Download Accounting Fraud: Booms, Busts, and Incentives to Perform

Accounting Fraud: Booms, Busts, and
Incentives to Perform
Robert H. Davidson
University of Chicago Booth School of Business
[email protected]
http://home.uchicago.edu/~rdavids2/
January 26, 2011
* I am grateful for many helpful comments from my dissertation committee: Abbie Smith (chair), Ray Ball, Ryan Ball,
and Christian Leuz. I would also like to thank Phil Berger, Aiyesha Dey, Merle Erickson, Joseph Gerakos, Andrei
Kovrijnykh, Michael Minnis, Valeri Nikolaev, Zoe-Vonna Palmrose, Haresh Sapra, Oren Yoeli, Shimeng Yu, and
Sarah Zechman as well as participants in seminars at the University of Chicago Booth School of Business.
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Abstract
In this paper, I examine whether macroeconomic conditions influence the propensity to commit
accounting fraud. I find that the incidence of observed accounting fraud is increasing in GDP
and is at its highest in the periods leading up to an economic peak. In addition, the incidence of
observed accounting fraud is decreasing in the average correlation between firm and market
returns and the average magnitude of analyst forecast errors; the relation is increasing in
average price-earnings ratios. When examining the relation between macroeconomic conditions
and firm-level fraud determinants I find that the association between CEO compensation
incentives and the propensity to observe accounting fraud is generally negative, but is positive
and significant during periods of high price sensitivity to earnings news. Analyzing accounting
fraud by type reveals that revenue fraud is increasing in the price sensitivity to revenue news;
this relation does not exist for expense or balance sheet fraud. Additionally, balance sheet fraud
is increasing in the default risk premium. These results are consistent with the hypothesis that
market-wide incentives for managers to manipulate earnings influence the decision to commit
accounting fraud above and beyond firm-level determinants.
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1. Introduction
Over the last 100 years, recessions have been routinely accompanied by revelations of
scandalous reporting failures at many firms. Fraudulent reporting was exposed after the Great
Crash in 1929, after the Savings & Loans scandal in the 1980s, and after the dot-com bubble of
the late 1990s/early 2000s1. Sweeping changes in regulation have often followed these scandals;
the Sarbanes-Oxley Act, for example, was drafted to reduce the likelihood of fraudulent
reporting after the dot-com crash. Additionally, Baker and Wurgler [2007] note the dramatic
decrease in stock prices that occurs during these periods. The overall cost of a wave of
accounting scandals to the economy - in terms of reduced firm value, reduced investor
confidence, bankrupt firms, increased unemployment, and additional regulation - is huge.
Managers have many financial incentives to meet their performance expectations. These
incentives include avoiding, among other things, termination, a decline in the value of their
stocks and options, a downgrade of the company’s debt, debt covenant violations, and
corporate bankruptcy. The dollar effect on stocks and options from failing to meet expectations
has varied over time. Likewise, the other financial incentives that can lead managers to commit
accounting fraud vary in strength over time. Moreover, differential effects across these
incentives are likely to exist as well.
Theoretical research predicts that the level of accounting fraud in the economy is
likewise not constant over time, and neither are financial incentives that the capital and labor
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Galbraith [1961] discusses the scandals that followed the Great Crash. Ball [2009] discusses the wave of scandals
that came to light after the dot-com bubble burst.
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markets exert on managers. Research on firm-level determinants of accounting fraud often
yields contradictory results and does not consider, first, how incentives can vary over time and,
second, that differential effects across incentives are likely. Do changes in the macro economy
influence managers’ reporting decisions above and beyond firm-level determinants? Are
certain hypothesized firm-level determinants of accounting fraud only important under certain
environmental conditions? Does the type of accounting fraud managers commit depend on the
source of their incentives?
To answer these questions I gather data on a comprehensive sample of instances of
accounting fraud that occurred in the United States between 1980 and 2005. Over 800 cases of
accounting fraud are revealed over the sample period, including such high profile cases as
Adelphia, Enron, Sunbeam, and WorldCom.
Having identified the period the violation began, I use survival analysis to test whether
observed accounting fraud is related to macroeconomic performance and whether we observe
more managers reporting fraudulently after a long ‚boom‛ period. I then construct several
macroeconomic proxies measuring the relative strength of incentives various markets create
and test whether these proxies explain the increase in fraudulent reporting observed during
certain periods. I interact hypothesized firm-level accounting fraud determinants with a market
measure of price sensitivity to short-term news to see whether the firm-level determinants affect
managers more strongly in certain environments. Finally, I split accounting fraud into three
groups – revenue fraud, expense fraud, and balance sheet fraud – to test for differential effects
across incentives to commit accounting fraud.
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I find that more managers start committing accounting fraud during periods of strong
macroeconomic performance, as measured by detrended gross domestic product, and in the
two years leading up to an economic peak. Conversely, I find that fewer managers start
committing accounting fraud in the two years following an economic trough.
In survival analysis including fraud firms as well as a random sample of non-fraud
firms, I find more observed cases of accounting fraud start in periods of strong aggregate
performance. This finding is robust to the inclusion of potential firm-level determinants of
accounting fraud. Moreover, I find that more managers start committing accounting fraud in
periods wherein firm returns are less correlated with the market return, in periods wherein
predicting earnings is easier, and in periods wherein price-earnings ratios are high.
In logistic analysis testing the relation between accounting fraud and firm-level fraud
determinants interacted with price sensitivity to news, I find that the delta of the CEO’s stock
holdings is positive and significantly related to accounting fraud only in periods of high price
sensitivity to short-term news. I also find that while the relation between raising capital and
accounting fraud is generally positive, it is statistically significant only in periods of high price
sensitivity to news.
When analyzing accounting fraud by type, I find more observed cases of revenue fraud
in periods of high price sensitivity to revenue news; expense fraud is similarly related to
earnings news, rather than revenue news. I also find that balance sheet fraud is positively
related to the default risk premium; no significant relation exists between revenue or expense
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fraud and the default risk premium. Overall, these findings are consistent both with the
environment playing a role in managers’ financial reporting decisions and with managers
responding to external market driven incentives by committing accounting fraud in periods
when those incentives are strongest.
This paper contributes to the literature in several ways. First, by demonstrating that a
given manager’s decision to commit accounting fraud is related to macroeconomic conditions, it
provides support for the argument that market driven incentives affect managers to such an
extent that they commit accounting fraud. Second, as logistic results suggest, given strong
enough financial incentives from a CEO’s incentive compensation or the firm’s need for external
financing, managers will resort to accounting fraud; in addition, these results explain the
channels through which market price sensitivity to news operates. Third, this paper documents
a differential effect across incentives to commit accounting fraud. For example, managers are
more likely to commit revenue fraud when the demand for top-line growth is high and are
more likely to commit balance sheet fraud when the risk of default is high. Finally, it provides a
novel explanation for the contradictory results reported in the literature and the lack of
consensus regarding the relation between accounting fraud and firm-level determinants thereof.
The remainder of this paper is organized as follows: section 2 reviews the relevant
literature; section 3 develops testable hypotheses; section 4 describes the data; section 5
describes my empirical tests and presents my results; section 6 discusses my tests for
robustness; section 7 presents future work to be included in a final draft of this paper and
section 8 concludes.
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2. Literature
Most research on the relation between accounting fraud and the environment in which it
occurs is theoretical. Though each of these model’s assumptions and mechanisms are different,
they all find that more managers start to commit accounting fraud in periods of strong
aggregate performance.
Povel, Singh, and Winton [2007] present a model of collective accounting fraud wherein
a given manager is motivated to obtain funding for a project; investors choose either to rely on a
public signal from the manager or to invest in costly but unbiased private monitoring before
deciding whether to invest. Depending on investor’s beliefs about the state of the world, a
manager with a bad project can increase his chances of obtaining funding by overstating the
project’s value. In summary, they find that the relation between accounting fraud, actual
performance, and expected performance is non-monotonic. Specifically, fraud peaks in good
but not great states of the world. The primary determinant of their findings is the level of
monitoring effort investors exert, which they assume is influenced by the macro economy.
Hertzberg [2005] develops a model wherein variation in the manager’s short-term and
long-term incentives in turn creates variation in the incentives to commit accounting fraud.
Managers are more likely to commit accounting fraud when their short-term incentives are
strong. Hertzberg, however, does not model which forces alter the composition of the
manager’s incentives. Short-term incentives could be increased by various changes within the
firm, by capital and labor market incentives, or by other forces in the environment.
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Rajgopal, Shivakumar, and Simpson [2007] develop and test a catering theory of
earnings management. In their model, the manager is concerned with obtaining the highest
possible price for his firm’s shares. They find that earnings management increases in periods of
high investor optimism, which they define as periods wherein positive earnings surprises
receive a larger price reaction. They do not, however, test whether earnings management
increases in periods wherein negative earnings surprises are severely punished.
Though Rajgopal et al [2007] do not model or directly test the link between earnings
optimism and actual performance, they allude to as much and cite some macroeconomic
theories (Taussig [1911], Keynes [1936], Lavington [1992], and Collard [1996]) that suggest that
periods of earnings optimism tend to coincide with periods of high real growth. In their model,
in periods wherein optimism coincides with high real growth, it is less costly for the manager to
overstate earnings because, during such periods, it is easier to replace borrowed funds later,
thus reducing the costs of earnings management. This model is in slight contrast to the one
presented by Povel et al [2007], who argue that when actual performance and expected
performance are both high, managers has less incentive to commit fraud because the true
performance of their firms’ is also high. This difference, however, may derive from the papers’
different focuses. Whereas Povel et al [2007] model the decision to commit accounting fraud,
which is a high risk decision that can lead to large fines and jail time, Rajgopal et al [2007]
appear to model cases of within GAAP earnings management which is a low-risk decision,
relatively speaking.
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Two recent empirical papers study the relation between accounting fraud and the
environment in which it occurs. Kedia, Koh, and Rajgopal [2010] find that misreporting can
have a contagion effect. Specifically, they find that managers are more likely to begin
misreporting when another firm in their industry is revealed to have misreported. They find
this effect only in the absence of SEC litigation of the initial misconduct. While it is not clear
whether this effect is due to managers perceiving either a lax regulatory environment (i.e., a
reduced cost to misconduct) or a change in social norms (i.e., that misconduct is condoned), it
does appear to be driven by environmental, as opposed to firm-level, forces.
Fernandes and Guedes [2009] document the circumstances under which accounting
fraud is more likely to occur and examine the relation between accounting fraud and expected
and actual performance. They find a positive relation between the occurrence of accounting
fraud and expected growth and a negative relation between accounting fraud and actual
performance. I, on the other hand, after controlling for the difference between actual and
expected output find a positive relation between accounting fraud and output. Several
measurement issues related to Fernandes and Guedes’ [2009] calculation of accounting fraud
could account for our different results. For example, they average Foreign Corrupt Practices
Act violations over a three year period, which makes it impossible to identify which conditions
were present when the manager decided to start committing accounting fraud. Such averaging
can create a mechanical bias that leads to both overstating and understating the true number of
fraud firms present in a given year. A second measurement issue arises from the years to which
they attribute violations. Over their time period, fraudulent reporting lasts, on average, just
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under three years. Over the same time period, government litigation releases tend to come
about three years after the fraud is detected. This means that a release in 2006 is likely to
address accounting fraud that began around 2001 and was committed through 2003. Fernandes
and Guedes [2009] average this release over the period 2004-2006.
Dechow, Ge, Larson, and Sloan [2010] use many hypothesized firm-level symptoms and
determinants of accounting fraud to develop a predictive model of fraud. They develop an
audit tool, the F-Score, which has the predictive power to identify fraud firms ex post. Though
their research question is different from mine, our papers have several similarities. First, to
build their sample of accounting fraud, Dechow et al [2010] hand collect, as I do, a large sample
(i.e., 2190) of Accounting and Auditing Enforcement Releases; theirs is also one of the few
papers to analyze several hundred fraud firms. The major difference between our reported
sample sizes is that while Dechow et al [2010] include in their analysis all fraud years for a
given firm, I include only the first year of fraudulent reporting. Second, both Dechow et al
[2010] and I test market-related motives for accounting fraud. Specifically, we both report a
positive association between accounting fraud and both the propensity to raise capital and
lagged abnormal returns. The motivation and findings that Dechow et al [2010] document for
these variables led me to include them in my analyses.
3. Hypothesis Development
Becker [1968] presents a rational theory of crime that reduces the decision to commit a
crime to a weighing of the benefits against the associated costs. A given manager’s decision to
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report truthfully or to commit accounting fraud can be analyzed in this context. The costs to the
manager, if his crime is detected, include substantial fines, jail time, and loss of reputation. Add
to this the fact that accounting fraud is a rare event and it seems clear that for the manager the
costs of accounting fraud are substantial. Therefore, the perceived costs of reporting truthfully
– or, conversely, the perceived benefits of reporting fraudulently - must also be substantial
before a manager would choose to commit accounting fraud.
Analytical research supports the claim that the level of accounting fraud in the economy
is not constant over time. That accounting scandals come in waves is also supported by
anecdotal evidence. The forces that greatly influence the perceived costs of truthfully reporting
poor performance do not appear to be constant over time. These forces can arise from within
the firm, but can also arise from the environment the firm operates in.
Survey evidence from Graham, Harvey, and Rajgopal [2005] indicates that executives
manipulate earnings to maintain or increase their firm’s share price. Baker, Ruback, and
Wurgler [2007] suggest that managers manipulate earnings to cater to market demands.
Following this evidence, I assume that a given manager will be motivated to keep the price of
his firm’s shares high and, when faced with reporting numbers below ex ante expectation, will
base his decision of whether to report truthfully or fraudulently on the presence of
environmental factors that affect the net benefits of accounting fraud. A given manager’s
market driven incentives are not constant over time, so we should observe an increase in
fraudulent reporting in periods wherein those incentives are strongest.
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3.1 Time Variance
Recent literature assumes that the correlation between accounting fraud and
macroeconomic performance is positive, though not necessarily monotonic2. Proposed
hypotheses for this correlation include the belief that monitors, such as external auditors, debt
holders, and shareholders reduce their effort during good times; the belief that investors in
general are more likely to believe what firms report during good times; and the belief that
managers have more to gain from insider trading during good times. Each of these specific
hypotheses, however, is difficult to test empirically because of difficulties in measuring monitor
effort, how willing investors are to believe financial statements, or a manager’s intent to engage
in insider trading from fraud3. These hypotheses seem to implicitly assume that managers are
always willing to commit accounting fraud and are simply waiting for the right opportunity to
do so.
Though all of the above forces could influence a manager’s decision to report
fraudulently, they do not consider the potential relation between the firm’s performance and
accounting fraud. A more comprehensive argument is that what matters to managers is their
performance measured both against their firm’s expected performance and performance
relative to peers and that performing poorly across either dimension can increase a manager’s
incentive to commit accounting fraud. Myers, Myers, and Skinner [2006] document the
2
Povel et al [2007], Hertzberg [2005], and Strobl [2008] all develop analytical models that support this result.
3
Agrawal and Cooper [2007] do find some evidence to suggest that managers engage in trades during the fraud
period, but it is difficult to know whether they committed accounting fraud expressly for the purpose of engaging
in insider trades.
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importance to managers of consistently beating expected performance. They find that firms
with long strings of positive earnings surprises have higher share prices than firms with similar
long run performance but with breaks in positive surprises. Martin and McConnell [1991] find
that takeover targets perform better than the market but worse than their peer groups. Antle
and Smith [1986] and Gibbons and Murphy [1992] both find that relative performance is
sometimes expressly written into compensation contracts. Share price, CEO compensation, and
job retention are all influenced by the firm’s performance, both relative to its own expected
performance and relative to that of its peers.
Ball *2009+ argues that ‚high growth becomes built into performance expectations
during an extended boom‛ and that ‚managers therefore come under peer and financial
pressures to deliver strong earnings growth and share market performance‛4. When,
inevitably, some firms experience declining performance relative to their own expectations and
to those of their peers, the managers of these firms find themselves unable to meet their
heightened expectations. Such managers likely feel a great deal of pressure from the capital and
labor markets, and some may resort to accounting fraud to meet these expectations.
Building on research related to investor sentiment, Baker and Wurgler [2007] argue that
during the boom phase of the late 1990s extraordinary investor sentiment pushed the prices of
many stocks to unfathomable heights that simply could not be justified by the facts at hand.
They further note that risks and ‚limits to arbitrage‛ made it too difficult for contrarian
4
Galbraith [1961] makes a similar case for the events leading up to the 1929 market crash.
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arbitrageurs to bring prices back down to appropriate levels. This extremely high optimism
was built into the market’s expectation of firm performance. Baker and Wurgler [2006] find that
investor sentiment has a greater effect on firms with higher growth prospects; on average, firms
have higher growth prospects when the aggregate economy is experiencing a high growth
boom. The prior literature therefore supports my first hypothesis:
H1A: The relation between macroeconomic performance and accounting fraud is positive.
Given that the costs associated with reporting truthfully increase dramatically for
managers when their firm is performing poorly relative to benchmarks, I expect to find that
periods characterized by high benchmarks and a reasonable number of firms starting to
perform below expectation have higher levels of observed cases of accounting fraud. These
characteristics describe the years leading up to an economic peak. Such periods are generally
characterized by years of sustained growth that does not continue far into the future. Many
firms experience high growth in the years leading up to the peak, and their current forecasts are
formulated, in part, on this prior growth. Ex post we know that a number of firms experience a
decrease in performance right before the economy peaks and starts to decline.
H1B: The relation between observed accounting fraud and the years before an economic peak is
positive.
A related area of research looks at the progression a firm and manager make to get to
the point where a given manager commits accounting fraud. Schrand and Zechman [2008]
argue that there is a slippery slope to accounting fraud wherein managers start off looking to
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plug small gaps in performance and may have no intention of committing accounting fraud. If
their performance does not improve in subsequent periods, a manager may continue to plug the
small gaps; over time, however, the amount of manipulated earnings can grow egregious and
the manager will then face either reporting truthfully and reversing their past entries, or
continuing down the path towards accounting fraud. Empirical and anecdotal evidence
support the slippery slope theory, and its existence does not change the predictions I make in
this paper. I argue that market driven incentives and the firm’s environment will influence
managers in the same way regardless of whether a given manager has shown a willingness to
slightly manipulate earnings in the past or would have to reverse his past entries if he chose to
report truthfully. In other words, while a manager who has previously manipulated earnings
may be more willing to commit accounting fraud, environmental factors likely influence all
managers in the same way.
3.2 Market Based Incentives
Knowing when managers are more likely to commit accounting fraud is a helpful
starting point for developing hypotheses about which forces influence managers to either
truthfully report poor performance or to commit accounting fraud. If more managers report
fraudulently in good times, then we can shift our focus to forces that are particularly strong in
these periods. The analysis above supports the formulation of a market driven incentives
hypothesis. Market wide incentives affect all firms in the economy at the same time and to a
highly correlated degree. Looking at changes in these incentives over time seems like an
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optimal way to test the theory that market driven incentives influence a manager’s reporting
decisions.
Many areas of research document that markets place significant pressure on managers
to perform5. Benmelech et al. [2010] show that while incentive-based compensation induces
managers to exert costly effort, it also induces them to conceal bad news about future growth
options and to choose sub-optimal investment policies. They argue that in periods with strong
market incentives, or periods wherein price is highly sensitive to short term news, incentive
compensation should be reduced. Ball [2009] also discusses the financial pressures that build
up during an extended boom. Though accounting fraud is not the focus of their paper,
Benmelech et al [2010] nevertheless mention that fraudulent reporting is one possible response a
manager can have to poor performance in periods wherein price sensitivity to news is high.
Such a manager has incentives to keep his firm’s share price high because this affects both his
compensation and job retention. Disappointing investors by performing poorly reduces the
price of the firm’s shares, reduces the value of a manager’s incentive based compensation, and
increases the likelihood of that manager being replaced.
H2: The relation between observed accounting fraud and market driven incentives is positive.
Empirically separating capital market from labor market incentives is difficult. Poor
performance reduces share price, increases the cost of capital, increases pressure from investors,
and increases the likelihood of replacement. If managers are ultimately concerned with their
5
For examples of the effects of market incentives, see Gray, Meek, and Roberts [1995], and Cao and Laksmana
[2010].
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net worth, then we can assume that appeasing both the capital and labor markets will be
important to them. This hypothesis, however, does not specify whether the manager is
responding to incentives driven primarily by the capital or labor market, owing to the
difficulties with separating the two. Hypothesis 3 attempts to isolate the effects on accounting
fraud of the value of the manager’s incentive plans and net worth.
3.3 Relation between market-based incentives and firm-level accounting fraud determinants
Research on firm-level determinants of accounting fraud often produces inconsistent
and in some cases contradictory results. Common issues in this area of research are small
sample sizes6 and samples collected over short periods7. These issues could mean that the
differences in results have nothing to do with econometric or measurement issues, but are
driven by fluctuations in the strength of market wide incentives that influence both the decision
to commit accounting fraud and managers’ operating, financing, and accounting choices.
Essentially, prior research may have overlooked an important factor when modeling firm-level
accounting fraud determinants.
For example, research on the link between incentive compensation and accounting fraud
has yielded mixed results. While most academics believe that incentive compensation does
induce the manager to exert costly effort, the extent to which it also gives the manager
incentives to commit accounting fraud remains debatable. Johnson et al. [2009] find a positive
6
Beneish [1997] analyzes 49 fraud firms drawn from AAERs. Erickson et al [2004] analyze the amount of tax 27
fraud firms pay on fraudulent inflated earnings.
7
Kedia and Phillipon [2007] study violations between 1996 and 2001.
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relation between CEO unrestricted stock holdings and accounting fraud while Armstrong et al.
[2009] find a negative relation between incentive compensation and accounting fraud.
Benmelech et al. [2010] argue that the effect of incentive compensation is not fixed and is, rather,
influenced by market behavior. In periods wherein the sensitivity of price to short-term news is
high, managers have increased incentives to conceal bad news. It is possible that a given
manager’s reporting decision will be influenced by his incentive compensation only in periods
wherein markets respond dramatically to short-term news.
H3: The relation between observed accounting fraud and CEO incentive compensation and net
worth is positive and more pronounced in periods wherein stock price is highly sensitive to shortterm news.
While Dechow et al. [1996] find that managers are often motivated in their decisions to
commit accounting fraud by concerns about the cost of external financing, Beneish [1999] finds
no support for this claim. Further, while Beneish [1999] finds that fraud firm managers are
more likely to sell their stock and exercise stock appreciation rights during the fraud period,
Dechow et al. [1996] find no support for this claim. The effect of firm performance on the cost of
external financing is not constant. It is possible that managers are more willing to commit
accounting fraud to raise capital on favorable terms in periods wherein the fraudulently inflated
earnings will have the greatest effect on the value of securities.
H4: The relation between observed accounting fraud and the decision to raise capital is positive
and more pronounced in periods wherein price is highly sensitive to short-term news.
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4. Data: AAERs
I use SEC Accounting and Auditing Enforcement Releases (AAERs) as a proxy for
accounting fraud. These releases summarize investigations the SEC brings against the agents of
firms for violations of SEC and Federal rules. AAERs clearly state whether the violation was for
accounting fraud or some other infraction (e.g., securities law violations). To collect my sample,
I read through AAERs 1 – 3148 which were released between May 17, 1982 and June 29, 2010.
After limiting my sample to violations for accounting fraud wherein the fraud had a
determinable start date and then removing redundant cases, I was left with 824 firms.
Determining which types of violations to include involves a degree of subjectivity.
Ultimately, I include in my final sample only those firms for which it can be determined that
their financial statements (or notes) were materially misstated. One exception to this rule is
violations due to options backdating, which I exclude from the sample. Only a small number of
AAERs involve options backdating, and their inclusion marginally improves the results of some
of the tests. I nevertheless exclude these cases because the motivation to backdate options
seems clear.
The choice to include violations for revenue (or asset) understatement is also subjective.
As with cases of options backdating, only a small number of AAERs (i.e., 4 cases) involve
violations for revenue or asset understatement.8 This could be due to fewer managers having
strong incentives to understate revenues or to the SEC having weaker incentives to prosecute
8
Xerox and Microsoft are two well known cases of revenue/asset understatement litigated by the SEC.
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these types of violations. Because my predictions relate to the incentives to overstate, rather
than understate, earnings or net assets, I exclude these cases.
Only firms under the jurisdiction of the SEC are prosecuted and included in the AAER
sample. Firms that do not issue debt or equity in the United States are not included. Given that
this paper tests the relation between market wide incentives and accounting fraud, collecting a
sample of fraud firms from the universe of firms under SEC jurisdiction should represent a fair
proxy, relative to the alternatives. Small private firms are not included because the owners of
these firms generally do not have concerns related to labor market incentives (i.e., they often
have a controlling interest) or to capital market incentives from shareholders (i.e., shareholders
are generally nonexistent). Debt holders tend to be concerned about downside risk and the cash
flows required to service debt, not about whether the firm exceeds high growth expectations.
The value of these firms to their owners does not fluctuate wildly based on quarterly
performance; often there are no traded shares whose value can be reduced greatly by a period
of poor performance.
Although a number of international firms do raise public capital in the United States and
fall under the jurisdiction of the SEC, the AAER sample contains only a handful of such firms.
It is not clear why the sample contains such a small number of international firms, but one
likely reason is the additional cost the SEC would incur to prosecute agents of firms who often
do not reside in the United States and whose firms have little to no physical presence in the
United States. I exclude these cases from my sample of 824 fraud firms available for time series
analysis.
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Data restrictions limit the number of fraud firm observations available for firm-level
analysis. Table 1 presents the number of firms lost at each stage. The two primary reasons for
the decline in sample size are the lack of any identifying code for the firm (363 firms) and the
absence of CRSP and Compustat data before and during the period wherein the fraud began
(190 firms). The primary survival analysis includes 271 firms. While this is large drop from my
original sample of 824 firms, it is large compared to many of the papers previously discussed
above9. Of these 271 firms, only 104 have compensation data available; I therefore conduct two
sets of analysis, one with and one without control variables for CEO compensation.
AAERs offer several advantages over other proxies for accounting fraud. First and
foremost, it is clear whether managers of the firms in the AAER sample actually committed
accounting fraud, making the probability of type 1 errors negligible. This is not true for
earnings restatements, which also occur because of clerical error, change in accounting policy,
or numerous other factors in addition to accounting fraud. This is also not true for observations
from the Stanford Law Database on Shareholder Lawsuits. Though many shareholder lawsuits
arise from intentional material misstatements many more arise for other reasons. Moreover,
many shareholder lawsuits allege intentional misstatements when there is no clear evidence to
support that claim.
9
Dechow et al [2010] is one important exception to the general trend of small sample sizes in prior research. Their
main analysis includes between 354 and 494 fraud firm years. My analysis, which includes several of the variables
they use in their most restrictive tests, is based on a sample of 271 individual fraud firms.
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In the earnings management literature, some measure of abnormal accruals is often used
as a proxy for earnings management. Whether these measures have much ability to discern
earnings management in the aggregate, however, is debatable. Ball [2009] points out that
‚much academic research on earnings management establishes such a weak burden of proof
that earnings management appears almost universal.‛ Ball [2009] goes on to state that one
advantage of focusing on negligent or fraudulent financial reporting is that ‚a proven case of
negligent or fraudulent financial reporting is an institutional ‘fact’, as distinct from an errorprone academic estimate.‛ Further, Correia *2010+ shows that accruals models are correlated at
less than 5 percent with ex post measures of accounting fraud.
Another advantage of using AAERs is that they provide a great deal of information
about the nature and timing of the violation. They generally provide clearer information about
the start and end dates of the violation than do releases of violations of the Foreign Corrupt
Practices Act’s (FCPA) books and records laws. Testing the environmental conditions present at
the time a manager starts to commit accounting fraud requires access to as detailed and
accurate information as possible about the start date of the violation.
AAERs do, however, have several limitations. One drawback of using any ex post
measure of accounting fraud (i.e., AAERs, restatements, FCPA releases) is that they only
document cases that are detected – a potentially important issue that is difficult to completely
control for, though attempts are made. If the SEC’s detection methods or litigation decisions
contain any bias, then this issue becomes particularly relevant to AAERs. That said, as Dechow
et al [2010] point out, the SEC identifies firms for review through anonymous tips, news reports,
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voluntary firm restatements, and their own review practices. Several independent sources
provide information regarding potential malfeasance, which should reduce the possibility of
bias in the SEC’s detection methods.
The SEC also faces budgetary constraints and only prosecutes those cases where there is
strong evidence against the firm. On average, these cases should be the ones in which
accounting fraud most likely occurred. That said, time-variance in budget constraints could
influence the composition of the sample. To control for this possibility, I include the SEC’s
annual budget appropriation in my models. However, analyzing my sample of AAERs
provides anecdotal evidence that budget constraints are not a serious problem. Indeed, my
sample includes 24 cases in which the manipulation amounted to less than $1 million dollars,
which suggests that the SEC has the resources to prosecute cases of relatively small
manipulations as long as there is strong evidence of malfeasance. Further, there is no statute of
limitations for litigating fraud firms.
Another concern related to using AAERs is that lags in detection and/or AAER release
could cause the number of new fraud cases to be under-represented in the last few years of the
sample period. To mitigate this possibility, I include in the sample only violations that started
before 2006. In the AAER sample, accounting fraud lasts on average 10 quarters and the
corresponding release is published on average three years after the violation was detected.
23
Therefore, performing tests through 2005 should greatly reduce the likelihood that the most
recent years contain an unrepresentative number of fraud cases10.
5. Tests and Results
I examine Hypotheses 1A and 1B using time-series and survival analysis. While the
small number of years in my sample, concerns about degrees of freedom, and concerns about
correlated omitted variables limits the ability of the time-series regressions to establish
causality, these regressions still provide descriptive evidence on when we observe more
managers choosing to commit accounting fraud and on the magnitude of the effect that changes
in the macro economy has on the propensity to observe new cases of accounting fraud. To test
Hypotheses 1A and 1B, I estimate time-series regressions of the following general form:
fraud    i macro _ performance   surprise   .
Fraud is measured as the number of managers who start committing fraud during a
given year scaled by the number of Compustat firms in that year. Macro performance is
measured using GDP (inflation adjusted and expressed in chained 2005 dollars) with the time
trend removed using the Hodrick-Prescot [1997] filter1112. To test Hypothesis 1B I include
indicator variables that measure the run up before an economic peak and the recovery after an
10
Results are not sensitive to the choice of cutoff year. I also used 2003 and 2004 as end years and found that the
significance of my results remained unchanged.
11
I also used the Baxter-King [1999] filter and found no change in my results.
12
Focusing on the consumption and investment components of GDP does not change the results. Neither does
using corporate profits.
24
economic trough as defined by the National Bureau of Economic Research (NBER). Peak takes
a value of 1 if the current year falls within the two years before an NBER defined peak and 0
otherwise and Trough takes a value of 1 if the current year falls within the two years following
an NBER defined trough. I also include an indicator variable, PTT, to measure the period
between the peak and trough, which means the years peak and trough are measured against
those that are more than two years removed from peaks or troughs. Including these indicators
allows me to track accounting fraud through the business cycle and to observe how accounting
fraud builds and falls. GDP Surprise is defined as the difference between actual GDP and
expected GDP as forecasted by the Survey of Professional Forecasters, provided by the
Philadelphia FED. Like GDP, GDP Surprise is detrended because the forecasts show evidence
of a trend. I include GDP Surprise because the decision to commit accounting fraud could be
related more to the performance-expectation gap than to performance per se. Because the vast
majority (over 75%) of observed cases of accounting fraud start in the fourth calendar quarter, I
measure and test macro variables contemporaneously with observed cases of accounting fraud
in both the time-series and survival analysis.
To control for the possibility that the level of accounting fraud is positively related to the
number of firms in the economy, I measure Fraud as a percentage. Figures 1 and 2 show the
effect of deflating accounting fraud by the number of Compustat firms. Figure 1 plots new
cases of detected accounting fraud by year. I find evidence of a time trend in accounting fraud.
However, as shown in Figure 2, when I replace the number of cases of accounting fraud with
the percentage of accounting fraud firms I no longer find such a trend over the sample period.
25
As presented in Table 2, the results from annual time-series regressions are consistent
with Hypotheses 1A and 1B. New cases of observed accounting fraud are positively related to
detrended GDP and economic peaks, and are negatively related to economic troughs. All timeseries standard errors are adjusted using the Newey-West correction. Because the dependent
variable is scaled by total Compustat firms, interpreting the magnitude of the coefficients yields
an increase in the percentage of fraud firms. The 0.05 coefficient on GDP in the base model of
Table 2 indicates that a one standard deviation increase in GDP ($141 billion) increases the
percentage of fraud firms by 0.07 percent. In 1996, for example, this increase translates into 6
additional cases of accounting fraud - a result that is both economically significant and feasible.
These findings are consistent with the theoretical literature, which predicts that managers are
more likely to commit accounting fraud in strong economic periods.
Survival analysis allows me to extend tests to the firm level and allows for both
macroeconomic and firm-level controls. To test Hypothesis 1A and 1B, I estimate a Cox
proportional hazards model of the following general form:
fraud    i macro _ perf   j macro _ controls   k firm _ controls   . Fraud is now an
indicator variable equal to 1 in the period during which failure occurs (i.e., when the manager
starts committing accounting fraud) and 0 otherwise. Macroeconomic controls include GDP
surprise, stock market volatility, the default risk premium, the number of IPOs in the previous
three years, and the SEC budget appropriation. Stock market volatility captures some aspect of
the information environment; accounting fraud could be related more to variance in
performance and expectation than to actual output per se. The default risk premium controls
26
for default risk and the rate at which future cash flows are discounted in the price-earnings ratio
analysis. Many AAERs report that the detection of fraud came after the firm became insolvent
and no longer had enough cash to service its debt obligations or continue operating. The
number of IPOs over a 3 year period controls for changes in the composition of firms in the
economy. Generally, the number of IPOs increases during periods of prosperity; if a large
number of fraud firms are IPO firms, then it is possible that the observed increase in accounting
fraud during strong economic periods is related to concurrent increases in the number of IPOs.
Though the number of IPOs is unlikely to influence individual fraud firms, it could still explain
a significant coefficient on GDP. The SEC’s budget appropriation controls for the SEC’s ability
to detect and litigate accounting fraud. As suggested in Dyck et al [2007] the SEC is often not
the first agent to detect accounting fraud, which means the SEC budget may not control for a
large portion of the detection environment. However, since my sample of fraud firms is
collected from SEC AAERs, the SEC budget should effectively control for the effect of litigation
constraints on the sample. I have no clear prediction for the relation between fraud and the SEC
budget. On the one hand, if the SEC having additional resources makes accounting fraud more
costly, then we should observe fewer cases of accounting fraud in periods wherein the SEC has
an increased budget. On the other, if these increases in the SEC budget are in response to
increase in fraudulent reporting, then we could observe a positive relation.
I include the following firm-level determinants of accounting fraud as controls: a raising
capital indicator variable, lagged abnormal returns, and deltas of CEO incentive compensation
and net worth. Capital takes a value of 1 if the firm issued debt or sold common shares in the
27
current period and 0 otherwise. Dechow [1996] argue that among the reasons managers commit
accounting fraud is that the inflated share price reduces the cost of raising capital. Dechow et al
[2010] test several different proxies of dependency on external financing and find that,
compared with other proxies, an indicator for whether the firm raised capital in the fraud year
has superior predictive power. Lagged abnormal return is defined as the value-weighted
market adjusted firm return for the previous year. Dechow et al [2010] argue that managers
whose firms have optimistic expectations built into their stock price may be more prone than
other managers to misstate their earnings for the purpose of hiding decreasing performance. A
significant coefficient on this variable supports the hypothesis that high expectations and
market driven incentives are among the reasons managers commit accounting fraud. Incentive
compensation is measured using the delta of current CEO option grants based on Black Scholes
values and the delta of all CEO stock options based on Execucomp values. Wealth is measured
using the delta of all shares held by the CEO based on Execucomp values. While the relation
between accounting fraud and incentive compensation is not clear, it is nevertheless an
important consideration. All variables are defined in Appendix A.
Table 3 presents Spearman and Pearson correlations for variables I use in survival
analysis. Univariate correlations between accounting fraud, macroeconomic performance, and
the market incentive proxies are consistent with Hypotheses 1 and 2. Table 4 presents
descriptive statistics for the variables used in survival analysis. Each year, approximately 1
percent of managers of firms listed on Compustat start committing fraud. GDP can show a
negative mean because the values are detrended. The value for the peak and trough indicators
28
is the percentage of years that meet the inclusion criteria. The firm-level variables (i.e., lagged
abnormal returns, current IC, option, and wealth) are winsorized at the 1 percent level to reduce
the influence of outliers.
Tables 5A and 5B report results supporting Hypotheses 1A and 1B. GDP and economic
peaks are significant and positively related to the hazard rate while economic troughs are
negatively related to the hazard rate. I find that more managers start to commit accounting
fraud in periods of strong aggregate performance and in the two years leading up to an
economic peak.
In addition, I find that the SEC variable is negative and significant. This finding
suggests that managers are less likely to commit accounting fraud in periods wherein the SEC
has more resources at its disposal. Incentive compensation is negatively related to accounting
fraud and is significant when measured using the delta of CEO stock options. The economic
effect appears small though; a base hazard rate of 1.000 indicates that the variable has no effect
on whether fraud occurs and the hazard rate for incentive compensation always falls between
0.998 and 0.999. To decrease the likelihood of accounting fraud by even 1 percent, the delta of
options would have to increase by several standard deviations.
The hazard rate for the risk premium is negative in the GDP models and positive in the
peak/trough models, though it is not significant in any estimation. As Table 3 shows, the risk
premium is highly correlated with GDP and economic troughs. Finding that the risk premium
is not highly correlated with non-trough periods could explain why the hazard rate switches
29
from negative to positive. The hazard rate for IPO is positive and significant in Table 5A but is
negative (though insignificant) in Table 5B. This switch, however, is not driven by a correlation
with IPO and compensation measures; what causes the switch is not clear, but in an economic
sense, the hazard rate of 0.999 in Table 5B is not significant. The relation between raising capital
and accounting fraud is positive, as predicted by the extant literature, but not significant. This
finding, however, could be due to the downward bias of coefficients that attends modeling rare
events in non-linear models, as documented by King and Zeng [2001]. Time variance in the
relation is explored in Table 9. The relation between lagged abnormal returns and accounting
fraud is positive, consistent with Dechow et al [2010], though it is significant only in Table 5A.
One explanation for this finding is that the relation is not strong enough to be detected with the
smaller number of fraud observations included in the tests presented in Table 5B.
To mitigate the potential of downward bias of the coefficients that typically attends
models of rare events, I perform my analysis using a subset of non-fraud firms. King and Zeng
[2001] provide a detailed discussion of the difficulty of modeling rare events. To summarize,
the difficulty arises for two reasons: (1) because the statistical properties of binary regression
models are not invariant to the (unconditional) mean of the dependent variable and (2) because
the method of computing probabilities of events in logit analysis is suboptimal in finite samples
of rare events data. Indeed, King and Zeng [2001] show that the biases can be as large as some
of the estimated effects reported in the literature. Given that fraud firms represent
approximately 1 percent or less of the population in most periods, accounting fraud qualifies as
a rare event. As using a random subsample of observations drawn from the total population is
30
one technique for dealing with this issue, I select 15% of Compustat firms during the sample
period.13 To be included in the analysis, firms must have two years of data.14
Because incentive compensation data is only available for a subset of firms and only
from 1992 onward, I report my results in two separate tables. The hazard models presented in
Table 5B include 104 fraud firms and 1046 non-fraud firms. In both tables, standard errors are
clustered by firm and by year.
I test Hypothesis 2 using survival analysis. Here, I extend the Cox proportional hazards
model I estimated to test Hypothesis 1 to include three proxies for market wide incentives. The
market-incentive proxies measure the average correlation between firm returns and the market
return, the average median absolute forecast error for the period, and the average priceearnings ratio for the period. Because these proxies are reasonably highly correlated with one
another, I test each proxy separately.
How much weight the market places on idiosyncratic news influences the benefit of
committing accounting fraud. Ang and Chen [2002] show that the correlation between firm
returns and the market return is systematically correlated with the business cycle. The
correlation is relatively high during recessions and relatively low during booms. During
13
I conducted hazard analysis using random samples of 5%, 10%, and 15% of the Compustat population and found
that the magnitude and statistical significance of my results remained unchanged. Further, 25 random samples
were generated to insure that the results were not determined by the specific firms in the random sample.
14
Two years of data is required to calculate lagged abnormal returns for the fraud year. My results are not
sensitive to this distinction. I repeated my analysis requiring three years of data and imposing no restriction on the
data with similar results in terms of magnitude and statistical significance. Requiring more than two years of data
severely reduces the size of my sample.
31
recessions, price is primarily determined by news about markets and industries with firm-level
news being of relatively low importance. During booms, on the other hand, the market places
greater weight on idiosyncratic news when setting price. In periods wherein firm news is more
important than market news, managers have more to gain from fraudulently inflating earnings.
In periods wherein strong inferences about individual ability are made from reported earnings,
managers may face a greater risk of losing their job if they report performance below their
firm’s expectations.
Earnings news is not highly informative without context. Whether it is explicitly written
into compensation contracts or used as a general evaluation metric, firm performance is
measured against that of other firms in the economy. A firm that misses its forecast is evaluated
differently in periods wherein many firms miss their forecasts than in periods wherein few
firms miss their forecasts. Uncertainty about performance can also affect incentives to commit
accounting fraud. In periods characterized by high uncertainty and low forecast accuracy, both
the ex ante forecast and whether or not that forecast is met are likely to have a dampened effect
on price. There are many explanations for high uncertainty about performance and low forecast
accuracy and many of these also explain poor performance. The less certain the market is when
forming expectations, the less likely it is to be surprised when a manager falls short. As such,
managers have fewer incentives to commit accounting fraud when predicting performance is
difficult.
The sensitivity of price to news can, over time, greatly change the incentives to commit
accounting fraud. In periods wherein earnings has a large effect on price, managers will
32
experience stronger incentives to avoid reporting poor performance. As mentioned in Dechow
et al [2010], class action lawsuits are often filed against firms when their stock prices suffer large
decreases. Managers have strong incentives to avoid these lawsuits and the large decreases in
their firm’s stock price that precede them. Additionally, the effect of fraudulently inflating
earnings on a manager’s wealth will be higher in periods wherein these inflated earnings have
the largest effect on price. Price is determined by discounting expected future cash flows.
Dechow et al [1998] find that earnings is a better predictor of future cash flows than is cash
flows itself. That investors revise their expectation of future cash flows based on current
earnings is well established. As such, while price-earnings ratios should capture the market’s
sensitivity to earnings news, they will also capture the discount rate the market applies to
future cash flows. I therefore include the market risk premium to control for the discount rate
and to allow the PE ratio to be interpreted as a measure of the sensitivity of price to earnings
news.
The results I obtain for Hypothesis 2, which are presented in Tables 6A and 6B, are
strong for all three proxies for market wide incentives. The hazard rates for covariance
asymmetry (CA), median absolute forecast error (MAFE), and price-earnings ratio (PE) are all
significant in the predicted directions. The proxies are significant at the 1 percent level in every
estimation except for one, that is, the MAFE in Table 6A, which is significant at 10 percent. The
results for Hypothesis 2 suggest that managers are more willing to commit accounting fraud
when share price is determined more by firm-level news than by market news. Ceteris paribus
33
it is reasonable to expect that as the market places more weight on managerial ability, more
managers will commit accounting fraud.
The results also suggest that managers will be more likely to commit accounting fraud in
periods wherein earnings are easier to predict. Several interpretations are consistent with this
result: on the one hand, it could be managers are penalized more severely for falling short in
periods wherein earnings are easier to predict; on the other, it could be that, in periods wherein
earnings are more difficult to predict, managers have several plausible explanations for their
failure to meet performance expectations. It could also be that managers feel more comfortable
borrowing from the future in periods wherein performance is easier to predict.
Finally, I find that more managers start to commit accounting fraud in periods of high
price sensitivity to short-term earnings news. This finding is consistent with the predictions
made in Benmelech et al [2010]. Both the penalty for reporting poor performance and the
benefit of exceeding expectation are generally higher when news has a stronger effect on price.
I include in this model the PE ratios of individual firms to discern whether increases in the
individual fraud firms’ PE ratios drive the significance on the aggregate PE ratio. Ultimately, I
find that the firm PE ratio is insignificant, while the aggregate PE ratio is positive and
significant. This finding suggests that what creates the strongest incentives to misreport for
managers is economy wide price sensitivity to news. Incentives for managers to commit fraud
are high when all firms in the economy have high expectations built into their share price.
34
Results for the risk premium, the SEC budget, IPO, capital, and lagged abnormal returns
are similar in direction and significance to those reported in Tables 5A and 5B. Results for
macroeconomic volatility are consistent across Tables 5A and 6A, but not across Tables 5B and
6B. Volatility is highly correlated with the market incentive proxies analyzed in Table 6, which
could explain the inconsistent results for this variable. Results for GDP surprise are similar to
the results for volatility – they are consistent across the A tables, but not across the B tables. The
reduced number of fraud firm observations in the B tables could lead to a lack of power to
detect weaker relations.
Macroeconomic variables are often correlated with multiple forces. Though the market
wide incentive proxies arguably do capture market wide incentives, they may also be correlated
with other factors that could influence a manager’s decision to commit accounting fraud. Two
related explanations for time-variance in accounting fraud are changes in the detection
environment and changes in the litigation environment. Though it is not clear in what direction
either would be correlated with my sample of accounting fraud, it is possible that they affect a
manager’s decision to commit fraud in the same way I argue market wide incentives do. In
periods wherein monitors exert high effort, it is reasonable to assume that a higher proportion
of accounting fraud will be detected. If this is true and if, in addition, periods wherein monitors
exert high effort are correlated with periods of strong aggregate performance, then an increase
in the number of detected cases of accounting fraud in these periods could derive from an
increased detection rate and not from an increase in the number of managers reporting
fraudulently. While measuring effort empirically is difficult, most theoretical research finds
35
that monitoring effort actually decreases during periods of strong aggregate performance. This
finding argues against the conclusion that increases in AAERs during periods of high
performance derive from an increased detection rate of accounting fraud.
On the other hand, if monitors do in fact reduce their effort during good times, then one
could argue that their reduced effort increases the true number of fraud firms in the economy,
which in turn may explain part of the increase in detected cases of accounting fraud. Again, the
difficulty in measuring effort makes this hypothesis hard to test or refute. Dyck et.al. [2007]
find that whistleblowers detect more cases of accounting fraud than other agents do. Further,
they find that a large amount of accounting fraud is detected by agents that are not employed as
monitors; they note in particular that the media and analysts detect a reasonable number of
fraud cases. It is not clear whether the benefits one stands to gain from breaking a big story or
the ethics and/or willingness of a lower level employee to speak up vary much over time, or are
correlated with market covariance asymmetry or price-earnings ratios. Whistleblowers in
particular do not monitor the firm per se and tend to uncover fraudulent activity simply in the
course of doing their job. If the effort levels of agents that detect the vast majority of accounting
fraud cases do not vary over time, then the detection environment is less likely to be a concern
in this setting.
The evidence suggests that the SEC budget effectively controls for changes in the
litigation environment. The relation between accounting fraud and the SEC budget is negative
and significant in almost every estimation, which is consistent with fewer managers committing
accounting fraud in periods wherein the SEC has more resources at its disposal. Controlling for
36
changes in the detection environment is more difficult. The SEC budget should control for the
SEC’s resources for detecting accounting fraud, but as mentioned above, many other agents
detect accounting fraud. Moreover, data on the corporate governance characteristics of firms or
measures of abnormal audit fees are only available from the late 1990s onward and can be
calculated only for approximately 10 percent of the fraud firms in my sample.
To reduce the likelihood that the statistical significance of the proxies for market wide
incentives derives from correlated omitted variables, I recalculate each of these proxies at the
industry level using the Fama-French 5 and 12 industry definitions and re-estimate the hazards
models. Doing so increases the cross-sectional variation in the proxies which should in and of
itself increase the reliability of the results. Assuming the cross-sectional variation reduces the
likelihood that the market incentive proxies also serve as proxies for changes in either the
detection or litigation environments, then these recalculations provide more support for the
market wide incentive hypothesis. As presented in Table 7, the results support Hypothesis 2.
The industry-level proxies for market wide incentives are statistically significant in the
predicted directions. To conclude that these results are driven by changes in detection or
litigation, I would need to find that the effort to detect accounting fraud or the propensity to
litigate for each separate industry changes with the proxies for market incentives measured at
the industry level. At the very least, such a finding is far less plausible than when the proxies
are measured at the economy level. The standard errors in Table 7 are clustered by industry
and by year. The control variables exhibit behavior similar to that reported in Table 6A. The
37
coefficient on the risk premium switches signs in the CA model (but is not significant); all other
variables behave similarly.
I use logistic regressions to test Hypotheses 3 and 4, namely, that the relations between
accounting fraud and incentive compensation and accounting fraud and raising capital,
respectively, are stronger in periods wherein price is highly sensitive to news. The evidence
presented in Table 8 supports Hypothesis 3. In general, the relation between CEO incentive
compensation or CEO net worth and the propensity to observe new cases of accounting fraud is
negative, as it is in Tables 5 and 6. However, when I interact the relation with PE ratios and test
the delta of all CEO shares held, it becomes positive and significant at the 5 percent level. These
findings are consistent with those of Benmelech et al [2010] and support the conclusion that a
CEO’s wealth positively influences their decision to commit accounting fraud in periods
wherein share price, and by extension wealth, is highly sensitive to short-term earnings news.
The difference in significance between shares held and options held could reflect a lack of
accuracy in the Execucomp values for total options/shares held. A superior approach would be
to calculate the CEO option delta using the Core and Guay [2002] methodology. I will make
this change in an updated version of this paper so that I can test a more accurate measure of
delta and the relation between CEO wealth and accounting fraud.
The evidence presented in Table 8 also supports Hypothesis 4. The relation between
raising capital and accounting fraud is positive and significant at the 10 percent level in periods
wherein price is highly sensitive to news. This result might explain the inconsistent results
found in the literature that study this question. Because the significance of this result is
38
relatively low, I re-estimate this regression substituting the net amount of new financing raised,
deflated by total assets, for the actual issuance indicator. This interaction coefficient is also
significant at the 10 percent level. Dechow et al [2010] use both of these measures in their initial
analysis but find stronger predictive power for the actual issuance indicator variable.
The results presented in Table 8 serve two purposes. First, they show the conditions
present when the incentives created by CEO compensation and a firm’s need to raise external
capital are strong enough to prompt more managers to commit accounting fraud. This aspect of
the results supports recent research on these firm-level determinants and can explain why past
studies have often reported contradictory results. Second, they show two of the channels
through which market wide incentives in the form of price sensitivity to news can influence
managers.
Table 9 presents the results of testing the differential effect of market incentives on fraud
type and provides further support for the Hypothesis 2. If capital and labor market incentives
are a primary determinant of accounting fraud, then we should observe a differential effect
when looking at different sources of incentives. In Table 9, I split fraud into three types revenue fraud, expense fraud, and balance sheet (i.e., non-income statement) fraud - and
analyze each using survival analysis. If revenue fraud is positively associated with price
sensitivity to revenue news, but expense fraud and balance sheet fraud are not, then I can
conclude more confidently that the results are driven by the sensitivity to news and not by
changes in the detection or litigation environments. In this table, the dependent variable takes a
value of 1 in periods wherein a manager commits accounting fraud of a certain type. For
39
example, in the revenue columns, fraud is equal to 1 if a manager commits revenue fraud and 0
otherwise.15 I measure the sensitivity of price to news using price-earnings and price-revenue
ratios and earnings response and revenue response coefficients. I did not include expense
measures because of their near perfect negative correlation with revenue measures.
Overall, results suggest that the type of fraud committed is related to sensitivity to news
of that type. Revenue fraud is positively and significantly associated with both the pricerevenue ratio and the revenue response coefficient, but not with the price-earnings ratio or the
earnings response coefficient. Expense fraud, on the other hand, shows the opposite results; it
is significantly associated with sensitivity to earnings news, but not with sensitivity to revenue
news. Expense fraud appears to be related to the portion of earnings news not related to
revenue news. Balance sheet fraud is not related to the sensitivity of price to news at all; given
that balance sheet fraud does not change earnings or revenues, this is to be expected.
Consistent with the notion that growth is valued in boom or high growth periods, the relation
between GDP and revenue fraud is positive and significant. The relation between GDP and
expense fraud is not significant.
Most cases of balance sheet fraud involve overstating reserves or improperly removing
debt from the balance sheet. One motivation for committing this type of accounting fraud is to
appear less debt constrained and less risky. Accounting fraud of this type is more beneficial in
15
The number of fraud firm observations across the three models does not equal 271 because of overlaps in the
type of fraud. In many cases, managers commit more than one type of fraud at a time. In untabulated results, I reestimate the hazard models, this time using firms that commit only one type of accounting fraud. While my results
are marginally stronger, this restriction significantly reduces the size of my sample.
40
periods wherein the risk of default is higher. Balance sheet fraud is positive and significantly
related to the default risk premium while revenue and expense fraud are not. This finding
further suggests that the relation between the incentive proxies and accounting fraud is driven
by changes in market wide incentives. Together, the results in Table 9 suggest that different
types of accounting fraud have different determinants.
These results suggest that studying the relation between all forms of accounting fraud
and market or firm-level determinants is not always the appropriate research design. To
properly test certain relations, the researcher may need to separate accounting fraud into
subgroups. It is possible that even my earlier tests which find significant relations between
accounting fraud and market covariance asymmetry and price-earnings ratios may sacrifice the
level of understanding we can gain from them. Table 3 reports that revenue fraud is negatively
correlated with covariance asymmetry, whereas expense fraud and balance sheet fraud are not.
Similarly, revenue fraud appears to be motivated by the component of PE ratios driven by
revenues whereas expense fraud appears motivated by the component that is not. Further,
balance sheet fraud does not appear to be motivated by PE ratios at all. Just as the total strength
of incentives managers have to misreport can vary over time, so too can the source of those
incentives.
6. Robustness Checks
My robustness tests are primarily intended to validate the proxies for market wide
incentives and to strengthen Hypothesis 2. Each proxy is measured in a second way and
41
retested in the same models. I use the r-squared from the following annual regression,
firm _ return     market _ return as an alternative measure of covariance asymmetry; this
proxy captures how well the market return explains firm returns each year. I use the
percentage of firms that fail to meet their consensus median analyst forecast as another measure
of the information environment and the predictability of earnings. This proxy captures how
often firms report earnings below their forecast, rather than capturing the magnitude of forecast
errors. Finally, I use an annual earnings response coefficient as another measure of the
sensitivity of share price to earnings news.
Untabulated results show that the additional market wide incentive proxies are
significant in the predicted directions. These results also suggest that the significant results I
find for the initial proxies are not likely the result of how the variables are constructed. These
results are available upon request. Further, including the hypothesized firm-level predictors of
accounting fraud from Dechow et al [2010] does not eliminate the significant predictive power
of the market incentive proxies. While this paper focuses on determinants of fraud and is not
attempting to create a prediction model, it is comforting to know that firm-level variables
cannot be used to explain the observed relation between macro variables and accounting fraud.
7. Future Work
The primary extension of this paper is to further develop the fraud type and differential
effects analysis. Having several clear and testable predictions supported by economic theory
will more strengthen the conclusion of my current analysis. Developing more proxies that
42
measure market wide incentives from different sources will strengthen the results presented in
Table 9. A related extension is to see whether less egregious forms of financial misreporting
(i.e., within GAAP earnings management) behave similarly to criminal fraud.
8. Conclusions
In this paper I find that more managers are observed committing accounting fraud
during periods of stronger aggregate performance and in the two years leading up to an
economic peak. Survival analysis suggests that proxies for market wide incentives that measure
the relative weight the market places on firm-level news, the ability of the market to predict
earnings, and the sensitivity of price to earnings news are all related to a given manager’s
decision to commit accounting fraud. These results support the conclusion that market driven
incentives influence whether a manager decides to commit accounting fraud. I find that
revenue fraud is positively related to the price response to revenue news and that balance sheet
fraud is positively related to the default risk premium. I addition, I find that the delta of the
CEO’s shares held and the decision to raise capital are both positively related to accounting
fraud in periods of high price sensitivity to earnings news.
43
References
Abbott, L., Park, Y., Parker, S., 2000. The Effects of Audit Committee Activity and
Independence on Corporate Fraud. Managerial Finance: vol. 26, issue 11, 55-68.
Agrawal, A., Chadha, S., 2005. Corporate Governance and Accounting Scandals. The Journal of
Law and Economics: vol. 48, no.2, 371-406.
Agrawal, A., Cooper, T., 2007. Insider Trading Before Accounting Scandals. Working Paper.
Ang, A., Chen, J., 2002. Asymmetric Correlations of Equity Portfolios. Journal of Financial
Economics: vol.63, 443-494.
Antle, R., Smith, A., 1986. An Empirical Investigation of the Relative Performance Evaluation of
Corporate Executives. Journal of Accounting Research: vol.24, no.1, 1-39.
Armstrong, C., Jagolinzer, A., Larcker, D., 2009. Chief Executive Officer Equity Incentives and
Accounting Irregularities. Journal of Accounting Research: vol.48, no.2, 225,271.
Baker, M., Ruback, R., Wurgler, J., 2007. Behavioral Corporate Finance: A Survey. Handbook of
Corporate Finance, no.1, chapter 4.
Baker, M., Wurgler, J., 2006. Investor Sentiment and the Cross-Section of Stock Returns.
Journal of Finance: vol.61, no.4, 1645-1680.
Baker, M., Wurgler, J., 2007. Investor Sentiment in the Stock Market. Journal of Economic
Perspectives: vol.21, no.2, 129-151.
Ball, R., 2009. Market and Political/Regulatory Perspectives on the Recent Accounting Scandals.
Journal of Accounting Research, no.47.
Ball, R., Shivakumar, L., 2008. Earnings Quality at Initial Public Offerings. Journal of
Accounting and Economics: vol.45, no.2-3, 324-349.
Bar-Gill, O., Bebchuk, L., 2003. Misreporting Corporate Governance. Working paper.
Bartov, E., Givoly, D., Hayn, C., 2002. The Rewards to Meeting or Beating Expectation. Journal
of Accounting and Economics: v.33, no.2, 173-2004.
Baxter, M., King, R., 1999. Measuring Business Cycles: Approximate Band-Pass Filters for
Economic Time Series. Review of Economics and Statistics, vol.81, 575-593.
44
Beasley, M., 1996. An Empirical Analysis of the Relation Between the Board of Director
Composition and Financial Statement Fraud. The Accounting Review: vol. 71, no.4, 443-465.
Beatty, R., Karaoglu, E., Sandino, T., 2006. Benchmarking Against the Performance of High
Profile ‘Scandal’ Firms. Working paper.
Becker, G., 1968. Crime and Punishment: An Economic Approach. Journal of Political
Economy: vol. 76, no.2, 169-217.
Beneish, M., 1999. Incentives and Penalties Related to Earnings Overstatements that Violate
GAAP. The Accounting Review, vol.74, no.4, 425-457.
Beneish, M., 2001. Earnings Management: A Perspective. Managerial Finance: vol.27, no.12, 317.
Benmelech, E., Kandel, E., Veronesi, P., 2010. Stock-Based Compensation and CEO (Dis)
Incentives. Working paper.
Bergstresser, D., Philippon, T., 2006. CEO Incentives and Earnings Management. Journal of
Financial Economics, no.80, 511-529.
Blanchard, O., Simon, J., 2001. The Long and Large Decline in US Output Volatility. Brookings
Papers on Economic Activity; issue 1, 165-173.
Bonner, S., Palmrose, Z., Young, S., 1998. Fraud Type and Auditor Litigation: an Analysis of
SEC Accounting and Auditing Enforcement Releases. The Accounting Review: vol.73, no.4,
503-532.
Cao, J., Laksmana, I., 2010. The Effect of Capital Market Pressures on the Association between
R&D Spending and CEO Option Compensation. Review of Quantitative Finance and
Accounting: vol.34, no.2, 273-300.
Caplan, D., 1999. Internal Controls and the Detection of Management Fraud. Journal of
Accounting Research: vol.37, no.1, 101-117.
Cogger, K., Fanning, K., 1998. Neural Network Detection of Management Fraud using
Published Financial Data. International Journal of Intelligent Systems in Accounting: vol.7,
issue 1, 21-41.
Collard, D., 1996. Pigou and Modern Business Theory. The Economic Journal: no.106, 912-924.
45
Core, J., Guay, W., 2002. Estimating the Value of Employee Stock Option Portfolios and Their
Sensitivities to Price and Volatility. Journal of Accounting Research: vol. 40, no.3, 613-630.
Correia, M., 2010. Political Connections, SEC Enforcement, and Accounting Quality. Working
paper.
Davis, L., Ricchiute, D., Trompeter, G., 1993. Audit Effort, Audit Fees, and the Provision of
Nonaudit Services to Audit Clients. The Accounting Review: vol.68, no.1, 135-150.
Dechow, P., Ge, W., Larson, C., Sloan, R., 2010. Predicting Material Accounting Manipulations.
Contemporary Accounting Research, forthcoming.
Dechow, P., Kothari, S., Watts, R., 1998. The Relation between Earnings and Cash Flows.
Journal of Accounting and Economics: vol.25, no.2, 133-168.
Dechow, P., Skinner, D., 2000. Earnings Management: Reconciling the Views of Accounting
Academics, Practitioners, and Regulators. Accounting Horizons: vol.14, no.2, 235-250.
Dechow, P., Sloan, R., Sweeney, A., 1996. Causes and Consequences of Earnings Manipulation:
An Analysis of Firms Subject to Enforcement Actions by the SEC. Contemporary Accounting
Research vol.13 1-36.
Desai, H., Hogan, C., Wilkins, M., 2006. The Reputation Penalty for Aggressive Accounting:
Earnings Restatements and Management Turnover. The Accounting Review: vol.81, issue 1, 83112.
Docking, D., Koch, P., 2005. Sensitivity of Investor Reaction to Market Direction and Volatility:
Dividend Change Announcements. The Journal of Financial Research: vol.28, no.1, 21-40.
Durtschi, C., Hillison, W., Pacini, C., 2004. The Effective use of Benford’s Law to Assist in
Detecting Fraud in Accounting Data. Journal of Forensic Accounting: vol.5, 17-34.
Dyck, A., Morse, A., Zingales, L., 2007. Who Blows the Whistle on Corporate Fraud? Working
paper.
Erickson, M., Hanlon, M., Maydew, E., 2006. Is there a Link between Executive Compensation
and Accounting Fraud? Journal of Accounting Research: vol.44, no.1, 113-143.
Erickson, M., Hanlon, M., Maydew, E., 2004. How much will Firms Pay for Earnings that do
not Exist? Evidence of Taxes Paid on Allegedly Fraudulent Earnings. The Accounting review:
vol.79, issue 2, 387-408.
46
Falk, A., Ichino, A., 2006. Clean Evidence on Peer Effects. Journal of Labor Economics: vol.26,
no.1, 39-57.
Fernandes, N., Guedes, J., 2009. Keeping up with the Joneses: a Model and a Test of Collective
Accounting Fraud. European Financial Management: vol.16, no.1, 72-93.
Feroz, E., Park, K., Pastena, V., The Financial and Market Effects of the SEC’s Accounting and
Auditing Enforcement Releases. Journal of Accounting Research: vol.29 supplement, 107-142.
Galbraith, J. K., 1961. The Great Crash, 1929. London: Penguin Books.
Gibbons, R., Murphy, K., 1992. Optimal Incentive Contracts in the Presence of Career Concerns:
Theory and Evidence. Journal of Political Economy: vol.100, no.3, 468-505.
Gimeno, J., Zhang, Y., 2007. Strategic Behavior under Capital Market Pressure: Evidence from
the U.S. Airline Industry. Working paper.
Goldman, E., Slezak, S., 2003. The Economics of Fraudulent Misreporting. Working paper.
Graham, J., Harvey, C., Rajgopal, S., 2005. The Economic Implications of Corporate Financial
Reporting. Journal of Accounting and Economics: vol.40, no.1, 3-73.
Gray, S., Meek, G., Roberts, C., 1995. International Capital Market Pressures and Voluntary
Annual Report Disclosures by U.S. and U.K. Multinationals. Journal of International Financial
Management and Accounting: vol.6, no.1 43-68.
Hertzberg, A., 2005. Managerial Incentives, Misreporting, and the Timing of Social Learning: A
Theory of Slow Booms and Rapid Recessions. Working paper.
Hodrick, R., Prescott, E., 1997. Postwar US Business Cycles: An Empirical Investigation.
Journal of Money, Credit, and Banking: vol.29, 1-16.
Hong, Y., Tu, J., Zhou, G., 2007. Asymmetries in Stock Returns: Statistical Tests and Economic
Evaluation. Review of Financial Studies: vol.20, no.5, 1547-1581.
Janakiraman, S., Lambert, R., Larcker, D., 1992. An Empirical Investigation of the Relative
Performance Evaluation Hypothesis. Journal of Accounting Research: vol.30, no.1, 53-69.
Jensen, M., 2003. Paying People to Lie: the Truth about the Budgeting Process. European
Financial Management: vol.9, no.3, 379-406.
Johnson, S., Ryan, H., Tian, Y., 2009. Executive Compensation and Corporate Fraud. Review of
Finance: vol.13, no.1, 115-145.
47
Kasznik, R., McNichols, M., 2002. Does Meeting Earnings Expectations Matter? Evidence from
Analyst Forecast Revisions and Share Prices. Journal of Accounting Research: vol.40, no.3, 727759.
Kedia, S., Koh, K., Rajgopal, S., 2010. Evidence on Contagion in Corporate Misconduct.
Working paper.
Kedia, S., Philippon, T., 2007. The Economics of Fraudulent Accounting. Review of Financial
Studies: vol.22, no.6, 2169-2199.
Keynes, J., 1936. The General Theory of Employment, Interest, and Money. London McMillan.
King, G., Zeng, L., 2001. Logistic Regression in Rare Events Data. Political Analysis: vol.9, no.2,
137-163.
Kroner, K., Ng, V., 1998. Modeling Asymmetric Comovements of Asset Returns. Review of
Financial Studies: vol.11, no.4, 817-844.
Lavington, F., 1922. The Trade Cycle: An Account of the Causes Producing Rhythmical Changes
in the Activity of Business. P.S. King and Sons.
Leary, M., Roberts, M., 2009. Follow the Leader: Strategic Interaction in Corporate Capital
Structure. Working paper.
Martin, K., McConnell, J., 1991. Corporate Performance, Corporate Takeover, and Management
Turnover. The Journal of Finance: vol.46, no.2, 671-687.
McConnell, M., Perez-Quiros, G., 2000. Output Fluctuations in the United States: what has
Changed Since the Early 1980’s? The American Economic Review: vol.90, no.5, 1464-1476.
Miller, G., 2006. The Press as a Watchdog for Accounting Fraud. Journal of Accounting
Research: vol.44, issue 5, 1001-1033.
Myers, J., Myers, L., Skinner, D., 2006. Earnings Momentum and Earnings Management.
Working paper.
Philippon, T., 2006. Corporate Governance over the Business Cycle. Journal of Economic
Dynamics and Control: vol.30, 2117-2141.
Povel, P., Singh, R., Winton, A., 2007. Booms, Busts, and Fraud. Review of Financial Studies:
vol.20, no.4, 1219-1254.
48
Rajgopal, S., Shivakumar, L., Simpson, A., 2007. A Catering Theory of Earnings Management.
Working paper.
Ramey, G., Ramey, V., 1995. Cross-country Evidence on the Link between Volatility and
Growth. The American Economic Review: vol.85, no.5, 1138-1151.
Rezaee, Z., 2003. Causes, Consequences, and Deterrence of Financial Statement Fraud. Critical
Perspectives on Accounting: vol.16, issue 3, 277-298.
Roychowdhury, S., 2006. Earnings Management through Real Activities Manipulation. Journal
of Accounting and Economics: vol.42, no.3, 335-370.
Sadka, G., 2006. The Economic Consequences of Accounting Fraud in Product Markets: Theory
and a Case from the US Telecommunications Industry (WorldCom). American Law and
Economics Review: vol.8, 439-475.
Schrand, C., Zechman, S., 2008. Executive Compensation and the Slippery Slope to Fraud.
Working paper.
Strobl, G., 2008. Earnings Manipulation and the Cost of Capital. Working paper.
Taussig, F., 1911. Principles of Economics. New York, McMillan.
Uzun, H., Szewczyk, S. Varma, R., 2004. Board Composition and Corporate Fraud. Financial
Analysts Journal: May/June, 33-43.
49
Appendix A
Variable Definitions
Variable
Definition
Data Sources
Fraud
GDP
Indicator variable equal to 1 in the period a manager begins committing accounting fraud.
2005 chained GDP, inflation adjusted, expressed in 100s of billions of dollars and detrended
using the Hodrick-Prescot filter.
Indicator variable - 1 if the year is 1 or 2 years before an economic peak.
Indicator variable - 1 if the year is 1 or 2 years after an economic trough.
Average correlation between monthly firm returns and the equal weighted market return.
Average median absolute forecast error. Calculated as the absolute value of EPS less the
consensus analyst forecast closest to the report date, scaled by beginning of period price.
Equal weighted average price-earnings ratio.
Detrended difference between GDP and GDP forecast as provided by the Philadelphia FED
'Survey of Professional Forecasters'.
Standard deviation of the monthly market return.
Difference between the long term Baa corporate bond rate and the 10 year US Treasury rate.
SEC annual budget, expressed in millions of dollars.
Number of IPOs in the last 3 years.
Indicator variable - 1 if the firm issued debt or stock in the current period.
Lagged annual firm return less the lagged annual value weighted market return.
Price/earnings ratio for the firm
Black Scholes delta of current CEO option grants.
Delta of all CEO option grants outstanding using the option value given in Execucomp.
Delta of all shares held by the CEO at year end using the option value given in Execucomp.
Equal weighted average price-revenue ratio.
Earnings response coefficient calculated by regressing earnings surprise on abnormal returns.
Revenue response coefficient calculated by regressing revenue surprise on abnormal returns.
SEC - AAER
Bureau of Economic Accounts
Peak
Trough
CA
MAFE
PE
Surprise
Volatility
Risk Premium
SEC
IPO
Capital
Lag Ab Return
PE - Firm Level
Current IC
Option
Wealth
PR
ERC
RRC
50
National Bureau of Economic Research
National Bureau of Economic Research
CRSP
IBES and Compustat
Compustat
Bureau of Economic Accounts
and the Philadelphia FED
CRSP
St.Louis FED
SEC
CRSP and Jay Ritter
Compustat
CRSP
Compustat
Execucomp
Execucomp
Execucomp
Compustat
Compustat and CRSP
Compustat and CRSP
Figure 1: Accounting Fraud by Year
90
80
New Cases of Accounting Fraud
70
60
50
40
Fraud Starts
30
20
10
0
Year
51
New Cases of Accounting Fraud Scaled by NYSE/AMEX/Nasdaq Firms
Figure 2: Percentage of Accounting Fraud by Year
1.2
1
0.8
0.6
Fraud Starts
0.4
0.2
0
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
52
Table 1
Descriptive Statistics: Fraud Sample
Sample collection period
1982-2010
Sample period of violations
1980-2005
Total AAERs
3148
AAERs not involving accounting fraud and redundant cases
2298
Total accounting fraud AAERs
852
Cases of options backdating
24
Cases of asset/revenue understatement
4
Number of fraud cases in time-series analysis
824
Firms without CRSP identifiers
329
Firms with CRSP identifiers but no data to calculate lagged returns
190
Remaining firms without Compustat identifiers/data
34
Firms available for firm-level survival analysis
271
Remaining firms without compensation data
167
Firms available for survival analysis with compensation data
104
Average Duration of Fraud
2.50 years
Median Duration of Fraud
2 years
Shortest Case
1 quarter
Longest Case
13 years
This table provides summary information about the AAER sample, including the number of fraud firms and years, and describes the
reductions in sample size due to data requirements.
53
Table 2
Time-Series Regressions
GDP
Base
Peak
Cycle
0.05 **
0.04
0.04
(0.48)
(0.40)
(2.06)
Peak
0.75 ***
(3.18)
PTT
0.64 **
(2.55)
-0.26
(-0.88)
Trough
-0.22 **
(-2.04)
Surprise
Constant
Observations
R-Squared
0.01
0.01
0.02
(0.23)
(0.14)
(0.26)
0.68 ***
0.97 ***
1.09 ***
(7.95)
(6.17)
(6.53)
26
26
26
6.00%
25.00%
30.00%
This table presents time-series regression estimates for fraud starts at the annual level. The time range in the regressions
is 1980-2005. Standard errors are adjusted using the Newey-West [1987] procedure. ***, **, and * represent significance
at the 1%, 5%, and 10% levels, respectively. T-statistics are presented in parentheses. The dependent variable is the
number of managers who start committing fraud in the current year scaled by the number of Compustat firms. GDP is
gross domestic product in chained 2005 billions of dollars, adjusted for inflation, and detrended using the Hodrick
Prescot (1980) filter. Peak is an indicator variable equal to 1 in the 2 years leading up to an economic peak as defined by
NBER. PTT is an indicator variable equal to 1 in the periods between a peak and a trough. Trough is an indicator
variable equal to 1 in the 2 years following an economic trough as defined by NBER. Surprise is GDP less forecasted
GDP as predicted by the Survey of Professional Forecasters provided by the Philadelphia FED.
54
Table 3
Correlation Matrix
Fraud
Revenue Expense
Balance
GDP
Peak
Trough
Surprise Volatility
Sheet
Fraud
Risk
SEC
IPO
CA
MAFE
PE
PR
ERC
RRC
Capital
Premium
Lab Ab
PE Firm
Return
Level
Option
Wealth
1
0.81
0.62
0.58
0.09
0.08
-0.08
0.01
0.09
0.06
0.03
0.00
-0.06
-0.04
0.06
0.09
0.04
0.03
0.01
0.03
0.01
-0.01
-0.01
Revenue
0.58
1
0.30
0.36
0.08
0.05
-0.04
0.00
0.09
0.01
0.03
0.01
-0.07
-0.03
0.00
0.09
-0.01
0.04
0.01
0.02
0.01
0.00
-0.01
Expense
0.45
0.31
1
0.53
0.03
0.03
-0.02
0.02
0.04
0.01
0.01
0.02
0.03
0.00
0.00
0.00
0.00
-0.02
0.03
0.01
-0.01
-0.01
0.01
Balance Sheet
0.43
0.36
0.55
1
0.05
0.04
-0.01
0.01
0.04
0.03
0.02
0.02
0.02
0.02
-0.03
0.00
-0.02
-0.02
0.03
0.01
0.00
0.00
0.02
GDP
0.08
0.06
0.02
0.03
1
0.82
-0.39
0.08
0.48
0.38
-0.04
-0.37
0.32
0.58
0.36
0.33
0.02
0.09
-0.01
0.03
0.04
0.07
0.07
Peak
0.06
0.08
0.01
0.02
0.78
1
-0.28
0.12
0.06
0.18
-0.10
-0.02
-0.15
0.25
-0.32
0.38
0.07
0.01
-0.01
0.03
0.05
0.06
0.06
Trough
-0.05
-0.03
-0.01
0.00
-0.40
-0.29
1
0.13
-0.17
0.45
0.31
-0.29
-0.18
-0.07
-0.17
-0.15
0.01
0.00
-0.04
0.03
-0.01
-0.04
-0.01
Surprise
-0.01
-0.01
0.02
0.00
0.06
0.13
0.09
1
-0.28
-0.30
-0.06
0.21
-0.18
-0.20
0.30
-0.16
-0.18
0.11
0.01
-0.05
0.00
0.06
0.03
Volatility
0.06
0.09
0.03
0.04
0.42
-0.04
-0.12
-0.30
1
0.59
0.27
-0.49
0.92
0.51
-0.60
0.34
-0.40
-0.21
-0.03
0.05
0.00
0.03
0.05
Risk Premium
0.07
0.04
0.01
0.02
0.37
0.11
0.45
-0.31
0.59
1
0.62
-0.83
0.43
0.55
-0.85
0.34
-0.23
0.20
-0.06
0.10
0.02
0.00
0.06
SEC
-0.01
0.03
0.02
0.02
-0.10
-0.20
0.30
-0.04
0.30
0.62
1
-0.31
0.29
-0.33
-0.48
0.22
0.04
0.72
-0.04
0.10
0.03
-0.01
0.04
IPO
0.00
0.03
0.02
0.03
-0.37
-0.12
-0.20
0.19
-0.38
-0.75
-0.26
1
-0.45
-0.55
0.61
0.03
0.23
-0.27
0.04
-0.05
-0.01
0.00
-0.04
CA
-0.03
-0.05
0.02
0.01
0.28
-0.15
-0.19
-0.17
0.87
0.41
0.31
-0.45
1
0.33
-0.42
-0.08
-0.43
-0.13
-0.02
0.01
-0.02
0.02
0.05
MAFE
-0.07
-0.04
0.00
0.02
0.48
0.12
0.06
-0.26
0.51
0.49
-0.34
-0.29
0.19
1
-0.58
-0.23
-0.34
-0.47
-0.01
0.02
0.01
0.00
0.02
PE
0.04
0.02
0.03
-0.01
0.23
-0.35
-0.11
0.25
-0.50
-0.79
-0.41
0.65
-0.44
-0.34
1
0.32
0.33
-0.09
0.04
-0.11
-0.03
0.01
-0.04
PR
0.03
0.05
0.01
0.00
0.29
0.38
-0.21
-0.06
0.21
0.26
0.22
-0.10
0.15
-0.10
0.39
1
-0.21
-0.05
-0.03
0.08
0.00
0.06
0.05
ERC
0.04
0.04
0.00
-0.01
0.02
-0.31
0.04
-0.20
-0.34
-0.19
0.05
0.25
-0.42
-0.18
0.35
-0.26
1
0.18
0.00
-0.01
0.03
-0.04
-0.05
RRC
0.06
0.09
-0.01
-0.02
0.07
-0.13
0.06
0.15
-0.25
0.17
0.73
-0.33
-0.08
-0.55
-0.16
0.11
0.09
1
-0.04
0.06
0.01
0.03
0.01
Capital
0.02
0.01
0.03
0.03
0.00
0.00
-0.04
0.01
-0.02
-0.03
-0.05
0.03
-0.01
0.00
0.04
-0.02
0.00
-0.04
1
-0.02
0.01
0.03
0.06
Lag Ab Return
0.02
0.02
0.01
0.01
0.01
0.01
0.04
-0.04
0.05
0.10
0.10
-0.04
0.01
0.02
-0.09
0.07
-0.01
0.05
-0.03
1
0.09
0.07
0.17
PE - firm level
0.01
0.01
-0.01
0.00
0.01
0.01
0.00
-0.01
0.00
0.02
0.02
0.01
-0.02
0.01
-0.01
-0.01
0.03
0.00
0.01
0.08
1
0.01
0.00
Option
0.00
0.00
-0.01
-0.01
0.07
0.05
-0.04
0.05
0.03
0.00
-0.01
0.01
0.02
0.01
0.01
0.08
0.14
0.15
0.03
0.08
0.00
1
0.37
Wealth
-0.01
-0.01
0.00
0.01
0.06
0.05
-0.02
0.03
0.03
0.03
0.04
-0.05
0.04
-0.02
-0.04
0.03
0.08
0.17
0.05
0.14
0.00
0.29
1
This table shows univariate correlations (Spearman above the diagonal and Pearson below the diagonal). Detailed variable definitions and their data source are included in Appendix A.
55
Table 4
Descriptive Statistics
Variable
Mean
Std. Dev.
25th
Median
75th
Fraud Start
1.02%
0.49
0.71%
0.93%
1.32%
Revenue
0.57%
0.40
0.20%
0.54%
0.75%
Expense
0.34%
0.33
0.18%
0.33%
0.55%
Balance Sheet
0.25%
0.20
0.11%
0.26%
0.46%
GDP
-4.12
140.78
-127.54
-51.32
111.83
Peak
27.00%
-
-
-
-
Trough
27.00%
-
-
-
-
Surprise
-4.13
143.61
-65.02
-25.1
65.37
Volatility
4.56%
0.01
3.30%
4.37%
5.00%
Risk Premium
2.06%
0.49
1.62%
1.97%
2.28%
SEC
326
101
294
320
337
IPO
950
477
484.5
972.5
1291.5
CA
25.00%
0.07
20.00%
26.00%
29.00%
MAFE
0.10
0.03
0.08
0.09
0.13
PE
12.21
2.81
9.89
12.98
14.64
PR
1.46
0.35
1.18
1.41
1.63
ERC
0.003
0.01
0.000
0.002
0.003
RRC
0.011
0.02
0.001
0.007
0.012
53.00%
-
-
-
-
Lag Ab Ret
0.08
0.42
-0.34
-0.05
0.25
PE - Firm Level
18.22
24.16
10.49
16.12
27.72
Option
18.97
12.24
0.35
2.13
8.58
Wealth
104.06
158.37
7.11
34.43
176.11
Capital
This table provides descriptive statistics for variables used in hazard and logistic analysis. The sample period is 1980-2005.
Detailed variable definitions and their data source are included in Appendix A.
56
Table 5 A
Hazard Analysis
GDP
GDP
Peak
1.317 **
(2.34)
Peak
1.343 **
(2.23)
Trough
0.611 **
(-2.32)
Surprise
1.113 **
(2.03)
1.090 **
(2.35)
Volatility
1.168 ***
(3.79)
1.149 ***
(3.74)
Risk Premium
0.821
(-0.71)
SEC
0.997 **
(-2.31)
0.996 ***
(-3.33)
IPO
1.001 **
(2.06)
1.001 *
(1.78)
Capital
1.269
(1.37)
1.268
(1.36)
Lag Ab Return
1.085 **
(2.21)
1.085 **
(2.32)
Fraud Firms
Non-Fraud Firms
1.217
(1.09)
271
2987
271
2987
This table presents results from estimates of a Cox proportional hazards model testing the macroecnomic and firm-level conditions
present when managers begin committing accounting fraud. The table presents hazard rates, not coefficient estimates. Standard
errors are clustered annually and by firm. Each column corresponds to a different way of measuring aggregate performance. Z-stats
are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively. GDP is gross domestic
product in chained 2005 100's of billions of dollars, adjusted for inflation and detrended using the Hodrick-Prescot filter. Surprise is the
detrended difference between actual GDP and forecasted GDP. Volatility is the standard deviation of the monthly market return. Risk
premium is the long term Baa corporate bond rate less the 10 year government treasury rate. SEC is the SEC annual budget, expressed
in millions of dollars. IPO is the number of IPOs in the last 3 years. Peak is an indicator variable equal to 1 if the year is in the 2 years
prior to an NBER defined economic peak. Trough is an indicator variable equal to 1 if the year is in the 2 years following an NBER
defined trough. Capital is an indicator variable equal to 1 if the firm issued debt or stock in the current period. Lag Ab Return is the
lagged annual firm return less the lagged annual market return.
57
Table 5 B
Hazard Analysis: Compensation Sub-Sample
GDP
Option
GDP
1.671 ***
(3.27)
GDP
Wealth
Peak
Option
Peak
Wealth
1.583 **
(2.59)
Peak
1.782 ***
(2.79)
Trough
0.199 **
(-2.46)
1.734 ***
(4.23)
0.199 **
(-2.32)
Surprise
0.919
(-1.04)
0.951
(-0.60)
1.045
(1.05)
1.075 **
(2.58)
Volatility
0.926
(-0.74)
0.952
(-0.50)
0.952
(-0.77)
0.959
(-0.71)
Risk Premium
0.610
(-0.98)
0.580
(-1.17)
1.186
(0.52)
1.128
(0.54)
SEC
0.993 **
(-2.20)
0.994 **
(-2.32)
0.99 ***
(-4.77)
0.99 ***
(-4.19)
IPO
0.999 **
(-0.72)
0.999
(-0.96)
0.997
(-1.27)
0.997
(-1.26)
Capital
1.432
(0.85)
1.498
(0.88)
1.431
(0.86)
1.499
(0.88)
Lab Ab Return
1.135
(0.90)
1.146
(1.00)
1.163
(1.12)
1.175
(1.31)
Option
0.999 **
(-2.01)
Wealth
Fraud Firms
Non-Fraud Firms
0.999 *
(-1.81)
0.999
(-1.04)
104
1046
0.999
(-1.08)
104
1046
104
1046
104
1046
This table presents results from estimates of a Cox proportional hazards model testing the macroecnomic and firm-level conditions
present when managers begin committing accounting fraud. The table presents hazard rates, not coefficient estimates. Standard
errors are clustered annually and by firm. Each column corresponds to a different way of measuring aggregate performance. Z-stats
are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively. GDP is gross domestic
product in chained 2005 100's of billions of dollars, adjusted for inflation and detrended using the Hodrick-Prescot filter. Surprise is the
detrended difference between actual GDP and forecasted GDP. Volatility is the standard deviation of the monthly market return. Risk
premium is the long term Baa corporate bond rate less the 10 year government treasury rate. SEC is the SEC annual budget, expressed
in millions of dollars. IPO is the number of IPOs in the last 3 years. Peak is an indicator variable equal to 1 if the year is in the 2 years
prior to an NBER defined economic peak. Trough is an indicator variable equal to 1 if the year is in the 2 years following an NBER
defined trough. Capital is an indicator variable equal to 1 if the firm issued debt or stock in the current period. Lag Ab Return is the
lagged annual firm return less the lagged annual market return. Option is the delta for total CEO option grants based on Execucomp
values. Wealth is the delta for all CEO shares held based on Execucomp values.
58
Table 6 A
Hazard Analysis: Market Incentive Proxies
CA
GDP
CA
1.109 *
(1.91)
MAFE
1.484 **
(2.54)
PE
1.869 ***
(4.13)
0.907 ***
(-2.76)
MAFE
0.943 *
(-1.88)
PE
1.166 ***
(2.77)
Surprise
1.13 **
(2.41)
1.113 **
(2.23)
1.064
(1.50)
Volatility
1.413 ***
(3.42)
1.213 ***
(3.69)
1.204 ***
(3.86)
Risk Premium
0.655
(-1.38)
0.722
(-1.12)
0.832
(-0.76)
SEC
0.997 **
(-2.42)
0.996 ***
(-3.62)
0.998 **
(-1.99)
IPO
1.001
(0.72)
1.001 **
(2.07)
1.002 ***
(3.46)
Capital
1.227
(1.36)
1.268
(1.36)
1.264
(1.33)
Lab Ab Return
1.083 **
(2.34)
1.085 **
(2.23)
1.085 **
(2.21)
PE - Firm Level
Fraud Firms
Non-Fraud Firms
1.001
(0.48)
271
2987
271
2987
271
2987
This table presents results from estimates of a Cox proportional hazards model testing the macroecnomic, market incentive, and firmlevel conditions present when managers begin committing accounting fraud. The table presents hazard rates, not coefficient estimates.
Standard errors are clustered annually and by firm. Each column corresponds to the different market incentive proxy tested. Z-stats
are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively. GDP is gross domestic product
in chained 2005 100's of billions of dollars, adjusted for inflation and detrended using the Hodrick-Prescot filter. CA is the average
correlation between monthly firm returns and the monthly equal weighted market return. MAFE is the average absolute median
forecast error for the period. PE is the average price-earnings ratio for the period. Surprise is the detrended difference between actual
GDP and forecasted GDP. Volatility is the standard deviation of the monthly market return. Risk premium is the long term Baa
corporate bond rate less the 10 year government treasury rate. SEC is the SEC annual budget, expressed in millions of dollars. IPO is the
number of IPOs in the previous 3 years. Capital is an indicator variable equal to 1 if the firm issued debt or stock in the current period.
Lag Ab Return is the lagged annual firm return less the lagged annual market return. PE - Firm Level is the PE ratio for individual firms.
59
Table 6 B
Hazard Analysis: Compensation Sub-Sample
CA
Option
GDP
CA
CA
Wealth
1.266 *
(1.92)
1.192 *
(1.75)
0.823 ***
(-2.80)
0.813 ***
(-2.93)
MAFE
MAFE
Option
MAFE
Wealth
2.412 ***
(5.38)
2.701 ***
(5.43)
0.614 ***
(-4.43)
0.628 ***
(-5.51)
PE
Option
PE
Surprise
1.141 *
(1.83)
1.202 **
(2.11)
1.009
(0.72)
1.012
(0.42)
Volatility
1.469 ***
(2.65)
1.683 ***
(2.88)
1.011
(0.40)
1.084
(0.54)
PE
Wealth
6.267 ***
(6.24)
7.932 ***
(5.15)
1.863 ***
(7.83)
1.849 ***
(6.57)
0.871 **
(-1.96)
1.140
(1.36)
0.889 *
(-1.66)
1.152
(1.42)
Risk Premium
0.330
(-1.54)
0.270 **
(-2.02)
0.389 *
(-1.90)
0.516 *
(-1.88)
0.392 **
(-2.14)
0.413 *
(-1.82)
SEC
0.996 *
(-1.73)
0.996 *
(-1.89)
0.990 **
(-2.00)
0.987 **
(-1.99)
0.998 *
(-1.88)
0.998
(-1.65)
IPO
0.999 **
(-0.93)
0.998
(-1.53)
0.996
(-1.63)
0.996
(-1.58)
1.001
(0.17)
0.999
(-0.06)
Capital
1.428
(0.85)
1.490
(0.87)
1.432
(0.90)
1.455
(0.87)
1.447
(0.87)
1.508
(0.89)
Lab Ab Return
1.140
(0.97)
1.147
(1.08)
1.166
(1.04)
1.202
(1.38)
1.139
(0.92)
1.148
(1.06)
1.001
(0.25)
1.001
(0.25)
PE - Firm Level
Option
0.999 **
(-1.98)
Wealth
Fraud Firms
Non-Fraud Firms
0.999 *
(-1.73)
0.999
(-1.03)
104
1046
104
1046
0.999 **
(-1.99)
0.999
(-1.04)
104
1046
104
1046
0.999
(-1.07)
104
1046
104
1046
This table presents results from estimates of a Cox proportional hazards model testing the macroecnomic, market incentive, and firmlevel conditions present when managers begin committing accounting fraud. The table presents hazard rates, not coefficient estimates.
Standard errors are clustered annually and by firm. Each column corresponds to the different market incentive proxy tested. Z-stats
are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively.
60
GDP is gross domestic product in chained 2005 100's of billions of dollars, adjusted for inflation and detrended using the HodrickPrescot filter. CA is the average correlation between monthly firm returns and the monthly equal weighted market return. MAFE is the
average absolute median forecast error for the period. PE is the average price-earnings ratio for the period. Surprise is the detrended
difference between actual GDP and forecasted GDP. Volatility is the standard deviation of the monthly market return. Risk premium is
the long term Baa corporate bond rate less the 10 year government treasury rate. SEC is the SEC annual budget, expressed in millions of
dollars. IPO is the number of IPOs in the previous 3 years. Capital is an indicator variable equal to 1 if the firm issued debt or stock in
the current period. Lag Ab Return is the lagged annual firm return less the lagged annual market return. PE - Firm Level is the PE ratio
for individual firms. Option is the delta for total CEO option grants based on Execucomp values. Wealth is the delta for all CEO shares
held based on Execucomp values.
61
Table 7
Hazard Analysis: Industry Level
Fama-French 5 Industry
CA
MAFE
PE
Fama-French 12 Industry
CA
MAFE
PE
GDP
1.216 *
(1.85)
1.269 **
(2.34)
1.252 ***
(2.75)
1.228 *
(1.92)
1.297 **
(2.22)
1.296 ***
(4.54)
Surprise
1.056
(1.09)
1.107
(1.12)
1.163 *
(1.74)
1.040
(0.98)
1.065
(0.65)
1.105
(0.87)
Volatility
1.052
(0.72)
1.209
(1.43)
1.244 **
(2.02)
1.071
(0.84)
1.185 **
(2.18)
1.226 ***
(3.40)
Risk Premium
1.508
(1.41)
0.823
(-0.63)
0.713
(-1.01)
0.998 *
(-1.71)
0.998 **
(-2.16)
SEC
0.997 **
(-2.36)
IPO
1.001 **
(2.25)
CA - industry
1.002 ***
(3.48)
1.301
(1.13)
0.997 **
(-2.04)
1.002 ***
(2.60)
0.972 **
(-2.11)
MAFE - industry
1.001
(1.60)
0.883
(-0.47)
0.742
(-1.02)
0.997 *
(-1.88)
0.999
(-1.80)
1.002 ***
(2.64)
1.002 ***
(3.10)
0.944 **
(-2.12)
0.978 **
(-2.03)
PE - industry
0.954 *
(-2.02)
1.311 **
(2.82)
1.229 **
(2.61)
Capital
1.049
(0.32)
1.012
(0.08)
1.010
(0.07)
1.006
(0.31)
1.094
(0.09)
1.114
(0.18)
Lab Ab Ret
1.074 *
(1.80)
1.069 *
(1.76)
1.066
(1.65)
1.053
(1.58)
1.060 *
(1.84)
1.074 *
(1.69)
PE - Firm Level
Fraud Firms
Non-Fraud Firms
1.002
(0.98)
271
2987
271
2987
1.003
(1.03)
271
2987
271
2987
271
2987
271
2987
This table presents results from estimates of a Cox proportional hazards model testing the macroecnomic, market incentive, and firmlevel conditions present when managers begin committing accounting fraud. The table presents hazard rates, not coefficient estimates.
All market incentive proxies are calculated at the industry level using the Fama-French industry definitions. Standard errors are
clustered annually and by industry. Each column corresponds to the different market behavior proxy tested using either 5 or 12
industries. Z-stats are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively.
62
GDP is gross domestic product in chained 2005 100's of billions of dollars, adjusted for inflation and detrended using the HodrickPrescot filter. Surprise is the detrended difference between actual GDP and forecasted GDP. Volatility is the standard deviation of the
monthly market return. Risk premium is the long term Baa corporate bond rate less the 10 year government treasury rate. SEC is the
SEC annual budget, expressed in millions of dollars. IPO is the number of IPOs in the previous 3 years. CA is the average correlation
between monthly firm returns and the monthly equal-weighted market return. MAFE is the average absolute median forecast error
for the period. PE is the average price-earnings ratio for the period. Capital is an indicator variable equal to 1 if the firm issued debt or
stock in the current period. Lag Ab Return is the lagged annual firm return less the lagged annual market return. PE - Firm Level is the
PE ratio for individual firms.
63
Table 8
Logistic Regression: Interactions
Current IC
PE
Current IC
0.408 **
(2.29)
Option
Wealth
0.469 **
(2.53)
0.456 **
(2.42)
Capital
0.133 *
(1.89)
-0.002
(-0.87)
Option
-0.002
(-0.53)
Wealth
0.002
(0.09)
Capital
0.100
(0.17)
Interaction
0.001
(0.81)
0.001
(0.42)
0.028 **
(2.06)
0.021 *
(1.90)
GDP
1.313 ***
(3.26)
1.416 ***
(3.33)
1.356 ***
(3.04)
0.434 ***
(2.85)
Surprise
-0.103
(-0.73)
-0.165
(-1.15)
-0.136
(-0.93)
Volatility
0.134
(0.96)
0.101
(0.66)
0.115
(0.71)
0.272 ***
(4.66)
Risk Premium
-0.007
(0.96)
0.231
(0.31)
0.188
(0.25)
0.453
(1.38)
SEC
0.001
(0.35)
0.002
(0.52)
0.002
(0.44)
0.002 **
(0.61)
IPO
-0.001
(-0.26)
-0.001
(-0.20)
-0.001
(-0.29)
0.002 ***
(4.57)
Capital
0.592 **
(2.12)
0.471
(1.66)
0.084
(1.37)
0.527 *
(1.81)
Lag Ab Return
-0.005
(-0.05)
-0.015
(-0.13)
0.014
(0.11)
0.073 *
(1.74)
PE - Firm Level
0.001
(0.24)
0.001
(0.15)
0.001
(0.14)
0.002
(0.60)
Constant
-9.135 *
(-1.94)
-10.383 **
(-2.05)
-9.989 *
(-1.95)
-8.578 ***
(-6.01)
Fraud Firms
Non-Fraud Firms
Pseudo R-Squared
104
1046
7.20%
104
1046
7.70%
271
2987
4.00%
104
1046
7.00%
This table shows results from logistic regressions testing the interaction between price sensitivity to news (measured using PE ratios)
and various potential firm-level determinants of accounting fraud. Standard errors are clustered annually and by firm and the
interaction has been adjusted using the Norton, Wang, and Ai correction technique. Each column corresponds to the specific interaction
tested. Z-stats are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively.
64
PE is the average price-earnings ratio for the period. Current IC is the Black-Scholes delta for current CEO option grants. Options is the
delta for total CEO option grants based on Execucomp values. Wealth is the delta for all CEO shares held based on Execucomp values.
Capital is an indicator variable equal to 1 if the firm issued debt or stock in the current period. The interaction is the PE ratio multiplied
by the variable listed beneath it in each model. GDP is gross domestic product in chained 2005 100's of billions of dollars, adjusted
for inflation and detrended using the Hodrick-Prescot filter. Surprise is the detrended difference between actual GDP and forecasted
GDP. Volatility is the standard deviation of the monthly market return. Risk premium is the long term Baa corporate bond rate less
the 10 year government treasury rate. SEC is the SEC annual budget, expressed in millions of dollars. IPO is the number of IPOs in the
previous 3 years. Lag Ab Return is the lagged annual firm return less the lagged annual market return. PE - Firm Level is the PE ratio
for individual firms.
65
Table 9
Hazard Analysis: Fraud Type
Revenue Fraud
Price
Response
Ratios
Coefficients
Expense Fraud
Price
Response
Ratios
Coefficients
GDP
1.213 **
(2.06)
1.217
(0.74)
PE Ratio
1.103
(1.60)
1.104 *
(1.95)
PR Ratio
1.201 ***
(3.63)
1.014
(0.68)
ERC
0.700
(-1.21)
RRC
Risk Premium
Surprise
1.344 ***
(2.88)
1.372
(1.40)
Balance Sheet Fraud
Price
Response
Ratios
Coefficients
1.030
(0.14)
0.923
(-1.45)
1.353 ***
(2.66)
1.139 **
(2.06)
1.093
(0.35)
1.027
(0.11)
1.149
(0.97)
1.112
(0.31)
1.080
(0.60)
1.214
(1.06)
1.113
(0.92)
1.565 *
(1.88)
1.532 *
(1.92)
0.990
(-0.11)
0.974
(-0.42)
1.096
(0.93)
1.128
(1.36)
1.270 *
(1.99)
1.222
(1.36)
1.123
(1.23)
1.339 ***
(3.99)
1.216 *
(1.87)
1.240
(1.50)
0.990 ***
(-3.66)
0.993 ***
(-3.12)
0.995 ***
(-3.23)
Volatility
1.328 ***
(3.80)
SEC
1.001
(0.25)
IPO
1.002 **
(2.50)
1.002 ***
(2.89)
1.001
(0.70)
1.002 ***
(3.00)
1.002 *
(1.91)
1.002 *
(1.69)
Capital
1.104
(0.60)
1.101
(0.58)
1.534
(1.47)
1.528
(1.45)
1.409 *
(1.67)
1.409 *
(1.67)
Lab Ab Return
1.075 *
(1.80)
1.079 *
(1.85)
1.077
(1.28)
1.083
(1.36)
1.076
(1.00)
1.086
(1.20)
Fraud Firms
Non-fraud Firms
155
3103
1.302 ***
(5.04)
0.997
(-1.33)
0.995 **
(-2.52)
92
3166
68
3190
This table presents results from estimates of a Cox proportional hazards model testing the relation between type of accounting fraud
and price sensitivity to that type of news. The table presents hazard rates, not coefficient estimates. The revenue columns represent
results for revenue fraud, expense for expense fraud, and balance sheet for balance sheet fraud. Standard errors are clustered annually
and by firm. Z-stats are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively. GDP is
gross domestic product in chained 2005 100's of billions of dollars, adjusted for inflation and detrended using the Hodrick-Prescot filter.
PE ratio is the average price-earnings ratio for the period. PR ratio is the weighted average price-revenue ratio for the period. ERC is
the earnings response coefficient for the period. RRC is the revenue response coefficient for the period. Risk premium is the long term
corporate bond interest rate less less the 10 year government treasury rate. Surprise is the detrended difference between actual GDP
and forecasted GDP. Volatility is the standard deviation of the monthly market return. SEC is the SEC annual budget, expressed in
millions of dollars. IPO is the number of IPOS in the previous 3 years. Capital is an indicator variable equal to 1 if the firm issued debt
or equity in the current period. Lag Ab Return is the lagged annual firm return less the lagged annual market return.
66