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Go before the whistle blows: an empirical analysis of director turnover and financial fraud

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Abstract

This study investigates whether outside directors are aware of financial fraud. Our analysis focuses on the abnormal turnover of these directors during the fraud committing period, before fraud is discovered and before lawsuits are filed. Our empirical analysis shows that, during the fraud committing period, outside directors in fraud firms exhibit an abnormal level of turnover. Examining the characteristics of outside directors and boards at these fraud firms, we find strong evidence that female directors, directors who have greater stock ownership in the firm, and directors with multiple directorships at other firms are more likely to depart fraud firms. We also find some evidence that board size, number of meetings, and fraction of financial experts are related to abnormal turnover in fraud firms during the fraud committing period. We show that abnormal director turnover is significantly higher for fraud that is considered more egregious (i.e., involving fictitious transactions and disclosure problems). Lastly, directors are more likely to depart fraud firms with more serious fraud, as proxied by higher ex-post settlement amounts and longer fraud duration.

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Notes

  1. Though this oversight has the potential to reduce principal–agent conflicts between managers and shareholders, Hermalin and Weisbach (2003) suggest that boards are endogenously determined institutions.

  2. However, Black et al. (2006) show that punitive penalties for outside directors are typically very small when financial reporting fraud is discovered by the U.S. Securities and Exchange Commission (SEC): In only 13 cases since 1980 have directors made a personal settlement or paid legal expenses. Brochet and Srinivasan (2014) show that only 11% of independent directors are named as defendants in litigation lawsuits.

  3. Despite the low likelihood of legal penalties (Black et al. 2006; Brochet and Srinivasan 2014), the fear of legal liability nonetheless serves as an effective deterrent for individuals serving as directors (Romano 1989; Sahlman 1990; Alexander 1991).

  4. Following the literature (e.g., Yermack 2004; Fahlenbrach et al. 2013), we define abnormal director turnover as the departure of outside directors who are below age 70 (i.e., non-retiring). In robustness analysis, we also consider total outside director turnover and include director age as an explanatory variable in the regression analysis. We obtain similar results and include the discussion in more details in Section 6.

  5. To alleviate the concern of potential endogeneity associated with the determination of corporate fraud and abnormal director departure, we alternatively construct a sample by matching the predicted likelihood (i.e., propensity score) of fraud for fraud firms with that of non-fraud control firms. Our results using the propensity score matching (PSM) sample (further discussed in Section 6) continue to show that abnormal director turnover is consistently higher in fraud firms, lending further support to our main findings.

  6. There is substantive literature examining the impact of gender on corporate management. For example, see Gul et al. (2008), Adams and Ferreira (2009), and Srinidhi et al. (2011) on monitoring effort; Bernardi and Arnold (1997) and Cohen et al. (1998) on ethical standards; and Jianakoplos and Bernasek (1998), Barber and Odean (2001), and Brooks and Zank (2005) on risk aversion.

  7. There is another small stream of literature that looks into other players in the process of fraud discovery. For example, Dyck et al. (2010) look into the role of employees, the media, and industry regulators as whistleblowers. Bowen et al. (2010) examine the characteristics of firms subject to employee allegations of corporate fraud.

  8. Though not tabulated here for brevity, we also consider outside directors with corporate expertise or certain academic qualifications, such as doctoral degrees and professorships, and obtain findings similar to those reported.

  9. We conduct additional analysis to examine the trading behavior of departing directors and discuss the results in Section 6.

  10. In additional analysis, we consider the reputational loss of departing multi-seat directors compared to that of departing non–multi-seat directors and staying directors at fraud firms. We discuss the results of the analysis in Section 6.

  11. Other studies argue that firms with directors of multiple board seats have better performance (e.g., Ferris et al. 2003), since these directors are motivated to better monitor their companies (Adams et al. 2010).

  12. Affiliated directors are those with potential conflicts of interest, such as consulting arrangements, family relationships, or interlocking board memberships (Srinivasan 2005).

  13. Slightly different from Agrawal and Chadha (2005), we use the fraction of financial experts on the audit committee because almost all firms in our sample have at least one financial expert on the audit committee. In addition, our results are robust to using the presence of financial experts instead of the fraction of financial experts.

  14. These categories include revenue misstatements, misstatements of other expense/shareholder equity accounts, the capitalization of costs as assets, accounts receivable misstatements, inventory misstatements, misstatements of costs of goods sold, liability misstatements, reserve account misstatements, misstatements of allowance for bad debts, misstatements of marketable securities, and misstatements of payables.

  15. For firms with multiple litigation cases, we treat each case as a separate firm–fraud observation. Our results are not affected if we delete firms with multiple litigation cases.

  16. Corporate Library data start in 2001 with initial coverage on only Standard & Poor’s (S&P) 1500 firms. The coverage increases to 2000 firms (including Fortune 1000 and Russell 1000) in 2003 and 3000 firms (including Fortune 1000 and Russell 3000) in 2006. The large number of firms dropped in the sample selection process is due to the fact that the Corporate Library only covers relatively large firms listed in the S&P 1500, Fortune 1000, and Russell 3000 indexes.

  17. We do not require that a firm’s coverage in the Corporate Library completely span the entire fraud committing period, because such a requirement would further reduce our final sample size due to the limited time coverage of the Corporate Library. Therefore, our measure of abnormal director turnover is likely to be understated, especially for fraud firms, which introduces a conservative bias against our findings.

  18. Using the more stringent requirement while considering only the top three firms yields similar results.

  19. However, it should be noted here that matched non-fraud firms in the control sample are still among the top 10 closest firms to fraud firms in terms of asset size. In untabulated robustness tests, we also find that our main results are qualitatively unaffected by a reduced sample when we require the matched control firms to be within the 80–120% range.

  20. Examples of trigger events include restatements and the firing of auditors (Karpoff et al. 2008).

  21. Karpoff et al. (2008) illustrate the sequence of events for a typical enforcement action case. In our sample, the timeline usually goes sequentially from (1) the class action beginning date through (5) the final status date, though in some special cases the first class action filing dates may be a few days before the class action ending dates. We use the class action ending dates or the first filing dates instead of trigger event dates because the trigger event dates are not disclosed for every case. Nonetheless, we consider trigger event dates and other possible news event dates in the robustness tests reported in Section 6. Our results remain unchanged.

  22. Admittedly, the classification of fraud involves some subjectivity. We compare our frequencies of types of fraud with those of prior research. Overall, we observe similar patterns. Consistent with Bonner et al. (1998), we show that omitted or improper disclosure is the most frequent type and that fictitious fraud is the least frequent type. Though Dechow et al. (2011) focus only on misstatements identified upon AAER and we focus on all financial frauds, we find that our misstatement subsample bears similar percentages for the misstatement of individual accounts as in Dechow et al. (2011). Finally, Schrand and Zechman (2012) find that a quarter of their sample firms engage in self-dealing, which is comparable to (though a bit less than) our sample observations. Note that the sum of the percentages of fraud types is not equal to one since one fraud case can have multiple types.

  23. There is little consensus in the literature as to the amount of stock holdings that makes a blockholder. For example, DeFond and Jiambalvo (1991) treat stock ownership above 5% as blockholdings, while Jensen (1993) finds that stock ownership for management and board members is quite low, with a mean and median of 2.7% and 0.2%, respectively. Thus, in the main analysis, we define an outside director as a blockholder (Block Holder) if the director holds at least 5% of outstanding shares. In robustness checks, we also consider 1% stock ownership, and our results are unaffected.

  24. All firm-level control variables except Institutional Holdings are measured at the fiscal year-end prior to the class action beginning date, while Institutional Holdings is measured at the last quarter-end prior to the class action beginning date. We alternatively measure these variables in the year of director turnover (or year of fraud discovery for staying directors) and obtain similar results.

  25. Untabulated results show that the average stock holdings of outside directors in our fraud and non-fraud control firms are 1% and less than 1%, respectively.

  26. Compared to prior studies, the average board size in our sample is quite similar to that of Fich and Shivdasani (2007), whose mean board sizes for fraud and control firms are 13.4 directors and 12.6 directors, respectively. We also observe a similar but slightly higher number of board meetings in our sample relative to their sample (7.63 and 7.55 for fraud and control firms, respectively); however, the fraction of outside directors in our sample is significantly higher than in theirs (52% and 55% for fraud and control firms, respectively). These differences are reasonable, since their sample coverage is from 1998 to 2002 while ours is concentrated in the period after 2001.

  27. The Corporate Library does have a variable called date retiring; however, it has mostly incomplete information and is not updated in a timely manner. Effective since August 23, 2004, the SEC requires firms to file director departures and elections in Form 8 K, Section 5.02, no later than four days after the event. Unfortunately, this information is not available for most of our sample. Hence, we follow the literature (e.g., Fahlenbrach et al. 2013), using the proxy statement to define director departure.

  28. Our assumption is relatively conservative since our assumed date of director departure would most likely be later than the actual date of director departure. For a few fraud firms (e.g., Enron), there were no future proxy statements once the fraud was discovered, because the firm was then in the bankruptcy process. To determine the departure dates of directors in such cases, we also take the conservative approach and assume that theses directors departed one year after the final proxy date. This approach will undercount the number of departing directors.

  29. Our turnover rates are comparable to those of Fich and Shivdasani (2007), who find that 5.6% of outside directors depart fraud firms in the first year after the class action filing dates.

  30. Since it is not the focus of our paper to examine the impact of these firm-level variables on abnormal director turnover, we do not discuss these coefficients in detail, so as to keep our manuscript and the tables at a manageable length. Nonetheless, the results are available from the authors upon request.

  31. Bonner et al. (1998) also show that fraud that involves the misstatement of important accounts does not lead to greater risk of auditor litigation. However, they do find frequent fraud more likely leads to auditor litigation. We attribute the difference in our findings to the fact that auditors may be held more responsible than directors in cases of frequent fraud, many of which involve accounting manipulation, such as premature revenue recognition, which auditors should be better able to detect given their qualifications.

  32. We compute the predicted probability of financial fraud as

    $$ p(Fraud)=\frac{\mathit{\exp}(PredictedValue)}{\left[1+\mathit{\exp}(PredictedValue)\right]} $$
    $$ \mathrm{Predicted}\ \mathrm{Value}=-7.893+0.79*\mathrm{rsst}\_acc+2.518*\mathrm{d}\_rec+1.191*\mathrm{d}\_inv+1.979*\%\mathrm{soft}\_\mathrm{at}+0.171*\mathrm{d}\_cs-0.932*\mathrm{d}\_roa+1.029*\mathrm{issue} $$

    where rsst_acc is total accruals; d_rec, d_inv, d_cs, and d_roa are changes in receivables, inventory, cash sales, and return on assets (ROA), respectively; %soft_at is the percentage of soft assets (i.e., assets on the balance sheet that are neither cash nor property, plant, and equipment); and issue is a dummy variable that equals one if a firm issues equity or debt and zero otherwise. All variables are defined exactly as reported in Table 7 of Dechow et al. (2011).

  33. We require the asset size of non-fraud control firms to be no less than 20% and no more than five times that of the fraud firms. Since we apply a relatively generous matching criterion for firm size, our PSM approach should not be considered a size-based matching method. Nonetheless, matching based on firm size is conducted as a robustness analysis and the results remain similar.

  34. Since the PSM sample has a much smaller sample size as compared to our size-matched sample, it makes estimation of the interaction model difficult. Thus, we estimate only the baseline model for the PSM sample.

  35. The percentage of shares traded is computed as the number of shares traded divided by shares held by the director.

  36. These untabulated results are available from the authors upon request.

  37. The results in this section are not tabulated for brevity but are available from the authors upon request.

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Acknowledgements

We appreciate the valuable comments from Patricia Dechow (the editor), an anonymous referee, David Farber, Weili Ge, and Oliver Li. Earlier versions of the paper were presented at the 2013 AAA Western Regional Meeting, the 2013 CAAA Annual Conference, the 2013 ATINER International Conference on Accounting, and the 2014 AAA Annual Meeting. We thank participants and discussants of the aforementioned conferences as well as doctoral seminar participants at City University of Hong Kong, Fudan University, and University of Waterloo for their comments and suggestions. We gratefully acknowledge financial support from the City University of Hong Kong, Fonds de Recherche du Québec – Société et Culture, and McGill University. All errors are, of course, ours.

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Correspondence to Desmond Tsang.

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Table 11 Variable definitions

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Gao, Y., Kim, JB., Tsang, D. et al. Go before the whistle blows: an empirical analysis of director turnover and financial fraud. Rev Account Stud 22, 320–360 (2017). https://doi.org/10.1007/s11142-016-9381-z

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