Stock Market Reaction to SEC Comment Letters PinS Report

7/28/2019 Stock Market Reaction to SEC Comment Letters PinS Report

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The Stock Market Reaction to SEC Comment Letters

Mallory J. Rubin


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Mallory J. Rubin

Rubin

A. Abstract

Professor Cook and I utilize basic event study methodology in order to evaluate

the stock market reaction to allegations of accounting irregularities in Comment Letters

issued by the U.S. Securities and Exchange Commission (SEC) since 2004. This is a

single phase study in which we calculate daily abnormal stock returns and cumulative

abnormal stock returns (CARs) for the sample as a whole as well as for five sub-samples

categorized by Comment Letter issue.

In the full sample test, we find that the average drop in stock price from the day

before the Comment Letter release to the day following the release is a mere 0.06%. Over

the window stretching from 30 days before the release to 60 days following the release,

the average total decrease in stock price is only 1.39% for the whole sample. Only the

abnormal returns in the (-30, 0) window are statistically significant, and only at a 10%

level of significance.

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Companies addressed by the SEC for issues surrounding acquisitions and similar

activities showed a significant loss of 0.20% in the window from 30 days after the event

to 60 days after the event (10% level of significance). Interestingly, companies with

issues concerning contractual obligations had a statistically significant gain of 0.36% in

the window from two days before the issue date to the issue date itself.

From our research, we can conclude that the SECs Comment Letters are more

proactive than its AAERs are in identifying possible earnings management and other

accounting fraud. However, the stock market is just slightly sensitive to the release of

SEC letters. The drastic difference between this studys results and Professor Cooks

findings in his 2006-2007 research of AAERs, including an average CAR of -14.07% in

the (-1, +1) window at the 1% level of significance, clearly demonstrate the small

proportions of stock market reactions to Comment Letter issuances. Also, we observe that

the market reaction to Comment Letters is much more gradual than that to AAERs, with a

longer average period of declining stock returns.

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B. Introduction

The event study Professor Cook and I have conducted examines the stock market

reaction to earnings management allegations implied by the SECs issuance of Comment

Letters to companies with questionable financial filing disclosures. Comment Letters,

which are typically released within two weeks of an alleged accounting irregularity,

represent an effort by the SEC to become more proactive in handling potential cases of

accounting fraud. Formerly, the primary public announcements of irregularities were the

SECs Accounting and Auditing Enforcement Releases (AAERs), which are publicly

released announcements of much more serious accounting frauds than are implicated by

the issuance of Comment Letters. They are often not generated until over two years after

an allegation.Our research on the market reaction to the release of Comment Letters is a

follow-up study to Professor Cooks sabbatical research on similar effects from the

SECs AAERs (Cook & Grove, 2007).

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after August 1, 2004 began being released to the public through the EDGAR database on

May 9, 2005 (U.S. Securities and Exchange Commission, 2005).

Comment Letters issued by the SEC cover a broad area of disclosure concerns.

In large part because Comment Letters represent a proactive approach by the SEC, the

issues they cover range from very minor accounting irregularities to severe accusations of

fraudulent corporate behavior. The widely different language utilized across our sample

of Letters demonstrates the extent of variation. For example, one common request in

Comment Letters written since 2004 is the following, which responds to Avnet, Inc.s

July 3, 2004 10-K filing:

Please expand future filings to make more detailed and specific disclosure abouthow you identify and measure impairment of long-lived assets. Also makedisclosures about how you measure fair value and describe the nature and extentof estimates and uncertainties that are inherent to that process.

(Todd, 2004)

The comment above, which addresses assumptions underlying asset impairment

valuations, points to an area of increasing scrutiny in the accounting world: asset

valuation. In fact, Nelson, Elliott, and Tarplay (2003) note similar comments about

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A significant portion of the companies in our sample (approximately 11%) were cited by

the SEC primarily for issues concerning basic disclosure requirements and/or (GAAP)

guidelines such as the one described above. In response to the wide range of different

issues addressed in the Comment Letters, we have classified the primary issue(s)

addressed in each Letter into one of ten categories which we describe in an appendix to

Table 1. They range from the highly specific and potentially severe, like Asset Valuation,

to the very vague and likely insignificant, like Overall Transparency and Other.

Several key concepts underlie our research topic of the stock market reaction to

SEC Comment Letters. Though Comment Letters are considered to be much more minor

than AAERs are, they still imply a companys insufficient disclosure. In recent years,

such a lack of transparency has become strongly associated with accounting fraud in the

form of earnings management. An overview of earnings management is provided below.

Additionally, our study, and the entire category of event studies, tests the semi-strong

form of the efficient market hypothesis (EMH), which suggests that stock prices reflect

all publicly available information (Fama, 1970). Hence, if our findings show that the

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One of the most daunting challenges faced in the field of financial accounting is

combating the influence of earnings management. Earnings management occurs when top

management resorts to operational alterations and reporting techniques that mislead

external stakeholders on the financial realities of a firm (Healy & Wahlen, 1999).

According to Nelson, Elliot, and Tarpley (2003), the definition above can include both

GAAP compliant and non-GAAP compliant methodologies. Additionally, earnings can

either be boosted or reduced to match managements desires (Nelson et al. 2003). Studies

have shown that managers actively alter reported earnings in order to enhance

compensatory benefits, to avoid regulation, and to match analysts forecasts (Healy &

Wahlen, 1999).

Earnings figures are typically viewed by investors as being more relevant to the

financial outlook of a company than cash flow data is (Healy & Wahlen, 1999). Though

it has been hypothesized that investors can see through earnings management, as

reflected by the reaction in stock prices to earnings announcements, studies have clearly

shown that stocks can drop drastically in value over the two years following an allegation

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paid to the phenomenon. According to a survey reported by Don Durfee (2006), over half

of chief financial officers (CFOs) at the end of 2006 claimed to have the authority to

impact reported earnings by a magnitude of at least 3 percentage points. Interestingly, the

CFOs also believed that external auditors were unlikely to observe such earnings

management, and were almost equally unlikely to respond in the case that an allegation

was made (Durfee, 2006).

CFOs can use either operational planning or accounting methods, or a

combination of both, to influence earnings figures. Operational methods include adjusting

the timing of large projects or inventory order cycle. On the accounting side, new policies

can be adopted at strategic times and critical estimates can be altered (Durfee, 2006).

From analyzing the Comment Letters, it becomes apparent that the SEC accountants are

concerned with both areas, addressing comments about operational reporting to the

Managements Discussion and Analysis and Results of Operations sections and those on

accounting management to the Accounting Policies and Estimates sections. The

Comment Letters demonstrate that top managements manipulation of earnings, both by

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The efficient market hypothesis (EMH) is a prominent theoretical framework in

finance. Fama (1970) gives a basic definition of an efficient market as one in which

prices always fully reflect available information (p. 383). The EMH has three sub-

sets, one of which is of particular importance to our study. There are weak, semi-strong,

and strong forms of the EMH. Tests of the weak form seek to prove that capital market

prices are determined by past prices and other historical data (Tung & Marsden, 1998).

Tests of the semi-strong form of the EMH, in contrast, seek to prove that market prices

account for publicly available data that is also current (Tung & Marsden, 1998). Finally,

tests of the strong form seek to prove that both public and private information are

reflected in market prices, making abnormal returns very unlikely (Tung & Marsden,

1998). The EMH has been dominant in the field for almost four decades, largely because

the alternative theories are less adequate to describe stock price fluctuations in the market

For example, anomalies, or events that contradict the EMH, including price overreaction

and underreaction, tend to balance themselves out on average (Fama, 1998).

The EMH is highly relevant to event study research because the notion of

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different types of announcements (Khotari & Warner, 2004). [S]ystematically nonzero

abnormal returns following an event are inconsistent with efficiency and imply a

profitable trading rule (Khotari & Warner, 2004, p. 12)

Also, because event studies include both historical and current market data in

calculating expected returns, they are specifically testing the semi-strong form of the

EMH. In fact, evidence shows that short-horizon event studies such as ours, or ones with

event windows under one year in length, strongly support market efficiency theories

(Khotari & Warner, 2004).

Professor Cook and I extend prior event study research by looking at the stock

market impact of the very recent phenomenon of publicly available Comment Letters.

Our study, with a sample of 274 diverse U.S. companies, provides an initial examination

of whether investors react to early reports of potential fraud and helps us to answer the

following questions: Are the Comment Letters issued more closely to the date the

financial press announces irregularities as compared to the SECs AAERs? Does the SEC

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A number of studies have analyzed the impact of earnings management on stock

market returns in the context of SEC regulation. A strong example is Professor Cooks

sabbatical research on the stock market reaction to AAERs issued by the SEC (Cook,

2007). The results of Professor Cooks research mimicked those of Beneishs 1999 study,

showing that companies alleged of earnings fraud have their market values drop

significantly within a few-day period surrounding the announcement Professor Cook

(Cook & Grove, 2007) found an average drop of 14% in the three-day period around the

event and 19% over an 11-day period. The study was developed, in part, to correct for the

design inadequacies of previous event studies, including the failure to account for

confounding events, nonsyncronous trading, and non-normal statistical distributions.

Professor Cook references the Feroz, Park, and Pastena (1991) and Dechow, Sloan, and

Sweeney (1996) studies to highlight the weaknesses in other event studies that measure

the capital market impact of earnings manipulation allegations.

In addition to Professor Cooks research, there are several studies that examine

the area of earnings management. Healy and Wahlen (1999) summarize the extensive

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AAERs. While this study thoroughly examines the role of the auditing process in

uncovering and controlling earnings management, it ignores the impact on the larger

capital market.

McDowell (2005) utilizes a basic event study construction to measure the impact

of SEC investigations on implicated companies stock returns, and finds that the

disclosure of either a formal or informal SEC investigation results, on average, in an

approximately 6% decrease in market value over a (-1, +1) window. The study accounts

for some confounding activities and focuses on the volatility of stock returns, the size of

firms in the sample, and calendar portfolios extending several months beyond the

announcement of an SEC investigation. Unlike AAERs and Comment Letters, SEC

investigations do not clearly state the specific areas of a companys financial and

accounting procedures that are inadequate.

Professor Cook and I extend the prior research on the connections between

earnings management, the role of the SEC, and the resulting market reaction by looking

at the stock market impact of the very recent phenomenon of publicly available Comment

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the time from two years prior to the relevant 10-K or Comment Letter issue date to one

year after that date. I then converted the prices into holding period returns (HPRs) in data

files to later be used with the SAS software to form SAS data sets. My next step was

finding the appropriate SEC Comment Letter for each company (according to a 10-K date

identified by the previous student researcher) on the EDGAR database within the SEC

website, reading the Letters, and creating a classification system for the various issues

addressed in the Letters. I organized every Comment Letter into a single category, with

the exception of companies in which I observed both expensing and revenue recognition

irregularities.

Next, I again utilized the Thompson DataStream database, this time extracting

descriptive data of the sample for the year before the issuance of the relevant Comment

Letter using Excels vlookup function. I calculated the means, medians, standard

deviations, and coefficients of variation of this company-specific financial data for all the

companies in order to summarize the characteristics of the sample. The variables

examined were total assets, sales, operating profit margin, earnings per share, and return

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Event Studies:

Our research structure is that of a basic short-horizon event study. Within the

finance/accounting field, an event study is a research project that investigates whether the

stock market reacts in a statistically significant way to a certain type of past event (Cook,

2007). Hence, measuring of the magnitude of an events impact on the market is a

primary objective of event studies (Khotari & Warner, 2004). By the end of 2004, more

than 500 event studies had been completed in financial research (Khotari & Warner,

2004). Many different kinds of events can be tested in this type of study, including

earnings, stock split, or merger announcements released by the firm itself, as well as SEC

investigations, analysts forecasts, and new legislative changes that occur externally.

Event studies assume that the conditions necessary for the semi-strong form of market

efficiency are present by testing a) whether information appears to be assimilated into

market prices, and b) how quickly (efficiently) the process of assimilation occurs (Cook,

2007). Additionally, event studies presuppose that stock prices reflect the present value of

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interval around and including the announcement, are also examined. Because the longest

event window is under a year in length, our study is considered to have a short horizon

(Khotari & Warner, 2004). Comparing event windows of various lengths is crucial in

testing market efficiency because the return to market equilibrium, or normal expected

returns, can be estimated by determining the approximate period with the highest

concentration of abnormal returns.

In order to compare the hypothetically abnormal returns during the event window

to normal (or expected) returns that are observed before earnings management occurs, an

estimation window must be developed in order to apply a stock price model to the firm.

Estimation windows utilize stock price data from sufficiently before an announcement in

order to estimate what the stock prices in the event window ought to be. These predicted

stock returns (in percentages), computed from a return generating model, are then

compared to the actual returns observed in the event window, and abnormal returns are

calculated and are then determined to be either statistically significant or insignificant.

An estimation window must cover a long enough period to generate reliable predictions

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where Rit is the expected stock return in time t, Rmt is the Wilshire 5000 value-weighted

index of stocks at time t, and i and i are precise, unbiased parameters (Cook, 2007).

The Market Model has become strongly established over the years in efficient market

research since Famas (1970) pioneer work in the field. Once the expected normal returns

have been calculated using the Market Model, abnormal returns can be determined by

subtracting the expected normal return from the actual observed return as follows:

ARit = Rit E(Rit | No announcement)

Hence, ARit is the abnormal return to an individual firm i in time t due to the public

release of an announcement, while Rit is the actual return in time t, and E(Rit | No

announcement) is the expected return to firm i in period t using the Market Model and

assuming the absence of an announcement. In other word, an AR is a stock prices

deviation from the expected return over a certain period of time. Because of the

undesirable nature of Comment Letters for firms, we anticipate negative abnormal returns

on average for our sample.

E t t di id th t it t l t th t k t

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effect of the announcement on firms over time, however, a cumulative statistic must be

generated by summing all of the AARs for all firms in the sample together (Cook, 2007).

The resulting number is termed a cumulative abnormal return, or a CAR.

CAR = AARt

Because stock market return models, including the Market Model, cannot always

estimate firms stock returns accurately, an abnormal return calculated through the steps

above must be shown to be statistically different from what the return model predicts as

the firms expected return. Statistical significance can be determined by establishing a

considerable difference between a computed CAR and zero. An efficient markets test of

significance effectively standardizes the CAR by dividing it by its own standard error:

CAR / (CAR)

The qualitative nature of the test design is important to the field of event studies.

Power is one element of the overall test design. In essence, the term power refers to the

ability of a specific test to accurately determine abnormal returns (Khotari & Warner,

2004) A goal then of event studies is to create high power tests Precise specification of

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because the average abnormal performance of stock returns is not highly concentrated in

the event window. This is also discussed in the Results section below.

E. Sample

Full Sample:

Our sample consists of all companies that received a SEC Comment Letter after

the public release date of August 1, 2004 and also have -2 years to +1 year of daily stock

returns immediately around the issue date which are accessible from the Thompson

DataStream database. A small number of companies that participated in business

combinations during our estimation window were eliminated from the sample. The final

sample has 274 U.S. companies. The companies come from 8 different industries and

represent 4 separate U.S. stock exchanges. As such, they range from large, multinational

firms with several subsidiaries to small, regionally-based corporations. The primary

comments each company received from the SEC also vary greatly across the sample,

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Our sample covers Comment Letters which contain many different issues

identified by the SEC. We chose to classify each Comment Letter based on its primary

issue of concern into ten categories: Revenue Recognition, Expensing, Acquisitions, etc.,

Asset Valuation, Contractual Obligations, Controls and Procedures, Hedging, Liquidity,

Overall Transparency, and Other. Specific descriptions for the characteristics of

companies in these categories are in an appendix to Table 1. Unsurprisingly, the two

categories with the greatest number of Letters are Expensing and Revenue Recognition,

with 62 and 67 Letters, respectively. This is in large part because these categories are the

only two that we chose to allow for overlapping among classifications. Expensing and

Revenue Recognition often occur simultaneously in earnings management, as described

in the Introduction. Acquisitions, Contractual Obligations, and Overall Transparency

concerns are also frequently cited, accounting for, respectively, 14%, 11%, and 12% of

the Comment Letters designations in the sample. Controls and Procedures and Asset

Valuation have significant representation with 26 and 19 Comment Letters, and all other

categories contain small numbers of Letters.

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coefficients of variation are also telling in this matter. Additionally, the low median

figures for total assets and sales show that there are a significant number of small stocks

in the sample. However, the proximity of the mean and median values for the profitability

measures shows that the small companies are able to maintain fairly competitive with

their larger counterparts.

Table 4 in the Appendix summarizes the industry distribution of the sample. By

far, the industry best represented in our sample is Manufacturing, with 131 companies.

This is followed by Finance, Insurance, and Real Estate with 42 companies, Services

(which is heavily computer-oriented) with 35 companies, Transportation and Utilities

with 27 companies, and Retail Trade with 22 companies represented. There are a handful

of companies in the Mining, Construction, and Wholesale Trade industries. The

weighting towards the Manufacturing industry is not intended and simply reflects the

abundance of Manufacturing companies fitting our two criteria of having data on

Thompson Datastream and receiving a SEC Comment Letter that has been made

available online.

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not represented in the first four groups. Essentially, we evaluate whether any of the main

issue categorizations have more significant abnormal returns than the sample as a whole.

The Revenue Recognition sub-sample consists of 67 events. As described in the

appendix to Table 1, companies with Letters in this category do not appear to be fully

disclosing their revenue recognition policies in terms of accrual timing and/or basis for

recognition. Indications of inappropriate revenue recognition are also cited frequently in

this classification.

The Expensing sub-sample consists of 62 events, many of which overlap with the

Revenue Recognition sub-sample. Because many companies that received comments for

one of these two issues were also addressed on issues concerning the other, we assign

both categorizations to them. Comment Letters classified with Expensing issues identify

problems like inappropriate aggregation of income statement expense line items,

incomplete explanations of the expense line items and their underlying assumptions, as

well as decreasing loss allowances that inaccurately portray expenditure trends in a

company.

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With 36 events represented, the Contractual Obligations sub-sample is the

smallest of our five sub-samples. Again, like the Acquisition, etc. group, the Contractual

Obligations sub-sample consists of all Comment Letters originally classified into this

category. Letters in this sub-sample inappropriately report debt covenant requirements,

off-balance-sheet liabilities, or pending litigation that is likely to incur significant

expenditures in the future. Companies with Letters falling under this category have a high

likelihood of future cash flow problems.

The final sub-sample, Overall Transparency and Other, is a catch-all for Letters

whose issues do not fall within the first four categories. There are 118 events in this

group. The range of issues in this sub-sample is the broadest of the five, going from

speculative investments and hedging, to inadequate internal controls, to general reporting

insufficiencies, and other issues like tax reserves and foreign currency conversion.

F. Results and Analysis

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window CAR, meaning that much of the overall loss is occurring closer to the edges of

the event window. Only the abnormal returns in the (-30, 0) window are statistically

significant, and at a weak 10% level of significance. Because this portion of the drop

represents nearly 50% of the total decrease, it may indicate that investors learn of the

accounting irregularities before the Comment Letters are released, or at least anticipate

some negative reports (Khotari & Warner, 2004). However, Beneishs 1999 study

demonstrates that the SEC typically hones in on companies with recent histories of poor

returns, so the -0.66% average change in stock price from day -30 to the event date may

corroborate these findings. Moreover, even though the bigger stock price declines occur

further away from the announcement date, they do not necessarily undermine the semi-

strong form of the efficient market hypothesis because the daily abnormal returns

themselves are so weak, with very little statistical significance.

Sub-Samples

Only three of the five sub-samples (Expensing, Acquisitions, etc., and Contractual

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mimic those of the other sub-samples and the full sample, with the CARs sloping

downward to an average sum between -0.50% and -1.50%.

Significant drops occur not only on a single day between 40 and 50 days after the

announcement date for the full sample Eventus study, but also in each of the five sub-

samples. This could be a possible indication that our event dates (day 0 in the event

window) do not accurately represent the point in time when the market learns the

contents of the Comment Letters. Accessed from the EDGAR database, our event dates

are the filing dates recorded on the SEC website, and as such, probably differ somewhat

from the dates that the information was publicly released about each company. It is

feasible that the market receives the Comment Letters a little more than a month after the

filing date provided by the SEC.

Issues Affecting Data Accuracy

There are a number of statistical issues that have the potential to bias the results of

our research. Certain characteristics of the sample create these issues, but we utilize

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test has a built-in function from Eventus that corrects for serial correlation. Another

problem with using daily returns is that of heteroskedasticity, which occurs when daily

returns have differing variances due to their non-normal distribution. In event studies, the

calculation of abnormal returns during the event window depend upon estimates

determined using the estimation window. However, there are often different levels of

variability, and hence different daily variances, in the event window as compared to the

estimation window. We utilize a test developed by Bohmer, Musumici and Paulsen

(1991) to eliminate the potential bias of heteroskedasticity (Cook & Grove, 2007). A final

problem with daily returns is their non-normal distribution. Daily returns are often

skewed, or have asymmetrical distributions, and exhibit kurtosis, or variances that are

responses to infrequent, extreme variations. We correct for these issues by incorporating

Cowans 1992 generalized sign test into our results. Tables 5 and 6 include this statistic

in our results.

Issues with Small Stocks in the Sample

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days with zero returns. To correct for this, we again utilize the generalized sign test, the

results of which are reported in Tables 5 and 6.

G. Conclusions

Our study of the stock market reaction to SEC Comment Letters extends prior

research by evaluating the interaction between a new type of SEC correspondence and

the capital market system. We utilize a sample of significant size, with 281 Comment

Letters written to 274 U.S. companies, and augment the classical statistical tests with

non-parametric tests to account for problematic elements of the data.

The results of our study enable us to address the questions considered above in the

Introduction. First, Comment Letters do represent a more proactive approach by the SEC

in identifying and combating earnings management and other accounting fraud. The vast

majority of Comment Letters in our sample were publicly released within one year of the

original 10-K filings to which they refer, and within that set, most were released closer to

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cumulative abnormal return for the entire 90-day event window, the (-30, 0) window is

statistically significant at a 10% level of significance. The drastic difference between this

studys results and Professor Cooks findings in his 2006-2007 research of AAERs,

including an average CAR of -14.07% in the (-1, +1) window at the 1% level of

significance, illustrate the small proportions of stock market reactions to Comment Letter

issuances. Though both forms of SEC documents are publicly available, only AAER

releases generate drastic stock market reactions. This phenomenon probably exists

because the content and implications of AAERs are better understood by the investing

world than the meaning of Comment Letter issuances. Also, the reaction to Comment

Letters is not only smaller than that to AAERs, but is also much more gradual. About 20

percentage points of the total average 32% decline in stock value in Professor Cooks

research occurred over a ten day period roughly surrounding the event date. However, in

our study of Comment Letters, the cumulative 1.39% drop in value was fairly steady

throughout the 90-day event window.

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H. Bibliography

Batterson, D., B. Boch, J. Burgdoerfer, J. Gromacki, T. Malik, R. Osborne, and W.Tolbert, Jr. 2003. SEC News Alerts, December 2003. Retrieved July 26, 2007,fromhttp://corp.jenner.com/alert_details_1079490383265.html

Beneish, M. The Detection of Earnings Manipulation. The Financial Analysts Journal55(5): 24-36. Retrieved August 8, 2007, from

http://0-web.ebscohost.com.bianca.penlib.du.edu/ehost/pdf?vid=11&hid=105&sid=24e4c4ba-42ea-492f-908a-c9118a16ac8c%40sessionmgr104

Cook, T. 2007, February 20. Report on My Sabbatical during Fall Quarter 2006 2007:The Stock Market Reaction to Allegations of Earnings Fraud. Reiman School ofFinance, Daniels College of Business, University of Denver.

Cook, T. and H. Grove. 2007, June 18. The Stock Market Reaction to Allegations ofEarnings Manipulation. Daniels College of Business, University of Denver.

Durfee, D. 2006. Management or Manipulation? CFO 22 (13): 28. Retrieved July 24,2007, from http://www.cfo.com/article.cfm/8189832/c_8341296?f=magazine_alsoinside

Fama, E. F. 1970. Efficient Capital Markets: A Review of Theory and Empirical Work.Journal of Finance 25 (2): 383-417. Retrieved July 26, 2007, fromhttp://web.ebscohost.com/ehost/detail?vid=22&hid=107&sid=5335e6a5-51bc-40f7-9168-70a4430717aa%40sessionmgr108

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McDowell, J. 2005. A Look at the Markets Reaction to the Announcements of SEC

Investigations. Glucksman Institute for Research in Securities Markets, New YorkUniversity.

Nelson, M. W., J. A. Elliott, and R. L. Tarpley. 2003. How Are Earnings Managed?Examples from Auditors.Accounting Horizons 17 (Supplement): 17-35.

Spirgel, L. 2004, December 14. Comment Letter to CMGI, Inc. Regarding Form 10-K forthe Fiscal Year Ended July 31, 2004. U.S. Securities and Exchange Commission.

Retrieved August 13, 2007, fromhttp://sec.gov/Archives/edgar/data/914712/000000000004039987/filename1.txt

Todd, G. 2004, December 14. Comment Letter to Avnet, Inc. Regarding Form 10-K forthe Fiscal Year Ended July 3, 2004. U.S. Securities and Exchange Commission.Retrieved August 13, 2007, fromhttp://sec.gov/Archives/edgar/data/8858/000000000004039996/filename1.txt

Tung, A. T., and J. R. Marsden. 1998. Test of Market Efficiencies Using ExperimentalElectronic Markets. Journal of Business Research 41 (2): 145-151. Retrieved July26, 2007, fromhttp://0-www.sciencedirect.com.bianca.penlib.du.edu/science?_ob=MImg&_imagekey=B6V7S-3SX6NG9-6-1&_cdi=5850&_user=1497530&_orig=browse&_coverDate=02%2F28%2F1998&_sk=999589997&view=c&wchp=dGLbVzW-zSkWW&md5=52ee89fe167350f4c405bfb49bb9c7a1&ie=/sdarticle.pdf

U.S. Securities and Exchange Commission. 2004, June 24. SEC Staff to Publicly ReleaseComment Letters and Responses. Retrieved July 9, 2007, from

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Comment Letter Issue Distribution 2004 2005 2006 Total %

Acquisitions, etc. 4 26 17 47 14.24%Asset Valuation 1 8 10 19 5.76%

Contractual Obligations 1 24 11 36 10.91%

Controls and Procedures 0 15 11 26 7.88%

Expensing 5 31 26 62 18.79%

Hedging 0 5 5 10 3.03%

Liquidity 2 8 2 12 3.64%

Overall Transparency 4 18 17 39 11.82%

Revenue Recognition 3 38 26 67 20.30%

Other 0 7 5 12 3.64%

100.00%*All companies have been designated one Comment Letter classification except for those exhibitingearnings manipulation in terms of both expensing and revenue recognition.

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Overall Transparency

SEC Comment Letters addressing a companys overall transparency emphasize a lackof appropriate detail in qualitative and non-financial information within financialstatement filings. Comments in these letters often advise improvements to the MD&Asection and to tabular disclosures. Companies receiving a large number of briefcomments on very distinct aspects of their filings have also been included in thiscategory.

Expensing

Comment Letters in this category highlight questionable accounting methods for anddisclosure of companies expenses. Common citations include aggregation of incomestatement expense line items, incomplete explanations of the expense line items and theirunderlying assumptions, and decreasing loss allowances that inaccurately portrayexpenditure trends in a company.

Revenue Recognition

This category incorporates Comment Letters with a primary concern on revenuerecognition policies. Issues frequently addressed in these Letters are return policies,special agreements with supply chain participants, and exact terms for the time-sensitiverecognition of normal operating revenues. Full disclosure of revenue recognitionprocedures for distinct operating segments and/or product lines is also a recurringcomment. Revenue recognition and expensing have been categorized separately because[r]evenues and expenses differ fundamentally in the accounting principles being

applied (Nelson et al., 2003, p. 21).

Contractual Obligations

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This category of Comment Letters addresses the valuation methods of many assetclassifications, including accounts receivable, inventories, long-lived assets, and

intangible assets. The issue of impairment is prominent in the latter two asset groups,with many citations for impaired properties and major goodwill impairment followingacquisition activity.

Acquisitions, etc.

Companies with inadequate overall disclosure relating to segment reporting and thesimilar areas of acquisitions, affiliate companies, and subsidiary relationships, have beenclassified underneath this umbrella category. Impairment of acquired assets and theaddition of new contractual obligations frequently appear within this category of SECComment Letters. Inappropriate segment aggregation and consolidated financialstatements in disclosures are also commonly recognized.

Liquidity

Comment Letters that stress limitations in cash flow disclosure, burdensome cashobligations, decreases in revenues, increases in accounts receivables, or any combinationof the above, concern the issue of a companys liquidity. Companies receiving suchletters often rely heavily upon non-operating cash flows and/or misclassify their cashflows. Incomplete disclosure of significant contractual obligations is also referred tofrequently by the SEC accountants.

Hedging

Companies derivative investments and other hedging activities receive the greatest

amount of attention in this group of Comment Letters. The most frequent citationhighlights inadequate explanation of the risks and assumptions underlying fair valuationof such investment instruments. The SEC cannot conclude that these companies

ti th d l i f d i ti d th h d i t t i l ith

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Nasdaq 96 35.04%

NYSE17

0 62.04%

OTC 6 2.19%

AMEX 2 0.73%

Total27

4 100.00%

Table 3:

Summary Financial DataTotal Assets* Sales* Oper. PM EPS ROE

Mean $19,850,125 $6,256,405 11.34% 1.38% 14.14%

Stand. Dev. $96,189,346 $14,255,519 22.30% 2.08% 63.89%

Median $2,152,186 $1,382,202 10.79% 1.22% 13.16%

Co. of Variation 0.21 0.44 0.51 0.66 0.22

*Assets and sales figures are presented in terms of thousands of dollars

Table 4:

SIC Code Distribution # %Mining 5 1.82%

Construction 3 1.09%

Manufacturing 130 47.45%

Transportation & Public Utilities 27 9.85%

Wholesale Trade 9 3.28%

Retail Trade 22 8.03%

Fi I R l E 42 15 33%

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-29 281 0.967 0.05% 0.12% 144:137 0.487

-28 281 0.729 0.07% 0.01% 142:139 -0.441

-27 281 -0.943 0.06% -0.01% 128:153 -0.665

-26 281 0.012 -0.04% -0.10% 136:145 -1.227-25 281 -0.823 -0.20% -0.16% 129:152 -0.89

-24 281 1.325$ -0.26% -0.06% 147:134) -1.217

-23 281 1.803* -0.10% 0.16% 151:130> 1.291$

-22 281 -0.227 -0.04% 0.06% 134:147 0.505

-21 281 0.848 -0.06% -0.02% 143:138 0.272

-20 281 -0.465 0.06% 0.13% 132:149 0.74

-19 281 -0.465 0.09% 0.02% 132:149 0.429

-18 281 -1.062 0.10% 0.01% 127:154 0.37

-17 281 -1.301$ -0.03% -0.13% 125:156( -0.875

-16 281 -0.585 -0.04% -0.01% 131:150 0.031

-15 281 -2.017* -0.16% -0.12% 119:162< -1.205-14 281 -0.465 -0.37% -0.21% 132:149 -1.802*

-13 281 0.967 -0.45% -0.08% 144:137 -0.237

-12 281 1.445$ -0.27% 0.17% 148:133) 2.621**

-11 281 -0.823 -0.28% -0.01% 129:152 -0.334

-10 281 -1.540$ -0.35% -0.07% 123:158( -1.218

-9 281 0.609 -0.28% 0.07% 141:140 -0.106

-8 281 0.49 -0.38% -0.09% 140:141 -1.099

-7 281 0.132 -0.28% 0.10% 137:144 0.772

-6 281 -0.943 -0.28% 0.00% 128:153 0.333

-5 281 -0.704 -0.31% -0.03% 130:151 -0.704

-4 281 -0.346 -0.48% -0.17% 133:148 -1.508$

-3 281 -0.346 -0.60% -0.11% 133:148 -0.212

-2 281 1.325$ -0.56% 0.04% 147:134) 0.959

-1 281 -2.017* -0.65% -0.09% 119:162< -1.625$

0 281 -1.062 -0.66% -0.01% 127:154 0.061

+1 281 -0.823 -0.61% 0.04% 129:152 0.469

+2 281 -0.465 -0.67% -0.06% 132:149 -0.867

+3 281 -0.227 -0.64% 0.02% 134:147 -0.982

+4 281 -0.585 -0.64% 0.00% 131:150 -0.818

+5 281 -0.107 -0.56% 0.08% 135:146 0.359

+6 281 -0.704 -0.64% -0.08% 130:151 -0.745

+7 281 0.49 -0.59% 0.06% 140:141 0.756

+8 281 -2.137* -0.67% -0.08% 118:163< -1.371$

+9 281 1.325$ -0.61% 0.06% 147:134) 0.68

+10 281 2.758** -0.51% 0.09% 159:122>> 1.334$

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Full Sample Cumulative Abnormal Returns using the Market Model

Days N Mean Cumulative Abnormal Return Patell Z Generalized Sign Z(-1,+1) 281 -0.06% -0.623 -0.465

(-5,+5) 281 -0.28% -1.449$ -1.062

(-10,+10) 281 -0.23% -1.168 0.49

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels,respectively, using a 1-tail test. The symbols (,< or ),> etc. correspond to $,* and show the significance

and direction of the generalized sign test.

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Figure 1: Full Sample Cumulative Average Abnormal Returns

Figure 1: Cumulative Average Abnormal ReturnsMa r k e t i n d e x d i s t i n c t i o n =V a l u e

- 1 . 5 0 %

- 1 . 4 0 %

- 1 . 3 0 %

- 1 . 2 0 %

- 1 . 1 0 %

- 1 . 0 0 %

- 0 . 9 0 %

- 0 . 8 0 %

- 0 . 7 0 %

- 0 . 6 0 %

- 0 . 5 0 %

- 0 . 4 0 %

- 0 . 3 0 %

- 0 . 2 0 %

- 0 . 1 0 %

0 . 0 0 %

0 . 1 0 %

0 . 2 0 %

D a y

- 3 0 - 2 0 - 1 0 0 +1 0 +2 0 +3 0 +4 0 +5 0 +6 0

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