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The effect of enforcement transparency: Evidence from SEC comment-letter reviews

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Abstract

This paper studies the effect of the public disclosure of the Securities and Exchange Commission (SEC) comment-letter reviews (CLs) on firms’ financial reporting. We exploit a major change in the SEC’s disclosure policy: in 2004, the SEC decided to make its CLs publicly available. Using a novel dataset of CLs, we analyze the capital-market responses to firms’ quarterly earnings releases following CLs conducted before and after the policy change. We find that these responses increase significantly after the policy change. These stronger responses partly occur while the review is ongoing and persist on average for two years. Corroborating these results, we also document a set of changes that firms make to their accounting reports following CLs. Our results indicate that disclosure of regulatory oversight activities can strengthen public enforcement.

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Fig. 1

Notes

  1. 1.

    In the aftermath of the financial crisis, for instance, regulators opted to publicly disclose stress tests to better inform investors on the risks taken by banks (e.g., Goldstein and Sapra 2013). More recently, the Reed–Grassley bill sought to publicly disseminate the Public Company Accounting Oversight Board’s (PCAOB) inspection reports of auditors (PCAOB Enforcement Transparency Act of 2017).

  2. 2.

    For instance, some politicians have raised concerns about “the utility of devoting hundreds of professional staff to a process that is not designed to detect fraudulent conduct.” (e.g., Paredes 2009; Katz 2010, 2011).

  3. 3.

    Several additional considerations suggest that CLs do matter. First, they allow the SEC to obtain answers to questions that are frequently dodged, dismissed, or ignored when asked by investors or analysts (Hollander et al. 2010). Second, the large backlog of Freedom of Information Act (FOIA) requests preceding the policy change suggests a vivid public interest in these letters (OIG 2007). Third, the SEC believes that these letters prompt firms to change their reporting practices (OIG 2008b). Finally, short-sellers use CLs (Dechow et al. 2016).

  4. 4.

    We focus on earnings announcements, rather than SEC filings, because prior work shows that these announcements are important disclosure events in terms of impact on security prices (e.g., Kothari 2001; Basu et al. 2013). We provide more insights on this choice in Section 3.3.

  5. 5.

    On its website, the SEC (2015) describes the objective of CL reviews as follows: “Much of the Division’s review involves evaluating the disclosure from a potential investor’s perspective and asking questions that an investor might ask when reading the document. When the staff identifies instances when it believes a company can improve its disclosure or enhance its compliance with the applicable disclosure requirements, it provides the company with comments.”

  6. 6.

    There could be economic reasons for firms to adjust or drop disclosures triggered by CLs a certain time after the review. For example, changes in a firm’s economics, modifications in the accounting standards, or changes in the materiality of a disclosure could lead firms to adjust or drop disclosures over time. The following quote by Wayne Carnall (the former chief accountant of the Division of Corporation Finance) illustrates this idea and suggests that the SEC does not necessarily expect a permanent effect from its CLs: “As with all disclosures, you provide what is right and meaningful and material. If it is not material, not relevant, companies do not have to continue to provide that disclosure” (PwC 2016b).

  7. 7.

    These cross-sectional results do not allow to unambiguously attribute our findings to one of the two mechanisms. For example, the presence of dedicated investors potentially increases reputation costs for the SEC, inducing the SEC to exert more effort in its reviews. Thus part of the evidence could also be consistent with supervisory discipline.

  8. 8.

    Dechow et al. (2010) report that many studies use discretionary accruals and restatements as measures of reporting quality. Consistent with this, Blackburne (2014) and Cunningham et al. (2017) use discretionary accruals and restatements as measures of reporting quality in the context of SEC CLs. The evidence in the work of Bozanic et al. (2017) suggests that the length of narratives is associated with the quality of firms’ disclosures.

  9. 9.

    For example, Beaver et al. (2018) show that the ERC increased following SOX, and Cohen et al. (2008) document a decline in accruals-based earnings management after SOX. More broadly, Coates (2007) cautions researchers to be careful in drawing strong inferences around times of multifaceted regulatory change.

  10. 10.

    Informed by our study, the European Securities and Markets Authority (ESMA), which coordinates the capital-market supervision of the national regulators in the European Union, recommends the public disclosure of oversight activities there (ESMA 2017).

  11. 11.

    The notion that firms start adjusting their reports, while under review, is supported by the inner-workings of the CL process revealed in the OIG’s audit of Bear Stearns’ CL review (OIG 2008b). John W. White noted that the SEC sent a CL to Bear Stearns related to the company’s fiscal year 2006 10-K on September 27, 2007. That letter included a focus on subprime mortgage matters. Soon after receiving this letter—and well before the public release of the CL and Bear Stearns’ collapse in March 2008—Bear Stearns improved its disclosures about subprime mortgage securities in its form 10-Q filed on October 10, 2007. (Specifically, details on net inventory markdowns related to losses in residential mortgages and leveraged finance areas were added.) John W. White also emphasized that the CL review of Bear Stearns was not unique and explained more generally how CLs improve disclosure. He said: “The goal of disclosure of material information to investors is achieved by seeking improvements to a company’s public disclosures in its periodic and current reports. Those reports are readily available to all investors. … Our experience is that, similar to the Bear Stearns review described above, a company may respond to staff comments in its public disclosure documents.” For confirmation purposes, we also exploit an alternative research design using the dissemination date as the beginning of the 360-day period (untabulated). We find consistent results.

  12. 12.

    We estimate equation (1) using weighted-least-squares (“robust”) regressions that place less weight on estimates with large absolute residuals. We perform robust regressions using Stata’s “rreg” procedure, which eliminates any observations with a Cook’s distance greater than one and weighs the remaining observations based on the absolute residuals. As explained in Section 5.6., our results are robust to alternative approaches to deal with extreme UE observations.

  13. 13.

    Our results are robust to using firm fixed effects instead of review fixed effects.

  14. 14.

    Table 2, Panel A, does not report the main effects of Public_Review and SEC_Budget, because these variables are subsumed by the fixed effects.

  15. 15.

    We also report results from tests including alternative fixed effect structures and additional interactions for our pooled sample in Table 9. The results hold.

  16. 16.

    Disclosure of CLs may also affect firms’ reporting credibility. As described in Section 5.4., we rerun our main specification and define the period between the first and last letter from the SEC as the treatment period. Under this alternative research design, the public is still unaware of the CL during the treatment period. We find results similar to those in Table 2 (untabulated), suggesting that changes in reporting credibility alone are unlikely to explain our findings.

  17. 17.

    While the results presented in Table 2, Panel B, columns 3–4 show that the ERC effect is significant in the public period, this is less clear for our pooled sample (Table 2, Panel A, column 2). To examine this, we test whether the difference 0.820 (0.803–0.037 – 0.034 + 0.088) – 0.769 (0.803–0.034) = 0.051 is statistically significant. We find that the difference is statistically significant at the p < 0.02 level, confirming a statistically significant increase in ERC following a CL in the public period.

  18. 18.

    The magnitude of our increase in ERC is modest in comparison to prior literature. For instance, Chen et al. (2014), who examine capital-market responses to unexpected earnings releases following material restatements, find a decrease in ERC of approximately 56% in the 11 quarters following the restatement. However, material restatements are much more severe and less frequent than CLs.

  19. 19.

    Prior research shows that many firms provide substantial qualitative disclosures and detailed GAAP financial statement information in their EAs (Chen et al. 2002; Baber et al. 2006; Wasley and Wu 2006; D’Souza et al. 2010). Regulators and practitioners also strongly encourage companies to include more detailed disclosures in the EAs. For example, the SEC Committee on Improvements in Financial Reports (CIFiR) urges companies to include in their EAs a balance sheet and cash flow statement, in addition to the income statement (SEC 2008).

  20. 20.

    The information provided in the earnings announcements is subject to the anti-fraud provisions of Section 10(b) and Rule 10b-5 of the Securities Exchange Act of 1934 (Steinberg 2009). Consistent with this, PwC (2016a) emphasizes the importance of providing information in the earnings announcements consistent with the information included in the subsequent filing, especially as the SEC staff reviews public information for consistency.

  21. 21.

    The proper identification of the mechanism requires exogenous (or independent) variation in SEC reputation costs and market monitoring. As such, our cross-sectional results do not allow to unambiguously attribute our findings to one specific mechanism.

  22. 22.

    Data on investor classification are retrieved from http://acct3.wharton.upenn.edu/faculty/bushee/IIclass.html.

  23. 23.

    We thank Terrence Blackburne for sharing this data with us.

  24. 24.

    Differences documented in Table 5 may be driven by SOX rather than by the 2004 SEC disclosure policy change. We examine this possibility by comparing the characteristics of private reviews conducted before and after SOX (untabulated). The only significant differences between these two types of private reviews are that, after SOX, the Time_from_Filing_Date is significantly shorter, and the number of core accounting topics is significantly smaller. Therefore it is unlikely that the pattern in Table 5 is driven by SOX.

  25. 25.

    Our results also hold when we use the standard deviation of the residuals as the dependent variable.

  26. 26.

    We cannot include the variable Big4_Audit (which equals 1 if the company has been audited by a Big Four accounting firm and 0 otherwise) and Public_Review, because they are subsumed by review fixed effects.

  27. 27.

    Bozanic et al. (2017) construct an index from various measures, such as length, readability, and tone, and find that the correlation between this index and the length of the narratives is very high (0.985).

  28. 28.

    We obtain this measure from Bill McDonald’s website (http://www3.nd.edu/~mcdonald/Word_Lists.html).

  29. 29.

    Using Audit Analytics’ restatement data, we observe that the announcement of a restatement usually occurs within 365 days after the filing date of the restated report.

  30. 30.

    The means of Accruals, Text_Length, and Restatements are 0.03, 25.5, and 0.11, respectively. Note that abnormal accruals increase following CLs conducted before the policy change. In other words, prior to the policy change, abnormal accruals increased following CLs, and after the policy change abnormal, accruals did not increase following CLs, explaining the negative and significant coefficient on Treatment*Public_Review.

  31. 31.

    We also analyze whether earnings announcements during the review period are more likely to be followed by price reversals. A price reversal would suggest that investors overreact to the accounting information (perhaps because they consider this information more credible) and subsequently adjust their views about the informativeness of these announcements (e.g., Ball and Brown 1968). In particular, we re-estimate equation (1) and modify the measurement of the dependent variable, CAR, by compounding returns over the (+2, +90) window after the earnings announcement and subtract the market return compounded over the same horizon. In untabulated tests, we find a pattern qualitatively similar to that in Table 2, but the coefficient on UE*Treatment_Period*Public_Review is insignificant in all specifications. This suggests that our main results are unlikely to be driven by a market overreaction to earnings announcements during the review period.

  32. 32.

    Before the SEC rule “Additional Form 8-K Disclosure Requirements and Acceleration of Filing Date” issued in 2004, the classification of events reported in firms’ 8-Ks is not reliable.

  33. 33.

    The FOIA Action Plan (2008) states: “There are primarily two entities filing thousands of requests per year to obtain access to these letters for commercial use, thereby creating the overwhelming backlog. … The FY 03 review was completed in May 2006. … The review of FY 04 requests is on-going.” (The report was written in 2008.) See also OIG report (2007).

  34. 34.

    Similarly, in the comment to the policy change, John Gavin wrote in 2004: “SEC Insight currently has over 6,000 pending FOIA requests for comment and response letters relating to completed reviews.”

  35. 35.

    In untabulated tests, we also conduct propensity-score matching tests, following the procedure used by Johnston and Petacchi (2017). We find consistent results.

  36. 36.

    Using this alternative control group addresses the concern that the population of firms not receiving a CL could contain cases where a firm uses other firms’ comment letters to anticipate and fix priority issues before it is reviewed, thereby negating a minor review that might have otherwise elicited a comment letter (e.g., Brown et al. 2018). In this case, it is less clear that disclosure of CLs would improve reporting.

  37. 37.

    In row (4), as the dates are counterfactually shifted from the true dates, the coefficient on β1 becomes smaller. (As expected, the coefficients remain significant because there is considerable overlap with the true dates.)

  38. 38.

    The SEC announced the disclosure policy change on June 24, 2004. While firms could have filed their reports before August 1, 2004, to avoid the public disclosure of a potential CL review, the time from the announcement to the start of the new disclosure policy was very short, thus limiting this possibility. We do not observe any unusual filing patterns between June 24 and August 1 of that year.

  39. 39.

    While the original compliance date of SOX 404 for accelerated filer was June 15, 2004 (SEC Release No. 33–8238, published on June 5, 2003), SEC Release No. 33–8392 published on February 24, 2004, extended the compliance date to November 15, 2004. Other SOX provisions, such as Regulation G, became effective as of March 28, 2003 (https://www.sec.gov/rules/final/33-8176.htm). In a similar manner, Section 408 of SOX, which requires the SEC to review each company at least once every three years, became effective with the passage of SOX in 2002. Similarly, firms had to be in compliance with Section 301 (https://www.sec.gov/rules/final/33-8220.htm) and Section 208(a) (https://www.sec.gov/rules/final/33-8183.htm) by the start of 2004.

  40. 40.

    For non-accelerated filers, Section 404 became effective for fiscal years ending on or after July 15, 2007 (SEC Release No. 33–8618, published on September 22, 2005).

  41. 41.

    Compared to the results presented in Table 2, Panel A, the magnitude of the coefficient on UE*Treatment_Period*Public_Review is larger. One potential explanation for the larger magnitude is that the policy change was unexpected, and therefore initially firms made more substantial changes around publicly disclosed CL, resulting in a stronger initial ERC effect. Another possibility is that firms have improved their reporting over time, and thus there is a decreasing need for CL reviews (Beaver et al. 2018).

  42. 42.

    While the SEC already aimed to review each firm at least once every three years before the Sarbanes-Oxley Act of 2002 (OIG 2000), Section 408 of SOX made the three-year frequency a requirement. SOX also mentions the following specific factors the SEC should consider when deciding which firms to review more frequently: “(1) issuers that have issued material restatements of financial results; (2) issuers that experience significant volatility in their stock price as compared to other issuers; (3) issuers with the largest market capitalization; (4) emerging companies with disparities in price to earnings ratios; (5) issuers whose operations significantly affect any material sector of the economy; and (6) any other factors that the Commission may consider relevant.”

  43. 43.

    The ROC curve plots the probability of detecting a true signal (sensitivity) and a false signal (1 – specificity) for the entire range of possible cutoff points (Kim and Skinner 2012). The area under the ROC curve (AUC), which ranges from 0 to 1, provides a measure of the model’s ability to discriminate. A value of 0.5 indicates no ability to discriminate, while a value of 1 indicates perfect ability to discriminate. A greater area indicates a better performance of the model. The usual convention is that a model with an area of less than 0.7 is considered to have no discrimination ability, a model with an area between 0.7 and 0.8 is considered to have acceptable discrimination ability, and a model with an area between 0.8 and 0.9 is considered to have excellent discrimination ability.

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Acknowledgements

We appreciate helpful comments from Patricia Dechow (the editor) and two anonymous reviewers. This paper has also benefited from the suggestions of Dan Amiram, Marc Badia, Terrence Blackburne, Zahn Bozanic, Pietro Bonetti, Fabrizio Ferri, Brandon Gipper, Jonathan Glover, Ian Gow, Trevor Harris, Colleen Honigsberg, Bob Herz, Bob Kaplan, Alon Kalay, Sharon Katz, Wayne Landsman (discussant), Tim Loughran (discussant), Christian Leuz, Stephen Penman, Oded Rozenbaum, Gil Sadka, Eugene Soltes, Ayung Tseng, Forester Wong, and workshop participants at Columbia Business School, Erasmus University in Rotterdam, European Securities and Markets Authority (ESMA), Harvard Business School IMO Conference, IESE Business School, Michigan State University, the 2018 Review of Accounting Studies Conference, University of Mannheim, University of Minnesota, the SEC’s Division of Economic and Risk Analysis (DERA), and WHU – Otto Beisheim School of Management. We also thank Olga Usvyatsky, vice president of research at Audit Analytics; Harvey Goldschmid, former SEC commissioner; Wayne Carnall, former chief accountant for the SEC’s Division of Corporation Finance; Brian Breheny, former SEC deputy director for legal and regulatory policy; and Steve Meisel, PwC’s partner and representative on the SEC Regulations Committee and the Center for Audit Quality Research Advisory Board. Miguel Duro acknowledges support from Columbia University CIBER, the Nasdaq Educational Foundation, and the Jerome Chazen Institute. Gaizka Ormazabal thanks the Marie Curie and Ramon y Cajal Fellowships and the Spanish Ministry of Science and Innovation, grant ECO2015-63711-P.

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Correspondence to Gaizka Ormazabal.

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Appendices

Appendix 1: Variable Definitions

The following variables are constructed using data from a proprietary dataset of comment letters obtained through FOIA requests [FOIA], Audit Analytics (Comment Letters, and Advanced Restatement Modules) [AA], Compustat [C], CRSP [CRSP], GAO Database (Restatements from 1998 to 2002) [GAO], Equilar [EQUILAR], Thomson Reuters and Bushee Investors’ Classification [TR + BUSHEE], IBES [IBES], the SEC’s EDGAR database [EDGAR], AGR MSCI (Metrics and Scores Modules) [AGR], Loughran and McDonald’s (2011) textual analysis measures [LM], and SEC (2002–2012) Annual reports [SEC].

A. Stock Price Reaction to Earnings Announcements
CAR Compounded return over the (−1, +1) day window around the earnings announcement less the CRSP market compounded return over the same period. [CRSP]
UE Unexpected earnings-per-share (EPS) divided by price. Unexpected EPS is defined as actual minus expected earnings, where the expected value of earnings is calculated as the I/B/E/S forecast, which is the median analyst forecast of EPS during the 90-day period before the disclosure of earnings. [C + CRSP+IBES]
Treatment_Period Indicator variable that equals 1 if quarterly earnings are announced in the 360 days following the date of the initial SEC comment letter and 0 otherwise. [AA+FOIA]
Public_Review Indicator variable that equals 1 if the CL review is public and 0 otherwise.
Size The log of market value of equity (in millions of dollars) measured at the prior fiscal year-end. [CRSP]
BM The ratio of the book value of equity to the market value of equity, measured at the prior fiscal year-end. [CRSP]
Leverage The ratio of total liabilities to total equity, measured at the prior fiscal year-end. [C]
Past_Returns Compounded return over one year prior to the earnings announcement less the CRSP market return over the same period. [CRSP]
Beta Coefficient from regressing daily returns for firm i on market returns over one calendar year, ending on the prior fiscal year-end date. [CRSP]
Persistence The autocorrelation parameter from Foster’s (1977) first-order autoregressive model in seasonally differenced earnings using the previous eight quarters. [C]
Loss Indicator variable that equals 1 if quarterly earnings are negative and 0 otherwise. [C]
SEC_Budget Annual fractional change in the budget of the SEC’s Division of Corporation Finance. [SEC]
%Dedicated_Investors Percentage of shares owned by “dedicated” institutional investors. We categorize institutional investors as “dedicated” using data from http://acct3.wharton.upenn.edu/faculty/bushee/IIclass.html. [TR + BUSHEE]
B. Characteristics of SEC Comment Letters
Review_Length Number of days from the first comment letter to the “no further comment” letter. [AA+FOIA]
Time from Filing Date Number of days from the firm’s 10-K filing to the start of the CL review (i.e., until the date of the first comment letter). [AA+FOIA]
Total Comments Total number of unique issues addressed in the comment letters. [AA+FOIA]
Accounting Comments Total number of comments classified as “Accounting Rules and Disclosure,” as described in Appendix 2. [AA+FOIA]
Accounting Core Comments Total number of comments classified as “Accounting Rules and Disclosure” and (sub)classified as “accounts receivable & cash reporting issues,” “depreciation, depletion, or amortization reporting issues,” “expense (payroll, SGA, other) recording issues,” “inventory, vendor, and/or cost of sales issues,” “lease, leasehold issues (FAS 13 (98) and IAS 17),” “Liabilities, payables, and accrual estimate issues,” “revenue recognition (including deferred revenue) issues,” “percentage of completion issues,” and “research and development issues,” as described in Appendix 2. [AA+FOIA]
Accounting Noncore Comments Total number of comments classified as “accounting rules and disclosure” and not coded as Accounting Core. [AA+FOIA]
Operational, Control and Risk Comments Total number of comments classified as such, as described in Appendix 2. [AA+FOIA]
Other Comments Total number of comments not coded as Accounting Comments or Operational, Control and Risk Comments. [AA+FOIA]
Number of Rounds Number of letters from the SEC in the review, representing the number of rounds from the first letter until the “no further comment” letter. [AA+FOIA]
Unresolved Comments Indicator variable that equals 1 if the firm has not resolved/replied to all comments raised by the SEC and 0 otherwise. [AA+FOIA]
Confidentiality Requests Indicator variable that equals 1 if the firm has requested that some portion of the comment letter be redacted because the letter contains proprietary information and 0 otherwise. [AA+FOIA]
Extension Requests Indicator variable that equals 1 if the firm has requested an extension to reply to the comment letter and 0 otherwise. [AA+FOIA]
Involvement of a Law Firm Indicator variable that equals 1 if an external law firm is in copy in the review and 0 otherwise. [AA+FOIA]
SEC-mandated restatements (filings under review) Indicator variable that equals 1 if the SEC has mandated that the firm restate or amend the filings under review and 0 otherwise. [AA+FOIA]
Supervisors Number of reviews divided by the number of supervisors [shared by Terrence Blackburne].
C. Additional Dependent Variables
Abnormal_Accruals Absolute value of abnormal accruals in the quarterly accounting information scaled by total assets. Accruals are computed as (ΔCA − ΔCash) − (ΔCL − ΔSTD − ΔTP) − Dep, where ΔCA is the change in current assets, ΔCash is the change in cash/cash equivalents, ΔCL is the change in current liabilities, ΔSTD is the change in debt included in current liabilities, ΔTP is the change in income taxes payable, and Dep is the depreciation and amortization expense. Abnormal accruals are based on the Dechow and Dichev (2002) model, which regresses accruals on past, current, and future quarterly operating cash flows scaled by total assets, denoted as CFOt-1, CFOt, and CFOt + 1. The absolute value of the residual from this model is Abnormal_Accruals. [C + CRSP+IBES]
Text_Length Number of words (in thousands) in the 10-K or 10-Q following the earnings announcement. [LM]
Restatements Number of restatements and amendments within 365 calendar days after the date of the earnings announcement. [AA+GAO]
Abn_Trade_Vol Abnormal trading volume computed as ΔTurnover – ΔTurnover_Mkt. ΔTurnover is defined as the mean Turnover over the (−1, +3) day window around the earnings announcement less the mean Turnover over the (−60, −5) window prior to the announcement divided by the standard deviation of Turnover over the same window. Turnover is daily share turnover (daily trading volume scaled by number of shares outstanding). ΔTurnover_Mkt is constructed in the same way replacing Turnover with Turnover_Mkt, which is the average daily trading volume of all firms in CRSP. [CRSP]
D. Additional Control Variables
ROA Quarterly return on assets. [C]
Firm_Age A firm’s age based on first time occurrence in Compustat. [C]
CEO_Chair Indicator variable that equals 1 if CEO is the chair of the board of directors and 0 otherwise. [AGR]
%Independent_Directors Percentage of the board of directors that qualify as “independent,” according to the listing requirements of the exchange where the firm is quoted. [EQUILAR]

Appendix 2: Types of Comments

I. Accounting Rules and Disclosures
Accounts receivable and cash reporting issues Acquisitions, mergers, and business combinations
Asset retirement obligation (FAS 143) issues Asset sales, disposals, divestitures, reorganization issues
Balance sheet classification of assets issues Capitalization of expenditures issues
Cash flow statement (FAS- 95 or IAS 7) classification errors Changes in accounting estimates issues
Changes in accounting principles and interpretation Comprehensive income (equity section) issues
Consolidation (Fin 46, variable interest, SIV, SPE and off-B/S) Consolidation, foreign currency/inflation issue
Contingencies and commit, legal, (FAS 5 or IAS 37) Debt and/or equity classification issues
Debt, quasi-debt, warrants and equity (BCF) security issues Deferred, stock-based and/or executive comp issues
Deferred, stock-based options backdating only Deferred, stock-based SFAS 123 only (subcategory)
Depreciation, depletion or amortization reporting Dividend and/or distribution issues
EPS, ratio and classification of income statement issues Expense (payroll, SGA, other) recording issues
Fair value measurement, estimates, use (incl. VSOE) Fin statement segment reporting ((FAS 131) subcategory) issues
Financial derivatives/hedging (FAS 133) accounting issues Foreign (affiliate or subsidiary) issues
Gain or loss recognition issues Intercompany accounting issues
Inventory, vendor and/or cost of sales issues Investment in subsidiary/affiliate issues
Investments (SFAS 115) and cash and cash equivalents issues Lease, leasehold issues (FAS 13 (98) and IAS 17)
Liabilities, payables, and accrual estimate issues Loans receivable, valuation and allowances issues
Loss reserves (LAEs, reinsurance) disclosure issues Non-monetary exchange (APB 29, EITF 01–2) issues
Pension and related employee plan issues Percentage of completion issues
PPE fixed asset (value/diminution) issues PPE issues—intangible assets and goodwill
Research and development issues Revenue recognition (incl. Deferred revenue) issues
Subsidiary issues—US or foreign (subcategory) Tax expense/benefit/deferral/other (FAS 109) issues
Tax rate disclosure issues  
II. Operational, Control and Risk
Accuracy of financial statement given disclosure control and internal control (DC/IC) deficiency Changes in internal controls (IC)—disclose
Incorrect language for DC/IC disclosure Material weakness in DC/IC—disclose who discovered
Material weakness in DC/IC—fully disclose Material weakness in DC/IC—impact on fin statements
Material weakness in DC/IC—proposed remedies Non-effectiveness of DCs/ICs—needs to be stated explicitly
Timetable needed for remedy of DC/IC deficiency 8-K Disclosure issues
Business overview issues (MD&A) Capital adequacy and/or calculation issues
Contingencies and commitments (MD&A) disclosure issues Contractual obligations
Credit ratings changes Critical accounting policies and estimates (MD&A)
Executive compensation plan disclosure issues Intellectual property risk and disclosure issues
Liquidity issues (MD&A) Loan covenant violations/issues
Market risk disclosures Oil, gas and mining reserve reporting issues
Results of operations (MD&A) US GAAP reconciliation to Foreign GAAP issues
Valuation of assets, liabilities or equity issues Risk factors—anti-takeover issues
Risk factors—accounting Policy Change Risk factors—capital adequacy and liquidity restrictions
Risk factors—barriers to entry Risk factors—clarify/quantify price volatility risks
Risk factors—change in shareholder rights Risk factors—compensation levels and expense
Risk factors—climate change matters Risk factors—conflicts of interest/related party issues
Risk factors—competition and competitors Risk factors—credit risk for accounts receivable
Risk factors—credit restrictions Risk factors—descriptive subheading issues
Risk factors—data protection and security breaches Risk factors—dividend payments
Risk factors—dissent over merger or offer Risk factors—fluctuations in currency or exchange rates
Risk factors—exchange listing issues Risk factors—government regulatory effects/changes
Risk factors—going concern Risk factors—ineffective internal or disclosure controls
Risk factors—inadequate disclosure issues Risk factors—information technology
Risk factors—information about industry Risk factors—international operations
Risk factors—intellectual property rights Risk factors—legal exposures, reliance, claims etc.
Risk factors—investments at risk Risk factors—limited operating history
Risk factors—licensing or regulatory agency approvals Risk factors—market for offered securities
Risk factors—loss reserves may prove inadequate Risk factors—merging and acquiring risks
Risk factors—market for products or services Risk factors—reliance on certain personnel
Risk factors—operating losses Risk factors—remove language downplaying or mitigating risk
Risk factors—reliance on suppliers, customers, governments Risk factors—revenue sources
Risk factors—remove or specify generic risks Risk factors—share dilution issues
Risk factors—seasonal fluctuations Risk factors—tax positions and assumptions
Risk factors—substantial debt Risk factors—unbundle discrete risks
Risk factors—technology reliance, feasibility, etc.  
III. Other
Event disclosure issues Registration issues
Federal securities laws Tender offers issues
Legal matters and Supreme Court decisions Other disclosure matters

Appendix 3: Determinants of the CL review

In this appendix, we analyze the determinants of receiving a CL related to firms’ 10-K filings, using logistic regression estimation. We follow Cassell et al. (2013) and include four sets of determinants: (i) those explicitly mentioned in Section 408 of SOX,Footnote 42 (ii) firm characteristics, (iii) auditor characteristics, and (iv) governance characteristics. The definitions of the variables corresponding to these three sets of determinants are included in Appendix 3.1. (except the variables already defined in Appendix 1).

We perform the analysis separately for the periods before and after the policy change. Table 11. below presents the details of the tests and the results. We find weak evidence that reviews are conducted on a selective basis, as the model’s ability to explain SEC firm selection, based on specific firm characteristics, is moderate in both periods (as the area under the ROC curve of the model is a bit higher than 0.7 in both periods).Footnote 43 However, the model’s explanatory power is marginally higher for public reviews. Overall, these findings suggest that the selectivity of firms subject to a CL review increases (although only marginally) over the sample period.

Table 11 Determinants of receiving a comment letter
Table 1 Descriptive statistics
Table 2 Stock price reaction to earnings announcements around SEC comment letters
Table 3 Timing and Persistence of the Effect of SEC Comment Letters on ERCs
Table 4 Cross-sectional partitions
Table 5 Characteristics of firms’ CL reviews
Table 6 Quarterly financial reporting information around comment letters
Table 7 Falsification tests
Table 8 Short window around the SEC’s policy change
Table 9 Alternative specifications
Table 10 Trading volume around earnings announcements

Variable Definitions

The following variables are constructed using data from a proprietary dataset of comment letters obtained through FOIA requests [FOIA], Audit Analytics (comment letters, auditor changes, late filers, internal controls, advanced restatement modules) [AA], Compustat [C], CRSP [CRSP], GAO Database (restatements from 1998 to 2002) [GAO], AGR MSCI (metrics and scores modules) [AGR], and Thomson Reuters and Bushee Investors’ Classification [TR + BUSHEE].

Letter Indicator variable that equals 1 if the firm receives a comment letter related to its 10-K filing in that year and 0 if the company did not receive a comment letter in the current or two previous years. [AA+FOIA]
SOX Section 408 Criteria:
Material Weakness Indicator variable that equals 1 if the firm has disclosed at least a quarterly internal control weakness (Section 302) in the current or previous two years and 0 otherwise. [AA]
Restatement Indicator variable that equals 1 if the firm has restated its financial reports in the current or previous two years and 0 otherwise. [AA+GAO]
High Volatility Indicator variable that equals 1 if the volatility of abnormal monthly stock returns (equal to the monthly return [RET] minus the value weighted return [VWRTD]) is in the highest quartile in a given fiscal year and 0 otherwise. [CRSP]
Other Firm Characteristics:
Sales Growth Change in annual sales from year t-1 to year t. [C]
Bankruptcy Rank Decile rank of the company’s Altman’s Z-score. Companies in the decile having the poorest financial health are assigned a value of 10 and so on down to 1 for the highest financial health. [C]
Segments Number of non-empty and unique segment industry codes reported in the Compustat Segments database. [C]
M&A Indicator variable that equals 1 if the company has performed an M&A corporate transaction during the previous two years and 0 otherwise. [AGR]
Restructuring Indicator variable that equals 1 if the company has experienced restructuring or reorganization in the previous year and 0 otherwise. [AGR]
External Financing Sum of equity and debt financing scaled by total assets. [C]
Litigation Indicator variable set equal to 1 if the company is in a highly litigious industry (four-digit SIC industry codes 2833–2836, 3570–3577, 3600–3674, 5200–5961, or 7370–7374) and 0 otherwise. [C]
Auditor Characteristics:
Big4 Indicator variable that equals 1 if the company has been audited by a Big Four accounting firm and 0 otherwise. [C]
Second Tier Indicator variable that equals 1 if the company has been audited by BDO Seidman, Crowe Horwath, Grant Thornton, or McGladrey & Pullen and 0 otherwise. [AA]
Auditor Tenure Number of years (through year t) during which the auditor has audited the company. [AA]
Auditor Resignation Indicator variable that equals 1 if the auditor resigned in the previous year and 0 otherwise. [AA]
Auditor Dismissal Indicator variable that equals 1 if the auditor was dismissed in the previous year and 0 otherwise. [AA]
Governance Characteristics:
% Institutional Non-Transient Ownership Total institutional holdings minus institutional holdings held by institutions categorized as “transient” in the quarter immediately preceding fiscal year-end divided by the total shares outstanding as of fiscal year-end. We identify transient institutions using data from http://acct3.wharton.upenn.edu/faculty/bushee/IIclass.html. [TR + BUSHEE]

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Duro, M., Heese, J. & Ormazabal, G. The effect of enforcement transparency: Evidence from SEC comment-letter reviews. Rev Account Stud 24, 780–823 (2019) doi:10.1007/s11142-019-09503-1

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Keywords

  • Disclosure rules
  • SEC comment-letter reviews
  • Public enforcement

JEL classifications

  • G18
  • L51
  • M41
  • M45