1. Corporate Securities Fraud & Securities Market Regulation
IFS Lecture
Tracy Yue Wang
June, 2013

2. Outline
Conceptual and empirical framework for understanding corporate securities fraud
How pervasive is corporate fraud?
Determinants of corporate fraud incentive
Executive compensation
Business conditions
Cost of committing fraud
To private parties
Negative externalities

3. Corporate Securities Market
Information asymmetry is the most important issue in financial markets.
A healthy and vibrant financial market requires a continuous flow of high quality information.
Relevant, truthful
The entire institutional design in the public securities markets centers around the infor. asymmetry problem.
SEC and securities regulation: protect outside investors and minority investors
Mandatory disclosure requirement
Antifraud provisions and enforcement
Financial intermediaries: facilitate infor. exchange
Collect, certify, and disseminate information

4. Corporate Securities Fraud
Securities fraud: intentional misreporting of material financial information to outside investors.
Earnings management
Use of managerial discretion in the preparation of financial statements
Reasonable or aggressive
Benchmark to GAAP, not securities laws

5. Corporate Securities Fraud
The “psycho” approach to crime (including white-collar crime)
Crimes cannot result from rational behavior
The “ethics” approach to crime
Intrinsic cost of engaging in illegal behavior
The “economics” approach to crime
People are utility maximizers and thus respond to economic incentives (economic benefits and costs).
People are forward looking. They try their best to anticipate the uncertain consequences of their actions.

6. Corporate Securities Fraud
The different approaches are not mutually exclusive.
The preference in the utility function can be broad
Heterogeneous preferences
Gibson et al. “Preferences for truthfulness: Heterogeneity among and within individuals” (AER forthcoming)
When there is an economic cost for telling the truth and no economic cost for lying, 32% of participants choose to tell the truth.
Participants’ incentive to tell the truth does respond significantly to varying economic cost of truth-telling.

7. Gary Becker’s Work
Many decisions in life can reflect rational economic decision making.
Illegal behaviors and crimes
Investment in human capital: education, accumulation of skills and knowledge.
Family: marriage, divorce, child care, relations among family members
Discrimination against minorities

8. Economics of Securities Fraud
Economic benefits
Decision to commit fraud
Prob. of being detected
Economic costs
Penalty upon detection

9. Benefits from Fraud Managers’ personal benefit
Compensation
Job security
Benefit for current shareholders (at the cost of other investors)
Getting financing at better terms
Acquisition using overvalued equity
Selling overvalued securities
Gaining competitive advantages in the product market

10. Cost of Committing Fraud
Deterrence of detection
Do fraud committers respond more to an increase in the prob. of detection or to an increase in the penalty upon detection?
Implication for the utility function of fraud committers. If fraud committers are
Risk neutral, then the two are equivalent;
Risk averse, then penalty has a larger deterrence effect;
Risk seeking, then detection prob. has a larger deterrence effect.
Implication for optimal provision of deterrence.

11. Cost of Committing Fraud
Costs to the private parties upon detection
Managers: being fired, reputation loss, monetary penalty, jail terms
Investors: loss of security value, loss of future cash flow, monetary penalty paid by the firm
Investors: resources used up to commit fraud, conceal fraud, and clean up after detection (real operations may be distorted)
Costs to the society
Negative externalities: fraud is not just a wealth transfer

12. Empirical Framework Challenge: Partial observability of fraud
We only observe frauds that are committed and later detected
Shared by other white-collar crimes: tax evasion, corruption and bribery, etc.

13. Empirical Framework Do not commit fraud Not detected Commit fraud
1-P(F)
P(F)
Not detected
1-P(D|F)
Commit fraud
P(D|F)
Detected
Observed
Prob.(Detected Fraud)
= Prob.(Fraud Commission)*Prob.(Detection | Commission)

14. Empirical Framework
Since the fraud detection process is not perfect, the probability of detected fraud can substantially underestimate the probability of fraud.
The realized probability of detected financial fraud is only about 4%.
What we observe may be the tip of the iceberg.
Equating P(F*D) to P(F) can lead to incorrect assessment of corporate or public policies that are designed to combat fraud.
Need to separate P(F) from P(D|F) in order to examine the deterrence of detection.

15. Empirical Framework Wang (JLEO, 2011): Fraud commitment:
Fraud detection:
Detected fraud:
The error terms u and v are assumed to follow a bivariate normal distribution.
Unobserved
Observed
Need a model to assess the firm’s fraud propensity

16. Identification
The F* and D* equations cannot have exactly the same set of explanatory variables.
Anticipatable detection risk: Deterrence of detection
some variables that affect detection risk (and can be anticipated) should also affect incentive to commit fraud, in the opposite direction.
Unanticipatable detection risk: Detection occurs after the commission of fraud.
There are factors that affect detection ex post but cannot be anticipated at time of fraud commission
The explanatory variables exhibit adequate variation in the sample
Continuous variables are better than indicator variables.

17. Empirical Specification
P(Z =1) = P(F=1) * P(D=1)
Existence of an accounting-related securities lawsuit (SEC enforcement + class action)
Frauds committed between 1993 and 2005
Sample restrictions to control for frivolous lawsuits
Focus on lawsuits post-PSLRA (after 1995)
Exclude court dismissals
Exclude cases with settlement < $2 million

18. P(Z =1) = P(F=1) * P(D=1) Industry boom/bust Profitability
External financing need
Leverage
Insider equity incentives
Firm investment
Institutional monitoring
Firm size, age, sector
Deterrence of detection;
Ex-ante detection risk, may deter fraud.
Firm investment
Institutional monitoring
Firm size, age, sector
Abnormal industry litigation
Unexpected performance shock
Abnormal return volatility
Abnormal stock turnover
Key identification condition: detection occurs after fraud commitment;
Ex-post detection risk, cannot be anticipated at time of fraud commission.

21. How pervasive is corporate fraud?
Using the bivariate probit model in Wang (2011)
Average P(F) ranges between 11% to 15%.
P(Z)=4%, then the implied P(D|F) ranges between 27% to 36%.
Estimates sensitive to sample & specification.
Crime detection probability:
In 2007, there were murder cases in the US, and estimated 61% of the cases were solved (not official stats).
On average the recovery rate from theft is 19.3%. Auto: 53%, jewelry: 4.6%, currency: 2.3%.

22. How pervasive is corporate fraud?
Dyck, Morse, and Zingales (2013) use two other methods and get consistent estimates.
Method 1: the fall of Arthur Andersen
Key assumption: P(D|F) close to 1 for firms that were clients of AA and were forced to change auditors, and new auditors had incentive to “clean the house”.
Estimated P(F)=14.5%. This is more for financial fraud for which auditors can play an important role at detection.
Estimates crucially depend on assumptions.

23. How pervasive is corporate fraud?
Dyck et al. (2013) method 2: Survey of MBA students at University of Chicago
“In your job you are asked to do something that is illegal. Example: Your boss asks you to lie in reporting sales.”
On average, 14.8% of students were asked to do something illegal in their prior jobs.
Unlikely to hide, but likely to be smaller scale illegal activities.
Piskorski, Seru, and Witkin (2013): Compare actual and disclosed mortgage loan characteristics in RMBS
At least 9% of the RMBS had misreported mortgage loan infor.

24. Managerial Compensation & Fraud
Equity-based compensation is a double-edged sword (Goldman and Slezak (JFE 2006), Benmelech et al. (QJE 2010)).
It induces both managerial effort in production and incentive to conceal bad news or outright misreport financial information.
As long as P(D|F) is not perfect, the optimal pay-for-performance sensitivity (PPS) should be lower than the first best, and the equilibrium amount of effort will be lower than the first best.
Benmelech et al. show that the equity component should not exceed 40% of total compensation for most firms.

25. Managerial Compensation & Fraud
Empirical evidence on the relation btw. equity-based comp. and the incidence of fraud is mixed.
Stock options: e.g., Peng and Röell (RofF 2008), Burns and Kedia (JFE 2006)
Restricted stocks: e.g, Johnson et al. (RofF 2009)
No effect: e.g., Erickson, Hanlon and Maydew (JAR 2006), Armstrong, Jagolinzer, and Larcker (JAR 2010)

26. Compensation Regulation
Managerial compensation contract should be set by private parties. Regulators should not directly intervene with the level or the structure of pay.
The role of the government is to create an enabling environment for the market participants
Increase transparency of pay
Empower shareholders with “Say on Pay” (in some countries non-binding, in others binding)
Ex ante power vs. ex post lawsuit (the case of Disney)
Require 100% compensation committee independence
Progressive taxation: increase the tax rate for the highest income earners
In 2005, the dismissal of a well-publicized, decade-long lawsuit to overturn a huge severance payout demonstrated the obstacles shareholders faced attempting to control executive pay using the courts. The Delaware Court of Chancery refused to overturn a $140 million severance package ($300,000 for every day as president of the company) paid to Michael Ovitz when he was forced to resign by Disney as its president in 1996.
Testimony and documents had described how the Disney compensation committee approved the compensation arrangement after spending only a small fraction of a one-hour meeting on the subject, without receiving any materials in advance, or any recommendations from an experts, and without even seeing a draft of the agreement. The court found the decision to pay Ovitz was simply one of the inherent risks shareholders take as owners for which businesses cannot be held liable, since Ovtiz's poor performance did not rise to the level of `malfeasance`, or a "breach of fiduciary duty and waste of corporate assets".

27. Business Conditions & Fraud
Povel, Singh, and Winton (RFS 2007)
Key idea: Investors’ belief about the business conditions affects their monitoring incentives, which in turn affect firms’ incentive to misreport.
A firm with either good investment prospect (good firm) or poor prospect (bad firm) seeks funding from an investor.
The investor either funds based on the firm’s reported prospect or pay a monitoring cost to get more information before the funding decision.

28. Business Conditions & Fraud
Povel, Singh, and Winton (RFS 2007)
When the investor believes that the business condition (the avg. prob. of a good firm) is weak or average, monitoring focuses on firms with strong reports → fraud is unattractive
When the investor believes that the business condition is good, the investor lessens scrutiny on firms with strong reports → fraud becomes attractive
When the investor believe that the business condition is extremely good, the investor even funds firms with weak reports without monitoring → no need to misreport

29. Business Conditions & Fraud
P(F) is a hump-shaped function of investors’ belief about the business conditions.

30. Business Conditions & Fraud
Hertzberg (2006)
Key idea: Investors’ belief about the business conditions affects the optimal managerial compensation contract, which in turn affect firms’ incentive to misreport.
Short-term compensation (i.e., comp. based on short-term performance) is effective at inducing effort, but also increases the fraud incentive.
When investors believe that the business condition is good, short-term comp is optimal.
When investors believe that the business condition is poor, long-term comp is optimal.
A linear effect rather than a hump-shaped effect.

31. Business Conditions & Fraud
Empirical evidence: Wang, Winton, and Yu (JF 2010)
Examine firms’ incentive to misreport during the IPO process.
Strong support for the PSW theory: investor’s monitoring incentive matters.
Some support for Hertzberg’s theory:
Compensation is related to both business conditions and fraud propensity, but does not appear to be the mechanism linking the two.

33. Business Conditions & Fraud
Implications for securities market regulation:
Some people argue that it should be up to investors to prevent and detect fraud (“Buyer be aware”).
But if investors monitor to fund good projects rather than to prevent fraud per se, then investor monitoring cannot effectively prevent fraud when business conditions are good.
Regulators should be especially vigilant during booms.
The dynamic of fraud can amplify business cycle fluctuations---negative externalities

34. Cost to Managers
Karpoff et al. (JFE, 2008): Substantial personal penalty upon fraud detection
93% lost their jobs
31% were barred by the SEC to serve as an officer or director in a publically traded company
28% face criminal charges and penalties, including jail terms that average 4.3 years
On average, a fraudulent manager lost $15.3 million through his equity holding in the firm, and $5.7 million due to legal penalty (could be partially covered by D&O insurance)
Is the penalty upon detection severe enough?
The estimated P(D|F) is about 27%
Do managers underestimate P(D|F)

35. Cost to Shareholder Value
Legal sanction
Market penalty: Measure the decline in equity value at the moment of fraud revelation.
Cost to fraud vs. revelation of bad news about fundamentals
Karpoff, Lee, and Martin (JFQA, 2008)
Legal penalty is relatively small: on average $23.5 million per firm
Market penalty in terms of loss of shareholder value is much larger: $1 of inflated value → $3.08 of extra value loss upon fraud detection
$0.36 due to expected legal penalty
$2.72 due to reputation loss

36. Cost to Shareholder Value
Resources used up in committing fraud, concealing fraud, and cleaning up after detection.
Wang (2006): Manager’s investment incentives can be distorted due to his incentive to manipulate the prob. of being caught.
Managers favor volatility-increasing investments (e.g., R&D vs. capex) even if the investments are negative NPV.
Benmelech et al. (QJE, 2010): Managers tend to overinvest to maintain the high investor expectation due to fraudulent reporting.
Kedia and Phillipon (RFS, 2009): Fraudulent firms increase investment and employment during the fraud period, and then shed assets and labor after fraud revelation.

37. Negative Externalities of Fraud
Increase information asymmetry in securities market
Undervaluation
Higher cost of capital for firms
Harder for good firms to access the market

38. Negative Externalities of Fraud
Amplification of the business cycle
Fraud incentives are pro-cyclical: Frauds are likely committed during the booms and get revealed during the downturns
Amplification effect especially strong in more competitive industries (Wang and Winton, 2013)
Fraud may distort industry common signals
Regulators have not paid enough attention to this issue

39. Negative Externalities of Fraud
Spillover effects of fraud on peer firms
Competition effect: Peer firms may benefit after fraud is revealed in the firm (little empirical support);
Information spillover: Peer firms may suffer because investors reassess financial statement information in similar firms (firms in the same industry) (empirical support from Gleason et al & Goldman et al. 2012)
Geographic spillover through the demand for local equity (Giannetti and Wang, 2013)

40. Negative Externalities of Fraud
Fraud and household stock market participation (Giannetti and Wang, 2013)
Revelation of fraud in some local companies can decrease households’ willingness to participate in the stock market;
The decrease in demand may generate large indirect value losses by increasing the cost of capital for other firms (i.e., firms that are not involved in fraud).

41. Summary
Firms’ incentive to misreport information is an important issue in capital markets.
Need to understand the economic incentives underlying firms’ incentive to misreport in order to set good policies.
Need to understand the negative externalities of misreporting in capital markets.
A fruitful area for future research!