
Business model shocks likely increase NDAijt and  ijt due to the inability of the accrual
models to capture these shocks. When researchers investigate the effect of a particular variable
(i.e., partitioning variable) on “earnings quality” as proxied by  ijt , false inferences can result if
the partitioning variable is correlated with business model shocks. We make this prediction after
controlling for the HN operating volatility measures.
13
For example, suppose Firm A introduced a new product based on a patented technology that gains considerable
market acceptance, and Firm A’s success obsoletes Firm B’s business model. Further assume it takes several years
before Firm B’s revenues are adversely affected. Firm B’s stock price adjusts as the market (i) learns of Firm A’s
success and comes to understand the implications of Firm A’s success on Firm B’s value, and (ii) how Firm B
changes its business model in response to Firm A’s success. Hence, business model shocks to Firm B (from Firm A)
in year t are not captured by the volatility of Firm B’s cash flow and revenue computed over years t-5 to t-1. 14 Prediction 5: Tests of the relation between earnings quality (proxied by  ijt ) and a
partitioning variable (PART) are biased in favor of finding a positive relation between
the two variables when PART is correlated with business model shocks.
Because business model shocks affect the overall fit of the accrual model, we predict a
spillover effect among firms in the same industry-year, again after controlling for the HN
operating volatility measures.
Prediction 6: Tests of the relation between earnings quality (proxied by  ijt ) and a
partitioning variable (PART) are biased in favor of finding a positive relation between
the two variables when PART is correlated with business model shocks experienced by
firm i’(i‘ i) in industry j.
3.
Data and Sample Selection
Our sample consists of the intersection of the annual Compustat file and the CRSP
monthly return file for fiscal years 1988 through 2010. We begin the sample in 1988 because our
analysis examines estimated abnormal accruals using data from the statement of cash flows
(unavailable until 1988) to avoid problems associated with the use of balance sheet data (Collins
and Hribar 2002). For each fiscal year observation we further require non-missing industry
identifiers and twelve non-missing CRSP monthly return observations, leaving a sample size of
125,964 firm-year observations, which hereafter we refer to as our "full" sample. Our accrual
model estimations impose additional data requirements. Specifically, we require non-missing
observations for accruals, cash flows, sales, PP&E, return-on-assets, and total assets. Next,
because we estimate our abnormal accrual model in industry-year cross sections, we delete
observations with fewer than twenty-five observations in a fiscal-year-industry group, resulting
in a sample of 90,822 observations.
Table 1 presents descriptive statistics of the variables in our analyses. Notably, the
median value of unsigned "abnormal" accruals from the original Jones model, nonlinear accrual
model, and performance control accrual model is 4.9%, 4.0%, and 4.4% of total assets,
respectively. In untabulated analyses we also note that the median unsigned "abnormal" accruals
are roughly the same magnitude for both negative and positive original Jones model accruals
(5.0% and 4.8% of total assets, respectively). Table 2 presents correlations among the variables
in our analyses, with Pearson (Spearman) correlations reported above (below) the diagonal.
15 Consistent with Hribar and Nichols (2007), unsigned abnormal accruals are positively correlated
with both cash flow volatility and revenue volatility, which suggests the need to control for these
variables in our multivariate analyses. We also note that the probability of a firm experiencing a
business model shock is associated with numerous firm characteristics including size, market-tobook, and leverage.
4.
Research design and empirical findings
4.1.
Business model shocks
As described by Teece (2010), business models are dynamic. Unfortunately, few firms
specifically disclose changes in their business strategies. Some managers only learn that their
current business model needs revamping after their financial performance deteriorates. Because
business model shocks arise for various reasons (e.g., technology, regulatory changes, entry and
exit) and are often difficult to identify empirically from financial statements (particularly
contemporaneously), we utilize a market-based measure as our primary empirical shock proxy.
In part to help validate this market-based proxy, we also consider a financial statement-based
proxy generated from events observable in Compustat. Each proxy has advantages and
disadvantages.
4.1.1
Market-based proxy for business model shocks (RetShock)
We consider firm-years which contain an unsigned monthly market-adjusted return in
excess of 20% to have experienced a large business model shock during that year. Using stock
price data from the CRSP monthly file, we create a firm-year variable called MaxMUARi,t, which
equals the largest monthly unsigned abnormal return (i.e., firm return less the value-weighted
CRSP index return) experienced by firm i in year t. Figure 2 documents that it is relatively
common for firms to experience at least one month in a year with a large unsigned abnormal
return. The modal largest unsigned monthly abnormal return in a year is 15%. We define an
indicator variable RetShocki,t that equals one if firm i experienced at least one month during year
t with an unsigned abnormal return of greater than 20%, and zero otherwise. 55% of all firmyears experience such an event.
We recognize that abnormal returns may also capture non-business model shocks (growth
shocks to the firm’s existing business model, liquidity shocks, and investor sentiment), which
add noise to using abnormal returns to proxy for business model shocks (Savor, 2012 and
16 Barberis, 1998).14 To examine the usefulness of using large unsigned abnormal returns to capture
business model shocks we randomly sampled 100 firm-months with unsigned monthly abnormal
return greater than 20%, read all the news stories on Factiva for that firm-month, and in
particular identified the particular day(s) with large daily abnormal returns in that month and the
stories released on that day. We also noted the unsigned Jones-model residual corresponding to
that fiscal year and the previous and following years. Appendix A provides four examples from
the 100 firms examined, with a few additional observations. Based on our examination of these
100 random large monthly abnormal returns we conclude that it is very difficult to pinpoint the
exact date when firms experience business model shocks. Business models seem to evolve over
time and shocks get impounded into stock prices as tangible evidence, usually in the form of
earnings, is released. While large abnormal monthly returns capture some of these business
model shocks, it does so with considerable error. And, large unsigned abnormal accruals are not
concentrated only in the year of the large abnormal returns. Rather, large business model shocks
(as captured by returns) tend to propagate through several years of abnormal accruals.
4.1.2. Operational proxies for business model shocks (OpShock)
As a secondary measure, we develop an operational shock proxy that relies on five
Compustat-reported data items to capture business model shocks: (i) the extent of acquisitions
(sale_fn), (ii) discontinued operations (do), (iii) four-digit SIC industry changes, (iv)
restructuring charges (rcp), and (v) special items (spi). To capture substantial business strategy
shocks using these operational variables, we create the following indicator variables:
LargeMergAcqi,t equals one if Compustat footnote data item “sale_fn” = "AB" (i.e., sales have
been "restated for/reflects a major merger or reorganization resulting in the formation of a new
company") (0.1% of sample, or 127 observations) and equals zero otherwise; LargeDiscOpsi,t
equals one if the magnitude of the income effect of discontinued operations is greater than five
percent of sales (i.e., |do/sale| > 0.05) (2.8% of sample, or 3,516 observations) and equals zero
otherwise; IndChangei,t equals one if firm i’s four digit SIC differs in years t-1 and t (3.4% of
sample, or 4,328 observations) and equals zero otherwise; LargeRestruci,t equals one if the
magnitude of restructuring charges is greater than five percent of sales (i.e., |rcp/sale| > 0.05)
(1.1% of sample, or 1,415 observations) and equals zero otherwise. LargeSpecItemi,t equals one
14
Growth shocks to the firm’s existing business models also can affect the estimation of abnormal accruals if firms
adjust contemporaneous working capital in anticipation of future growth. Working capital adjustments and sales
changes occurring in different years reduce the accrual model’s goodness of fit.
17 if the magnitude of special items is greater than five percent of sales (i.e., |spi/sale| > 0.05)
(12.2% of sample, or 15,324 observations) and equals zero otherwise. Further, we define an
indicator OpShocki,t that equals one if at least one of the preceding five event indicator variables
equals one (16.6% of sample, or 20,899 observations), and equals zero otherwise (i.e., OpShock
equals one if firm i experienced at least one of the above described five large operational
shocks).
About 17% of all firm years experience at least one of the five operational shock proxies.
A couple of observations are warranted. These five operational shock proxies will to varying
degrees be mechanically associated with abnormal accruals. For example, restructuring charges
in year t are mechanically related to abnormal accruals in year t. While these five operational
variables have some intuitive appeal as proxies for business model shocks, the five variables and
the cutoffs for defining “large” are ad hoc. Further, these five operational shock proxies unlikely
capture all business model shocks.
4.1.3. Association between market-based and operational shock proxies
The rationale for the tests in this section is two-fold: (i) to establish that large marketadjusted returns are capturing changes in future cash flows due to business models shocks
(acquisitions, divestitures, restructuring charges, etc.), and are not merely capturing changes in
growth rates of firms with stable business models, and (ii) to show that it can take several years
from when the market first learns of the business strategy revision to when the change manifests
in one of the operational shock proxies. We estimate the association between our market-based
proxy (RetShock) and the five operational shocks discussed above using the following logit
model:
Pr(OpShocki ,l  1) 
1
,
1  e z
(3)
z   0  1 RetShocki ,t   2 RetShocki ,t 1   3 RetShocki ,t  2 ,
where all variables are as defined above.
Table 3 presents the results of separately estimating Eq. (3) for the five individual
components of OpShock, and for the aggregate OpShock indicator variable. The results are
consistent with our conjecture that contemporaneous and prior two-year lagged large unsigned
abnormal returns are associated with operational shocks. Note that our set of operational shocks
is but a subset of business model shocks. In addition, consistent with Gerakos and Kovrijnykh
18 (2012) the statistically significant coefficients on the one- and two-year lagged RetShock indicate
that business model shocks can take several years from when the market first learns of the shock
until it manifests in the financial statements. The findings in Table 3 suggest that simple crosssectional accrual models are likely unable to capture the complex dynamic and persistent
financial effects induced by business model shocks.
4.2.
Abnormal accrual models
The above analysis suggests that business model shocks are common in our sample. We
next examine how the prevalence of business model shocks affects the estimation of abnormal
accruals. For simplicity, we begin by estimating abnormal accruals using the original model from
Jones (1991) in the cross-section by industry-year, as follows:
TotalAccrualsi ,t   0  1SalesChangei ,t   2 PPEi ,t   i ,t
(4)
where TotalAccruals is taken from the statement of cash flows (Collins and Hribar 2002). As
mentioned earlier, our inferences are unchanged when using working capital accruals instead of
total accruals. SalesChange equals the change in sales from year t-1 to t, and PPE equals gross
property, plant and equipment. All variables are scaled by beginning-of-period total assets, and
Winsorized at the top and bottom one percent by two-digit SIC. As is standard, we define a
measure of abnormal accruals, AbAccruali,t, as the residual from estimating Eq. (4). Further, we
define an unsigned form of this residual, UAA, as |AbAccrual|.
Next, we repeat our analyses using a nonlinear modification to the Jones model as
proposed by Ball and Shivakumar (2006). Specifically, we estimate the following model in the
cross-section by industry-year:
TotalAccrualsi ,t   0  1SalesChangei ,t   2 PPEi ,t   3CFi ,t   4 DCFi ,t  5 DCF * CFi ,t
  6 ABNRETi ,t   7 DABNRETi ,t  8 DABNRET * ABNRETi ,t   i ,t ,
(5)
where CF is operating cash flows scaled by average total assets, DCF is an indicator that equals
one if CF is less than zero and equals zero otherwise, ABNRET is firm i's abnormal stock return
during fiscal year t (based on the CRSP equal-weighted market index), DABNRET equals one if
ABNRET < 0 and zero otherwise, and all other variables are as defined above. We denote the
residual from Eq. (5) as AbAccrual_NL, and the corresponding unsigned residual as UAA_NL.
Finally, we use a variant of the original Jones model that adds a control for firm
performance, as suggested by Kothari et al. (2005):
19 TotalAccrualsi ,t   0  1SalesChangei ,t   2 PPEi ,t   3 ROAi ,t   i ,t ,
(6)
where ROA is calculated as net income divided by average total assets, and all other variables are
as previously defined. We denote the residual from Eq. (6) as AbAccrual_PF, and the
corresponding unsigned residual as UAA_PF.
Figure 3 plots histograms of the R2 from the 878 industry-year estimations of Eqs. (4) (6). There is wide variation in the model fit across estimations. In the original Jones model, most
industry-years have R2s of less than 10%, suggesting that most industry-year models are
estimated relatively imprecisely. While there is substantially improved model fit using both the
nonlinear and performance control accrual models from Eqs. (5) and (6), the sample mean and
median values of the abnormal accruals from these models are still relatively large (see Table 1).
For example, 42% of sample firm-years (untabulated) have an abnormal accrual from the
nonlinear model (i.e., UAA_NL) greater than 5% of total assets.
The literature generally attributes the entire abnormal accrual to "discretion," or earnings
management.15 However, given the large magnitude of the mean and median unsigned abnormal
accruals, such earnings management would be difficult to disguise from the external auditors
(Ball and Shivakumar, 2008). In applying generally accepted auditing standards auditors seek
reasonable assurance that the financial statements are free from material misstatement whether
from errors, fraud, or management’s use of unreasonable estimates. Since a number of accounts
require management judgment (deferred taxes, inventory obsolescence, uncollectible receivables,
warranty obligations, pension liabilities, impaired assets), auditors must test the reasonableness
of these estimates, and “indicate a possible bias on the part of the entity's management.” (AU
Sections 312.37 and 9312A PCAOB).16 Auditors often base their materiality assessment on a 5%
of net income rule, which holds that “a reasonable investor would not be influenced in
15
For example, Cohen, et al. (2008) use unsigned abnormal accruals to measure accounting-based earnings
management and report median modified-Jones unsigned abnormal accruals of 6% of total assets over their sample
period 1987-2005. 16
PCAOB standard AU 9312A reads, “The auditor should also consider whether the difference between estimates
best supported by the audit evidence and the estimates included in the financial statements, which are individually
reasonable, indicate a possible bias on the part of the entity's management. For example, if each accounting estimate
included in the financial statements was individually reasonable, but the effect of the difference between each
estimate and the estimate best supported by the audit evidence was to increase income, the auditor should reconsider
the estimates taken as a whole. In these circumstances, the auditor should reconsider whether other recorded
estimates reflect a similar bias and should perform additional audit procedures that address those estimates.” 20 investment decisions by a fluctuation in net income less than or equal to 5%” (Vorhies 2005).17
In contrast, the median Jones model abnormal accrual is 67% of unsigned net income.18 These
unsigned abnormal accruals clearly exceed the auditor’s materiality threshold of 5% of net
income, and hence are likely to have been examined by the external auditor and deemed to be
free from material misstatement (including unreasonable management bias). On the assumptions
that auditors detect and prevent material misstatements (including unreasonable management
estimates) and that they apply a 5% of net income materiality threshold, then if a firm has an
abnormal accrual of 67% of unsigned net income it indicates that abnormal accruals as a proxy
for managerial accounting discretion have a noise-to-signal ratio of roughly 12 (62% ÷ 5%).
4.3.
Association between unsigned abnormal accruals and business model shocks
We next examine whether the existence of business model shocks affect the estimation of
abnormal accruals. We first test Prediction 1 on the relation between the magnitude of abnormal
accrual estimates and business model shocks in the current and previous years. We focus on
unsigned abnormal accruals because business model shocks can increase or decrease accruals.
We begin by estimating the following OLS model:
UAAi ,t   0  1 BMShocki ,t   2 BMShocki ,t 1   3 BMShocki ,t  2
 4 CFOi ,t   5 REVi ,t   i ,t ,
(7)
where UAA is unsigned Jones model abnormal accruals and BMShock is, alternately, OpShock
and RetShock, i.e., the indicators for the presence of business model shocks, as discussed above.
σCFO and σREV capture operating volatility, measured as the standard deviation of cash flow
and sales, respectively, scaled by assets over the current and previous four years (Hribar and
Nichols, 2007). We also estimate a logit specification that tests for a relation between business
model shocks and large unsigned abnormal accruals, as follows:
Pr( LUAAi ,t  1) 
1
,
1  e z
(8)
17
In SAB 99 the SEC cautions auditors to avoid “exclusive reliance on certain quantitative benchmarks to assess
materiality in preparing financial statements and performing audits.” Moreover, “the staff has no objection to such a
‘rule of thumb’ as an initial step in assessing materiality. But quantifying, in percentage terms, the magnitude of a
misstatement is only the beginning of an analysis of materiality; it cannot appropriately be used as a substitute for a
full analysis of all relevant considerations.” The 5% net income rule of thumb is consistent with Dichev’s et al.
(2012) finding that CFOs believe about 10% of reported earnings is managed for firms that manage earnings.
18
Because we are interested in the magnitude of abnormal accruals relative to the magnitude of net income, we
calculate |abnormal accrualsi,t | ÷ |net incomei,t |. Inferences are similar using the nonlinear and performance control
accrual models in Eqs. 5 and 6.
21 z   0  1 BMShocki ,t   2 BMShocki ,t 1   3 BMShocki ,t  2
 4 CFOi ,t   5 REVi ,t ,
where LUAA is an indicator that equals one if UAA is greater than the sample median (i.e., the
absolute value of abnormal Jones model accruals is greater than 5% of total assets) and equals
zero otherwise. All other variables are as previously defined. We also repeat the estimation of
Eqs. (7) and (8) using both the nonlinear and the performance controlled accrual models.
Before estimating Eqs. (7) and (8), Figure 4 presents time-series plots of the proportion of
sample firms each year with business model shocks and large abnormal accruals (i.e., OpShock =
1, RetShock = 1, LUAA = 1, LUAA_NL = 1, and LUAA_PF = 1) to give a sense of associations
between time trends in these variables. Figure 4 shows that 40% of all firms consistently have
unsigned abnormal accruals in excess of 5% across all model specifications. Further, our
instruments for business model shocks indicate that in excess of 20% and 60% of firms have
operational shocks and large return shocks, respectively, in most years. Moreover, these
variables are strongly correlated through time. For example, the correlation (untabulated)
between the percentage of firms with OpShock = 1 and RetShock = 1 is 0.71 and the correlation
between the percentage of firms with LUAA = 1 and RetShock = 1 is 0.72.
Table 4 reports estimation of Eqs. (7) and (8). As shown in Panel A, contemporaneous
operational shocks (OpShock) are positively associated with the magnitude of unsigned abnormal
accruals and the presence of a large abnormal accrual for all three abnormal accrual models we
estimate. Results are generally weaker for lagged operational shocks. As shown in Panel B,
contemporaneous and both one and two-year lagged stock return shocks are positively associated
with both measures of abnormal accrual magnitude across all models, where the strength of the
association generally diminishes with the lag of the return shock. These results support
Prediction 1. The operating volatility control variables are positively associated with the
likelihood of a firm having a large unsigned Jones model abnormal accrual, consistent with
Hribar and Nichols (2007). So even after controlling for cash flow (σCFO) and revenue (σREV)
volatility, which likely also capture business model shocks, our operational shock (OpShock) and
market shock (RetShock) proxies (including lagged variables) continue to explain both the
magnitude and the presence of large unsigned abnormal accruals.
To test Prediction 2 on the spillover effect from other firms in the same industry-year, we
estimate the following OLS model:
22 UAAi ,t   0  1MaxMUARi ,t   2 MaxMUARi ,t 1   3 MaxMUARi ,t 2
 4OtherMaxMUARi ,t   5OtherMaxMUARi ,t 1   6OtherMaxMUARi ,t 2
 7 CFOi ,t   8 REVi ,t   i ,t ,
(9)
where MaxMUARi,t is firm i's maximum monthly unsigned absolute market-adjusted return
during year t and OtherMaxMUARi,t is the average MaxMUAR across all other firms in firm i's
industry during year t. The results are presented in Table 4 Panel C. We find similar results
regarding a firm’s own business model shocks, in that MaxMUAR is positively associated with
large abnormal accrual magnitudes. These results confirm those in Panel B and support
Prediction 1. Turning to the coefficients on OtherMaxMUAR, i.e., shocks experienced by the
other firms, we find uniformly positive and significant coefficients on OtherMaxMUARi,t across
the three accrual models, indicating a spillover effect from the business model shocks
experienced by other firms due to their effect on the accrual model fit. These results support
Prediction 2. The coefficients on the lagged OtherMaxMUAR variables are insignificant.
4.4.
Effect of sample "business model shock" contamination on abnormal accruals
To further illustrate the dynamics behind our key results in Table 4, in this section we
examine how inclusion of an increasing number of "contaminated" firm-year observations with
large business model shocks affect the estimation of accrual models. For parsimony, we focus on
how including or excluding observations with business strategy shocks in either years t, t-1 and t2 impact estimated abnormal accruals where the existence of a shock is proxied by an unsigned
monthly abnormal stock return during the year in excess of 20% (RetShock =1).
We begin by estimating the abnormal accrual models of Eqs. (4)-(6) by industry-year
using only "uncontaminated" observations (i.e., RetShockt = RetShockt-1 = RetShockt-2 = 0), again
requiring at least 25 observations in each industry-year regression. The resulting
"uncontaminated" sample consists of 10,844 observations. We report the average unsigned
abnormal accrual (UAA, UAA_NL, UAA_PF) and the percent of observations with a large
unsigned abnormal accrual (LUAA, LUAA_NL, LUAA_PF). Then, we iteratively repeat the
estimations after increasing the number of "contaminated" observations in the sample.
Specifically, in stepwise fashion we randomly select 12.5%, 25%, 37.5%, … 100% of the
contaminated observations to include in the sample, recording the average unsigned abnormal
accrual and the percent of observations with a large unsigned abnormal accrual in each iteration.
We predict that each of these statistics will increase with the number of contaminated
23 observations included in the sample. Each iteration tracks these statistics separately for the
original uncontaminated 10,844 observations to separately document how inclusion of
contaminated observations affects abnormal accruals of the uncontaminated sample.
Table 5 presents the results from this analysis. Panel A reports the sample composition
across the nine iterations. The number of “uncontaminated” observations increases as more
“contaminated” observations are included because more industry-years acquire the 25
observations required to estimate the regression. Panel B tabulates the average unsigned
abnormal accruals, percent of observations with a large abnormal accrual, and median industryyear R2 from estimations using all three abnormal accrual models. Figure 5 presents these
statistics in graphical form. For brevity, we will narrate the abnormal accrual results only for the
Jones model, as the results from the other models are inferentially equivalent. Focusing on
columns (1) and (2) in Panel B1, the sample of purely uncontaminated observations iteration (1)
has an average UAA of 0.033 (i.e., abnormal accrual of 3.3% of total assets), where 19% of the
observations have a large abnormal accrual (i.e., UAA > 0.05). In stark contrast, estimation with
the full sample (iteration 9) generates an average UAA of 8.1%, where 49% of the observations
have a large abnormal accrual. As can be seen in both Panel B and Figure 5, consistent with our
expectation there is a monotonically increasing pattern in both the average UAA and the
percentage of observations with a large abnormal accrual (LUAA) as the number of contaminated
observations in the sample increases. This is further evidence in support of Prediction 1.
Interestingly, the increasing pattern is concave, which reveals that it only takes a relatively small
number of contaminated observations to significantly affect the outputs of the accrual model.
Stated differently, if one starts with the fully contaminated sample and begins removing
contaminated observations, little is accomplished unless a substantial number of contaminated
observations are removed. Therefore, studies that simply exclude one or a few specifically
identified events such as mergers are likely not substantially altering model performance.
Columns (3) and (4) of Panel B present the effect on the initial 10,848 uncontaminated
observations of adding an increasing number of contaminated observations to estimating the
accrual model. A similar pattern emerges, in that the constant sample of uncontaminated
observations likewise exhibits a monotonic increase in both the average UAA and the percent of
observations with a large abnormal accrual as the number of contaminated observations in the
sample increases. Thus, not only do contaminated observations themselves display problematic
24 statistics from estimation of accrual models, but their inclusion in the model estimation likewise
affects the efficiency with which accrual models are estimated for the uncontaminated
observations. Whereas 19% of the original 10,844 uncontaminated observations have a large
abnormal accrual when the model is estimated without contamination (iteration 1), 27% of the
same 10,844 observations have a large abnormal accrual when the full sample, including all
contaminated observations, are included in the model estimation (iteration 9). The above findings
are consistent with Prediction 2’s spillover effect.
Column (5) of Panel B in Table 5 reports the median industry-year R2 across sample
contamination iterations. In both the Jones and nonlinear models, there is a nearly monotonic
decrease in industry-year R2 as sample contamination increases, which reinforces the inference
that contamination reduces the fit of the accrual models, i.e., there is a decreasing pattern in R2 as
the number of contaminated observations in the sample increases, and supports Prediction 3.
Interestingly, as shown in Panel B3, when ROA is included in the accrual model, industry-year
R2 increases with the degree of sample contamination. This is inconsistent with Prediction 3 and
is likely caused by the correlation between return shocks and ROA. Nonetheless, even in this
case, the average unsigned abnormal accrual and the proportion of observations with large
unsigned abnormal accruals increases as sample contamination increases.
To further examine the relation between the presence of shocks and accrual model fit, we
regress R2 from individual industry-year accrual models on the percentages of firms in that
industry-year regression that that have RetShock = 1 in years t, t-1, and t-2. In untabulated results
we find negative coefficients on the percentage of firms experiencing business strategy shocks in
year t for the Jones and nonlinear accrual models, but a positive coefficient on the performance
control accrual model (consistent with Table 5 Panel B3). Also, the coefficient on the percentage
of firms with large return shocks in t-2 is negative and statistically significant in the nonlinear
accrual model.
4.5.
Cash operating cycle variability and business model shocks
In this section, we test Prediction 4 on whether business model shocks are associated
with greater volatility in firm operating cycles by estimating the following regression.
Pr(OPCYCVOLUPi ,t  1) 
1
,
1  e z
z   0  1 BMShocki ,t
25 (10)
where OPCYCVOLUPt is an indicator that equals one if firm i's 4-quarter operating cycle
volatility increased from year t-1 to t, and equals zero otherwise. To calculate operating cycle
volatility in year t, we first compute the operating cycle for each quarter in year t as "days sales
outstanding" + "days inventory outstanding" – "days payables outstanding." We then take the
standard deviation of these four quarterly operating cycles as year t's operating cycle volatility. If
this standard deviation increases from year t-1 to t, then OPCYCVOLUPt = 1.19
We report the results from Eq. (10) in Table 6, with the operational shocks in column (1)
and return shocks in column (2). The results in both columns confirm that business model shocks
in year t are associated with a higher likelihood of increases in firm operating cycle volatility,
consistent with Prediction 4. As firms’ operating cycles become more volatile due to business
model shocks, the empirical accrual models’ assumptions of intra-industry homogeneity and firm
stationarity are likely violated, reducing the fit of the models.
4.6.
Business model shocks and inferences using abnormal accruals
4.6.1. Rejection frequencies from simulations – unsigned abnormal accruals
This section tests Predictions 5 and 6 on how business model shocks affect researchers’
inferences regarding earnings management/earnings quality measured using unsigned Jones
model abnormal accruals. We follow the methodology in HN, where we construct a partitioning
variable (PART) as a weighted combination of MaxMUARi,t and a random variable. We draw 250
random samples of 1,000 observations each from our sample and run a regression of unsigned
Jones model abnormal accruals on PART for each random sample. We record the number of
times out of 250 the t-statistic on the PART rejects the null hypothesis of no correlation between
unsigned abnormal accruals and PART at the 5% level in a one-tailed test. We repeat this process
11 times at different levels of correlation between MaxMUARi,t and PART. Within a randomly
selected sample, we expect no systematic association between earnings management/earnings
quality and large return shocks, suggesting rejection rates of around 5% in properly designed
tests. The actual rejection rates are plotted in Figure 6 Panel A, with the diamond-dotted line for
regressions with no control variables and the square-dotted line for regressions with the
following controls: LnSize, MktToBook, Leverage, and the HN operating volatility measures of
19
We do not include the lagged business model shocks in Eq. 10 due to ambiguous predictions on the relation
between lagged shocks and the change in operating cycle volatility. For example, a large positive shock in year t-1
likely leads to higher operating cycle volatilities in year t-1 and it may also increase the operating cycle volatility in
year t if the shock affects multiple years’ accruals generating processes. As a result, the implication of the lagged
shock on the change in operating cycle volatility from year t-1 to year t is unclear.
26 σCFO and σRev. Focusing first on the diamond line with no control variables, we find excessive
rejection rates (i.e., > 5%) in all cases when there is a positive correlation between MaxMUARi,t
and PART, starting from 0.1. Adding the control variables substantially lowers the rejection
rates. However, the test still over-rejects the null even at modest levels of correlations such as 0.2
after controlling for the HN volatility measures. These findings support Prediction 5 that
business model shocks can create false inferences in research on “earnings quality” due to
correlations between researchers’ partitioning variables and firms’ business model shocks.
We test Prediction 6 by repeating the above analysis replacing MaxMUARi,t with
OtherMaxMUARi,t. The rejection rates are plotted in Panel B of Figure 6, again with the
diamond-dotted line for regressions with no control variables and the square-dotted line for
regressions with the same control variables in Panel A. When no control variables are included,
the rejection rate is greater than 5% at 0.1 correlation and climbs quickly as the correlation rises.
Even with the controls, the test over rejects the null at very modest levels of correlation and the
rejection rates quickly become elevated. These results support the spillover effect in Prediction 6
and suggest a new dimension of potentially correlated omitted variables not considered in prior
literature – business model shocks experienced by other firms in the industry affect the overall fit
of accrual models.
4.6.2. Illustrative example
To further illustrate the issues with drawing inferences based on the use of unsigned
abnormal accruals to measure “discretion” in the presence of frequent business model shocks, we
revisit the setting examined in Bharath et al. (2008) (hereafter BSS). A key question addressed
by BSS is how "accounting quality" affects the interest rate paid on private debt, i.e., bank loans.
The authors state that "we measure accounting quality using the magnitude of operating accruals
to proxy for the influence of discretionary accounting choices. Large abnormal operating
accruals ... make it harder for the lenders to reliably estimate future operating cash flows" (p. 2).
To measure accounting quality, the authors use the first principle component from three
measures of abnormal operating accruals, one being the modified Jones model estimated by
industry-year. Among other things, BSS provides evidence that a higher level of accounting
quality (i.e., lower abnormal operating accruals) leads to lower cost of private debt.
As our study focuses on abnormal accruals from the Jones model, we re-examine the
question of BSS using this single proxy for discretionary accruals, rather than their principle
27 component approach using multiple measures. As such, this example is not intended to challenge
that study's key inference that accounting quality is associated with cost of bank debt. Rather, we
use their setting to illustrate issues associated with making inferences based on the use of accrual
model residuals as a specific proxy for “discretionary accruals.”
We first obtain a sample of bank loan facilities issued during the period 1988-2010 from
Dealscan, and similar to BSS we exclude very short-term loans (i.e., maturities less than three
months). We match the Dealscan loan facilities with our Jones model sample using the most
recent annual accounting data available prior to the issuance date for a given loan, and delete
observations with missing data required by our analysis. The final loan sample contains 23,738
distinct loan facilities. Using this loan sample, we estimate the following OLS specification used
by BSS in column (1) of their Table 5 (p. 17), where we likewise follow BSS and cluster
standard errors at the firm level: Spreadi ,l   0   1UAAi ,t   2 Leveragei ,t   3 LnAssetsi ,t   4Tangibilityi ,t
 5CurrRatioi ,t   6 MktToBooki ,t   7 DefaultRiski , m   8 LnFacilityi ,l
(11)
 9 LnMaturityi ,l   10 Securedi ,l   i ,l .
Spread is the loan basis point spread over LIBOR, Tangibility is net PP&E divided by total
assets, LnAssets is the natural log of total assets, and CurrRatio is current assets divided by
current liabilities. DefaultRisk is a market-based measure of default likelihood for the most
recent month prior to loan initiation, calculated based on the Black-Scholes-Merton equation
closely following the approach in Hillegeist et al. (2004). LnFacility is the natural log of the loan
amount, LnMaturity is the natural log of the loan maturity in months, and Secured is an indicator
that equals one if the loan has a collateral requirement and equals zero otherwise. All variables
are described more fully in Appendix B.
We present the results from estimating Eq. (11) in column (1) of Table 7. Inferences are
consistent with the key findings in BSS. Specifically, higher values of UAA (i.e., lower
"accounting quality") are associated with higher loan spreads (coefficient estimate of 80.58 with
a t-statistic of 4.83). This result would lead to the same inference made by BSS that low
accounting quality (as proxied by large unsigned abnormal accruals) leads lenders to charge a
higher interest rate because of "information risk." The signs and significance of the control
variables are generally consistent with those reported in BSS.
28 Next, we introduce an indicator variable for the presence of business model shocks (i.e.,
"contaminated" observations), and its interaction with UAA, to examine whether the association
between UAA and spread is different for contaminated versus uncontaminated observations:
Spreadi ,l   0   1Contami ,t   2UAAi ,t   3Contam *UAA   4 Leveragei ,t   5 LnAssetsi ,t
 6Tangibilityi ,t   7CurrRatioi ,t   8 MktToBooki ,t   9 DefaultRiski ,m
(12)
 10 LnFacilityi ,l   11 LnMaturityi ,l   12 Securedi ,l   i ,l .
Columns (2) and (3) of Table 7, respectively, report results from estimating Eq. (12)
using our RetShock and OpShock proxies for the presence of business model shocks within the
current or previous two years. In these specifications, the association between UAA and Spread
are only significant for those observations that have experienced a business model shock.
Focusing on column (2), the coefficient estimate on UAA (25.18; t-statistic 0.66) captures the
insignificant relation between UAA and Spread for firms that have not experienced a business
model shock. In contrast, the coefficient sum UAA + Contam*UAA captures the relation between
UAA and Spread for firms experiencing a business model shock. As indicated by the associated
χ2 statistic, the sum of 73.25 is statistically significant at the p < 0.0001 level. These findings
suggest that the significant relation documented in column (1) between UAA and Spread is
driven by firms with business model shocks within the three years prior to loan inception. This
raises questions about attributing the findings in column (1) to "information risk" arising from
discretionary accounting choices. Alternatively, the relation between UAA and Spread may
simply reflect firms undergoing business model shocks being riskier than firms not experiencing
a strategy shock in a manner not captured by market-based default risk measures.
4.6.3. Rejection frequencies from simulations – signed abnormal accruals
Kothari et al. (2005) show that accrual models are likely to be misspecified, i.e.,
researchers tend to over-reject the null of no earnings management, for firms with extreme
characteristics. Following their methodology, we group all 90,822 observations in our Jones
model sample into quartiles each year based on the following variables -- market-to-book, sales
growth and firm size. We combine the observations in each quartile across the different years
and randomly select 200 firms without replacement from each quartile and calculate the mean
and t-statistic of the signed abnormal accruals from the Jones model. We repeat this process 250
times for each quartile, from which we compute the rejection rates for the null hypothesis of
AbAccrual = 0 for both the alternative hypotheses (Ha) of AbAccrual < 0 and Ha of AbAccrual >
29 0 at the 5% level using one-sided tests. The null rejection rates for the market-to-book quartiles
are reported in Table 8 Panel A, those for size are in Panel B, and sales growth in Panel C.
Rejection rates that are significantly different from the 5% nominal significance level (i.e., below
2% or above 8%) are bolded in the table. Below each row that presents the proportion of random
samples in each quartile where the null is rejected, we tabulate the actual number of random
trials (out of the 250) where the null is rejected. For brevity we will discuss only the results in
Panel A (partitions based on market-to-book), as inferences in Panels B and C are similar.
Again focusing on the full sample (Columns 1-6), in random sampling from the entire
pool of 90,822 sample observations we find that rejection rates are not statistically different from
5%, similar to findings in prior studies (Column 1). However, we find that firms in the extreme
market-to-book quartiles tend to exhibit excessive rejection rates on the negative side, i.e. 33.6%
(16.8%) for the lowest (highest) market-to-book quartile, supporting Ha of AbAccrual < 0. This
is consistent with inferences from Kothari et al. (2005) for the Jones model in these extreme
portfolios. Even though Kothari et al. (2005) do not report the rejection rates for the two middle
quartiles, we show excessive rejection rates there (42% and 46.8%, respectively) supporting Ha
of AbAccrual > 0. These findings suggest that excessive rejection of the null is not limited to the
extreme portfolios. Column (6) reveals that for the 1,000 combined random trials across the four
quartiles, AbAccrual > 0 (AbAccrual < 0) is accepted 226 (126) times, in contrast to an expected
acceptance frequency of 50 in a well specified model (i.e., 5% of 1,000 trials).
To investigate the effect of business model shocks, we next exclude all observations with
large absolute abnormal returns in the current year (resulting in a much smaller subsample of
33,027 firm-year observations) and re-estimate the Jones model by industry-year and then repeat
the above rejection tests, and present results in Columns (7)-(12). This analysis shows that the
over-rejection problem is greatly mitigated when observations with business model shocks are
excluded from the analysis, and the cell-by-cell change in the rejection rates is generally
statistically significant based on a two-sample Wald test. To get an overall sense of the
improvement in model specification that comes from removal of shock observations, it is useful
to compare Columns (6) and (12). Whereas for the full sample 226 (126) out of 1,000 trials result
in the inference AbAccrual > 0 (AbAccrual < 0), for the no-shock subsample, only 78 (66) out of
1,000 trials results the inference AbAccrual > 0 (AbAccrual < 0). Accordingly, over-rejection of
the null in both directions is greatly mitigated when shock observations are excluded.
30 The above analysis indicates that the presence of business model shocks likely generates
false inferences when signed abnormal accruals are used to capture earnings management. We
emphasize that even though the reduced sample produces better specified tests, throwing out
observations with large absolute returns is not a panacea, as it can lead to greatly reduced sample
sizes. The reduced sample in Table 8, which excludes observations with large returns in the
current year only, is about 1/3 the size of the overall sample. Further excluding firms with large
returns in prior years (because business model tend to persist through several years’ financial
statements) would have contributed to even further loss of observations.
5.
Conclusion
In this paper we argue that economic insights pose serious challenges to the two
maintained assumptions underlying empirical accrual models: intra-industry homogeneity and
firm-stationarity. In particular, economics teaches that technological innovation, changes in
regulation, and entry by new (or existing firms) create shocks to existing firms’ business models.
These shocks cause revisions in strategies and hence changes in real investment, operating, and
organizational policies, and eventually different accrual generating processes.
Business model shocks cause firms to revise their business strategies by undertaking real
transactions such as acquisitions, divestitures, outsourcing, insourcing, and reorganizations, all of
which generate accruals. Because firms in the same industry can react to the same shock with
diverse business strategy revisions (because they are seeking differentiated strategies and have
different core competencies), a given business model shock does not have the same effect on
total accruals for all firms in the industry. Therefore, including an instrument for the business
model shock in the cross-sectional industry-year accrual model only captures the average effect
of the shock in “normal” or “non-discretionary” accruals. The firm-specific effect of the shock
on total accruals ends up in the regression residual and is mischaracterized as “discretionary”
earnings management. Moreover, since it often takes firms several years to adapt to a business
model shock, past business model shocks propagate through several years of firm’s accruals and
cause instability of a firm’s accrual generating process.
We provide evidence that business model shocks are pervasive, and have consequences
for the firm-stationarity and intra-industry-homogeneity assumptions underlying empirical
accrual models. In particular, large unsigned abnormal accruals are associated with
contemporaneous and lagged proxies for business model shocks and with business model shocks
31 experienced by other firms in the same industry-year. Business model shocks reduce accrual
model goodness of fit, and it takes relatively few firm years contaminated by a business model
shock to substantially affect the outputs of accrual models. As more “contaminated” observations
are included in the sample, the Jones model R2 declines monotonically in a convex fashion, and
the magnitude of abnormal accruals rises monotonically in a concave pattern. Finally, we
document that the inclusion of business model shock observations in industry-year abnormal
accrual estimations can lead to spurious inferences in studies that use either unsigned or signed
abnormal accruals as proxies for earnings management / earnings quality. We further note that
little is accomplished in terms of goodness of fit and smaller abnormal accruals unless a
substantial number of contaminated observations are removed from the sample. Studies that
simply exclude one or a few specifically identified events, such as mergers or acquisitions,
unlikely alter substantially model performance.
We see no clear roadmap to resolve the accrual model misspecification problems we raise
because business model shocks have large effects on the accrual generating process, they occur
randomly, their effects on accruals can persist over several years, observable proxies for these
shocks are noisy, and firms in the same industry respond to shocks in diverse ways causing
different implications for signed abnormal accruals. Contributing to the difficulty of developing
better specified accrual models that explicitly recognize intra-industry heterogeneity and firmnon-stationarity is the absence of satisfactory theoretical and empirical industrial organizational
models that explain entry, growth, and survival across different markets and regulatory regimes
(Sutton, 2007). Although our investigation has focused specifically on abnormal accruals, our
results have implications for a broader set of “earnings quality” measures. These include the
Dechow and Dichev (2002) accrual quality measure because business model shocks can
similarly affect their model’s goodness-of-fit and their measure of earnings quality. Other
earnings quality measures such as earnings persistence and earnings smoothness that rely on
time-series properties of accounting earnings may also suffer from the confounding effects due
to volatilities induced by business model shocks.
Finally, our analysis has implications for the stream of literature that investigates the
consequences of having lower “earnings quality,” including documenting an association between
“earnings quality” and costs of capital or firm investment behavior (Dechow et al., 2010 review
this literature). The association between “earnings quality” and some variable of interest such as
32 cost of capital often is explained by “information risk” caused either by managers reporting
lower quality earnings to obfuscate the true nature of the firm’s underlying economic
performance, or by the innate information risk due to the complexity of the firm or its
environment. Our evidence suggests that firms face inherent risk in their business models and
that many measures of “earnings quality” likely proxy for this risk. In other words, in most
accounting studies of “earnings quality,” business model risk is a correlated omitted variable and
the various instruments for “earnings quality” capture this risk. If proxies for information risk are
indeed driven by inherent business model risk, then one needs robust models of the relation
between business model risk and innate information risk to estimate the discretionary portion of
information risk (Zimmerman 2013).
33 Appendix A
Examples of Firms with Large Unsigned Monthly Abnormal Returns, the Associated News
Stories in that Month, and the Lead, Lagged, and Contemporaneous Unsigned Abnormal
Accruals
We randomly selected 100 firm-year observations with unsigned monthly abnormal returns of at least
20%. We searched all news stories on Factiva for the corresponding month with the large unsigned
monthly abnormal return. The following are four examples.
1.
Mercury Computer Systems Inc (Market cap: $246 million)
Large monthly abnormal return: 128%
Month of large unsigned monthly abnormal return: 12/08
Lagged unsigned Jones-model abnormal accrual: 0.01
Contemporaneous unsigned Jones-model abnormal accrual: 0.08
Lead unsigned Jones-model abnormal accrual: 0.13
Various News stories (12/15/08): “Visage Imaging Receives FDA 510(k) Clearance for its Latest Thin Client”
(12/20/08) “Mercury Computer Systems Inc., said this week its Visage unit received Food and Drug Administration
clearance to sell a medical- imaging unit. The Visage CS 3.1 performs a range of imaging functions, including CAT
scans and magnetic resonance imaging. In addition, the Chelmsford manufacturer of computer, signal, and imageprocessing systems and software is launching the Echotek Series of Virtex digital receivers.”
Abnormal returns: 16% (12/2/08), 11% (12/3/08), 13% (12/9/08), 20% (12/19/08)
2.
CompuCredit Corporation (Market cap: $325 million)
Large monthly abnormal return: -52%
Month of large unsigned monthly abnormal return: 1/02
Lagged unsigned Jones-model abnormal accrual: 0.00
Contemporaneous unsigned Jones-model abnormal accrual: 0.38
Lead unsigned Jones-model abnormal accrual: 0.24
News story (1/29/02): “CompuCredit announced preliminary fourth quarter 2001 net income of $5.6 million, or
$0.12 per share. Wall Street analysts on average were expecting the Company to earn $0.28 per share in the same
period, according to Multex Global Estimates. The Company cited reduced loan growth and increased expenses as
the primary reasons for its expected earnings shortfall. The Company also announced that it has reduced its
workforce by approximately 70 employees. CompuCredit is a credit card company that uses analytical techniques,
including sophisticated computer models, to market general-purpose credit cards and related fee-based products and
services.”
Abnormal return on 1/30/02: -42%
3.
Tenet Healthcare Corp. (Market cap: $5.1 billion)
Large monthly abnormal return: -25%
Month of large unsigned monthly abnormal return: 1/04
Lagged unsigned Jones-model abnormal accrual: 0.08
Contemporaneous unsigned Jones-model abnormal accrual: 0.08
Lead unsigned Jones-model abnormal accrual: 0.04
34 News story (1/28/04): “Tenet Healthcare plans to sell more than a quarter of its hospitals this year and its dim nearterm outlook signaled that a turnaround is more distant than anticipated.”
Abnormal return on 1/28/04: -17%
4.
Geo Group (Market cap: $741 million)
Large monthly abnormal return: 46%
Month of large unsigned monthly abnormal return: 3/06
Lagged unsigned Jones-model abnormal accrual: 0.04
Contemporaneous unsigned Jones-model abnormal accrual: 0.10
Lead unsigned Jones-model abnormal accrual: 0.12
News story (3/31/06): “The GEO Group, Inc. today increased its previously issued earnings guidance for the first
quarter of 2006 by $0.10 per share to a range of $0.39 to $0.41 per share. This increased guidance is primarily
attributable to increased occupancy at GEO's facilities, including those formerly operated by Correctional Services
Corporation. GEO is increasing its first quarter 2006 revenue guidance by $2.0 million to a range of $184 million to
$188 million. … GEO has previously announced its intention to restructure its relationship with CentraCore
Properties Trust ("CPT"), from whom GEO leases 11 of its correctional and detention facilities (the "Leased
Facilities"). … GEO has previously stated that it may elect to not exercise its exclusive option to renew certain of
the Expiring Leases in favor of the construction and development through government-sponsored bonds or other
third party financing of new replacement facilities in close proximity to the facilities covered by the Expiring
Leases. In furtherance of this strategy, GEO is announcing that it has acquired three properties in close proximity to
certain of the expiring Leased Facilities, and has entered into an agreement to buy a fourth property adjacent to an
expiring Leased Facility. GEO is also in negotiations to purchase additional properties in close proximity to the
Leased Facilities. … GEO is a world leader in the delivery of correctional, detention, and residential treatment
services to federal, state, and local government agencies around the globe. GEO offers a turnkey approach that
includes design, construction, financing, and operations. GEO represents government clients in the United States,
Australia, South Africa, Canada, and the United Kingdom. GEO's worldwide operations include 61 correctional and
residential treatment facilities with a total design capacity of approximately 49,000 beds.”
Abnormal return on 1/30/02: 14%
Additional observations:
Very few stories corresponding to the large daily abnormal returns specifically mentioned that the firm
was revising its business strategy, although a few did (see GEO Group). Many of the stories associated
with large daily abnormal returns involved earnings releases. Within these stories the firm offers more
texture about the success or failure of its current business model. For example, CompuCredit reported
lower earnings citing reduced loan growth, and announced layoffs. Its stock price fell 42% on that day.
Such a story provides the market with tangible information about the success (or failure in this case) of
the firm’s strategy. Interestingly, the current and following years’ unsigned abnormal accruals are 38%
and 24% of total assets, respectively. Other large monthly abnormal returns are associated with new
products, joint ventures, and FDA drug approvals. For example, Mercury Computer’s stock price rose
128% in December 2008. Various stories started to appear during December that the FDA approved the
sales of its medical imaging system. Mercury’s lagged, contemporaneous, and lead abnormal accruals
were 1%, 8%, and 13% of total assets, respectively.
35 Appendix B
Variable Definitions
σCFOi,t
Standard deviation of cash flow from operations (Compustat oancf)
deflated by total assets (Compustat at) over the current and prior four
years
σREVi,t
Standard deviation of sales (Compustat sale)deflated by total assets
(Compustat at) over the current and prior four years
AbAccruali,t
Firm i's year t abnormal accrual, estimated as the residual from the
estimation of the cross-sectional version of the original Jones model by
industry-year.
AbAccrual_NLi,t
Firm i's year t abnormal accrual, estimated as the residual from the
estimation of the cross-sectional version of the nonlinear accrual model
(Ball and Shivakumar 2006) by industry-year.
AbAccrual_PFi,t
Firm i's year t abnormal accrual, estimated as the residual from the
estimation of the cross-sectional version of the accrual model with ROA
performance control (Kothari et al., 2005) by industry-year.
ABNRETi,t
Firm i's cumulative abnormal stock return during fiscal year t (using the
CRSP monthly returns file), where expected return is measured by the
equal-weighted CRSP index.
CFi,t
Firm i's cash flow from operations (Compustat oancf) in year t, scaled
by average total assets (Compustat at).
ContamFSi,t
An indicator variable that equals one if OpShockt=1 or OpShockt-1=1 or
OpShockt-2=1 .
ContamReti,t
An indicator variable that equals one if RetShockt=1 or RetShockt-1=1 or
RetShockt-2=1 .
CurrRatioi,t
Firm i's quarter t current ratio, measured as current assets divided by
current liabilities (Compustat act/lct).
DABNRETi,t
An indicator variable that equals one if ABNRET < 0, and equals zero
otherwise.
DCFi,t
An indicator variable that equals one if CF < 0, and equals zero
otherwise.
DefaultRiski,l
A market-based measure of firm i's probability of default as of the end
of the month immediately preceding the inception of loan l. Based on
the Black-Scholes-Merton model.
36 IndChangei,t
An indicator that equals one if firm i changed four digit sic industries
from year t-1 to year t, and equals zero otherwise.
LUAAi,t
An indicator that equals one if firm i has a "large" unsigned abnormal
accrual in year t, and equals zero otherwise, where “large” is defined as
an abnormal accrual with magnitude greater than 5% of total assets,
which is roughly the median UAA (UAA > 0.05)
LUAA_NLi,t
An indicator that equals one if firm i has a "large" unsigned abnormal
accrual (based on the nonlinear accrual model) in year t, and equals
zero otherwise, where “large” is defined as an abnormal accrual with
magnitude greater than 5% of total assets (UAA_NL > 0.05)
LUAA_PFi,t
An indicator that equals one if firm i has a "large" unsigned abnormal
accrual (based on the ROA performance control accrual model) in year
t, and equals zero otherwise, where “large” is defined as an abnormal
accrual with magnitude greater than 5% of total assets (UAA_PF >
0.05)
LargeDiscOpsi,t
An indicator that equals one if firm i has discontinued operations
(Compustat do) greater than five percent of sales in year t, and equals
zero otherwise.
LargeMergAcqi,t
An indicator that equals one if firm i in year t engaged in a large
merger/acquisition (as indicated by Compustat sales footnote code
"AB"), and equals zero otherwise.
LargeRestruci,t
An indicator that equals one if firm i has restructuring charges
(Compustat rcp) greater than five percent of sales in year t, and equals
zero otherwise.
LargeSpecItemi,t
An indicator that equals one if firm i has special items (Compustat spi)
greater than five percent of sales in year t, and equals zero otherwise.
Leveragei,t
Firm i's quarter t leverage, measured as total liabilities (Compustat lt)
divided by total assets (Compustat at).
LnAssetsi,t
The natural log of firm i's quarter t ending total assets (Compustat at).
LnFacilityi,l
The natural log of the face amount of firm i's loan facility l (Dealscan
facilityamt), in millions of U.S. dollars.
LnMaturityi,l
The natural log of the maturity in months of firm i's loan facility l
(Dealscan maturity).
LnSizei,t
The natural log of firm i's quarter t ending market value of common
equity (Compustat prcc_c*csho).
37 MaxMUARi,t
The maximum monthly abnormal return experienced by firm i during
year t, where abnormal return is measured as CRSP firm return less the
return on the CRSP value weighted index.
MktToBooki,t
Firm i's market-to-book ratio at the end of quarter t (Compustat
[prcc_c*csho]/ceq)
OpCycVolUPi,t
An indicator that equals one if firm i's four-quarter operating cycle
volatility (i.e., std. deviation) increased from year t-1 to t. We measure
operating cycle as "days sales outstanding" (90/(saleq/avg_rectq)) +
"days inventory outstanding" (90/(cogsq/avg_invtq)) - "days payables
outstanding" (90/(cogsq/avg_apq)).
OpShocki,t
An indicator variable that equals one if firm i in year t experiences at
least one of five specifically identified operational shocks (from
financial statements), and equals zero otherwise; specifically, this
variable equals one if at least one of the following variables equals one:
IndChange,
LargeDiscOps,
LargeMergAcq,
LargeRestruc,
LargeSpecItem.
OtherMaxMUARi,t
The average maximum monthly abnormal return experienced by all
other firms in firm i's industry in year t, where abnormal return is
measured as CRSP firm return less the return on the CRSP value
weighted index.
PPEi,t
Firm i's quarter t gross property, plant and equipment balance, scaled by
beginning-of-period total assets.
RetShocki,t
An indicator variable that equals one if firm i in year t experiences a
large monthly abnormal stock return, defined as MaxMUAR > 20%.
ROAi,t
Firm i's year t return on assets, computed as net income divided by
average total assets.
SalesChangei,t
Firm i's change in sales from year t-1 to t, scaled by year t-1 total assets.
Securedi,l
An indicator variable that equals one if loan facility l requires collateral,
and equals zero otherwise (DL-secured).
Spreadi,l
interest rate on loan facility l in excess of LIBOR, in basis points (DLallindrawn)
Tangibility,i,t
Asset tangibility, measured as property, plant and equipment, scaled by
total assets (Compustat ppent/at).
TotalAccrualsi,t
Firm i's year t total accruals, taken from the statement of cash flows.
UAAi,t
Firm i's year t unsigned abnormal accrual, calculated as |AbAccrual|.
38 UAA_NLi,t
Firm i's year
|AbAccrual_BS|.
t
unsigned
abnormal
accrual,
calculated
as
UAA_PFi,t
Firm i's year
|AbAccrual_PF|.
t
unsigned
abnormal
accrual,
calculated
as
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43 Figure 1
Variance of Operating Cycle in the Airline Industry
Figure 1 presents a time-series plot of the time-series and cross-sectional variation in the operating cycles of several
major airline companies.
Airlines’ Operating Cycle (Days)
30
American
20
10
Alaska
0
1985
1990
1995
2000
2005
2010
2015
US Air
American
‐10
Delta
‐20
Southwest
United
‐30
Frontier
Delta
‐40
‐50
‐60
44 Figure 2
Frequency histogram of large stock return shocks
Figure 2 presents a histogram of sample realizations (using the full sample of 125,956 observations) of the
magnitude of the largest monthly abnormal stock return in each firm-year (MaxMUAR).
MaxMUAR
25000
Frequency
20000
15000
10000
5000
0
Absolute Percentage
45 Figure 3
Histogram of R2 from estimation of the cross-sectional Jones model by industry-year
Figure 3 presents frequency histograms of the estimated R2s from the 878 industry-year estimations within our
sample.
Industry‐year Jones Model R2 Frequency
160
140
120
Frequency
100
80
60
40
20
0.000
0.025
0.050
0.075
0.100
0.125
0.150
0.175
0.200
0.225
0.250
0.275
0.300
0.325
0.350
0.375
0.400
0.425
0.450
0.475
0.500
0.525
0.550
0.575
0.600
0.625
0.650
0.675
0.700
More
0
R2
Industry‐year Nonlinear model R2 Frequency
45
40
35
Frequency
30
25
20
15
10
5
0
R2
Industry‐year Model with ROA Performance Control R2 Frequency
70
60
50
Frequency
40
30
20
10
0
R2
46 Figure 4
Time-series plots of the proportion of sample firms in each year that experience shocks and large
unsigned abnormal accruals
Figure 4 presents time-series plots of the proportion of the "full sample" firms in each year (i.e., 125,956
observations) that experience large operational shocks (i.e., OpShock = 1), large return shocks (i.e., RetShock = 1),
large unsigned abnormal accruals from the original Jones model (i.e., LUAA = 1), large unsigned abnormal accruals
from the nonlinear Jones model (i.e., LUAA_NL = 1), and large unsigned abnormal accruals from a Jones model that
includes ROA (i.e., LUAA_PF = 1). All variables are further defined in Appendix B.
% Firms with Shocks/Large Abnormal Accruals
1
0.9
0.8
0.7
0.6
% OpShock
% RetShock
0.5
% LUAA
0.4
% LUAA_NL
% LUAA_PF
0.3
0.2
0.1
0
47 Figure 5
Graphical presentation of Table 5, Panel B results
All Observations ‐ Nonlinear model
0.60
Initial Uncontaminated Obs ‐ Nonlinear model
0.09
0.30
0.050
0.045
0.08
0.50
0.25
0.040
0.07
0.40
0.035
0.20
0.06
0.030
0.05
0.15
0.30
0.04
0.025
PcntUAA
PcntUAA
0.020
AvgUAA
0.20
0.10
0.03
0.02
0.010
0.05
0.10
0.01
0.00
0.005
0.00
0
1
2
3
4
5
6
7
8
AvgUAA
0.015
0.000
1
9
2
3
4
5
6
7
8
9
Model Number (see Panel A)
Model Number (see Panel A)
Initial Uncontaminated Obs ‐ model with ROA performance control
All Observations ‐ model with ROA performance control
0.60
0.30
0.09
0.050
0.045
0.08
0.25
0.50
0.040
0.07
0.40
0.035
0.20
0.06
0.030
0.05
0.15
0.30
0.04
PcntUAA
0.020
AvgUAA
0.20
0.025
PcntUAA
0.10
0.03
0.015
0.02
0.10
0.010
0.05
0.01
0.005
0
0.00
1
2
3
4
5
6
7
8
0.00
2
3
4
5
6
Model Number (see Panel A)
48 0.000
1
9
Model Number (see Panel A)
7
8
9
AvgUAA
Figure 5, continued
Original Jones
0.20
0.18
0.16
0.14
0.12
0.10
Mean IY R‐sq.
0.08
Median IY R‐sq
0.06
0.04
0.02
0.00
1
2
3
4
5
6
7
8
9
Model Number (see Panel A)
Nonlinear model
0.64
0.62
0.60
0.58
0.56
0.54
Mean IY R‐sq
0.52
Median IY R‐sq
0.50
0.48
0.46
0.44
1
2
3
4
5
6
7
8
9
Model Number (see Panel A)
Model with ROA performance control
0.40
0.38
0.36
0.34
0.32
0.30
Mean IY R‐sq
0.28
Median IY R‐sq
0.26
0.24
0.22
0.20
1
2
3
4
5
6
Model Number (see Panel A)
49 7
8
9
Figure 6
Rejection rate frequencies (Predictions 5 and 6)
Fig. 6 presents the frequency of rejecting the null hypothesis of no relation between absolute discretionary Jones
model accruals and a partitioning variable that is correlated with business model shocks. In Panel A, the correlation
is with a firm's own shock during year t (MaxMUAR). In Panel B, the correlation is with shocks to other industry
firms during year t (OtherMaxMUAR). The "diamond" lines are from univariate tests at the 5% level, and the
"square" lines are from multivariate tests with the following controls: LnSize, MktToBook, Leverage, σCFO, and
σRev. Results are based on 250 trials using a sample size of 1,000 randomly drawn observations.
Panel A: Own-firm shocks (MaxMUAR) (Prediction 5)
100.00%
90.00%
Rejection Rate Frequency
80.00%
70.00%
60.00%
50.00%
NoControls
40.00%
Controls
30.00%
20.00%
10.00%
0.00%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Correlation between partition variable and MaxMUAR
Panel B: Other firm shocks in the industry (OtherMaxMUAR) (Prediction 6)
100.00%
90.00%
Rejection Rate Frequency
80.00%
70.00%
60.00%
50.00%
NoControls
40.00%
Controls
30.00%
20.00%
10.00%
0.00%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Correlation between partition variable and OtherMaxMUAR
50 0.9
1
Table 1
Descriptive statistics
Table 1 presents descriptive statistics for key variables used in our Jones model analyses. |AbAccrual| is the
unsigned abnormal accrual from estimating the original Jones model by industry-year (i.e., the absolute value of the
residual from Eq. 4). |AbAccrual_NL| is the unsigned abnormal accrual from estimating the nonlinear accrual model
by industry-year (i.e., the absolute value of the residual from Eq. 5). |AbAccrual_PF| is the unsigned abnormal
accrual from estimating the Jones model with inclusion of ROA by industry-year (i.e., the absolute value of the
residual from Eq. 6). All variables are further defined in Appendix B.
N
Mean
Std
P1
P25
Median
P75
P99
TotalAccruals
90,822
-0.070
0.141
-0.581
-0.112
-0.054
-0.010
0.291
SalesChange
90,822
0.108
0.349
-0.724
-0.022
0.057
0.197
1.357
PPE
90,822
0.573
0.516
0.000
0.211
0.453
0.825
2.079
ROA
90,822
-0.036
0.239
-1.059
-0.050
0.029
0.074
0.283
CF
90,822
0.036
0.189
-0.753
-0.001
0.069
0.129
0.345
ABNRET
90,822
0.008
0.688
-0.980
-0.389
-0.102
0.214
3.025
MktToBook
90,615
2.780
4.081
-8.317
1.055
1.811
3.225
26.204
Leverage
90,664
0.507
0.260
0.044
0.307
0.502
0.673
1.342
LnSize
90,657
5.261
2.320
0.377
3.553
5.176
6.881
10.780
σCFO
65,050
0.076
0.076
0.006
0.028
0.050
0.091
0.376
σREV
65,982
0.174
0.158
0.006
0.065
0.124
0.227
0.733
|AbAccrual|
90,822
0.080
0.102
0.001
0.021
0.049
0.099
0.497
|AbAccrual_PF|
90,822
0.070
0.084
0.001
0.019
0.044
0.089
0.399
|AbAccrual_NL|
90,822
0.068
0.091
0.001
0.017
0.040
0.083
0.451
51 Table 2
Correlation matrix
Table 2 presents Pearson (Spearman) correlations above (below) the diagonal among key variables we use in our analyses. Correlations significant at the p < 0.05
level are reported in bold. Variables are defined in Appendix B.
(1)
TotalAccruals (1)
SalesChange (2)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
0.141
-0.138
-0.049
0.080
-0.036
-0.127
0.085
-0.155
-0.048
-0.375
-0.190
-0.378
-0.116
0.045
0.091
0.205
0.111
-0.036
0.088
-0.003
0.133
0.076
0.105
0.050
0.021
0.200
0.045
-0.038
0.090
0.086
-0.153
-0.114
-0.076
-0.084
-0.078
-0.097
0.113
-0.088
-0.050
0.293
-0.508
-0.112
-0.285
-0.294
-0.307
-0.231
0.219
-0.071
0.142
0.003
0.016
0.017
0.057
-0.005
0.061
-0.069
0.166
0.171
0.041
0.092
0.133
0.118
0.034
0.046
-0.083
0.043
0.048
-0.012
0.011
-0.035
-0.364
-0.308
-0.217
-0.202
-0.205
-0.395
0.375
0.412
0.468
0.402
0.317
0.226
0.217
0.207
0.243
0.795
0.855
0.221
0.691
0.216
0.161
PPE (3)
-0.186
0.081
CF (4)
-0.303
0.189
0.281
ABNRET (5)
0.118
0.264
0.079
0.239
MktToBook (6)
0.020
0.263
0.009
0.160
0.304
-0.085
-0.054
0.126
-0.085
-0.053
-0.102
LnSize (8)
0.056
0.145
0.120
0.358
0.246
0.380
0.073
σCFO (9)
-0.085
0.000
-0.203
-0.250
-0.096
0.061
-0.205
-0.452
σREV (10)
-0.044
0.120
-0.090
-0.094
-0.054
0.015
-0.029
-0.331
0.483
0.010
0.025
-0.135
-0.241
-0.061
0.035
-0.049
-0.249
0.373
0.234
|AbAccrual_PF| (12)
-0.065
0.060
-0.133
-0.154
-0.032
0.080
-0.081
-0.241
0.425
0.235
0.678
|AbAccrual_NL| (13)
-0.047
0.004
-0.137
-0.221
-0.126
0.070
-0.104
-0.242
0.369
0.228
0.633
0.480
LUAR
-0.113
-0.019
-0.124
-0.246
-0.073
-0.054
-0.067
-0.391
0.395
0.285
0.245
0.244
Leverage (7)
|AbAccrual| (11)
52 0.232
0.270
Table 3
Operational shocks and large unsigned abnormal returns
Table 3 presents results from estimating the probit specification of Eq (3). OpShocki,t is an indicator that equals one if firm i has an observed operational shock in
year t, and equals zero otherwise. RetShocki,t is an indicator that equals one if firm i has a large monthly stock return shock in year t, and equals zero otherwise.
All variables are further defined in Appendix B. Robust t-statistics clustered by both firm and year are reported in parentheses. *, **, and *** indicate
significance (two-sided) at the 10%, 5% and 1% levels, respectively.
Dep. Var.:
Column:
Intercept
RetShockt
RetShockt-1
RetShockt-2
N
Pseudo-R2
LargeMergAcq
(1)
-7.520***
(-29.968)
0.561**
(2.102)
0.435**
(2.281)
-0.025
(-0.127)
IndChange
(2)
-3.881***
(-35.992)
0.562***
(6.872)
0.239***
(3.176)
0.089
(0.942)
LargeDiscOps
(3)
-4.122***
(-26.029)
0.488***
(5.683)
0.263***
(3.548)
0.200***
(2.849)
LargeRestruc
(4)
-6.382***
(-23.499)
1.238***
(6.749)
0.980***
(3.330)
0.452**
(2.130)
LargeSpecItem
(5)
-3.124***
(-48.554)
0.977***
(17.729)
0.583***
(7.408)
0.213***
(6.179)
OpShock
(6)
-2.554***
(-49.583)
0.837***
(18.557)
0.485***
(9.416)
0.188***
(7.283)
125,964
0.010
125,964
0.014
125,964
0.014
125,964
0.066
125,964
0.062
125,964
0.051
53 Table 4
Business model shocks and large abnormal accrual estimates (Predictions 1 and 2)
Table 4 presents results from estimating Eqs. (7), (8), and (9). LUAA is an indicator that equals one if firm i's year t unsigned original Jones model abnormal
accrual is greater than 5% of total assets, and equals zero otherwise. LUAA_NL is an indicator that equals one if firm i's year t unsigned nonlinear Jones model
abnormal accrual is greater than 5% of total assets, and equals zero otherwise. LUAA_PF is an indicator that equals one if firm i's year t performance Jones model
abnormal accrual is greater than 5% of total assets, and equals zero otherwise. OpShocki,t is an indicator that equals one if firm i has an observed operational
shock in year t, and equals zero otherwise. RetShocki,t is an indicator that equals one if firm i has a large monthly stock return shock in year t, and equals zero
otherwise. MaxMUAR is firm i's maximum monthly unsigned absolute return during year t. OtherMaxMUA is the average MaxMUAR across all other firms in
firm i's industry during year t. All variables are further defined in Appendix B. R2 refers to adjusted- R2 (pseudo- R2) in columns (1)-(3) (columns 4-6). Robust tstatistics clustered by both firm and year are reported in parentheses. *, **, and *** indicate significance (two-sided) at the 10%, 5% and 1% levels, respectively.
Panel A: Operational Shocks reflected in Financial Statements (Prediction 1)
Model:
Dep. Var.:
Column:
Intercept
OpShockt
OpShockt-1
OpShockt-2
σCFO
σREV
N
R2
OLS
UAA
(1)
0.025***
(19.248)
0.046***
(30.481)
0.003**
(2.073)
0.001
(0.641)
0.435***
(24.818)
0.048***
(10.330)
64,193
0.212
OLS
UAA_NL
(2)
0.019***
(19.413)
0.045***
(34.962)
0.005***
(3.544)
0.003**
(2.358)
0.386***
(21.966)
0.034***
(8.039)
64,193
0.209
OLS
UAA_PF
(3)
0.023***
(28.768)
0.026***
(25.684)
0.001
(1.466)
-0.002
(-1.399)
0.449***
(33.528)
0.024***
(5.092)
64,193
0.239
54 Logit
LUAA
(4)
-0.985***
(-18.168)
0.725***
(41.616)
0.071***
(2.920)
0.027
(1.040)
6.859***
(21.733)
1.350***
(13.001)
Logit
LUAA_NL
(5)
-1.376***
(-29.866)
0.849***
(30.514)
0.113***
(3.166)
0.057**
(2.073)
7.376***
(32.863)
1.205***
(15.109)
Logit
LUAA_PF
(6)
-1.258***
(-32.802)
0.606***
(21.242)
0.033
(1.568)
-0.014
(-0.553)
9.311***
(30.591)
1.037***
(10.830)
64,193
0.076
64,193
0.092
64,193
0.095
Table 4, continued
Panel B: Market-based shocks (Prediction 1)
Model:
Dep. Var.:
Column:
Intercept
RetShockt
RetShockt-1
RetShockt-2
σCFO
σREV
N
R2
OLS
UAA
(1)
0.021***
(13.708)
0.015***
(10.185)
0.006***
(6.326)
0.004***
(2.790)
0.438***
(23.333)
0.040***
(7.517)
64,599
0.184
OLS
UAA_NL
(2)
0.015***
(11.486)
0.016***
(10.664)
0.006***
(6.813)
0.005***
(3.806)
0.387***
(20.903)
0.025***
(5.177)
OLS
UAA_PF
(3)
0.019***
(23.096)
0.009***
(15.797)
0.004***
(7.170)
0.003***
(3.007)
0.441***
(31.255)
0.017***
(3.565)
Logit
LUAA
(4)
-1.189***
(-18.769)
0.395***
(11.316)
0.208***
(5.938)
0.182***
(5.015)
5.823***
(19.192)
1.019***
(10.481)
Logit
LUAA_NL
(5)
-1.657***
(-30.443)
0.556***
(11.988)
0.216***
(5.797)
0.215***
(5.548)
6.211***
(26.108)
0.829***
(10.938)
Logit
LUAA_PF
(6)
-1.488***
(-38.312)
0.382***
(18.327)
0.217***
(7.963)
0.184***
(6.095)
8.035***
(26.829)
0.719***
(8.173)
64,599
0.178
64,599
0.227
64,599
0.075
64,599
0.093
64,599
0.098
55 Table 4, continued
Panel C: Shocks of other industry firms (Prediction 2)
Dep Var:
Column:
Intercept
UAA
(1)
0.003
(0.959)
0.021***
(7.292)
0.008***
(4.422)
0.007***
(2.864)
0.075***
(5.761)
-0.013
(-0.616)
0.003
(0.139)
0.417***
(23.899)
0.040***
(7.222)
MaxMUARt
MaxMUARt-1
MaxMUARt-2
OtherMaxMUARt
OtherMaxMUARt-1
OtherMaxMUARt-2
σCFO
σREV
N
Adj.-R2
64,599
0.193
56 UAA_NL
(2)
-0.006
(-1.641)
0.022***
(7.440)
0.010***
(5.134)
0.008***
(5.168)
0.069***
(5.965)
-0.005
(-0.258)
0.014
(0.761)
0.361***
(19.468)
0.024***
(5.116)
64,599
0.189
UAA_PF
(3)
0.008***
(2.696)
0.013***
(5.077)
0.005***
(3.003)
0.001
(0.848)
0.046***
(7.478)
-0.002
(-0.161)
0.003
(0.188)
0.434***
(30.649)
0.018***
(3.744)
64,599
0.232
Table 5
Business model shock sample contamination and abnormal accruals (Prediction 3)
Table 5 presents results from iterative estimation of accrual models to show the effect of inclusion of
"contaminated" observations in the cross-sectional industry-year estimation on the incidence of large unsigned
absolute accruals and industry-year estimation R2s. We begin by estimating the accrual models with only
uncontaminated observations (i.e., observations with RetShockt = 0 and RetShockt-1 = 0 and RetShockt-2 = 0), then
successively add 12.5% of the contaminated sample observations (i.e., observations with RetShockt = 1 or RetShockt1 = 1 or RetShockt-2 = 1) and re-estimate the models. Panel A presents the sample characteristics of each estimation
iteration. Panel B reports the median industry-year R2 from each estimation, mean unsigned abnormal accruals
(UAA), and the percentage of sample observations with a large unsigned abnormal accrual (LUAA = 1). All variables
are defined in Appendix B.
Panel A: Sample compositions across contamination iterations
Iteration
% of Contaminated
Obs. Included
1
2
3
4
5
6
7
8
9
00.0%
12.5%
25.0%
37.5%
50.0%
62.5%
75.0%
87.5%
100.0%
Number of Observations
Uncontaminated
Contaminated
(1)
(2)
10,844
12,722
13,937
14,867
15,502
15,938
16,400
16,701
16,917
57 0
6,447
14,426
23,833
33,634
43,659
53,753
63,831
73,905
Table 5, continued
Panel B: Estimation results from sample iterations (Prediction 3)
Column:
Initial 10,844
Uncontaminated Obs.
(3)
(4)
All Observations
(1)
(2)
Panel B1: Jones Model
Iteration
% LUAA
1
0.1936
2
0.3413
3
0.3968
4
0.4340
5
0.4567
6
0.4706
7
0.4810
8
0.4873
9
0.4927
Avg. UAA
0.0331
0.0562
0.0647
0.0705
0.0740
0.0762
0.0783
0.0794
0.0805
(5)
% LUAA
0.1936
0.2278
0.2378
0.2464
0.2542
0.2585
0.2635
0.2646
0.2678
Avg. UAA
0.0331
0.0364
0.0373
0.0380
0.0386
0.0389
0.0394
0.0396
0.0399
Med. I-Y R2
0.1403
0.1152
0.1049
0.0991
0.0940
0.0948
0.0913
0.0853
0.0836
Panel B2: Nonlinear Accrual Model
Iteration
% LUAA_NL
Avg. UAA_NL
1
0.1117
0.0233
2
0.2659
0.0434
3
0.3291
0.0535
4
0.3668
0.0591
5
0.3853
0.0625
6
0.3996
0.0646
7
0.4109
0.0665
8
0.4154
0.0675
9
0.4201
0.0684
% LUAA_NL
0.1117
0.1629
0.1718
0.1810
0.1848
0.1904
0.1968
0.1969
0.1942
Avg. UAA_NL
0.0233
0.0285
0.0296
0.0304
0.0311
0.0313
0.0316
0.0318
0.0318
Med. I-Y R2
0.6016
0.5583
0.5274
0.4844
0.4731
0.4680
0.4607
0.4630
0.4545
Panel B3: Model with ROA Performance Control
Iteration
% LUAA_PF
Avg. UAA_PF
1
0.1872
0.0320
2
0.3211
0.0498
3
0.3714
0.0574
4
0.4024
0.0617
5
0.4201
0.0644
6
0.4318
0.0663
7
0.4413
0.0678
8
0.4464
0.0689
9
0.4515
0.0697
% LUAA_PF
0.1872
0.2086
0.2124
0.2124
0.2151
0.2193
0.2200
0.2190
0.2195
Avg. UAA_PF
0.0320
0.0342
0.0349
0.0350
0.0353
0.0355
0.0356
0.0357
0.0358
Med. I-Y R2
0.2466
0.3095
0.3406
0.3371
0.3334
0.3380
0.3425
0.3497
0.3464
58 Table 6
Business model shocks and increases in operating cycle volatility (Prediction 4)
Table 6 presents results from estimating Eq. (10). OpCycVolUPt is an indicator that equals one if firm i's 4-quarter
operating cycle volatility increased from year t-1 to t, and equals zero otherwise. To calculate operating cycle
volatility in year t, we first calculate the operating cycle for each quarter in year t as "days sales outstanding" +
"days inventory outstanding" - "days payables outstanding." We then take the standard deviation of these four
quarterly operating cycles as year t's operating cycle volatility. If this standard deviation increases from year t-1 to t,
the dependent variable = 1. OpShocki,t is an indicator that equals one if firm i has an observed operational shock in
year t, and equals zero otherwise. RetShocki,t is an indicator that equals one if firm i has a large monthly stock return
shock in year t, and equals zero otherwise. All variables are further defined in the Appendix. Robust t-statistics
clustered by both firm and year are reported in parentheses. *, **, and *** indicate significance (two-sided) at the
10%, 5% and 1% levels, respectively.
Dep. Var.:
Column:
Intercept
OpShockt
OpCycVolUPt
(1)
-0.070**
(-1.97)
0.230***
(6.26)
OpCycVolUPt
(2)
-0.059
(-1.42)
RetShockt
N
R2
0.047*
(1.66)
47,056
0.001
47,056
0.0001
59 Table 7
Unsigned abnormal accruals and cost of debt
Table 7 presents results from estimating Eqs. (11) and (12). Spread is the loan interest rate in excess of LIBOR.
UAA is the absolute value of firm i's year t abnormal accrual, estimated as the residual from the estimation of the
cross-sectional version of the original Jones model by industry-year. ContamRet is an indicator that equals 1 if firm i
has a return shock (RetShock) in either year t, t-1, or t-2, and equals 0 otherwise. ContamOS an indicator that equals
1 if firm i has an observed operational shock (OpShock) in either year t, t-1, or t-2, and equals 0 otherwise. All
variables are further defined in Appendix B. Robust t-statistics clustered by firm are reported in parentheses. *, **,
and *** indicate significance (two-sided) at the 10%, 5% and 1% levels, respectively.
Dep. Var.:
Contam:
Column:
Intercept
Spread
(1)
497.049***
(28.707)
Contam
UAA
80.577***
(4.834)
Contam*UAA
Leverage
LnAssets
Tangibility
CurrRatio
MktToBook
DefaultRisk
LnFacility
LnMaturity
Secured
84.024***
(11.295)
-8.023***
(-6.334)
-11.998*
(-1.930)
-2.706**
(-2.251)
-1.887***
(-5.499)
167.856***
(12.877)
-20.067***
(-16.872)
5.783***
(3.927)
89.018***
(34.296)
HO: UAA + Contam*UAA = 0
χ2 Statistic
Prob. > χ2
N
Adj.-R2
23,738
0.423
60 Spread
ContamRet
(2)
452.044***
(26.098)
38.601***
(11.626)
25.176
(0.660)
48.069
(1.151)
78.758***
(10.796)
-5.716***
(-4.692)
-9.751
(-1.616)
-3.661***
(-3.083)
-1.775***
(-5.266)
162.351***
(12.709)
-19.828***
(-17.293)
6.632***
(4.563)
84.172***
(32.949)
Spread
ContamOS
(3)
484.130***
(28.463)
15.454***
(5.021)
30.486
(1.061)
45.391
(1.334)
80.769***
(10.919)
-7.972***
(-6.461)
-11.635*
(-1.909)
-2.823**
(-2.372)
-1.924***
(-5.674)
164.284***
(12.782)
-19.600***
(-16.818)
5.941***
(4.100)
88.322***
(34.186)
73.245***
17.69
0.0000
23,738
0.435
75.877***
15.04
0.0001
23,738
0.427
Table 8
Specification tests of signed abnormal accruals
Table 8 reports the proportion of 250 random samples of 200 firms each where the null hypothesis of zero abnormal accruals is rejected at the 5% level using a
one-tailed t-test (the assocated actual numbers of random trials out of the 250 where the null is rejected is presented in brackets). The samples are drawn at
random within each sample quartile based on several different variables: Market-to-book, Size, and Sales Growth. Figures in bold signify rejection rates that are
significantly different from the 5% significance level of the test (two-tailed), where proportions above (below) 0.05 indicate that tests are biased against (in favor)
of accepting the null hypothesis of zero abnormal accruals. The first five columns ('Full Sample') use the full sample in the Jones model estimation and random
sampling pools. The second five columns ('Subsample') includes only those firm-years where LUARt = 0 (i.e., only retains those firm-years without a
contemporaneous shock) from both the initial Jones model estimation and the random sampling pool. * indicates that the proportion in the no-shock subsample
analysis is different from the corresponding proportion in the full sample analysis at the 5% level based on a two-sample Wald test.
Column
(1)
(2)
Ha : AbAccrual < 0
0.044
0.420
[105]
0.468
[117]
0.008
[2]
0.068
[226]
0.336
[84]
0.000
[0]
0.000
[0]
0.168
[42]
[126]
0.000
[0]
0.088
[22]
0.564
[141]
[243]
0.468
[117]
0.024
[6]
0.320
[80]
0.004
[1]
0.000
[0]
[124]
Panel C: Sales Growth
Ha : AbAccrual > 0
0.060
0.004
[1]
0.444
[111]
0.592
[148]
0.008
[2]
Ha : AbAccrual < 0
0.044
0.248
[62]
0.000
[0]
0.000
[0]
0.124
[31]
61 (7)
0.008
[2]
0.060
0.060
(6)
All
Sum 1-4 33,027
Panel A: Market-to-Book
Ha : AbAccrual > 0
Panel B: Size
Ha : AbAccrual > 0
(5)
4
22,705
All
1
90,822 22,705
0.044
(4)
Full Sample
2
3
22,705
22,705
Sample Quartile
N
Ha : AbAccrual < 0
(3)
0.036
0.068
0.036
0.068
[262]
0.036
[93]
(8)
(9)
(10)
(11)
Subsample With No Year t Shocks
1
2
3
4
8,256
8,256
8,256
8,256
(12)
Sum 1-4
0.008
[2]
[78]
0.004
[1]
0.048*
[12]
0.032*
[8]
0.208
[52]
[66]
0.000
[0]
0.024*
[6]
0.004*
[1]
0.072*
[18]
0.036*
[9]
0.092*
[23]
0.040*
[10]
0.040*
[10]
0.016
[4]
0.044*
[11]
0.120*
[30]
0.024*
[6]
0.144*
[36]
0.028
[7]
[77]
0.004
[1]
0.088
[22]
[40]
0.124*
[31]
0.020*
[5]
0.132*
[33]
[20]
[57]