SEC monitoring of foreign firms’ disclosures in the presence of foreign regulators

Abstract

SEC comment letters indicate that the SEC has reviewed the firm’s filings and identified a disclosure issue. Using the existence of an SEC comment letter as a proxy for SEC monitoring, we document a negative association between the level of SEC monitoring of foreign firms and the strength of those foreign firms’ home-country institutions, consistent with the idea that the SEC implicitly shares its regulatory duties with international securities regulators. We find that foreign cross-listed firms are subject to lower monitoring intensity than foreign firms listed only on US exchanges, but do not find a statistically significant difference in monitoring between foreign firms listed only on US exchanges and US firms. These findings suggest that it is the presence of another regulator that drives the intensity of SEC monitoring. We also find that US investor holdings are positively associated with the level of SEC oversight, suggesting that the SEC focuses its resources on firms that pose a greater risk to US investors. Collectively, our analyses show that two countervailing forces drive the SEC’s choice to monitor foreign firms. On the one hand, the SEC reduces monitoring intensity when it can rely on the public and private enforcement institutions in the foreign firm’s home country. On the other hand, the SEC provides increased monitoring of certain foreign firms when investors on US exchanges have greater investment exposure in those firms.

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Fig. 1

Notes

  1. 1.

    For example, the New York Stock Exchange had a total global market value of $26 trillion in 2006, which included 424 non-US issuers valued at $10 trillion. Source: http://www.nyse.com/press/1190629848623.html

  2. 2.

    If weaker standards are associated with lower levels of compliance, then weaker standards would be associated with a higher incidence of comment letters. However, if weaker standards simply imply reduced disclosure requirements, technical compliance rates for foreign issuers may be higher and there would be fewer SEC comment letters issued.

  3. 3.

    An example of this type of foreign private issuer is Alibaba, which is only listed on the NYSE.

  4. 4.

    Our conversations with SEC staff also revealed that the volume of comment letters closely tracks SEC review activity. In other words, when more resources are provided for reviews, more comment letters are produced.

  5. 5.

    Explicit agreements between regulators typically take the form of bilateral or multilateral memorandums of understanding. Unlike a treaty, a memorandum of understanding is simply a statement of intent (known as “soft law”) and is not enforceable under international law. These memoranda identify the scope, cost, permissible uses, and confidentiality obligations associated with information sharing.

  6. 6.

    See https://www.sec.gov/divisions/corpfin/internatl/foreign-private-issuers-overview.shtml for more information.

  7. 7.

    We exclude comment letters on other filings such as prospectuses.

  8. 8.

    The percentage of reviews that produce a comment letter has declined in recent years, which may be due to a shift in the focus of the SEC to only issue comment letters in response to material issues. For example, see http://www.auditanalytics.com/blog/comments-pending-companies-without-recent-comment-letters/.

  9. 9.

    https://www.sec.gov/divisions/corpfin/internatl/companies.shtml

  10. 10.

    https://www.sec.gov/news/press/2005-72.htm

  11. 11.

    Eliminating the small number of comment letters for 6-K filings and focusing exclusively on 20-F comment letters does not influence our results or change any of our conclusions.

  12. 12.

    In untabulated results, we find that our conclusions are unchanged when we conduct our analyses using the foreign firm’s headquarters instead of the location of the primary non-US exchange, suggesting that there is little difference across the two approaches of identifying firms’ home country. The primary non-US exchange and the firm’s headquarters are located in the same country for approximately three-quarters of foreign firms in our sample.

  13. 13.

    https://www.adrbnymellon.com/

  14. 14.

    The coefficient on Disclosure Requirements is −0.149 (t-statistic = −4.171). Therefore, we calculate the reduction in likelihood as (0.75–0.42) x − 0.149 = −0.0492.

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Acknowledgements

We appreciate helpful suggestions and comments from Patricia Dechow, Bret Johnson, Alistair Lawrence, Russell Lundholm, Sarah McVay, James Ryans, Lakshmanan Shivakumar, Roger Silvers, Aida Wahid, Clare Wang, Beverly Walther, Maria Wieczynska, Holly Yang, and workshop participants at Colorado Summer Accounting Research Conference, Conference on Financial Economics and Accounting, Journal of Accounting, Auditing and Finance Conference, UBCOW Conference, AAA Annual Meeting, University of California Berkeley, University of British Columbia, Arizona State University, University of Miami, and Burton Conference at Columbia University.

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Correspondence to Rafael Rogo.

Appendix 1: Variable description and data sources

Appendix 1: Variable description and data sources

Variable Description Data Source
Panel A: Dependent variables and main variable of interest
SEC_Review Indicator variable set equal to 1 for period t if the firm received a comment letter for a period t filing, and 0 otherwise. Audit Analytics
Sec_Review_Words The number of words in each comment letter issued as part of a conversation between the SEC and the firm. Audit Analytics
SEC_Review_Filing Count variable equal to the number of financial filings with comment letters. Audit Analytics
SEC_Review_2 Indicator variable that takes the value 1 for period t if the firm received a comment letter for a period t filing and at least one comment letter in the previous two years, and 0 otherwise. Audit Analytics
SEC_Review_Alt Indicator variable equal to 1 for period t when (a) the firm receives a comment letter during period t or (ii) the firm has not received a comment letter for the period t-2 through period t filing, and 0 otherwise. Audit Analytics
Foreign_Firm Indicator variable set equal to 1 if firm is classified as a foreign private issuer by the SEC for fiscal year t, and 0 otherwise. SEC Website
Single_Listed Indicator variable set equal to 1 if foreign firm’s shares only trade on a US exchange, and 0 otherwise. BNY Mellon
Cross_Listed Indicator variable set equal to 1 if foreign firm’s shares are cross-listed on both a US and foreign exchange, and 0 otherwise. BNY Mellon
US_Exposure The percent of the firm’s market capitalization that is traded on US exchanges, determined as the ratio of the US market capitalization to the total market capitalization of the firm. Bloomberg or by manually inspecting the firm’s 20-F filing
Panel B: Measures of public enforcement
Rules_Enforcement Formal Public Enforcement Index. The arithmetic mean of: (1) supervisor characteristics index; (2) its rule-making power index; (3) its investigative powers index; (4) orders authority index; and (5) criminal authority index, as La Porta et al. (2006) describe. La Porta, Lopez-de-Silanes, and Shleifer (2006)
Staff_Resources Staff Resource Based Public Enforcement Index. The number of the securities regulators’ staff in 2005, divided by the country’s population in millions based on the extended sample, as Jackson and Roe (2009) describe. Jackson and Roe (2009)
Budget_Resources Budget Resource Based Public Enforcement Index. The securities regulators’ 2005 budget divided by the country’s GDP based on the extended sample, as Jackson and Roe (2009) describe. Jackson and Roe (2009)
Panel C: Measures of private enforcement
Disclosure_ Requirements The index of disclosure equals the arithmetic mean of: (1) nature of liability on a prospectus; (2) extent compensation must be disclosed; (3) shareholders’ disclosure; (4) extent inside ownership must be disclosed; (5) extent irregular contracts must be disclosed; (6) and the extent that related party and irregular transactions must be disclosed, as La Porta et al. (2006) describe. La Porta et al. (2006)
Liability_ Standards The index of liability standards equals the arithmetic mean of: (1) liability standard for the issuer and its directors; (2) liability standard for the distributor; and (3) liability standard for the accountant, as La Porta et al. (2006) describe. La Porta et al. (2006)
Panel E: Controls for accounting quality
Material_Weakness Indicator variable set equal to 1 if the internal control audit opinion (under SOX Section 404) or the management certification (under SOX Section 302) as reported in Audit Analytics is qualified for a material weakness in any years of t, t – 1, or t – 2, and 0 otherwise. Audit Analytics
Restatement Indicator variable set equal to 1 if the firm filed a 10-K restatement in any years of t, t – 1, or t – 2, and 0 otherwise. Audit Analytics
IFRS Indicator variable equal to 1 for period t when the firm reports using IFRS. Audit Analytics
Small_NI Indicator variable set equal to 1 for firm-years when annual net income scaled by total assets is between 0 and 0.01, a 0 otherwise (see Lang, et al. 2006). Compustat
Panel F: Auditor related controls
Auditor_Big4 Indicator variable set equal to 1 if the firm’s auditor is a Big 4 audit firm, and 0 otherwise. Audit Analytics
Auditor_2Tier Indicator variable set equal to 1 if the firm’s auditor is a second tier audit firm (i.e., BDO Seidman, Crowe Horwath, Grant Thornton, or McGladrey & Pullen), and 0 otherwise. Audit Analytics
Auditor_Tenure The number of years during which the auditor has audited the firm. Audit Analytics
Auditor_Dismiss Indicator variable set equal to 1 if the auditor was dismissed in any years of t, t – 1, or t – 2, and 0 otherwise. Audit Analytics
Auditor_Resigned Indicator variable set equal to 1 if the auditor resigned in any years of t, t – 1, or t – 2, and 0 otherwise. Audit Analytics
Panel G: Other variables
RetVol_High Indicator variable set equal to 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. Return volatility is calculated as the standard deviation of monthly stock returns for the 36-month period ending in the last month of the fiscal year. CRSP
MarketCap The natural log of market capitalization, calculated as shares outstanding at fiscal year-end (CSHO) times the share price at fiscal year-end (PRCC_F). Compustat
Firm_Age The age of the firm. Compustat
Loss Indicator variable set equal to 1 if net income (NI) is negative in any years of t, t – 1, or t – 2, and 0 otherwise. Compustat
Sales_Growth The mean of sales growth in years t, t – 1, and t – 2, where sales growth is measured as the percentage change (in decimal form) in annual sales (REVT). Compustat
Bankruptcy The decile rank of Altman’s Z-score, where companies with the weakest financial health are assigned to decile 10. Altman’s Z-score is measured following Altman (1968). Compustat
Num_Segments The number of business segments reported in the Compustat segments database. Compustat
Ext_Financing The sum of equity financing and debt financing scaled by total assets measured in t + 1. Equity financing equals the sales of common and preferred stock (SSTK) minus the purchases of common and preferred stock (PRSTKC) and dividends (DV). Debt financing equals long-term debt issued (DLTIS) minus long-term debt reduction (DLTR) minus the change in current debt (DLCCH). Compustat
Restructuring Indicator variable set equal to 1 for non-zero restructuring costs as reported on a pre-tax basis (RCP) in any years of t, t – 1, or t – 2, and 0 otherwise. Compustat
M&A Indicator variable set equal to 1 for non-zero mergers or acquisitions as reported on a pre-tax basis (AQP) in year t, and 0 otherwise. Compustat
Litigation Risk Indicator variable set equal to 1 if the firm’s SIC code is one of the following: 2833–6, 3570–7, 3600–74, 5200–5961, or 7370–4, and 0 otherwise. Compustat
Inst_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, winsorized to 1.00. We identify transient institutions using data from http://acct3.wharton.upenn.edu/faculty/bushee/IIclass.html Thomson (S34)
GDP The log of 2005 gross domestic product in US dollars. Derived from GDP variables downloaded from World Bank Data and Statistics website, as Jackson and Roe (2009) describe. Jackson and Roe (2009)

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Naughton, J.P., Rogo, R., Sunder, J. et al. SEC monitoring of foreign firms’ disclosures in the presence of foreign regulators. Rev Account Stud 23, 1355–1388 (2018). https://doi.org/10.1007/s11142-018-9467-x

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Keywords

  • Regulatory coordination
  • Cross-list
  • Bonding
  • SEC
  • Comment letters

JEL codes

  • G15
  • G18
  • M41