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Are a Few Huge Outcomes Distorting Financial Misconduct Research?*

* This paper is a replication of an original study

Author

Listed:
  • Emre Kuvvet

Abstract

Research on financial misconduct uses data on enforcement outcomes, such as the penalties that the firm pays. The distribution of most enforcement outcomes shows extreme observations or outliers, but you wouldn’t necessarily glean that by a casual examination of some of the leading research on financial misconduct. In this paper I describe the public-policy context of such research and raise the issue of whether the extreme-values problem has been given adequate attention. I touch on a number of papers, and then focus on a 2018 Journal of Accounting Research article by Andrew Call, Gerald Martin, Nathan Sharp, and Jaron Wilde (CMSW), which purports to show a positive association between the severity of enforcement outcomes and the involvement of a whistleblower. I show that the top one percent of the enforcement outcomes (11 observations) in CMSW’s large sample of 1,133 enforcement actions drive their results; a number of robustness checks suggest that the extreme-values problem is serious. Moreover, I explain numerous sources of fuzziness in their whistleblower coding, and explain that research with the extreme-values problem is highly sensitive to such fuzziness because a few dubious codings can change the results. Unfortunately, CMSW do not disclose how each coding was arrived at, so we cannot peer into the fuzziness to see how the extreme observations came to be coded as they are. I suggest that CMSW could have been upfront about the looming problem of a few extreme values in enforcement outcomes, should have shown how the outliers affect their results, and should have explained and resolved (to the extent possible) the mysteries surrounding the coding. Furthermore, as regards the larger issue of public policy, it should be emphasized that we would expect strong and natural correlations among severity of misconduct, likelihood of penalties, and whistleblowing, like the correlations among the severity of health emergencies, likelihood of medical interventions, and calls to 9-1-1. Correlation is not causation. CMSW do not give this point the emphasis that it deserves. It looms especially large in light of my findings that CMSW’s results are not robust to removing the outliers. Accordingly we should perhaps be surprised that CMSW did not find a statistically significant correlation for one of the enforcement-outcome categories.

Suggested Citation

  • Emre Kuvvet, 2019. "Are a Few Huge Outcomes Distorting Financial Misconduct Research?," Econ Journal Watch, Econ Journal Watch, vol. 16(1), pages 1-1–34, March.
    • Handle: RePEc:ejw:journl:v:16:y:2019:i:1:p:1-34
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    References listed on IDEAS

    as
    1. Rafael La Porta & Florencio Lopez‐De‐Silanes & Andrei Shleifer, 2006. "What Works in Securities Laws?," Journal of Finance, American Finance Association, vol. 61(1), pages 1-32, February.
    2. Jonathan M. Karpoff & D. Scott Lee & Gerald S. Martin, 2014. "The Consequences to Managers for Financial Misrepresentation," Springer Books, in: Roberto Pietra & Stuart McLeay & Joshua Ronen (ed.), Accounting and Regulation, edition 127, chapter 0, pages 339-375, Springer.
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    8. repec:cup:jfinqa:v:46:y:2011:i:06:p:1865-1891_00 is not listed on IDEAS
    9. Alexander Dyck & Adair Morse & Luigi Zingales, 2010. "Who Blows the Whistle on Corporate Fraud?," Journal of Finance, American Finance Association, vol. 65(6), pages 2213-2253, December.
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    11. Cumming, Douglas & Dannhauser, Robert & Johan, Sofia, 2015. "Financial market misconduct and agency conflicts: A synthesis and future directions," Journal of Corporate Finance, Elsevier, vol. 34(C), pages 150-168.
    12. Andrew C. Call & Gerald S. Martin & Nathan Y. Sharp & Jaron H. Wilde, 2018. "Whistleblowers and Outcomes of Financial Misrepresentation Enforcement Actions," Journal of Accounting Research, Wiley Blackwell, vol. 56(1), pages 123-171, March.
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    Replication

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    Keywords

    whistleblower; tipster; outlier;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • K22 - Law and Economics - - Regulation and Business Law - - - Business and Securities Law
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • M48 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Government Policy and Regulation

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    1. Are a Few Huge Outcomes Distorting Financial Misconduct Research? (EJW 2019) in ReplicationWiki

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