*University of Pennsylvania. ** University of Chicago. Correspondence to Catherine Schrand, e-mail schrand@wharton.upenn.edu. The authors thank Gavin Cassar for helpful discussions. We thank Ray Ball, Ed Maydew and Greg Miller and audiences at MIT, Penn State University, the Stanford 2007 Summer Camp, and the University of Minnesota Mini-Conference on Empirical Accounting for helpful comments. Sarah Zechman thanks the Deloitte Foundation and the University of Chicago Booth School of Business for providing financial support.

Executive Overconfidence and the

Slippery Slope to Fraud

Catherine M. Schrand* and Sarah L. C. Zechman**

First Draft: July 2007

This draft: November 2008

Abstract We propose that executive overconfidence increases the likelihood that a firm commits financial reporting fraud. A manager that faces an earnings shortfall is more likely to manage earnings to overcome it if he believes the shortfall is temporary and, hence, the earnings management will be a one-off event that likely will go undetected. If performance does not improve, however, the manager, faced with reversals of prior-period earnings management and continuing poor performance, may choose to engage in the type of egregious financial reporting that the SEC prosecutes. Overconfident managers with unrealistic beliefs about future performance are more likely to find themselves in this situation. Using industry-level proxies for executive overconfidence, we find industries that attract overconfident executives have a greater proportion of frauds. Our analysis that uses firm-level proxies for overconfidence suggests that there are two types of frauds: Those associated with moderate levels of overconfidence, perpetrated by executives who ex post fall down the slippery slope, and those perpetrated by executives with extreme overconfidence that commit fraud for opportunistic reasons ex ante. Analysis of individual executives supports the notion that there are two types of overconfident executives that engage in fraud. Those with opportunistic motives are more likely to be from a founding family, have greater commitment to the firm, earn more total and have a higher percent of variable cash compensation, and are less likely to have accounting experience. Finally, we document that a matched sample of non-fraud firms do not have stronger governance mechanisms that prevent fraud. This result mitigates the possibility that it is weak governance rather than executive overconfidence that is a significant determinant of fraud.

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1. Introduction

In the mid-1990s, papers such as DeBondt and Thaler (1995) and Daniel, Hirshleifer, and

Subrahmanyam (1998) generated renewed academic interest in the impact of overconfidence on

decision making, especially in corporate finance decisions.1 Since then, empirical studies have

documented that executive overconfidence is associated with what are viewed as distorted

financing decisions (e.g., Ben-David, Graham, and Harvey, 2007) and corporate investment (e.g.,

Malmendier and Tate, 2005). In an attempt to explain the observation that humans, in general,

and corporate executives and entrepreneurs, in particular, are overconfident despite its negative

impact on decision-making,2 a burgeoning literature models overconfidence as an optimal

endogenous behavior choice by individuals, specifically in a corporate setting (e.g., Bénabou and

Tirole, 2002; Compte and Postlewaite, 2004; Brunnermeier and Parker, 2005; Goel and Thakor,

2000; Gervais and Goldstein, 2007; Gervais, Heaton, and Odean, 2007). The upshot of this

literature is that overconfidence has benefits that make it an optimal trait overall, despite its

negative effects on particular decisions.

Our purpose in this paper is to examine whether overconfidence is associated with a

greater likelihood of one particular corporate decision – financial reporting fraud. This

prediction recognizes that financial reporting fraud often represents the escalation of minor

earnings management infractions. Earnings management in minor amounts in a given period is

likely to go undetected. If performance does not improve in the next period, however, the

manager is forced either to manage earnings in an increasing amount to cover reversals and to

1 The literature that examines behavioral biases as they relate to economic decision making under uncertainty has a long history (e.g., Kahneman and Tversky, 1979). Even in 1937, Adam Smith recognized the bias of overconfidence: “The chance of gain is by every man more or less over-valued and the chance of loss is by most men under-valued.” (Cited in Arabsheibani, de Meza, Maloney, and Pearson, 2000). 2 See Englmaier (2004), Heaton (2002), and Schultz and Zaman (2001) and the references therein, for surveys of this literature.

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keep up the earnings expectations he has created or to reveal the poor performance. Eventually,

the level of earnings management required to hide a series of bad performance realizations can

be obtained only if the manager “cooks the books” and makes the kinds of accounting

misstatements that are prosecuted by the SEC.

We propose that an overconfident manager with unrealistic beliefs about future

performance is more likely to underestimate the need for more egregious earnings management

and thus is more likely to start down the slippery slope to fraud. As a result, he is more likely to

be in the position that egregious earnings management is the optimal choice, conditional on

getting a bad draw on realized earnings.3 This explanation for fraud assumes a moderate level of

overconfidence that leads to a naïve misperception of the earnings distribution. It does not

envision a manager who exhibits a more extreme level of overconfidence associated with

narcissism or sensation-seeking (e.g., Grinblatt and Keloharju, 2001; Puri and Robinson, 2005;

Rosenthal and Pittinsky, 2006).

Descriptive evidence on a subsample of frauds used in the paper indicates patterns in the

timing of unmanaged earnings and the associated earnings management that are consistent with

this explanation. At the firm level, we document that a subsample of fraud firms associated with

moderate levels of overconfidence (or non-opportunistic frauds) have a similar level of earnings

in the year prior to the fraud as that of a matched sample. However, going forward into Year 1

and then Year 2 of the fraud there is a decreasing pattern in unmanaged performance and an

3 The Waste Management fraud is consistent with this scenario: “Defendants' improper accounting practices were centralized at corporate headquarters. Each year, Buntrock, Rooney, and others prepared an annual budget in which they set earnings targets for the upcoming year. During the year, they monitored the Company's actual operating results and compared them to the quarterly targets set in the budget. To reduce expenses and inflate earnings artificially, defendants then primarily used "top-level adjustments" to conform the Company's actual results to the predetermined earnings targets. The inflated earnings of prior periods then became the floor for future manipulations. The consequences, however, created what Hau (the Waste Management vice president, corporate controller, and chief accounting officer) referred to as a "one-off" problem. To sustain the scheme, earnings fraudulently achieved in one period had to be replaced in the next.” (AAER 1532)

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increasing percent of loss firms in the fraud firms that is not evident in the matched sample. In

addition, the fraud firms increase the amount of earnings management in the second year of the

fraud relative to the first year. This pattern is consistent with the increasing amount of earnings

management required to fill the growing gap between unmanaged earnings and a benchmark,

which is consistent with the slippery slope explanation.

Our evidence on the association between overconfidence and fraud comes from three sets

of analyses. First, we use industry-level and firm-level characteristics as proxies for executive

overconfidence and we predict industry level and firm level fraud propensity (“fraud prediction”

tests). Our fraud sample includes firms accused of frauds in SEC Accounting and Auditing

Enforcement Releases (AAERs) in the 1990s and 2000s.

We find that industries with high sales growth and that face significant idiosyncratic risk

have a higher proportion of frauds. These results are consistent with the locus-of-control

literature (e.g., Rotter, 1966) which predicts and finds that overconfident executives who exhibit

control-seeking behavior are attracted to work in risky, dynamic, high growth environments.

At the firm level, we create proxies for overconfidence based on prior literature that

documents that executive overconfidence is associated with corporate financing, investing, and

compensation choices (e.g., Heaton, 2002; Ben-David, Graham, and Harvey, 2007). Assuming

that an overconfident manager will be overconfident with respect to all decisions, not just their

financial reporting decisions, we use these firm characteristics to identify overconfident

executives. We find that firms with lower dividend yields, greater tax avoidance, more total cash

compensation, and a greater percent of variable compensation are more likely to commit fraud,

which is consistent with the joint hypothesis that these firm characteristics are associated with

overconfidence and that over confidence is associated with fraud. However, we find little or no

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evidence in support of an association between the probability of fraud and capital structure or

investment decisions that have been linked to overconfidence.

In our second type of test, we select two industries – software and hardware – and create

an executive-level OC-SCORE for the CEOs of all firms in the industry. The score is a function

of the CEO’s prevalence in photographs in the firm’s annual report and the CEO’s cash and non-

cash pay relative to that of the second highest paid executive at the firm. The sample and

overconfidence score follow Chatterjee and Hambrick (2007). We find strong evidence that

higher overconfidence scores are associated with a greater propensity for fraud.

In our third analysis, we use a subsample of the AAERs identified in Erickson, Hanlon,

and Maydew (2006), and the executives named in those frauds. We compare characteristics of

the executives from the fraud sample to those of the executives from the matched sample of non-

fraud firms. The smaller sample allows for hand collection of data and a more contextual

analysis of the nature of the frauds. The characteristics of the executives at the fraud firms

relative to the matched executives are fairly similar. However, within the fraud firms there are

significant differences between the characteristics of the executives as a function of whether the

SEC suggests the motives are based on opportunism or not. Opportunistic executives are more

likely to be from a founding family and have a longer tenure with and commitment to the firm,

where commitment is measured as the number of years the executive was at the firm prior to the

fraud relative to the total number of years the firm was public prior to the fraud. Each of these

characteristics is consistent with ex ante predictions that overconfidence is associated with a

greater sense of control, personal investment in the outcomes, and self-attribution. In addition,

these executives receive more total cash compensation and a greater percent of variable

compensation. These compensation patterns are consistent with predictions that extreme

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overconfidence is associated with higher incentive pay due to the distortion in value of such

compensation, but low or moderate levels of overconfidence are associated with lower executive

pay as less is necessary to induce effort (de la Rosa, 2007).

Further analysis suggests that frauds that the SEC does characterizes as opportunistic are

distinct. These frauds are perpetrated by executives that the psychology literature might

characterize as narcissists, or at the extreme end of the overconfidence continuum. Extremely

OC fraud-firm executives, or those that commit opportunistic frauds, are more likely than

moderately OC fraud-firm executives to be from founding families and have a greater time

commitment at the firm. These executives are less likely to be CPAs and have audit experience.

They have significantly higher total compensation and a significantly greater percentage of their

total pay is variable (bonus scaled by bonus plus salary).

Finally, we examine the association between governance mechanisms and fraud. This

analysis recognizes that overconfidence is not an undesirable trait of an executive, and may even

be an optimal trait, when considering its effects on “net” performance (Bénabou and Tirole,

2002; Compte and Postlewaite, 2004; Brunnermeier and Parker, 2005; Gervais and Goldstein,

2007; Gervais, Heaton, and Odean, 2007). If the Board or higher-level executives recognize

overconfidence and its potential impact on earnings management, then mitigating forces in the

form of better governance could be used to control the effects of overconfidence on this

particular behavior. Thus, we explore the hypothesis that all executives are equally

overconfident, but that better governance was in place to control certain executives, such that

they did not commit fraud.

In summary, we observe no significant differences between the fraud firms and the

matched sample firms with respect to commonly studied governance mechanisms including

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block ownership, board size, board composition, and measures of the entrenchment and busy-

ness of the inside, outside, and gray board members. Combined with our earlier results, the lack

of correlation between governance and fraud may suggest that fraud firm executives are more

overconfident than those at the non-fraud firms and that better governance mechanisms were not

in place to mitigate their tendency to commit fraud. These results are not consistent with the

alternative hypothesis that fraud firm and non-fraud firm executives are equally overconfident,

but that better governance mitigated the adverse effects of their overconfidence on earnings

management decisions.

The notion that manager behavioral characteristics may be related to fraud is not new.

The fraud literature recognizes “attitudes” as a potential risk factor, with specific indicators

including an aggressive, evasive, or disrespectful manner; high turnover; undue emphasis on

meeting projections or maintaining stock price; and a poor reputation, prior irregularities, or a

history of violations (e.g., Loebbecke, Eining, and Willingham, 1989; SAS 99, 2002). These

attitudes represent outcomes of fraudulent behavior, however, rather than more fundamental firm

or executive characteristics that might be associated with it. The COSO report (1999) on

fraudulent financial reporting also concludes that characteristics such as “tone at the top” and

managerial “integrity” are important, but it provides no evidence on this issue. Our analysis

attempts to examine the relation between a more fundamental behavioral trait and fraud.

The paper is organized as follows. Section 2 develops the hypotheses and predictions.

Sections 3 summarizes the various analyses in the paper and provides descriptive evidence on the

frauds. Section 4 presents results of the fraud prediction tests using both industry-level and firm-

level proxies for overconfidence. Sections 5 and 6 present the analyses of the smaller

subsamples of overconfident executives. Finally, Section 7 concludes.

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2. Hypothesis development

The study requires a careful definition of overconfidence. The term is used inconsistently

in the academic literature, and it is often used interchangeably with optimism. In some studies,

optimism is unrealistic (positive) beliefs about the levels of outcomes (i.e., cash flows) whereas

overconfidence is unrealistic (positive) beliefs about some aspect of the distribution of an

uncertain outcome (e.g., underweighting the likelihood of negative outcomes or underestimating

the range). Other studies use optimism to describe a dispositional trait whereas overconfidence

is used to describe a characteristic that derives from the decision-maker’s experience (e.g.,

education) or that is related to the task (e.g., the controllability of the outcome). The first

perspective dominates the economics-based literature while the second is more prominent in the

psychology-based literature.4 Throughout the paper, we refer to the behavioral bias of having

unrealistic (positive) beliefs about any aspect of the distribution of an uncertain outcome, such

that the mean is overstated, as overconfidence. Overconfidence can derive from the decision-

maker’s innate temperament or his experience.

The psychology literature and the emerging corporate finance literature on

overconfidence identify characteristics of overconfident individuals. The recent studies of

financial decisions assess overconfidence using questionnaire responses (e.g., self life

expectancy assessments in Puri and Robinson, 2005; forecasts of stock market performance in

Ben-David, Graham, and Harvey, 2007) or unique datasets (e.g., results of required

psychological tests for Finnish males in Grinblatt and Keloharju, 2001).

4 Clearly, this is a broad generalization. Weinstein and Klein (1996), for example, describe “optimism” as one-sided – underestimating the likelihood of negative outcomes (as opposed to also overestimating the likelihood of positive). Grinblatt and Keloharju (2001) attempt to distinguish two behavioral biases that may lead to similar observed decision outcomes: overconfidence (miscalibration of risk) and sensation-seeking (innate desire for greater risk).

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One of the most studied phenomenon and robust findings in the psychology literature is

that the greater the perceived controllability of an event, the greater the tendency to exhibit

optimistic bias (Weinstein and Klein, 1996). Another common theme is that individuals are

more likely to be overconfident about the outcomes or the risk of a project when they have a

personal stake in it. Psychologists suggest that optimistic bias is a mechanism individuals use to

preserve self-esteem and the danger to one’s self-esteem is greatest if the event is one in which

the individual has a personal stake. A final common theme is that overconfident individuals

exhibit self-attribution bias: They attribute successes, especially recent successes, to ability,

while attributing failures to bad luck.

Our prediction that overconfidence is associated with fraud results because the manager

makes an earnings management decision that incorporates expectations about future earnings

management.5 Assume the manager’s expected utility is the sum of his expected payoffs in time

t and t + 1. The manager’s expected payoffs depend on current period reported earnings and

expected future reported earnings. The manager faces an expected cost associated with earnings

management – which varies with the amount of managed earnings during the period. The cost

function embeds expectations about both the probability of detection and the penalty.6

The manager makes, and anticipates making, an earnings management decision at each

financial reporting date. At any given date t, the manager chooses the amount of period t

earnings management after observing unmanaged earnings for the period. He has expectations

over the next period’s earnings and he understands the rate at which his period t earnings

management will reverse in period t+1.

5 The idea that overconfidence becomes relevant in the two period setting is based on ideas in Brunnermeier and Parker (2005) who model optimism as an endogenous choice that maximizes average “felicity” over two periods. 6 Several studies indicate that fines for fraud and restatements are large, executives face jail time, and reputational penalties are substantial (COSO, 1999; Karpoff, Lee, and Martin, forthcoming; and Karpoff and Lott, 1993).

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At time t, if the manager has unrealistic expectations about future period unmanaged

earnings, he anticipates greater slack in period t+1 earnings. Thus, he anticipates less (or no)

earnings management at t+1, which increases the expected utility of managing earnings at t. A

manager that has unrealistic expectations at t about t+1 earnings is more likely to underestimate

the likelihood of needing to manage earnings in period t+1, and thus is more likely to be in a

position at t+1 in which egregious earnings management is the optimal choice. A similar

prediction holds if the manager has unrealistic (overconfident) expectations about the likelihood

of detection.

The assumed utility function that underlies our predictions is based on the definition of

overconfidence stated above. However, the economics and psychology literature view

overconfidence as a continuum. The economics-based literature that models self-confidence

yields an optimal level (Bénabou and Tirole, 2002; Compte and Postlewaite, 2004; Brunnermeier

and Parker, 2005), which implies that there can be a greater-than-optimal level. Empirical

studies have characterized extremely overconfident executives as “sensation seeking” (e.g.,

Grinblatt and Keloharju, 2001) or as making “imprudent decisions” (Puri and Robinson (2005)).

The psychology-based literature links excessive confidence and unrealistically optimistic

perceptions to narcissism (Rosenthal and Pittinsky, 2006; Post, 1993). Narcissism in leaders is

characterized by self-serving attitudes and the choice of actions “principally motivated by their

own egomaniacal needs and beliefs” (Rosenthal and Pittinsky, 2006).

Our slippery slope explanation for fraud envisions the moderately overconfident

executive and predicts that fraud is the outcome. An extremely overconfident executive may

increase utility by beating the system or may believe that he deserves to opportunistically cheat

shareholders. While this sort of extreme overconfidence may also be associated with a greater

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likelihood of fraud, it is not the interest of this study. Therefore, throughout the analysis we

identify individuals that the SEC indicates engaged in the fraud based on self-serving motives

and we identify frauds for which the AAER suggests that the primary motive is insider trading

profits, bonuses, or other compensation. This distinction is important because these types of

frauds ex ante do not fit the slippery slope explanation; self-serving opportunism rather than an

unrealistic expectation is the explanation for the fraud according to the SEC.

3. Research design, samples, and descriptive evidence

We test the basic prediction that overconfidence is associated with a greater propensity

for fraud using three types of analyses, and appropriate samples, as described in the following

three sections.

3.1 Fraud prediction tests

We use industry-level and firm-level proxies for executive overconfidence to predict the

proportion of frauds within industries, defined at the three digit level, and within firms,

respectively. We refer to these tests as the “fraud prediction” tests. In the industry-level

analysis, we assume that overconfident managers will self-select to certain industries. Hence,

such industries are more likely to have overconfident managers, and we predict a greater

proportion of frauds in such industries. By estimating the proportion of frauds in the industry,

these tests control for motives and opportunities for fraud that vary with industry characteristics.

In the firm-level analysis, we model the likelihood that a firm will engage in fraud as a

function of firm characteristics that proxy for the overconfidence of the firm’s executives. We

assume that executives that are overconfident will exhibit overconfidence with respect to all of

their decisions, not just their financial reporting decisions. Empirical and survey literature links

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overconfidence to corporate finance and investment decisions, such as capital structure. Thus,

we use these other firm characteristics as a proxy for the likelihood that the firm has

overconfident executives.

In both the industry-level and firm-level fraud prediction tests, we use the sample of

misstating firms from Dechow, Ge, Larson, and Sloan (DGLS, 2008). Dechow et al. create a

sample of firms subject to SEC AAERs between 1982 and 2005. They find 447 firms subject to

at least one annual period of manipulation, of which 350 have stock price data available on

Compustat. Of these 350 firms, 121 have all the required data for our analysis.

3.2 Matched sample tests

The subsample of frauds that we analyze for the more contextual analysis in a matched

sample research design is 49 of the 50 sample firms from Erickson, Hanlon, and Maydew (EHM,

2006) that were subject to AAERs from January 1996 to November 2003.7 EHM do not

eliminate firms in particular industries or impose other data constraints that induce obvious

selection bias in this sample, but it exhibits some industry clustering. Eleven firms (22%) are in

the 2-digit DNUM covering computer programming, data processing, and miscellaneous

business services and six firms are in the computer and office equipment category.8

Table 1 provides a summary of the smaller subsample of frauds. Revenue recognition is

the most frequent fraudulent activity. Over 50% of the revenue recognition cases involve

premature revenue recognition. This pattern in our subsample is consistent with evidence in

7 We eliminate Thor Industries from their sample. The AAER accuses a subsidiary-level controller of managing earnings to hide his theft of cash from corporate-level executives. The SEC does not accuse the firm of securities law (10b-5) violations. 8 Prior research on fraud has noted industry concentrations but using different benchmarks to identify a concentration. For example, there is over-representation in banking, high-tech, and savings and loans and under-representation in education, government and other not-for-profit among a particular set of audit clients (Bell and Carcello, 2000); high frequencies (raw counts) in computer hardware and software, other manufacturing, financial services, and healthcare (COSO, 1999) and high-tech industries (Dechow et al., 1996). The studies speculate that greater opportunities for fraud given the nature of the activities in these industries could explain the patterns.

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larger samples in Dechow, Sloan and Sweeney (1996), Farber (2005), and COSO (1999). The

most frequently alleged goal (84%) is overstatement of earnings. These two facts are important

because they are consistent with the types of earnings management activities and objectives that

are envisioned in the slippery slope explanation for fraud. The extreme firms engage in

fraudulent behavior with respect to multiple accounts and they are more often accused of

reporting fictitious as opposed to just premature revenue. They also are more likely to have as a

goal hiding debt off the balance sheet.

The “fraud period” is from the first periodic report in which the SEC alleges fraudulent

reporting to the last period in which the SEC alleges fraudulent behavior.9 The average fraud

period is 3.78 years with a minimum of one year and a maximum of eight years.

To create a matched sample of non-fraud firms, each fraud firm is matched to a firm in

the same industry on the basis of firm size, measured as total assets (ASSETS) as of the end of

the year before the earnings management began (CLEANYR). Foreign firms, firms missing

DNUM classifications, and firms that are not included in the Compustat and CRSP databases

from the CLEANYR to the final year of the fraud are excluded from the potential pool of match

firms. We insure that each matched firm is itself not the subject of an AAER during the fraud

period.10

Matching on industry attempts to control for the firm’s opportunity to manage earnings

given that revenue recognition and asset and expense over and understatement are the primary

fraud vehicles. Matching on size is important because the sample represents firms selected by

the SEC to be accused of fraud and size may be correlated with SEC scrutiny. Beneish (1999)

also discusses this issue and matches on firm age to control for SEC scrutiny.

9 For all but one firm, the periodic reports are the 10-K or 10-Q. For Microstrategy, the fraudulent reports are IPO and SEO registration statements. 10 Two of the original matched sample firms were replaced as a result of this procedure.

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This procedure generates a match at the four-digit level with total assets within 10% for

27 of the 49 firms. We match at the three-digit level (two-digit level) for 15 (7) of the firms.11

The AAER suggests that the primary motive is insider trading profits, bonuses, or other

compensation for 13 frauds, and we set an indicator variable OPP_FRAUD equal to 1. Of the

remaining 36 frauds, the AAER suggests that the firm was attempting to meet either internal

targets such as budgets or external targets such as Wall Street forecasts or investor expectations

for 26. The AAER emphasizes external financing transactions as a motive for six firms. Other

motivations include influencing merger transactions, moving exchanges, or hiding details that

would reveal bad business decisions (e.g., credit losses/store closings).

Table 2 shows that the fraud firms are not significantly different from the matched

sample with respect to size measured by total assets (ASSETS) at the end of CLEANYR, which

was the matching criterion. They also are not different with respect to size measured by net sales

(SALES) or the market value of the firm (MVSIZE), which is the sum of the market value of

equity, the book value of long-term debt, and the book value of preferred stock.

Table 2 also reports characteristics of the samples that prior literature has identified as

determinants of fraud. We include these comparisons to assess the ability of the matched sample

to control for opportunities, incentives, and motives to commit fraud that are not related to

overconfidence. All variables are measured during or at the end of CLEANYR. We draw the

control variables from three primary (recent) sources that study fraud determinants: Beasley

(1996), the COSO report (1999), and EHM (2006). (Appendix A provides details on

construction of the variables defined in this and subsequent sections.)

The first set of variables relates to pre-fraud performance, broadly speaking, which may

11 The Compustat DNUM for Enron Corp. is 5172 (Petroleum, Ex Bulk Statn-Whsl); the DNUM for Enron Corp.-Old is 4923 (Natural Gas Transmis and Distr). Enron Corp. is matched to Public Service Enterprise Group, Inc., in DNUM 4923, because there were no reasonable size matches to Enron in DNUM 5172.

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be associated with greater incentives to commit fraud. We measure operating performance using

return on assets; net income scaled by sales; and income before extraordinary items scaled by

sales. Following EHM (2006), we measure financial health using a market-based debt-to-equity

ratio. Following Beasley (1996), we measure financial health as a binary variable that equals 1 if

the firm had at least three loss years during the six year period preceding the fraud and equals

zero otherwise.12 We also include four measures of financial health that have been used in other

contexts: S&P bond ratings; the current ratio; the quick ratio; and the interest coverage ratio. As

summary measures of operating performance and financial health, we use the book-to-market

ratio and the earnings/price ratio following EHM (2006). Consistent with COSO (1999), we

measure pre-fraud performance based on stock returns.

The overall conclusion from the comparison of these variables is that the matched sample

effectively controls for performance-related earnings management incentives. The median

values of NISALES and IB4XSALES are not significantly different. The means are significantly

lower for the fraud sample due to several extreme negative observations at high-tech startups (-1,

0, and 3 years old) who are matched to more established firms. The fraud firms’ coverage ratios

also are marginally lower, although their debt-equity ratios are not higher, which is consistent

with the earnings deficiencies relative to the match firm sample. The returns are greater for the

opportunistic fraud firms than their matched sample, though not for the non-opportunistic firms

relative to their matches. Finally, the volatility of returns does not differ between either of the

fraud groups and their respective match samples.

The second set of control variables for fraud determinants are proxies for external

financing demands. The non-opportunistic fraud firms are significantly younger than their

12 Results for similarly constructed TROUBLE variables over shorter windows (2 years and 4 years) to maximize data availability are similar.

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matched sample,13 and they have significantly lower free cash flow consistent with the

predictions of EHM (2006) and Beasley (1996). However, their merger activity is not

significantly different. These results suggest that the fraud sample firms may have greater

external financing demands, and thus greater incentives to commit fraud. The opportunistic

fraud firms do not differ significantly from their matched sample on any of these dimensions.

The third set of control variables measures the nature of the firm’s operations and growth

to assess potential differences in the motivation and opportunity to manage earnings.14 The non-

opportunistic fraud firms have significantly higher asset and sales growth than their matched

sample, especially in recent periods. This difference is worth noting because Ben-David et al.

(2007) find a positive association between overconfidence and 5-year sales growth, which they

use to represent past performance. In summary, the comparison indicates that the non-

opportunistic sample may have greater financing needs and growth than their matched sample,

which suggests that differences in incentives and motives to commit fraud may still be a concern

in the subsequent analysis.

3.3 Time-series patterns in earnings management and unmanaged earnings

The slippery slope explanation for fraud assumes that the realization of unmanaged

earnings in the second year of the fraud is more likely to be lower than expected for firms with

overconfident managers. Thus, for the slippery slope explanation to hold, it should be the case

that unmanaged earnings in Year 2 of the fraud deviate more from the benchmark than did Year

1 earnings. Assuming a fixed benchmark, this leads to a prediction of a decrease in unmanaged

13 For three fraud firms, AGEPUB equals -1, which indicates that the first year of the fraud was the year of the IPO. For one fraud firm (Finehost), AGEPUB = -5; the fraud started prior to their 1996 IPO. 14 We also examine external auditors as a potential determinant of (deterrent to) fraud, consistent with the early literature on fraud determinants, however, there is little variation in either sample. All but four of the fraud firms use Big-8 auditors, two use named second tier firms. All but five of the fraud firms use Big-8 auditors, two use named second tier firms.

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earnings from Year 1 to Year 2. It should also be the case that the dollar amount of earnings

management increases from Year 1 to Year 2 as more egregious earnings management is

required to fill the gap. These predictions are unique to the slippery slope story. We do not

expect such a pattern for the frauds associated with opportunism.

Table 3 first reports the time-series changes in the dollar amounts of managed revenues

and net income (Panel A) for the subsample of EHM frauds. These amounts are hand collected

from reported restatements in 10-K filings and cannot be observed for the full sample or the non-

fraud sample. The dollar amount of the earnings management escalates from one period to the

next. The mean (median) amount of revenue and earnings restatement of the non-opportunistic

fraud firms increases by 221% (97%) and 40% (18%) between the first year and the second year

of the fraud. While the increasing pattern in earnings management is not direct evidence of the

slippery slope explanation for fraud, it is nonetheless consistent with the story. The same change

in revenue restatements in not observed for the firms with opportunistic frauds.

Panels B and C report patterns in unmanaged performance-related variables.

Performance is measured as unmanaged net income in dollars. For all years for the match

sample firms, and for CLEANYR for the fraud firms, performance equals net income (data item

#172) from Compustat. For the fraud firms in Years 1 and 2 of the fraud, performance equals to

net income less restatement amounts if restatement amounts are available and it is missing

otherwise. %LOSS FIRMS is an indicator equal to 1 for firms with negative unmanaged

performance and equal to 0 otherwise.

The non-opportunistic fraud firms have a median decline in unmanaged earnings from

CLEANYR to Year 1 of 33%, which is consistent with the need to manage earnings initially.

Unmanaged earnings decline by a greater amount (86%) from Year 1 to Year 2. A similar time-

17

series pattern is evident from the relative percents of loss firms in the samples (Panel C).

The opportunistic fraud sample, however, does not exhibit the same deterioration in

unmanaged earnings in Year 2. While they show a decrease in median unmanaged earnings of

5% from CLEANYR to Year 1, there is an increase of 65% from Year 1 to Year 2 and the

percent of loss firms declines in Year 2. Thus, the opportunistic firms continue the fraud, and

even elevate it as noted in Table 1, despite the fact that performance was improving. The

matched samples also do not exhibit a pattern of deterioration in performance during this period.

4. Fraud prediction tests

4.1 Industry-level proxies for overconfidence

We hypothesize that overconfident executives self-select to work in certain industries,

and thus there will be a greater likelihood of fraud in these industries. The locus-of-control

literature (e.g., Rotter, 1966) provides a basis for identifying industries that attract overconfident

executives. This literature distinguishes “internal” individuals who perceive control over the

outcomes in their lives from “external” individuals who believe outcomes are beyond their

control. Broadly speaking, the studies suggest that internals – or control-seeking individuals –

are attracted to innovation and risk-taking, dynamic/heterogeneous environments, and jobs that

require proactive rather than passive management. Based on the link between overconfidence

and control, we predict that overconfident executives are more likely to cluster in industries that

require innovation and risk-taking, have dynamic/heterogeneous environments, and jobs that

require proactive rather than passive management.

We use the following proxies to measure these industry characteristics. We measure

industry-level innovation by intangibles intensity and capital expenditures. We proxy for risk-

18

taking using return volatility. The concept of a growing or more dynamic work environment is

measured by growth in sales and in operating cash flows, and by whether growth is attributed to

merger activity. We also measure the length of the operating cycle, consistent with the notion

that internals are attracted to “futurity” (Miller, Kets de Vries, and Toulouse, 1982).15 Finally,

we measure heterogeneity and complexity based on the number of business (or geographic)

segments. We measure each variable at the firm level on an annual basis and compute the

median and mean of the observations at the 3-digit industry level. Values for industries with less

than five annual observations are set to missing. We use the industry medians in the year 1995,

the middle of the period.16

Finally, we predict that overconfident executives, who seek control, are attracted to

industries in which the returns are driven by idiosyncratic rather than macroeconomic factors.

Our proxy for this industry-level characteristic is the average variance about a market model

regression estimated using monthly returns over the period 1988-2002 for firms in each three-

digit industry (NONSYNC). This industry-level return synchronicity measure follows the

country-level return synchronicity measure in Li, Morck, Yang, and Yeung (2003). Higher

values of NONSYNC indicate less synchronicity in firm stock returns within the industry, which

suggests more individual control over outcomes for the executive. Thus, we predict a positive

association between NONSYNC and fraud propensity. Anecdotally, a notable feature of the fraud

sample and earlier studies is that frauds by firms in commodity-based industries, in which

15 Miller, Kets de Vries, and Toulouse (1982) test the internal/external theories using proprietary proxies for firm characteristics that would attract internals. Their proxies include a “technocratization” score, which reflects the prevalence and importance of engineers, scientists and the like; a “scanning” score, which reflects the need to understand shifts in customer tastes (a “dynamic” environment); and a “futurity” score which reflects a longer planning horizon. 16 Results for grand averages of the annual medians or means over the period 1990-2004 for all variables except NUMSEG, for which we compute the grand averages pre and post-SFAS 131, are similar.

19

managers are subject to price/demand/supply shocks that are outside their control, are rare.17

We estimate the relation between these industry-level characteristics and industry level

fraud concentration. Fraud concentration in industry i ( %FRAUDi ) equals the total number of

number of firms in industry i subject to enforcement action by the SEC scaled by the average

number of firms in the industry from 1990 – 2004. We also estimate the relation between the

industry-level characteristics and restatement concentration based on the number of firms in the

3-digit industry with financial restatements between January 1997 and September 2005, as

identified by the Government Accountability Office (GAO). %GAOi equals the total number of

restatements in industry i scaled by the average number of firms in the industry from 1997 –

2005. Because the dependent variable is the proportion of firms in the industry that misstate, our

primary model specification uses an arc sine-square root transformation of the dependent

variable.18

Table 4 reports the results. Industries with less return synchronicity (higher values of

NONSYNC) have greater percentages of AAERs and restatements (NONSYNC is significant at

p-value<0.10 and <0.01, respectively). Assuming that low-synchronicity industries attract more

overconfident managers, this positive relation is consistent with more overconfident managers

having a greater propensity for fraud. Industries with high sales growth have a higher proportion

17 Bell and Carcello (2000), based on SAS 53, predict that a high “sensitivity of operating results to economic factors” is a risk factor for fraud because such firms have greater incentives for earnings management, which is opposite to our conjecture. They find no evidence, however, that auditors view sensitivity to economic factors as an audit risk factor. 18 The arc sine transformation is one of two commonly suggested transformations for models of proportions data with multiple “trials” across a treatment group (e.g., Draper and Smith, 1998). The other commonly suggested transformation is the log-odds transformation of the dependent variable, and a WLS estimation of the model; observations are weighted by the reciprocal of the variance of the log-odds ratio. The log-odds transformation is less appropriate for our sample given the large number of industries with no frauds or no restatements. In the log-odds transformation, such observations are assigned an arbitrarily small proportion > 0. We estimate the models using the log-odds transformation and it provides similar inferences. We also estimate separate Tobit regression models of %FRAUDi and %GAOi and a logistic regression model using a logistic transformation of the proportions data. Industries with no frauds or no restatements during the sample period are assigned an arbitrarily small proportion = 0.0001. The results are consistent from these alternative model specifications.

20

of frauds. At the same time, there is a negative and statistically significant relation between

operating cash flow growth and both AAERs and restatements. Taken together, one

interpretation of these findings is that industries that are dynamic and have high sales growth are

attractive to overconfident executives, but the current low operating cash flow growth provides a

greater motivation to manage earnings.

A weakly significant positive coefficient on the number of geographic segments suggests

that the frauds are clustered in more complex industries. We find no support for the notion that

frauds or restatements are clustered in industries that provide more innovation, risk, or “futurity”

as measured by the operating cycle.

4.2 Firm-level proxies for overconfidence

We hypothesize that overconfident executives are likely to be consistently overconfident

in all of their corporate finance and investment decisions, and thus we can use firm

characteristics to proxy for overconfidence.19 We use five firm characteristics to identify firms

that are likely to have overconfident executives: Capital structure, dividend policy, tax

avoidance, capital expenditures, and executive compensation. Recent studies suggest that

overconfidence is associated with these firm-characteristics. If the firm’s managers are

overconfident with respect to these other corporate decisions, then we expect they also will be

overconfident with respect to their financial reporting decisions.

Related to capital structure choice, we hypothesize that overconfident executives are

associated with a stronger pecking order preference and riskier debt. Studies that link capital

structure choice to managerial overconfidence suggest that overconfidence can have two effects

on inputs to a firm’s optimal decision about financing corporate investment (Heaton, 2002;

19 Ideally, we would predict only those frauds in which the CEO or CFO was involved. We are in the process of getting these data.

21

Hackbarth, 2007). An optimistic manager overestimates cash flows from investment projects.

An optimistic manager also believes external financing costs (both debt and equity) are too high.

Predictions about the net impact of these two forces on capital structure choice depend on the

relative degree of optimism about each. Heaton (2002) generates standard pecking order

preferences by optimistic managers. Hackbarth (2007) generates predictions of a pecking order

preference if the first effect dominates and a reverse pecking order preference if the second effect

dominates.

Our predictions of a stronger pecking order preference and riskier debt for the fraud firms

are consistent with the survey results of Ben-David et al.(2007) who find that overconfident

executives are at firms with higher debt ratios and longer debt duration, which are their proxies

for riskier debt. We examine two capital structure variables. The first proxy is the debt-to-

equity ratio which is long-term debt scaled by the market value of the firm. The second proxy is

an indicator variable equal to 1 if the firm uses either convertible debt or preferred stock.

Related to dividend policy, we use a low dividend yield as a proxy for overconfident

executives.20 Ben-David et al. (2007) find that overconfident executives are less likely to pay

out dividends. Their explanation is that overconfident executives preserve cash because they

expect to have valuable investment opportunities.

Related to investment policy, we examine capital expenditures. Ben-David et al. (2007)

expect overconfident managers to invest in more projects than non-overconfident manager.

Overconfident managers underestimate the risk of the project, thus estimate a lower discount rate

and perceive a greater number of projects to have positive net present values. Consistent with

this prediction, they find firms with overconfident managers invest more in capital expenditures.

We measure capital expenditures as the log of (capital expenditures plus 0.001). 20 Our results are robust to using an indicator variable that equals 1 if the firm pays dividends and 0 otherwise.

22

Related to tax aggressiveness, we hypothesize that more aggressive executives are more

overconfident. We measure tax avoidance following Dyreng, Hanlon, and Maydew (2008) who

suggest that a low cash-based effective tax rate represents aggressive tax avoidance. CASHETR

is the ratio of cash taxes paid during the first year of the fraud to pretax income excluding special

items. Following Dyreng et al., CASHETR is set to missing for firms that have a negative

denominator, and values less than zero (greater than one) are truncated to zero (one).

Related to compensation, the optimal contract with an overconfident manager trades off

two forces: overconfident managers “overvalue” success-based incentive pay, but less incentive

pay is necessary to induce effort from them (de la Rosa, 2007). This model predicts that extreme

overconfidence is associated with higher incentive pay because of the manager’s distorted

preference for success-based pay, but low or moderate levels of overconfidence are associated

with lower incentive pay.21 We examine two compensation variables: The natural log of salary

plus bonus (CASHCOMP) and the ratio of bonus to salary plus bonus as a measure of variable

pay (VAR$PAY).

While we include compensation proxies in these large sample fraud prediction tests, the

predictions are ambiguous because the sign of the relation depends on the degree of the

manager’s overconfidence. We are able to test the compensation hypothesis more directly in the

matched sample. However, inclusion of the compensation variables in the fraud prediction tests

is nonetheless important as a control variable. If overconfident executives have more variable

compensation and if variable compensation creates greater incentives for earnings management,

then compensation is a potential omitted variable in our analysis. The severity of this potential

problem is not clear, however, because empirical evidence on the relation between compensation

21 Gervais, Heaton, and Odean (2007) predict that moderately overconfident executives require less convexity in an optimal contract as some overconfidence reduces the classic principal/agent conflict and better aligns manager’s incentives with shareholders. However, for extremely overconfident executives, more convexity is optimal.

23

and fraud suggests a weak relation, if any.22

The capital structure, dividend, investment, and compensation variables are measured for

year t - 1 for non-fraud firms and in the year prior to the first year of the alleged fraud for the

fraud sample. The CASHETR is computed for the first year of the fraud for the fraud sample

and for year t for the non-fraud sample, as we expect that the earnings management and tax

avoidance are concurrent.

In summary, we predict that the likelihood of fraud will be increasing with debt-to-equity

ratios, risky debt, aggressive tax practices (or low CASHETR), capital expenditures, and total

and variable compensation and decreasing with dividends, because these firm-level

characteristics are associated with overconfidence. We test these predictions using a logistic

regression model. The dependent variable equals one in the first year of the fraud for firm i and

equals zero for all firms that are not the subject of an AAER in any year from 1989-2001. We

control for firm size in the model because the AAER sample represents firms selected by the

SEC to be accused of fraud and size may be correlated with SEC scrutiny. We control for pre-

fraud performance using return on assets and book-to-market ratios because it may be associated

with greater incentives to commit fraud. We control for firm growth, in sales and assets, because

the nature of the firm’s operations and growth may be associated with differences in motivation

and opportunities to commit fraud. Finally, we include free cash flow as a proxy for external

financing demands.23 Standard errors are clustered by firm and fiscal year.

Table 5 reports the results. Consistent with the notion that overconfident executives are 22 For example, Dechow, Sloan, and Sweeney (1996) do not find a relation between earnings-based bonus plans and fraud, and EHM (2006) do not find evidence of any relation between fraud and the sensitivity of equity to stock price. Johnson, Ryan, and Tian (2008), however, find that fraud executives receive greater total compensation in the fraud years and that the sensitivity of their unrestricted holdings is higher than that of the matched sample. Related studies of restatements find a positive association with option-compensation delta (Burns and Kedia, 2006) and levels of in-the-money option holdings (Efendi, Jap, Srivastava, and Swanson, 2006). 23 Results are robust to alternative controls for firm size (SALES or LOGMVE), growth (ASSETGRO4, PPE, or RDINTENSE), and external financing demands (MERGE).

24

less likely to pay out dividends, we find that the fraud firms have a lower dividend yield than

non-fraud firms. When the sample is restricted to firms with compensation data available in the

ExecuComp database (columns 3-4), we find that fraud firms are more likely to be aggressive on

taxes. Finally, fraud firms pay more total cash compensation and a great percent of variable

compensation than the firms without AAERs. Capital expenditures and capital structure are not

related to fraud propensity. Contrary to the findings of Ben-David et al. (2007), we find a

negative relation between DERATIO and fraud propensity. However, when we use their metric

of the ratio of total debt to total assets, this negative relation disappears. Further, in untabulated

univariate tests between the EHM fraud sample and match firms, there is no difference in either

of these variables across the samples.

5. Subsample analysis of overconfident executives

In this section we focus on a small subsample of executives for which we compute an

executive-specific measure of overconfidence. We select a sample of executives in the software

(primary SIC 737) and hardware (SIC 357) industries between 1992 – 2004 for which we are able to

create a proxy for their overconfidence following Chatterjee and Hambrick (CH, 2007).24 The

focus on a single industry is necessary because of the significant hand-data collection for the

photoscore, and the restriction on the sample period is necessary because we require a digital form of

the annual report. CH focus on these two industries because they had a large number of publicly held

firms to aid in data collection and the industries are not subject to a high degree of external

constraints or regulations to allow for greater variation in managerial characteristics. The CH

industries happen to be appropriate for our study because of the cross-sectional variation in fraud

24 Malmendier and Tate (2008) use another ex ante measure of overconfidence based on the text of news sources that describes a CEO as “optimistic” or “confident.”

25

frequency within them. The industries have nine firms accused of fraud in an AAER during our

sample period and 98 firms not accused. Thus, given that the measures used by CH have

established validity for this industry, we focus on the same industries.

Our sample contains 111 executives from 107 companies over the sample period.25

Similar to CH, we required that a CEO had to have begun their tenure in 1991 or later and have a

minimum of four years tenure with the firm.

For each of the 111 executives in the sample, we create a proxy for overconfidence based

on two inputs: 1) the CEO’s prevalence in photographs in the firm’s annual report, and 2) the

CEO’s cash and non-cash pay relative to that of the second highest paid executive at the firm.

The photoscore is specified as follows: four points if the CEO’s photo in the annual report

included no other individuals and was at least equal to one half page in size; three points if the

CEO’s photo included no other individuals and was less than one half page in size; two points if

there were other individuals pictured with the CEO; and one point if there was no photograph of

the CEO. Relative cash compensation is equal to the ratio of the CEO’s salary plus bonus to the

salary plus bonus of the second highest paid executive. Relative non-cash compensation is

similarly measured using total compensation on Execucomp (TDC1) less cash compensation.

The combined score “OC-SCORE” is equal to the sum of the standardized values of the

photoscore and relative compensation variables. Each variable is equal to the average across the

CEO’s second and third years of tenure with the firm.26 Data are available to compute an OC-

SCORE for 102 of the 111 executives. We estimate the probability of fraud at the executive’s

firm as a function of the OC-SCORE in a logit regression model. The model includes control

variables consistent with the firm-level fraud prediction analysis.

25 This is similar to the sample of 111 CEO’s in 105 firms used by CH. 26 Results are qualitatively consistent using an alternative relative non-cash proxy measured using the Black Scholes value of options granted.

26

Table 6 presents the results. While all standardized components of OC-SCORE are

positively correlated with the occurrence of fraud, only the relative non-cash compensation

variable based on total compensation less cash compensation is significant at traditional

significance levels (Column 1). When the variables are combined into OC-SCORE, the results

are consistent with overconfidence increasing the propensity for fraud, as defined by the

existence of an AAER (Columns 2 and 3). The OC-SCORE is positively associated with fraud

(p-value < 0.05). The sample size is less than the original 102 firms due to missing data.

6. Subsample analyses of the EHM frauds

6.1 Analysis of accused executives in the EHM subsample

We compare the accused executives of the EHM subsample of fraud firms to their

counterparts in the matched sample firms. The AAERs and related litigation releases identify

224 respondents that are employees of the 49 fraud firms. Of these, we classify 107 as

executives and 117 as non-executive employees that do not appear in proxy statements. Forty of

the frauds involve at least one executive, and the average number of executives for these 40

frauds is 2.7.

We match each fraud firm executive to an executive at its matched firm. The matching is

not straightforward because organizational structures/titles differ across firms. Our goal is to

match on the executive’s level of decision-making authority over a particular kind of decision

(e.g., financial vs. operational).

For the 107 fraud firm executives, we are able to find reasonable matches for 99.27 Table

27 The fraud sample contains 26 CEOs, 23 of which are matched to CEOs. The fraud sample contains 35 financial executives (CFOs, controllers, chief accounting officers, VP-finance, or other financial titles), 32 of which are matched to similarly titled financial executives. Board membership was not a requirement for matching, but ex post there is moderate correlation between the two samples. Of the 57 fraud firm executives that are on the BOD, 32

27

7 Panel A presents univariate comparisons of executive-level proxies for overconfidence across

the fraud firm and matched sample executives. Panel B presents comparisons within the sample

of 107 accused executives at the 49 EHM fraud firms. There are 32 accused executives at firms

in which the SEC characterizes the fraud as opportunistic (OPP_FRAUD = 1) and 75 accused

executives at firms for which OPP_FRAUD = 0.

Panel C distinguishes individual executives that the SEC characterizes as opportunistic.

If an AAER reports that the accused was required to disgorge funds directly linked to fraud-

related trading or specifically alleges self-serving compensation motives for the fraud, the

indicator variable OPP_EXEC equals 1, and equals zero otherwise.28 This person-level variable

is distinct from the firm-level variable (OPP_FRAUD), but they are related. Eleven of the 13

firms that primarily have an opportunistic motivation have at least one respondent with insider

trading allegations against them. However, 11 of the 36 firms not classified as primarily

opportunistic also have at least one opportunistic respondent. There are 40 accused executives

defined as opportunistic (OPP_EXEC = 1) based on insider trading allegations and 67 accused

executives for which OPP_EXEC = 0.

Finally, we separately examine the characteristics of the 73 EHM sample firm executives

that the SEC indicates “orchestrated” the fraud, the 23 that “participated” in the fraud, and the

seven that were “wreckless in not knowing” about the fraud. 29 Due to small numbers, we do not

present the results of this analysis, but the distinction provides additional context for interpreting

some of the tabulated results. match to executives on the match firm’s BOD. Of the 20 fraud firm executives that are BOD chairs, 13 match to executives that are BOD chairs. 28 If an AAER states that the executive is required to disgorge funds but does not specifically link the disgorgement to the repayment of ill-gotten gains from insider trading, then the executive is not classified as opportunistic. 29 Based on discussion in the AAER, we are able to determine the role for all but four executives. This classification is less subjective than it appears. The terms in quotations (orchestrated, participated, and wreckless in not knowing) are frequently used in the AAERs. Even when these terms are not used, the classification is fairly straightforward. For example, AAER 1749 states that executive x, “acting at the direction of executive y, …”

28

We measure several individual characteristics of the executives to proxy for

overconfidence. Broadly speaking, the proxies are observable individual characteristics, such as

education, that prior literature has linked to overconfidence. As discussed previously, this

literature indicates that overconfidence is associated with controllability, a sense of personal

investment in outcomes, and self-attribution. Before we describe the specific proxies, it is worth

noting that all of the proxies for overconfidence are endogenous at some level. For example,

there are documented patterns between education and overconfidence, but it is not clear whether

overconfidence leads individuals to attain greater education levels or whether education leads to

overconfidence. While this endogeneity is emphasized in the psychology literature, it is not

relevant to this study as long as the proxy variable measures the degree of an executive’s

overconfidence at the time of the earnings management decision, and its determinants are not

correlated with determinants of fraud.

Our first executive-level characteristic is founding family status. FOUNDER equals 1 if

the executive is a founder or co-founder of the firm, or is part of the founding family, and equals

zero otherwise.30 Consistent with the finding that entrepreneurs tend to be overconfident (e.g.,

Bernardo and Welch and the cites therein, 2001; Puri and Robinson, 2005), we propose that

executives that are founders or members of founding families are more likely to be

overconfident. Founders, however, also may have greater private benefits of control.

Table 7 Panel A reports that fraud firm executives are marginally more likely to be from

30 Earlier studies of fraud/restatements/earnings management have considered founding family status, however, they measure this variable at the firm-level – whether the top executives (or CEO) at the firm are founders – not at the level of the executive involved in the fraud. That specification confounds interpretation of the results because founders may have different incentives to manage earnings than non-founders, but they also may have different incentives to monitor earnings management by others. Dechow, Sloan, and Sweeney (1996) suggest that a founder-CEO implies weak governance (greater influence combined with less accountability) and find a positive association between fraud and CEO-founders, and Loebbecke, Eining, and Willingham (1989) find that closely held firms are more likely to be charged with fraud. Agrawal and Chadha (2005), however, find that the probability of an accounting restatement is lower for firms in which the CEO is part of a founding family, and Wang (2006) finds firms with founding family members in positions of influence are associated with higher quality earnings.

29

founding families (p-value of 15%). Further analysis suggests that executives that orchestrate

the fraud are significantly more likely to be part of a founding family relative to executives who

were merely wreckless (not tabulated). Founding family influence is more related to

opportunistic frauds than non-opportunistic frauds (Panel B) and to self-serving fraud behavior

(Panel C). These results, however, are due to the four founding family members involved in the

Adelphia fraud. Excluding these executives, the percentages of opportunistic frauds involving

founding family members is higher than for the non-opportunistic frauds, but the difference is

not significant in either classification for opportunism.

Our second executive-level characteristic is the executive’s tenure at the firm. We

predict that an executive with longer tenure is more likely to have pet projects about which he

would be overconfident. We measure the executive’s “commitment” to the firm two ways: 1) as

a continuous variable equal to the number of years between the executive’s start date at the firm

and the first year of the fraud (TENURE) and 2) as the ratio of the number of years between the

executive’s start date at the firm and the first year of the fraud scaled by the number of years the

firm has been public at the first year of the fraud (COMMIT).31 COMMIT equals 1 for

executives who are with the firm at the time of listing, for which COMMIT would have been

greater than 100%. When we cannot determine the exact start year, we substitute the earliest

year that we could determine the executive was with the firm.32

Table 7 Panel A indicates that the fraud firm executives have significantly shorter tenures

overall (TENURE) and as a percentage of the firm’s life (COMMIT).33 This pattern for the

31 When we cannot determine the exact start year, we substitute the earliest year that we could determine the executive was with the firm. We made this substitution, which will create an understatement of tenure levels, to maximize available observations. The results are not sensitive to this substitution. 32 We made this substitution, which will create an understatement of tenure levels, to maximize available observations. The results are not sensitive to this substitution. 33 Three executives have negative tenures. Two arrived and escalated existing earnings management (revenue recognition) practices; one arrived at the firm and participated in an already egregious situation. None of them were

30

general sample is contrary to the prediction that the greater overconfidence is associated with

fraud, if overconfidence is correlated with having a personal stake in projects. However,

untabulated analysis indicates that the shorter tenure and lower commitment of the fraud firm

executives is only significant when the non-opportunistic executives are compared to their

respective matches. Consistent with this interpretation, the executives that orchestrate the fraud

have significantly longer tenures than those that are simply wreckless.

Our third executive level characteristic is the executive’s position measured by binary

variables that equal one (and zero otherwise) if the executive is on the board or is chair of the

board. We predict that board members and chairs are more overconfident than their counterparts

who are matched based on title. The fraud firm executives are no more likely to serve on the

board than the match firm executives. However, the orchestrators, relative to those who simply

participated or were wreckless in not knowing of the fraud, are significantly more likely to be on

the board and to be the board chair (not tabulated).

The psychology literature suggests that experts tend to be more overconfident than

novices (e.g., Griffin and Tversky, 1992). One might imagine that experts would be more

realistic (i.e., less biased) because of their expertise, but the offsetting force is that their expertise

causes them to believe they are better than average. Our first proxy for perceived expertise is

education. More educated executives are likely to be overconfident, consistent with evidence in

Puri and Robinson (2005) and Ben-David et al. (2007). We measure education using a discrete

variable that measures the highest level of education attained: No college (0); Bachelor’s degree

only (1); Master’s degree (2); two Master’s degrees (2.5); JD or PhD (3). In addition, we have

separate indicator variables if the executive’s Master’s degree is an MBA or Master’s of Finance

at opportunistic firms or were considered to be opportunistic executives. Without these three executives, the tenure results in Table 7 are unchanged.

31

as business education may create a sense of overconfidence related to financial reporting.34

We examine two additional proxies for expertise that we predict make an executive

overconfident, specifically in the fraud context, about his ability to avoid detection. CPA is a

binary variable equal to 1 if the manager is CPA and equal to zero otherwise.35 AUDITOR is a

binary variable equal to 1 if the executive has external audit experience and equal to zero

otherwise. External audit experience includes work at a Big 8 or second-tier firms or with a bank

regulator (one case).

In the full sample, education and financial expertise are not associated with the likelihood

of fraud. The orchestrators are less likely to be CPAs or auditors (untabulated), which suggests

that the “accountant” does not direct the fraud, but his participation is necessary to achieve it.36

The opportunistic executives are significantly less likely to be CPAs and have audit experience

(Panel C). This pattern suggests that ex-auditors (all of which are CPAs) may engage in fraud,

but they do not appear to engage in self-serving frauds.

The next executive level characteristic is gender. Men are more overconfident than

women, in general contexts, and empirical evidence documents this phenomenon specifically for

investment trading decisions (e.g., Barber and Odean, 2001; Estes and Hosseini, 2001). An

indicator variable (FEMALE) equals 1 if the executive is a woman, and equals zero otherwise.

Fraud firm executives are significantly more likely to be male. The opportunistic executives also

34 Englmaier (2004) similarly suggests that military service is associated with overconfidence. We were able to identify only two fraud firm executives (John J. Rigas of Adelphia Communications and “Chainsaw” Al Dunlap from Sunbeam, who was a paratrooper at Westpoint) and one matched sample executive (Kenneth E. Hyatt from Walter Industries, Inc.) with military service. Because of the small numbers, and our uncertainty about whether our data on this variable are complete, we do not analyze military service. 35 The CPA variable is also coded = 1 if the manager is a member of the ACCA (Association of Chartered Certified Accountants) in the United Kingdom or is a Chartered Accountant in Canada. 36 The executives that are cited as “wreckless” are high level executives (board chairs, CEOs or CFOs), and CPAs and executives with audit experience. These patterns are almost tautological; one can only be considered wreckless in not knowing about the fraud if he should have known. The SEC appears to expect board chairs, CEOs and CFOs, and individuals with financial expertise to detect at least the significant types of financial reporting defalcations in this sample.

32

are marginally more likely to be female (p-value = 12%), but this result is misleading given the

overall low number of female executives. The finding is based on one female executive (i.e., a

member of the Rigas family at Adelphia) accused of fraud who is defined as opportunistic.

We also examine executive age. The median age of the fraud firm executives is lower,

although only significant in a one-sided test and the result is tenuous given that we are measuring

age coarsely in years, not months. An executive’s age can be correlated positively or negatively

with factors that affect overconfidence. Empirical evidence on the relation between age and

decisions/forecasts that reflect overconfidence is context-specific with results of mixed sign,

linearity, and significance (e.g., Puri and Robinson, 2005; Arabsheibani, de Meza, Maloney, and

Pearson, 2000; Grinblatt and Keloharju, 2001; Malmendier and Tate, 2005; Ben-David et al.

2007). Thus, the results on the relation between age and fraud is descriptive.

Consistent with the regression results in Table 5, the compensation for the fraud sample

executives does not differ from that of the matched sample. However, within the fraud sample, a

greater percent of variable compensation and more total compensation are associated with

opportunistic frauds (Panel B) or opportunistic executives (Panel C). This result is consistent

with theoretical work that predicts that extreme overconfidence is associated with higher

incentive pay because of the distorted preference for such pay, but that lower levels of

overconfidence are associated with lower incentive pay because it is not necessary to induce

effort (de la Rosa, 2007).

6.2 Analysis of governance mechanisms in the EHM subsample

In this section, we examine the role of governance in monitoring the overconfident

executive. We compare the fraud firms to the matched sample firms with respect to commonly

studied governance mechanisms including block ownership, board size, board composition, and

33

measures of the entrenchment and business of the inside, outside, and gray board members. The

purpose of the analysis is to provide additional evidence about whether differences in executive

overconfidence are responsible for the observed differences in earnings management between the

EHM fraud sample firms and the matched sample. If all executives are equally overconfident,

but better governance mitigates the tendencies of the executives at the matched sample firms to

commit fraud, then we should observe better governance at the non-fraud firms.

In summary, the results in Table 8 do not indicate significant differences in governance

mechanisms across the fraud firms and the matched sample firms. None of the variables that

measure blockholdings are significantly different, in contrast to existing evidence that non-fraud

firms are more likely to have a blockholder or a larger percentage of shares held by blockholders

(DSS, 1996, Farber, 2005). The samples do not differ with respect to board size or whether the

CEO is the chair, in contrast to existing evidence that fraud firms have larger boards (Beasley,

1996), are less likely to have audit committees (DSS, 1996), and are more likely to have a CEO

as the chair (DSS, 1996; Farber, 2005; EHM, 2006).37 The fraud firms do not have significantly

different monitoring by inside, gray, or outside directors, as measured by their relative

representation on the board, their tenures, or their outside directorships. These results differ

from those of other matched sample studies in which fraud firms have a larger percentage of

outside and/or gray directors with greater total percentage share ownership and longer tenures

(Beasley, 1996; DSS, 1996; Farber, 2005); and have less busy outside directors (Beasley, 1996).

For the variables that we measure similarly to the prior literature, differences in the results are

likely due to our matching (and possibly smaller sample size).

These (non) results are consistent with the explanation that the fraud firm executives are

37 Efendi, Srivastava, and Swanson (2006) similarly find a positive association between the CEO being the board chair and accounting restatements.

34

more overconfident than those at the non-fraud firms and that better governance mechanisms

were not in place to mitigate their tendency to commit fraud. That is, the overconfident manager

was able to engage in the fraud because he was not better monitored, although he should have

been. The non-results are not consistent with the notion that the matched sample executives are

equally overconfident, but that better governance mitigated the adverse effects of their

overconfidence on earnings management decisions. This final interpretation is subject to the

caveat that our measures capture the sorts of governance mechanisms that would detect and

prevent fraud.

7. Conclusion

This paper examines whether overconfidence is associated with one particular executive

decision – earnings management. While accountants have long recognized the influence of risk-

taking preferences or behavioral biases on individual decision making, the analysis of the impact

of these biases has been limited mainly to auditor decisions, analyst forecasting, and to a lesser

extent, management forecasting. Even in these cases, studies seem to insist on a traditional

economic rationale for the behavioral bias, such as direct monetary incentives like restricted

stock, or indirect incentives such as Institutional Investor competitions and a tournament-style

promotion process in the analyst community. With an expanded view of the role of

overconfidence along the lines of Bénabou and Tirole (2002) and Compte and Postlewaite

(2004), one can observe biased decision making in the absence of direct or indirect monetary

incentives. This broader view of decision making implies interesting applications to external

reporting decisions, such as voluntary disclosure, or earnings management as in this study.

We also examine whether internal and external monitoring mitigates the predicted

35

adverse effects of overconfidence on earnings management decisions. The question of whether

individuals are aware of the impact of their overconfidence on decision-making is common in the

psychology literature. For example, Weinstein and Klein (1996) state: “One must wonder

whether these people recognize when they are at risk and whether their comforting beliefs

interfere with taking precautions to reduce their risk.” Similarly, the recent economics-based

literature characterizes overconfidence as an optimal behavioral choice despite self-awareness

(Compte and Postlewaite, 2004) or as a form of self-deception or “endogenous lack of

awareness” (Bénabou and Tirole, 2002; Brunnermeier and Parker, 2005).38 The examination of

governance mechanisms is related to these ideas in that it addresses whether principals recognize

the potential for biased financial reporting decision making by their agents.

38 See also Schelling (1978) for a non-rigorous discussion of similar ideas.

36

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Appendix A: Variable Definitions OPP_EXEC = executive level variable equal to 1 if an AAER states that the accused was required to

disgorge funds directly linked to fraud-related trading or alleges that the fraud was engaged in for self-serving compensation motives, 0 otherwise

OPP_FRAUD = firm level variable equal to 1 if the discussion of fraud in the AAER indicates opportunistic reasons as the primary motive for the fraud, 0 otherwise

Size SALES = total sales (data12) ASSETS = total assets (data6) MVSIZE = total market value of the firm, equal to the sum of the market value of equity

(data199*data25) plus the book value of long-term debt (data9+data34) plus the book value of preferred stock (data130)

Performance/Financial Health: ROA = return on assets, equal to net income (data172) divided by total assets (data6) NISALES = net income (data172) divided by total sales (data12) IB4XSALES = net income before extraordinary items (data18) divided by total sales (data12) DERATIO = debt to equity ratio, equal to long-term debt (data9) plus current portion of long-term

debt (data34) divided by MVSIZE TROUBLE = 1 if the firm had at least three loss years during the six years prior to the fraud, 0

otherwise SPBOND = S&P long-term domestic credit rating (data280), renumbered to be consecutive from 1

(AAA) to 24 (D) CURRENT = current ratio, equal to current assets (data4) divided by current liabilities (data5) QUICK = quick ratio, equal to cash and short-term investments (data1) divided by current

liabilities (data5) COVRATIO = interest coverage ratio, equal to the sum of pretax income (data170) plus interest

expense (data15) divided by the sum of interest expense plus capitalized interest (data147)

BKMKT = book-to-market ratio, equal to the book value of equity (data60) divided by MVSIZE E-P = earnings/price ratio, equal to net income (data172) divided by share price (data199) 1YR_RET = cumulative return in the twelve months prior to the year end of the last clean fiscal year

using CRSP monthly returns with distributions When the fraud firm and its matched pair do not have the same FYE, we accumulate returns over the appropriate 12 months prior to the fraud firm fiscal year end and for the same 12 calendar months for the matched firm, regardless of its fiscal year end.

1YR_RETV = volatility of the monthly returns with distributions used in 1YR_RET Need for external financing: AGEPUB = the number of years the firm has been public, equal to CLEANYR less the start year in

CRSP. FCF = free cash flow (data308) in year t less average capital expenditures (data128) of the

three years prior to year t, scaled by current assets (data4) at t-1. MERGE = 1 if evidence of significant acquisition activity (data249>0 or data129>0), 0 otherwise Growth: ASSETGRO4 (2) = growth in total assets (data6) from four (two) years before the fraud) SALESGRO = one-year percentage change in sales (data12) for the year prior to the fraud PPE = property, plant and equipment (data187) divided by MVSIZE OPCFGRO = One-year percentage change in operating cash flow (data308). OPCYCLE = Length of operating cycle in days (average days in inventory plus average days in

accounts receivable less average days in accounts payable). This variable is set to missing for financial institutions.

41

Appendix A (continued) Innovation: INTANGS = Intangibles intensity, equal to intangible assets (data33) / total assets CAPEX = Capital expenditures from the Statement of Cash Flows (data128) LOGCAPEX = log (CAPEX + 0.001) Risk-taking: RETVOL = equal to 1) the standard deviation of daily stock returns from CRSP (in descriptive

statistics) or 2) a transformed measure that equals the logarithm of the variance of daily stock returns consistent with Coles, Daniel, and Naveen (2006) (in regression analysis).

Complexity/heterogeneity: NUMSEG = Number of business (or geographic) segments. Idiosyncratic risk: NONSYNC = average in each industry I of the nI firm-specific estimates of variance from a market

model estimated for each firm j that has at least 50 monthly return observations from January 1988 through December 2002. The firm-specific estimate of the variance

about the regression is 22 )(1

1)( jj

j est

e−

=σ , where 2)( jes is the sum of squares about

the regression for firm j and tj is the number of return observations for firm j. We use multiple market indices (the results are similar)

Executive characteristics FOUNDER = 1 if the executive is a founder or co-founder of the firm, or is part of the founding

family, 0 otherwise TENURE = the number of years between the executive’s start date at the firm and the first year of

the fraud COMMIT = TENURE scaled by the age of the firm, using the firm start year on CRSP. If the

executive was with the firm prior to the public listing (and start year on CRSP), this variable is 1.

BOD = 1 if the executive is a member of the board, 0 otherwise BODCHAIR = 1 if the executive is the chair of the board, 0 otherwise CEO = 1 if the executive is the chief executive officer (CEO) of the firm, 0 otherwise CFO = 1 if the executive is the chief financial officer (CFO) of the firm, 0 otherwise EDUCATION = 0 if the executive did not attain a college degree, 1 Bachelor’s degree, 2 Master’s

degree, 2.5 two Master’s degrees, or 3 JD or PhD, based on the highest level of education obtained.

MBA = 1 if executive holds a MBA or Master’s of Finance, 0 otherwise AGE = executive’s age in the first year of the fraud CPA = 1 if the executive holds a CPA (or CPA equivalent from another country), 0 otherwise AUDITOR = 1 if the executive worked as an external auditor, 0 otherwise FEMALE = 1 if the executive is a female, 0 if male Compensation: CASHCOMP = Natural log of salary plus bonus VAR$PAY = Ratio of bonus to salary plus bonus

42

Appendix A (continued) Overconfidence score: PHOTOSCORE = four points if the CEO’s photo in the annual report included no other individuals and

was at least equal to one half page in size; three points if the CEO’s photo included no other individuals and was less than one half page in size; two points if there were other individuals pictured with the CEO; and one point if there was no photograph of the CEO.

REL_CASHCOMP = the ratio of salary plus bonus for the CEO to that of the second highest paid executive, from ExecuComp.

REL_NONCASH = the ratio of non-cash compensation for the CEO to that of the second highest paid, where non-cash compensation is equal to total compensation (TDC1) less cash compensation from ExecuComp.

OC-SCORE = equal to the sum of the standardized values of three proxies for executive overconfidence based on those used by Chatterjee and Hambrick (2007): PHOTOSCORE, REL_CASHCOMP, and REL_NONCASH. Each proxy is the average value over the second and third years of the CEO’s tenure.

Capital structure/Dividend policy/Tax avoidance: RISKYDEBT = An indicator variable = 1 if either the ration of convertible debt (data39) to assets or

preferred stock (data130) to assets is > 0, and = 0 otherwise. DIVYLD = dividend yield, equal to dividends per share (data26) divided by share price (data199)

for the firms that pay dividends (and missing otherwise) CASHETR = cash effective tax rate, equal to the ratio of cash taxes paid (data317) during the first

year of the fraud to pretax income excluding special items (data170-data17) Blockholders: BH_IND = An indicator variable = 1 if the proxy identifies at least one blockholder, and = 0

otherwise BH_NUM = Number of reporting persons identified in the proxy statement who are the beneficial

owners of more than 5% of the common stock outstanding as defined under Section 13(d) of the Securities Exchange Act of 1934, as amended

BH_PCT = Percent of common shares held by identified blockholders Board characteristics: BDSIZE = Number of directors on the board CEOCHAIR = An indicator variable = 1 if the CEO is the chair and = 0 otherwise AUDITCOMM = An indicator variable = 1 if the board has an audit committee and = 0 otherwise PERCENT = Percentage of directors of the noted type ENTRENCH = An indicator variable = 1 if the director served more than 5 years or 100% of the firm’s

life (AVGTEN% = 1) and = 0 otherwise AVGTEN% = Average tenure on the board in years from the first year of the directorship to the

meeting date (as per IRRC) scaled by firm age (truncated at 1) for directors of the noted type

AVGBUSY = Average number of other directorships held by directors of the noted type

43

Table 1. Summary of the subsample of 49 EHM fraud firms Table 1 first summarizes the effect of the fraud on the financial statements. The column labeled “Occurred” indicates the percent of AAERs in which the SEC alleges that a particular type of earnings management occurred. The column labeled “Primary” indicates which of the earnings management activities we identified as the most significant activity based on the AAER discussion. The second section describes the goals of the fraud. The third section reports the average length of the frauds.

EHM sample firms Non-opportunistic frauds (OPP_FRAUD=0)

Opportunistic frauds (OPP_FRAUD=1)

Occurred Primary Occurred Primary Occurred Primary Financial statement effect: Revenue recognition Premature 51.0% 28.6 58.3 33.3 38.5 15.4 Fictitious 40.8 18.4 36.1 16.7 53.9 23.1 Unclear 18.4 - 19.4 - 15.4 - Any type 63.3 44.9 63.9 47.2 61.5 38.5 Overstate assets 26.5 8.2 22.2 8.3 38.5 7.7 Other income increasing 46.9 16.3 41.7 16.7 61.5 15.4 Capitalize expenses 20.4 6.1 19.4 5.6 30.8 7.7 Create (or use) hidden reserves 18.4 8.2 22.2 11.1 15.4 - Off-balance sheet financing 6.1 6.1 2.8 2.8 15.4 15.4 Improper income statement classification 18.4 2.0 19.4 2.8 15.4 - Illegal transactions39 6.1 - 2.8 - 15.4 - Related party 16.3 4.1 8.3 - 38.5 15.4 Goals: Increase income 83.7% 86.1 76.9 Smooth income 6.1 8.3 - Hide debt off-balance sheet 6.1 2.8 15.4 Length of fraud period: Average number of years 3.78 3.58 4.31 Minimum/Maximum 1/8 1/7 2/8

39 We do not classify falsifying documents, which is alleged in many revenue recognition cases, as an illegal transaction.

44

Table 2. Comparison of characteristics of the 49 EHM fraud firms and the matched sample Descriptive characteristics of the fraud and matched samples. All variables, which are defined in Appendix A, are measured at the end of or during CLEANYR. * (**){***} in the “Matched sample” columns indicate that the mean [median] of the matched sample is significantly different from that of the corresponding fraud sample at the 10% (5%) {1%} level. * (**){***} in the “Fraud firms” columns for the extreme firms indicates significant differences from the moderate fraud firms. The p-values of paired t-tests of differences in means across the samples assume equal variances unless equality is rejected at 10% level. The p-values of differences in medians across the samples are for a two-sided Wilcoxon rank-sum test.

Non-opportunistic frauds (OPP_FRAUD = 0) Opportunistic frauds (OPP_FRAUD=1) Fraud firms Matched sample Fraud firms Matched sample Mean Median Mean Median Mean Median Mean Median ASSETS 5,050 204.8 4,747 200.0 4,198 284.3 3,822 286.4 SALES 4,305 243.7 4,681 293.5 2,813 249.0 2,308 365.5 MVSIZE 8,317 440.8 9,598 218.0 7,518 2,613.6 6,926 522.8

ROA -0.03 0.04 0.01 0.06 0.05* 0.06 0.03 0.07 NISALES -0.51 0.03 -0.02 0.04 -0.51 0.06 0.06 0.09 IB4XSALES -0.40 0.03 -0.02 0.04 -0.51 0.06 0.06 0.10 DERATIO 0.15 0.10 0.16 0.10 0.21 0.18 0.28 0.19 TROUBLE 0.19 0 0.15 0 0.29 0 0 0 SPBOND40 8.25 8.5 6.9 6 9.0 8.0 10.0 9.0 CURRENT 3.00 2.08 2.14 1.84 1.85* 1.67 2.89 2.66 QUICK 1.35 0.37 0.66 0.24 0.61 0.55 1.30 0.26 COVRATIO -2.42 4.51 38.31 5.81 14.42 3.56 67.26 4.92 BKMKT 0.42 0.32 0.42 0.38 0.32 0.36 0.59* 0.48 E-P 5.18 0.68 10.15 0.54 3.32 1.05 0.51 1.10 1YR_RET 0.38 0.20 0.42 0.23 0.57 0.50 0.02** 0.04* 1YR_RETV 0.14 0.12 0.12 0.11 0.13 0.11 0.14 0.13

AGEPUB 10.19 6 15.55 12* 13.92 11 12.25 8 FCF 0.03 0.04 0.04 0.10* 0.11 0.13 0.09 0.08 MERGE 0.40 0 0.28 0 0.55 1.00 0.55 1 Growth: ASSETGRO4 1.90 0.45 0.81 0.25 1.11 0.79 0.77 0.63 ASSETGRO2 0.41 0.18 0.15* 0.10** 3.38 0.36 1.02 0.22 SALESGRO 0.53 0.18 0.21* 0.13* 1.81 0.31 0.21 0.11* PPE 0.31 0.15 0.39 0.30 0.40 0.28 0.39 0.29

40 N = 8 (10) in the non-opportunistic fraud (matched firm) sample. N = 5 (4) in the opportunistic fraud (matched firm) sample.

45

Table 3 Comparisons of time-series changes in restatement amounts and unmanaged earnings Panel A shows comparisons of percent changes in restatements in Year 2 of the fraud relative to Year 1 for the non-opportunistic and opportunistic fraud firms. Panel B shows changes in performance for the non-opportunistic and opportunistic fraud firms and their matched samples. Performance is measured as pre-managed (or restated) net income. For all years for the match sample firms, and for CLEANYR for the fraud firms, performance equals net income (data item #172) from Compustat. For the fraud firms in Year 1 and Year 2, performance is restated net income if restatement amounts are available and it is missing otherwise. The percent change in restatement amounts and percent growth in performance (Panels A and B) are measured at the firm level. Averages across the firms are presented. Panel C shows the percent of firms with negative performance, where % LOSS FIRMS is an indicator equal to 1 for firms with negative performance and equal to 0 otherwise. * (**){***} in the “Matched sample” columns indicate that the mean [median] of the matched sample is significantly different from that of the corresponding fraud sample at the 10% (5%) {1%} level. There are no significant differences between the means or medians of any variables for the opportunistic fraud firms and the non-opportunistic fraud firms. The p-values of t-tests of differences in means across the samples assume equal variances unless equality is rejected at 10% level. The p-values of differences in medians across the samples are for a two-sided Wilcoxon rank-sum test. For binary variables, p-values are for a χ2 test.

Non-opportunistic frauds (OPP_FRAUD = 0) Opportunistic frauds (OPP_FRAUD=1) Fraud firms Matched sample Fraud firms Matched sample Mean Median Mean Median Mean Median Mean Median

Panel A: Firm-specific % change in restatement amounts Revenue: Year 1 – Year 2 221% 97 -54 26 Net income: Year 1 – Year 2 40% 18 58 71 Panel B: Firm-specific % growth in restated performance CLEANYR – Year 1 -87% -33 63 -8 327 -5 49 18 Year 1 – Year 2 -174% -86 -75 -1 408 65 -49 -36 Panel C: % LOSS FIRMS Year 0 26% 0 14 0 23 0 23 0 Year 1 42% 0 22 0 63 1 31 0 Year 2 53% 1 18** 0*** 43 0 31 0

46

Table 4. Industry effects on fraud propensity

Models of the determinants of the proportion of frauds in an industry during the period 1990 - 2004 and the proportion of restatements in an industry during period 1997-2005. For both samples, industry is defined at the 3-digit SIC code level. %FRAUDi equals the total number of AAERs against firms in industry i scaled by the average number of firms in the industry from 1990-2004. %GAOi equals the total number of restatements in industry i scaled by the average number of firms in the industry from 1997-2005. The dependent variable in the model is the arc sine-square root transformation: %FRAUDarcsin%ASFRAUD ii = and %AOGarcsin%ASGAO ii = . The model is estimated using weighted least squares (WLS); observations are weighted by the average number of firms in the industry. Independent variables are estimated using the median value of the variable for the respective industry group for the year 1995, with the exception of SYNCSSE. Significance levels are indicated by ***, **, *, and # representing 1%, 5%, 10%, and 15% levels, respectively, 2-tailed. FRAUD RESTATEMENT Intercept 0.0040 0.5669* INTANGS 0.0031 0.1470 CAPEX 0.0001 -0.0001 SALESGRO 0.2591** 0.2540 OPCFGRO -0.1018*** -0.0901* OPCYCLE 0.0002 -0.0009*** MERGE -0.0254 -0.0082 RETVOL -0.1814 -2.3341 NUMSEG (Business) -0.0937 -0.0313 NUMSEG (Geographic) 0.2009# -0.0619 NONSYNC 1.1513* 2.8225*** N 115 115 Adj R2 19.76% 14.72%

47

Table 5. Firm-level fraud prediction model Models of the determinants of fraud at the firm level during the period 1989 – 2001. The dependent variable is an indicator equal to 1 in the first year the firm was accused of fraud in an AAER and is equal to zero for all non-AAER firms. The model is estimated using a logit regression with standard errors clustered by firm and fiscal year. Variable definitions of the independent variables are provided in Appendix A. Significance levels are indicated by ***, **, and * representing 1%, 5%, and 10%, respectively, 1-tailed if predicted and two-tailed otherwise. Dependent variable: 1 = AAER firm (YR1 of fraud in year t)

0 = non-AAER firm (no fraud in year t) Intercept -6.210*** -7.128*** -10.694*** -7.291***

DERATIO + -1.195** -1.561** -3.369*** -3.370***

RISKYDEBT + 0.139 -0.078 -0.388 -0.369

DIVYLD - -38.382*** -32.789*** -21.653*** -21.461***

CASHETR - 0.355 0.271 -1.274** -1.374**

LOGCAPEX + 0.004 -0.194 0.061 0.079

CASHCOMP + 0.753***

VAR$PAY + 2.228***

LOG(ASSETS) 0.162** 0.448*** 0.093 0.202**

ROA -1.286 0.345 -3.124 -3.392

BKMKT -0.242 -0.286 0.822 0.870

ASSETGRO2 0.271*** 0.326*** 0.250 0.178

SALESGRO 0.547*** 0.405** 0.733** 0.593

FCF -1.378***

N 34,620 30,628 10,145 10,145 Log likelihood -765.87 -696.93 -258.85 -257.67

48

Table 6. Fraud prediction using a subsample of overconfident executives Estimation of fraud prediction models for a sample of executives of firms in the software (SIC 737) and hardware (SIC 357) industries. The executive’s OC-SCORE aggregates the standardized values of three variables capturing executive overconfidence. The three components capture the prominence of the CEO’s picture in the annual reports, the ratio of CEO cash pay to that of the second highest paid executive, and the ratio of non-cash pay. The dependent variable is an indicator equal to 1 if the firm was accused of fraud in an AAER and is equal to zero for all non-AAER firms. The model is estimated using a logit regression. Non-cash pay is defined as total compensation less cash pay. Control variables are winsorized at 5%. Definitions of the independent variables are provided in Appendix A. Significance levels are indicated by ***, **, and * representing 1%, 5%, and 10%, respectively, 1-tailed if predicted and two-tailed otherwise. 1=AAER firm

0=Non-AAER Intercept -5.697** -5.727** -6.611**

OC-SCORE components:

PHOTOSCORE + 0.325

REL_CASHCOMP + 0.360

REL_NONCASH + 0.735

OC-SCORE + 0.441** 0.586**

LOGASSETS 0.429 0.420 0.458

ROA 1.583 1.397 1.648

DERATIO -2.884 -2.966 -25.425

BKMKT 1.094 1.192 3.023

ASSETGRO2 -0.102 -0.091 0.215

SALESGRO 0.758 0.810 0.496

FCF 2.029

N 100 100 92 Pseudo R2 15.15% 14.78% 23.03%

49

Table 7. Comparison of executive characteristics Means (medians of continuous variables) of characteristics of accused executives at the EHM subsample of fraud firms and the matched sample firms. Panel A presents statistics for the 107 executives of the EHM sample firms and their matched sample counterparts. Panel B presents statistics for 32 of the 107 accused executives at firms in which the SEC characterizes the fraud as opportunistic (OPP_FRAUD = 1) and for 75 of the 107 accused executives at firms for which OPP_FRAUD = 0. Panel C presents statistics for 40 of the 107 accused executives defined as opportunistic (OPP_EXEC = 1) based on insider trading allegations and for 67 of the 107 accused executives for which OPP_EXEC = 0. For continuous variables, significance is based on p-values of paired t-tests of differences in the means across the samples assuming equal variances unless equality is rejected at 10% level or on p-values of two-sided Wilcoxon rank-sum tests of differences in the medians. For binary variables, significance is based on p-values of a χ2 test. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level.

Panel A: EHM sample vs. matched sample

Panel B: EHM sample divided by the nature of the fraud

Panel C: EHM sample divided by executive overconfidence

N EHM firms N Match Opportunistic Non-opp Extreme Moderate Max N 107 99 32 75 40 67 FOUNDER 99 15.15% 99 10.10 28.13 10.67 ** 25.00 10.45 **

TENURE 88 5.97 88 9.53 *** 8.75 4.90 *** 7.95 5.02 ** (3) (7) *** (8) (3) *** (5) (3) **

COMMIT 71 60.60% 71 76.01 ** 82.15 55.63 *** 68.38 60.77 (76.92%) (100.00) ** (100.00) (58.57) *** (100.00) (80.00)

BOD 98 54.08% 98 46.94 58.06 52.00 57.50 51.52 BODCHAIR 98 20.41% 98 22.45 25.81 16.00 22.50 16.67

EDUCATION 35 1.43 35 1.61 1.73 1.50 1.82 1.46 * (1) (2) (1) (1) (2) (1)

MBA 35 20.00% 35 37.14 20.83 25.00 27.27 20.59

AGE 99 48.05 years 99 49.12 46.69 48.44 47.55 48.13 (47 years) (49)

CPA 61 29.51% 61 27.87 24.14 33.96 19.35 37.25 * AUDITOR 56 23.21% 56 19.46 14.81 23.40 10.71 26.09 FEMALE 99 1.01% 99 5.05 * 3.13 0 2.5 0

VAR$PAY 44 25.34% 44 31.29 30.69 24.10 34.00 18.32 *** (22.63%) (37.32) (48.63) (28.90) (41.52) (5.70) ***

CASHCOMP 44 5.98 44 6.14 6.40 5.73 *** 6.37 5.50 *** (5.79) (6.05) (6.26) (5.63) *** (6.26) (5.36) ***

50

Table 8 Comparison of governance mechanisms Comparison of governance mechanisms for the fraud sample and matched sample. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. The p-values of the t-test of the differences in the means across the samples assume equal variances unless equality is rejected at 10% level. The p-values of tests of differences in the medians across the samples are for a two-sided Wilcoxon rank-sum test. For binary variables, significance is based on p-values of a χ2 test.

Fraud firms Matched sample firms Test of dif Mean Median Mean Median Mean Median N = 32 N = 32 Blockholders: BH_IND 81.82% 1 71.88% 1

BH_NUM 1.94 2 1.81 1

BH_PCT 20.14% 16.87 21.59% 18.95

Board characteristics: BDSIZE 8.82 8 7.94 8

CEOCHAIR 78.79% 81.25%

AUDITCOMM 69.70% 84.38%

PERCENT: Inside 23.01% 20.00% 23.08% 20.00%

Gray 21.01% 17.75% 17.75% 12.50%

Outside 56.36% 60.00% 59.18% 64.58%

Board member characteristics: ENTRENCH: Inside 74.19% 80.65%

Gray 81.82% 76.19%

Outside 80.00% 72.41%

AVGTEN%: Inside 55.30% 68.33% 70.99% 93.75%

Gray 55.35% 81.60% 75.43% 91.67%

Outside 54.60% 50.00% 60.78% 53.33%

AVGBUSY: Inside 1.09 0.17 0.70 0

Gray 1.12 0.50 1.24 0.83

Outside 1.64 1.67 1.21 1.00 *