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The economic consequences of ceasing option backdating

Review of Accounting Studies (2022)Cite this article

Abstract

The 2002 enactment of Section 403(a) of the Sarbanes-Oxley Act (SOX403) made option backdating less viable for firms. I examine whether the loss of the benefits obtained from option backdating is associated with more fraud after the enactment of SOX403. For firms suspected of backdating options (suspect firms), I find an increase in fraudulent financial reporting after the enactment of SOX403. The increase in fraud is more prominent for suspect firms more affected by SOX403. I also find an increase in insider trading profits from fraud for individuals who formerly benefited from option backdating. My study highlights an unintended consequence of SOX403. The opportunistic timing of executive option compensation appears to be replaced with fraudulent activities that are likely more value-destroying.

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Fig. 1

Notes

  1. For executives and directors reporting on Form 4, the deadline for reporting the receipt of option grants was the 10th day of the month following the grant date. For those reporting on Form 5, the filing deadline was 45 days after the end of the fiscal year.

  2. Executives may also have increased other perquisites, such as pensions, although this may alarm shareholders—exactly what firms had hoped to avoid by backdating options in the first place (Fried 2008). I explore the potential increase in other perquisites in additional analyses.

  3. To avoid confusion, “fraud” in this paper refers to financial reporting fraud other than option backdating. Section 2 discusses the ambiguous legality of option backdating and how it differs from other forms of financial reporting fraud where managers intentionally violate securities regulations.

  4. Jagolinzer et al. (2011) note that 180 days is generally chosen in studies of insider trading because profits earned within a shorter period are subject to the short-swing-profit rule pursuant to Section 16(b) of the Securities and Exchange Act of 1934. The short-swing-profit rule requires insiders to return any profit earned from the purchase and sale of the company’s equity securities that occur within a period of less than six months.

  5. The SEC indicated its intention to accelerate the filing of insider trading first in April 2002 (Release No. 33–8090) and suggested the goal is “to provide investors with prompt disclosure of this information, so that investors will be able to make investment and voting decisions on a better-informed and more timely basis.” This eventually leads to SOX403. Per the SEC’s statements, the elimination of option backdating is more like a byproduct rather than the major goal of the rule.

  6. According to the SEC complaint, the 4.8 million options granted to top executives other than the CEO were granted on February 7, 2001, when the stock price was around $21. However, the grant date was backdated to January 17, 2001, when the stock price was $16.81. The Black-Scholes value of these options was $43 million based on the strike price of $16.81 and $54 million based on the strike price of $21. Hence the backdating increased these executives’ option compensation by around 26% (= $54/$43–1) and their total compensation by around 6% (=$54 / ($43 + $8) – 1, where $8 (million) is the value of compensation other than options as reported by the company) (Securities and Exchange Commission v. Nancy R. Heinen and Fred D. Anderson, Case No. 07–2214-HRL (Lloyd), April 24, 2007).

  7. The former CEO of KB Home received 8 months of home confinement rather than several years in prison and was even acquitted of all counts of securities fraud (Dobuzinskis 2010).

  8. In general, the most favorable day is the day with the lowest stock price of the month. The only exception is option exercises that are classified as exercise-and-sell transactions with a disposition of shares to the company, for which the most favorable day is the day with the highest stock price of the month. The classification method of option exercises is described by Cicero (2009) and Biggerstaff et al.. I also apply the same sample selection criteria as do Biggerstaff et al., such as excluding option grants that are regularly scheduled. These numbers of observations are comparable to the 18,815 likely backdated grants and the 3717 likely backdated exercises that comprise the sample set of Biggerstaff et al. I use the same periods as Biggerstaff et al. to identify firms that backdated options. For firms with backdating events after 2002 (including those after 2005, the end of my sample period in the main analysis), they are classified as “noncompliant firms,” as discussed later in the paper.

  9. Focusing only on CEOs, the same procedure results in 264 CEOs probably backdating options, which is comparable to the 249 suspect CEOs of Biggerstaff et al.

  10. There are 33 firms subjected to SEC option backdating legal actions according to SEC’s option backdating website. Of these firms, 20 meet my classification criteria, eight do not, and the remaining five are missing data on option grants and exercises and therefore are not included in the analyses. Similarly, there are 257 firms implicated in the Glass Lewis data, among which 115 meet my classification criteria, 125 do not, and the remaining 17 are missing data.

  11. For firms implicated in the Glass Lewis data, 172 are classified as noncompliant firms. The remaining 68 (= 115 + 125–172) firms are classified as suspect firms.

  12. Although this method does not eliminate lawsuits without merit, it does not cause bias in favor of finding an increase in fraudulent financial reporting for suspect firms after 2002 unless the suspect firms became more likely than nonsuspect ones to settle lawsuits without merit. Suspect firms may have become more likely to settle lawsuits if the plaintiffs retroactively added an option backdating allegation to their complaints. Hence I exclude all lawsuits alleging option backdating.

  13. Karpoff et al. (2017) argue that there is usually a lag between the date the fraud is first revealed and the date a securities class action is filed or an AAER occurs. For example, fraud may occur in 2001 and be revealed at the beginning of 2002, but a securities class action may be filed at the end of 2002, and the SEC usually takes enforcement actions even later. This lag does not affect my definition of a fraud year, which is the year a firm misstates its financial reports (i.e., 2001 in the preceding example). However, there may be a right-truncation problem due to the lag. In the preceding example, I would misclassify 2001 as not involving fraud if I considered only lawsuits filed by the end of 2001 rather than those filed by the end of 2002. Thus I use AAERs issued until 2016 and securities class actions filed until 2010 to identify fraud that occurred between 1999 and 2005 to minimize the misclassification.

  14. Executives could also extract rents through other means, such as pensions. I examine supplemental executive retirement pensions (SERPs), which have been identified as a significant source of excess executive compensation (Bebchuk and Fried 2004; Kalyta and Magnan 2008). To identify firms that adopted SERPs, I start with 602 nonsuspect firms and 52 suspect firms with suspect CEOs (i.e., CEOs suspected of option backdating), that engaged the same CEO from 2001 to 2003 and that have CEO compensation data in ExecuComp. I then manually collect SERPs information from these firms’ proxy statements from 2001 to 2005. As firms were required to disclose the existence but not the value of SERPs before 2006, vague disclosure provided an opportunity for executives to extract rents. To address the concern that some firms might not have complied with the disclosure requirement of SERPs, I also examine whether these firms disclosed SERPs after 2006, when the SEC adopted new rules requiring more disclosure of pension values. I find only two firms, both of which are nonsuspect, that appear likely to have concealed the existence of SERPs before 2006, as they started to disclose SERPs in 2007 but did not provide any information about the adoption year of the plans. I find 18 suspect firms (= 35% of suspect firms) that adopted SERPs before 2002 and kept the plans afterward, but none of the other suspect firms adopted SERPs by the end of 2005. In contrast, 300 nonsuspect firms (=50% nonsuspect firms) adopted SERPs before 2002, and all of them kept the plans afterward. And 15 other nonsuspect firms adopted SERPs after 2002. Finding no increase in SERPs for suspect firms does not support the notion that suspect individuals replaced option backdating with other perks. Finding a smaller proportion of suspect firms adopting SERPs, relative to nonsuspect firms, indicates that suspect firms might have chosen to backdate options before 2002 because they could not obtain shareholder approval of SERPs or other perks.

  15. For example, suppose that 3% firms initiated fraud in 2003. If none of the frauds were detected by the end of 2003, the same firms would continue to commit fraud in 2004. If another 1% firms initiated fraud in 2004, the proportion of firms committing fraud in 2004 would be 4%. So the proportion of firms committing fraud in 2003 and 2004 is 3% and 4%, but the proportion of firms initiating fraud is 3% and 1%. Moreover, the yearly change in ongoing fraud may not equal the proportion of firms initiating fraud. Suppose that 2% firms initiated fraud in 2002 and all frauds were detected in the same year. Then the proportion of firms committing fraud from 2002 to 2003 is 2% and 3%, and the proportion of firms initiating fraud is also 2% and 3% rather than 2% and 1%.

  16. As fraud involving option backdating is removed from the sample in panel A, restatements involving option backdating are removed from the sample in panel B. In untabulated results, I find that, if restatements involving option backdating are included in the sample and NONCOMPLY and SOX403 × NONCOMPLY are added to model 2, the coefficient on SOX403 × SUSPECT is still significantly positive, and the coefficient on SOX403 × NONCOMPLY is not statistically significant.

  17. The coefficients on SOX403 × SUSPECT and SOX403 × SUSPECT×OPIMPORT in column 2 are affected by the zero incidence of restatements involving fraud for suspect firms with OPIMPORT = 0 after 2002. Univariate analyses show that RESTAT increased by 0.6 percentage points for suspect firms with OPIMPORT = 1 (from 0.002 to 0.008) but declined by 0.5 percentage points for suspect firms with OPIMPORT = 0 (from 0.005 to 0). The difference in the mean change between the two groups is 1.1 percentage points (= 0.6 – (−0.5)) and is statistically significant (two-tailed p value < 10%).

  18. Jagolinzer et al. (2011) note that this measure avoids the biases in long-run buy-and-hold returns. Nevertheless, the inferences remain the same if I use six-month buy-and-hold abnormal returns following Lee et al. (2014) (not tabulated).

  19. As I do not have compensation data for every insider, I use the CEO as an example and find that suspect CEOs did not have positive trading profits during fraud periods before 2002. However, they earned positive trading profits during fraud periods after 2002, and their annualized trading profits are equivalent to an average of 14% of their total compensation in the year before the fraud period.

  20. Biggerstaff et al. (2015) show that suspect CEOs were more likely to commit fraud over their sample period of 1990 to 2005 without further distinguishing between fraud cases involving and those not involving option backdating. In an untabulated multivariate analysis, I find that, before 2002, suspect firms had a higher fraud risk than nonsuspect firms only if I include fraud cases involving option backdating. Nevertheless, even after including option backdating cases, suspect firms still exhibited an incremental increase in fraud, relative to nonsuspect ones, after 2002. In sum, my results remain qualitatively the same, regardless of whether I include or exclude option backdating cases.

  21. As I match each suspect firm with a nonsuspect firm based on their data in 2001 rather than doing a year-by-year matching, their total numbers of years with available data over my sample period are not exactly the same. Hence the total number of firm-year observations is not exactly double that of suspect firms. Standard errors clustered at both the industry and year levels are not estimable when I use the Glass Lewis data to identify suspect firms because the sample size is too small. Table 9 shows standard errors clustered at the industry level. Clustering standard errors at the year level yields quantitatively similar results (untabulated).

  22. SEC’s webpage for option backdating (https://www.sec.gov/spotlight/optionsbackdating.htm) shows that the commission did not have any speeches or testimony related to option backdating before 2006, and that only two out of 33 firms involved in the commission’s enforcement actions regarding option backdating were sued before 2006.

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Acknowledgements

This paper is an extension of my doctoral dissertation, completed at the University of Rochester. I am grateful for the invaluable guidance I received from Jerry Zimmerman, Joanna Wu, and Shane Heitzman. I would also like to thank Richard Sloan (editor), an anonymous referee, Luzi Hail, Edward Owens, Jaewoo Kim, Robert Novy-Marx, Jerry Warner, Ron Kaniel, Ming-Yi Hung, Haifeng You, Brian Cadman, and workshop participants at the University of Rochester, University of Minnesota, Drexel University, National Taiwan University, University of Hong Kong, Chinese University of Hong Kong, Hong Kong University of Science and Technology, and National University of Singapore for helpful comments. I am grateful to the Hong Kong University of Science and Technology for financial support. I take responsibility for all errors.

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  1. Department of Accounting, Business School, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Szu-fan Chen

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Appendices

Appendix A. Post-SOX403 change in types of fraud

The sample in the main tests includes all financial reporting fraud cases that caused an inflation in stock prices during the fraud period, as managers either increased stock prices by issuing misleading good news or delayed stock price declines by withholding bad news. Insiders can benefit from fraud by selling their shares at an inflated price before the market learns the truth. Executives whose compensation is sensitive to performance can also benefit from fraud through increasing compensation, although such extra compensation could be subject to being clawed back. Therefore managers can benefit from all fraud cases in my sample.

My sample does not include fraud that does not involve misleading financial reporting, such as the dissemination of misleading market demand information and the intentional omission of material risk factors. These cases are rare in AAERs but common in securities class actions. I term fraud involving misleading financial reporting as “GAAP fraud” and that not involving misleading financial reporting as “non-GAAP fraud.” Although non-GAAP fraud also inflates stock prices, its benefits could be smaller than those from GAAP fraud, as non-GAAP fraud affects stock prices to a lesser degree and for a shorter period than GAAP fraud.

Table 10 shows the comparison of GAAP and non-GAAP fraud using securities class actions starting between 1999 and 2005. Compared with GAAP fraud, non-GAAP fraud has a much smaller average settlement amount (two-tailed p value < 10%) and a shorter fraud period (two-tailed p value < 1%). Consistent with a smaller benefit from non-GAAP fraud, the average insider trading profits during the fraud period is also smaller for non-GAAP fraud than for GAAP fraud (two-tailed p value < 5%). If suspect individuals commit fraud to mitigate their wealth loss after the enactment of SOX403, they would commit more GAAP fraud than non-GAAP fraud, as the benefits from the former are greater than those from the latter.

To examine whether the change in fraud differs across types of fraud, I use a multinomial logistic model to estimate the probability of non-GAAP fraud and that of GAAP fraud. The dependent variable equals one for non-GAAP fraud, two for GAAP fraud, and zero if there is no fraud. The model estimates the probability that the dependent variable equals one or two, relative to zero. The independent variables are the same as those in model 1. Table 11 shows the results. The coefficient on SOX403 × SUSPECT is significant only for GAAP fraud, suggesting that the main finding of a smaller decrease in fraud for suspect firms is mainly due to the change in GAAP fraud rather than the change in non-GAAP fraud. These results are consistent with my hypothesis that managers commit more fraud to mitigate their loss from the cessation of option backdating.

Appendix B. Propensity-Score Matching

I estimate the likelihood of committing fraud as the propensity score by estimating a logistic model using FRAUD as the dependent variable and the control variables in model (1) as independent variables. Table 12 shows the logistic regression results using suspect and nonsuspect firms.

I then match each suspect firm with a nonsuspect firm that is in the same industry (two-digit SIC codes) and that has the closest propensity score in 2001, the year before SOX403 was enacted. I also restrict the absolute difference in propensity scores to be less than 0.01 and perform the matching with replacement. Table 13 shows the covariate balance results between the 461 matched pairs using the Biggerstaff et al. approach. The majority of variables are not statistically significantly different between the matched pairs.

Table 10 Differences between GAAP and non-GAAP fraud
Full size table
Table 11 Post-SOX403 changes in GAAP and non-GAAP fraud
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Table 12 Estimation of the probability of fraud
Full size table
Table 13 Covariate balance between the matched pairs
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Chen, Sf. The economic consequences of ceasing option backdating. Rev Account Stud (2022). https://doi.org/10.1007/s11142-022-09681-5

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Keywords

  • Securities regulation
  • Financial reporting fraud
  • Option backdating
  • Insider trading

JEL classification

  • G18
  • G34
  • K22