Rick Johnston*
Krannert School of Management
Purdue University
West Lafayette, IN 47907 johnstr@purdue.edu
Reining Petacchi
MIT Sloan School of Management
Cambridge, MA 02142 rnchen@mit.edu
January 30, 2012
Abstract:
We explore the content, determinants and resolution of Securities and Exchange Commission (SEC) comment letters and then examine the informational consequences of letter resolution. The content analysis shows that nearly half of all comments involve accounting application, financial reporting and disclosure issues. Our determinants model suggests that firms with historically poor reporting quality as evidenced by previously amended filings and restatements are more likely to receive a letter. More than 17% of our sample immediately amend filings to resolve comment letters and financial statements and/or footnotes are frequently revised. Following comment letter resolution, ERCs increase, return volatility and trading volume around earning announcements decline, analyst forecast accuracy improves and forecast dispersion declines. We conclude the SEC’s oversight has beneficial informational effects.
JEL Classification: G12, G14, G18, M48
Key Words: Securities and Exchange Commission (SEC), Comment Letter, Disclosure,
Enforcement, Regulation
This paper is a revision of a previous manuscript titled “The Effect of Regulator Oversight on Firms’ Information
Environment: Securities and Exchange Commission Comment Letters”. We appreciate the helpful comments and suggestions of an anonymous referee as well as from Andrew Karolyi, Cathy Schrand, Ro Verrecchia, and workshop participants at the 2009 AAA Financial Accounting and Reporting Section meeting, the AAA annual meeting, the
2 0 10 LBS conference, and the 2010 HKUST conference and the following universities: City University London,
MIT, National University of Singapore, The Ohio State University, University of Illinois at Chicago, University of
Pennsylvania, and University of Technology Sydney. We thank Akash Kumar, Anthony Meder, Shannon Nurse,
KoEun Park, Wei Xiang and Yunyan Zhang for data assistance. Special thanks to Srinivasan Sankaraguruswamy for his SEC amended filings and S filings data and Sundaresh Ramnath for his assistance with comment letters.
Johnston conceived of this project and completed an early draft while at The Ohio State University, significant progress was made while Johnston was visiting The Wharton School, University of Pennsylvania.
* Corresponding author
Electronic copy available at: http://ssrn.com/abstract=1291345

1. Introduction
In this paper we examine the content, determinants, resolution, and ensuing informational consequences of the Securities and Exchange Commission’s (SEC) comment letters. The SEC has an oversight role of financial reporting through its review of the filings (10Q, 10K, S1, etc.) submitted to them.
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If the review identifies potential deficiencies, the SEC staff send the company a comment letter seeking clarification, additional information, and ultimately perhaps, revision of the filing or future filings. Prior to its resolution, the SEC enquiry is generally unknown to the public. In 2005, the SEC began to publicly release comment letters and company responses from resolved cases. These letters provide a unique opportunity to investigate the monitoring role of the SEC and its economic consequences.
The stated goal of the SEC review process is to improve the quality of material disclosure to investors in a timely manner. Given current accounting and disclosure standards, the U.S. information environment is already rich, so it seems plausible that comment letters may have little economic effect. Alternatively, we hypothesize three other possible effects. Releasing the letters could act as a signal to the market that letter firms are poor quality reporters. Also, if the comment letter process results in substantive reporting changes it could have at least two informational consequences. Changes in accounting or disclosure could improve the firm’s earnings quality or the perception of its earning quality. Enhanced or additional disclosure may also provide useful information to investors thus enhancing a firm’s information environment.
For example, an expanded or clarified revenue recognition policy disclosure could improve user forecasts of earnings, resulting in less surprise at future earnings announcements.
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Section 2 describes additional institutional details about comment letters.
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Electronic copy available at: http://ssrn.com/abstract=1291345

We collect all comment letters from the SEC’s website for 2004-2006 and retain those related to 10Ks and 10Qs. Our final sample contains 6,057 letters from 2,374 cases for 2,256 firms. Content analysis of a sub-sample of letters reveals that a significant portion of SEC comments address accounting, financial reporting and disclosure issues. We then explore the attributes of letter recipients. The Sarbanes-Oxley Act of 2002 (SOX) outlines several review selection criteria for the SEC to use. We investigate those and other criteria and find that the biggest factors affecting the probability of receiving a letter are previously amended filings or restatements.
To explore the reporting changes arising from comment letters, we examine the sub- sample of firms that amend filings to resolve the SEC review. 17% of our sample (402 instances) amend one or more filings. Approximately 40% of these amendments address what we consider to be legal technicalities, revisions relate primarily to wording of internal control and audit reports. In the remainder of the sub-sample, financial statements and/or footnotes are being revised in more than half the cases. Furthermore, in the non-technical cases, we find negative stock returns around the amended filings, providing some evidence of the informational relevance of the amendments. In addition to the amendments, we find companies also commit to a significant number of other changes in future filings.
Examining the change in earnings response coefficients (ERCs) in the eight quarters following comment letter resolution is our approach to test the reporting quality signal hypothesis or a change in earnings quality. We replicate Wilson’s (2008) methodology. She finds a temporary decline in earnings credibility (ERCs are the proxy) following restatements. To evaluate the impact of the comment letters on the firms’ information environment we use two approaches. One, we study the change in market reactions around the eight earnings
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announcements that follow comment letter resolution. A sustained decline in price reactions suggests that the quality of the information environment prior to the announcement has improved. Lower volume reactions indicate less investor disagreement regarding the information
2 content of the earnings announcement and higher consensus of firm value. Two, we examine the change in analyst forecast performance after comment letters.
We find ERCs increase in the immediate post comment letter period. Absolute abnormal returns and trading volume around earnings announcements following comment letter resolution decline and the magnitude of the change is economically significant. Similarly, analyst forecast accuracy improves and forecast dispersion declines. These results suggest comment letters enhance earnings quality and reduce information asymmetry. The improvement in information environment is greater for severe cases. Our proxy for which is the time it takes to resolve the case. Resolutions which include revisions to financial statements or footnote disclosures also have large effects. The information environment results are robust to the inclusion of a propensity score matched control sample. Finally, we find no evidence that firms subject to SEC review increase the quantity or change the type of their voluntary disclosures, thus eliminating an alternative explanation for our results.
Our study contributes to three streams of research. The first investigates the economic consequences of companies that voluntarily commit to higher levels of disclosure (e.g., Welker,
1995; Leuz and Verrecchia, 2000; Brown et al., 2004). These studies provide evidence that increases in disclosure levels reduce information asymmetry, increase stock liquidity and reduce a firm’s cost of capital. Our paper complements these studies by examining the consequences of
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The above argument is similar to Bailey et al. (2003) and Bailey et al. (2006).
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reporting and disclosure changes that arise from a regulator’s direct monitoring of corporate reporting.
The second research area explores private and public enforcement of securities laws. La
Porta et al. (2006) and Djankov et al. (2008a) conclude that public enforcement of securities laws has limited value. Jackson and Roe (2009) however, find that these papers underestimate the extent to which public enforcement is associated with capital market development. Leuz and Hail
(2006) find that firms from countries with more extensive disclosure requirements, stronger securities regulation and stricter enforcement mechanisms have a significantly lower cost of capital. These international studies construct their enforcement measures indirectly through lawyer surveys (Leuz and Hail 2006, La Porta et al. 2006, Djankov et al. 2008a) or resources devoted to regulators (Jackson and Roe 2009). In contrast, our study examines actual oversight activities undertaken in the U.S. by the SEC. Unlike LaPorta et al. (2006) and Djankov et al.
(2008a) but similar to Jackson and Roe (2009), our results suggest that there are positive benefits of public enforcement.
The third research stream examines the SEC Accounting and Auditing Enforcement
Releases (AAER) (see for example, Feroz et al., 1991; Dechow et al., 1996; Beatty et al., 1998;
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Beneish, 1999; Farber, 2005). These papers explore the impact of these enforcement actions on corporate governance, managers, auditors, underwriters, and market participants. AAERs are in general more involved, take longer to resolve and are far more infrequent than comment letters.
Comment letters can lead to AAERs. By studying comment letters, we add another dimension to the research that assesses the impact of the SEC on the U.S. markets.
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One could view the third stream as a sub-set of the second. We distinguish it here because of its historic presence in the accounting literature.
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Our study offers early evidence on the content, determinants, resolution and consequences of SEC comment letters. We document a beneficial effect of the oversight role played by the SEC in enhancing and maintaining the reporting quality of U.S. listed firms. This oversight evidence is important, because practitioners and academics often focus on the SEC’s an important factor that contributes to the quality of the U.S. markets. The results could be of interest to policymakers and to the SEC itself, particularly because the Commission has devoted
5 significant resources in conducting these reviews. Moreover, the results could have implications for other countries who wish to replicate the success of U.S. markets or evaluate the implications of publically releasing the results of regulator reviews. Finally, this paper is of relevance to the financial statement analysis (FSA) literature since one of the realities of FSA is that managers often have incentives to omit or obfuscate important information. The comment letters represent a sample of enquiries that a knowledgeable financial statement reader might generate during a review of a 10K. The otherwise “private” information that is revealed in response to the enquiry demonstrates the potential value of reviewing financial reports.
The paper is organized as follows. Section 2 provides institutional details about the
SEC’s comment letter process and our comment letter content analysis. We develop our hypotheses in Section 3, and in Section 4 we outline our research design. Section 5 describes the sample, the comment letter determinants and resolutions. Empirical analyses are presented in
Section 6 and Section 7 concludes.
4 For example, the 1992 revision of executive compensation disclosure rules (Lo 2003), the 1997 SFAS 131 revisions of segment reporting (Wysocki 1998), and the 2000 Regulation Fair Disclosure (Bailey et al., 2003; Heflin et al., 2003)
5 In the SEC’s 2006 Audit report #401, it states that the Division of Corporate Finance has 515 staff, of which 80% are assigned to review filings. The costs of operations for the Division amount to $125 million.
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2. SEC Comment Letters: Institutional Background and Content Analysis
Public companies file quarterly (10Q) and annual financial reports (10K) with the SEC.
The Sarbanes-Oxley Act of 2002 (SOX) requires the SEC review a company’s filings at least once every three years but they may review companies more frequently based on their risk-based model. The SEC states:
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“Currently, the Division of Corporation Finance achieves the goal of improving the quality and timeliness of material disclosure to investors by selectively reviewing the periodic financial and other disclosures made by public companies.”
Review objectives include identifying potential or actual material accounting, auditing, financial reporting or disclosure deficiencies; influencing accounting standards and practices; proposing
7 new and amended disclosure rules; and offering guidance and counseling. Feroz et al. (1991) cite an SEC official who claimed that half of all SEC enforcement leads came from these reviews.
The SEC does not reveal when a firm will be subject to review, so only if a firm receives a comment letter does it become aware of the review. Many reviews are completed without issuing any comments. Section 408 (b) of SOX requires the Commission to consider the following factors in scheduling reviews:
(1) issuers that issued a material restatement of financial results; (2) issuers that experience significant volatility in their share 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.
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Taken from sec.gov, 2008. Prior to SOX, the SEC reviewed approximately 20% of the filings each year.
7 In a speech on July 19, 2000, the SEC Chief Accountant, Robert A Bayless made the following remark, “the review and comment process in the Division of Corporation Finance unearths a surprising number of accounting errors, disclosure deficiencies, and tortured interpretations of GAAP in filings with the Commission.”
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When a comment letter is issued, the company has ten business days to respond. The company can either submit a response letter or amend the filing under review. Follow-up comment letters and responses occur until all issues are resolved, at which point the SEC advises the filer that the review is complete.
Prior to 2005, public access to comment letters and the related responses were only available through a Freedom of Information Act request. In 2005, the SEC began to publicly release, on their website, comment letters and responses relating to filings made after August 1,
2004, but no earlier than 45 days after completion of the review. Therefore, in most cases the public learns of the existence and content of the letters only upon SEC release.
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We include two complete letters as examples, (Landec Corporation, Appendix 1 and The
Charles Schwab Corporation, Appendix 2). To provide some descriptives on the nature and frequency of comments in the letters, we read and manually code 157 of the early letters released by the SEC in 2005. We classify the letter comments into 79 types. Appendix 3 details the 79 comment types as well as the tabulation of our coding.
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The comments types are grouped into four categories. The first, Accounting Issues , includes big-picture problems. Comments relate to issues such as adherence to GAAP, materiality and auditor issues. The second group, Accounting/Financial Reporting/Disclosure
Topics , comprises comments specific to accounting balances or transactions such as revenue recognition, inventory, and related party transactions. The third group, Business Issues , represent
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Firms rarely voluntary disclose that they are under the investigation of the SEC. We randomly select 400 letters issued in 2004 and 2005 and find only four cases that the company voluntary discloses they have received SEC comment letters.
For sensitive information, companies can request confidential treatment under Rule 83 (17 CFR 200.83). If the request is granted, companies can exclude the confidential information from publicly available filings, and only provide the information to the SEC. We found such requests to be rare based on our work in Section 5.
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Ertimur and Nondorf (2006) are the source of our 79 comment types. We found that their descriptions and categories accurately depict the content of the letters. We exclude items that do not apply to our setting.
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more generic business issues, such as liquidity, competitive environment, and risk factors. The fourth group, Tone and Level of Disclosure , is editorial in nature, the comments address presentation issues and requests are to emphasize or de-emphasize, clarify or disaggregate certain items.
The 157 letters contain 1,499 comments, slightly less than ten per letter, on average.
Forty-five percent of the comments fall into the second group. Within that group, questions about claims, commitments and contingencies are the most frequent, followed by revenue recognition and then expenses. The other three groups are approximately equal in terms of the percentage of comments. In the first group, the most common comment is a request for a cite from authoritative literature to support an accounting treatment. Other frequent comments include; a request to clarify an accounting policy, reasons to explain why the company is not following
GAAP, and a request to disclose certain material information. In the third group, both MD&A disclosure and liquidity issues receive substantial attention. In the editorial category, the most common comments are a request for something to be clarified or to quantify an amount related to a disclosure.
3. Hypothesis Development
Comment letters may have no economic consequence if the SEC raises non-substantive issues or only inconsequential information results. Alternatively, the comment letter process, including the ultimate release of the SEC letters and the companies’ responses could have at least three potential effects. First, public revelation of the letters could act as a signal of poor reporting quality for comment letter firms. Second, if the additional disclosures or reporting changes that occur as a result of the review are substantive, those changes could enhance the firms’ earnings
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quality or the perception of its earnings quality. Similarly, more or better disclosures could also reduce information asymmetry between the firm and financial statement users or between investors. We discuss each of these alternatives below.
Given the high quality accounting standards, disclosure requirements, and the U.S. requirement that public companies be audited, it seems reasonable to question whether there would be any benefit from an SEC review, which in substance is merely the SEC staff reading the corporate filings. A long-standing economic question is the justification of regulating corporate disclosures (Healy and Palepu, 2001). Schulte (1988) summarizes the paradox of regulation by arguing that information is similar in nature to public goods. Without regulation, public goods tend to be under produced because of the free-rider problem. The tendency in regulation, however, is to oversupply the public good, because users of information always overstate their demand. If comment letters merely create excess disclosure or an oversupply, then there would no subsequent improvement in the firm's information environment.
Comment letters may also lack substance due to regulatory capture, a theory that suggests regulated firms manipulate the agency regulating them (see Dal Bo, 2006 for a review). If the
SEC is subject to filer influence, then comment letters may avoid substantive issues, thus creating no economic benefits. Ertimur and Nondorf’s (2006) lack of results may be evidence of this. Related anecdotal evidence may exist in the Commission’s 2005 and 2006 annual reports in which they report various metrics to track and report on SEC effectiveness. For comment letters, the reports in summary state that the SEC is unable to quantify significant improvements or
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actions related to the letters.
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La Porta et al. (2006) and Djankov et al. (2008a) suggest that a regulator’s role is best in setting rules as opposed to enforcing them.
All firms filing with the SEC are subject to review at least once every three years however, not all firms receive comment letters. The issuance of a letter suggests at a minimum a lack of clarity in a filing as a knowledgeable reader has an enquiry. Comments may also address completeness or appropriateness of either disclosure or accounting application hence raising doubt about either the ability or integrity of the firm’s management. As a result, firms receiving letters may be perceived by investors to be lower quality reporters since their filings have been determined to be deficient or suspect in some way. Thus, upon revelation of the letters the market may ascribe less credibility to the financial reports of these firms. In a previous study of credibility, Wilson (2008) examines a sample of 215 firms that issued earnings restatements.
Testing the change in market reaction to earnings in the post restatement period, she finds that
ERCs are significantly lower initially but then return to historic levels by the fourth quarter after the restatement. If comment letters are a reporting quality signal, any decline in ERCs or credibility effect is also likely be a short-term phenomenon.
Other hypothesized effects may be more contingent on the substance of the letter comments. If comment letters result in accounting changes and/or additional or enhanced disclosures, those revisions could impact earnings quality, the perception of earnings quality or financial statement users’ understanding of the economics of the firm. Abarbanell et al. (1995) show that the sensitivity of price change to an earnings surprise is increasing in the precision of the announcement. Therefore, if the comment letter process enhances earnings quality or the
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“Divisions of Corporation Finance and Investment Management continued to work toward establishing a means for accurately tracking data on comments that result in significant enhancements in financial and other disclosures or other significant actions to protect shareholders. The divisions will provide data for this indicator once such tracking methods are in place.” See Exhibit 2.23 in the 2006 SEC annual report available at www.sec.gov.
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perception of earnings quality, one would expect greater price responses to earnings (higher
ERCs) after the letters. Such a result would be opposite the reporting credibility effect outlined above.
Moreover, if comment letters expand or enhance disclosures with economic substance, an improved information environment would result. A large body of research supports such a relation. For example, Welker (1995) shows that a well-regarded disclosure policy reduces information asymmetry and increases liquidity in equity markets. Leuz and Verrecchia (2000) examine German firms that commit to higher disclosure levels by adopting International or U.S.
Accounting Standards and find these firms experience a decline in cost of capital. Brown et al.
(2004) show that conference calls lead to a reduction in information asymmetry among equity investors. Brown and Hillegeist (2007) find that disclosure quality reduces the likelihood that investors discover and trade on private information.
Further, if an information environment effect exists, then it seems reasonable to expect cross-sectional variation based on the substance of the letter comments. The resolution of severe letters is more likely to improve the information environment. Severe letters may address more issues, thus creating a larger cumulative impact. For example, the Landec Corporation letter
(Appendix 1) contains 32 comments, while the letter issued to Charles Schwab (Appendix 2) only has one comment. Or severe letters may address more important issues. For example, we observe great heterogeneity in comment types in Appendix 3. We expect the severity of the letter to be related to the information environment effect arising from the resolution of the issue(s). An empirical challenge however is how to capture letter severity, an issue we address in the results section.
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4. Research Design
Our primary research design is to examine the change in our variables of interest following the resolution of the comment letter case. We define the pre period as the eight quarters prior to the first comment letter issued by the SEC and the post period as the eight quarters following case resolution (See Exhibit 1 for a timeline). We use eight post quarters to attempt to distinguish between transitory effects (i.e. lower credibility) and longer term effects of improved accounting and disclosure. We examine earnings response coefficients (ERCs) and four other variables related to firms’ information environment, absolute cumulative abnormal returns (ACARs), cumulative abnormal volume (CAV), analyst absolute forecast errors
(ABSFE) and analyst forecast dispersion (DISPERSION). To capture any comment letter effect we apply the following regression model:
Variable of Interest =
β
0
+
β
1
POST+
γ
Controls +
ε
POST is a dummy variable(s) representing the quarters following case resolution. Controls are defined based on the relevant existing literature related to each specific variable of interest and are detailed below. We cluster standard errors by firm to correct for possible correlations across observations of a given firm (Rogers, 1993; Petersen, 2009).
4.1 Earnings information content
Higher quality earnings should lead to larger ERCs in the post period, and lower quality earnings or the perception of such, the opposite. Wilson (2008) examines the information content of earnings following restatements. We apply her ERC model for our sample with one modification, we examine up to eight quarters post (she uses six). There are eight post quarter dummy variables (one for each quarter), each interacted with unexpected earnings capturing the quarterly change in the ERC.
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The regression variables are defined as follows. CAR is the three day cumulative abnormal return surrounding each firm’s quarterly earnings announcement, where abnormal returns are CRSP firm specific returns less CRSP value-weighted market returns. SUE is unexpected earnings for each respective firm quarter, based on the median of analyst forecasts issued within 60 days of the quarter’s earnings announcement deflated by share price at the beginning of the quarter.
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Analyst forecasts and actual earnings are taken from IBES.
We include the control variables in Wilson (2008). They are standard control variables for ERC regressions based on an extensive ERC literature. NONLINEAR is defined as SUE x
|SUE| and is a control for the nonlinearity in the price-earnings relation (Freeman and Tse 1989).
Extreme earnings outcomes are less value relevant. PREDICT is the variance of the absolute values of unexpected earnings based on a seasonal random walk over the two years prior to the earnings announcement. Lipe (1990) finds a negative relation between unexpected returns and the predictability of earnings. PERSIST is based on Foster’s (1977) model estimated over the two years prior to the earnings announcement. Kormendi and Lipe (1987) find a positive relation between ERCs and earnings persistence. MTB is the market-to-book ratio at the end of the quarter. Collins and Kothari (1989) document an association between ERCs and MTB. BETA is the market model regression coefficient estimated over the year prior to the earnings announcement. Collins and Kothari (1989) find a negative relation between ERCs and risk, where market Beta is the risk proxy. SIZE is the natural log of the market value of equity.
Wilson (2008) makes no prediction on the direction of this control variable due to potential correlation with other control variables. LOSS is a dummy variable equal to one if the firm reports negative earnings in the quarter and zero otherwise. Hayn (1995) finds negative earnings
11 Wilson’s (2008) primary specification deflates by share price at the end of the quarter. Her footnote 7 states that the results are robust to deflating by share price at the beginning of the quarter.
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have lower information content. Q4 is a dummy variable equal to one if the earnings announcement is for the fourth quarter and zero otherwise. Mendenhall and Nichols (1988) find fourth quarter earnings reports to have lower information content.
4.2 ACAR and CAV
We use stock price and trading volume reactions around earnings announcements as proxies for firms’ information environment. These two proxies are motivated by theoretical work of Diamond and Verrecchia (1991) and Kim and Verrecchia (1991a, b; 1994; 1997) that show stock price reaction to a public announcement decreases with the precision of pre-announcement information. Therefore, a decrease in return volatility around earnings announcements indicates a smaller information gap between the firm and investors regarding the upcoming announcements and thus, an improvement in the pre-announcement information environment. These papers also show that stock trading volume arises from differences in the quality (precision) of investors’ private information. Consequently, a reduction in trading volume around earnings announcements reflects less information asymmetry across investors and greater consensus regarding firm value.
If the information environment improves after the comment letters because of more or better disclosure or higher quality accounting, both return volatility and abnormal trading volume should decrease around ensuing earnings announcements. In contrast, if the additional information arising from the review process is just an oversupply or is non-substantive, then we would expect no change in market responses. Several empirical papers in accounting and finance have used this approach to study information environment changes.
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For example, Bailey et al. (2006) use return and volume reactions around earnings releases to study the change in a firm’s information environment when it cross-lists in the US market. Bailey et al. (2003) use return and volume reactions to investigate the information environment change around Regulation Fair Disclosure. Another FD study,
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We measure stock price reaction as the three-day absolute cumulative abnormal return
(ACAR) around quarterly earnings announcements. We define the earnings announcement date as day zero ( t=0 ), and compute ACAR=| _
+1 t=-1
(1+AR )-1| where AR is the abnormal return based on one-factor market model residuals estimated over the period t -11 to t -200 trading days. CAV is the three-day cumulative abnormal trading volume measure.
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Abnormal trading volume is the difference between announcement-window (-1, +1) trading volume and the mean of pre- announcement window (-200, -11) trading volume, divided by the mean of the pre- announcement volume.
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Consistent with earlier studies, we include a set of control variables related to market behavior around earnings announcements. Firms with inherently higher price volatility tend to have higher price reactions around earnings releases (Heflin et al. 2003; Black, 1976; Christie,
1982; Nelson, 1991). We use RETVOL and NEGCAR to capture firm’s inherent price volatility.
RETVOL is the standard deviation of the firm’s returns during the market model estimation period. NEGCAR equals one if the cumulative abnormal return over the window (-64, +1) is negative, and zero otherwise. Heflin et al. (2003) find that larger information flow yields greater market reactions. ABSCAR is the absolute value of the cumulative abnormal return over the window (-64, +1) and is used to capture the overall information flow around the earnings announcement. We include LOSS because the market may react less when earnings are less
Heflin, Subramanyam and Zhang (2003) use return volatility around earnings announcements to study the change in the flow of financial information to the capital markets before and after the implementation of Regulation FD.
13 Some papers in the trading volume literature use a “volume market model” in the preannouncement window to calculate expected trading volume (e.g., Tkac (1999); Bailey et al. (2006)). We opt not to follow this approach given the highly skewed volume data, a linear model tends to poorly specify the underlying data structure. Moreover, such a model requires more computational cost but provides little improvement in power (Bamber et al. 2009).
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Our focus on earnings announcements is supported by the large literature on the economic and statistical significance of market reactions to them (Kothari 2001). There is also a literature on market reactions around SEC filings. We choose not to focus on filings given the results of Li and Ramesh (2009) who find that market reactions to 10Qs only occur when an earnings announcement is concurrently issued.
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informative (Hayn, 1995). BONDYIELD is the yield on the CRSP 30-year bond index at the end of the quarter. Collins and Kothari (1989) find that price reactions to earnings announcements are decreasing in interest rates. We control for investor expectations by including analyst forecast errors. ABSFE_QTR is the quarterly analyst forecast errors defined as the absolute value of the difference between the actual earnings per share and the median individual analysts earnings forecasts prior to the earnings announcement, scaled by the stock price. Finally, we include SIZE because Atiase (1985) shows that smaller firms tend to have larger market reactions to an earnings shock.
4.3 Analyst forecasts
We examine the change in analyst forecast accuracy and forecast dispersion in the post comment letter period as a complementary approach to ACAR and CAV. Analysts are sophisticated users of financial statements and their forecasts are common proxies for firms’ information environment. Lang and Lundholm (1996) find enhanced disclosure is associated with greater analyst forecast accuracy and less dispersion.
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We use individual current quarterly forecasts issued between earnings announcements.
ABSFE is the absolute value of forecast errors and is a common proxy for analyst forecast accuracy. Forecast errors (FE) are company actual quarterly earnings less individual analyst earnings forecasts. Actual earnings and analyst forecasts are taken from IBES. Forecast errors are deflated by the stock price at the beginning of the quarter. To avoid FE measurement problems that could arise from small deflators, we delete observations with price deflators that are less than $1.36, which represents the fifth percentile of price deflators, we also winsorize the
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They also examine analyst following. We do not focus on following as it is a more indirect measure of the information environment. Instead we focus on the more direct measures - forecasts.
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top and bottom percentile of forecast errors. Forecast dispersion (DISPERSION) is the standard deviation of FE.
Control variables include forecast horizon as well as company and analyst characteristics that previous research has shown to affect analyst forecasts. Since information arrives over time, forecast error should decline as the earnings announcement approaches (O’Brien, 1988).
HORIZON is the number of days from the analyst forecast date to the company’s earnings announcement. Brown (1999) finds that forecast errors differ between loss and profit companies.
A dummy variable, LOSS, controls for forecast differences between profitable and non- profitable quarters. To control for analyst coverage, the regressions contain FOLLOWING, which is the number of analysts who provide forecasts on a company during the quarter (See Lys and Soo, 1995). Clement (1999) finds greater forecast accuracy for more experienced analysts and for those from larger brokerage firms. He finds accuracy declines with task complexity. We include experience, brokerage size, and complexity to control for the effect of individual analyst characteristics on their forecasts. EXPERIENCE measures the analyst’s tenure as a sell-side analyst, measured as the number of years they appear in the IBES database. BROKERSIZE is the yearly decile ranking of IBES brokerage firms based on the number of analysts at each firm.
COMPLEXITY represents the number of companies an analyst covers. Individual forecast accuracy also affects collective forecast dispersion, hence, we consider the same controls for both regressions.
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5. Data, Comment Letter Determinants and Resolutions
5.1 Data
We search EDGAR, the SEC database of public company filings, for comment letters and retain those relating to 10Qs and 10Ks. For the period 2004 to 2006 we obtain 9,206 letters relating to 4,134 cases for 3,815 firms. Since all of our tests require data from Compustat and
CRSP, we require our sample firms to have GVKEYs and PERMNOs. Requiring non-missing
GVKEYs eliminates 1,571 letters from 1,084 cases and 898 firms.
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Requiring non-missing
PERMNOs further reduces the sample by 1,578 letters from 676 cases and 661 firms. Our selection process results in a final sample of 6,057 letters representing 2,374 cases for 2,256 firms. Table 1, Panel A summarizes the sample selection process.
Table 1, Panel B shows most of the sample cases arise in 2005 and 2006. This time clustering exists because the SEC began publicly disclosing comment letters in 2005 and only intended to post letters relating to filings made after August 1, 2004. Panel C shows that the majority of the sample firms are the focus of only one SEC investigation. Out of 2,256 firms, only 116 firms are the subject of two cases and one firm is the subject of three cases. In Panel D, we see that on average, a case lasts for 88 days and has two and a half comment letters. In Panel
E, we report the industry distribution of both comment letter firms and the Compustat universe in the sample period. The industry classification follows Fama and French (1997). Insurance is slightly over-represented among comment letter firms (5.05% compared to 2.69%), and utilities are slightly underrepresented (2.78% compared to 4.1%). Otherwise, letter representation appears to be proportional.
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Some initial exploration suggests these firms are partnerships.
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5.2 Determinants of receiving an SEC comment letter
Whether a firm receives an SEC comment letter in a particular year depends on whether the firm is subject to review in that year and its reporting quality. We develop our determinant model based on these two factors.
We include a set of proxies for the SOX review criteria (See Section 2). SOX specifies restatements as a basis for review. A firm’s restatement and amendment history is representative of its historic reporting quality which may impact the likelihood of review and receiving a letter.
Since recent restatements or amendments may be of greater relevance, we allow separate coefficients for restatements and amendments within a year of the letter and those filed prior to that. RESTATE_1YR and AMEND_1YR are dummy variables that equal one if the firm restates or amends respectively in the year prior to receiving the letter, and zero otherwise.
RESTATE_B4 and AMEND_B4 are dummy variables that equal one if the firm has a restatement or amendment previously, excluding the prior year, and zero otherwise.
We measure a firm’s price volatility as its idiosyncratic volatility in the stock market where IDIOSYNCRATIC_VOL = ln(
1 R
2
R
2
) , and R
2 is the R-square from the market model estimated one year prior to receiving the comment letter (Durnev et al., 2004; Ferreira and Laux,
2007). IDIOSYNCRATIC_VOL measures firm price volatility relative to market-wide variation which we believe best captures the SOX criteria. SIZE is the natural log of the firm’s market capitalization at the fiscal year-end prior to receiving a comment letter. To measure whether a firm is an emerging company with a disparate PE ratio, we include a firm’s age and its earnings- per-share to share price ratio ( E/P ). AGE is the quartile ranking of the number of years the firm appears on CRSP. EP is the quartile ranking of the E/P ratio, calculated at the fiscal year-end prior to the comment letter. We use the E/P ratio rather than a P/E ratio because some of the
19

sample firms have zero earnings. To measure the impact of a firm’s operation on any material sector of the economy, we calculate each company’s proportion of their respective industry revenue at the fiscal year end prior to the comment letter and denote the variable as
REVENUE_PROP.
In addition to the SOX review criteria we include three additional factors that relate to a firm’s reporting quality. First, firms with high uncertainty in their operating environment are likely to use greater estimation and more approximations in their financial reports. Accordingly, we expect such firms to be more subject to reporting errors. We use the volatility of a firm’s operating cash flow as our proxy for operating uncertainty. CFO_VOL is the standard deviation of cash flows from operation (CFO) over the five years prior to receiving the comment letter. We scale CFO by total assets. Second, dominant audit suppliers are likely to provide higher quality audits because they have more resources and wish to protect their reputational capital. We expect companies audited by these large audit firms to have higher reporting quality, and hence to be less likely to receive a comment letter. The dominant audit suppliers in our sample period are the so-called "Big 4" public accounting firms: Deloitte and Touche, Ernst and Young, KPMG, and
PriceWaterhouseCoopers. BIG4 equals one if the firm is audited by one of these audit firms and zero otherwise. Third, prior research (e.g., Teoh, et al., 1998) shows that firms may manage earnings around initial public offerings (IPO). We include a dummy variable IPO that equals one, if the firm went public within four years prior to receiving the letter and zero otherwise.
1 7
As an additional factor for review we consider firms registering to issue securities. The
SEC may have greater concern for the quality of disclosure for firms issuing new securities.
17
A private conversation with a former SEC staff member also suggests that the Commission may monitor more frequently companies that just became public.
20

SFILE_1YR is a dummy variable that equals one if the firm files an S filing in the year prior to receiving the comment letter and zero otherwise.
1 8
Of the 2,374 comment letter cases, 2,308 have complete data for the determinant test.
There are 9,718 non-comment-letter firm-years in Compustat during the sample period that have similarly complete data. For comment-letter firms, we measure all variables prior to the date of the first letter. For non-comment-letter firms, we measure all variables at the prior year’s fiscal year-end date.
Table 2, Panel A provides descriptive statistics and the results of the univariate tests.
Comment letter firms differ from non-comment letter firms on most dimensions. On average, we find that firms that receive an SEC comment letter are more likely to have a restatement and amendment history, in general, as well as within the year prior to receiving the letter. Comment letter firms are more likely to have filed an S filing. Contrary to our expectations, comment letter firms tend to have smaller price volatilities than non-comment letter firms. Comment letter firms are slightly larger, represent a larger proportion of their industry revenue, have been listed longer, and have a higher E/P ratio. The average CFO_VOL of the comment letter sample is significantly larger than the non-comment-letter sample, while the medians are economically similar although statistically different. We find no significant difference in the proportion of firms that are audited by a Big 4 audit firm. Comment letter firms are less likely to have recently become a public company.
Panel B of Table 2 presents Pearson correlations. The largest significant correlations are a negative correlation of -0.698 between firms’ age and IPO, followed by a negative correlation of
- 0.652 between firm size and price volatility. The majority of other correlations fall between
18
We obtain the restatement information from the Government Office of Accountability database. We thank
Srinivasan Sankaraguruswamy for generously providing us with the amendment filing and S filing data.
21

- 0.20 and +0.20, which suggests that the variables included in our determinant model capture distinct firm attributes.
We run our determinant model using a Logit regression and Panel C of Table 2 presents the results. The model yields a Wald _
_ of 268.5, which is significant at the one percent level or better, thereby rejecting the null hypothesis that all the coefficients equal zero. In addition to the coefficient estimates, we present the marginal effect of each variable. Whether the firm has amended a filing(s) in the past has the largest marginal effect (11.6 and 9.9%) on the probability of receiving an SEC comment letter. The next largest marginal effect comes from restatements.
Consistent with the SOX criteria, a restatement increases the likelihood of receiving a comment letter. These results suggest that the SEC pays attention to historic reporting quality.
The coefficient on SFILE_1YR is positive and significant, suggesting that the SEC is more likely to review firms who wish to issue securities. Consistent with the SOX guideline, we find that larger firms and firms with higher stock price volatility face a higher probability of getting a letter. Inconsistent with the SOX criteria, however, we find that older firms are more likely to receive a letter. We find no evidence of the firm’s share of industry revenue or its E/P ratio being related to getting a letter. Firms with a more uncertain operating environment and not audited by Big 4 auditors face a higher probability of receiving a letter. The univariate analyses and the Logit model results in Table 2 are fairly consistent.
19
To evaluate the Logit model’s goodness-of-fit, in Panel D of Table 2, we present the
Hosmer-Lemeshow statistic. we create ten ordered groups based on the fitted probabilities and
19
In untabulated analysis we explore the market reaction when a firm receives the first comment letter from the
SEC. Of 2,256 firms, we are able to find 2,202 firms (2,316 cases) with non-missing return data on the first letter date ( t=0 ). We find no statistically significant stock market reactions in terms of either daily abnormal returns or cumulative abnormal returns over (-1, +2). This result is not surprising, since the existence of an SEC enquiry is unknown to the public until the case is resolved and the SEC releases the letters. Obviously, we are interested in the market reaction on the date that the SEC releases the letters, but we do not have those dates of release.
22

then compare the actual number in the each group (observed) to the number predicted by the
Logit model (expected). Across our ten groups, the frequency observed is very close to the number expected, both for the comment letter firms and for the non-comment letter firms. The
Hosmer-Lemeshow test statistic further confirms that the model prediction does not significantly differ from the observed ( p-value = 0.28). Therefore, our Logit model fits the data well.
5.3 Comment letter resolutions – amended filings
To document the reporting changes that result from the comment letter process, we examine amended filings caused by comment letters. We focus on amendments because requiring a company to amend a filing provides evidence of the importance and relevance of the issues raised in the comment letters.
We identify the amended filings caused by a comment letter as follows. We obtain all amended filings from the SEC’s EDGAR database. We select comment letter firms’ amended filings which occur after the comment letter case starting date but no later than 1 year after the case ending date. Of the 2,374 cases, 880 have such amendments. We manually check whether the comment letters cause the amended filings. We do so by reviewing the comment letter issues and the response letters from the companies. Some issues are explained away or contested by the company without making any changes. Otherwise the response letters identify what the companies propose to revise in the amended filings. We randomly cross check these proposals to the actual amendments. We find 402 cases, 17 percent of our sample result in an amended filing.
To tabulate the nature of the revisions in the amended filings, we code the revisions into five categories: MD&A ; Financial Statements ; Footnotes ; Other; and Future Filings . MD&A represents changes to the Management Discussion & Analysis section of the filing. Financial
Statements captures presentation, classification and numeric changes to any of the four financial
23

statements in the amended filing. Footnotes represents revisions of the notes to the Financial
Statements. Other quantifies revisions to any other part of the filing. Generally these represent internal control report wording and/or audit report issues, i.e. a missing signature, date or office location. Future Filings represents issues which the company does not address in the amendment but commits to correct in a later filing.
Table 3, Panel A presents the results. For the 402 amendments there are 2,628 total revisions, or 6.5 per case, on average. Since all these revisions relate to amended filings, the minimum resolution is one but the maximum is 55. Future Filings represents the largest single category total with 1,070, slightly more than 40 percent of the all revisions. Hence, 1,558 actual revisions occur in the amended filings, approximately four per case. It is surprising that the SEC allows such a large number of changes to occur in the future, given that the filing is amended .
Other , is the next largest category with 579 revisions, slightly more than a third of the 1,558 immediate revisions. Changes to financial statements and footnotes are about equally common, with 334 and 369 revisions respectively. MD&A is the smallest with 276 revisions.
Of the 402 amendments, 157 have revisions only in the Other category. For the remaining 245 cases, we recalculate the mean and median resolution per category. For MD&A ,
Financial Statements , and Footnotes , the average revisions per case increase. MD&A increases from 0.7 to 1.1, Financial Statements increases from 0.8 to 1.4, and Footnotes increases from 0.9 to 1.5. The median of one for both Financial Statement and Footnotes shows that more than half of these cases result in changes to the financial statements or the footnotes. These 245 amendments are likely to be more economically important than the other 157 cases.
To assess whether market participants consider these revisions economically important, we examine the abnormal returns around the amended filing date. Of the 402 cases, we are able
24

to find non-missing filing date returns for 388. Of the 388, 151 result in only technical revisions
( Other ).
For comparison, we compute similar abnormal returns for all amendments filed in the same time period. There are 18,630 amended 10Ks and 10Qs filed from 2004 to 2008. Of the
18,630 amendments, 12,745 have non-missing GVKEYs and 8,813 have non-missing
PERMNOs. After excluding the amendments related to the 388 comment letter cases and requiring non-missing return data on the amendment filing dates, we are left with 8,015 other amendments.
Panel B of Table 3 presents the abnormal returns. We find negative market reactions to the amended filings for the 388 comment letter cases. The median abnormal return on the amendment filing date and the day after is about -0.2%. The median three-day cumulative abnormal return over (-1, +1) is approximately -0.5%. These reactions are nearly twice the magnitude of the reactions to all amendments. For example, the median three-day CAR (-1, +1) for all amendments is -0.26%.
The comment letter negative market reactions are driven by the cases that are more than just revisions in the Other category. The 151 amendments that only contain technical revisions
( Other ) show statistically insignificant abnormal CARs around the amended filing dates. The remaining 237 amendments show a negative two day CAR (-1, 0) of -0.45% and a negative three-day CAR (-1, +1) of -0.67%, both which are statistically greater (more negative) than the market reactions to all amendments (10% statistical significance level). The returns evidence provides support for the importance and relevance of the non-technical amendments resulting from comment letters.
25

6. Informational Changes
6.1 Earnings credibility
We employ the ERC analysis as detailed in Section 4.1 to assess the change in the information content of earnings post-comment letters. Table 4 presents the results. Descriptive statistics and univariate analyses are shown in Panel A. CAR declines in the post period and SUE becomes more optimistic. The univariate comparisons of these variable’s means and medians in the pre and post comment letter periods show that the changes are statistically significant.
Although there are also differences in some of the control variables, the magnitude of the changes is small.
Table 4, Panel B presents the regression analyses. The first two specifications include all observations with complete data. The first is without firm fixed effects, which the second includes. The third specification requires that there be at least four quarters of available data for each firm observation in both the pre and post comment letter period. This restriction results in a decline in the number of quarterly observations. The results for all three models are generally consistent. The coefficient on SUE, the ERC, is positive and statistically significant in all three specifications. The ERC is larger is the restricted case.
2 0
Most relevant to our study, the results show ERCs increase early in the post period (based on the interactive variables POSTQ1 through
POSTQ5 x SUE) and decline in post quarter six and seven.
21
Most of the control variables when interacted with SUE are statistically significant and in the direction expected, except for MTB which is consistently negative in all three specifications. Overall, the incrementally larger ERC
20
Kothari (2001) states empirical estimates of ERCs range from 1 to 3, page 128. Our estimates are consistent with that. Wilson (2008) found extremely high ERCs (in excess of 5) perhaps due to her small sample size.
21
In untabulated analysis we also restrict the sample to require a full balanced panel (plus and minus 8 quarters).
Overall the results are similar, although weaker. Only 3 post quarter x SUE coefficients are statistically significant, but they are all positive. They are Q1, Q7 and Q8. The sample size however is only 4,160 observations.
26

results suggest that in the year immediately following the comment letters, on average, the market perceives the comment letter firms’ earnings to be of higher quality. The decline in the sixth and seventh post quarter is puzzling but may be related to other events in these later periods. The larger ERCs is in contrast to a poor reporting quality signaling hypothesis.
In untabulated analysis, we also examine the 1,131 cases that report quarterly earnings following the resolution of the comment letter case but prior to 45 days thereafter. Hence, for this group, the market is likely unaware of the comment letter case for the first post letter earnings announcement but would be aware for the second post letter earnings announcement.
2 2
If comment letters are a signal of poor reporting quality, then we would expect that the second post quarter ERC would be dampened relative to the first. Accordingly, we repeat the ERC analysis for this sub-sample and test the equality of the coefficients on POSTQ1 x SUE and POSTQ2 x
SUE . The overall results for this sub-sample are similar to the full sample and we are unable to reject the equality of the first two post interactive coefficients, both of which are positive, providing further evidence against the signaling hypothesis.
6.2 Market reactions around earnings announcements
If SEC comment letters improve disclosure and enhance firms’ information environment, we expect less information asymmetry between the firm and its investors as well as among investors. Our information asymmetry proxy is market reactions around earnings announcements. Less information asymmetry would lead to dampened market reactions. Table 5 presents the results.
Panel A of Table 5 provides descriptive statistics. After requiring non-missing data, we have 1,597 comment letter cases for the share price volatility test and 1,600 cases for the trading
22
See Section 2. Letters are released to the public no earlier than 45 days after the case is closed.
27

volume test. The univariate comparisons show an increase in both ACAR and CAV following resolution of the letter. However, almost all the control variables also experience changes, which may cause the increase in the market reactions, hence the importance of our multivariate analysis that follows.
Panel B of Table 5 presents the regression results with ACAR as the dependent variable.
We present the average result in column (1) and the cross-sectional results in columns (2) to (7).
In general, after controlling for other explanatory factors, we find the price reactions around earnings announcements after comment letter resolution are on average significantly lower. In column (1), the coefficient on POST is negative and statistically significant. The reduction in the three-day ACAR of 3.9% is economically significant. Relative to the pre-comment letter period sample mean of 5.1%, this results suggests comment letter firms experience a 76% decrease in
ACARs. In untabulated analysis, we find that the reduction in price reactions lasts through to the eighth quarter following the letter resolution and the magnitude of the reduction does not diminish through that time. The long term reduction suggests the result is more likely driven by an improvement in disclosure than by a temporary loss of market confidence.
We next examine whether the reduction in price reaction varies cross-sectionally based on the severity of letter content. We use two proxies to capture the seriousness of the letter. Our first proxy for letter severity is time to resolution (TTR), the duration of the letter period. We conjecture that if it takes longer to resolve the comment letter issues, then the issues are more likely to be substantial, or there are more issues to resolve, or both. Therefore, these letters are more likely to lead to measureable changes in disclosure and the firm’s information environment.
The advantage of using TTR as a proxy for letter severity is that it is an observable and objective measure. It would be a challenge for any researcher to evaluate the correctness or importance of
28

any or all of the comments for each case. The negotiation between the SEC and the firm under review is likely to distill the issues down to those that are relevant and important. The more important the issue(s), the longer the process is likely to take. The downside is that this proxy does not capture the different characteristics of comments raised in a letter. To address this problem, we employ a second proxy, the type of resolution for a sub-sample of letters. We conjecture that revisions of financial statements and footnotes are more likely to have economic impact than revisions of technical presentation details. We discuss the classification in detail in
Section 5.3. Although it relates to the types of comments, our second proxy only applies to a subsample, hence facing the limitation of generalizability. Since each of the two proxies has their own strengths and weakness, we employ both to test the cross sectional variations in changes in information environment.
We split the sample based on the median TTR and find that the reduction in price reaction is driven by longer duration cases. In column (2), the sample includes cases with greater than median TTR and the coefficient on POST is negative and significant. Moreover, the magnitude of the coefficient increases from -3.9% (i.e., the average effect) to -5.3%. In contrast, for cases with smaller than median TTR, we do not find any statistically significant reduction in
ACARs (see column (3)).
We then split the amendment sub-sample based on whether the comment letter cases only contain technical comments (OTHER = 1). In columns (4) and (5), the coefficients on POST are not significant. To introduce the importance of resolutions we further examine the remaining resolution types. Since revisions related to MD&A tend to just repackage information presented elsewhere in the filing, we conjecture that issues related to financial statements and footnotes are more important. For the OTHER=0 sub-sample, we define IMPORTANT=1 for cases where
29

amendment issues are related to either financial statements or footnotes and have no issues to be amended in future filings. There are 50 such cases. We find that these 50 cases experience a large reduction in price reactions subsequent to the comment letters. In column (6), the coefficient on POST is almost -20%. In contrast, for the IMPORTANT=0 sub-sample, we find no price reaction reduction (see column (7)).
The control variable coefficients generally have the expected signs. Firms with inherently higher price volatility tend to have higher price reactions around earnings releases, as indicated by the positive coefficients on RETVOL and NEGCAR (Heflin et al. 2003; Black, 1976; Christie,
1982; Nelson, 1991). The positive coefficients on ABSCAR suggest that larger information flow yields greater market reactions (Heflin et al., 2003). The coefficients on LOSS are negative and significant, consistent with the theory that the market reacts less when the earnings numbers are less informative (Hayn, 1995). Consistent with Bailey et al. (2006), we find a positive association between analyst forecast error and market reaction, suggesting that the market reacts more to an earnings shock. Neither BONDYIELD nor SIZE is significant in our specification.
Since the effect of control variables may differ in the pre- and post-comment letter periods, we allow their coefficients to vary by interacting them with POST . Except for BONDYIELD , we do not find a significant change in the effect of the control variables, as indicated by insignificant coefficients on their interaction terms with POST.
2 3
In Panel C of Table 5, we present the results with abnormal trading volume as the dependent variable. The structure and presentation of Panel C is similar to Panel B. Consistent with the price reaction results, the coefficient on POST is negative and significant, indicating a reduction in abnormal trading volume around earnings releases following comment letter
23
Our results are robust to dropping the full set of interaction terms between POST and the control variables.
30

resolution. Comparing the magnitude of this coefficient with the pre-comment letter sample mean shows that these firms experience a 98% reduction in their volume reactions. Again, this reduction lasts through the eighth quarter, post-comment letter (untabulated). These results suggest that the SEC comment letters are effective in enhancing disclosure levels, which leads to a lower divergence of opinion among investors in the long-run. The cross-setional results are also consistent with Panel B. We find that the reduced volume reactions are driven by cases with longer duration. Revisions involving financial statements and footnote disclosures also have a large effect. In contrast to the ACAR model, most of the interaction terms between POST and the control variables are significant, suggesting that allowing the coefficients on the control variables to differ across periods is important.
Overall, the above evidence that both price and trading volume reactions decrease over a long period following the resolution of the comment letters combined with the earlier results of enhanced earnings quality, is consistent with the interpretation that the SEC comment letters achieve its goal of enhancing corporate disclosure.
6.3 Analyst analyses
If SEC comment letters improve firms’ disclosure and information environment, we would expect to see improvement in analyst forecast performance. Table 6 presents the results.
Univariate analysis in Panel A shows that both absolute analyst forecast errors and forecast dispersion increase in the post comment letter period, tests of means and medians are all significant at the one percent level. However, it is important to consider that other changes occur as well. Our interest is in evaluating analyts use of information in the post period. To do so, entails controlling for other known forecast determinants. For example, the frequency of losses increases significantly in the post period. Previous research has shown that analyst forecast
31

performance is much worse for losses. Forecast horizon increases on average and analyst following declines in the post period, both of which would have a negative effect on analyst forecast accuracy.
24
The analyst characteristics also show changes in the post period. The average broker firm is smaller and analyst coverage (complexity) increases – both likely to negatively affect analyst forecast performance. Hence we rely on our multivariate analysis in
Panels B and C to draw our inferences.
After requiring non-missing variables, we have 1,900 comment letter cases for analyst forecast accuracy analysis and 1,746 cases for the forecast dispersion analysis. Panel B of Table
6 reports the analyst forecast accuracy regression results. We find that on average analyst forecast accuracy improves subsequent to the resolution of the SEC comment letters. In column
(1) the coefficient on POST is negative and significant, indicating analyst forecast errors have decreased in the post-comment letter period. This decrease is economically significant.
Comparing the coefficient on POST to the pre-comment letter mean ABSFE , we find that comment letter firms experience a 76% improvement in analyst forecast accuracy.
2 5
We find, this improvement is mostly driven by cases with longer duration (column (2)). Cases with resolutions involving financial statements and footnotes (column (6)) have a large effect. All these results are consistent with the earlier findings on ACAR and CAV.
The control variables are generally as expected based on past research. An economically large factor is firm losses. Analyst forecast errors increase when the firm reports a loss. Accuracy declines with horizon (ABSFE increases). Analyst following is in general associated with better
24
One could argue that these variables are also reflective of the information environment. However, our primary interest is in the impact of new information revealed due to comment letters and so focus on more direct measures – forecast accuracy and dispersion.
25
For both Panels B and C, we scale the dependent variables, ABSFE and DISPERSION , by multiplying by 1000 for presentation purpose.
32

accuracy. We do not find the number of firms an analyst covers and the analyst’s experience to have an an effect on forecast accuracy. We allow for the effect of the control variables to vary in the post period by interacting them with POST .
In Panel C of Table 6, we examine the changes in analyst forecast dispersion. Consistent with the forecast accuracy results, we find that on average analyst forecast dispersion declines in the post-comment letter period as indicated by the negative and significant coefficient on POST in column (1). Moreover, the reduction is driven by cases with longer durations (columns (2) and
(3)). However, when we use types of comments to proxy for letter severity, we do not find any statistically significant results (column (6)).
In summary, Table 6 shows that analyst forecast performance improves subsequent to the resolution of issues raised by the comment letters. The improvement in analyst performance is consistent with our earlier findings on ACAR and CAV, suggesting the SEC comment letters enhance firms’ disclosure and information environment.
6.4 Robustness tests - difference-in-difference method with a matched control sample
We use propensity score matching to select the control sample. Each control firm has a hypothetical comment letter period based on the matching comment-letter firm. We then conduct a difference-in-difference test, examining the change in the information environment between the comment-letter firms and the matched control firms.
A firm’s propensity score is the probability of receiving an SEC comment letter conditional on the firm’s observable characteristics. We estimate each firm’s propensity score based on the determinant model from Section 5.2. We then select a control firm that has the closest propensity score to each comment letter firm without replacement. The difference-in- difference model takes the form:
33

Variable of Interest = α t
+
β i
+
β
1
Post +
β
2
CL*Post +
γ
+ Γ POST x Controls +
ε where i indexes firm, t indexes time. We have four specifications, the dependent variables are the four proxies for the information environment (i.e., ACAR , CAV , ABSFE , and DISPERSION ). α t and β i are year and firm fixed effects that control for any market wide changes in information environment and unobserved fixed difference across firms. CL is a dummy variable, which equals one if the firm receives an SEC comment letter. Because the specification includes firm fixed effects, it is not necessary to include the non-interacted CL dummy.
2 6
The coefficient of interest is β
2
, which represents the differential change in information environment between the comment letter firms and the matched controls. If the SEC’s review process is effective in enhancing a firm’s information environment, then we would expect
β 2
<0 (i.e., lower market reactions and smaller analyst forecast errors and dispersion).
The above equation essentially compares the change in the comment-letter firms’ information environment to the change for the control firms. An important assumption of the difference-in-difference methodology is that shocks contemporaneous with the comment letters affect the treatment and control groups similarly. This assumption can be problematic if the treatment and control groups have dissimilar characteristics. However, since we select the control firms based on propensity scores, we create a quasi-randomized experiment (D’Agostino,
1998). That is the two firms, one in the comment-letter group and one in the control group, have the same propensity score, it is as if these two similar firms were randomly assigned to each group. This design mitigates the concern that shocks contemporaneous with the comment letters affect the two groups differentially thus biasing the results.
26
This specification is similar to Bertrand and Mullainathan (1999, 2003), and Low (2009).
34

Table 7 reports the results of the difference-in-differences method. Columns (1) and (2) present the results of changes in market reactions and columns (3) and (4) present the results of changes in analyst forecast performance. For some comment letter cases we cannot find a sufficiently close propensity score match, hence the number of cases declines slightly compared to our main tests. The results in Table 7 are consistent with those reported earlier. Across all models, the coefficients on POST x CL are negative and significant, providing evidence of the robustness of our main results suggesting that the resolution of the SEC comment letters reduces informaton asymmetry and enhances analyst forecast performance..
6.5 Measuring the treatment effect
Although the SOX requires that the SEC review each registrant at least once every three years, SOX also outlines a risk-based approach to select filings for review. Therefore the
Commission may pay particular attention to certain types of companies and such companies may be more likely to be reviewed and receive a letter more than once every three years. If the SEC can identify firms with information issues in advance of their review, then our results may be overstated relative to a result that would arise if firms were only selected for review once every three years. Our main results estimate the effect of the comment letter process as it currently exists. A policy maker might be interested in the effect of a review program without a risk based review model.
We address this issue in two ways. First, our propensity score matched sample analysis in
Section 6.4 is one way of addressing the effect of any nonrandom treatment assignment arising from the SEC’s risk based selection model. As a supplementary approach we also re-run our analyses after eliminating all firms which appear multiple times in our sample. Firms appearing multiple times in our sample period likely only occur because of the SEC’s risk based approach.
35

In untabulated analyses we find little substantive difference in our ACAR and CAV results but a reduction in the magnitude of the effect in the analyst analyses, although little change in the statistical significance. The results of both of these approaches suggest that the effect of comment letters is not only because the SEC can identify problematic firms but also a general result.
6.6 Voluntary disclosure
An alternative explanation for our results is that management makes more or better quality voluntary disclosures after receving an SEC comment letter. A greater quantity of or better quality disclosures could potentially enhance the information environment which could explain the decrease in market reactions to earnings announcements and the improvement in analyst forecast performance. We investigate this alternative explanation using management earnings forecasts as a proxy for voluntary disclosures.
We obtain management forecast data from First Call’s Corporate Issued Guidance database for our ACAR matched sample. The data contain managers’ estimates or discussions of current periods’ and future periods’ earnings, where periods are both quarters and years. For each fiscal quarter, we calculate the number of management forecasts from the day after the previous quarter’s earnings announcement to the day of the current quarter’s earnings announcement. We also decompose the forecasts into point estimates, range estimates, and other qualitative descriptions. We find that neither the change in the total number of management forecasts nor the change in the composition of the forecasts differ between comment letter firms and control firms (untabulated). Therefore, we find no support for a a change in voluntary disclosures as the explanation for our enhanced information environment results.
36

7. Conclusion
We explore the content, determinants and resolution of SEC comment letters and then examine the informational effects of letter resolution. Our sample includes 2,256 firms that receive comment letters in the 2004-2006 period. The content analysis shows that nearly half of the comments involve accounting application, financial reporting and disclosure issues. Our determinants model suggests that firms with historically poor reporting quality, as evidenced by previous amendments and restatements, are more likely to receive a letter. 17% of our sample immediately amends filings to resolve comment letters. These amendments often involve revisions to financial statements and/or footnotes.
Our results show ERCs increase and abnormal return volatility and trading volume around earnings announcements decline in the period after comment letter resolution.
Corroborating our market evidence is an improvement in analyst forecast accuracy and decline in forecast dispersion. Moreover, the changes in market reactions and analyst performance are largely driven by cases with longer resolution durations suggesting more issues or more important issues to resolve. Resolutions involving changes to footnote disclosures or financial statements have large informational effects. Our information environment results are robust to the inclusion of a propensity score matched control sample with a difference-in-difference research design. We also find no evidence of a change in voluntary disclosure behavior by comment-letter firms, thus eliminating an alternative explanation. We conclude that the SEC comment letter process has beneficial informational consequences by both enhancing the information content of earnings and the firm’s information environment. However, whether comment letters create costs that exceed these benefits remains an open question.
37

To our knowledge, our paper provides the first large-sample evidence on the financial reporting oversight role of the SEC. We find that regulators can improve firms’ information environment by monitoring corporate reporting. These results contrast with recent papers that question the role of public enforcement (La Porta et al. (2006); Djankov et al. (2008a)). Our findings could be of interest to policy makers, both domestic and foreign, as well as the SEC who wish to evaluate the effectiveness of this regulatory effort.
We note that our paper is not without its limitations. Our sample of SEC comment letters is clustered in a short time frame and hence the generalizability of our results may be a concern.
In addition, the SEC comment-letter process has many different objectives (see Section 2) and thus may create other effects that we do not explore.
38

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43

Exhibit 1: Comment Letter Timeline – Hypothetical Example Centered on 10K Filing
Pre-comment letter period
Q t-1 through Q t- 8
(relative to first CL issued)
EA t-8
EA t-1
10 K filed
Comment letter period - omit
- first CL issued
Post-comment letter period
Q t+1 through Q t+8
(relative to last CL issued)
- last CL issued
44

Appendix 1
Mail Stop 0510
February 10, 2005
Via U.S. mail and facsimile
Gary T. Steele, President and Chief Executive Officer
Landec Corporation
3 6 03 Haven Avenue
Menlo Park, CA 94025
RE: Form 10-KSB for the fiscal year ended May 30, 2004
Form 10-QSB for the period ended August 29, 2004
File No. 0-27446
Dear Mr. Steele:
We have reviewed these filings and have the following comments. If you disagree with a comment, we will consider your explanation as to why our comment is inapplicable or a revision is unnecessary. Please be as detailed as necessary in your explanation. In some of our comments, we may ask you to provide us with supplemental information so we may better understand your disclosure. After reviewing this information, we may or may not raise additional comments.
Please understand that the purpose of our review process is to assist you in your compliance with the applicable disclosure requirements and to enhance the overall disclosure in your filing. We look forward to working with you in these respects. We welcome any questions you may have about our comments or on any other aspect of our review. Feel free to call us at the telephone numbers listed at the end of this letter.
FORM 10-K FOR THE YEAR ENDED MAY 30, 2004
Comments applicable to your overall filing
1 . Where a comment below requests additional disclosures or other revisions to be made, please show us in your supplemental response what the revisions will look like. These revisions should be included in your future filings.
Item 7. Management`s Discussion and Analysis of Financial Condition and Results of Operation
Critical Accounting Policies and Use of Estimates
Revenue Recognition, page 23
2 . Please expand your disclosure to define what you refer to as "recycled" revenue.
Results of Operations
Revenues
Apio Trading, page 25
3 . Please expand your disclosure here and in footnote 12 to include further information regarding the concentration of your International sales in Asia and any other material geographies.
Corporate, page 26
4 . You have disclosed the reason for the decrease in revenue is due to a decrease in licensing revenue with UCB and a decrease in research and development revenue associated with a medical device company. Please expand your disclosure to include further details regarding the closing of these agreements. Please include in your disclosure whether the product licensed to UCB can and will be licensed to other potential customers; whether any additional revenue from royalties or licensing is expected as a result of the research and development work performed for the
45

medical device company; and what your expectations are for the coming year relating to licensing and research and development revenue.
Gross Profit
Apio Trading, page 27
5 . You have disclosed on page 26 a change in certain export contracts. Please expand your disclosure to include any impact these contract changes had or will have on gross profit, if any.
Liquidity and Capital Resources, page 32
6 . You have disclosed on page 12 you are currently shipping products to L`Oreal of Paris. You have also disclosed you will receive royalty payments from Alcon on sales of the PORT(tm) device through 2012. You have further disclosed on page 39, that you may not receive royalties on future sales of QuickCast(tm) and PORT(tm) because you no longer have control over the sales of these products. Please expand your disclosure to include your expectations regarding revenue from these products and any other new products, product lines, or licensing and research and development agreements. Also, please include in your disclosure how not having control of these products may affect your ability to receive royalties on these products.
7 . You have disclosed on page 13 information regarding potential milestone payments relating to an exclusive licensing and one year research and development collaboration with a medical device company. Please expand your disclosure to discuss the terms and status of this agreement and whether or not you expect to meet any of these milestones. Please also disclose the timing on if and when you anticipate revenue will be earned through royalties.
Contractual Obligations, page 34
8 . Please revise your table of contractual cash obligations to include estimated interest payments on your debt.
Because the table is aimed at increasing transparency of cash flow, we believe these payments should be included in the table. Please also disclose any assumptions you made to derive these amounts.
Additional Factors That May Affect Future Results
Our Indebtedness Could Limit Our Financial and Operating Flexibility, page 35
9 . You have disclosed you may be obligated to make future payments to the former shareholders of Apio of up to
$1.2 million for the future supply of produce. Please expand your disclosure to include the terms and conditions that would cause you to incur this additional liability. Please include in your disclosure any amounts that were accrued for the periods presented and where these amounts were recorded in the balance sheet and statement of operations.
Please also indicate when payments on these amounts are expected to be paid, if applicable.
Financial Statements
Statements of Operations, page 49
1 0 . Please revise your statements of operations to breakout separately the cost of service revenue, related party.
Statements of Cash Flows, page 51
1 1 . Please tell us which of the cash outflows and inflows related to your notes and advances receivable are included in operating activities and which are included in investing activities. Please explain to us how you determined which amounts belonged in each classification. In providing us a response, please also tell us where the cash flows related to each of the loans shown in Note 4 are included and explain why each loan was classified where it was. Naturally, we understand that interest earned on these notes and advances receivable would be included in operating activities, regardless of where the principal amounts are classified. In the event the repayments you receive exceed the original principal amounts, for reasons other than stated interest payments, please tell us how these amounts are treated in your cash flow statement as well. If a portion of the repayments on these receivables occurs with consideration other than cash, please disclose how this works and how you take into account these non-cash payments in preparing your statement of cash flows. If all of the cash flows related to your investments in farming activities are not included in the notes and advances receivable cash flows, please separately address your classification for these cash flows as well. Refer to paragraphs16, 17, 22 and 23 of SFAS 95.
1 2 . Please present the cash inflows and outflows related to your notes and advances receivable on a gross basis.
Otherwise, please explain to us how they meet the criteria in SFAS 95 for netting.
46

Only cash flows stemming from investments, loans and debt with original maturities of three months or less may be reported on a net basis.
1 3 . Please present cash flows related to the change in other assets separately from those related to the change in other liabilities. Please also present these cash flows on a gross basis, rather than a net one. Please supplementally tell us how you determined that these cash flows represented investing cash flows. Refer to paragraphs 16 and 17 of
SFAS 95.
1 4 . Please present sales of common stock and repurchases of common stock on a gross basis. Please also present your stock repurchases separately in your statement of changes in shareholders` equity. Please disclose in a footnote the timing, nature and terms of your stock repurchases. If these stock repurchases occurred under a stock repurchase program, please discuss it as well.
Notes to Financial Statements
1 5 . Please disclose the types of expenses that you include in the cost of sales line item and the types of expenses that you include in the selling, general and administrative expenses line item. Please also disclose whether you include inbound freight charges, purchasing and receiving costs, inspection costs, warehousing costs, internal transfer costs, and the other costs of your distribution network in the cost of sales line item. With the exception of warehousing costs, if you currently exclude a portion of these costs from cost of sales, please disclose: * in a footnote the line items that these excluded costs are included in and the amounts included in each line item for each period presented, and * in MD&A that your gross margins may not be comparable to those of other entities, since some entities include all of the costs related to their distribution network in cost of sales and others like you exclude a portion of them from gross margin, including them instead in a line item, such as selling, general and administrative expenses.
1 . Organization, Basis of Presentation, and Summary of Significant Accounting Policies
Related Party Transactions, page 56
1 6 . Your disclosure states that you have loss exposure on the subleases from the agricultural land you lease from the
Apio CEO. Please expand your disclosure to include the amount of revenue generated during the periods presented and the portion of the leased land that was subleased as of May 30, 2004.
1 7 . Please expand your disclosure to discuss the terms and conditions of the "earnout liability" between you and the
Apio CEO. Please include in your disclosure any balance remaining as of May 30, 2004 and what line item this is included in on your balance sheets. If applicable, please disclose when the remaining amount is expected to be paid.
Investment in farming activities, page 57
1 8 . Your disclosure regarding your significant accounting policies discusses your policies relating to investments in farming activities. Please expand your disclosure to explain how you determined these investments would not meet the criteria for consolidation under FIN 46(R), given that these advances were in exchange for a percentage ownership in the proceeds of the crops and that you appear to bear the risk of loss if the net proceeds of the crops are not sufficient to cover the expense. In your discussion, please specifically address the analysis you used in concluding that you lacked any of the three characteristics of a controlling financial interest relating to these investments as discussed in paragraphs 5(b)(1) to (3) of FIN 46(R). Please also include in your discussion whether or not substantially all of these activities are conducted your behalf.
1 9 . Please expand your disclosure to include the facts and circumstances that led to the gains and losses, for which you refer, relating to you investments in farming activities.
Property and Equipment, page 58
2 0 . Your disclosure indicating the estimated useful lives of furniture and fixtures, computers, capitalized software, machinery, equipment and autos range from three to ten years is not very helpful to readers. Please separately disclose the useful lives for each category shown in Note 5.
2 1 . Please expand your disclosure relating to capitalized software development costs to include the amount of amortization recognized for the periods presented and which line item these costs are included in on your statements of operations.
47

Per Share Information, page 59
2 2 . Please expand your disclosure to include potentially dilutive securities that were not included in your calculation of diluted EPS because the securities would have had an antidilutive effect. Refer to paragraph 40(c) of SFAS 128.
Accounting for Stock-Based Compensation, page 62
2 3 . You have disclosed that no stock options were granted above Grant date market prices for the periods presented.
Did you mean to say that no stock options were granted below grant date market prices? If not, please expand your disclosure to include information relating to stock options issued at below market prices on the grant dates.
Please include the following information in your disclosure:
* The number of shares issued below market prices
* The market price on the date of grant
* The price at which the stock options were issued
* The vesting period of the stock options
* The reason why the stock options were issued
* The amount of compensation expense recorded, if any, how it was calculated, and the line items for which the amounts are included in on the financial statements.
3 . Exit of Fruit Processing and Domestic Commodity Vegetable Business, page 64
2 4 . I n your Form 10-K for the year ended October 27, 2002, you state under Note 1 on page 50 that you adopted
SFAS 144 as of the beginning of that year. In June 2002, you recorded a $436,000 gain on the sale of a fruit processing facility and included it in other income. Under the Other heading on page 29 of your MD&A, you indicate that Other includes gain or loss on the sale of assets. Gains and Losses on the sale of long-lived assets that are not a component of an entity are required to be included in arriving at your operating income (loss). Gains and losses on the sale of long-lived assets that are a component of an entity should be treated as discontinued operations.
Please tell us how you considered the criteria in paragraphs 41 to 45 of SFAS 144 in reaching the conclusion that this gain should be included in the other income, net line item.
2 5 . You have disclosed the $1.1 million charge recorded in fiscal year 2003 primarily relates to inventory and notes receivable. Please revise your disclosure here and in your statements of operations to include the portion of the writedown relating to inventory in cost of revenue, or explain to us why that classification is not appropriate.
7 . Shareholder`s Equity
Common Stock, Stock Purchase Plans and Stock Option Plans, page 69
2 6 . You have disclosed that the exercise price for non-statutory stock options may be no less than 85% of the fair market value of Landec`s common stock on the date the option was granted to non- Named executives. Please expand you disclosure to include the following:
* The number of shares issued below fair market value
* The fair market value on the date of grant
* The price at which the stock options were issued
* The vesting period of the stock options
* The reason why the stock options were issued
* The amount of compensation expense recorded, if any, how it was calculated, and the line items for which the amounts are included in on the financial statements Please include the above mentioned information here and in the section under this heading entitled "Landec Ag Stock Plan."
Index of Exhibits, page 88
2 7 . Please update your Exhibit filed entitled "Subsidiaries of the Registrant," to include the most current information relating to your subsidiaries.
FORM 10-Q FOR THE PERIOD ENDED AUGUST 29, 2004
Comments applicable to your overall filing
2 8 . Please address the comments above in your interim Forms 10-Q as well.
Item 1. Financial Statements
48

Balance Sheets, page 3
2 9 . Please revise your balance sheet to include the par value and the number of shares issued and outstanding.
Notes to Financial Statements
3 0 . Please expand your disclosure to include information relating The balances and gains or losses incurred on your investments in Farming activities, as disclosed in your Form on 10-K.
7 . Debt, page 9
3 1 . Please expand your disclosure to indicate whether or not you have been in compliance with the restrictive covenants established under the Loan Agreement with Wells Fargo Bank N.A for the six months ended November
2 8 , 2004.
Item 2. Management`s Discussion and Analysis of Financial Condition and Results of Operations
Results of Operations
Gross Profit, page 17
3 2 . You have disclosed components that have contributed to the increase in gross profits for the three and six months ended November 29, 2004 compared to the same periods in the prior year. Please expand your disclosure to quantify the affects each of these components have had on the increase in gross profits.
Please respond to these comments within 10 business days, or tell us when you will provide us with a response.
Please provide us with a supplemental response letter that keys your responses to our comments and provides any requested supplemental information. Detailed letters greatly facilitate our review. Please file your supplemental response on EDGAR as a correspondence file. Please understand that we may have additional comments after reviewing your responses to our comments.
We urge all persons who are responsible for the accuracy and adequacy of the disclosure in the filings reviewed by the staff to be certain that they have provided all information investors require for an informed decision. Since the company and its management are in possession of all facts relating to a company`s disclosure, they are responsible for the accuracy and adequacy of the disclosures they have made.
In connection with responding to our comments, please provide, in writing, a statement from the company acknowledging that:
* the company is responsible for the adequacy and accuracy of the disclosure in their filings;
* staff comments or changes to disclosure in response to staff comments do not foreclose the Commission from taking any action with respect to the filing; and
* the company may not assert staff comments as a defense in any proceeding initiated by the Commission or any person under the federal securities laws of the United States.
In addition, please be advised that the Division of Enforcement has access to all information you provide to the staff of the Division of Corporation Finance in our review of your filing or in response to our comments on your filing.
If you have any questions regarding these comments, please direct them to Meagan Caldwell, Staff Accountant, at
(202) 824-5578 or, in her absence, to the undersigned at (202) 942-1774.
Sincerely,
Rufus Decker
Accounting Branch Chief
49

Appendix 2
April 20, 2005
Mail Stop 4-8
By U.S. Mail and facsimile to (415) 636-5877.
Christopher V. Dodds
Chief Financial Officer
The Charles Schwab Corporation
1 2 0 Kearny Street
San Francisco, CA 94108
Re: The Charles Schwab Corporation
Form 10-K
Filed March 2, 2005
File No. 001-09700
Dear Mr. Dodds:
We have reviewed your filing and have the following comment. We have limited our review to only the issue raised in our comment. Where indicated, we think you should revise your document in response to this comment in future filings. If you disagree, we will consider your explanation as to why our comment is inapplicable or a revision is unnecessary. Please be as detailed as necessary in your explanation. In our comment, we may ask you to provide us with supplemental information so we may better understand your disclosure. After reviewing this information, we may or may not raise additional comments.
Please understand that the purpose of our review process is to assist you in your compliance with the applicable disclosure requirements and to enhance the overall disclosure in your filing. We look forward to working with you in these respects. We welcome any questions you may have about our comments or any other aspect of our review.
Feel free to call us at the telephone numbers listed at the end of this letter.
Consolidated Financial Statements
Note 5. Discontinued Operations - page 41
1 . Please supplementally tell us, and revise your document to include a disclosure of the specific factors you considered in determining that the expected cash flows generated from the contract with UBS are not material direct cash flows of the disposed component. In addition, supplementally tell us the following related to the contract, and explain how you considered each factor in determining that the contract did not constitute significant continuing involvement in the disposed component:
* The significance of the contract or arrangement to the overall operations of the disposed component
* The extent to which you are involved in the operations of the disposed component
* The rights conveyed to each party by the contract
* The pricing terms of the contract or arrangement. Refer to paragraph 42 of SFAS 144 and EITF 03-13.
Please respond to this comment within 10 business days or tell us when you will provide us with a response. Please furnish a cover letter that keys your response to our comment, indicates your intent to include the requested revisions in future filings and provides any requested supplemental information. Please understand that we may have additional comments after reviewing your responses to our comment.
We urge all persons who are responsible for the accuracy and adequacy of the disclosure in the filing reviewed by the staff to be certain that they have provided all information investors require for an informed decision. Since the company and its management are in possession of all facts relating to a company`s disclosure, they are responsible for the accuracy and adequacy of the disclosures they have made.
50

In connection with responding to our comments, please provide, in writing, a statement from the company acknowledging that:
* the company is responsible for the adequacy and accuracy of the disclosure in the filing;
* staff comments or changes to disclosure in response to staff comments do not foreclose the Commission from taking any action with respect to the filing; and
* the company may not assert staff comments as a defense in any proceeding initiated by the Commission or any person under the federal securities laws of the United States.
In addition, please be advised that the Division of Enforcement has access to all information you provide to the staff of the Division of Corporation Finance in our review of your filing or in response to our comments on your filing.
You may contact Rebekah Moore, Staff Accountant, at (202) 824-5482 or me at (202) 942-1782 if you have questions.
Sincerely,
Paul Cline
Senior Accountant
Page 3 of 3
51

Appendix 3
I.
Accounting Issues
These items represent issues or questions that the SEC posed relating to how specific accounting items were represented in the financial statements and disclosures.
1 . Accounting Cite : A request for a specific citation from accounting literature as a basis for the treatment that the firm used to account for a particular transaction.
2 . Accounting Change : A request for further information regarding a change is accounting principle or change in accounting estimate that was either inadequately disclosed or not disclosed at all.
3 . Audit Issue : A request for additional information regarding the firm’s relationship with its audit firm, including issues with auditor changes, issues with matters disclosed (or that should have been disclosed) in the audit report, and issues with the auditor’s consent letter for the offering.
4 . Clarify Accounting Policy : A general request to clarify or provide more information about the firm’s accounting treatment regarding a particular transaction or series of transactions. This request is more about how a firm applies a given standard, not what accounting standard was used (see Accounting Cite).
5 . Critical Accounting Policies and Estimates : Questions or issues about the firm’s critical accounting policy disclosures (or lack of disclosures) and disclosures about the firm’s bases for accounting estimates.
6 . Financial Statement Formatting : Comments about the general formatting of the financial statements and tables in the footnote disclosures.
8 . Internal Controls : Questions about the firm’s internal control systems and the testing, if any, of controls.
9 . Materiality Issues : Comments or questions about the firm’s obligation to disclose material information in the filing, including the reiteration of the definition of materiality.
1 0 . New Accounting Pronouncements : Comments regarding a firm’s disclosures of the effects of newly issued accounting pronouncements, particularly the firm’s consideration of any material impact that the pronouncements may have on the firm’s financial results.
1 1 . Not Following GAAP : An indication by the reviewer that the firm does not appear to be following the tenets of
GAAP in recording a particular type of transaction or series of transactions.
1 2 . Pro Forma Disclosures : Questions or critiques about either the firm’s pro forma disclosures (effects of changes in the firm’s capital structure based on the offering or effects of a merger transaction) and non-GAAP financial disclosures (EBITDA or another non-GAAP measure).
1 3 . Quality of Earnings or Cash Flows
: Explicit comments or questions regarding the quality of the firm’s earnings or cash flows as the firm has presented their results, usually accompanied by comments to balance the tone of the disclosure or make risks/negative results a more prominent part of the disclosure.
1 4 . Reportable Conditions : Request for additional information and disclosure related to a reportable condition or other irregularity that was identified by management related to the firm’s internal controls.
II. Accounting/Financial Reporting/Disclosure Topics
These items represent questions or issues posed by the SEC related to the accounting, financial reporting. or disclosure of specific transactions or classes of transactions.
1 6 . Acquisitions: Questions or comments about the accounting treatment and disclosures of business combination transactions, including purchase price allocations.
1 7 . Capital Expenditures: Questions or issues about the firm’s investment in property, plant and equipment, particularly its accounting treatment related to capitalization of these items.
1 8 . Claims, Commitments and Contingencies: Issues or comments raised about the firm’s accounting for and disclosure of it obligations and long-term commitments, including legal matters.
1 9 . Contra Asset Accounts: A request for information about contra asset-type accounts, such as the allowance for doubtful accounts or loan losses for loan receivables.
2 0 . Depreciation/Amortization: Questions or issues related to the firm’s depreciation and amortization policies.
2 1 . Derivatives: Questions related to the accounting treatment for the firm’s derivative and hedging programs, including the application of hedge accounting models and hedge effectiveness assessments.
2 2 . Environmental Reserves: Questions or comments related to the firm’s environmental remediation obligations.
2 3 . Earnings per Share: Questions related to the computation of earnings per share disclosures.
2 4 . Employee Stock Options and Fair Value: Questions or comments related to the application of SFAS 123(R),
Share-Based Payments , particularly regarding the valuation methods used, including assumptions such as expected volatility and expected term.
2 5 . Expenses and Cost Allocations: Requests for information about expense items and cost allocations.
52

2 6 . Goodwill and Impairment: Questions or comments related to the firm’s goodwill balance and impairment testing, including the definition of reporting units and valuation issues.
2 7 . Intangibles:
Questions or comments regarding the firm’s accounting treatment for intangible assets, including how they were valued and/or whether they should have an indefinite life.
2 8 . Intercompany Accounts: Requests for information about a firm’s accounting and disclosures for intercompany transactions.
2 9 . Inventories: Questions or comments about a firms inventory and related accounting policies.
3 0 . Investments: Questions or comments about a firms investment balances, including the accounting treatment based on ownership percentages and fair value determinations.
3 1 . Leases: Questions or comments about the accounting for leasing transactions, including terms of leases, the treatment of rental escalations, and the treatment of leasehold improvements.
3 2 . Minority Interests: Questions or comments regarding the accounting for minority interests.
3 3 . Off-Balance Sheet Arrangements: Questions or comments relating to the understanding of off balance sheet arrangements, including special purpose entities, and their material effects.
3 4 . Other Fair Value Assessments: Questions or comments regarding valuation assessments for all balance sheet items, excluding acquisition-related and stock option-related fair value determinations.
3 5 . Pensions and Other Employee Benefits: Questions or issues about the assumptions and estimates, including the assumed discount rate, and funding obligations related to a firm’s benefit obligations.
3 6 . Preferred Stock:
Questions or comments regarding the firm’s preferred stock.
3 7 . Related Party Transactions: Requests for additional clarification or details surrounding the accounting for the firm’s transactions with related parties, including management, board members, and other insiders.
3 8 . Reserve Accounts: Questions or comments regarding the accounting and disclosure for reserve liabilities such as warranties and other accrued liabilities.
3 9 . Restructuring Reserves: Questions or comments specifically related to restructuring reserve liabilities, including severance costs.
4 0 . Revenue Recognition: Questions or comments related to a firm’s method of accounting for revenues and material considerations in evaluating the quality and uncertainties surrounding their revenue generating activity.
4 1 . Segment Reporting: Questions about the identification of operating segments, aggregation of operating segments, and information about geographic areas in which the firm operates.
4 2 . Shareholders’ Equity: Questions regarding the accounting treatment of items included as part of shareholders’ equity, including other comprehensive income and retained earnings (accumulated deficits).
4 3 . Statement of Cash Flow Classification: Questions or comments about the classification and presentation of the statement of cash flows. emphasis is made on ensuring an accurate presentation of the firm’s actual cash receipts and cash payments based on activity (operating, investing, or financing).
4 4 . Subsequent Events: Requests for additional information and/or disclosure related to event occurring after the date the financial statement were prepared as of.
4 5 . Tax Accounting: Questions or comments regarding the firm’s income tax disclosures, particularly items disclosed in their income tax footnotes such as the allowance on deferred tax assets.
III. Business Issues
These items represent questions or comments about the firm’s operating, financing, or investing matters.
4 6 . Backlog: Questions about the firm’s disclosures of its order backlog.
4 7 . Competitive Environment:
Comments or questions about the firm’s competitive environment and its strategies in addressing competitive forces.
4 8 . Components of Revenue: Requests for information about disclosure and reporting about the firm’s various sources of revenue, including the separation of product and service revenues.
4 9 . Customer Profiles: Requests for information about the firm’s key customers, including any customer concentration.
5 0 . Debt Covenants: Questions or issues surrounding the company’s contractual covenants related to its outstanding debt, including disclosure about the firm’s compliance with these covenants.
5 1 . Dividends: Requests for more information regarding the firm’s dividend policy, including recent past dividend declarations and/or payouts and support for statements regarding the firm’s intention to pay future dividends.
5 2 . Going Concern: Questions or comments regarding the firm’s ability to continue as a going concern.
5 3 . Intellectual Property: Questions or comments about the firms disclosure of the terms of their intellectual property and any claims against their intellectual property.
53

5 4 . Key Performance Indicators: Requests for additional information about disclosures of the key performance metrics in a firm’s industry.
5 5 . Liquidity:
Questions or comments regarding the firm’s liquidity disclosures, including how the statement of cash flows translates into operating cash inflows and outflows, and sensitivity analysis related to future cash flow needs.
5 6 . Material Contracts: Comments regarding material contracts and their terms that are disclosed or should be disclosed and included as exhibits in the firm’s registration document.
5 7 . Management Discussion and Analysis: Questions or comments about the type of information disclosed or that should have been disclosed as part of the filing.
5 8 . Properties and Facilities: Questions or comments surrounding the description of the locations in which the firm operates.
6 0 . Risk Factors: Questions or comments regarding the identification and disclosure of the firm’s material risk factors, including the potential impact of the factors on the firm’s operations and cash flows.
6 1 . Research and Development Projects: Comments regarding the identification and disclosure of the firm’s material R&D projects.
6 2 . Terms of Debt/Credit Arrangements: Questions or comments about the disclosures of the material terms of the firm’s debt and credit arrangements.
6 3 . Trends: A request to provide additional information regarding the material trends underlying the firm’s reported operations and cash flows, as well as any forward-looking information about the effects of trends on future operations and cash flows.
IV. Tone and Level of Disclosure Issues
These items represent requests for additional information or questions about the manner in which the firm presented its disclosures and the level of disclosure that the firm presented.
6 4 . Balanced Discussion: A request for management to balance the overly-positive tone of their disclosures with more discussion of the risks and downside of their business and operating environment.
6 5 . Clarify Subject: General requests to provide more specific information regarding a disclosure issue.
6 6 . Confidentiality Request: Represents the SEC’s acknowledgement of the firm’s request for confidential treatment of various components of the firm’s responses to the SEC comment letters.
6 7 . Confusing Format: A notation by the SEC that a particular disclosure or presentation is in a difficult to follow format. It is often accompanied by a suggestion from the SEC for improved presentation.
6 8 . Disaggregation: A request to provide a finer level of detail related to disclosures or questions about line-item classification.
6 9 . Forward-looking Information: Comments or questions about the firm’s disclosure of forward looking information.
7 0 . General Formatting: Comments or questions regarding the overall, non-financial statement formatting of the offering document.
7 1 . Inaccuracies: An observation that there are inaccuracies in the filing document, including misstatements of fact or numbers that do not reconcile within the document.
7 2 . Incomplete: An annotation that the document is incomplete.
7 3 . Inconsistencies: A comment regarding disclosures that conflict with each other.
7 4 . Independent Support: A request for independent, third-party support for statements included in the filing document, often, these requests relate to disclosures about fair value disclosures or the firm’s market position.
7 5 . Make Prominent: A request to alter the format of the filing to highlight or improve the visibility of a particular disclosure.
7 6 . Plain English: Requests to modify the language used in the disclosures to eliminate obfuscating language, industry-specific terminology, or excessive use of acronyms.
7 7 . Quantify Amounts : Request to quantify amounts in disclosures where an issue is discussed in qualitative terms.
7 8 . Repetitive Disclosures: A comment that management has unnecessarily repeated information or disclosures throughout sections of the filing without providing additional substance.
7 9 . Specific to Firm: A request to make the firm’s disclosures less generic and boilerplate, and to add content that applies the disclosures to the particular circumstances of the firm.
8 0 . Supplemental Information: Requests for supplemental information that would support assertions in the firm’s disclosures; this information may or may not be further incorporated in the disclosures, but may just be information that the SEC wanted to review (e.g., reviewing a drug effectiveness study that supports certain disclosures made by the firm in its filing).
54

8 1 . Supporting Calculations: A request to provide detailed support for the calculations that result in the numbers or figures disclosed in the document.
8 2 . Too Detailed: Comments that certain portions of the filing documents, such as the summary sections, contained too much detail and information that would be more appropriately included in later sections of the filing.
Note: items 7, 15 and 59 were removed – they related specifically to IPO firms only.
1 .
2 .
3 .
4 .
5 .
6 .
8 .
9 .
Accounting Cite
Accounting Change
Audit Issue
Clarify Accounting Policy
Critical Accounting Policies and Estimates
Financial Statement Formatting
Internal Controls
Materiality Issues
1 0 .
1 1 .
1 2 .
1 3 .
1 4 .
Accounting Issues
Not Following GAAP
Pro Forma Disclosures
New Accounting Pronouncements
Quality of Earnings or Cash Flows
Reportable Conditions
Accounting/Financial Reporting/Disclosure Topics
1 6 . Acquisitions
1 7 . Capital Expenditures
1 8 . Claims, Commitments and Contingencies
1 9 . Contra Asset Accounts
2 0 . Depreciation/Amortization
2 1 . Derivatives
2 2 . Environmental Reserves
2 3 . Earnings per Share
2 4 . Employee Stock Options and Fair Value
2 5 . Expenses and Cost Allocations
2 6 . Goodwill and Impairment
2 7 . Intangibles
2 8 . Intercompany Accounts
2 9 . Inventories
3 0 . Investments
3 1 . Leases
3 2 . Minority Interests
3 3 . Off-Balance Sheet Arrangements
3 4 . Other Fair Value Assessments
3 5 . Pensions and Other Employee Benefits
3 6 . Preferred Stock
3 7 . Related Party Transactions
3 8 . Reserve Accounts
3 9 . Restructuring Reserves
4 0 . Revenue Recognition
4 1 . Segment Reporting
4 2 . Shareholders’ Equity
4 3 . Statement of Cash Flow Classification
4 4 . Subsequent Events
4 5 . Tax Accounting
Number of Comments
53
1
18
49
27
7
30
48
12
39
20
1
3
3 08
56
40
34
23
9
30
0
9
18
16
8
9
45
13
23
39
22
15
30
46
32
16
14
2
3
16
15
10
66
20
6 79
55
Percentage
3 .5 %
0 .1 %
1 .2 %
3 .3 %
1 .8 %
0 .5 %
2 .0 %
3 .2 %
0 .8 %
2 .6 %
1 .3 %
0 .1 %
0 .2 %
2 0 .5
%
1 .0 %
0 .7 %
4 .4 %
1 .3 %
0 .9 %
0 .1 %
0 .2 %
1 .1 %
2 .0 %
3 .1 %
2 .1 %
1 .1 %
1 .5 %
2 .6 %
1 .5 %
1 .0 %
0 .5 %
0 .6 %
3 .0 %
0 .9 %
0 .0 %
0 .6 %
1 .2 %
1 .1 %
3 .7 %
2 .7 %
2 .3 %
1 .5 %
0 .6 %
2 .0 %
4 5 .3
%

Business Issues
4 6 . Backlog
4 7 . Competitive Environment
4 8 . Components of Revenue:
4 9 . Customer Profiles
5 0 . Debt Covenants
5 1 . Dividends
5 2 . Going Concern
5 3 . Intellectual Property
5 4 . Key Performance Indicators
5 5 . Liquidity
5 6 . Material Contracts
5 7 . Management Discussion and Analysis
5 8 . Properties and Facilities
6 0 . Risk Factors
6 1 . Research and Development Projects
6 2 . Terms of Debt/Credit Arrangements
6 3 . Trends
Tone and Level of Disclosure Issues
6 4 . Balanced Discussion
6 5 . Clarify Subject
6 6 . Confidentiality Request
6 7 . Confusing Format
6 8 . Disaggregation
6 9 . Forward-looking Information
7 0 . General Formatting
7 1 . Inaccuracies
7 2 . Incomplete
7 3 . Inconsistencies
7 4 . Independent Support
7 5 . Make Prominent
7 6 . Plain English
7 7 . Quantify Amounts
7 8 . Repetitive Disclosures
7 9 . Specific to Firm
8 0 . Supplemental Information
8 1 . Supporting Calculations
8 2 . Too Detailed
Total
2
41
2
3
11
24
2
3
0
1 00
0
2
19
1
27
3
10
18
0
2 68
4
19
8
14
16
4
37
8
91
6
4
5
2
2
0
18
6
2 44
1 4 99
0 .1 %
0 .0 %
1 .2 %
0 .4 %
0 .4 %
0 .3 %
0 .3 %
0 .1 %
0 .3 %
2 .5 %
0 .5 %
6 .1 %
0 .3 %
1 .3 %
0 .5 %
0 .9 %
1 .1 %
1 6 .3
%
0 .0 %
6 .7 %
0 .0 %
0 .1 %
1 .3 %
0 .1 %
1 .8 %
0 .2 %
0 .7 %
1 .6 %
0 .1 %
0 .2 %
0 .1 %
2 .7 %
0 .1 %
0 .2 %
0 .7 %
1 .2 %
0 .0 %
1 7 .9
%
10 0 %
56

Table 1: Sample Description
This table provides information on the sample composition. Panel A reports the sample selection process. Panel B
(C) presents the distribution of the comment letter cases by year (firm). Panel D provides information on the case length and the number of letters per case. Panel E reports the industry distribution, where the industry is defined by
Fama and French (1997).
Panel A: Sample selection process
10 K & Q comment letters on EDGAR from 2004 to 2006
Less: Firms without COMPUSTAT GVKEY
Less: Firms without CRSP PERMNO
Final sample
Number of letters
9 2 06
(1571)
(1578)
6 0 57
Number of cases Number of firms
4 13 4
(1084)
(676)
2 37 4
38 1 5
(898)
(661)
22 5 6
Panel B: Frequency of the comment letter cases by year
Comment letter case issue year
2 0 0 4
2 0 0 5
2 0 0 6
Total
Number of comment letter cases
5 5
10 6 1
12 5 8
23 7 4
Panel C: Frequency of the comment letter cases by firm
Cases
1
2
3
Total
Number of firms
2 13 9
1 1 6
1
2 25 6
Percentage of firms
9 4 .8 1
5 . 14
0 . 04
1 0 0
Total comment letter cases
2 13 9
2 3 2
3
2 37 4
Panel D: Descriptive statistics on comment letter cases
Case length (days)
Number of letters
Mean
8 7 .5 5
2 .5 5
Median
6 5
2
Percentage
2 .3 2
4 4. 6 9
5 2. 9 9
1 0 0
Std. Dev
8 5 .6 1
1 .1 4
57

Table 1 (continued)
Panel E: Comment letters by industry (in percentage) relative to Compustat
Industry
Agriculture
Comment letter firms
0. 3 4
COMPUSTAT population
0. 2 7
Aircraft
Alcoholic Beverages
0. 1 7
0. 2 1
0 .4
0. 3 2
Apparel
Automobiles and Trucks
Banking
Business Services
1. 2 6
1. 3 9
9. 5 2
1 0 .1 1
0. 9 1
1. 1 5
9. 8 3
1 0 .8 7
Business Supplies
Candy and Soda
Chemicals
Coal
Computers
Construction
0. 9 3
0. 2 1
1. 4 7
0. 2 1
4. 0 4
1. 1 8
0. 8 3
0 .2
1. 6 2
0. 2 6
3. 3 2
0. 8 2
Construction Materials
Consumer Goods
Defense
Electrical Equipment
Electronic Equipment
Entertainment
Fabricated Products
Food Products
Healthcare
Insurance
Machinery
Measuring and Control Equip
Medical Equipment
Miscellaneous
Nonmetallic Mines
Personal Services
Petroleum and Natural Gas
Pharmaceutical Products
Precious Metals
Printing and Publishing
Real Estate
Recreational Products
Restaurants, Hotel, Motel
Retail
Rubber and Plastic Products
Shipbuilding, Railroad Equip.
Shipping Containers
Steel Works, Etc.
Telecommunications
Textiles
Tobacco Products
Trading
Transportation
Utilities
Wholesale
Total
1 .6
1. 1 4
0. 2 5
1. 0 1
6. 6 6
1. 1 8
0. 1 7
1. 3 9
1. 8 5
5. 0 5
2. 7 8
2. 3 2
3. 6 2
1. 0 5
0. 4 2
1. 3 1
4
6. 1 1
0. 2 5
0. 5 9
0. 6 7
0. 5 9
1. 3 1
4 .3
0. 6 3
0. 1 3
0. 2 9
1. 2 2
2. 6 1
0. 2 9
0. 0 4
6. 2 3
2. 2 3
2. 7 8
2. 8 6
1 0 0
1. 3 2
1. 0 8
0. 1 4
0. 7 3
5. 3 4
1. 4 3
0. 2 1
1. 2 5
1 .2
2. 6 9
2. 3 6
1. 6 3
2. 8 8
1. 9 5
2. 6 8
0. 7 7
5. 4 8
6. 2 9
1. 7 8
0. 7 2
1. 0 3
0. 5 9
1. 3 6
3. 5 8
0. 6 8
0. 1 5
0. 1 9
1. 1 2
3. 4 3
0 .2
0. 1 1
5. 5 2
2. 5 3
4 .1
2. 6 8
1 0 0
58

Table 2: Determinants of Receiving an SEC Comment Letter
This table explores determinants associated with receiving SEC comment letters. Panel A presents summary statistics, Panel B the Pearson correlations, Panel C the Logit model estimates, Panel D the goodness of fit diagnostic for the Logit model. For comment letter firms, we measure all variables prior to the date of the first letter. For non-comment-letter firms, we measure all variables at the prior year’s fiscal year end date. RESTATE_B4 equals one if the firm files a restatement before the prior year, and zero otherwise.
RESTATE_1YR equals one if the firm files a restatement within the prior year, and zero otherwise. AMEND_B4 equals one if the firm files an amendment before the prior year, and zero otherwise. AMEND_1YR equals one if the firm files an amendment within the prior year, and zero otherwise. SFILE_1YR equals one if the firm files an S filing within the prior year, and zero otherwise. IDIOSYNCRATIC_VOL is the relative idiosyncratic volatility estimated from the market model over the prior year. SIZE is the natural log of the market value of equity at the end of the prior year. REVENUE_PROP is the firm’s share of industry revenue in the prior year. AGE is the quartile rank of the number of years the firm appears on CRSP. EP is the quartile rank of the EPS-to-price ratio at the end of the prior year. BIG4 equals one if the firm is audited by the big 4 audit firms, and zero otherwise. CFO_VOL is the standard deviation of cash flows from operation over the prior five years. IPO equals one if the firm went public in the prior 4 years, and zero otherwise. In Panel B bold text indicates significance at the 0.05 level or better. In Panel C t -statistics are in brackets and are calculated based on White heteroskedastic consistent standard errors adjusted for clustering by firm. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
Panel A: Descriptive statistics on firm characteristics
Variable
RESTATE_B4
RESTATE_1YR
Comment letter firm-years (n = 2308)
Mean Median Std. Dev.
0 . 15 7
0 . 07 5
0
0
0 .3 6 4
0 .2 6 3
AMEND_B4
AMEND_1YR
0 . 46 6
0 . 26 1
SFILE_1YR 0 . 53 2
IDIOSYNCRATIC_VOL 2 . 88 4
0
0
1
2 .1 4 8
0 .4 9 9
0 .4 3 9
0 .4 9 9
2 . 18
SIZE
REVENUE_PROP
AGE
EP
BIG4
CFO_VOL
IPO
6 . 26 1
0 .0 1
1 . 77 5
1 . 51 9
0 . 75 3
0 . 16 4
0 . 14 4
6 .0 9 4
0 .0 0 1
2
2
1
0 .0 4 4
0
2 .1 1 5
0 .0 2 6
1 .0 9 2
1 .0 9 9
0 .4 3 1
1 .8 5 4
0 .3 5 1
Non-comment letter firm-years (n = 9718)
Mean Median Std. Dev.
0 .1 1 9
0 .0 5 7
0
0
0. 3 24
0. 2 32
0 .3 3 6
0 .2 1 6
0 .4 6 6
3 .0 4 3
6
0 .0 0 8
1 .5 3 3
1 .4 6 9
0. 7 5
0 .1 0 7
0 .2
0
0
0
2 .4 0 6
5 .9 1 6
0 .0 0 1
2
1
1
0 .0 4 7
0
0. 4 72
0. 4 12
0. 4 99
2. 1 93
2. 0 26
0. 0 23
1 .0 9
Comparison
Test of Means Test of Medians
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
1. 1 17 *
0. 4 33
0 .4 6
0 . 4
* * *
* * *
* * *
* * *
* * *
* *
* * *
* * *
59

Table 2 (continued)
Panel B: Correlations
Variable
(1) RESTATE_B4
(2) RESTATE_1YR
(3) AMEND_B4
(4) AMEND_1YR
(5) SFILE_1YR
(6) IDIOSYNCRATIC_VOL
(7) SIZE
(8) REVENUE_PROP
(9) AGE
(10) EP
(11) BIG4
(12) CFO_VOL
(13) IPO
(2)
0 .0
9 6
-
(3)
0.
1 27
0.
0 7 8
-
(4)
0 .0
3 9
0 .2
4 2
0 .4
0 4
-
(5)
0 .0
3 3
0 .0 0 6
0 .0
8 4
0 .1
2 2
-
(6) (7) (8) (9) (10) (11)
0 .0
4 6
- 0 . 01 4
0 .0
4 5
0 .1
0 8
-
0 03 3
0 . 00 2
0 .0
3 2
0 .0
1 9
0 .0
5 0 0 .
02 2 - 0. 0 11
0 .
12 9 0 .0
6 4
0 05 6 0 .0 1 6 -
0.
0 87
0.
0 22
0.
1 54
0. 0 10
0.
0 2 3
0 .0
2 9
0 .0
4 7
0 .0 1 0
0
0
.0
.0
2
3
0
2
0 .0 1 1
0 .1
0 1 0 .0
7 3
0 .1
1 1 0 .0
5 5
0 65 2 0 .2
1 1 0.
1 2 6 0 .1
9 8 0 .3
7 0
- 0 .4
2
-
8 0.
0.
1
1
16
03
-
0
0
0
.3
.1
.1
-
0
6
9
4
8
0
0
0
0
0
.4
.1
.0
.0
-
2
7
6
4
4
1
4
1
(12) (13)
- 0 . 01 3 0 .
09 6
0 .0 1 4 0 02 4
0 .0 0 9 0 17 3
0 .0
3 0
0 .0
3 7
0 .
01 9
0 .
03 1
0 .0
4 4 0 .
07 4
0 .0
6 0 0 .
04 2
0 .0
3 0 0 .
04 3
0 .0
4 9 0 .
69 8
0 .0
8 1 0 .
10 4
0 .0
4 7 0 .
04 1
- 0 05 2
-
60

Table 2 (continued)
Panel C: Logistic regression of the determinants of receiving a comment letter
Variable
Intercept
RESTATE_B4
RESTATE_1YR
AMEND_B4
AMEND_1YR
SFILE_1YR
IDIOSYNCRATIC_VOL
SIZE
REVENUE_PROP
AGE
EP
BIG4
CFO_VOL
IPO
Coefficient
- 3. 0 36 * * *
[-16.467]
0 .1 3 9*
[1.753]
0 .1 7 5*
[1.791]
0 .7 8 2 * * *
[11.471]
0 .6 6 6 * * *
[8.835]
0 .1 4 2 * * *
[2.805]
0 .0 4 2 * * *
Marginal effect
[2.669]
0 .1 1 2 * * *
[5.509]
- 0 . 08 5
[-0.066]
0 .1 6 8 * * *
[4.623]
0 .0 0 5
[0.210]
- 0 .1 3 3 *
[-1.944]
0 . 05 1 * *
[2.398]
0 .1 1 6
[1.171]
0 . 02 1
0 . 02 6
0 . 11 6
0 . 09 9
0 . 02 1
0 . 00 6
0 . 01 7
- 0. 0 13
0 . 02 5
0 . 00 1
- 0 .0 2
0 . 00 8
0 . 01 7
Observations
Pseudo R
2
Wald χ
Prob > χ
1 2,026
0 .0 3 2 4
2 6 8 .5
< 0.001
61

Table 2 (continued)
Panel D: Goodness of fit test for the Logit model
8
9
1 0
4
5
6
7
Comment letter firm-years Non-comment letter firm-years
Group Prob (comment letter = 1) Observed Obs Expected Obs Observed Obs Expected Obs Total
1
2
3
0 . 10 4 1
0 . 11 9 2
0 . 13 9 6
1 2 5
1 3 6
1 6 0
11 2 .9
13 4 .3
15 4 .6
10 7 8
10 6 7
10 4 2
1 0 9 0. 1
1 0 6 8. 7
1 0 4 7. 4
12 0 3
12 0 3
12 0 2
0 .1 6 9
0 . 19 2 9
0 . 21 1 5
0 . 22 9 5
0 . 25 0 5
0 . 28 2 1
0 . 86 9 6
1 6 8
2 3 1
2 4 8
2 5 8
2 8 8
2 8 9
4 0 5
1 8 5
21 8 .8
24 3 .4
26 5 .3
2 8 8
3 1 9
38 6 .7
10 3 5
9 7 1
9 5 5
9 4 5
9 1 4
9 1 4
7 9 7
10 1 8
98 3 .2
95 9 .6
93 7 .7
9 1 4
8 8 4
81 5 .3
12 0 3
12 0 2
12 0 3
12 0 3
12 0 2
12 0 3
12 0 2
Hosmer and Lemeshow Goodness-of-Fit Test: with 8 degree of freedom = 9.80
Prob > χ = 0.28
62

Table 3: Comment Letter Resolutions – Amended Filings
This table summarizes the types and frequency of revisions undertaken or committed to undertake to resolve a comment letter case in the 402 cases where one or more filings were amended. Panel A reports categories of revisions reported in the amended filings. Panel
B reports median market reactions around amendment filing dates. MD&A captures changes to the Management Discussion &
Analysis of the filing. Financial Statements represents presentation, classification and numeric changes to any of the four financial statements in the amended filing. Footnotes represents any revisions to the notes to the Financial Statements. Other quantifies revisions to any other part of the filing, primarily these changes represent Internal Control report wording and/or audit report issues, i. e. a missing signature, date or office location. Future Filings represents issues which the company commits to correct in later filings.
AR is the abnormal returns around amendment filing dates and is calculated based on firm specific returns adjusted for value-weighted market returns. CAR is the cumulative abnormal returns around amendment filing dates. In Panel A, if the resolution involved only
Other issues (157 cases), the mean and medians for the categories are re-calculated in the bottom of the table excluding those cases.
* * * , **, and * indicate significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
Panel A: Revisions in the amended filings caused by comment letters
Resolutions
Total
Mean per case
Median per case
Minimum
Maximum
Number Of Cases
Mean per case,
Excluding
Other Only
Median per case,
Excluding
Other Only
Number Of Cases
Excluding
Other Only
MD&A
2 7 6
0 .7
0
0
1 2
1 .1
0
Financial
Statements
3 3 4
0. 8
0
0
2 2
1. 4
1
Footnotes
3 69
0 .9
0
0
11
1 .5
1
Other
5 7 9
1. 4
1
0
1 7
1. 1
0
Future
Filings
1 ,070
2 .7
1
0
2 7
2 .8
1
Total
2 ,628
6 . 5
5
1
5 5
4 0 2
7 . 9
6
2 4 5
Panel B: Market reactions around the amended filing dates
Time AR
- 1 - 0 .0 0 08
All
N = 388
Amendments caused by Comment Letters
Excluding Other only
All amendments filed from 2004-2008
Other category only
CAR
- 0 . 00 0 8
AR
N = 237
CAR
- 0. 0 01 3 - 0 .0 0 1 3
AR
- 0 .0 0 00
N = 151
CAR
- 0 . 00 0 0
AR
N = 8015
CAR
- 0 .0 0 0 9* * * - 0 .0 0 0 9 * * *
0
1
- 0 .0 0 21 * * - 0 . 00 3 4 * * * - 0. 0 01 4
- 0 .0 0 20 * * - 0 . 00 4 9 * * * - 0. 0 02 0
- 0 .0 0 4 5 * * - 0 .0 0 34 * * - 0 . 00 1 8
- 0 .0 0 6 7 * * * - 0 .0 0 22 - 0 . 00 2 0
- 0 .0 0 1 0* * * - 0 .0 0 1 6 * * *
- 0 .0 0 1 2* * * - 0 .0 0 2 6 * * *
63

Table 4: ERCs – Signaling versus information content
This table reports changes in ERCs after the SEC comment letters. Panel A presents descriptive statistics. Panel B presents regression analysis with cumulative abnormal returns ( CAR ) as the dependent variable. CAR is the three day cumulative abnormal return surrounding each firm’s quarterly earnings announcement, where abnormal returns are CRSP firm specific returns less CRSP value- weighted market returns. SUE is unexpected earnings for each respective firm quarter based on the median of analyst forecasts issued within 60 days of the quarter’s earnings announcement deflated by share price at the beginning of the quarter. Analyst forecasts and actual earnings are taken from IBES. NONLINEAR is SUE x |SUE|. PREDICT is the variance of the absolute values of unexpected earnings based on a seasonal random walk over the two years prior to the earnings announcement . PERSIST is based on Foster’s
(1977) model estimated over the two years prior to the earnings announcement. MTB is the market-to-book ratio at the end of the quarter. BETA is the market model regression coefficient estimated over the year prior to the earnings announcement. SIZE is the natural log of the market value of equity. LOSS is a dummy variable equal to 1 if earnings is negative and 0 otherwise. Q4 is a dummy variable equal to 1 if the earnings announcement is for the fourth quarter and 0 otherwise. POSTQ1 – POSTQ8 are dummy variables equal to one if the observation is from the respective post-letter quarter, and zero otherwise. ***, **, and * indicate significance at the
1 %, 5%, and 10% levels, respectively (two tailed).
Panel A: Descriptive Statistics
Variable
CAR
SUE
NONLINEAR
PREDICT
PERSIST
MTB
BETA
SIZE
LOSS
Q4 n Mean
Comment letter firm-quarters
Pre-comment letters
Median Std. Dev.
8 ,009 0 .0 0 1 4 0 . 00 2 1 n
0 .0 7 5 8 8 ,892
Post-comment letters
Mean Median Std. Dev. Test of Means Test of Medians
0 .0 0 1 3 - 0 .0 0 0 5 0 .0 8 4 0
Comparison
* * *
8 ,009 - 0. 0 00 2 0 . 00 0 5
8 ,009 - 0. 0 00 4 0 . 00 0 0
0 .0 2 1 7 8 ,892 - 0 . 00 2 6
0 .0 1 9 1 8 ,892 - 0 . 00 2 5
0. 0 00 4
0. 0 00 0
0 .0 5 7 8
0 .1 1 6 9
* * *
*
* *
* *
8 ,009 0 .0 0 1 5 0 . 00 0 0
8 ,009 0 .6 2 1 1 0 . 72 5 9
8 ,009 3 .2 1 2 5 2 . 40 4 6
8 ,009 1 .1 8 2 6 1 . 10 0 6
8 ,009 7 .3 9 7 0 7 . 35 1 1
8 ,009 0 .1 9 8 5
8 ,009 0 .2 3 3 5
0
0
0 .0 0 9 6 8 ,892 0 .0 0 2 4 0. 0 00 0
0 .7 4 7 7 8 ,892 0 .7 0 9 9 0. 8 74 4
4 .9 4 3 6 8 ,892 3 .2 2 8 7 2. 3 47 9
0 .5 7 2 0 8 ,892 1 .1 4 1 8 1 .1 0 0
1 .7 1 8 1 8 ,892 7 .4 9 0 5 7. 4 42 1
0 .3 9 8 9 8 ,892 0 .2 0 7 7
0 .4 2 3 1 8 ,892 0 .2 3 5 5
0
0
0 .0 3 2 2
0 .7 3 5 4
5 .2 9 2 0
0 .5 0 4 4
1 .7 7 8 6
* *
* * * * * *
*
* * *
* * * * * *
0 .4 0 5 7
0 .4 2 4 3
64

Table 4 (continued)
Panel B: Regression analyses
Parameter
Intercept
POSTQ1
POSTQ2
POSTQ3
POSTQ4
POSTQ5
POSTQ6
POSTQ7
POSTQ8
SUE
POSTQ1 x SUE
POSTQ2 x SUE
POSTQ3 x SUE
POSTQ4 x SUE
POSTQ5 x SUE
POSTQ6 x SUE
POSTQ7 x SUE
POSTQ8 x SUE
NONLINEAR
PERSIST
PREDICT
BETA
MTB
SIZE
LOSS
Q4
SUE x PERSIST
SUE x PREDICT
SUE x BETA
SUE x MTB
SUE x SIZE
SUE x LOSS
SUE x Q4
Firm FE
Num. Obs
R-sq
Predicted
Sign
?
(-)
(+)
(-)
(-)
(+)
?
(-)
?
?
?
?
(+)
?
?
?
(-)
(+)
(-)
(-)
(+)
?
(-)
(-)
All Observations All Observations
Coefficient Coefficient
0 .0 0 1 2
0 .0 0 0 7 0 .0 0 3 5
- 0 .0 0 44
0 .0 0 0 9
- 0 .0 0 16
0 .0 0 4 6
- 0 .0 0 37
0 .0 0 0 1
0 .0 0 4 4
0 .0 0 3 1
0 .0 0 0 5
0 .0 0 3 0
0 .0 0 6 2
0 .0 0 3 7
0 .0 0 1 3
0 .8 3 9 1 * * *
1 .3 3 3 3 * * *
0 .4 6 0 9 * * *
0 .4 1 3 0 * * *
0 .2 4 5 1 * * *
0 .2 0 1 3 * *
- 0 .3 7 37 * * *
0 .0 0 0 0
0 .8 5 8 9
1 .4 1 3 8
0 .5 0 4 4
0 .6 2 0 5
0 .3 0 2 3
0 .2 2 2 8
*
* *
* * *
* * *
* * *
* * *
* * *
* *
- 0 .3 5 91 * * *
- 0 .1 8 39 * * *
0 .3 8 6 1 * * *
- 0 .4 9 05 * * *
0 .0 0 1 1
- 0 .0 0 64
0 .0 0 1 4
- 0 .0 0 00
- 0 .0 0 01
- 0 .0 1 36 * * *
0 .0 0 2 6 *
0 .1 7 3 9 * * *
- 1 .0 1 19 * * *
0 .0 0 0 7
- 0 .0 3 81 * * *
0 .0 9 1 3 * * *
- 0 .8 2 54 * * *
- 0 .4 0 67 * * *
No
1 6,901
5 .7 %
- 0 .1 5 58
0 .4 1 9 7
- 0 .5 0 91
0 .0 0 0 1
- 0 .0 3 36
0 .0 0 0 6
- 0 .0 0 01
- 0 .0 2 19
- 0 .0 0 85
0 .0 0 2 2
0 .1 9 6 5
- 1 .0 3 10
0 .0 0 0 0
- 0 .0 3 71
0 .0 9 2 2
- 0 .8 5 52
- 0 .4 1 79
Yes
1 6,901
18 . 2%
* *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
Minimum: 4 Qtrs
Pre and Post
Coefficient
0 .0 0 2 3
- 0 . 00 2 2
0 .0 0 0 9
0 .0 0 1 9
0 .0 0 1 9
0 .0 0 4 1
0 .0 0 0 1
- 0 . 00 2 6
2 .3 6 2 2 * * *
1 .0 3 6 3 * * *
- 0 . 29 9 5
0 .8 3 2 3 * * *
0 .1 7 6 6 *
- 0 . 03 1 4
- 0 . 62 8 6 * * *
- 0 . 51 1 5 * * *
- 0 . 02 2 0
- 0 . 45 4 6 * * *
- 0 . 00 0 5
- 0 . 05 5 6
0 .0 0 2 3
- 0 . 00 0 3 *
- 0 . 01 8 2 * * *
- 0 . 00 7 4 * * *
0 .0 0 1 7
0 .2 2 3 7 * * *
0 .0 0 7 2
- 0 . 10 1 8 *
- 0 . 03 1 7 * * *
0 .0 5 1 5 * *
- 1 . 76 0 6 * * *
- 0 . 48 2 8 * * *
Yes
1 2,524
16 . 4%
65

Table 5: Changes in Market Reactions around Earnings Announcements
This table reports changes in market reactions around earnings announcements after the SEC comment letters. Panel A presents summary statistics. Panel B presents regression analysis with absolute cumulative abnormal return ( ACAR ) as the dependent variable. Panel C presents regression analysis with cumulative abnormal trading volume ( CAV ) as the dependent variable. ACAR is the absolute value of daily abnormal returns around earnings announcement, where abnormal returns are one-factor market model residuals estimated from day -200 to day -11. CAV is the abnormal trading volume around earnings announcement, where abnormal trading volume is the difference between trading volume and the mean of daily volume over the (-200, -11) window, normalized by the mean volume. RETVOL is the standard deviation of the firm’s returns during the market model estimation period. NEGCAR equals one if the cumulative abnormal return over the window (-64, +1) is negative, and zero otherwise. ABSCAR is the absolute value of the cumulative abnormal return over the window (-64, +1). LOSS equals one if earnings is negative, and zero otherwise. BONDYIELD is the yield on the CRSP 30-year bond index at the end of the quarter. ABSFE_QTR is the absolute value of the difference between the actual earnings per share and the median individual analysts’ most recent earnings forecast prior to the earnings announcement, scaled by the stock price. SIZE the natural log of the market value of equity at the end of the quarter. t -statistics are in brackets and are calculated based on
White heteroskedastic consistent standard errors adjusted for clustering by firm. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
Panel A: Descriptive statistics
Variable n
Pre-comment letters
Mean Median Std. Dev.
ACAR(-1, +1) 1 01 8 0 0 .0 5 1 0 . 03 4
CAV(-1, +1) 1 02 0 0 2 .4 6 7 1 . 51 6
RETVOL
NEGCAR
ABSCAR
1 01 8 0
1 01 8 0
1 01 8 0
0 .0 2 4
0 .5 3 8
0 .1 1 7
0 . 02 1
1
0 . 08 3
0 .0 5 2
3 .5 9 7
0 .0 1 1
0 .4 9 9
0 .1 1 1
LOSS 1 01 8 0 0 .1 1 2 0
BONDYIELD 1 01 8 0 4 .7 2 1 4 . 72 1
ABSFE_QTR
SIZE
1 01 8 0
1 01 8 0
0 .0 0 3
7 .2 0 2
0 . 00 1
7 . 03 8
0 .3 1 5
0 .2 8 3
0 .0 0 7
1 .7 5 2 n
Post-comment letters Comparison
Mean Median Std. Dev. Test of Means Test of Medians
10 3 4 7 0 .0 5 7 0. 0 39
10 3 7 3 2 .5 7 7 1. 7 68
10 3 4 7 0 .0 2 4 0. 0 22
10 3 4 7 0 .5 4 9 1
10 3 4 7 0 .1 2 5 0. 0 91
0. 0 55
3. 4 08
0. 0 11
0. 1 15
* * *
* *
0. 4 98
* * *
* * *
* * *
* * *
* * *
10 3 4 7 0 .1 3 5
10 3 4 7 4 .6 9 7
0
4 .7 5
10 3 4 7 0 .0 0 5 0. 0 01
10 3 4 7 7 .3 1 5 7. 1 87
0. 3 42
0. 2 61
0. 0 11
1. 7 97
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
66

Table 5 (continued)
Panel B: Changes in absolute cumulative abnormal returns around earnings announcements
Post
RETVOL
NEGCAR
ABSCAR
LOSS
BONDYIELD
ABSFE_QTR
SIZE
POST x RETVOL
POST x NEGCAR
POST x ABSCAR
POST x LOSS
POST x BONDYIELD
POST x ABSFE_QTR
POST x SIZE
Average
Effect
-0 . 0 0 3
[-0.865]
0 . 0 0 8 * *
[2.396]
-0 . 1 5 9
[-1.164]
-0 . 0 0 0
[-0.888]
-0 . 0 0 0
[-1.055]
0 .1 4 7
[1.290]
-0 . 0 0 2
[-1.317]
-0 . 0 0 0
[-0.046]
(1)
-0 .0 3 9 * *
[-2.311]
0 . 7 9 9 * * *
[9.263]
0 . 0 0 4 * * *
[4.741]
0 . 1 2 7 * * *
[17.796]
-0 . 0 1 2 * * *
[-5.181]
0 .0 0 3
[1.439]
0 . 6 3 4 * * *
[5.866]
Cross Sectional Effects Based on Letter Severity
Proxy 1: Time to Resolution
TTR>=Med TTR< Med OTHER=1
-0 .0 0 3
[-0.796]
0 .0 1 1 * *
[2.084]
-0 .1 0 7
[-0.598]
-0 .0 0 0
[-0.623]
-0 .0 0 0
[-0.833]
0 .1 2 5
[0.759]
-0 .0 0 1
[-0.532]
-0 .0 0 3
[-0.216]
(2)
-0 .0 5 3 * *
[-2.056]
0 . 7 7 0 * * *
[6.867]
0 . 0 0 3 * * *
[2.873]
0 . 1 2 3 * * *
[13.285]
-0 . 0 1 1 * * *
[-3.710]
0 .0 0 3
[1.169]
0 . 6 3 1 * * *
[4.456]
-0 . 0 0 1
[-0.292]
0 .0 0 7
[1.408]
-0 . 2 0 5
[-1.023]
-0 . 0 0 0
[-0.665]
-0 . 0 0 0
[-0.507]
0 .1 5 0
[0.964]
-0 . 0 0 3
[-1.320]
0 .0 0 2
[0.117]
(3)
-0 . 0 3 0
[-1.299]
0 . 8 1 5 * * *
[6.253]
0 . 0 0 5 * * *
[3.863]
0 . 1 3 3 * * *
[12.240]
-0 . 0 1 3 * * *
[-3.687]
0 .0 0 2
[0.751]
0 . 6 2 8 * * *
[3.860]
0 .0 0 1
[0.625]
0 .6 5 5
[1.217]
-0 . 0 1 9 * * *
[-3.246]
-0 .0 0 7
[-0.133]
0 .0 0 5
[0.276]
-0 .0 1 0
[-0.623]
-0 .5 6 7
[-1.363]
-0 .0 0 2
[-1.021]
(4)
0 .0 6 9
[0.830]
0 .5 2 7
[1.282]
0 . 0 1 4 * * *
[3.409]
0 . 1 6 9 * * *
[4.337]
-0 .0 2 8 * *
[-2.188]
0 .0 0 4
[0.407]
0 .7 3 3 * *
[2.262]
-0 . 0 0 5
[-1.117]
0 .0 0 1
[0.040]
-0 . 0 0 8
[-0.785]
0 .0 1 6
[1.470]
-0 . 0 0 6
[-0.745]
0 .7 2 7
[1.363]
0 .0 0 2
[1.058]
0 .2 0 7
[0.506]
-0 . 3 7 4
[-0.619]
-0 .0 0 4 * *
[-2.212]
Proxy 2: Comment Types
OTHER=0 OTHER = 0
(5)
-0 . 0 4 7
[-0.883]
IMPORTANT=1 IMPORTANT=0
(6) (7)
-0 . 1 9 8 * *
[-2.415]
0 .0 3 9
[0.588]
0 . 7 1 4 * *
[2.381]
0 . 0 0 6 *
[1.822]
0 . 0 9 9 * * *
[4.829]
-0 . 0 0 4
[-0.523]
0 . 8 3 5
[1.622]
-0 . 0 0 4
[-0.734]
0 . 1 0 2 * * *
[3.321]
-0 . 0 3 2 * *
[-2.129]
0 . 6 1 6 *
[1.732]
0 . 0 1 2 * * *
[2.890]
0 . 0 9 7 * * *
[3.680]
0 .0 1 0
[0.892]
-0 . 0 1 9
[-1.536]
1 . 8 4 8 * * *
[3.333]
-0 . 0 0 1
[-0.346]
-0 . 1 9 3
[-0.326]
0 . 0 0 6
[0.797]
-0 . 0 0 4
[-0.077]
0 . 0 1 8
[0.982]
0 . 0 4 5 * * *
[2.681]
-1 . 4 3 3 * *
[-2.213]
-0 . 0 0 2
[-0.941]
0 .0 0 2
[0.205]
0 .3 9 7
[0.625]
0 . 0 0 4 *
[1.951]
0 .3 2 2
[0.649]
-0 .0 1 3 * *
[-2.121]
-0 .0 0 5
[-0.169]
-0 .0 1 8
[-1.401]
0 .0 0 0
[0.021]
-0 .1 0 3
[-0.146]
-0 . 0 0 4 *
[-1.699]
Industry FE
Year FE
Observations
Number of cases
Adjusted R
2
Yes
Yes
2 0 ,527
1 5 9 7
0 . 2 1
Yes
Yes
1 0 ,414
8 0 3
0 .1 9 8
Yes
Yes
1 0 ,113
7 9 4
0 .2 2 9
67
Yes
Yes
9 8 9
7 8
0 .2 2 0
Yes
Yes
1 ,571
1 3 1
0 .1 9 5
Yes
Yes
6 0 5
5 0
0 . 1 8 8
Yes
Yes
9 6 6
8 1
0 .2 1 3

Table 5 (continued)
Panel C: Changes in cumulative abnormal trading volumes around earnings announcements
Post
RETVOL
NEGCAR
ABSCAR
LOSS
BONDYIELD
ABSFE_QTR
SIZE
POST x RETVOL
POST x NEGCAR
POST x ABSCAR
POST x LOSS
POST x BONDYIELD
POST x ABSFE_QTR
POST x SIZE
[3.971]
0 . 0 3 9
[1.389]
-1 6 . 8 2 6 * *
[-2.090]
-0 .1 2 8
[-1.344]
-1 . 5 3 8 * *
[-2.446]
0 . 2 5 4
[1.274]
0 . 6 5 6 * * *
[2.587]
-1 7 . 0 5 3 * *
[-2.007]
-0 .0 0 8
[-0.234]
Average
Effect
Cross Sectional Effects Based on Letter Severity
(1)
-2 . 4 0 8 *
Proxy 1: Time to Resolution
TTR >= Med TTR < Med OTHER=1 OTHER=0
(2)
-3 . 6 4 4 *
[-1.931] [-1.955]
-2 2 . 8 1 0 * * * -3 0 . 9 2 2 * * *
[-3.445]
0 . 3 5 6 * * *
[-3.486]
0 . 2 7 8 * * *
(3)
-1 . 2 3 1
[-0.722]
-1 5 . 7 4 5
[-1.633]
0 .4 4 7 * * *
(4)
5 . 1 8 8
[1.029]
-4 9 . 8 3 6
[-1.303]
1 .0 5 5 * * *
Proxy 2: Comment Types
(5)
-1 .2 3 3
[-0.264]
-7 4 . 0 4 0 * * *
[-3.500]
0 .3 9 8
IMPORTANT=1 IMPORTANT=0
(6)
-1 3 . 5 3 0 * *
[-2.030]
OTHER = 0
-1 0 6 . 0 5 8 * * *
[-3.620]
0 .6 5 6 *
(7)
4 .6 7 3
[0.796]
-5 5 . 9 3 8 * *
[-2.039]
0 .3 9 1
[5.050]
8 . 7 6 7 * * *
[17.157]
-1 . 3 6 5 * * *
[2.859]
8 . 3 8 0 * * *
[12.948]
-1 . 2 9 1 * * *
[-8.351]
-0 .0 3 9
[-5.832]
-0 .0 1 0
[-0.265] [-0.051]
2 8 . 4 4 3 * * * 3 3 . 5 4 9 * * *
[4.442]
9 .1 5 3 * * *
[11.748]
-1 .4 0 8 * * *
[-5.908]
-0 . 0 5 6
[3.274] [1.538]
1 0 . 8 2 0 * * * 8 . 6 6 3 * * *
[3.768]
-1 . 4 6 4
[-1.135]
-0 . 3 9 5
[-0.256] [-0.603]
2 5 . 9 4 2 * * * 4 3 . 6 6 3 * *
[5.158]
-0 .6 4 2
[-1.173]
-0 .4 4 6
[-0.772]
4 1 . 2 2 0
[1.862]
1 0 . 7 0 9 * * *
[4.492]
-0 . 7 4 9
[-0.734]
-0 . 9 8 6
[-1.101]
1 8 9 .6 4 5 * * *
[1.063]
7 . 7 8 6 * * *
[3.509]
-0 . 5 1 4
[-0.759]
-0 . 0 8 8
[-0.123]
1 .8 8 2
[3.086]
0 . 0 2 4
[0.624]
-1 5 . 3 8 3
[-1.355]
-0 .1 1 8
[-0.907]
-2 . 0 8 1 * *
[-2.516]
0 . 1 5 6
[0.552]
0 .8 9 4 * *
[2.374]
-1 6 . 6 4 8
[-1.317]
0 . 0 0 1
[0.024]
[2.794]
0 .0 7 3 *
[1.670]
-1 8 . 7 9 5 *
[-1.675]
-0 . 1 4 6
[-1.067]
-0 . 9 7 3
[-1.032]
0 . 4 2 2
[1.498]
0 . 4 3 9
[1.259]
-1 9 . 9 0 7 *
[-1.757]
-0 . 0 2 1
[-0.407]
[2.119]
0 . 3 5 2 *
[1.917]
-2 5 . 8 7 4
[-0.656]
-1 . 1 1 3 * *
[-2.398]
-0 . 5 5 8
[-0.171]
-0 . 2 6 9
[-0.224]
-0 . 3 4 5
[-0.365]
-4 . 9 1 6
[-0.165]
-0 . 2 9 8
[-1.586]
[1.124]
0 .0 9 9
[0.791]
3 6 . 7 2 0
[1.211]
-0 .5 4 5
[-1.587]
-1 .4 1 0
[-0.706]
-0 .2 1 6
[-0.285]
0 .3 0 5
[0.310]
-1 3 . 7 5 5
[-0.354]
-0 .0 3 3
[-0.235]
[3.047]
-0 . 2 2 7
[-1.185]
4 9 . 2 8 0
[1.491]
-0 . 4 4 6
[-0.972]
-4 . 8 4 6
[-1.666]
-0 . 1 8 5
[-0.179]
2 .6 3 2 *
[1.967]
-1 6 0 . 3 6 4 * * *
[-2.807]
0 .1 4 4
[0.831]
[0.049]
0 .2 0 5
[1.247]
3 1 . 7 0 2
[0.849]
-0 . 6 3 9
[-1.315]
-0 . 2 3 4
[-0.092]
-0 . 2 2 1
[-0.259]
-0 . 8 0 6
[-0.645]
2 7 . 4 8 8
[0.723]
-0 . 1 3 1
[-0.700]
Industry FE
Year FE
Observations
Number of cases
Adjusted R
2
Yes
Yes
2 0 ,573
1 6 0 0
0 . 1 0
Yes
Yes
1 0 ,433
8 0 4
0 . 0 9 2
Yes
Yes
1 0 ,140
7 9 6
0 . 1 1 4
68
Yes
Yes
9 8 9
7 8
0 . 1 5 0
Yes
Yes
1 ,574
1 3 1
0 .1 0 7
Yes
Yes
6 0 8
5 0
0 .1 4 0
Yes
Yes
9 6 6
8 1
0 .1 0 3

Table 6: Changes in Analyst Forecast Performance
This table reports changes in analyst forecast performance after the SEC comment letters. Panel A presents summary statistics. Panel B presents regression analysis with absolute analyst forecast errors ( ABSFE ) as the dependent variable. Panel C presents regression analysis with analyst forecast dispersion ( DISPERSION ) as the dependent variable. Analyst forecasts are individual current quarterly forecasts issued between earnings announcements. ABSFE is the absolute value of forecast errors. Forecast errors (FE) are company actual quarterly earnings less individual analyst earnings forecasts, deflated by the stock price at the beginning of the quarter. Actual earnings and analyst forecasts are taken from IBES.
To avoid FE measurement problems that could arise from small deflators, we delete observations with price deflators that are less than $1.36 which represents the fifth percentile of price deflators and we winsorize the top and bottom percentile of FE. DISPERSION is the standard deviation of FE. HORIZON is the number of days from the analyst forecast date to the company’s earnings announcement. LOSS equals one if quarterly earnings are negative and zero otherwise. FOLLOWING is the number of analysts who provide forecasts on a company during the quarter. EXPERIENCE is the number of years an analyst appears in the IBES database . BROKERSIZE is the decile rank of brokerage firm size based on the number of analysts at each firm. COMPLEXITY represents the number of companies an analyst covers based on forecasts issued during the year. t -statistics are in brackets and are calculated based on White heteroskedastic consistent standard errors adjusted for clustering by firm. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
Panel A: Descriptive statistics
Variable
ABSFE n
Pre-comment letters
Mean Median Std. Dev.
8 4,767 0 .0 0 3 0. 0 01 0 .0 0 6 n
Post-comment letters
Mean Median Std. Dev. Test of Means Test of Medians
8 8 ,614 0 .0 0 5 0 .0 0 2 0. 0 13 * * *
Comparison
* * *
DISPERSION 8 2,529 0 .0 0 2 0. 0 01
LOSS 8 4,767 0 .1 0 5 0
HORIZON 8 4,767 5 4 .4
FOLLOWING 8 4,767 1 1 .5
5 7 .0
1 0 .0
0 .0 0 3
0 .3 0 7
2 9 .6
7 .5 7
8 6 ,682 0 .0 0 2
8 8 ,614 0 .1 1 7
8 8 ,614
8 8 ,614
54 . 5
11 . 3
0 .0 0 5
0
5 8 .0
1 0
0. 0 05
0. 3 22
3 0 .2
7 .2 0
* * *
* * *
* * *
* * *
* * *
* * *
BROKERSIZE 8 4,767 9 .1 8
COMPLEXITY 8 4,767 1 6 .0
EXPERIENCE 8 4,767 4 .1 6
1 0 .0
1 5 .0
3 .0 0
1 .4 3
9 .3 6
3 .6 7
8 8 ,614
8 8 ,614
8 8 ,614
9. 0 9
17 . 0
4. 5 5
1 0 .0
1 6
3 . 00
1 .5 5
7 .4 9
3 .7 1
* * *
* * *
* * *
* * *
* * *
* * *
69

Table 6 (continued)
Panel B: Changes in absolute analyst forecast errors
POST
LOSS
HORIZON
FOLLOWING
BROKERSIZE
COMPLEXITY
EXPERIENCE
POST x LOSS
POST x HORIZON
POST x FOLLOWING
POST x BROKERSIZE
POST x COMPLEXITY
POST x EXPERIENCE
[-0.003]
8 . 6 1 1 * * *
[5.044]
0 . 0 0 5 *
[1.850]
0 .0 0 8
[0.300]
0 .0 7 4
[1.454]
0 .0 1 1
[1.335]
0 .0 6 3 * *
[2.376]
Dependent variable = ABSFE * 1000
Average
Effect
Cross Sectional Effects Based on Letter Severity
(1)
Proxy 1: Time to Resolution
TTR >= Med TTR < Med OTHER = 1 OTHER = 0
(2)
-2 .2 9 4 * * -3 . 5 2 7 * *
(3)
-0 . 7 3 9
(4)
-1 .5 4 4
Proxy 2: Comment Types
(5)
-1 . 4 3 1
OTHER = 0
IMPORTANT = 1
(6)
-2 . 8 1 4 *
IMPORTANT
=0
(7)
0 . 0 5 3
[-2.490]
8 . 0 5 7 * * *
[13.661]
0 . 0 1 1 * * *
[-2.394]
9 . 2 5 5 * * *
[10.595]
0 . 0 1 4 * * *
[-1.008]
6 . 7 0 4 * * *
[9.226]
0 . 0 0 8 * * *
[-1.049]
4 . 6 9 3 * * *
[3.775]
0 . 0 0 6 *
[-1.208]
7 . 0 6 2 * * *
[6.155]
0 . 0 1 5 * * *
[-1.883]
6 . 6 3 7 * *
[2.497]
0 . 0 2 0 * * *
[0.039]
7 . 5 8 9 * * *
[5.715]
0 . 0 1 4 * *
[6.635]
-0 .0 0 5
[-0.408]
-0 . 0 9 1 * * *
[-3.112]
-0 .0 0 4
[-0.835]
-0 .0 0 0
[5.952]
0 . 0 2 3
[1.259]
-0 . 1 1 5 * * *
[-2.795]
0 . 0 0 4
[0.493]
-0 . 0 3 0 *
[3.650]
-0 .0 4 1 * *
[-2.266]
-0 . 0 3 5
[-0.973]
-0 . 0 0 8 *
[-1.801]
0 .0 2 1
[1.804]
-0 .0 1 5
[-0.173]
-0 .0 9 4
[-1.511]
0 . 0 0 1
[0.277]
0 . 0 2 5
[3.590]
-0 . 0 2 7
[-0.673]
-0 . 0 6 2
[-0.923]
0 .0 0 4
[0.721]
-0 . 0 4 3
[3.601]
-0 . 1 3 0 * * *
[-2.916]
-0 . 0 7 9
[-1.116]
-0 . 0 0 2
[-0.334]
-0 . 0 2 1
[2.445]
-0 . 0 2 7
[-0.311]
-0 . 0 9 9
[-1.148]
0 . 0 2 6
[1.431]
-0 . 0 9 9
[-1.708]
1 1 . 8 5 5 * * *
[4.533]
0 . 0 0 6
[1.324]
-0 .0 0 0
[-0.005]
0 .1 5 8 * *
[2.064]
0 . 0 0 9
[0.589]
0 .0 9 4 * *
[2.168]
[1.263]
4 . 2 3 4 * * *
[3.326]
0 .0 0 5
[1.560]
0 .0 1 2
[0.623]
-0 . 0 3 8
[-0.724]
0 . 0 2 1 * *
[2.508]
0 .0 2 1
[0.971]
[1.051]
1 . 5 3 2
[1.000]
0 . 0 0 7
[1.220]
0 . 0 6 2
[0.826]
0 . 0 9 4
[0.886]
-0 .0 0 8
[-0.372]
0 . 0 1 3
[0.358]
[-0.689]
3 . 2 9 1 * *
[2.052]
0 .0 1 2
[1.373]
-0 . 0 2 6
[-0.857]
0 .0 8 0
[0.855]
0 .0 2 8 *
[1.774]
0 .1 0 0
[1.415]
[-0.509]
1 .8 5 0
[1.002]
0 .0 0 1
[0.130]
0 .0 2 5
[0.861]
0 . 2 5 0 * *
[2.436]
0 .0 0 4
[0.291]
-0 . 0 1 2
[-0.197]
[-1.206]
4 . 2 7 6 * *
[2.217]
0 . 0 1 8
[1.430]
-0 . 0 3 4
[-0.636]
0 . 0 1 6
[0.142]
0 . 0 1 9
[0.770]
0 . 1 2 3
[1.400]
Industry FE
Year FE
Observations
Number of cases
Adjusted R
2
Yes
Yes
1 7 3 ,381
1 9 0 0
0 .2 1 0
Yes
Yes
9 0 ,592
9 4 1
0 . 2 3 4
Yes
Yes
8 2 ,789
9 5 9
0 .2 0 5
Yes
Yes
6 ,152
1 0 1
0 . 3 5 3
Yes
Yes
1 2 ,370
1 6 4
0 .4 8 1
Yes
Yes
5 ,106
6 0
0 .4 0 4
Yes
Yes
7 ,264
1 0 4
0 . 5 4 2
70

Table 6 (continued)
Panel C: Changes in analyst forecast dispersion
POST
LOSS
HORIZON
FOLLOWING
BROKERSIZE
COMPLEXITY
EXPERIENCE
POST x LOSS
POST x HORIZON
[1.756]
2 . 7 0 2 * * *
[3.163]
0 .0 0 1
POST x FOLLOWING
[1.265]
0 .0 0 6
[0.424]
POST x BROKERSIZE 0 .0 5 6 * *
Dependent variable = DISPERSION * 1000
Average
Effect
Cross Sectional Effects Based on Letter Severity
(1)
Proxy 1: Time to Resolution
TTR >= Med TTR < Med OTHER = 1 OTHER = 0
(2)
-0 .9 0 6 * * -1 . 5 9 9 * *
(3)
-0 . 1 3 3
(4)
-0 .7 8 9
Proxy 2: Comment Types
(5)
0 .2 6 5
OTHER = 0
IMPORTANT = 1
(6)
-0 .0 1 7
IMPORTANT
=0
(7)
0 . 5 2 9
[-1.994]
4 . 4 4 0 * * *
[11.613]
-0 . 0 0 7 * * *
[-2.323]
4 .6 6 6 * * *
[8.551]
-0 .0 0 7 * * *
[-0.274]
4 .1 3 4 * * *
[7.977]
-0 .0 0 8 * * *
[-1.199]
1 .8 6 7 * *
[2.053]
-0 .0 0 4 * *
[0.383]
4 . 7 8 1 * * *
[4.682]
-0 . 0 0 6 * * *
[-0.018]
4 .2 7 2
[1.653]
-0 . 0 0 5 * * *
[0.597]
4 . 8 6 0 * * *
[4.004]
-0 . 0 0 5 *
[-8.341]
0 .0 1 9 * *
[2.274]
-0 . 0 4 3 * * *
[-3.043]
0 .0 0 0
[0.145]
0 . 0 1 4 *
[-6.287]
0 .0 2 5 * * *
[2.599]
-0 .0 5 4 * * *
[-3.041]
0 .0 0 8 *
[1.655]
-0 . 0 0 6
[-6.288]
0 . 0 1 0
[0.740]
-0 . 0 2 2
[-1.111]
-0 .0 0 5 *
[-1.685]
0 .0 3 0 * * *
[-2.048]
0 .0 1 0
[0.321]
-0 .0 4 8
[-1.537]
-0 .0 0 1
[-0.490]
0 .0 1 4
[-3.147]
0 .0 3 0
[1.205]
-0 . 0 5 3 *
[-1.707]
0 .0 0 2
[0.442]
0 .0 1 5
[-2.831]
-0 .0 2 5
[-0.782]
-0 .0 1 1
[-0.488]
-0 .0 0 0
[-0.051]
0 . 0 2 3 *
[-1.789]
0 .0 7 2 *
[1.688]
-0 . 0 8 5 *
[-1.810]
0 . 0 0 6
[0.788]
-0 . 0 3 1
[-0.619]
3 .8 3 6 * * *
[2.989]
0 . 0 0 1
[0.583]
0 . 0 2 3
[1.016]
0 . 0 9 0 * *
[2.847]
1 . 2 0 8
[1.408]
0 . 0 0 2
[1.583]
-0 . 0 0 9
[-0.575]
0 . 0 0 9
[1.183]
2 . 2 7 6 *
[1.960]
-0 .0 0 0
[-0.158]
0 .0 4 8
[1.405]
0 .0 6 3
[0.626]
-0 .1 2 4
[-0.120]
0 .0 0 2
[0.558]
-0 . 0 4 1 *
[-1.976]
0 .0 4 5
[1.762]
-0 .3 5 5
[-0.190]
0 .0 0 3
[1.165]
0 .0 1 6
[0.887]
0 .0 3 0
[-1.292]
0 . 6 2 0
[0.419]
-0 . 0 0 1
[-0.181]
-0 . 0 8 4 * *
[-2.229]
0 . 0 5 5
POST x COMPLEXITY
POST x EXPERIENCE
[2.201]
0 .0 0 1
[0.198]
0 .0 0 5
[0.380]
[2.540]
-0 . 0 0 0
[-0.047]
0 . 0 1 9
[0.970]
[0.300]
0 . 0 0 6
[1.115]
-0 . 0 1 4
[-1.181]
[1.537]
-0 .0 0 3
[-0.332]
0 .0 0 7
[0.316]
[0.907]
0 .0 1 3
[1.437]
0 .0 2 2
[1.053]
[0.802]
0 .0 0 9
[0.805]
-0 .0 2 4
[-1.073]
[0.782]
0 . 0 1 3
[1.016]
0 .0 4 5 *
[1.775]
Industry FE
Year FE
Observations
Number of cases
Adjusted R
2
Yes
Yes
1 6 9 ,211
1 7 4 6
0 .2 4 6
Yes
Yes
8 8 ,648
8 6 9
0 . 2 7 7
Yes
Yes
8 0 ,563
8 7 7
0 . 2 2 5
Yes
Yes
5 ,718
6 7
0 .4 4 2
Yes
Yes
1 1 ,580
1 1 5
0 .5 4 5
Yes
Yes
4 ,880
4 5
0 .4 9 6
Yes
Yes
6 ,700
7 0
0 . 6 2 9
71

Table 7: Robustness Tests – Difference-in-differences Method with a Matched Control Sample
This table reports robustness tests of changes in market reactions and analyst forecast performance using a difference-in-difference research design. The matched control firms are determined based on the probability of receiving a comment letter. CL equals one if the firm receives an SEC comment letter, zero otherwise. See
Tables 5 and 6 for the remaining variable definitions. t -statistics are in brackets and are calculated using White heteroskedastic consistent standard errors adjusted for firm clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
POST
POST x CL
RETVOL
NEGCAR
ABSCAR
LOSS
BONDYIELD
ABSFE_QTR
SIZE
POST x RETVOL
POST x NEGCAR
POST x ABSCAR
POST x LOSS
POST x BONDYIELD
POST x ABSFE_QTR
POST x SIZE
Firm FE
Year FE
Observations
# of cases
Adjusted R
2
- 0 .0 0 3
[-1.143]
0. 0 07 * * *
[2.679]
- 0 .1 7 7
[-1.625]
- 0 .0 0 1 * * *
[-2.743]
Yes
Yes
3 5 ,132
1 39 8
0 .2 4 9
[8.092]
0 .0 0 2
[1.484]
0 .0 0 3
[0.035]
- 0 .0 0 1
[-0.493]
0 .0 0 7
[0.849]
Changes in Market Reactions
ACAR (-1, +1) CAV (-1, +1)
(1) (2)
- 0. 0 25 * - 1. 1 69
[-1.919]
- 0. 0 02 *
POST
[-1.208]
- 0 .1 3 4 * POST x CL
[-1.875]
0. 2 53 * * *
[3.054]
0. 0 04 * * *
[-1.650]
- 5 8 .3 4 7* * * LOSS
[-9.999]
0 .3 3 1 * * * HORIZON
[4.908]
0. 1 11 * * *
[18.774]
- 0 .0 0 7 * * *
[-3.047]
0. 0 04 * * *
[2.647]
0. 7 47 * * *
[6.032]
7 .7 7 3 * * *
[20.281]
[-5.309]
0 .0 8 3
[0.713]
FOLLOWING
- 0 . 81 1 * * * BROKERSIZE
COMPLEXITY
4 9. 3 6 4 * * * EXPERIENCE
[7.319]
0 .8 1 1 * * *
[9.804]
- 3 3 .2 2 6* * * POST x HORIZON
[-5.350]
- 0. 0 52
[-0.688]
- 1 .0 6 5* *
[-2.208]
0. 3 83 * *
[2.464]
0 .5 3 2 * * *
POST x LOSS
POST x FOLLOWING
POST x BROKERSIZE
POST x COMPLEXITY
POST x EXPERIENCE
[2.760]
- 1 9 .9 8 6* * *
[-2.635]
- 0 . 07 9 * * *
[-2.660]
Yes
Yes
3 5 ,185
1 4 0 1
0 .1 9 0
Firm FE
Year FE
Observations
# of cases
Adjusted R
2
0 .0 0 4
[1.000]
- 0. 0 04
[-0.442]
5 .5 7 1 * * *
[8.185]
0. 0 04 * *
[2.381]
- 0. 0 12
[-0.922]
0. 0 56 * *
[2.118]
0 .0 1 6 * * *
[2.650]
0 .0 1 3
[0.874]
Changes in Analyst Forecast Performance
ABSFE x 1000 DISPERSION x 1000
(3) (4)
- 1 . 30 4 * * * - 0 .5 3 6 * * *
[-3.397]
- 0 .3 9 2 *
[-3.019]
- 0. 2 56 * *
[-1.866]
5 .7 1 9 * * *
[11.169]
0 .0 1 4 * * *
[12.306]
- 0. 0 11
[-0.718]
- 0 . 04 9 * * *
[-2.863]
[-2.364]
2 .8 6 5 * * *
[9.182]
- 0 .0 0 4 * * *
[-7.671]
0 .0 4 5 * * *
[6.016]
- 0 .0 2 2 * * *
[-3.210]
0 .0 0 1
[0.689]
0 .0 0 3
[0.636]
1 .7 1 8 * * *
[4.960]
0 .0 0 1*
[1.850]
0 .0 0 2
[0.246]
0 .0 4 4 * * *
[3.731]
0 .0 0 4*
[1.916]
0 .0 0 2
[0.343]
Yes
Yes
27 5 ,984
1 5 6 2
0 .3 2 9
Yes
Yes
2 5 4 ,235
1 3 1 6
0 .4 3 5
72