THE UNIVERSITY OF CHICAGO

ACCOUNTING FRAUD: BOOMS, BUSTS, AND INCENTIVES TO PERFORM

A DISSERTATION SUBMITTED TO

THE FACULTY OF THE UNIVERSITY OF CHICAGO

BOOTH SCHOOL OF BUSINESS

IN CANDIDACY FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

BY

ROBERT HENRY DAVIDSON

CHICAGO, ILLINOIS

JUNE 2011

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Copyright © Robert Henry Davidson 2011

DEDICATION

I dedicate this dissertation to my father,

Daniel Robert Davidson

iv

TABLE OF CONTENTS

List of Tables v

List of Figures vii

Acknowledgements viii

Abstract ix

Introduction 1

Literature 4

Hypothesis Development 9

Data: AAERs 19

Tests and Results 25

Robustness Checks 58

Conclusion 65

References 66

Appendix 1 70

v

LIST OF TABLES

Table 1: Descriptive Statistics: Fraud Sample 22

Table 2: Time-Series Regressions 30

Table 3: Correlation Matrix 33

Table 4: Descriptive Statistics 35

Table 5A: Hazard Analysis 37

Table 5B: Hazard Analysis: Compensation Sub-Sample 39

Table 6A: Hazard Analysis: Market Incentive Proxies 43

Table 6B: Hazard Analysis: Compensation Sub-Sample 46

Table 7: Logistic Regressions: Interactions 52

Table 8: Hazard Analysis: Fraud Type 56

Table 9: Chi Squared Tests 57

Appendix Table 1: Variable Definitions 70

Appendix Table 2: Descriptive Statistics 71

Appendix Table 3: Hazard Analysis: SEC Chair Fixed Effects 72

Appendix Table 4: Hazard Analysis: Industry Level 73

vi

Appendix Table 5: Hazard Analysis: Firm-Level Variables 74

Appendix Table 6: Hazard Analysis: Market Incentive Proxies 75

Appendix Table 7: Hazard Analysis: Industry Level 76

Appendix Table 8: Logistic Regressions: Interactions 79

Appendix Table 9: Hazard Analysis: Fraud Type – ERC 80

vii

LIST OF FIGURES

Figure 1: Accounting Fraud by Year 28

Figure 2: Percentage of Accounting Fraud by Year 29

viii

ACKNOWLEDGMENTS

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, Georgetown

University, The University of Texas at Dallas, The University of Illinois at Urbana-Champaign,

and Purdue University.

ix

ABSTRACT

In this thesis, 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

market 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. I also find that

although the association between the firm’s need for external financing and the propensity to

observe accounting fraud is positive, it is only significant during periods of high price

sensitivity to earnings news. Analyzing accounting fraud by type reveals that revenue fraud is

increasing in price sensitivity to revenue news; this relation does not exist for expense or

balance sheet fraud. 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.

1

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 often followed these scandals; for

example, the Sarbanes-Oxley Act was drafted to reduce the incidence 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. Reduced firm value, reduced investor confidence,

increased bankruptcies, increased unemployment, and increased regulation all contribute to the

total welfare cost from widespread accounting fraud.

Prior research finds that managers commit accounting fraud partly in response to strong

financial incentives2. 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 strength of these financial incentives varies

over time. Moreover, the strength of these incentives varies cross-sectionally as well.

Theoretical research predicts that the level of accounting fraud in the economy is not

constant over time; neither is the strength of the manager’s financial incentives created by the

1 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.

2 Dechow et al [1996] find that managers commit fraud to access the capital markets on favorable terms. Johnson

et al [2009] find that managers commit fraud due to financial incentives from their compensation packages.

Graham et al [2005] find survey evidence that managers manipulate earnings to keep their firm’s share price high.

2

capital and labor markets. Research on firm-level determinants of accounting fraud often yields

contradictory results and does not consider, first, how the strength of incentives varies over

time and, second, differential effects across these incentives. Do changes in the macro economy

influence managers’ reporting decisions above and beyond firm-level accounting fraud

determinants? Are certain firm-level accounting fraud determinants 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, Tyco, 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 three

macroeconomic proxies measuring the relative strength of incentives the capital and labor

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 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.

3

In survival analysis including both fraud firms and a random sample of non-fraud firms,

I find more observed cases of accounting fraud start in periods of strong aggregate performance

and in the two years leading up to an economic peak. 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.

In survival analysis of 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

price sensitivity 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 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.

4

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 hypothesis that market wide incentives influence managers’ reporting

decisions incremental to firm-level effects. Second, as logistic results suggest, the macro

environment affects the strength of the financial incentives from CEO incentive compensation

or the firm’s need for external financing; managers’ reporting decisions are only influenced by

these incentives when they are at their strongest. 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

and section 7 concludes.

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,

5

they all predict 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. Povel et al [2007] 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 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 activity, or by other forces in the environment.

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

6

high investor optimism, which they define as periods wherein positive earnings surprises

receive a larger price reaction.

Though Rajgopal et al [2007] do not model or directly test the link between earnings

optimism and actual macro performance, they allude to as much and cite some macroeconomic

theories (Lavington [1922] and Collard [1996]) that suggest that periods of earnings optimism

tend to coincide with periods of high real growth. 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 have less incentive to commit fraud because the true

performance of their firms’ is high. This difference, however, may derive from the papers’

different focuses. Whereas Povel et al [2007] model the decision to commit accounting fraud, 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.

Several recent empirical papers study the relation between accounting fraud and the

environment in which it occurs. Kedia, Koh, and Rajgopal [2010] find that fraudulent reporting

has a contagion effect. Specifically, they find that managers are more likely to commit

accounting fraud after 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 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.

7

Fernandes and Guedes [2009] examine the relation between accounting fraud and

expected and actual macro performance. They find a positive relation between the occurrence

of accounting fraud and expected performance 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 aggregate

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 either overstating or

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 under three years. Over the same time period, government

litigation releases tend to occur about three years after the fraud is detected. This means that a

release in 2005 is likely to address accounting fraud that began around 2000. Fernandes and

Guedes [2009] average this release over the period 2003-2005.

Gerety and Lehn [1997] test internal and external forces that can influence the decision to

commit accounting fraud. They find that fraud is more likely in markets wherein it is more

difficult to value assets. Though their tests are done in the cross-section, comparing accounts

such as research and development to property, plant, and equipment, it is possible that there is

also a temporal effect. If assets in general are more difficult to value in certain periods, then this

8

could lead to either increased cases of fraud or decreased detection of fraud. An additional

implication is that fraud should be more prevalent in industries with assets that are more

difficult to value.

Cunningham [2005] also presents a theory of fraud that is related to valuation

difficulties. He argues that the growing prominence of the fair-value movement and the

elevation of cash flows give managers more discretion over reported numbers, which now lack

verifiability, and has led to an increase in fraudulent reporting. This could explain a general

increase in accounting fraud over this time period (starting in the early 1970s), but is less likely

to explain variation in the level of accounting fraud over the business cycle.

Ebert and Gagne [2007] develop a monopoly model of fraud in which managers choose

to commit fraud because of their ability to shift the costs onto the company. The manager’s

ability to shift costs is related to his power and ability to subvert internal controls. In this

model, the costs of accounting fraud increase at a slower rate for the manager than for the firm.

If the power of the average CEO has increased over the last 30 years, then this model provides

an explanation for the increase in the magnitude of the average fraud case over the last 30 years.

As accounting frauds become larger, the CEO takes on a decreasing fraction of the costs.

Miller [2006] investigates the role of the press as a watchdog for accounting fraud. If the

press’ incentives to investigate possible fraud firms vary over time, this could lead to variation

in the number of perpetrated or detected cases of accounting fraud. Miller [2006] finds that in

the cross section, firms with a richer information environment are more likely to be cited in the

9

press. He also finds that the type of fraud matters; insider trading ‚sells‛ for example. There is

no evidence of a temporal shift in the efforts of the press to uncover and report accounting

fraud.

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 ability 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 SEC Accounting and Auditing Enforcement Releases; theirs is also one of the few

papers to analyze several hundred fraud firms. The 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.

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

report truthfully or to commit accounting fraud can be analyzed in this context. The costs to the

manager, if his crime is detected, are substantial and include fines, jail time, and loss of

reputation. Therefore, the perceived costs of reporting truthfully – or, conversely, the perceived

10

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 supported by anecdotal

evidence. Thus, 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 performance below ex ante expectation,

will base his decision of whether to report truthfully 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.

3.1 Time Variance

Recent literature predicts that the relation between accounting fraud and

macroeconomic performance is positive, though not necessarily monotonic3. Proposed

3 Povel et al [2007], Hertzberg [2005], and Strobl [2008] all develop analytical models that find this result.

11

hypotheses for this relation include the belief that monitors, such as external auditors, debt

holders, and shareholders reduce their effort during periods of strong aggregate performance

and the belief that investors are more likely to believe what firms report during periods of

strong aggregate performance. These specific hypotheses, however, are difficult to test

empirically because of difficulties in measuring monitors’ effort or how willing investors are to

believe financial statements.

These hypotheses seem to implicitly assume that the opportunities managers have to

commit accounting fraud are the key determinant of fraud because they do not address the

manager’s motivations to commit fraud. A more comprehensive argument considers both. I

argue that managers care about their firm’s performance measured against both their firm’s

expected performance and performance relative to peers and that performing poorly across

either dimension can increase a manager’s incentives to commit accounting fraud. Myers,

Myers, and Skinner [2006] document the importance to managers of consistently exceeding

expectation. They find that firms with long strings of positive earnings surprises have higher

share prices than do firms with similar long run performance but without the consecutive

strings of 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 explicitly written into

compensation contracts. Share price, job retention, and CEO compensation are all influenced by

the firm’s performance, relative to both its own expected performance and to that of its peers.

12

Ball [2009] argues that high growth is built into performance expectations during a

boom period 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/or their peers, the

managers of these firms find themselves unable to meet their heightened expectations. Such

managers know the consequences of poor performance and have particularly strong incentives

to meet expectation.

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 could not be justified by the facts at hand. They

further note that risks and ‚limits to arbitrage‛ made it too difficult for contrarian 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 leads me to 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

4 Galbraith [1961] makes a similar case for the events leading up to the 1929 market crash.

13

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

plug small gaps in performance and may have no intention of committing accounting fraud. If

the firm’s 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 his past

entries, or continuing down the path towards accounting fraud. Empirical and anecdotal

evidence support the slippery slope theory; this evidence does not change the predictions I

make in this paper. I posit that market driven incentives and the firm’s environment 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

14

report truthfully. 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 direction, even if by varying degree.

3.2 Market Based Incentives

Knowing in which environments managers are more likely to commit accounting fraud

is a helpful starting point for developing hypotheses about which market forces create such

strong incentives that managers respond to them by committing accounting fraud. If more

managers report fraudulently in good times, then the focus can shift to stimulants that are

particularly strong in these periods. Market wide incentives affect all firms in the economy at

the same time and to a highly correlated degree. Therefore, looking at changes in these

incentives over time is required to test the theory that market driven incentives influence a

manager’s reporting decisions.

Many areas of research document that markets create significant incentives for

managers to perform5. Benmelech, Kandal, and Veronesi [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. Though accounting fraud is not

the focus of their paper, Benmelech et al [2010] nevertheless mention that fraudulent reporting

5 For examples of the effects of market incentives, see Gray, Meek, and Roberts [1995], and Cao and Laksmana

[2010].

15

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 the probability of 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 the 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. My main argument is that the incentives to commit

accounting fraud originate in the labor market, but normally include equity sensitive

compensation. If managers are ultimately concerned with their net worth, then we can assume

that meeting the expectations of both the capital and labor markets is important to them.

Hypothesis 3 attempts to isolate the effects on accounting fraud of the value of the manager’s

incentive plans and net worth and on his firm’s need to access the capital markets.

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

16

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 reporting decisions.

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 Benmelech et al [2010]

show that incentive compensation induces the manager to exert costly effort, the extent to

which it also gives the manager incentives to commit accounting fraud remains unclear.

Johnson et al. [2009] find a positive 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 constant 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.

H3A: The relation between observed accounting fraud and price sensitivity of the CEO’s net

worth is positive and more pronounced in periods wherein stock price is highly sensitive to short-

term news.

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 Philippon [2007] study violations between 1996 and 2001.

17

While Dechow et al. [1996] find that accounting fraud is often motivated by concerns

about the cost of external financing, Beneish [1999] finds no support for this claim. The effect of

firm performance on the cost of external financing is not constant. It is possible that the

incentives arising from a need to access the capital markets are strong enough to influence the

manager’s reporting decisions only in periods wherein the fraudulently inflated earnings will

have the greatest effect on the value of securities.

H3B: 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.

3.4 Differential effects across type of accounting fraud committed

Financial statements report the aggregation of many individual transactions and entries.

Managers can commit accounting fraud by intentionally misrepresenting any combination of

these individual transactions. For example, a manager can increase net income by overstating

revenue, understating expenses, or by manipulating both. This choice is likely strategic and

reflects specific expectations the manager wishes to meet. Revenue, expense, and balance sheet

information is used by numerous agents for numerous purposes. A manager can have strong

incentives to improve along one of these dimensions while having not nearly as strong of

incentives to improve along the others. If market driven incentives are an important

determinant of accounting fraud, then we should observe managers committing certain types of

accounting fraud when the incentives for improvement along that dimension are particularly

strong. Ghosh, Gu, and Jain [2005] find the that two components of earnings are differentially

18

informative, suggesting that the interpretation of a positive earnings surprise is in part

determined by whether that surprise is driven by increased revenues, or decreased expenses.

Further, Ertimur, Livnat, and Martikainen [2003] find that revenue surprises are more

important for growth firms and during high growth periods. The market demand for revenue

growth varies independently from that of earnings growth. When the demand for top line

growth is high we should observe an increase in revenue fraud but, ceteris paribus, have no

reason to expect an increase in expense or balance sheet fraud.

H4A: The relation between revenue fraud and sensitivity of price to revenue news is positive and

incremental to any relation to sensitivity of price to earnings news, and is related to a stronger

degree than is expense or balance sheet fraud.

In addition to revenue and expense information having differential interpretations, the

income statement and balance sheet are often used by different agents for different purposes.

For example, Watts *2003+ notes the prevalence of the balance sheet’s use in contracting when

discussing conservatism in accounting. Common covenants written into debt contracts include

restrictions against issuing more debt, requiring a minimum level of working capital, and

placing limits on certain ratios such as interest coverage and debt-to-equity. When analyzing

developments in credit risk management, Altman and Saunders [1998] cite the extensive use of

the balance sheet in credit risk assessment. They also note that an important goal of the last 10

years has been to properly analyze the balance sheet in light of the rise of many forms of off-

balance sheet debt. Bernanke and Gertler [1989] develop a model of the business cycle where

higher net assets reduce the agency costs of financing real capital investments. In their model,

19

the balance sheet is more important in periods of high financial distress. Many of the incentives

for a strong balance sheet relate to credit risk or bankruptcy risk and the strength of these

incentives varies through time.

H4B: The relation between balance sheet fraud and the default risk premium is positive and is

related to a stronger degree than is revenue or expense fraud.

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 a violation is 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 has a

determinable start date and then removing redundant cases, I am 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. 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 most of the time, the illegal act related to forging

documents or failing to disclose the backdating to shareholders, not to overstating net earnings

20

or assets. 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 these types of violations. Because my hypotheses 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 public debt or equity in the United States are not included.

Given that I test the relation between capital and labor market 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 public shareholders

(i.e., public shareholders are often 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.

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.

Several explanations are consistent with this finding: international firms may commit

8 Xerox and Microsoft are two well known cases of revenue/asset understatement litigated by the SEC.

21

accounting fraud, as defined by the SEC, less frequently than domestic firms, increased costs to

prosecute agents who do not reside in the United States, or lower demand to prosecute these

agents because foreign firms have fewer U.S. shareholders. My macro variables are calculated

using U.S. centric data (e.g. U.S. GDP and surprise, and SEC budget data) and my market wide

incentives proxies are calculated using Compustat North America data. Therefore, I exclude

firms headquartered outside of the U.S. from my sample of 824 fraud firms.

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 a large drop from

my original sample of 824 firms, it is large compared to prior research9. Of these 271 firms, only

104 have Execucomp compensation data available; I therefore conduct two sets of analysis, one

with and one without variables for CEO compensation.

AAERs offer several advantages relative to other proxies for accounting fraud. First and

foremost, it is clear whether the managers of firms in the AAER sample actually committed

accounting fraud, making the probability of type 1 errors negligible. I am specifically interested

in violations of Section 13(a) and Section 13(b)(2)(A) of the Securities Exchange Act of 1934

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.

22

which ‚requires issuers to make and keep books, records, and accounts, which, in reasonable

detail, accurately and fairly reflect the transactions and dispositions of assets of the issuer.‛

AAERs are often issued for violations of Section 13 of the Securities Exchange Act and they

clearly note which violations occurred, making them an appropriate choice to test my

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 AAERs 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.

23

hypotheses. This is not necessarily 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 necessarily 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.

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

of the academic research on earnings management establishes a rather weak burden of proof.

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 error-prone 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 as detailed and accurate

information as possible about the start date of the violation. One last important advantage of

using AAERs is that most of them provide clear information regarding which accounts or totals

24

were manipulated. AAERs denote whether the primary manipulations increased revenue,

reduced expenses, or increased net assets on the balance sheet. This information is required to

test Hypothesis 4. In many cases, information regarding the specific entries booked or illegal

agreements entered into is provided, ensuring I can accurately categorize accounting fraud into

types.

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

address. 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, 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 faces budgetary constraints and only prosecutes those cases where there is

strong evidence against the firm. 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 do not create much bias. 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

25

strong evidence of malfeasance. Further, there is no statute of limitations for litigating cases of

accounting fraud.

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 understated 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 2.5 years and the corresponding

release is published on average 3 years after the violation is detected. 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 test Hypotheses 1A and 1B using time-series and survival analysis. While the small

number of years in my sample 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:

_ifraud macro performance surprise . (1)

10

Results are not sensitive to the choice of cutoff year. I also used 2003 and 2004 as end years and find that the

significance of my results remains unchanged.

26

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 100s of billions of

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 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 and 0 otherwise. I also include an

indicator variable, PTT, to measure the period between the peak and trough. Including these

indicators allows me to track accounting fraud through the business cycle and to observe how

accounting fraud rises 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. 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 analyses.

11

I also use the Baxter-King [1999] filter and find 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.

27

Fraud is measured as a percentage to control for the possibility that the level of

accounting fraud is positively related to the number of firms in the economy. 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. There is clear 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 there is no longer such a trend

over the sample period.

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. All time-series 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 ($139 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

28

010

20

30

40

50

60

70

80

90

New Cases of Accounting Fraud

Ye

ar

Figu

re 1

: A

cco

un

tin

g Fr

au

d b

y Ye

ar

Frau

d S

tart

s

29

0

0.2

0.4

0.6

0.81

1.2

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

New Cases of Accounting Fraud Scaled by NYSE/AMEX/Nasdaq Firms

Year

Figu

re 2

: Per

cent

age

of A

ccou

ntin

g Fr

aud

by Y

ear

Frau

d St

arts

30

Table 2

Time-Series Regressions

Base Peak Cycle

GDP 0.05 ** 0.04 0.04

(2.06) (0.48) (0.40)

Peak 0.75 *** 0.64 **

(3.18) (2.55)

PTT -0.26

(-0.88)

Trough -0.22 **

(-2.04)

Surprise 0.01 0.01 0.02

(0.23) (0.14) (0.26)

Constant 0.68 *** 0.97 *** 1.09 ***

(7.95) (6.17) (6.53)

Observations 26 26 26

R-Squared 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.

proportional hazards model of the following general form:

_ _ _i j kfraud macro perf macro controls firm level controls (2)

Fraud is now measured with 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. In

31

the base models I include a measure for GDP change as an additional proxy for macro

performance to see if the results appear to be driven primarily by the level or change in

detrended GDP. Macroeconomic controls include GDP surprise, stock market volatility, the

default risk premium, the SEC budget appropriation, the average time to detect new cases of

accounting fraud, and the number of IPOs in the previous three years. Stock market volatility

captures a component of the information environment; accounting fraud could be related more

to variance in performance and expectation than to actual performance per se. The default risk

premium controls 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 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.

The average time to detect new cases of accounting fraud controls for changes in monitor effort.

The proxy is imperfect, but it is probable that in years where cases of fraud are detected quickly

that monitors are exerting more effort to detect fraudulent reporting. 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

32

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.

I include the following firm-level determinants of accounting fraud as controls: a raising

capital indicator variable, lagged abnormal returns, and the delta of CEO option and stock

holdings. Capital takes a value of 1 if the firm issued debt or sold common shares in the 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 overstate 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. Wealth is

measured using the delta of all options and shares held by the CEO using the Core & Guay

[2002] methodology. While the relation between accounting fraud and incentive compensation

is not clear, it is nevertheless an important consideration. All variables are defined in Appendix

Table 1.

33

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20

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60

.09

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.06

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40

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0.6

53

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34

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. Approximately 3.5% of the

observations used in survival analysis are first year fraud firms. GDP can show negative values

because of the effect of time detrending. The value for the peak and trough indicators is the

percentage of years that meet the inclusion criteria. The firm-level variables (i.e., lagged

abnormal returns, PE – firm level, and wealth) are winsorized at the 1 percent level to reduce

the influence of outliers. Appendix Table 2 presents descriptive statistics for the firm-level

variables split between fraud and non-fraud firms. Fraud firms are more likely to raise external

financing and have much higher lagged abnormal returns on average (though the median

values are similar). Fraud and non-fraud firms have similar PE ratios and the delta for fraud

firm CEOs is a little more than twice as large as it is for non-fraud firm CEOs.

Tables 5A and 5B report results supporting Hypotheses 1A and 1B. GDP, GDP Change,

and economic peaks are significant and positively 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 Table 5A, the 1.295 hazard rate on GDP

means that for a $100 billion increase in GDP there is a corresponding 29.5% increase in

accounting fraud. A one standard deviation increase in GDP leads to approximately an 80%

increase in observed accounting fraud. Over my sample period, the average year has 31 new

35

Table 4

Descriptive Statistics

Variable Mean Std. Dev. 25th Median 75th

Fraud 3.54% - - - -

Revenue 2.27% - - - -

Expense 1.41% - - - -

Balance Sheet 1.24% - - - -

GDP 1.44 138.78 -120.61 -32.29 103.77

Peak 27.00% - - - -

Trough 27.00% - - - -

Surprise 36.84 18.19 24.77 38.94 47.09

Volatility 4.50% 2.00% 3.32% 4.21% 4.89%

Risk Premium 2.06% 0.47% 1.70% 1.96% 2.21%

SEC 322 115 251 318 344

Detect 2.38 0.40 2.12 2.35 2.68

IPO 1039 484 582 1075 1492

CA 24.97% 6.00% 20.33% 25.26% 28.33%

MAFE 0.10 0.03 0.08 0.10 0.13

PE 11.89 2.79 9.82 12.69 14.30

PR 1.82 0.49 1.42 1.79 2.27

ERC 0.003 0.01 0.000 0.002 0.003

RRC 0.011 0.02 0.001 0.007 0.012

Capital 53.00% - - - -

Lag Ab Ret 8.00% 0.92 -35.00% -5.00% 26.00%

PE - Firm Level 14.81 53.46 1.47 12.33 21.39

Wealth 1002.00 3909 32.00 98 443

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 Table 1.

36

cases of accounting fraud. This one standard deviation increase in GDP increases the number of

fraud firms in the economy from 31 to 55. In addition, I find that the SEC variable is generally

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. Detect is also

negative but only significantly so in one out of six models. In practical terms, wealth is not

related to the hazard rate. The economic effect is small; 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 0.999 and 1.000 (the z-stat is between -0.01 and 0.00).

The hazard rate for the risk premium is negative in all models, significantly so in four of

them. 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 between IPO and

compensation; what causes the switch is not clear, but in an economic sense, the hazard rate of

0.995 – 0.998 in Table 5B is not significant. The hazard rate on the risk premium indicates a

large effect on fraud, but when placed in context is not so large. In the GDP model of Table 5A,

a 1 unit (1 percent) increase in the risk premium is associated with a 48% reduction in

accounting fraud. But, the standard deviation of the risk premium is less than 0.5%. So, a two

standard deviation change in the risk premium is needed to observe the 48% reduction in fraud.

The relation between raising capital and accounting fraud is positive, as predicted by the extant

literature, but not significant. This lack of significance could be due to the downward bias of

coefficients that attends modeling rare events in non-linear models, as documented by King and

37

Table 5 A

Hazard Analysis

GDP Change Peak

GDP 1.295 **

(2.20)

GDP Change 1.538 **

(2.21)

Peak 1.340 ***

(3.48)

Trough 0.681 ***

(-3.06)

Surprise 1.013 ** 1.005 1.012 ***

(2.59) (0.91) (3.02)

Volatility 1.285 *** 1.255 *** 1.252 ***

(3.51) (2.62) (3.46)

Risk Premium 0.516 0.351 ** 0.750

(-1.38) (-2.20) (-0.83)

SEC 0.999 0.998 ** 0.998 ***

(-1.57) (-2.38) (-3.04)

Detect 0.539 0.285 ** 0.598

(-1.16) (-2.42) (-1.20)

IPO 1.001 ** 1.000 1.001 *

(2.01) (0.49) (1.67)

Capital 1.270 1.269 1.270

(1.37) (1.36) (1.37)

Lag Ab Return 1.084 ** 1.088 ** 1.085 **

(2.21) (2.25) (2.34)

R-Squared 0.12 0.14 0.11

Fraud Firms 271 271 271

Non-Fraud Firms 2987 2987 2987

This table presents results from estimates of a Cox proportional hazards model testing the macroeconomic 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. GDP Change is the change in detrended GDP in 100s of billions of dollars. 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. Surprise is the 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

38

Table 5A: Continued

rate. SEC is the SEC annual budget, expressed in millions of dollars. Detect is the average time to detection in years for accounting

frauds beginning in the current year. IPO is the number of IPOs in the last 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.

Zeng [2001]. In economic terms, issuing capital is associated with approximately a 27% increase

in accounting fraud. Time variance in the relation is explored in Hypothesis 3. The relation

between lagged abnormal returns and accounting fraud is positive, consistent with Dechow et

al [2010], though it is statistically 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 firm

observations included in the tests presented in Table 5B. The results indicate that every 1%

increase in lagged abnormal returns is associated with approximately an 8.5% increase in

probability of accounting fraud.

To mitigate the potential of downward bias of 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.13 Given that fraud firms

represent less than 1 percent of the population in most periods, accounting fraud qualifies as a

rare event. Using a random subsample of observations drawn from the total population is one

technique for dealing with this issue, so I select 15% of Compustat firms during the sample

period.14 Firms are selected so that each separate year has 15% of Compustat firms for that year

13

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 logistic analysis is suboptimal in finite samples of rare events data.

14 I conduct hazard analysis using random samples of 5%, 10%, and 15% of the Compustat population and find that

the magnitude and statistical significance of my results does not vary. Further, 25 random samples were

generated to insure that the results are not determined by the specific firms in any one random sample.

39

Table 5 B

Hazard Analysis: Compensation Sub-Sample

GDP Change Peak

GDP 1.611 ***

(2.96)

GDP Change 1.870 *

(1.75)

Peak 2.232 ***

(5.41)

Trough 0.125 **

(-2.46)

Surprise 0.997 0.988 1.007

(-0.27) (-0.71) (1.11)

Volatility 1.095 1.171 1.079

(0.56) (1.00) (0.95)

Risk Premium 0.108 ** 0.075 ** 0.197 ***

(-2.22) (-2.25) (-3.28)

SEC 0.996 * 0.996 * 0.992 ***

(-1.91) (-1.66) (-2.10)

Detect 0.199 0.099 0.194

(-1.56) (-1.34) (-0.99)

IPO 0.998 0.997 0.995

(-1.42) (-1.19) (-1.20)

Capital 1.577 1.567 1.567

(1.17) (1.15) (1.15)

Lag Ab Return 1.084 1.088 1.125

(0.36) (0.37) (0.55)

Wealth 0.999 1.000 0.999

(-0.01) (0.00) (-0.01)

R-Squared 0.22 0.12 0.17

Fraud Firms 104 104 104

Non-Fraud Firms 1046 1046 1046

This table presents results from estimates of a Cox proportional hazards model testing the macroeconomic 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. GDP Change is the change in detrended GDP in 100s of billions of dollars. 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. Surprise is the 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. Detect is the average time to detection in years for accounting

40

Table 5B: Continued

frauds beginning in the current year. IPO is the number of IPOs in the last 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.

Wealth is the delta of all shares held by the CEO, calculated using the Core & Guay methodology.

included in the analysis. To be included in the analysis, firms must have two years of data.15

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 estimate 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 market price-

earnings ratio for the period. Because these proxies exhibit strong correlations with one

another, I test each proxy separately. Additionally, I estimate an index from a principal

components analysis to test whether the market incentive proxies have incremental explanatory

power16.

15

Two years of data is required to calculate lagged abnormal returns for the fraud year. My results are not

sensitive to this distinction. I repeat 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.

16 Principal components analysis can produce unreliable results when done on a small number of observations.

Given that the market-incentive proxies have only 26 independent values, this is a legitimate concern in my

analysis.

41

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 relatively high during recessions and relatively low during

booms. During recessions, price is determined more so 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, managers have more to gain from fraudulently inflating earnings. In

periods wherein strong inferences about individual performance 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 lessened 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,

42

managers have weaker incentives to commit accounting fraud when predicting performance is

difficult.

The sensitivity of price to news can greatly influence the incentives to commit

accounting fraud. In periods wherein earnings has a large effect on price, managers will have

stronger incentives to avoid reporting poor performance. Dechow et al [2010], note that 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.

The results I obtain for Hypothesis 2, 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 at the 5 percent level in every estimation. The principal components index

is only significant at the 10 percent level. Further, the r-squared value for this model is smaller

than it is for other models17. This is odd, as the PCI should not have less explanatory power

than the individual components. This could be due to the potential unreliability of principal

components analysis when the index is determined by a small number of independent

observations. The economic significance associated with a 1 unit change in the PCI is rather

17

Rouam, Moreau, and Broet [2011] provide one technique for estimating an r-squared value in hazards models.

43

Table 6 A

Hazard Analysis: Market Incentive Proxies

CA MAFE PE PCI

GDP 1.172 ** 1.567 *** 1.814 *** 1.498 ***

(2.62) (4.39) (3.99) (3.84)

CA 0.918 ***

(-2.83)

MAFE 0.897 ***

(-2.69)

PE 1.162 **

(2.38)

PCI 0.855 *

(-1.85)

Surprise 1.014 *** 1.012 *** 1.009 * 1.008 *

(3.85) (2.75) (1.93) (1.72)

Volatility 1.771 *** 1.381 *** 1.315 *** 1.263 ***

(4.82) (3.47) (3.10) (2.82)

Risk Premium 0.461 ** 0.359 ** 0.499 * 0.578

(-2.11) (-2.24) (-1.75) (-1.48)

SEC 0.999 0.997 *** 0.999 0.999

(-1.41) (-3.46) (-0.56) (-1.01)

Detect 0.543 0.410 0.527 0.495

(-1.26) (-1.42) (-1.10) (-1.28)

IPO 1.001 1.001 1.002 *** 1.001 **

(0.89) (1.61) (3.05) (2.48)

Capital 1.269 1.268 1.265 1.272

(1.37) (1.36) (1.33) (1.38)

Lag Ab Return 1.083 ** 1.085 ** 1.084 ** 1.085 **

(2.34) (2.25) (2.23) (2.28)

PE - Firm Level 1.001

(0.48)

R-Squared 0.16 0.14 0.15 0.13

Fraud Firms 271 271 271 271

Non-Fraud Firms 2987 2987 2987 2987

This table presents results from estimates of a Cox proportional hazards model testing the macroeconomic, market incentive, 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 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-

44

Table 6A: Continued

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. PCI is a principal

components index of CA, MAFE, and PE. Surprise is the 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. Detect is the average time to detection in years for accounting

frauds beginning in the current year. 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.

large though. A one standard deviation change in the PCI reduces the hazard rate to 0.73.

Regardless, the analysis does not show that the market incentive proxies have much

incremental explanatory power relative to one another. 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. The economic effect is large; for example,

moving from the median to the 75th percentile (a 3% movement) is associated with a 24.6%

decrease in observed accounting fraud. Ceteris paribus it is reasonable to expect that as the

market places more weight on managerial performance, more managers will commit

accounting fraud.

The results also suggest that managers are more likely to commit accounting fraud in

periods wherein earnings is easier to predict. A one standard deviation increase in MAFE (3

units) is associated with a 30.9% decrease in observed accounting fraud. Several interpretations

are consistent with this result: on the one hand, it could be that 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

45

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.18

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 of the aggregate PE ratio19. Ultimately, I

find that the firm-level PE ratio is insignificant, while the aggregate PE ratio is positive and

significant. The economic effect is large. A one standard deviation increase in PE ($2.80) is

associated with a 45.4% increase in observed accounting fraud. This finding suggests that what

creates the strongest incentives for managers to misreport 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.

Interpreting the economic significance of the results across the A and B tables requires

some adjustments. The data in the B tables is only from 1992 onwards so the mean and

18

While price-earnings ratios capture the market’s sensitivity to earnings news, they also capture the discount rate

the market applies to future cash flows. Including the market risk premium should control for the discount rate

and allow the PE ratio to be interpreted as a measure of the sensitivity of price to earnings news.

19 I do not include firm level measures for CA or MAFE. I cannot calculate the correlation between 1 firm’s annual

return and the market return – this is just a correlation between two numbers. The correlation between monthly

returns would not likely provide much more insight. The firm-level MAFE would not provide much insight into the

firm’s information environment, it would just show whether one firm hit its forecast or not.

46

Table 6 B

Hazard Analysis: Compensation Sub-Sample

CA MAFE PE

GDP 1.376 4.384 *** 6.836 ***

(1.33) (5.76) (5.97)

CA 0.829 ***

(-2.89)

MAFE 0.662 **

(-2.19)

PE 1.951 **

(2.55)

Surprise 1.016 0.999 0.978

(1.40) (-0.04) (-1.09)

Volatility 2.706 *** 1.332 ** 1.268 ***

(2.91) (2.03) (3.42)

Risk Premium 0.117 ** 0.024 * 0.180 ***

(-2.25) (-1.89) (-3.48)

SEC 0.998 0.988 ** 1.000

(-1.04) (-2.25) (0.11)

Detect 0.341 0.116 0.280

(-0.96) (-1.32) (-0.68)

IPO 0.998 * 0.995 1.000

(-1.90) (-1.48) (0.12)

Capital 1.581 1.579 1.577

(1.19) (1.19) (1.57)

Lag Ab Return 1.074 1.085 1.109

(0.56) (0.64) (0.48)

PE - Firm Level 1.001

(0.69)

Wealth 0.991 0.991 0.998

(-0.48) (-0.50) (-0.06)

R-Squared 0.26 0.20 0.28

Fraud Firms 104 104 104

Non-Fraud Firms 1046 1046 1046

This table presents results from estimates of a Cox proportional hazards model testing the macroeconomic, market incentive, 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 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-

47

Table 6B: Continued

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. PCI is a principal

components index of CA, MAFE, and PE. Surprise is the 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. Detect is the average time to detection in years for accounting

fraudsbeginning in the current year. 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. Wealth is the delta of all shares held by the CEO, calculated using the Core & Guay

methodology.

standard deviations of the macro variables are different here. For example, in Table 6B, moving

from the median to the 75th percentile for the CA variable is associated with a 61% decrease in

the number of fraud firms, compared to a 25% decrease in Table 6A. One reason for the

difference is that moving from the median to the 75th percentile is a 3.6 unit increase in Table 6B

instead of a 3 unit increase. Other reasons are the different years in the sample and the smaller

number of observations in the sample, leading to certain years having more influence on the

results. Results for volatility, the risk premium, the SEC budget, detection time, IPO, capital,

and lagged abnormal returns are similar in direction and significance to those reported in

Tables 5A and 5B. Results for GDP Surprise are not consistent within or across the B tables.

Macroeconomic variables are often correlated with multiple forces. Though the market

wide incentive proxies likely 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

48

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 result 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 predicts

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 are caused by 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 do other agents. Further,

they find that a large amount of accounting fraud is detected by agents who 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 a reporter 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

49

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 most estimations, which is consistent with fewer managers committing

accounting fraud in periods wherein the SEC has more resources at its disposal. The SEC

budget should control for the SEC’s resources for detecting accounting fraud, but as mentioned

above, many other agents detect accounting fraud. The relation between accounting fraud and

average fraud detection time is also negative but rarely significant. This suggests that managers

are less likely to commit fraud when detection effort is low. This result is plausible if it is due to

higher detection rates when monitoring effort is higher. That said, these variables are difficult

to interpret because it is not clear whether managers are aware of changes to the SEC’s

resources or to fraud detection times and respond accordingly, or if changes to these variables

are responses that come after a large increase in aggregate fraudulent reporting. The political

environment could influence both the SEC’s litigation practices and the effort exerted by certain

monitors. Appendix Table 3 re-estimates that hazards models presented in Table 6A but

includes SEC chairperson fixed effects. The SEC chairperson is appointed by the president and

often only serves for a few years. Some chairpersons might be much more litigation prone than

others and may have been appointed with an agenda in mind. This proxy cannot control for all

the changes in the political environment, but over my sample period there are seven

presidential elections and seven different SEC chairpersons; the chairperson does change with

50

political regimes and following elections. The results in Appendix Table 3 are consistent in

direction and magnitude with those presented in Table 6A except for that GDP Surprise is now

never significant, and IPO has switched from positive to negative (though not significantly so).

The explanatory power of the models goes up a great deal, from around 15% to 22%.

To reduce the likelihood that the statistical significance of the proxies for market wide

incentives is due to 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

Hypothesis 2. As presented in Appendix Table 4, the results do support Hypothesis 2. Here, I

substitute the market incentive proxies for two variables: the proxy calculated for the industry

the firm is in, and the proxy calculated for the rest of the firms in the market. Both measures are

used so that the results can be attributed to industry variance in the measure and not simply

because most industries have to be correlated with the market20. The industry-level proxies for

market wide incentives are statistically significant in the predicted directions in five out of six

models. To conclude that these results are driven by changes in detection or litigation, the effort

to detect accounting fraud or the propensity to litigate for each separate industry would need to

20

Because the industry and market measures are correlated with one another I also estimate the models using

only the industry incentive proxies with no change in the results.

51

change with the proxies for market incentives measured at the industry level when controlling

for changes in the rest of the market. 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 Appendix Table 4

are clustered by industry and by year. The control variables exhibit behavior similar to that

reported in Table 6A.

I use logistic regressions to test Hypotheses 3A and 3B, 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 7 supports Hypothesis 3A. In general, the relation between CEO

delta and the propensity to observe new cases of accounting fraud is positive but not significant

(z-stat of 0.41). However, the interaction of CEO delta and PE ratio is positive and significant at

the 10 percent level. These findings are consistent with those of Benmelech et al [2010] and

support the conclusion that a CEO’s wealth positively influences his decision to commit

accounting fraud in periods wherein share price, and by extension wealth, is highly sensitive to

short-term earnings news. Interpreting the magnitude of the interaction coefficient of two

continuous variables in a logistic regression is not easy, but there is reason not to dismiss the

0.0054 coefficient on the interaction as economically insignificant. The aggregate PE ratio in this

sample is around 13 and the interaction coefficient is approximately 1/13th the size of the

coefficient on wealth. Further, PE has a standard deviation of approximately 3 units and wealth

52

Table 7

Logistic Regressions: Interactions

Wealth Capital

PE 0.488 * 0.080

(1.67) (1.02)

Wealth 0.071

(0.41)

Capital 0.091

(0.16)

Interaction 0.005 * 0.021 *

(1.86) (1.79)

GDP 1.448 *** 0.382 **

(2.89) (2.40)

Surprise -0.024 0.017 **

(-1.10) (2.41)

Volatility 0.228 0.316 ***

(1.06) (4.45)

Risk Premium -0.685 -0.450

(-0.31) (-0.94)

SEC 0.004 0.004 ***

(1.00) (3.32)

Detect -1.044 -1.249 ***

(-0.57) (-2.76)

IPO 0.001 0.001 ***

(0.05) (3.66)

Capital 0.631 **

(2.05)

Lag Ab Return -0.094 0.072 *

(-0.67) (1.66)

PE - Firm Level -0.001 0.002

(-0.05) (1.41)

Constant -8.737 -5.163 ***

(-0.78) (-2.82)

Pseudo R-Squared 8.40% 5.20%

Fraud Firms 104 271

Non-Fraud Firms 1046 2987

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.

53

Table 7: Continued

PE is the average price-earnings ratio for the period. Wealth is the delta for all CEO shares calculated using the Core & Guay

methodology. 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 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. Detect is the average

time to detection in years for accounting frauds beginning in the current year. 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.

a standard deviation of 4 units. A 1 unit change in the interaction term likely represents a

rather small change, so a small coefficient is not surprising.

The evidence presented in Table 7 also supports Hypothesis 3B. The relation between

raising capital and accounting fraud is positive but not significant; the coefficient on the

interaction of raising capital and price sensitivity to news is positive and significant at the 10

percent level. This finding indicates that incentives to commit accounting fraud arising from a

dependency on external financing are only strong enough to lead to fraud in periods of high

price sensitivity to news. The effect of price sensitivity to news can be determined for firms that

do and do not issue capital in this model. The interaction coefficient (a log odds ratio) implies

that the relation of PE to accounting fraud is approximately 2.3% greater for firms that raise

external financing. In this model, the base effect of PE is approximately 8.5%, so raising capital

increases the effect of PE on observed accounting fraud by 27%. This finding also might explain

the inconsistent results found in the literature when studying this question. The standard errors

for the logistic regressions presented in Table 7 are adjusted using the Norton, Wang, and Ai

[2004] correction technique.

The results presented in Table 7 serve two purposes. First, they show the conditions

present when the incentives created by CEO compensation and a firm’s need to raise external

54

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.

I use survival analysis to test Hypothesis 4, that managers commit different types of

accounting fraud in response to different incentives. In this analysis, I group fraud into three

categories: revenue fraud, expense fraud, and balance sheet fraud. I perform the analysis in two

ways: allowing overlap and disallowing overlap. In the analysis allowing overlap a manager

who commits multiple types of accounting fraud is included in each fraud type model. This

allows me to keep as many fraud firms in the sample as possible but could introduce noise into

the analysis. A manager who commits multiple types of accounting fraud is less likely to be

influenced by incentives from one source alone. In the analysis disallowing overlap firms are

only included when the AAER notes that the primary motivation was limited to one type of

accounting fraud as defined above. This allows for better identification but reduces the sample

size.

I have to rely on the information in the AAERs to determine the type of manipulation

and the motivation, particularly with regards to balance sheet fraud. Every entry (or non entry)

will eventually affect the balance sheet, if only through retained earnings, and could thusly be

considered balance sheet fraud. I categorize a fraud as a balance sheet fraud only where there is

evidence that a major part of the motivation was to strengthen the balance sheet. For example,

55

executives at PowerLinx, Inc were indicted for accounting fraud related to improperly

recognizing consignment sales as revenue. In this case, the balance sheet was fraudulently

represented because receivables were overstated and inventory was understated, and

eventually retained earnings might be overstated depending on what happens with the

consigned goods. Nevertheless, I treat this just as revenue fraud because the primary

motivation of PowerLinx’s executives was to overstate revenues and because the effect of the

fraud on net assets is driven through the effect of the fraud on revenues. Conversely,

sometimes the primary motivation for fraud is to strengthen the balance sheet but there is a

residual effect on the income statement. A number of executives have been indicted for

improperly removing debt from the balance sheet. While it is likely that in some cases the

correct amount of interest expense was not recognized, the primary motivation was to remove

debt to improve the balance sheet and the amount by which expenses are understated is usually

immaterial. I treat such a fraud as a balance sheet fraud only.

Table 8 presents results consistent with Hypothesis 4A. In both sets of analysis revenue

fraud is significant and positively related to price revenues ratios, but the relation between

revenue fraud and price earnings ratios is not significant. Surprisingly, the statistical and

economic significance is higher for the analysis involving overlap. This could be because the

analysis disallowing overlap lacks power, as it has only slightly more than half of the fraud firm

observations the overlap analysis has. Price revenues ratios are not significantly related to

either expense or balance sheet fraud. I present coefficient estimates in Table 8 instead of

hazard rates because I test the difference between these coefficients in Table 9. Chi squared

56

Table 8

Hazard Analysis: Fraud Type

Revenue Fraud Expense Fraud Balance Sheet Fraud

With Without With Without With Without

Overlap Overlap Overlap Overlap Overlap Overlap

GDP 0.162 *** 0.234 ** 0.188 0.202 0.090 -0.613 ***

(2.84) (2.37) (1.15) (0.95) (0.62) (-3.03)

PE Ratio 0.150 0.153 0.153 * 0.088 0.029 -0.81 ***

(1.12) (1.26) (1.96) (1.26) (0.36) (-4.38)

PR Ratio 0.133 ** 0.089 * 0.034 0.039 0.008 0.007

(2.11) (1.90) (0.63) (0.70) (0.48) (0.43)

Risk Premium -0.424 -0.790 0.247 -1.078 * 0.446 * 2.851 **

(-0.48) (-0.59) (0.43) (-1.81) (1.94) (2.11)

Surprise 0.005 0.010 0.009 0.010 0.029 ** 0.086 ***

(0.65) (0.76) (0.92) (1.07) (2.20) (3.44)

Volatility 0.308 * 0.278 0.118 0.116 0.312 -0.034

(1.89) (0.93) (1.33) (0.84) (1.50) (-0.13)

SEC -0.001 0.000 -0.004 * -0.001 -0.003 0.002

(-0.37) (0.02) (-1.80) (-0.56) (-1.23) (0.67)

Detect -0.054 -0.010 0.392 -0.375 -0.480 -4.002 **

(-0.05) (-0.06) (0.61) (-0.48) (-0.50) (-2.50)

IPO 0.002 *** 0.002 0.001 -0.001 0.003 ** 0.004 **

(3.01) (1.51) (1.50) (-0.92) (2.36) (2.08)

Capital 0.084 0.042 0.428 0.252 0.337 * 0.520

(0.50) (0.18) (1.44) (0.52) (1.69) (0.86)

Lag Ab Return 0.062 * 0.080 * 0.073 0.133 * 0.057 0.144

(1.79) (1.83) (1.34) (1.96) (0.89) (1.19)

PE - Firm Level 0.002 ** 0.002 0.000 -0.003 0.001 -0.008 ***

(2.21) (1.22) (0.04) (-0.55) (0.80) (-3.26)

R-Squared 0.23 0.24 0.18 0.21 0.30 0.31

Fraud Firms 155 81 92 30 68 27

Non-fraud Firms 3103 3177 3166 3228 3190 3231

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 coefficient estimates. The revenue columns represent results for revenue

fraud, expense for expense fraud, and balance sheet for balance sheet fraud. The with overlap columns include all fraud firms of that

type. The without overlap columns include fraud firms that only committed that type of 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 average price-revenue ratio for the period. Risk

premium is the long term corporate bond interest rate less less the 10 year government treasury rate. Surprise is the 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. Detect is the average time to detection in years for accounting frauds beginning in the current

57

Table 8: Continued

year. 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. PE - Firm Level is the PE

ratio for individual firms.

tests show that the coefficient on price revenue ratios for the revenue fraud models is

significantly larger than the coefficient on price revenue ratios for either of the expense or

balance sheet fraud models at the 10 percent level or better.

Table 8 also presents results consistent with Hypothesis 4B. Both sets of analysis

document a positive and significant relation between balance sheet fraud and the default risk

premium. However, the results for Hypothesis 4B are stronger in the analysis without overlap.

It appears as though the smaller sample size does not present power issues for this analysis.

The risk premium is not significantly related to revenue or expense fraud, except in one case,

where it is negatively related to expense fraud. Chi squared tests reported in Table 9 show that

Table 9

Chi Squared Tests

Price - Revenue Tests

Revenue and Expense Frauds Revenue and Balance Sheet Frauds

Chi Squared Value 2.97 4.43

P Value 0.0847 0.0352

Risk Premium Tests

Balance Sheet and Revenue Frauds Balance Sheet and Expense Frauds

Chi Squared Value 5.95 9.17

P Value 0.0147 0.0025

This table presents results from Chi-squared tests testing the difference between coefficients across two fraud type models. Coefficients

tested are those for Price-Revenue Ratio and Risk Premium from the revenue, expense, and balance sheet fraud type models estimated

in Table 8.

58

the coefficient on the risk premium for the balance sheet fraud model is significantly larger than

the coefficient on the risk premium for either of the revenue or expense fraud models at the 5

percent level or better.

Overall, the results suggest that the type of fraud committed is driven by incentives to

improve performance along that specific dimension. Just as the total strength of incentives

managers have to misreport can vary over time, so too can the source of those incentives.

Further, 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 analysis which finds significant relations between

accounting fraud and market-wide incentives may sacrifice the level of understanding we can

gain from them. Table 3 reports that revenue fraud is negatively correlated with covariance

asymmetry (-0.05), whereas expense fraud (0.02) and balance sheet fraud (0.01) are not. The

significant relation between Fraud and CA appears entirely driven by the relation between

Revenue and CA.

6. Robustness Checks

My robustness tests are primarily intended to show that my results are not sensitive to

variable choice or measurement. Results are presented in the appendix tables, many of which

have already been discussed. Prior research finds some evidence that accounting fraud is

positively related to the need to raise external financing, lagged abnormal returns, and CEO

59

incentive compensation. I also find some evidence of these relations, but correlations between

these firm-level variables and the macro variables included in my models could influence the

direction or strength of the relations. In Appendix Table 5 I estimate a hazards model including

only firm-level variables. The results are similar to those reported in previous tables. The

association between external financing and fraud is positive, and is significant in the

compensation sub-sample. Lagged abnormal returns are positively associated with fraud; the

result is significant in the full sample. The relation between fraud and both firm-level PE ratios

and CEO delta is positive, but never close to significant statistically or economically. The

inclusion of macro variables in the survival analysis does not influence the direction or

significance of potential firm-level determinants of accounting fraud.

To reduce concerns that results for Hypothesis 2 are driven by how the market incentive

proxies are measured, I calculate each proxy in a different way and re-estimate the hazards

models presented in Table 6A. I calculate the relative weight markets give firm-level versus

market news by taking the r-squared value from the following regression:

_ _firm return market return for each year. This value should be a close proxy for the

correlation between firm returns and the market return. I choose to present main results using

CA because the literature on covariance asymmetry between firm returns and the market return

generally refers to the underlying construct as the correlation between the two.

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

60

magnitude of forecast errors. I do not believe this proxy captures the underlying construct I am

trying to measure as well as the MAFE does. The environment where X number of firms report

below their forecast by $0.01 is different from the environment where X firms report below their

forecast by $0.50. But, this variable can provide insight regarding the tradeoff between the

number of managers that have some positive incentive to commit accounting fraud and the

strength of the incentive. Taken to the extremes, if all firms fail to meet their earnings forecast,

then many managers have a positive incentive to inflate earnings, but the incentive is relatively

weak. Given how risky it is to commit accounting fraud and how rarely it occurs, it is

reasonable that the manager’s incentives have to be strong before taking such a risk. At the

other extreme, if all firms but one beat their forecast, the manager of the one firm that

performed poorly likely has very strong incentives to inflate earnings. A significant result

could indicate that the number of firms performing poorly is a stronger determinant than the

magnitude of poor performance. An insignificant result could indicate that the above tradeoff

is not dominated by one side; fraud does not peak when many firms are performing poorly or

when few firms are performing poorly, but somewhere in the middle.

I use the annual average earnings response coefficient as a substitute for the average

market PE ratio to test market price sensitivity to news. I expect the two to behave similarly. I

use the PE ratio in my main tests for two reasons. First, if markets are efficient, then price

should incorporate all available information and update future expectations properly when

responding to earnings news. Second, the nature and strength of the ERC relationship is

debatable. The ERC represents the slope coefficient of a linear equation between unexpected

61

earnings and returns. However, it is not clear that this relation is linear. Further, ‚unexpected‛

earnings is likely measured with noise. The results reported in Appendix Table 6 are generally

consistent with those reported in Table 6A. The hazard rates for R-squared and ERC are

significant in the predicted directions at the 1% level. The relation between accounting fraud

and forecast is negative, but not significant. This is not necessarily an unexpected result given

what this variable is capturing. These results support Hypothesis 2 and reduce concerns that

the results for CA and PE, presented in Table 6A, are driven by variable choice or measurement.

Wang and Winton [2010] find that firms in competitive industries have a strongly pro-

cyclical propensity to commit accounting fraud. This suggests that firms in different industries

have different sets of incentives to report fraudulently. Therefore, I re-estimate the industry

hazards models presented in Appendix Table 4 and report results for each separate industry21.

This analysis is only performed using the Fama-French 5 industry definitions because the

number of fraud firm observations becomes rather small in some industries when using 12

industries. In this analysis, I use only the given industry’s value for CA, MAFE, and PE. The

results, presented in Appendix Table 7, are generally consistent with Hypothesis 2 and with

Wang and Winton [2010]. Panel A documents that CA is negative and significantly associated

with accounting fraud in four of five industries. A great deal of variance exists in both the

statistical and economic significance of the results, suggesting that the strength of different

incentives does vary across industry, but the relation between CA and fraud is consistent and

21

Because the models are estimated for each industry separately, I cluster standard errors by year and firm instead

of by year and industry.

62

strong. Industries 2 and 4 show results most different from previous analysis. Both GDP and

lagged abnormal returns have negative hazard rates (though not significant); those variables are

consistently positive in previous analysis. Both variables have positive hazard rates in the other

three industries, significantly so in two of them. Industries 2 and 4 have 32 and 25 fraud firms,

respectively, spread out over 20 years. This could explain why results look different in these

industries. Of course, it could also be that fraud is driven by additional incentives in these

industries as well. Further, results could be skewed for many of the macro proxies because they

should be measured at the industry level instead of economy level. This would be ideal, but

cannot be done for GDP, GDP surprise, or the SEC budget. Also, detect would become highly

volatile as in some industry years there are only one or two new cases of accounting fraud; this

would not likely be a good proxy when measured at the industry level. Looking at Panel B,

MAFE is negatively associated with accounting fraud in four industries, significantly so in three

of them. Here though, the relation is positive for industry 1. Other variables behave similarly

to Panel A. Finally, looking at Panel C, PE is positive and significantly related to accounting

fraud in three of five industries. The relation is negative in industries 2 and 5, though it is not

significant. Overall, most individual industries show results consistent with Hypothesis 2, and

the only statistically significant results are always consistent with H2. The variance across

panels could be due to the different incentive proxies capturing different incentives that could

vary in strength across industry. The small number of observations in some industries could

also influence the results. For example, in industry 2, where there are only 32 fraud

63

observations, if many of these frauds take place in two or three years, then the results are driven

by possibly one or two large values over the 25 year period.

Prior research on the link between CEO incentive compensation and accounting fraud

uses multiple measures for incentive compensation. I use the delta of all of the CEO’s

stockholdings because my prediction is that the sensitivity of the CEO’s net worth to changes in

price creates incentives strong enough that some managers commit fraud. However, it is

possible that the incentives are created simply by the firm providing a great deal of incentive

compensation to the CEO irrespective of the net worth effect. To investigate this possibility, I

re-estimate the logistic regression presented in Table 7 and substitute CEO delta with the

number of option and stock grants the CEO has received. The results are presented in

Appendix Table 8. The relation between CEO stock grants and accounting fraud is positive, but

not significant economically or statistically. The coefficient on the interaction is zero to the 4th

significant digit. The combined results indicate that the incentives to commit fraud are driven

by the net worth effect to the CEO and are only strong enough to lead to fraud in periods of

high price sensitivity, and thus high net worth sensitivity, to earnings news.

Dechow et al [2010] initially test three different measures for a need for external

financing. They find that an indicator variable for whether or not the firm raises external

financing in the fraud year has superior power to predict accounting fraud. Therefore, I use this

measure in my main analysis. But, to rule out that my results are driven by variable

measurement, I re-estimate the logistic regression presented in Table 7 using the other two

measures of external financing presented in Dechow et al [2010]. Xfin takes a value of 1 if cash

64

flow from operations less average capital expenditures over the last three years all scaled by

current assets is less than -0.5 and 0 otherwise. CFF is the level of external financing raised in

the current year scaled by average total assets. Results are presented in Appendix Table 8 and

are similar to those reported in Table 7. The coefficient on Xfin is positive and significant at the

5% level while the coefficient on CFF is positive and significant at the 10% level. The results

testing the relation between accounting fraud and the interaction between a need for external

financing and price sensitivity to news are robust to how a need for external financing is

measured.

As discussed above, I use average annual earnings response coefficients as a second

proxy for price sensitivity to earnings news in Appendix Table 6 and the results suggest the

ERC is a good substitute for the PE ratio. In Appendix Table 9 I report results from re-

estimating the fraud type hazards models substituting the ERC for the PE ratio. The results are

consistent with those reported in Table 8. The coefficient on the revenue response coefficient is

positive and significantly related to revenue fraud, but not significantly related to expense or

balance sheet fraud. This suggests that the results related to revenue and expense fraud are not

driven by using PE ratios as a proxy for price sensitivity to news. Though there is not much

reason to have predicted otherwise, the coefficient on the risk premium remains positive and

significantly related to balance sheet fraud and remains not significantly related to revenue or

expense fraud.

65

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 wide

incentives influence whether a manager decides to commit accounting fraud. I find that the

delta of all CEO stockholdings and the decision to raise capital are both positively related to

accounting fraud in periods of high price sensitivity to earnings news. Finally, I find that only

revenue fraud is positively related to the price response to revenue news and that only balance

sheet fraud is positively related to the default risk premium.

66

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70

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71

Appendix Table 2

Descriptive Statistics

Variable Mean Std. Dev. 25th Median 75th

Fraud Firms

Capital 59.20% - - - -

Lag Ab Ret 16.60% 0.907 -44.70% -6.20% 42.40%

PE - Firm Level 16.85 66.87 4.17 11.01 24.99

Wealth 2195 6157 84 226 1030

Non Fraud Firms

Capital 52.60% - - - -

Lag Ab Ret 4.90% 0.669 -34.20% -5.30% 25.30%

PE - Firm Level 14.69 52.52 1.32 12.38 21.21

Wealth 913 3675 30 93 427

This table provides descriptive statistics for firm-level variables for fraud firms and non-fraud firms.

Detailed variable definitions and their data source are included in Appendix Table 1.

72

Appendix Table 3

Hazard Analysis: SEC Chair Fixed Effects

CA MAFE PE

GDP 1.076 1.392 * 1.626 *

(0.44) (1.94) (1.94)

CA 0.915 **

(-1.99)

MAFE 0.857 *

(-1.92)

PE 1.152 **

(2.03)

Surprise 1.013 1.000 1.004

(1.26) (0.00) (0.46)

Volatility 1.793 ** 1.263 ** 1.245 **

(2.18) (2.66) (2.53)

Risk Premium 0.635 0.956 1.039

(-0.83) (-0.76) (0.08)

Detect 0.999 0.999 1.001

(-0.02) (-0.76) (1.25)

IPO 0.701 0.516 0.735

(-0.90) (-1.47) (-0.75)

Capital 1.231 1.231 1.220

(1.46) (1.45) (1.39)

Lag Ab Return 1.220 ** 1.230 ** 1.220 **

(2.09) (2.19) (2.09)

PE - Firm Level 1.001

(0.91)

SEC Chair Fixed Effects Yes Yes Yes

R-Squared 0.22 0.23 0.22

Fraud Firms 271 271 271

Non-Fraud Firms 2987 2987 2987

This table presents results from estimates of a Cox proportional hazards model testing the macroeconomic, market incentive, 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 the different market incentive proxy tested. Z-stats

are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively.

Detailed variable definitions and their data source are included in Appendix Table 1.

73

Appendix Table 4

Hazard Analysis: Industry Level

Fama-French 5 Industry Fama-French 12 Industry

CA MAFE PE CA MAFE PE

GDP 1.138 ** 1.203 * 1.742 *** 1.142 ** 1.221 * 1.539 *

(2.04) (1.90) (2.63) (2.05) (1.91) (1.70)

Surprise 1.015 *** 1.014 ** 1.013 ** 1.015 *** 1.014 ** 1.014 *

(2.80) (2.27) (2.03) (2.95) (2.26) (1.76)

Volatility 1.432 *** 1.303 *** 1.376 *** 1.472 *** 1.303 *** 1.373 ***

(3.21) (2.69) (2.97) (3.38) (2.70) (3.17)

Risk Premium 0.563 0.772 0.536 * 0.534 0.739 0.53

(-1.29) (-0.48) (-1.70) (-1.41) (-0.57) (-1.59)

SEC 0.999 0.999 1.001 0.999 0.999 1.000

(-1.24) (-0.64) (0.53) (-1.28) (-0.60) (0.24)

Detect 0.523 0.575 0.559 0.525 0.57 0.554

(-1.14) (-0.97) (-0.98) (-1.12) (-0.99) (-1.04)

IPO 1.000 1.002 *** 1.002 *** 1.000 1.002 *** 1.002 ***

(0.43) (2.68) (4.10) (0.20) (2.72) (3.34)

CA - Industry 0.004 *** 0.001 ***

(-3.21) (-3.12)

MAFE - Industry 0.787 ** 0.862 *

(-2.29) (-1.73)

PE - industry 1.155 * 1.103

(1.82) (0.97)

Market 5.455 ** 1.042 *** 0.993 7.385 *** 1.009 * 0.988

(2.41) (3.22) (-0.45) (4.28) (1.69) (-0.87)

Capital 1.113 1.02 0.994 1.155 1.014 0.991

(0.73) (0.13) (-0.04) (0.96) (0.09) (-0.06)

Lag Ab Ret 1.048 1.061 1.067 * 1.05 1.069 * 1.065 *

(1.16) (1.44) (1.84) (1.34) (1.80) (1.77)

PE - Firm Level 1.002 *** 1.002 **

(2.61) (2.57)

R-Squared 0.18 0.13 0.17 0.19 0.12 0.16

Fraud Firms 271 271 271 271 271 271

Non-Fraud Firms 2987 2987 2987 2987 2987 2987

This table presents results from estimates of a Cox proportional hazards model testing the macroeconomic, market incentive, and firm-

level 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.

Detailed variable definitions and their data source are included in Appendix Table 1.

74

Appendix Table 5

Hazard Analysis: Firm-Level Variables

Full Sample Wealth

Capital 1.249 1.694 *

(1.56) (1.68)

Lag Ab Return 1.196 * 1.127

(1.78) (0.46)

PE - Firm Level 1.001 1.001

(1.03) (0.47)

Wealth 1.002

(0.29)

R-Squared 0.014 0.034

Fraud Firms 271 104

Non-Fraud Firms 2987 1046

This table presents results from estimates of a Cox proportional hazards model testing the 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.

Detailed variable definitions and their data source are included in Appendix Table 1.

75

Appendix Table 6

Hazard Analysis: Market Incentive Proxies

R2 Forecast ERC

GDP 1.286 *** 1.259 ** 1.374 ***

(3.90) (2.10) (5.92)

R2 0.876 ***

(-3.53)

Forecast 0.982

(-0.56)

ERC 1.403 ***

(4.94)

Surprise 1.009 ** 1.012 1.002

(2.33) (1.41) (0.68)

Volatility 1.398 *** 1.236 *** 1.270 ***

(3.96) (3.04) (3.33)

Risk Premium 0.611 0.409 * 1.492

(-1.16) (-1.79) (1.08)

SEC 0.998 *** 0.999 0.519

(-3.66) (-0.88) (-1.41)

Detect 0.539 0.381 ** 0.991 ***

(-1.22) (-1.96) (-5.36)

IPO 0.999 1.000 1.002 ***

(-0.16) (0.69) (3.86)

Capital 1.273 1.276 1.271

(1.39) (1.39) (1.37)

Lag Ab Return 1.260 *** 1.270 *** 1.083 **

(2.89) (2.95) (2.27)

R-Squared 0.17 0.12 0.14

Fraud Firms 271 271 271

Non-Fraud Firms 2987 2987 2987

This table presents results from estimates of a Cox proportional hazards model testing the macroeconomic, market incentive, 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 the different market incentive proxy tested. Z-stats

are presented in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels respectively.

Detailed variable definitions and their data source are included in Appendix Table 1.

76

Appendix Table 7

Hazard Analysis: Industry Level

Panel A

Fama - Fama - Fama - Fama - Fama -

French 1 French 2 French 3 French 4 French 5

GDP 1.168 ** 0.998 1.491 ** 0.987 1.222

(2.09) (-0.02) (2.32) (-0.04) (0.84)

CA - Industry 0.849 *** 0.908 ** 0.975 * 1.020 0.918 *

(-3.31) (-2.05) (-1.86) (0.39) (-1.72)

Surprise 1.053 *** 1.012 0.993 1.017 1.022

(3.29) (0.61) (-0.70) (1.05) (1.32)

Volatility 2.506 *** 0.996 1.360 * 1.065 1.291

(3.41) (-0.02) (1.71) (0.15) (1.19)

Premium 0.164 1.091 0.658 2.399 0.313

(-1.63) (0.06) (-0.45) (0.48) (-0.85)

SEC 0.999 0.999 0.998 0.993 0.995

(-0.10) (-0.04) (-0.90) (-1.26) (-0.90)

Detect 0.308 2.843 0.211 * 4.002 0.839

(-1.03) (0.76) (-1.69) (0.79) (-0.13)

IPO 0.996 * 1.001 1.002 1.004 ** 0.997

(-1.86) (0.28) (1.55) (2.39) (-1.52)

Capital 1.136 1.687 1.149 1.136 2.942 ***

(0.42) (1.13) (0.58) (0.30) (2.62)

Lag Ab Ret 1.160 ** 0.824 1.063 ** 0.935 1.130

(1.98) (-0.41) (2.03) (-0.67) (1.34)

Fraud Firms 77 32 89 25 48

Non Fraud Firms 562 612 751 264 798

This table presents results from estimates of a Cox proportional hazards model testing the macroeconomic, market incentive, and firm-

level 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.

Detailed variable definitions and their data source are included in Appendix Table 1.

77

Appendix Table 7: Continued

Hazard Analysis: Industry Level

Panel B

Fama - Fama - Fama - Fama - Fama -

French 1 French 2 French 3 French 4 French 5

GDP 1.099 ** 0.728 1.247 ** 1.215 1.078

(2.01) (-0.62) (2.11) (0.31) (0.17)

MAFE - Industry 2.301 0.605 ** 0.867 0.077 ** 0.742 *

(0.56) (-2.08) (-1.62) (-2.11) (-1.71)

Surprise 1.028 1.014 1.004 1.036 * 1.009

(1.35) (0.90) (0.44) (1.83) (0.68)

Volatility 1.851 0.917 1.251 1.206 1.007

(1.45) (-0.41) (1.63) (0.85) (0.04)

Premium 0.099 3.990 1.119 1.679 0.535

(-1.40) (0.63) (0.11) (0.38) (-0.40)

SEC 1.003 0.997 0.998 0.997 0.994

(0.47) (-0.69) (-0.96) (-0.56) (-1.12)

Detect 0.090 4.032 0.508 2.705 0.458

(-1.23) (0.94) (-0.86) (0.67) (-0.65)

IPO 1.000 1.001 1.002 *** 1.005 ** 0.999

(0.28) (0.29) (2.66) (1.97) (-0.36)

Capital 1.155 1.689 1.139 1.118 2.929 **

(0.47) (1.14) (0.54) (0.26) (2.62)

Lag Ab Ret 1.138 * 0.843 1.065 * 0.911 1.121 **

(1.74) (-0.35) (1.93) (-0.82) (2.29)

Fraud Firms 77 32 89 25 48

Non Fraud Firms 562 612 751 264 798

78

Appendix Table 7: Continued

Hazard Analysis: Industry Level

Panel C

Fama - Fama - Fama - Fama - Fama -

French 1 French 2 French 3 French 4 French 5

GDP 1.249 0.704 1.658 ** 2.748 ** 1.042

(1.57) (-0.85) (2.35) (2.10) (0.10)

PE - Industry 1.143 * 0.849 1.062 ** 1.216 ** 0.930

(1.90) (-1.39) (2.29) (2.26) (-0.55)

Surprise 1.029 1.032 1.001 1.056 ** 1.005

(1.56) (1.50) (0.14) (2.28) (0.31)

Volatility 1.940 * 0.935 1.407 ** 1.675 * 1.017

(1.95) (-0.32) (2.25) (1.73) (0.10)

Premium 0.072 * 0.705 0.384 1.007 0.341

(-1.95) (-0.22) (-1.01) (0.01) (-0.79)

SEC 1.001 1.001 0.999 1.008 0.994

(0.25) (0.36) (-0.24) (0.93) (-0.81)

Detect 0.102 1.233 0.228 6.155 0.312

(-1.47) (0.17) (-1.31) (1.08) (-0.89)

IPO 1.001 1.001 1.002 ** 1.012 *** 0.999

(0.32) (0.32) (2.50) (3.59) (-0.28)

Capital 1.112 1.646 1.145 1.091 2.823 **

(0.34) (1.07) (0.57) (0.20) (2.58)

Lag Ab Ret 1.146 * 0.867 1.034 * 0.919 1.186 *

(1.88) (-0.31) (1.83) (-0.68) (1.70)

PE - Firm 1.003 1.001 1.002 * 1.003 0.994 *

(1.37) (0.45) (1.67) (0.76) (-1.77)

Fraud Firms 77 32 89 25 48

Non Fraud Firms 562 612 751 264 798

79

Appendix Table 8

Logistic Regressions: Interactions8

Grant Xfin CFF

PE 0.611 ** 0.067 0.086

(2.44) (0.88) (1.16)

Grant 0.014

(0.93)

Xfin -0.298

(-0.54)

CFF -0.434

(-0.64)

Interaction -0.000 0.068 ** 0.099 *

(-0.09) (2.01) (1.83)

GDP 1.732 *** 0.386 ** 0.404 **

(3.05) (2.47) (2.59)

Surprise -0.026 0.014 ** 0.014 **

(-1.14) (2.14) (2.10)

Volatility 0.119 0.282 *** 0.304 ***

(0.53) (3.98) (4.26)

Risk Premium -1195 -0.442 -0.482

(-0.48) (-0.91) (-0.98)

SEC 0.002 0.004 *** 0.004 ***

(0.57) (2.95) (3.08)

Detect -1.382 -1.215 ** -1.172 **

(-0.68) (-2.61) (-2.50)

IPO -0.002 0.001 *** 0.001 ***

(-0.43) (3.55) (3.15)

Capital 0.517 *

(1.66)

Lag Ab Return -0.069 0.064 0.044

(-0.57) (1.50) (1.02)

PE - Firm Level -0.000 0.002 * 0.001

(-0.09) (1.95) (1.44)

Constant -4.735 -4.538 ** -4.772 **

(-0.38) (-2.43) (-2.59)

Fraud Firms 104 271 271

Non-Fraud Firms 1046 2987 2987

Pseudo R-Squared 7.90% 4.70% 5.10%

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.

Detailed variable definitions and their data source are included in Appendix Table 1.

80

Appendix Table 9

Hazard Analysis: Fraud Type - ERC

Revenue Fraud Expense Fraud Balance Sheet Fraud

GDP 0.246 *** 0.477 *** 0.059

(3.40) (3.81) (0.51)

ERC -0.297 ** 0.387 *** 0.301

(-2.04) (4.07) (1.59)

RRC 0.233 ** 0.103 0.078

(2.10) (1.37) (-0.48)

Surprise 0.001 0.002 0.007

(0.11) (0.22) (0.61)

Volatility 0.294 *** 0.355 *** 0.551 ***

(3.12) (4.75) (3.21)

Risk Premium 0.003 0.122 0.333 *

(0.20) (1.43) (1.85)

SEC -0.005 * -0.015 *** -0.014 ***

(-1.79) (-3.77) (-2.94)

Detect -0.220 -0.593 -2.096 ***

(-0.32) (-1.28) (-3.00)

IPO 0.003 *** 0.004 *** 0.004 ***

(3.07) (6.25) (5.79)

Capital 0.080 * 0.423 0.327

(0.47) (1.42) (1.60)

Lag Ab Return 0.058 * 0.069 0.056

(1.68) (1.28) (0.85)

PE - Firm Level 0.002 ** 0.000 0.002

(2.33) (0.11) (0.89)

R-Squared 0.17 0.20 0.26

Fraud Firms 155 92 68

Non-Fraud Firms 3103 3166 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 coefficient estimates. The revenue column represents 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.

Detailed variable definitions and their data source are included in Appendix Table 1.