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Regulatory interventions in response to noncompliance with mandatory derivatives disclosure rules

Review of Accounting Studies (2022)Cite this article

Abstract

We investigate regulatory actions in response to violations of mandatory derivatives disclosure rules (SFAS 161) and the outcomes of these regulatory interventions using a hand-collected sample of derivatives disclosures. Derivatives are used by nearly two-thirds of U.S. nonfinancial firms, and they are one of the most complex types of financial contracts. Consequently, inadequate derivatives disclosures could pose significant challenges to financial statement users in assessing the risk and financial health of enterprises. First, we document that firms with high proprietary and agency costs are less likely to comply with SFAS 161. Next, by examining derivatives-related SEC comment letters, we further show that this noncompliance significantly increases the likelihood of receiving a comment letter. We also find that comment letter resolution is longer for firms with strong proprietary motivations than for those with strong agency incentives. Finally, we find that compliance with regard to derivatives disclosures following comment letter resolution improves for firms with high agency costs but not for firms with high proprietary costs. Collectively, our results imply that, when derivatives-related proprietary costs are high, benefits of noncompliance likely outweigh the costs. Moreover, the SEC’s review effectiveness depends crucially on whether firms’ initial motivation for noncompliance is proprietary versus agency.

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Notes

  1. For example, Regulation S-K (Reg S-K), mandated by the Securities and Exchange Commission (SEC), requires firms to report the identities of major customers, but Ellis et al. (2012) find that only 45% of their sample firms fully comply. SFAS 133 requires companies to disclose the amount of accumulated other comprehensive income from cash flow hedges expected to be reclassified into net income in the next year. However, Zou (2016) finds that this information is disclosed in only 44% of her sample observations from the U.S. airline industry.

  2. Robinson et al. (2011) report that noncompliance with the SEC’s mandatory executive compensation disclosures is positively associated with excess CEO compensation (a proxy for agency cost). Ellis et al. (2012) observe proprietary motives behind noncompliance with Reg S-K.

  3. Derivatives affect major components of accounting earnings, such as sales, cost of goods sold, interest expense, research and development (R&D) expenditure, unrealized holding gains/losses, among others. Estimates of notional market size of derivatives exceed $640 trillion (BIS 2019).

  4. For example, Bozanic et al. (2017) find that firms enhance their disclosures following a comment letter but that these modifications are attenuated for firms that push back on the regulator’s demands for expanded disclosures by submitting Rule 406, Confidential Treatment Requests. Johnston and Petacchi (2017) find that comment letter resolution is associated with reduced information asymmetry (adverse selection components of the bid-ask spread) and higher earnings response coefficients (ERCs). Duro et al. (2019) document a 10% increase in ERCs following public comment letter reviews and find that the average increase in ERCs persists for the next two years.

  5. The standard permits hedge accounting whereby a derivative can hedge exposures to (i) changes in the fair value of a recognized asset/liability or a firm commitment, (ii) variability in cash flows of a recognized asset/liability or a forecasted transaction, or (iii) currency risk related to foreign activities (Chang et al. 2016). Under the standard, hedge accounting enables gains/losses on hedging instruments to be recognized in earnings in the same period as offsetting losses/gains on hedged items (Ramirez 2015). Unrealized gains/losses that result from transactions not qualifying for hedge accounting or from hedge ineffectiveness are recognized in earnings as they occur (i.e., no offset).

  6. In a comment to the SFAS 161 exposure draft, Edison Electric expressed concern that disclosing information about forecasted purchases of oil when the forecasted purchase is hedged may divulge sensitive information to current and future competitors about the company’s cost structure and hence profitability.

  7. The following anecdote supports this concern. Hershey’s CEO expressed concern, in a testimony to the Senate Banking, Housing and Urban Affairs Committee, that if Hershey suffered losses on cocoa derivatives and disclosed these losses, competitors would know that the firm’s cost was higher than the market cost and could use that information to price products and gain market share (Wolfe 1997).

  8. Derivatives are one of the most complex types of financial contracts, and even experts struggle to understand their full implications (Chang et al. 2016). Consequently, ascertaining the degree of noncompliance requires expert knowledge and many SEC reviews do not involve specialists (e.g., Robinson et al. 2011). Moreover, financial engineering has led to the development of innovative derivatives, and it is often difficult to determine how the current standard applies to novel instruments.

  9. For example, consider the following excerpt from Cumulus Media Inc.’s response (dated May 21, 2010) to the SEC comment letter issued on May 12, 2010: “We have determined the put option with Clear Channel requires physical settlement (i.e., transfer of stations for cash), and therefore does not meet the provision for net settlement as defined in ASC 815–10-15. However, S99–4 of ASC 815–10 provides guidance for the accounting treatment for written options and states, ‘The SEC Observer noted that the SEC staff’s longstanding position that written options initially should be reported at fair value and subsequently marked to fair value through earnings.’ As a result, we concluded the put option should be bifurcated from the Transaction and treated as a separate freestanding liability (i.e., a freestanding derivative) and marked to market in accordance with this accounting guidance.”

  10. Consider the following excerpt from the SEC’s response to Keurig Green Mountain Inc.’s explanation for nondisclosure, dated April 23, 2009: “We understand from your response to prior comment 3 that you would prefer not disclosing to investors the percentage of your expected annual green coffee requirements covered by futures contracts because coffee purchases represent a significant cost. We believe that you should quantify and separately tabulate pounds of coffee covered by both fixed price and variable price purchase commitments and futures contracts, to allow readers to understand your exposure to changes in the market price of this commodity.”

  11. Descriptive evidence from our comment letter sample provides preliminary support for this notion. We find that firms characterized by high proprietary costs amend their financial statements 23% of the time after a derivatives-related comment letter resolution. The corresponding percentage for firms with high agency costs is nearly double (about 45%).

  12. We use an extensive set of keywords related to derivatives use to search each 10-K to ascertain whether a firm is using derivatives. We retain firms in our sample even if they do not provide any derivative disclosures but mention in their 10-Ks that they are using derivatives. We assume that these firms (about 8% of our sample) are using derivatives but not providing the disclosures required by SFAS 161.

  13. We exclude disclosures of derivatives gains and losses in other comprehensive income (OCI) for cash flow hedges from the computation of SCORE due to the following reason. SFAS 161, effective from 2009, requires firms to disclose cash flow hedge gain/loss amounts deferred to OCI and transferred from OCI to earnings in a footnote accompanying the financial statements. In addition, ASU 2011–05, effective for fiscal periods beginning after Dec. 15, 2011, requires firms to separately report each component of OCI and to report the gain/loss amounts transferred from OCI to earnings (including those related to cash flow hedges) in the financial statements (FASB 2011). Since we cannot disentangle the effect of one from the other, we exclude disclosures of derivatives gains and losses in OCI from our scoring scheme. However, in untabulated analyses, we repeat all of our tests including these OCI disclosures, and our inferences are unchanged.

  14. We empirically check whether our scoring mechanism can distinguish noncompliance from less disclosure. Firms with higher (lower) levels of derivatives use likely have higher (lower) levels of derivatives disclosures. If our scoring is simply capturing derivatives usage, we would expect a significantly positive correlation between derivatives usage and SCORE. We measure derivatives usage by the magnitude of fair value of derivatives. Fair value of derivatives is computed as the sum total of derivatives assets (derac + deralt) and derivatives liabilities (derlc + derllt). We find that the correlation between derivatives usage and SCORE is negative (−0.122) and insignificant, suggesting that SCORE quantifies noncompliance and not merely less disclosure.

  15. For example, a firm provides the following description: “the approximate fair values of these foreign currency derivative contracts were insignificant.” The statement implies that the impact of derivatives on the balance sheet is immaterial. Similarly, another firm reports: “the related impact on the consolidated statements of operations was not material.” This sentence suggests immaterial impact on the income statement.

  16. Definition of IMMATERIAL is provided in Appendix Table 11.

  17. The assumption has empirical support. Giambona et al. (2018) report that 76% of foreign exchange users hedge anticipated transactions/investments, and 63% of foreign exchange users hedge contractual (unbooked) commitments. In contrast, 54% of interest rate users use derivatives to swap from a floating to a fixed rate, while 39% use them to fix the rate/spread of new debt.

  18. We redo this ranking on an annual basis, and there is variation in IND_HEDGE values from year to year. As a result, when we introduce industry fixed effects based on two-digit SIC code in our regression models, IND_HEDGE is not perfectly subsumed by industry fixed effects. However, we re-estimate our models without industry fixed effects when IND_HEDGE is included as an explanatory variable, and our inferences are unchanged.

  19. We run the following sensitivity test using a subsample of new derivatives users to validate our assumption. We follow the three-step approach outlined by Zhang (2009) to classify new users into two groups: effective/efficient hedgers and speculative/ineffective hedgers. The classification is based on a comparison of new users’ actual risk exposure with their expected risk exposure in the post-initiation period. See Zhang (2009) for more details. We observe that the percentage of nonhedge accounting users in the speculative category is 58.1, while nonhedge accounting user percentage in the efficient group is 38.5, and the difference is significant at the 1% level.

  20. In an untabulated analysis, we use an alternative measure for the length of the comment letter resolution—number of rounds a firm goes through till the resolution of the issues cited in the comment letter. Our inferences are identical using this alternative measure.

  21. Appendix Table 12 shows an example of full derivatives disclosures under SFAS 161. In its 10-K, Nike Inc. discloses fair value (and gains/losses) of derivatives by types of derivatives, types of hedge, balance sheet line items, and income statement line items affected.

  22. Since all of our dependent variables are discrete, we use discrete modelling choices (e.g., ordered logit, probit and negative binomial models). However, our inferences are unchanged using ordinary least square estimations.

  23. Although LITIGATION is an indicator variable identifying litigious industries, it is not fully subsumed by the industry fixed effects. We follow the classification outlined by Francis et al. (1994) to identify industries with high litigation risk using four-digit SIC codes. While industry fixed effects are based on two-digit SIC codes, and these broader categorizations cannot accurately capture the significantly more granular classification of litigious industries. As a result, industry fixed effects do not fully subsume litigation risk classification. For example, household appliances (SIC code 3630) and magnetic and optical recording media (SIC code 3695) have the same industry fixed effect based on the two-digit SIC code, but the LITIGATION variable is set to 1 for the former and 0 for the latter.

  24. One potential validity concern is that HEDGE_SALES could represent firms’ operating risk as those with higher operating risk are more likely to hedge their sales price or inventory cost using foreign exchange or commodity price derivatives. However, there is no theoretical argument or empirical evidence that would suggest that higher operating risk leads to lower levels of disclosure. Nevertheless, we run the following test to rule out the alternative explanation. We use sales volatility as a proxy for operating risk. It is measured as the standard deviation of quarterly sales for the last two years. We classify a firm into high (low) operating risk category if the firm is above (below) the median value of the sample sales volatility. If HEDGE_SALES simply proxies for operating risk, the negative association between HEDGE_SALES and SCORE should be significantly stronger for the high operating risk category. We, however, find no difference between the two groups with regard to the association between HEDGE_SALES and SCORE.

  25. We calculate the marginal effect of SCORE on the likelihood of receiving an SEC comment letter. We find that the probability of receiving a letter decreases by approximately 1.5% when SCORE increases by 1.

  26. We do not interact SCORE with the disclosure cost proxies because these interactions are designed to capture the extent to which the negotiation process varies with the motivation for noncompliance, and we do not expect that the SEC will be able to determine the motivation for noncompliance based just on its initial review. Theoretical arguments and empirical evidence suggest that proprietary and agency motivations can coexist in a long-run equilibrium as long as outsiders cannot fully unravel these incentives from publicly available disclosures (e.g., Grossman 1981; Milgrom 1981; Bens et al. 2011). Thus withholding is futile if users can see through these motivations from public announcements. Further, given the complexity of derivatives disclosures, it is unlikely that the SEC will be able to reliably determine, from its initial review, the motives for noncompliance.

  27. We calculate the predicted counts at each level of HEDGE_SALES (0 and 1) and IND_HEDGE (0 and 1), holding all other variables in the model at their means. When HEDGE_SALES (IND_HEDGE) changes from 0 to 1, the predicted number of days to comment letter resolution increases by eight days (10 days). This increase appears to be economically significant, given that the mean NO_OF_DAYS is 46 days.

  28. Interestingly, the coefficient on the TREAT×POST interaction term is insignificant in both columns in Panel B, suggesting that firms with low agency cost tend not to improve their disclosures even after the regulatory scrutiny.

  29. The odds ratio of the coefficient on the three-way interaction (TREAT×POST×PTY) is 0.994 in Column (1) and 1.040 in Column (2) in Panel A. That is, for comment letter firms with high proprietary costs, the odds of improving their derivatives disclosures after comment letter resolution are 0.994 to 1.040 times the benchmark (TREAT×POST×PTY = 0). On the other hand, the odds ratio of the interaction coefficient for TREAT×POST×AGY is 5.569 in Column (1) and 1.563 in Column (2) in Panel B. These odds ratios indicate that comment letter firms with high agency costs are 1.6 to 5.7 times more likely to enhance derivatives disclosures than the benchmark (TREAT×POST×AGY = 0).

  30. We do not use the pair of HEDGE_SALES and NON_HEDGE in this mispricing test because the test requires nonmissing, nonzero values of unrealized gains/losses on cash flow hedges. That is, the test focuses only on cash flow hedgers (firms that elect to apply hedge accounting). Since NON_HEDGE takes the value of 1 if a firm does not use hedge accounting, it is impossible to construct a noncompliance sample of cash flow hedgers with high agency cost measured by NON_HEDGE.

  31. We also perform a similar analysis of comparing hedge returns for high/low proprietary costs firms and high/low agency cost firms in the pre-comment letter period. Mispricing exists in all four subsamples prior to receiving an SEC comment letter.

  32. As prior research contends, these generic proxies are less powerful, as they try to capture overall proprietary and agency costs using a broad brush instead of quantifying specific costs arising out of derivatives use. Predictably, the results are somewhat weaker, but our main results still flow through at conventional levels of significance.

  33. Following Lisic et al. (2019), we define financial statement restatement as an indicator variable set equal to 1 if the annual financial statements were misstated (as revealed through a restatement) and 0 otherwise. As outlined by Cheng et al. (2018), internal control weaknesses are coded as an indicator variable that takes a value of 1 if a firm reports material internal control weaknesses in its internal control over financial reporting (ICFR) document and 0 otherwise.

  34. NONDERCL is an indicator variable that equals 1 if a firm receives a comment letter where the issues raised are not derivatives-related in the current year and 0 otherwise.

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Acknowledgments

We are especially grateful to Lakshmanan Shivakumar (editor), Thomas Linsmeier, and an anonymous reviewer for many helpful suggestions. In addition, we would like to thank Sean Cao, Qiang Cheng, Daniel Cohen (SMU Accounting Research Summer Camp discussant), Jenna D’Adduzio (AAA discussant), Russ Hamilton, Doug Hanna, Xiumin Martin, Susan Riffe, Srini Sankaraguruswamy, Samuel Tan, Rencheng Wang, Yuan Zhang, Youli Zou, participants at the 2019 SMU Accounting Research Summer Camp, 2019 European Accounting Association annual meeting, 2019 American Accounting Association annual meeting, and seminar participants at the Lehigh University, Seoul National University, Southern Methodist University, and Yonsei University for numerous constructive comments. Hye Sun Chang gratefully acknowledges the research grant under Singapore Ministry of Education (MOE) academic research Tier 1.

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Authors and Affiliations

  1. Cox School of Business, Southern Methodist University, 6212 Bishop Blvd, Dallas, TX, 75275, USA

    Nilabhra Bhattacharya

  2. School of Accountancy, Singapore Management University, Singapore, Singapore

    Hye Sun Chang

  3. College of Business, Lehigh University, Bethlehem, PA, USA

    Raluca Chiorean

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  1. Nilabhra Bhattacharya
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Appendices

Appendix 1

Table 10 Disclosure score
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Appendix 2

Table 11 Variable definitions (Compustat mnemonics are in parentheses)
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Appendix 3

Table 12 Disclosure example. FY 2010 10-K for Nike Inc
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Bhattacharya, N., Chang, H.S. & Chiorean, R. Regulatory interventions in response to noncompliance with mandatory derivatives disclosure rules. Rev Account Stud (2022). https://doi.org/10.1007/s11142-022-09685-1

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Keywords

  • Mandatory disclosures
  • Derivatives
  • Proprietary costs
  • Agency costs
  • SEC comment letters

JEL classification

  • G32
  • G38
  • M41