Business Model Shocks and Abnormal Accrual Models

Edward Owens Joanna Shuang Wu Jerold Zimmerman

Simon School of Business, University of Rochester, Rochester, NY 14627

October 2013 Abstract Basic economics challenge the specification of discretionary 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 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. Further, 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. Our results suggest that removing observations contaminated by business model shocks leads to better specified accrual models, and reduces both Type I and Type II errors in tests of earnings management. 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, Wayne Guay, Shane Heitzman, Robert Resutek, Jerry Warner, Charles Wasley and seminar participants at Dartmouth University, University of Rochester, the MIT Asia Conference in Accounting, and Penn State Accounting Research Conference.

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“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).

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 (e.g., Dechow et al. 2010).1 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; Healy 1996; Dechow et al. 2010), and there is a general belief that such

improvements can indeed be discovered. 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 basic economics challenge the fundamental assumptions underlying “normal”

(non-discretionary) 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.

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processes) and intra-industry homogeneity (i.e., industry peers should have similar accrual

generating processes). 3 However, a key economic principle dictates that profit-seeking firms

seek rents by developing a unique, sustainable market niche that differentiates its business

strategy from other firms (Brickley and Zimmerman 2010), which 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 alter their existing business strategies. These business model revisions/shocks, if

frequent, 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). Consider that

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 in idiosyncratic fashion. Since firms in the same industry may react

differently to similar 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, a shock likely impacts 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

                                                            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 time-series 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.

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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 likely unpredictable ways as this firm and other firms in the same market

dynamically adjust their business strategies. 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.

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 monthly abnormal returns as our primary indicator for the presence of business model

shocks during a firm-year. 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. 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

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with stable business models. Moreover, our finding that one- and two-year lagged unsigned

abnormal returns are correlated with contemporaneous operational shocks suggests that business

model shocks can affect several years of financial statements, including accruals.

To examine 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 significantly 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 document that as more “contaminated” observations (i.e., those

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with business model shocks) are included in the industry-year Jones model estimation, the mean

unsigned abnormal accrual in the sample increases monotonically. Moreover, the magnitude of

unsigned abnormal accruals remains large unless a substantial number of contaminated

observations are excluded.

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. Further, we 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. 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 Finally, simulation tests show that removal

of observations with business model shocks increases the power to detect earnings management.

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.”

                                                            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).

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Our analysis provides an economics-based framework and associated evidence on how one

driver of fundamental performance, business model shocks, affects measurement of 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 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 non-discretionary components that arise from pervasive business model

shocks. Instead, our results suggest that removing observations that have experienced business

model shocks leads to better specified accrual models, and reduces both Type I and Type II

errors in tests of earnings management.

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 development of predictions

This section summarizes the literatures that challenge the firm stationarity and intra-

industry homogeneity assumptions underlying the estimation of accrual models. Then we

summarize accounting studies that have recognized and tried to address 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," which forces both incumbent firms and new entrants to modify their business

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strategies in response to entry and to adjustments in business models by competitors (e.g., digital

imaging destroying traditional photography). 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

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).

Several empirical regularities further challenge the validity of the firm-stationarity and

intra-industry-homogeneity assumptions. For example, considerable heterogeneity exists across

industries in firm size, survival, entry and exit rates, and market structures (Berry and Reiss 2007

and 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). Further, various

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operating characteristics of firms such as growth rates, idiosyncratic risk and survival rates of

newly listed firms have changed over time (Comin and Mulani, 2006, Fama and French, 2004,

Irvine and Pontiff, 2006, and Brown and Kapadia, 2007).

Another general view from both economics (Sutton 2007) and strategic management

suggests that business models are dynamic, and 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,

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. 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.

                                                            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.

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As further illustration of the veracity of the firm stationarity and intra-industry

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 has declined.

2.2. Related accounting studies of abnormal accruals

To parse “discretionary accruals” from total accruals, which are then used as proxies for

earnings management or earnings quality, researchers compute regression residuals derived from

various models of "normal accruals," typically following a version of the model used in Jones

(1991). 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. Others conjecture that

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estimates of “discretionary accruals” are likely misspecified due to inadequate considerations of

firm economic fundamentals such as uncertain economic environments and sales growth and

accounting fundamentals (Healy 1996 and McNichols 2001). 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 intra-

industry homogeneity, which are likely violated when business model shocks are present.

Ball and Shivakumar (2006) 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

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assumption of intra-industry homogeneity. While the prior literature’s efforts 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 type of 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

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acquisitions and discontinued operations). Many subsequent studies using cash flow statement

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 begin by considering the following generic cross-sectional accrual model:

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N

ijt jt njt nijt ijtn

TA X

(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 ijt ijt ijtDA NDA (2)

                                                            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.  

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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.

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 may

react to a similar shock with a different strategy responses which affect accruals differently. For

example, a 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

                                                            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 value-destroying acquisitions; and later, these firms divested or restructured to thwart their own takeover. 

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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

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.

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.

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

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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 business model shocks. We next

investigate whether business model shocks bias researchers’ inferences regarding the

determinants and effects of “earnings quality” when ijt is used to proxy for ijtDA 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 ( ijtNDA ), then business model shocks create bias in

unsigned abnormal accruals ( ijt ) as an instrument for unsigned discretionary accruals ( ijtDA ).

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

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cash flow and revenue volatilities from t-5 to t-1, business model shocks in year t will cause

large unsigned non-discretionary accruals ( ijtNDA ) 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 ijtNDA 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.

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.

                                                            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. 

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Because business model shocks introduce noise into the measurement of discretionary

accruals, removal of "contaminated" observations should lead to cleaner identification of "true"

earnings management as measured by signed discretionary accruals, therefore reducing the rate

of Type I and Type II errors in tests of earnings management.

Prediction 7: Tests of earnings management (as captured by signed abnormal accruals) will exhibit less Type I and Type II errors in samples where observations with business model shocks are removed.

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. 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, which hereafter we

refer to as our "full" sample.

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

18  

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.

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 (LUAR) is associated with numerous firm characteristics including size,

market-to-book, and leverage.

4. Research design and empirical findings

4.1. Business model shocks

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 reported 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

19  

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. Fifty-five percent of

all firm-years experience such an event.

We recognize that abnormal returns also capture things other than business model shocks

(e.g., liquidity shocks, investor sentiment), which add noise to using abnormal returns to proxy

for business model shocks (Savor 2012; Barberis 1998). 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% and 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 observations we conclude that firms are

indeed experiencing significant business events during months with large abnormal returns,

although 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.

20  

4.1.2. Operational proxies for business model shocks (OpShock)

As a secondary measure, we develop a proxy for business model shocks that relies on

five Compustat-reported data items: (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 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

21  

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 market-

adjusted returns are indeed capturing changes in future cash flows due to business models shocks

(acquisitions, divestitures, restructuring charges, etc, 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 using the following logit model:

,1Pr( 1) ,

1i l zOpShock

e

(3)

0 1 , 2 , 1 3 , 2 ,i t i t i tz RetShock RetShock RetShock

where all variables are 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. In addition, consistent with Gerakos

and Kovrijnykh (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 cross-sectional accrual models are likely unable to capture the complex dynamic and

persistent financial effects induced by business model shocks.

22  

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:

, 0 1 , 2 , ,i t i t i t i tTotalAccruals SalesChange PPE (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:

, 0 1 , 2 , 3 , 4 , 5 ,6 , 7 , 8 , ,

** ,

i t i t i t i t i t i t

i t i t i t i t

TotalAccruals SalesChange PPE CF DCF DCF CFABNRET DABNRET DABNRET ABNRET

(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.

23  

Finally, we use the Kothari et al. (2005) variant of the original Jones model that adds a

control for firm performance.:

, 0 1 , 2 , 3 , , ,i t i t i t i t i tTotalAccruals SalesChange PPE ROA (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 model fit is substantially improved 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

                                                            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.  

24  

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 (Vorhies 2005).17 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 begin by

estimating the following OLS model:

                                                            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.” 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).

25  

, 0 1 , 2 , 1 3 , 24 , 5 , , ,

i t i t i t i t

i t i t i t

UAA BMShock BMShock BMShockCFO REV

(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:

,1Pr( 1) ,

1i t zLUAA

e

(8)

0 1 , 2 , 1 3 , 24 , 5 , ,

i t i t i t

i t i t

z BMShock BMShock BMShockCFO REV

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)

26  

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 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 values) 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:

, 0 1 , 2 , 1 3 , 2

4 , 5 , 1 6 , 2

7 , 8 , , ,

i t i t i t i t

i t i t i t

i t i t i t

UAA MaxMUAR MaxMUAR MaxMUAROtherMaxMUAR OtherMaxMUAR OtherMaxMUAR

CFO REV

    (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. Turning to the coefficients

on OtherMaxMUAR, i.e., shocks experienced by the other firms, we find uniformly positive and

27  

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

In this section we examine how inclusion of an increasing number of "contaminated"

firm-year observations with business model shocks in either years t, t1 or t2 affect the

estimation of accrual models. 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

observations included in the sample. For each iteration we report 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

28  

“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 industry-

year 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

29  

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

statistics from estimating accrual models, but their inclusion in the 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 industry regression (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, which 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

30  

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.

,1Pr( 1) ,

1i t zOPCYCVOLUP

e

(10)

0 1 ,i tz BMShock

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 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.                                                             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.

31  

4.6. Business model shocks and inferences using unsigned abnormal accruals

4.6.1. Simulations

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/quality and

large return shocks, suggesting rejection rates of around 5% in properly specified 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

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.

32  

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. Reinterpretation of Bharath et al. (2008)

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). BSS examine 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.

33  

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

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 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 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: 

, 0 1 , 2 , 3 , 4 ,

5 , 6 , 7 , 8 ,

9 , 10 , , .

i l i t i t i t i t

i t i t i m i l

i l i l i l

Spread UAA Leverage LnAssets TangibilityCurrRatio MktToBook DefaultRisk LnFacilityLnMaturity Secured

(11)

 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 using the approach in Hillegeist et al. (2004)

based on the Black-Scholes-Merton equation. 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

34  

equals one if the loan has a collateral requirement and equals zero otherwise. All variables are

described 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). 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:

, 0 1 , 2 , 3 4 , 5 ,

6 , 7 , 8 , 9 ,

10 , 11 , 12 , ,

*

.

i l i t i t i t i t

i t i t i t i m

i l i l i l i l

Spread Contam UAA Contam UAA Leverage LnAssetsTangibility CurrRatio MktToBook DefaultRiskLnFacility LnMaturity Secured

(12)

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) suggests an

insignificant relation between UAA and Spread for firms that have not experienced a business

model shock. In contrast, the sum of the coefficients on 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"

35  

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.

4.7. Business model shocks and inferences using signed abnormal accruals

4.7.1. Type I errors

Kothari et al. (2005) show that accrual models are likely 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 > 0 at the 5% level

using one-sided tests. The null rejection rates are reported in Table 8 Panel A (market-to-book),

Panel B (size), and Panel C (sales growth). 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.

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%,

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similar to findings in Kothari et al. (2005) (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, consistent

with our Prediction 7.

37  

4.7.2. Type II errors

Because removal of shock observations results in better specified models of abnormal

accruals, we predict that removal of shock observations will not only mitigate over-rejection of

the null of no earnings management, but will also improve the power of the test to detect

earnings management when present (Prediction 7). To examine this prediction, we again follow

Kothari et al. (2005) and conduct a simulation wherein we seed various levels of earnings

management into both our full sample and our "no shock" sample, and compare the test power

across the samples for different sample sizes. Specifically, separately for our full sample and "no

shock" sample, we randomly draw 200 observations and add various seed levels (1%, 2%, 4%,

10%, -1%, -2%, -4%, and -10% of total assets) to total accruals for those observations, then

estimate the discretionary accrual models. We repeat this process 250 times, and record the

frequency with which the null of no earnings management is rejected out of the 250 trials. To

determine how the differential test power changes across sample sizes, we repeat these tests with

randomly drawn sample sizes of 100 and 500 observations.

Panel A (Panel B) of Table 9 reports these simulation results for the alternative

hypothesis of abnormal accruals less than (greater than) zero, where we seed negative (positive)

earnings management. As reported in Table 9, across all sample sizes we consider, the power of

the test improves when shock observations are removed, particularly for more plausible (i.e.,

relatively low seed) levels of earnings management. Focusing on the 200 observation results in

Panel A for discussion, we first note that when no earnings management is seeded into the

sample, the test is well specified for both samples. Specifically, the rejection rate of the null of

no earnings management is not different from five percent in either sample (6.8% and 4.8% in

the full and no shock samples, respectively). As we begin seeding in earnings management, the

38  

superior power of the test in the no shock sample becomes clear. For example, when we seed in

earnings management of one percent of total assets, the test detects earnings management 64% of

the time in the no shock sample, but only 30.8% of the time in the full sample.

The above simulation-based analyses indicate that the presence of business model shocks

likely generates false inferences in tests that utilize either unsigned or signed abnormal accruals.

We emphasize that even though the reduced sample produces better specified tests in terms of

both Type I and Type II errors, removing shock observations is not a panacea, as some earnings

management samples are inherently confounded by business model shocks. In such samp