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Jonathan Karpoff, Scott Lee, Gerald Martin

Conference on Contemporary Issues of Firms and Institutions Chinese University of Hong Kong December 14, 2007 “ What about liars, cheats, and thieves? ”. Jonathan Karpoff, Scott Lee, Gerald Martin University of Washington, Texas A&M University, American University.

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Jonathan Karpoff, Scott Lee, Gerald Martin

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  1. Conference on Contemporary Issues of Firms and InstitutionsChinese University of Hong KongDecember 14, 2007“What about liars, cheats, and thieves?” Jonathan Karpoff, Scott Lee, Gerald Martin University of Washington, Texas A&M University, American University

  2. … A summary of three related papers on the consequences and causes of financial misrepresentation Question #1: What are the consequences at the firm level? Paper title: “The cost to firms of cooking the books” Question #2: What are the consequences for the individual perpetrators? Paper title: “The consequences to managers for financial misrepresentation” Question #3: What motivates the decision to misrepresent financial statements? Paper title: “The determinants of managerial decisions to cook the books”

  3. The invisible hand “[Each person] generally, indeed, neither intends to promote the public interest, nor knows how much he is promoting it … by directing [his] industry in such a manner as its produce may be of the greatest value, he intends only his own gain, and he is in this, as in many other cases, led by an invisible hand to promote an end which was no part of his intention… By pursuing his own interest he frequently promotes that of the society more effectually than when he really intends to promote it.” – Adam Smith (IV.ii.6-9, page 456 of the 1776 Glasgow Edition of Smith’s works; vol. IV, ch. 2, p. 477 of 1776 U. of Chicago Edition.) Not always? When does the pursuit of self-interest not promote society’s interest?

  4. What about liars, cheats, and thieves? John Rigas, Founder and CEO of Adelphia Communications. He took home the popcorn.

  5. Dennis Kozlowski, CEO of Tyco International, Ltd. – “One of theTop 25 Managers of the Year” (Business Week magazine in 2001) – Now Prisoner 05A4820, in jail

  6. Kenneth Lay, former CEO of Enron Corp. - Convicted of fraud, false statements (died, July 5, 2006, before sentencing)

  7. Cheating, and its effects, have become important concerns in financial markets • “In many countries, expropriation of minority shareholders and creditors by the controlling shareholders is extensive.” • La Porta, Lopez-de-Silanes, Shleifer, and Vishny (JFE 2000, p.4) • “…[T]he nexus between the accounting firms and the corporations, and the aggressive attitudes of CFOs and CEOs, has created a pattern of deception; that has created a situation which has eroded public confidence in the sanctity of the numbers which is the basis of our markets today.” • ... Arthur Levitt, SEC Chairman 1993-2001, quoted in http://www.pbs.org/wgbh/pages/frontline/shows/regulation/lessons/

  8. What to do? • “A more activist SEC is what’s needed.” – The Christian Science Monitor • La Porta et al. (JF 2006) - public enforcement does not work • Jackson and Roe (wp 2007) - public enforcement does work • “It’s time to stop coddling white-collar crooks. Send them to jail ... Enough is enough: They lie, they cheat, they steal and they’ve been getting away with it for too long.” – Fortune magazine

  9. What else can we do? “The first thing we do, let's kill all the lawyers.” – William Shakespeare, in Henry VI “…[L]et's kill all the accountants.” – New York Daily News

  10. The economic problem • Fraudulent, deceptive, and opportunistic behavior creates a lemons problem • Information and contracts are costly • Contracts are incomplete • Contracts are costly to enforce • Buyers will demand a discount for the expected amount of cheating by sellers

  11. The economic problem, continued • All sellers know better than their counterparties whether they are trustworthy • At “average” terms, only sellers with less-than-average trustworthiness will engage in trade • Buyers know this, and demand even higher “cheating discounts” • The market breaks down

  12. What keeps it all together? Why isn’t fraud, self-dealing, and opportunistic cheating the norm in most transactions? • Agency theory and internal governance • External discipline and the market for control • International law and finance • Business misconduct, markets, and reputation • Examine the exceptional transactions, where someone acts opportunistically • What can we learn about most normal transactions – in which no one gets cheated or ripped off? The focus of these slides

  13. Concern about business misconduct drives business school curriculae • Ethics teaching is big business • AACSB accreditation requires ethics courses • “Beyond Grey Pinstripes” – A World Resources Institute annual award for teaching ethics (and sustainability) • “Teaching the Moral Leader” – well-publicized HBS course • Finance scholars have little influence in these areas • Have abandoned the topic – both the budgets and the intellectual leadership – to non-economics based disciplines • But, we have a lot to say as a positive science

  14. Question #1:What are the consequences at the firm level? • Data: All (788) SEC and DOJ enforcement actions for financial misrepresentation initiated from 1978 through September 30, 2006 for: • Books and records violations (15 USC § 78m(b)(2)(A)) • Internal controls violations (15 USC § 78m(b)(2)(B)) • Data sources: • Lexis-Nexis database • SEC website (all public releases since September 19, 1995) • DOJ staff (direct communications) • DOJ’s Corporate Fraud Task Force website • SEC’s EDGAR database • Stanford Law School’s Securities Class Action Clearinghouse

  15. Cumulative abnormal returns and total dollar losses Large losses

  16. All SEC, DOJ, and state-level fines, plus class action and derivative lawsuit awards Firm Losses Partitioned… Present value of higher future financing costs and lower net revenues Estimate of the amount by which firm value was artificially inflated by the cooked books

  17. Takeaways regarding the consequences to firms • Large losses in share values (~ 40%) • Most of the loss is due to lost reputation, i.e., the value of higher financing costs and lower net revenues • Consistent with prior law and economics literature on the impact of cheating on operations • For each dollar that firm value was inflated due to the inflated earnings and assets… • The firm loses this dollar… • Plus an additional $3.08 • $0.36 due to legal costs and settlements • $2.71 due to lost reputation • Evidence of the magnitude of Jensen’s agency cost of overvalued equity

  18. Question #2:What happens to the culpable individuals? • Job loss • Determinants of being replaced • Other consequences: • Debarment from executive positions (affects future employment opportunities) • Regulatory fines • Direct wealth losses through shareholdings • Criminal charges • Jail time

  19. Job survival rates for culpable executives Execs in general Culpable execs Non-culpable execs in targeted firms

  20. Culpable managers tend to be fired (contrary to most “normal” turnover)

  21. What determines who loses his/her job? • Logistic regressions (dependent var = 1 if terminated) • Report each coefficient’s odds ratio • > 1 indicates a positive effect • < 1 indicates a negative effect

  22. Turnover is facilitated by better governance(dependent variable = 1 if culpable manager loses job) Board independence is positively related to the likelihood of ouster

  23. Turnover is facilitated by better governance(dependent variable = 1 if culpable manager loses job) Shareholdings by non-culpable blockholders and other insiders is positively related to the likelihood of ouster

  24. Takeaways regarding culpable individuals • When misrepresentation is exposed, the perpetrators experience significant costs: • Job loss (91% of culpable execs) • Losses through shareholdings • SEC fines (average $14.4 million) • Class action/derivative lawsuit awards • Debarment (42% of all culpable execs) • Criminal charges (26% of all culpable execs) • Jail time (average sentence = 5.7 yrs) • Internal governance plays an important role • The sky’s not falling, i.e., “They lie, they cheat, they steal, and they’ve been getting away with it for too long” is wrong

  25. Question #3: Getting caught is really costly … So why do they risk it? Impacts on firms: • 40% loss in share value • About 25% of that is revaluation • Most (66%) of the loss in value is lost reputation, i.e., higher future costs, lower revenues Impacts on perpetrators: • 91% lose jobs (60% explicitly are fired) • 42% barred from serving as officers/directors • SEC fine = $14.4 million • Shareholdings decline from $6m – $39m • 26% indicted on criminal charges • Avg jail time = 5.7 years

  26. Ideas from prior research: Managers manipulate financials to… Boost compensation • Beneish (TAR 1999) • Erickson et al. (JAR 2006) • Denis, Hanouna, Sarin (JCF 2006) • Burns, Kedia (JFE 2006) Facilitate new financing • Dechow, Sloan, Sweeney (CAR 1996) • Richardson, Tuna, Wu (wp 2002) • Efendi et al. (JFE 2007) • Dechow,Ge,Larson,Sloan (wp 2007) Meet earnings thresholds • Robb (JFR 1998) • Degorge et al. (JB 2000) • Payne, Robb (JAAF 2000) • Schilit (book, 2002) Forestall distress-related problems • Maksimovic, Titman (RFS 1990) • DeFond, Jiambalvo (JAE 1994) • Efendi et al. (JFE 2007)

  27. More ideas from prior research: Managers are constrained by… Good governance • Beasley (TAR 1996) • Klein (JAE 2002) • Dechow, Sloan, Sweeney (CAR 1996) • Agrawal, Chadha (JLE 2005) Well-structured ownership • Gerety, Lehn (MDE 1997) • Cornett et al. (wp 2006) • Denis et al. (JCF 2006) Firm transparency • Gerety, Lehn (MDE 1997) • Dechow,Ge,Larson,Sloan (wp 2007) • Kedia, Philippon (RFS 2007) Other factors • Povel, Singh, Winton (RFS 2007) • Cohen, Dey, Lys (wp 2005)

  28. However…Prior results are all over the place

  29. Another example of mixed prior results

  30. A second issue:Prior results are based on small samples • Median sample size = 101 misrepresentation events Mean = 176 • In actual tests, sample sizes are severely reduced due to data availability problems

  31. A third issue: Prior tests have omitted variables issues Eight types of hypotheses

  32. Like the blind men and the elephant(?) “…[I]t is not feasible to consider all proposed motivations…” – Dechow, Sloan, and Sweeney (CAR 1996)

  33. Most focus on one or two types of motive Agrawal-Chadha (JLE 2005) Efendi et al. (JFE 2007) Burns-Kedia (JFE 2006)

  34. So we try a megastudy approach • Group various conjectures, theories into eight types of motives or constraints • Call them “hypotheses” • Avoid – or at least reduce – omitted variable bias • Consider many proxies for each hypothesis • We have more than 100 proxy variables • Preliminary results use a subset of these proxies • Use large, comprehensive sample • Most prior studies have small samples

  35. Ways to construct a sample • Our approach: All 868 SEC/DOJ enforcement actions from 1978-June 30, 2007 for financial misrepresention • 15 U.S.C. §§ 78m(b)(2)(A) - requires accurate books and records • 15 U.S.C. §§ 78m(b)(2)(B) - requires internal controls • Securities class action (10b-5) lawsuits (e.g., Gande-Lewis, JFQA 2007; Fich-Shivdasani JFE 2007) • 46% of our sample events have corresponding 10b-5 lawsuits • Accounting and auditing enforcement release (AAER) • AAER is a secondary designation assigned when the enforcement release names an accountant or auditor • Created in 1982 • May not have anything to do with financial misrepresentation • GAO (2002, 2003) restatement database • Currently popular, it’s easy

  36. Universe of financial misrepresentations Our sample • Type I error (miss events in which misrepresentation occurred) may be high • Type II error (events include innocent firms or individuals) is essentially zero • AAER samples • AAERs miss 18% of enforcement actions, and 29% of all Administrative and Litigation Releases - so Type I error is higher • AAERS include many instances in which there is no financial misrepresentation (e.g., Boston Scientific – Securities Exchange Act Release 34-43183, also assigned AAER-1295) - so Type II error is non-trivial. • GAO restatement sample • 1997–June 2002 (total = 919) • Attempted to screen for fraud, but SEC now says that over 50% are not misrepresentations or violations (implying that Type I and Type II error rates are very large) • Hand-collected restatement database in Palmrose-Scholz (e.g., CAR 2004) does not have these problems

  37. Other data issues • What is the event we wish to examine? • We focus on SEC/DOJ actions for financial misrepresentation • 15 U.S.C. §§ 78m(b)(2)(A) - requires accurate books and records • 15 U.S.C. §§ 78m(b)(2)(B) - requires internal controls • Others (e.g., Dechow et al. 1996) look at 13(a) “disclosure” violations • … Failure to report (e.g., 10-Qs and 10-Ks), failure to disclose information that should be disclosed, inaccurate info other than in financial statements (inaccuracies in financial statements trigger violations in bullet item above) • Example #1: Changing from LIFO to FIFO and not disclosing the gain • Example #2: Filing a 10-Q late • Is it “fraud?” • Most researchers say they examine “fraud” • But 25% of all enforcement actions do not include fraud charges Called 13(b) (of the SEC Act of 1934) violations

  38. (15 USC § 78m(b)(2)(A)) (15 USC § 78m(b)(2)(B)) (15 USC § 78m(b)(5)) { Lexis-Nexis SEC website (since Sept. 19, 1995) DOJ staff (direct communications) DOJ’s Corporate Fraud Task Force website Our sample

  39. Univariate comparisons (Table 3) … Performance-based pay is relatively high in violation firm-years

  40. Univariate comparisons (Table 3) … Some need-for-financing variables are relatively high in violation firm-years

  41. Univariate results - summary • Many things are correlated with the incidence of financial misrepresentation when viewed in isolation. • But all univariate comparisons suffer from omitted variables (and possibly problems of simultaneity and endogeneity) • Question: Which hypotheses (stories) are most important? • And which proxies should be used in empirical tests? • Use of multiple proxies causes attenuation bias

  42. Our current approach – 4 types of tests Kitchen sink approach Straw-man (a bit unfair) version of many previous papers’ approaches Comprehensive (if you believe the factors) Recognize multiple proxies and cherry-picking problems

  43. Example with compensation hypothesis variables Table 4: Factor loadings The loadings make sense The factor explains a substantial amount of the covariance in these four proxies

  44. Table 5: Random effects cross-sectional time-series logistic regressions • Columns 1 & 2 focus on one hypothesis at a time. • Columns 3 & 4 reflect joint tests of all 8 hypotheses. • The factor analysis captures the information from multiple proxy variables as a linear combination of the proxies.

  45. Table 5: Random effects cross-sectional time-series logistic regressions • Columns 1 & 2 focus on one hypothesis at a time. • Columns 3 & 4 reflect joint tests of all 8 hypotheses. • The factor analysis captures the information from multiple proxy variables as a linear combination of the proxies.

  46. Table 5: Random effects cross-sectional time-series logistic regressions • Columns 1 & 2 focus on one hypothesis at a time. • Columns 3 & 4 reflect joint tests of all 8 hypotheses. • The factor analysis captures the information from multiple proxy variables as a linear combination of the proxies.

  47. Table 5: Random effects cross-sectional time-series logistic regressions

  48. Summary of preliminary results Hypotheses about motives • Compensation – Yes • New financing – Yes (probably) • Earnings threshold – Possibly • Factor is significant, with intuitive sign • Distress – No • Prior stock performance seems important • Hypotheses about constraints • Governance – No • (CEO/Chm duality seems important • Ownership – No • (Some individual proxies seem important) • Transparency – No • (Significant only when other hypotheses are omitted) • External factors: • Macro trends (Povel et al.) – No • Regulatory regimes – Yes (probably)

  49. Ongoing steps in this project • Data reduction techniques to consider more of the 100+ proxies in our dataset • Other empirical methods issues • Unconditional vs. conditional factor analysis • Factor rotation • Uneven data limitations (e.g., use all available data for each factor?) • Multiple factors/eigenvalue cut offs • Datamine for “best” factors, then use these in empirical tests? • Alternative empirical methods (e.g., the binomial test in my paper with Boone, Field, and Raheja)? • Compare violation firm-years to non-violation firm-years for same firms (or used fixed firm effects)? • Be more explicit about using both factor loadings and factor coefficients in drawing inferences?

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