The Effect of Regulator Oversight on Firms’ Information Environment:

Securities and Exchange Commission Comment Letters

Reining Chen

MIT Sloan School of Management Cambridge, MA 02142

rnchen@mit.edu

Rick Johnston Krannert School of Management

Purdue University West Lafayette, IN 47907

johnstr@purdue.edu

July 9, 2010

Abstract:

We explore the content and determinants of Securities and Exchange Commission (SEC) comment letters and then examine whether letter resolution affects both the firm’s information environment and that of its peers. Our content analysis confirms that the comment emphasis is disclosure. If the resolution of an enquiry improves disclosure thereby enhancing the preannouncement information environment, we would expect dampened market reactions around ensuing earnings announcements. Our results support the hypothesis showing reduced return volatility and trading volume after the comment letter for targeted firms, and where SEC scrutiny is intense, reduced return reactions for industry peers. This spillover effect is consistent with our determinants model, which shows that the probability of receiving a letter is higher for industry leaders suggesting that peers mimic industry leaders. We conclude the SEC’s oversight has both direct and indirect 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 “Securities and Exchange Commission Comment Letters: Enforcing Accounting Quality and Disclosure”. We appreciate the helpful comments and suggestions from Andrew Karolyi, Sundaresh Ramnath, Cathy Schrand, Ro Verrecchia, and workshop participants at the 2009 AAA Financial Accounting and Reporting Section meeting and the annual meeting, the 2010 LBS conference and the following universities: City University London, MIT, National University of Singapore, The Ohio State University, University of Pennsylvania, and University of Technology Sydney. We thank Anthony Meder and Yunyan Zhang for data assistance. Johnston thanks The Wharton School, University of Pennsylvania and The Ohio State University for financial support.

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1. Introduction

In this paper we investigate whether regulatory oversight of financial reporting affects

companies’ information environment. We address the question by examining the content,

determinants, and ensuing market consequences of the Securities and Exchange Commission’s

(SEC) comment letters. The SEC reviews a significant portion of the filings (10Qs, 10Ks, S1-4

etc.) submitted to them. If the SEC staff identify potential deficiencies, they send a comment

letter to the company seeking clarification, more information, or revision of the filing or future

filings. Unless the company voluntarily discloses it, the SEC enquiry is unknown to the public

during its existence. In 2005, the SEC began to publicly release comment letters of resolved

cases. 1 These letters provide a unique opportunity to investigate the monitoring role of the SEC

in the U.S. capital market.

The stated goal of the SEC review process is to improve the quality of material disclosure

to investors in a timely manner. However, whether the review process has any effect on the

information environment is unclear. On the one hand, disclosure may improve as a result of the

review, thereby providing useful information to investors and 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.

Alternatively, any additional disclosure may be an oversupply of information and thus have little

economic consequence. This alternative seems highly plausible, given that under current

accounting and disclosure standards, the U.S. information environment is already rich.

1 Section 2 describes additional institutional details on comment letters.

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Moreover, we might expect cross-sectional variation in any potential effects. Some letters might

address more issues or issues of greater severity and thus create a larger improvement.

Alternatively, effects may vary across firm size. Smaller firms may not have the same quality of

information environment, so the impact of the SEC’s comments may be greater. Finally, the

changes in disclosure that arise from comment letters could be mimicked by industry peers,

hence we explore industry spillover effects. We expect any such effects to be most pronounced

where SEC scrutiny is intense within an industry.

We collect the comment letters from the SEC’s website for 2003-2006 and retain those

related to 10Ks and 10Qs. We conduct content analysis on a subsample and document that the

vast majority of letter comments address disclosure issues. We then explore the attributes of

letter recipients. Whether or not a firm receives a comment letter depends on two factors: the

type of firms that the SEC targets for review in that year, and the firm’s reporting quality. The

SEC's review selection criteria are unknown. However, the Sarbanes-Oxley Act of 2002 (SOX)

outlines several factors for the SEC to use when it selects firms for review. We use those criteria

and other factors related to financial reporting quality and find that firms more likely to receive a

SEC comment letter are industry leaders, those that have been public longer and those that have

previously restated their financial results. Greater cash flow volatility and higher Earnings/Price

ratios also increase the likelihood of receiving a letter.

To evaluate the impact of the comment letters on the firms’ information environment we

study the changes in market behavior around earnings announcements. Prior research shows how

information asymmetry and differential information processing by investors affect price and

volume reactions to public announcements (Diamond and Verrecchia, 1991; Kim and

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Verrecchia, 1991a, b, 1994, 1997; Harris and Raviv, 1993; Kandel and Pearson, 1995). In

particular, a decline in price reactions around earnings announcements suggests that the quality

of the information environment prior to the announcement has improved. Lower volume

reactions indicate less investor disagreement regarding the information content of the earnings

announcement and higher consensus of firm value.2

We find that abnormal return volatility and trading volume around earnings

announcements decline subsequent to the SEC comment letters, and that the magnitude of the

change is economically significant. The return volatility change appears to be concentrated in

small firms and the volume results apply to letter cases which are severe. Our findings are robust

to various approaches that address potential letter selection biases.

In our test of industry spillover effect, we do not find any change in return volatility for

industry peers, on average. However, in industries that receive greater attention from the SEC,

peer firms do show a reduction in price reactions. The magnitude of this reduction is as large as

the comment letter firms. We find no change in volume reactions for peer firms.

Our study is related to four 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 the

quality or quantity of disclosure has positive effects. Increases in disclosure levels tend to

generate greater stock liquidity and reduce a firm’s cost of capital and information asymmetry.

2 The above argument is similar to Bailey et al. (2003) and Bailey et al. (2006). Some might conjecture that the reverse of the story is the case, that a higher quality disclosure is associated with a stronger market reaction. This argument is true if earnings quality improves, leading to a stronger ERC. In Section 2 we examine a subsample of 157 comment letters and find that the majority of the comments are related to disclosure, not earnings quality. Therefore, on average, we expect dampened market reactions after the letters.

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Our paper complements these studies by examining the market consequences of disclosure

changes that arise from the regulator’s direct monitoring of corporate reporting.

The second research path studies the market consequences when the SEC issues a new

regulation, for example, Regulation Fair Disclosure (FD) (Bailey et al., 2003; Heflin et al.,

2003). This research finds that the quantity of voluntary disclosure increases after the adoption of

the regulation, but provides mixed evidence on market related aspects, such as volatility around

earnings announcements and the degree of information asymmetry. Our study explores the

impact of monitoring and enforcement of regulations rather than of issuing regulations.

A third research area which explores private and public enforcement of securities laws

concludes that public enforcement of securities laws has limited value (La Porta et al., 2006;

Djankov et al., 2008a). 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. Leuz and Hail base their enforcement construct on a survey of lawyers. In contrast, we

examine a sample of actual enforcement 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 fourth research stream explores the SEC Accounting and Auditing Enforcement

Releases (AAER) (see for example, Feroz et al., 1991; Dechow et al., 1996; Beatty et al., 1998;

Beneish, 1999; Farber, 2005). This research examines the impact of these enforcement actions on

corporate governance, managers, auditors, underwriters, and market participants. Comment

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letters can lead to AAERs but AAERs are rare. By studying comment letters, we add another

dimension to the research that assesses the impact of the SEC on the U.S. markets.

A working paper by Ertimur and Nondorf (2006) examines a sample of comment letters

for 95 firms that undergo the Initial Public Offering (IPO) process.3 They find no association

between the SEC comment letters and IPO underpricing, bid-ask spreads, or market depth. Our

study differs from theirs along several important dimensions. First, they focus on IPO firms

whereas we study public companies which allows us to examine the change in information

environment after the comment letters. Second, they categorize the content of the letters into

several groups and that grouping may not be representative of the relative importance of various

comments. We use a more objective measure, time to resolution, to proxy for the severity of the

letter content. Finally, while they fail to find any association between comment letter attributes

and the firm’s information environment, we find evidence that targeted firms and, in some

circumstances, their industry peers experience an improvement in their information environment

subsequent to the SEC comment letters.

Our study offers early evidence on the content, determinants and consequences of SEC

comment letters and adds to the literature on the positive effect of quality accounting and

disclosure on the information environment of U.S. firms. We document the beneficial effect of

the oversight role played by the SEC in enhancing and maintaining the quality of the information

environment of firms listed in the U.S. markets. This oversight evidence is important, because

practitioners and academics often focus on the SEC’s role in terms of creating regulations. Our

3 Their paper was undertaken independently and at the same time as ours.

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findings suggest that the SEC’s review process is also an important factor that contributes to the

quality of the U.S. markets. The results could be of interest to policymakers and the Commission

itself, particularly in terms of demonstrating the value of their review efforts. Moreover, the

results could have implications for other countries who wish to replicate the success of U.S.

markets. Finally, this paper is of relevance to the financial statement analysis literature, in that it

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 the results of our letter content analysis. We develop our

hypotheses in Section 3 and in Section 4 we outline our research design. Section 5 details the

data and explores the determinants of receiving a letter. Section 6 presents the empirical analyses

and Section 7 concludes.

2. SEC comment letters: institutional background and content analysis

The Securities Exchange Act of 1934 requires public companies to file quarterly (10Q)

and annual 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. Prior to SOX, the SEC reviewed

approximately 20% of the filings each year. The SEC motivates the review program as follows:4

“The full disclosure system for public companies is the foundation of the federal securities laws. 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.” (emphasis added)

4 Taken from sec.gov, 2008.

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Various stated review objectives include identifying potential or actual material accounting,

auditing, financial reporting or disclosure deficiencies; influencing accounting standards and

practices; proposing new and amended disclosure rules; and offering guidance and counseling,

either informally or through no action letters.5 Feroz et al. (1991) cite an SEC official who

claimed that half of all SEC enforcement leads came from reviews of financial statements and

securities filings.

The SEC does not disclose 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.

When comments are issued, the company receives a letter from the SEC and 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 can be made until the issues are resolved

to the Commission’s satisfaction, at which point the SEC staff advises the filer that the review is

complete.

5 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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Prior to 2005, public access to comment letters and the related responses were only

available through a Freedom of Information Act request. Due to an increasing number of such

requests, in 2005 the SEC began to publicly release, on their website, comment letters relating to

filings made after August 1, 2004 but no earlier than 45 days after the review is completed.

Therefore, in the absence of a company’s voluntary disclosure regarding the letter, the public can

learn about the existence and content of the letter only when the SEC releases it. Of course by

then, all the relevant issues have been resolved and interim filings have been enhanced.6

We provide two complete letters as examples, one for the Landec Corporation (Appendix

1) and a second one for Charles Schwab Corp. (Appendix 2). In addition, to provide some

context for the nature and frequency of comments in the letters, we read and manually code the

first batch of the letters posted to the SEC website. The first batch contains 157 letters from

2004-2005. We apply extracts of the Ertimur and Nondorf (2006) taxonomy, since we find that

they accurately represent the nature of comments found in the letters. We present the 80 possible

comment types and a description of each in Appendix 3.

The comments fall into four groups. The first group, Accounting Issues, address big-

picture problems. Comments relate to issues such as adherence to GAAP, materiality, and

auditor issues. The second group, Accounting/Financial Reporting/Disclosure Topics, comprise

comments specific to accounting balances or transactions such as revenue recognition, inventory,

6 Proprietary costs are a major counter force to the incentive to disclose. 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.

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related party transactions, and capital expenditures. The third group, Business Issues, address

more generic business issues, such as liquidity, competitive environment, and risk factors. The

fourth group, Tone and Level of Disclosure Issues, is editorial in nature, the comments address

presentation and requests are to emphasize or de-emphasize, clarify or disaggregate, certain

items.

The 157 letters contain 1,504 comments, slightly less than ten per letter, on average.

Forty-five percent of the comments fall into the second group. Within the second group,

questions about revenue recognition are the most frequent, followed by claims, commitments and

contingencies, 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 comment is a request for something to be clarified. The second most common

request is to quantify an amount related to a disclosure.

The descriptions of the 80 comment items in Appendix 3 indicate that most of the issues

deal with disclosure. It seems unlikely that earnings or accounting balances would be altered

frequently as a result of the review process. However, there is one exception, item 11 in the first

group, "Not Following GAAP" (see Appendix 3). If this SEC claim is supported, then earnings

may change. To provide further evidence on the nature of the comments, we code the second

group comments into disclosure-related or change in accounting numbers. If the comment

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suggests that the filer provide more information in the 10K or 10Q, we code it as a disclosure

issue. If the comment is more likely to require a change in a financial statement figure, then we

treat it as an accounting issue. We find ninety-six percent of the comments are disclosure related.

This preliminary analysis suggests that the SEC review process is more likely to impact

disclosure, rather than to revise accounting figures.

3. Hypothesis Development

A large body of research has established a positive relation between enhanced disclosure

and firms’ information environment. For example, Welker (1995) shows that a well-regarded

disclosure policy reduces information asymmetry and increases liquidity in equity markets.

Healy, Hutton, and Palepu (1999) find that when firms expand their voluntary disclosure, they

can attract more analyst following and improve stock liquidity. Leuz and Verrecchia (2000)

examine German firms that commit to higher disclosure levels by adopting International

Accounting Standards or U.S. GAAP and find that such firms experience a decline in the

information asymmetry component of the cost of capital, subsequent to adoption. Brown et al.

(2004) show that conference calls lead to long-term 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.

If the disclosure revisions that result from the comment letter process are substantive,

then we would expect an enhanced information environment thereafter. Further, if an effect

exists, then ex ante it seems reasonable to expect cross-sectional differences along at least two

dimensions. First, the resolution of severe letters is more likely to improve the information

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environment. For example, some letters may address a greater number of issues, thus creating a

cumulative impact. Or some letters could address an issue that is uniquely important. Neither

scenario is mutually exclusive, but it suggests that some measure of severity of the letter would

be related to the market effect arising from the resolution of the issue(s). Second, we conjecture

that the effect of the comment letters on the information environment of small firms is larger. On

average, small firms have poorer information environments. They generally have lower analyst

following and institutional ownership and are likely to have higher disclosure costs (Lang and

Lundholm 1993). This poor (pre-letter) information environment suggests that if there is any

impact created by the comment letters, then it should be relatively greater for small firms.

The effect of comment letters, if any, may extend beyond targeted firms. Peer companies

may mimic comment letter firm disclosure to adhere to industry norms or avoid review. Further

Big 4 audit firms may also encourage conformity across clients. If peers do mimic the

disclosures arising from comment letters, then the information environments of firms that do not

receive letters could be enhanced. We hypothesize that this indirect or spillover potential is most

likely where the SEC performs an intense review of a particular industry.

However, 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, because it is difficult to exclude

others from using it and its consumption by one user does not diminish its availability to others.

Without regulation, public goods tend to be underproduced 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.

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Based on Schulte’s (1988) argument, if the SEC is merely creating excess disclosure or

an oversupply, then comment letters should contain little substance and result in minimal

improvement in the firm's information environment. Given the high quality accounting

standards, disclosure requirements, and the requirement in the U.S. that public companies be

audited, we question whether there would be any benefit from an SEC review, which in

substance is merely the SEC staff reading the corporate filings.

Another rationale for why we might expect comment letters to have no economic effect is

regulatory capture theory, which suggests that regulated firms manipulate the agency responsible

for 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 would support either of these rationales.

Some anecdotal evidence of the concern about the effectiveness of the comment letters

appears in the Commission’s own annual reports (2005 and 2006), in which they report various

metrics to track and report on SEC effectiveness. For comment letters, they state that they are

currently unable to quantify significant improvements or actions related to the letters (see

footnote for exact statement). 7

7 “For corporate filings, comments are issued to elicit better compliance with applicable disclosure

requirements and improve the information available to investors. Many instances, amendments involve financial restatements. Determination of “significance” stems from the nature of the change (e.g., restating positive income as a loss) or the size of the company. Analysis of divisions of corporation finance and 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. 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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In summary, whether the SEC’s review of registrant filings improves firms’ information

environment is an empirical question. On one hand, if the review process is successful in

enhancing disclosure to investors, we would expect a better information environment. This

improvement is likely to vary with the severity of the letter and the firm’s original information

environment. On the other hand, if any additional information required by the review is an

unnecessary oversupply or lacks substance, we would expect no change in the firms’ information

environment.

4. Research Design

To assess whether SEC oversight enhances firms’ information environments, we compare

market behavior around earnings releases for the two quarters before and after the receipt of an

SEC comment letter. Our focus on earnings announcement reactions as a proxy for firms’

information environment is motivated by theoretical work of Diamond and Verrecchia (1991)

and Kim and Verrecchia (1991a, b; 1994; 1997). These papers show that 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 an improvement in the

pre-announcement information environment. These papers further show that stock trading

volume arises from differences in the quality (precision) of investor’s private information.

Consequently, a reduction in trading volume around earnings announcements reflects less

information asymmetry across investors and greater consensus regarding firm value. Several

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papers in accounting and finance have used a similar approach to study information environment

changes.8

4.1 Baseline model

We measure stock price reaction as the four-day absolute cumulative abnormal return

(ACAR) around quarterly earnings announcements. We define the earnings announcement date

as day zero (� = 0), and compute ACAR as ���� = | ∏ (1 + ��) − 1| 2

��1 where we compute

AR as the abnormal return based on one-factor market model residuals estimated over the period

t-11 to t-200 trading days. CAV is the four-day cumulative abnormal trading volume around the

quarterly earnings announcement.9 We define abnormal trading volume as the difference

between announcement-window (-1, +2) trading volume and the mean of pre-announcement

window (-200, -11) trading volume, normalized by the mean volume.

To test the effect of the SEC oversight on the information environment, we employ the

following model:

������ �������� = �0 + �1������� + �2�������_���� + ������!� + " (1)

where Market Reaction is either absolute return or volume reaction to earnings announcements;

ComtLtr equals one if the firm receives a SEC comment letter, and zero otherwise; and

8 For example, 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. Another FD study, Bailey et al. (2003) use return and volume reactions to investigate the information environment change around the standard. 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. 9 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 because given the highly skewed volume data, a linear model tends to poorly specify the underlying data structure. Moreover, such model requires more computational cost but provides little improvement in power (Bamber, Barron and Stevens 2009).

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ComtLtr_Post equals one if the firm receives a letter and the observation is from the post-letter

period, and zero otherwise. We define the pre-letter period as the two quarters before the date of

the first letter and the post-letter period as the second quarter after the date of the last letter. We

omit the first quarter after the letter to ensure that the equity market has been exposed to any

change in disclosure. We require the comment letter firms to have both a pre-letter and a post-

letter observation.

In Equation (1), β0 measures the average market reactions of the benchmark firms, β1

captures the difference in market reactions between the comment letter firms and the benchmark

firms, and β2 captures the change in market reactions for comment letter firms. If the SEC’s

review process substantively improves corporate disclosures, then we would expect more muted

market responses to earnings announcements following the resolution of the letters, which would

be reflected by a negative β2. In contrast, if the additional information resulting from the review

process is just an oversupply, then we would expect no change in market responses and thus β2 to

be indistinguishable from zero.

Consistent with earlier studies, we include a set of control variables related to market

behavior around earnings announcements, all of which we discuss in the results section. We

estimate Equation (1) with industry fixed effects, where we define industry as in Fama and

French (1997). We cluster standard errors by firm and year to correct for possible correlations

across observations (Rogers, 1993; Petersen, 2009).

To investigate whether the change in the information environment varies with the

severity of the letters, we alter Equation (1) by replacing the comment-letter dummy with a

variable based on the severity of letter content. We use the duration of the letter period as our

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proxy for the seriousness of the letter content. 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.

The change in a firm's information environment is also likely to vary with the firm’s pre-

letter information environment. We use firm size as our proxy for a firm’s original information

environment. To capture the differential effects, we partition the comment letter dummy in

Equation (1) into three size dummies.

4.2 Comment letter selection issues

If a firm receives an SEC comment letter, it is unlikely to be a random event. For

example, Section 408 of the SOX identifies various firm characteristics for consideration by the

SEC staff in choosing which companies to review. Therefore, certain types of firms are more

likely to attract the SEC’s attention. In addition, firms with certain characteristics may be more

likely to have reporting deficiencies and hence, receive a letter. This non-random treatment

assignment implies that there may be systematic differences between firms that receive a SEC

letter and firms that do not. The systematic differences could lead to biased estimates of the

treatment effect, i.e., changes in market reactions to earnings announcements.

One way to address the selection concern would be to construct the benchmark control

sample based on firms subject to SEC review, but for which no letter is issued. However, we are

unable to identity all the firms the Commission chooses to review.10 Hence, we construct the

10 Despite making several phone calls to the Division of Corporate Finance and also requesting the data under the Freedom Of Information Act.

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benchmark control sample based on firms not receiving a letter in the year, i.e., these firms can

either have been reviewed by the SEC but did not receive a letter or not have been reviewed at

all. However, we further screen the benchmark firms, which we describe next.

We start by building the determinant model which relates firm characteristics to the

probability of receiving a letter.11 The firm characteristics we consider are those listed on Section

408 of the SOX and also the firm’s operating uncertainty and audit quality. We conjecture that

firms are more likely to get a comment letter if they fall under the SOX Section 408 criteria and

are more subject to reporting errors. For every year, we run a logit model and assign each firm a

predicted probability of receiving a comment letter in that year. Then we require both the

comment letter firms and the benchmark firms to have a common predicted probability range.

We remove firms that fall out of the range and exclude any unmatched comment-letter firm.

In addition, we conduct several robustness tests. First, we apply a standard two-stage

Heckman model. The first stage is a probit regression that models the probability of receiving a

letter. The second stage is Equation (1) augmented with the inverse Mills ratio from the probit

model to control for any self-selection bias.

Second, we use propensity score matching to select a matched control sample. We then

conduct a difference-in-difference test, examining the change in the information environment

between the comment-letter sample and the matched control sample. The propensity score for a

firm is the probability of receiving a SEC comment letter conditional on the firm’s observable

characteristics. Propensity score matching provides two advantages. First, it allows us to control

11 (See Section 5.2)

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for many company covariates simultaneously by matching on a single scalar variable (i.e., the

propensity score), and second, we can create a quasi-randomized experiment (D’Agostino,

1998). If we find two firms, one in the comment letter group and one in the control group, with

the same propensity score, then it is as if these two firms were randomly assigned to each group

in the sense that they are equally likely to be treated or control (conditional on the observed firm

characteristics). Each matched control firm has a hypothetical comment letter period based on

the corresponding comment-letter firm.

The difference-in-difference model is:

������ �������� = �0 + �1���� + �2������� + �3������� ∗ ���� +

+ ������!� + "

(2)

�1 captures how both treatment and control firms are influenced by time. The time-invariant

difference, if any, in information environment between the treatment and the control is captured

by �2. β3 represents the differential change in information environment between the comment

letter firms and the matched control firms. If the SEC’s review process is effective in enhancing

a firm’s information environment, then we would expect �3 < 0.

The difference-in-difference design ensures that our results are not driven by some

unspecified macro time trend. However, propensity score matching is also subject to the

limitation that it can only remove selection biases based on observable firm characteristics. It is

less robust to (unobservable) omitted conditioning variables than the Heckman approach

(Heckman and Navarro-Lozano, 2004). Since the Heckman and the matching approach have

their own strengths and weaknesses, we use both to test the robustness of our findings.

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5. Data and comment letter determinants

5.1 Sample description

We search EDGAR, the SEC database of public company filings, for comment letters

relating to 10Qs and 10Ks. For the period 2003 to 2006 we obtain 9,212 letters relating to 4,138

cases for 3,818 firms.

Table 1, Panel A summarizes the sample selection process. Of the 3,818 firms in our

sample, there are 307 firms that have 627 cases. For our pre- and post-letter research design, we

want to ensure that issues are fully resolved in the post-letter period. Therefore, for these 627

cases, we use two criteria to decide whether the later cases are actually a continuing investigation

of an earlier case and hence the two should be combined. First, if the two cases have overlapping

letter periods, we consider them as one case. We find 90 cases meet this criterion and merge

them into 45 cases. Second, if the period between the end date of the earlier case and the

beginning date of the later case is less than six months, 232 cases meet this criteria. Merging

these cases reduces the number of cases by 118. (Two firms have three cases and one has four

cases.) In addition, we require the 3,818 sample firms have a Compustat GVKEY. This

requirement eliminates 903 firms and 921 cases. Our selection process results in a final sample

of 3,054 cases that represent 2,915 firms.

Table 1, Panel B shows that most of the cases in the sample 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 2,915 firms,

only 135 firms are the subject of two cases and two firms are the subject of 3 cases. In Panel D,

20

we see that on average, a case lasts for 69 days and has two comment letters. Panel E reports 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 (4.32% compared to 2.66%), and utilities are slightly

underrepresented (2.95% compared to 4.05%). Otherwise, letter representation appears to be

proportional.12

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 selected for review in that year and its reporting quality. We develop our determinant

model based on these two factors.

Our proxies for the SOX review criteria follow. Restate is a firm’s restatement history,

which we define as the number of restatements the firm has filed based on the Government

Office of Accountability database. We measure a firm’s price volatility as its idiosyncratic

volatility in the stock market so %&���'�������(�! = ln (1�)2

)2), and �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). This proxy is relative to market-wide variation which we believe best

captures the SOX criteria. MarketCap is the firm’s market capitalization at the fiscal year-end

12 In untabulated analysis we explore the market reaction when a firm receives the first comment letter from the SEC. Of 2,915 firms, we are able to find 2,324 firms (2,376 cases) with non-missing return data on the first letter date. We find no statistically significant stock market reaction in terms of either daily abnormal return or cumulative abnormal return 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.

21

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 (EPS) to share price ratio

(E/P). Age is the number of years the firm appears on CRSP. E/P is 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

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

RevProportion. We define industries based on their two-digit SIC codes.

In addition to the SOX review criteria we include 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. CFOVol 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.

Dominant audit suppliers are likely to provide higher quality audits because they have more

resources and also face a greater risk to their reputation if they conduct poor-quality audits. 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.

22

Of the 3,054 comment letter cases, 2,190 have complete data for the determinant

variables. There are 9,098 non-comment-letter firm-years in Compustat that have similarly

complete data. Table 2, Panel A provides descriptive statistics and the results of the univariate

tests we use to compare firms that receive an SEC comment letter with those that do not. 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.

On average, we find that firms that receive an SEC comment letter are more likely to

have a restatement history and lower idiosyncratic volatility, be larger in market capitalization

and represent a larger proportion of their industry revenue, have been listed longer, and have a

higher E/P ratio (median only). The univariate results on operating cash flow volatility that we

use as a proxy for operating uncertainty are mixed; the average CFOVol of the comment letter

sample is significantly larger than the non-comment-letter sample, while the median CFOVol of

the comment-letter firms is significantly smaller than the non-comment-letter firms. We find no

significant difference in the proportion of comment letter firms and non-comment-letter firms

that are audited by a Big 4 audit firm.

Panel B of Table 2 presents Pearson correlations. The largest significant correlations are a

negative correlation of -0.377 between Big4 and IdiosyncraticVol, followed by a positive

correlation of 0.259 between RevProportion and MarketCap. The majority of other correlations

fall between ±0.15, which suggests that the variables included in our determinant model capture

distinct firm attributes.

We model the probability of a firm receiving a comment letter as a function of the above-

mentioned firm characteristics by using the following logistic regression.

23

���,(�������) = -(�0 + �1������� + �2%&���'�������(�!

+�3��������. + �4��/���.������ + �5�0�

+�61� + �7�23(�! + �84�04)

(3)

Panel C of Table 2 presents the results. In addition to the coefficient estimates, we calculate the

marginal effect of each variable. Doing so can provide insight into which firm attributes are most

important in determining the likelihood of receiving a letter from the Commission. We find that

share of industry revenue has the largest marginal effect on the probability of receiving an SEC

comment letter. This result suggests that the SEC pays more attention to these firms because they

play an important role in the economy. A previous restatement and large P/E disparity also

increase the probability of receiving a letter. However, inconsistent with the SOX guideline, we

find that older firms are more likely to receive a letter. And we find that firms with a more

uncertain operating environment, as reflected in the operating cash flow volatility, face a higher

probability of receiving a letter. Our determinant model yields a Wald 52 of 124.81, which is

significant at the 1% level or better.

Overall, the univarite analyses and the logit model results in Table 2 suggest that the SEC

tends to pay more attention to industry leaders and more established firms and firms that have a

restatement history and higher operating uncertainty.

6. Market reactions to earnings announcements

Following Heflin, Subramanyam, and Zhang (2003), in Figure 1 we plot the mean

absolute cumulative abnormal returns (ACARs) from 64 trading days (the approximate number

of trading days in a quarter) before to two days after earnings announcements for both the pre-

24

and post-comment-letter periods. The post-comment-letter ACARs are consistently smaller than

are their pre-comment-letter counterparts, as reflected in the line below in Figure 1. Since

ACARs represent the information gap between the pre-announcement price and the full-

information post-announcement price, the figure suggests a reduction in the information gap

after the comment letters, and therefore an enhanced pre-announcement information

environment.

6.1. Changes in market reactions

Our requirement that comment-letter and benchmark firms have a common range of

predicted probability of receiving a letter in the year leaves 2,070 comment-letter firms and

3,832 benchmark firms. We further require non-missing data from Compustat, CRSP, and IBES,

so for the share price volatility test, our sample includes 1,286 comment-letter firms and 2,067

benchmark firms. For the trading volume test, we have 1,897 comment-letter firms and 3,540

benchmark firms. The price volatility test sample is smaller because it requires IBES data.

Panel A of Table 3 provides descriptive statistics for the market reaction variables. The

univariate comparisons show a decline in both ACAR and CAV for the comment-letter firms

following resolution of the letter, although only the ACAR change is statistically significant. On

average, we see no difference in ACARs between benchmark firms and comment-letter firms

prior to the letter, but we do observe larger CAVs for the comment-letter firms. Comparing the

post-comment-letter reactions for comment-letter firms to the benchmark firms, we see lower

ACAR reactions for comment-letter firms and no statistically significant difference in CAV

reaction.

25

Panels B1 and B2 of Table 3 present the regression results when we use ACAR as the

dependent variable. We present nine columns of analysis. In Panel B1, Columns (1) and (2), we

present the baseline model, with and without industry fixed effects respectively; in Columns (3)

and (4) we present some robustness tests. Panel B2 presents the additional analyses that we use

to address the potential selection bias problem, and also the cross-sectional analyses based on

letter severity and firm size. The results of the baseline model show that, after controlling for

various firm characteristics, the price reactions of the comment-letter firms are significantly

different from those of the benchmark firms as the coefficients on ComtLtr are positive and

significant. More importantly, for comment-letter firms, the price reactions become significantly

lower after the receipt of an SEC comment letter as the coefficients on ComtLtr_Post are

negative and statistically significant at the 1% level.

To give a sense of the magnitude of the changes in ACARs, we compare the coefficients

on ComtLtr_Post with the sum of the coefficients on the constant and on ComtLtr. The

comparison using the coefficients in Column (1) indicates that comment-letter firms experience a

decrease of about 155% in ACARs. Column (2) shows that including the industry fixed effects

reduces the magnitude of the decline to 32%.

The control variable coefficients generally have the expected sign. 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

26

less informative (Hayn, 1995). The coefficients on Size are negative, suggesting larger reactions

for smaller firms (Atiase, 1985). For smaller firms, investors may have less incentive to gather

pre-disclosure information, and therefore the market reacts more to an earnings shock. We do not

find that bond yield (Collins and Kothari, 1989) or analyst forecast error are significant in our

specification. The results of Columns (3) and (4) in Table 3 confirm that these results are robust

to using only the one quarter before the comment letter in contrast to two in the earlier

specification (balanced panel). Our results are also robust to calculating the abnormal return

market model only once, just prior to the comment letters (no overlap) for both the pre- and post-

comment-letter reactions.

Panel B2 begins with a standard Heckman model. Column (1) presents the probit model

results. The results are consistent with those presented in Section 5. Large industry leaders and

firms with a restatement history are more likely to receive an SEC comment letter. Column (2)

presents the second-stage OLS results. The coefficient on the inverse Mills ratio, λ, is significant

and positive, indicating the presence of an upward selection bias toward the coefficient on

ComtLtr. After including the inverse Mills ratio, the coefficient on ComtLtr switches from

positive (Panel B1 column (2)) to negative. More importantly, the coefficient on the

ComtLtr_Post term remains negative and significant. After correcting for the potential selection

bias, we find an 87.5% reduction in return volatility for post-comment-letter earnings events.

In addition, we select a propensity-score-matched control sample and conduct a

difference-in-difference test. Among the 1,286 comment-letter firms in the return volatility test,

we find matches for 1,227 firms. After the match, the means of all the determinant variables

(Restate, IdiosyncraticVol, MarketCap, RevProportion, Age, E/P, CFOVol, and Big4) are not

27

significantly different between the comment letter firms and the control sample firms (not

tabulated). This lack of differences confirms that the matching successfully removes observable

systematic differences between the two samples.

Column (3) of Panel B2 presents the matched sample results. The coefficient on ComtLtr

is no longer significantly different from zero, which further confirms that the matched control

sample has firm features similar to those of the treatment sample. The coefficient on Post is also

not significant, indicating that the control firms do not experience any change in their abnormal

return reactions to earnings announcement. The sum of the coefficients on Post and ComtLtr x

Post is negative and significantly different from zero (p-value = 0.0043), implying a decline in

ACARs for comment-letter firms. The coefficient on the interaction term ComtLtr x Post

captures the difference-in-difference between the comment-letter firms and the control firms and

it is negative and significant at the 1% level. The results of all four analyses consistently show

that companies generally experience a reduction in return reactions around earnings

announcements after the resolution of SEC comment letters. The reduced reactions imply an

enhanced pre-announcement information environment and a smaller information gap between

firm managers and investors.

In Table 3, Columns (4) and (5) of Panel B2 we present the cross-sectional analysis using

the matched sample. As our proxy for letter severity we use the time to resolve the comment

letter case. We introduce two dummy variables, Severe for cases that are the top decile of the

distribution, and Non-Severe for the others. Although the Column (4) results are consistent for

both groups, the reduction magnitude is much greater for the severe cases as the interactive post

coefficient is -0.018 compared to -0.006 for the non-severe cases.

28

We also split the sample into three groups based on market capitalization and present the

results in Table 3, Column (5). The results show that the comment-letter effect is concentrated in

the small firm group. These firms likely have a lower-quality initial information environment.

However, regulatory capture theory is an alternative explanation for our size results. If larger

firms have influence over the SEC that small firms do not, then they may be able to avoid any

substantive changes required from a review.

In Panels C1 and C2 of Table 3, we present the results where abnormal trading volume is

the dependent variable. In these models, we control for the contemporaneous price reaction

because prior research finds a positive association between volume reaction and the same

window price reaction (e.g., Kim and Verrecchia 1991a, 1997; Atiase and Bamber 1994). ACAR

is the absolute value of the cumulative abnormal return in the announcement window (-1, 2).

Given the importance of the relation between ACAR and trading volume, we also allow the

coefficient on ACAR to differ in the pre- and post-comment-letter period. Consistent with prior

research, we find that ACAR is positively associated with volume reactions. Similar to the price

reaction specification, we control for the amount of surprise to investors. However, rather than

using analyst forecasts as our proxy for investor expectations, we use last year’s quarterly

earnings since Bamber (1986) shows that price reactions to earnings announcements is best

related to analyst-based unexpected earnings, while trading volume reactions is more related to

random-walk based unexpected earnings. EarnSurprise is the absolute value of seasonal changes

in earnings per share deflated by price at the end of the quarter. Surprisingly, we find no relation

to earnings surprise.

29

The structure and presentation of Panel C is similar to Panel B. The baseline model

(Columns (1) and (2)) show that the coefficients on ComtLtr_ Post are significant and negative,

indicating a reduction in abnormal trading volume around earnings releases for comment-letter

firms. The results imply that the SEC comment letters are effective in enhancing disclosure

levels, which leads to a lower divergence of opinion among investors. The results shown in

Column (3) confirm that our results are robust to using only the quarter before the comment

letter (balanced panel). However, the results do not hold when calculating the abnormal volume

model strictly prior to the comment letters. We believe this result arises due to the time series

trend of increasing trading volume.

Panel C2 presents the test results which control for selection bias as well our cross-

sectional analysis. The baseline model result is robust to a Heckman self-selection bias

correction shown in Column (2). Of the 1,897 comment-letter firms in the volume test, we are

able to find 1,873 matched control firms. Column (3) shows the difference-in-difference results

with the matched control sample. The coefficient on ComtLtr x Post becomes insignificant in this

specification. The cross-sectional analysis shows that the lack of results in Column (3) seems to

be in part atrributable to the non-severe cases, which show no change in Column (4) in contrast

to the severe cases. We do not find any cross-sectional differences by firm size (Column 5).

We find some evidence of a trading volume decline following comment letters, but the

results are sensitive to the specification and the effect appears to be strongest for the severe cases.

30

6.2 Industry externality

The previous results suggest that once the SEC’s comments are resolved, comment-letter

firms experience a reduction in price and volume reactions around earnings announcements. The

lower reactions imply an enhanced information environment and an effective SEC review

process. In this section, we explore whether the SEC review process creates an industry spillover.

We define industry peers as all companies in the same four-digit SIC industry level as

comment-letter firms that do not receive a comment letter in the same year. We further require

any industry year to have at least five firms. For every year, we denote an industry comment-

letter period as starting from the earliest date of all the cases issued to the industry in that

industry year and ending on the last date of those cases. We define the pre-comment-letter period

for the industry peers as the the two fiscal quarters before the starting date of the industry

comment-letter period and the post-comment-letter period is the second fiscal quarter after the

end date of the industry comment-letter period. After we apply the data requirements, we are left

with 861 comment-letter firms and 1,331 industry peers for the return volatility test; for the

trading volume test we have 1,424 comment letter firms and 2,499 industry peers.

Panel A of Table 4 reports the regression results of changes in return reactions. In

Columns (1) and (2), we compare the change in return reactions among peer firms. In Column (1)

the coefficient on Post is not statistically significant, indicating that on average, peer firms

experience no change in their information environment, suggesting no positive industry

externality. However, Column (1) does not differentiate between whether or not the industry is

receiving intense SEC attention. We investigate this possibility in Column (2). We define an

industry as receiving intense SEC attention if the industry has received more than eight

31

comment-letter cases in the year. Eight represents the upper quartile. We partition the Post

dummy into Post_ Intense, and Post_ Nonintense, according to the industry group to which each

firm belongs. The results indicate that peer firms in nonintense industries do not experience any

change in their return reactions around earnings announcements, however, peer firms in intense

industries experience a decrease in theirs. The negative and significant coefficient on

Post_Intense provides some evidence that issuing comment letters creates positive industry

spillover, and that firms in industries to which the SEC reviews intensely improve their

information environment even without receiving a letter.

In Table 4, Columns (3), (4), and (5), we compare the change in return volatility between

peer and comment letter firms. If the externality is strong, then the reduction in return volatility

for the industry peers may be the same as comment-letter firms. Column (3) presents the overall

results. The coefficient on Post x Treat is negative and significant, suggesting that the decrease

in return reactions is larger for firms that actually receive a letter. The coefficient on Post is not

significant. Peer firms do not experience any change in their price reactions, on average. From

the results of Columns (1) and (2), however, we know that the nonsignificance of Post comes

from peer firms in the nonintense industries.

Therefore we examine intense industries and nonintense industries seperately. Column (4)

presents the results for intense industries. Consistent with the result in column (2), the coefficient

on Post is negative and significant: firms in intense industries experience a reduction in return

reactions even without receiving a comment letter. The nonsignificant coefficient on Post x Treat

indicates that comment-letter firms and peer firms experience a similar decrease in return

reactions.

32

Column (5) shows that peer firms in nonintense industries do not experience any change

in return reactions, and that comment-letter firms experience a greater decrease in return

reactions. The evidence of an industry spillover suggests that the Table 3 results may actually

understate the comment-letter effect, since the benchmark or control firms are also affected by

SEC oversight, thereby reducing the contrast between comment-letter and peer firms.

We repeat the externality analysis with trading volume as the dependent variable and

present the results in Panel B of Table 4. From Columns (1) and (2), we find that peer firms do

not experience a decrease in their trading volume reactions. When we compare the decrease in

volume reactions between peer firms and comment-letter firms in Columns (3) – (5), we find that

whether or not a firm receives a comment letter makes no difference. These results differ from

Table 3 due to the different sample applied for each.

In summary, Table 4 documents some evidence of a positive industry externality from

comment letters. Peer firms who do not receive a letter but are in the same industry as comment

letter firms receiving intense SEC scrutiny also experience a reduction in return reactions around

earnings releases and the magnitude of the reduction for industry peers is equivalent to that for

comment letter firms.

7. Conclusion

We study both the direct and indirect impact of SEC comment letters on the information

environment of firms by examining stock market reactions to earnings announcements before

and after a firm resolves a letter. Our sample includes 2,915 firms that receive comment letters in

the 2003-2006 period. Our results provide evidence consistent with a change in the firm’s

33

information environment following a comment letter. This change is evidenced by lower

abnormal return volatility and trading volume around ensuing earnings announcements.

Generally, our results are robust to correction for potential selection bias associated with which

firms receive a letter. However, the trading volume results appear to be concentrated in

comment-letter cases that are severe. Moreover, we find some evidence that there is an industry

spillover effect. We find that industry peers that do not receive letters also experience benefits,

suggesting that they adopt disclosure changes of comment-letter firms. Hence, we conclude that

the SEC comment letters enhance the quality of firms’ accounting and disclosure, which results

in improved information environments.

Our paper provides what we believe is 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

suggest no benefit to public enforcment (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.

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Appendix 1

Mail Stop 0510 February 10, 2005 Via U.S. mail and facsimile Gary T. Steele, President and Chief Executive Officer Landec Corporation 3603 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

40

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 10. Please revise your statements of operations to breakout separately the cost of service revenue, related party. Statements of Cash Flows, page 51 11. 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. 12. 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.

41

Only cash flows stemming from investments, loans and debt with original maturities of three months or less may be reported on a net basis. 13. 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. 14. 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 15. 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 16. 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. 17. 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 18. 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. 19. 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 20. 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. 21. 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.

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Per Share Information, page 59 22. 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 23. 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 24. In 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. 25. 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 26. 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 27. 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 28. Please address the comments above in your interim Forms 10-Q as well. Item 1. Financial Statements

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Balance Sheets, page 3 29. Please revise your balance sheet to include the par value and the number of shares issued and outstanding. Notes to Financial Statements 30. 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 31. 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 28, 2004. Item 2. Management`s Discussion and Analysis of Financial Condition and Results of Operations Results of Operations Gross Profit, page 17 32. 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

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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 120 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.

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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

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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. 7. Financial Statement Restatement: Questions about a restatement of the financial statements presented in the offering document. 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. 10. 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. 11. 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. 12. 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). 13. 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. 14. 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. 16. Acquisitions: Questions or comments about the accounting treatment and disclosures of business combination transactions, including purchase price allocations. 17. 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. 18. 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. 19. 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. 20. Depreciation/Amortization: Questions or issues related to the firm’s depreciation and amortization policies. 21. 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. 22. Environmental Reserves: Questions or comments related to the firm’s environmental remediation obligations. 23. Earnings per Share: Questions related to the computation of earnings per share disclosures.

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24. 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. 25. Expenses and Cost Allocations: Requests for information about expense items and cost allocations. 26. 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. 27. 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. 28. Intercompany Accounts: Requests for information about a firm’s accounting and disclosures for intercompany transactions. 29. Inventories: Questions or comments about a firms inventory and related accounting policies. 30. Investments: Questions or comments about a firms investment balances, including the accounting treatment based on ownership percentages and fair value determinations. 31. 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. 32. Minority Interests: Questions or comments regarding the accounting for minority interests. 33. Off-Balance Sheet Arrangements: Questions or comments relating to the understanding of off balance sheet arrangements, including special purpose entities, and their material effects. 34. 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. 35. 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. 36. Preferred Stock: Questions or comments regarding the firm’s preferred stock. 37. 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. 38. Reserve Accounts: Questions or comments regarding the accounting and disclosure for reserve liabilities such as warranties and other accrued liabilities. 39. Restructuring Reserves: Questions or comments specifically related to restructuring reserve liabilities, including severance costs. 40. 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. 41. Segment Reporting: Questions about the identification of operating segments, aggregation of operating segments, and information about geographic areas in which the firm operates. 42. Shareholders’ Equity: Questions regarding the accounting treatment of items included as part of shareholders’ equity, including other comprehensive income and retained earnings (accumulated deficits). 43. 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). 44. Subsequent Events: Requests for additional information and/or disclosure related to event occurring after the date the financial statement were prepared as of. 45. 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. 46. Backlog: Questions about the firm’s disclosures of its order backlog. 47. Competitive Environment: Comments or questions about the firm’s competitive environment and its strategies in addressing competitive forces. 48. 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. 49. Customer Profiles: Requests for information about the firm’s key customers, including any customer concentration. 50. 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. 51. 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.

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52. Going Concern: Questions or comments regarding the firm’s ability to continue as a going concern. 53. Intellectual Property: Questions or comments about the firms disclosure of the terms of their intellectual property and any claims against their intellectual property. 54. Key Performance Indicators: Requests for additional information about disclosures of the key performance metrics in a firm’s industry. 55. 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. 56. 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. 57. Management Discussion and Analysis: Questions or comments about the type of information disclosed or that should have been disclosed as part of the filing. 58. Properties and Facilities: Questions or comments surrounding the description of the locations in which the firm operates. 60. 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. 61. Research and Development Projects: Comments regarding the identification and disclosure of the firm’s material R&D projects. 62. Terms of Debt/Credit Arrangements: Questions or comments about the disclosures of the material terms of the firm’s debt and credit arrangements. 63. 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. 64. 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. 65. Clarify Subject: General requests to provide more specific information regarding a disclosure issue. 66. 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. 67. 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. 68. Disaggregation: A request to provide a finer level of detail related to disclosures or questions about line-item classification. 69. Forward-looking Information: Comments or questions about the firm’s disclosure of forward looking information. 70. General Formatting: Comments or questions regarding the overall, non-financial statement formatting of the offering document. 71. Inaccuracies: An observation that there are inaccuracies in the filing document, including misstatements of fact or numbers that do not reconcile within the document. 72. Incomplete: An annotation that the document is incomplete. 73. Inconsistencies: A comment regarding disclosures that conflict with each other. 74. 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. 75. Make Prominent: A request to alter the format of the filing to highlight or improve the visibility of a particular disclosure. 76. Plain English: Requests to modify the language used in the disclosures to eliminate obfuscating language, industry-specific terminology, or excessive use of acronyms. 77. Quantify Amounts: Request to quantify amounts in disclosures where an issue is discussed in qualitative terms. 78. Repetitive Disclosures: A comment that management has unnecessarily repeated information or disclosures throughout sections of the filing without providing additional substance. 79. 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.

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80. 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). 81. Supporting Calculations: A request to provide detailed support for the calculations that result in the numbers or figures disclosed in the document. 82. 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 15 and 59 were removed – they related specifically to IPO firms only.

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Figure 1

Price Discovery Before and After Comment Letters

The figure depicts mean absolute cumulative abnormal returns (ACAR) around earnings announcements pre- and post-comment letters. We define the earnings announcement as day 0. ACAR is the absolute value of daily abnormal returns summed over (-t, +2), where abnormal returns are one-factor market model residuals estimated over (-200, -11).

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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, 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

Number of letters

Number of cases Number of firms

10K & Q comment letters on EDGAR from 2003 to 2006 9212 4138 3818

Less: Cases with overlapping letter periods (45)

Less: Cases with less than 6 months gap (118)

Less: Firms without COMPUSTAT data (2088) (921) (903)

Final sample 7124 3054 2915

Panel B: Frequency of the comment letter cases by year

Comment letter case issue year Number of comment letter cases Percentage

2003 1 0.03

2004 58 1.9

2005 1508 49.38

2006 1487 48.69

Total 3054 100

Panel C: Frequency of the comment letter cases by firm

Cases Number of firms Percentage of firms Total comment letter cases

1 2778 95.3 2778

2 135 4.63 270

3 2 0.07 6

2915 100 3054

Panel D: Descriptive statistics on comment letter cases

Mean Median Std. Dev

Case length (days) 69.33 49 77.62

Number of letters 2.33 2 1.16

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Table 1 (continued)

Panel E: Frequency of the firms receiving comment letters by industry (in percentage)

Industry Comment letter firms COMPUSTAT population

Agriculture 0.34 0.26

Aircraft 0.21 0.39

Alcoholic Beverages 0.17 0.33

Apparel 1.24 0.92

Automobiles and Trucks 1.37 1.16

Banking 8.96 9.96

Business Services 11.22 11.17

Business Supplies 0.86 0.84

Candy and Soda 0.17 0.21

Chemicals 1.61 1.63

Coal 0.17 0.24

Computers 3.77 3.36

Construction 1 0.82

Construction Materials 1.3 1.34

Consumer Goods 1.34 1.14

Defense 0.24 0.14

Electrical Equipment 0.93 0.73

Electronic Equipment 6.01 5.3

Entertainment 1.58 1.44

Fabricated Products 0.21 0.22

Food Products 1.17 1.27

Healthcare 1.75 1.21

Insurance 4.32 2.66

Machinery 2.99 2.42

Measuring and Control Equip 2.23 1.63

Medical Equipment 3.6 2.87

Miscellaneous 1.61 1.83

Nonmetallic Mines 0.69 2.44

Personal Services 1.13 0.79

Petroleum and Natural Gas 3.64 5.27

Pharmaceutical Products 6.07 6.15

Precious Metals 0.31 1.69

Printing and Publishing 0.58 0.71

Real Estate 0.89 1.02

Recreational Products 0.65 0.6

Restaurants, Hotel, Motel 1.48 1.41

Retail 4.19 3.63

Rubber and Plastic Products 0.75 0.7

Shipbuilding, Railroad Equip 0.1 0.15

Shipping Containers 0.31 0.2

Steel Works, Etc. 1.2 1.12

Telecommunications 3.29 3.48

Textiles 0.38 0.21

Tobacco Products 0.03 0.12

Trading 5.87 5.5

Transportation 2.09 2.5

Utilities 2.95 4.05

Wholesale 3.02 2.76

Total 100 100

53

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 presents the Pearson correlations. Panel C presents the logit model estimates.

���,(�������) = -(�0 + �1������� + �2%&���'�������(�! + �3��������. + �4��/���.������ + �5�0� + �61� + �7�23(�! + �84�04) Restate is the number of restatements the firm has filed before. IdiosyncraticVol is the logistic transformed relative idiosyncratic volatility estimated from the market model over the prior year. MarketCap is the firm’s market capitalization (in $millions) in the prior year. RevProportion is the firm’s share of industry revenue in the prior year. Age is the number of years the firm appears on CRSP. E/P is the EPS-to-Price ratio at the end of the prior year. CFOVol is the standard deviation of cash flows from operation over the prior five years. Big4 equals one if the firm is audited by the big 4 audit firm, and zero otherwise. 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. In Panel B bold text indicates significance at the 0.05 level or better. White heteroskedastic consistent standard errors are in brackets. t-statistics are clustered by firm . ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively (two-tailed). Panel A: Descriptive statistics on firm characteristics

(1) (2) (1) - (2)

Variable Comment letter firm-years (n=2190)

Non-comment letter firm-years (n=9098)

Mean Median Std. Dev. Mean Median Std. Dev. Diff in mean W-test

Restate 0.317 0 0.668 0.238 0 0.592 (5.47)*** (5.97)***

IdiosyncraticVol 2.858 2.129 2.219 3.011 2.379 2.215 (-2.9)*** (-3.89)***

MarketCap 5046 478 20163 3513 393 12912 (4.41)*** (4.86)***

RevProportion 0.031 0.002 0.118 0.018 0.001 0.063 (7.11)*** (4.78)***

Age 17.222 12.008 16.119 13.428 9.173 13.57 (11.31)*** (11.83)***

E/P -0.017 0.037 0.286 0.486 0.033 54.79 (-0.43) (3.45)***

CFOVol 0.173 0.047 1.903 0.111 0.051 0.419 (2.83)*** (-2.68)***

Big4 0.796 1 0.403 0.788 1 0.409 (0.82) (0.82)

54

Table 2 (continued)

Panel B: Correlations

Variable Restate IdiosyncraticVol MarketCap RevProportion Age E/P CFOVol Big4

Rstate - -0.049 0.006 0.041 0.091 0.027 -0.009 0.017

IdiosyncraticVol - -0.187 -0.126 -0.173 0.005 0.046 -0.377

MarketCap - 0.259 0.182 -0.002 -0.024 0.120

RevProportion - 0.113 0.001 -0.023 0.097

Age - 0.009 -0.044 0.092

E/P - -0.001 0.005

CFOVol - -0.062

Big4 -

Panel C: Logistic regression of the determinants of receiving a comment letter

Variable Coefficient Marginal effect

Intercept -1.681

[0.091]***

Restate 0.154 0.024

[0.044]***

IndiosyncraticVol -0.007 -0.001

[0.013]

MarketCap x 1000 0.001 0.0002

[0.002]

RevProportion 1.346 0.207

[0.320]***

Age 0.015 0.002

[0.002]***

E/P x 1000 -0.492 -0.076

[0.226]**

CFOVol 0.072 0.011

[0.021]***

Big4 -0.052 -0.008

[0.072]

Observations 11288

Pseudo R-squared 0.02

Wald 52 124.81

Prob > 52 <0.001

55

Table 3

Changes in Market Reactions around Earnings Announcements

This table reports changes in market reactions around earnings announcements before and 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.

���� = �0 + �1������� + �2�������_���� + 1���(�! + 26�0��� + 3�,���� + 4���� + 54��&7��!&

+ 62�������1���� + 78�9� + " Panel C presents regression analysis with cumulative abnormal trading volume (CAV) as the dependent variable.

��( = �0 + �1������� + �2�������_���� + 1���(�! + 26�0��� + 3�,���� + 4���� + 51���8:�.����

+ 6���� + 7���� ∗ ���� + 88�9� + " ACAR is the four-day absolute value of daily abnormal returns summed over (-1, +2), where abnormal returns are one-factor market model residuals estimated from day -200 to day -11. CAV is the four-day abnormal trading volume summed over (-1, +2), 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. ComtLtr equals one if the firm receives an SEC comment letter, and zero otherwise. ComtLtr_Post equals one if the firm receives a SEC comment letter and the observation is from the post-letter period, and zero otherwise. 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, +2) is negative, and zero otherwise. AbsCar is the absolute value of the cumulative abnormal return over the window (-64, +2). Loss equals one if the firm reports a loss in the quarter, and zero otherwise. BondYield is the yield on the CRSP 30-year bond index at the end of the quarter. ForecastError is the absolute value of analyst forecast error deflated by price at the end of the quarter. Size is the firm’s market capitalization at the end of the quarter (in $millions). EarnSurprise is the absolute value of seasonal changes in earnings per share deflated by price at the end of the quarter. Post is the earnings announcement following resolution of the letter. White heteroskedastic consistent standard errors are in brackets. t-statistics are clustered by firm and by year. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively (two-tailed).

56

Panel A: Descriptive statistics on firm characteristics

Comment letter firm-quarters Benchmark firm-quarters Comparison

Pre-comment letters Post-comment letters

(1) (2) (3) (2) - (1) (1) - (3) (2) - (3)

Variable n Mean Median Std. Dev. n Mean Median Std. Dev. n Mean Median Std. Dev. Diff in mean Diff in mean Diff in mean

ACAR 2182 0.058 0.040 0.060 1111 0.051 0.035 0.051 13346 0.058 0.041 0.059 (-3.22)*** (-0.66) (-4.18)***

CAV 3843 5.232 1.844 54.699 1911 4.724 1.862 36.234 28681 3.65 1.385 31.676 (-0.37) (2.83)*** (1.42)

RetVol 3843 0.027 0.023 0.018 1911 0.026 0.023 0.015 28681 0.028 0.025 0.016 (-2.54)** (-4.47)*** (-6.37)***

NegCar 3843 0.550 1 0.498 1911 0.546 1 0.498 28681 0.565 1 0.496 (-0.29) (-1.79)* (-1.61)

AbsCar 3843 0.140 0.098 0.152 1911 0.135 0.094 0.14 28681 0.145 0.101 0.153 (-1.11) (-1.86)* (-2.6)***

Loss 3843 0.182 0 0.386 1911 0.183 0 0.387 28681 0.201 0 0.401 (0.14) (-2.93)*** (-1.91)*

Bond30 3843 4.662 4.697 0.263 1911 4.712 4.75 0.227 28681 4.743 4.75 0.27 (7.12)*** (-18.09)*** (-4.92)***

Size 3843 4998 496 19109 1911 5241 548 20190 28681 3217 427 11962 (0.45) (8.53)*** (6.78)***

EarnSurprise 3843 0.026 0.006 0.125 1911 0.030 0.006 0.147 28681 0.104 0.007 5.217 (1.01) (-0.93) (-0.62)

ForecastError 2182 0.004 0.001 0.017 1111 0.004 0.001 0.020 13346 0.004 0.001 0.037 (1.12) (-0.95) (-0.02)

57

Table 3 (continued)

Panel B1: Changes in absolute cumulative abnormal returns around earnings announcements

Baseline model

Baseline model (1) (2) Balanced panel (3) No overlap (4)

Intercept 0.0005 0.019 0.019 0.018

[0.019] [0.019] [0.026] [0.020]

ComtLtr 0.004 0.003 0.003 0.003

[0.001]*** [0.001]*** [0.002]** [0.001]***

ComtLtr_Post -0.007 -0.007 -0.006 -0.007

[0.002]*** [0.002]*** [0.003]** [0.002]***

RetVol 1.021 0.823 0.823 0.805

[0.057]*** [0.057]*** [0.068]*** [0.069]***

NegCar 0.003 0.003 0.003 0.003

[0.001]*** [0.001]*** [0.001]*** [0.001]***

AbsCar 0.149 0.147 0.147 0.147

[0.003]*** [0.002]*** [0.003]*** [0.003]***

Loss -0.012 -0.01 -0.01 -0.01

[0.002]*** [0.002]*** [0.002]*** [0.002]***

BondYield 0.003 0.003 0.004 0.004

[0.004] [0.004] [0.005] [0.004]

ForecastError 0.032 0.041 0.046 0.043

[0.029] [0.034] [0.038] [0.034]

Size x 106 -0.096 -0.087 -0.091 -0.085

[0.014]*** [0.017]*** [0.020]*** [0.014]***

Industry FE No Yes Yes Yes

Observations 16639 16639 15240 16639

Adjusted R2 0.18 0.19 0.19 0.19

58

Table 3 (continued)

Panel B2: Changes in absolute cumulative abnormal returns around earnings announcements

Controlling for self selection and cross-sectional test

Probit model

(1) Heckman model

(2) Matched sample

(3) Severity cut

(4) Size cut

(5)

Intercept -0.963 Intercept 0.025 Intercept 0.016 Intercept 0.015 Intercept 0.018

[0.077]*** [0.019] [0.010] [0.016] [0.011]

Restate 0.05 ComtLtr -0.017 ComtLtr 0.002 Nonsevere 0.001 Small 0.006

[0.027]* [0.004]*** [0.001] [0.002] [0.001]***

IdiosyncraticVol 0.016 ComtLtr_Post -0.007 ComtLtr_Post -0.007 Severe 0.005 Medium -0.001

[0.014] [0.002]*** [0.001]*** [0.004] [0.002]

CFOVol -0.014 Nonsevere_Post -0.006 Large -0.006

[0.075] [0.003]** [0.001]***

MarketCap 0.000 Severe_Post -0.018 Small_Post -0.014

[0]*** [0.005]*** [0.002]***

RevProportion 0.696 Medium_Post 0.000

[0.231]*** [0.001]

Age 0.008 Large_Post -0.001

[0.001]*** [0.001]

E/P -0.009 RetVol 0.806 RetVol 0.645 RetVol 0.644 RetVol 0.599

[0.015] [0.058]*** [0.106]*** [0.136]*** [0.113]***

Big4 0.037 NegCar 0.003 NegCar 0.004 NegCar 0.004 NegCar 0.004

[0.067] [0.001]*** [0.000]*** [0.001]*** [0.000]***

AbsCar 0.147 AbsCar 0.149 AbsCar 0.149 AbsCar 0.149

[0.002]*** [0.003]*** [0.010]*** [0.003]***

Loss -0.01 Loss -0.01 Loss -0.01 Loss -0.01

[0.002]*** [0.002]*** [0.003]*** [0.002]***

BondYield 0.003 BondYield 0.004 BondYield 0.004 BondYield 0.004

[0.004] [0.001]*** [0.002]* [0.001]***

ForecastError 0.041 ForecastError 0.09 ForecastError 0.09 ForecastError 0.09

[0.034] [0.065] [0.071] [0.068]

Size x 106 -0.04 Size x 106 -0.1 Size x 106 -0.1 Size x 106 -0.064

[0.014]*** [0.008]*** [0.031]*** [0.006]***

Post -0.001 Post -0.001 Post -0.001

[0.003] [0.002] [0.003]

λ 0.012

[0.002]***

Observations 5746 Industry FE Yes Industry FE Yes Industry FE Yes Industry FE Yes

Pseudo R2 0.02 Observations 16639 Observations 6724 Observations 6724 Observations 6724

Adjusted R2 0.19 Adjusted R2 0.19 Adjusted R2 0.19 Adjusted R2 0.19

59

Table 3 (continued)

Panel C1: Changes in cumulative abnormal trading volumes around earnings announcements

Baseline model

Baseline model (1) (2) Balanced panel (3) No overlap (4)

Intercept -1.413 -0.729 -0.591 2.814

[0.413]*** [2.008] [2.398] [0.547]***

ComtLtr 0.807 0.806 0.139 0.273

[0.545] [0.547] [0.703] [0.092]***

ComtLtr_Post -3.6 -3.568 -4.247 0.544

[1.064]*** [1.063]*** [1.105]*** [0.285]*

RetVol -294.388 -277.411 -254.867 -54.172

[78.962]*** [70.431]*** [86.412]*** [4.176]***

NegCar -1.655 -1.685 -1.544 -0.671

[0.153]*** [0.141]*** [0.145]*** [0.062]***

AbsCar 16.978 17.147 15.235 4.685

[2.586]*** [2.681]*** [4.186]*** [0.438]***

Loss -1.602 -1.365 -1.409 -1.784

[0.243]*** [0.440]*** [0.393]*** [0.099]***

EarnSurprise 0.009 0.009 0.009 0.006

[0.012] [0.013] [0.012] [0.009]

ACAR 197.185 198.762 178.714 38.283

[55.897]*** [56.485]*** [70.366]** [1.706]***

ACAR_Post 67.567 67.616 90.168 22.149

[99.250] [98.896] [108.903] [4.744]***

Size x 103 0.013 0.016 0.016 -0.003

[0.005] [0.007]** [0.010]* [0.001]**

Industry FE No Yes Yes Yes

Observations 34435 34435 32423 34435

Adjusted R2 0.19 0.19 0.19 0.26

60

Table 3 (continued)

Panel C2: Changes in cumulative abnormal trading volumes around earnings announcements

Controlling for self selection and cross-sectional test

Probit model (1)

Heckman model (2)

Matched sample (3)

Severity cut (4)

Size cut

(5)

Intercept -0.992 Intercept -2.363 Intercept -4.954 Intercept -4.804 Intercept 2.993

[0.05]*** [2.310] [0.825]*** [0.824]*** [0.278]***

Restate 0.087 ComtLtr 7.849 ComtLtr 0.789 Nonsevere -0.181 Small 0.03

[0.023]*** [3.729]** [0.722] [0.740] [0.263]

IdiosyncraticVol 0.003 ComtLtr_Post -3.593 ComtLtr_Post 0.419 Severe 9.529 Medium 0.254

[0.008] [1.063]*** [1.233] [1.674]*** [0.128]**

CFOVol 0.025 Nonsevere_Post 1.387 Large -0.092

[0.036] [1.266] [0.088]

MarketCap 0 Severe_Post -8.113 Small_Post 0.067

[0] [2.876]*** [0.062]

RevProportion 0.681 Medium_Post 0.193

[0.204]*** [0.523]

Age 0.01 Large_Post 0.244

[0.001]*** [0.342]

E/P 0.001 RetVol -275.707 RetVol -334.975 RetVol -340.212 RetVol -68.62

[0.007] [69.990]*** [23.984]*** [23.970]*** [7.759]***

Big4 -0.05 NegCar -1.694 NegCar -2.16 NegCar -2.158 NegCar -0.55

[0.039] [0.143]*** [0.590]*** [0.589]*** [0.203]***

AbsCar 17.178 AbsCar 22.143 AbsCar 22.186 AbsCar 5.03

[2.697]*** [2.533]*** [2.530]*** [0.550]***

Loss -1.327 Loss -0.457 Loss -0.331 Loss -1.339

[0.451]*** [0.898] [0.898] [0.238]***

EarnSurprise 0.009 EarnSurprise 0.03 EarnSurprise 0.031 EarnSurprise 0.012

[0.013] [0.039] [0.039] [0.001]***

ACAR 198.81 ACAR 261.012 ACAR 260.477 ACAR 37.171

[56.502]*** [4.938]*** [4.932]*** [1.628]***

ACAR_Post 68.108 ACAR_Post -56.389 ACAR_Post -55.702 ACAR_Post 2.822

[98.709] [8.667]*** [8.658]*** [3.230]

Size x 103 0.007 Size x 103 0.015 Size x 103 0.01 Size x 103 -0.002

[0.006] [0.021] [0.021] [0.002]

Post 3.594 Post 3.551 Post -0.252

[0.998]*** [0.997]*** [0.110]**

λ -4.084

[1.536]***

Observations 9791 Industry FE Yes Industry FE Yes Industry FE Yes Industry FE Yes

Pseudo R2 0.02 Observations 34435 Observations 11423 Observations 11423 Observations 11423

Adjusted R2 0.19 Adjusted R2 0.28 Adjusted R2 0.28 Adjusted R2 0.29

61

Table 4

Industry Externality

This table reports industry externality of SEC comment letters. Panel A presents changes in absolute cumulative abnormal returns (ACAR) among industry peers. Panel B presents changes in abnormal trading volume (CAV) among industry peers. ACAR is the four-day absolute value of daily abnormal returns summed over (-1, +2), where abnormal returns are one-factor market model residuals estimated from day -200 to day -11. CAV is the four-day abnormal trading volume summed over (-1, +2), 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, +2) is negative, and zero otherwise. AbsCar is the absolute value of the cumulative abnormal return over the window (-64, +2). Loss equals one if the firm reports a loss in the quarter, and zero otherwise. EarnSurprise is the absolute value of seasonal changes in earnings per share deflated by price at the end of the quarter. BondYield is the yield on the CRSP 30-year bond index at the end of the quarter. ForecastError is the absolute value of analyst forecast error deflated by price at the end of the quarter. Size is the firm’s market capitalization at the end of the quarter (in $millions). Intense equals one if the industry has received more than eight comment letter cases in the year, and zero otherwise. Nonintense equals one if the industry has received less than eight comment letter cases in the year, and zero otherwise. We define industry by 4-digit SIC codes. Post is the earnings announcement following resolution of the letter. White heteroskedastic consistent standard errors are in brackets. t-statistics are clustered by firm and by year. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively (two-tailed).

Panel A: Changes in absolute cumulative abnormal returns for industry peers

Compare among peer firms Compare peer firms with treatment firms

Variable Variable All cases Intense cases Non intense cases

(1) (2) (3) (4) (5)

Intercept 0.009 0.003 Intercept 0.011 0.013 0.002

[0.016] [0.016] [0.013] [0.018] [0.017]

Post -0.002 Post -0.002 -0.005 0.000

[0.002] [0.001] [0.002]** [0.002]

Post_Intense -0.006 Treat 0.003 0.003 0.002

[0.002]** [0.002] [0.002] [0.002]

Post_ Nonintense 0.001 Post x Treat -0.006 -0.003 -0.010

[0.002] [0.003]** [0.004] [0.004]***

RetVol 0.703 0.691 RetVol 0.57 0.321 0.937

[0.151]*** [0.152]*** [0.135]*** [0.162]** [0.220]***

NegCar 0.005 0.005 NegCar 0.004 0.007 0.001

[0.002]*** [0.002]*** [0.001]*** [0.002]*** [0.002]

AbsCar 0.13 0.13 AbsCar 0.144 0.152 0.134

[0.013]*** [0.013]*** [0.011]*** [0.015]*** [0.015]***

Loss -0.005 -0.005 Loss -0.006 -0.007 -0.001

[0.004] [0.004] [0.003]** [0.004]* [0.006]

BondYield 0.003 0.004 BondYield 0.003 0.003 0.003

[0.003] [0.004] [0.003] [0.004] [0.004]

ForecastError 0.064 0.067 ForecastError 0.035 -0.019 0.041

[0.074] [0.074] [0.051] [0.067] [0.067]

Size x 106 -0.048 -0.048 Size x 106 -0.061 -0.094 -0.001

[0.044] [0.044] [0.024]** [0.028]*** [0.054]

Industry FE Yes Yes Industry FE Yes Yes Yes

Observations 5602 5602 Observations 7845 4219 3626

Adjusted R2 0.19 0.19 Adjusted R2 0.2 0.21 0.20

62

Table 4 (continued)

Panel B: Changes in cumulative abnormal trading volumes for industry peers

Compare among peer firms Compare peer firms with treatment firms

Variable Variable All cases Intense cases

Non intense cases

(1) (2) (3) (4) (5)

Intercept 4.974 4.977 Intercept 1.893 0.594 2.489

[2.925]* [2.926]* [2.917] [0.982] [4.506]

Post -35.866 Post -27.351 -0.748 -44.331

[22.929] [19.686] [3.973] [24.982]*

Post _Intense -35.37 Treat 1.159 0.331 1.603

[22.720] [0.590]** [0.389] [1.119]

Post_Nonintense -36.348 Post x Treat -1.408 0.805 -1.449

[23.165] [1.812] [1.133] [3.022]

RetVol -334.369 -333.738 RetVol -359.201 -168.224 -474.574

[149.444]** [149.327]** [140.066]** [37.357]*** [182.393]***

NegCar -2.102 -2.13 NegCar -2.031 -1.477 -1.97

[0.745]*** [0.758]*** [0.529]*** [0.348]*** [0.900]**

AbsCar 24.135 24.136 AbsCar 25.499 15.068 22.984

[9.552]** [9.553]** [8.506]*** [5.515]*** [9.389]**

Loss -5.182 -5.189 Loss -3.184 -1.786 -5.041

[1.869]*** [1.870]*** [1.070]*** [0.604]*** [1.960]**

EarnSurprise 0.012 0.011 EarnSurprise 0.007 -0.088 0.018

[0.012] [0.012] [0.013] [0.128] [0.015]

ACAR 103.094 103.027 ACAR 153.449 103.279 203.725

[22.832]*** [22.824]*** [42.294]*** [27.238]*** [72.628]***

ACAR x Post 654.99 655.246 ACAR x Post 508.921 24.839 772.752

[414.356] [414.488] [359.765] [74.981] [431.471]*

Size x 103 0.0495 0.0496 Size x 103 0.0409 0.0000935 0.089

[0.034] [0.034] [0.025] [0.004] [0.047]*

Industry FE Yes Yes Industry FE Yes Yes Yes

Observations 13446 13446 Observations 17782 9003 8779

Adjusted R2 0.43 0.43 Adjusted R2 0.39 0.25 0.53