signals sent by financial statement...
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Signals Sent by Financial Statement Restatements
Katsiaryna SalaveiDepartment of Finance
University of [email protected]
Norman MooreDepartment of Finance
University of Connecticut
Draft, Revised May 12, 2005
Abstract
This study examines signals sent by financial statement restatements. We hypothesize that restatements generate both information and wealth effects, and that these two effects are pronounced to a different extent in different types of restatements. The evidence shows that the wealth effect of different types of restatements differs in both timing and magnitude. Mismanagement has a negative effect on the market response to restatements for all reasons; however the effect is significant only for revenue, cost, M&A, and restructuring restatements. Restructuring and securities related restatements that are not associated with mismanagement are positively related to abnormal returns.
Information effect, measured as the change in the idiosyncratic flow of information, is also heterogeneous. Securities and restructuring restatements generate positive information effect, while revenue recognition, merger and acquisitions, reclassification and multiple reason restatements mostly increase the idiosyncratic flow of information subsequent to the announcement. Companies, restatements of which have positive information effect, are much larger and are more levered. Results are mainly consistent with our hypothesis that restatements of inherently noisy items improve the precision of investors’ information set, while the restatements of precise figures increase the noisiness of investor’s information set.
We gratefully acknowledge useful comments by Mine Ertugrul, Carmelo Giaccotto, Joseph Golec, Neeraj Gupta,Chester Spatt, Keith Moore and especially Karen Teitel. All mistakes remain our own.
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Signals Sent by Financial Statement Restatements
1. Introduction
Publicly traded companies disclose financial information on a regular basis even
when such disclosure is not required by the government. Several papers examine the
incentives for companies to disclose information, and the market reactions to various
types of disclosures (Diamond (1985), Verrecchia (2001), Admati and Pfliederer (2000),
Kim and Verrecchia (1991) and (2001)). Diamond (1985) showed that the release of
information, even when costly to the companies, is beneficial for both the company and
investors because it improves risk sharing and allows all investors to abstain from
gathering of costly information. A company releases financial information via financial
statements and through various signaling mechanisms. Market reaction to both earnings
announcements and signals sent by management (such as dividend policy, self-tender
offers and capital structure decisions) have been studied intensively in the finance and
accounting literature. It has been shown that the information in such events is not
preempted by other news announcements and results in significant market reaction. In
this paper we analyze another disclosure event: financial statement restatements.
When companies announce earnings, they report standardized financial
statements. The form of the announcement of restatements is more heterogeneous.
Besides adjusting the bottom line, restatements often specify which item was improperly
reported in the original statement. This gives an opportunity to study the marginal
importance of different items in the financial statements rather then the joint impact of
accounting reports. In addition to revealing information to the market about a company’s
performance, restatements may also signal that management is not efficient in analyzing
and reporting financial data and in extreme case is intentionally misleading investors.
Moreover, the restatement event might indicate to the market that financial data from that
company is not reliable.
The Securities and Exchange Commission (SEC) expressed great concern
regarding the increasing number of financial statement restatements and the significant
capital losses associated with them. In particular, the SEC is alarmed with the increasing
number of revenue recognition restatements that have been the main reason for
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restatements in recent years. Several studies document significant negative abnormal
returns surrounding restatement announcements.1 Restatements that are related to
fraudulent activity and that affect core accounts have been blamed for the most damage to
shareholders’ wealth [Palmrose, Richardson and Scholz (2004), Wu (2002), Anderson
and Yohn (2002)]. The literature focuses primarily on identifying which restatements are
associated with more severe negative returns.
In this study we examine different signals sent by financial statement restatements
and the market’s reaction to those signals. We argue that restatements send different
signals depending on the reason for restatement. We hypothesize that restatements affect
investors’ information set in two ways. First, restatements can affect the perception
regarding the net present value of the future cash flows of a company. If restatements
revise expectations of the NPV upwards (downwards), we anticipate a positive (negative)
market reaction. This is the wealth effect. Second, restatements can alter the precision of
investors’ information set. Because risk-averse investors dislike uncertainty, if
restatements lead to the resolution of uncertainty, markets should react positively even if
the expectations regarding the NPV of future cash flows are not altered. Conversely, if
restatements increase the noisiness of investors’ information sets, then markets will react
negatively. Many of the items reported under GAAP are based on estimates. Some
estimates are often inherently imprecise, in particular, bad debt reserves, restructuring
charges, pension plan rates of return, long term restructuring plans and securities-related
entries. The restatements of such items provide more accurate estimates based on realized
numbers. Hence, they improve the precision of the investors’ information set pertaining
to company prospects and are likely to generate a positive market reaction. This second
effect is an information effect.
Using an initial sample of 788 companies restating financial statements in 1997
through June 2002, we empirically examine both wealth and information effect of nine
types of restatements. The wealth effect is measured using market model adjusted
abnormal returns. To examine information effect, we estimate abnormal returns
1 Palmrose, Richardson and Scholz (2004) found a 9% negative cumulative average abnormal returns around a two-day restatement announcement period in a sample of 403 restatements in 1995 to 1999. Anderson and Yohn (2002) find a 3.49% negative cumulative abnormal return during a 7-day window
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conditional on change in variance during the announcement window and test for a change
in variance of the market model residuals.
We find that restatements due to the same reason send different signals to
investors. The wealth effect of different types of restatements differs both in timing and
magnitude, with several types exhibiting short term reversals. Restatements that are due
to cost/expense and revenue recognition cause negative abnormal returns when
restatements are associated with litigation. However, revenue recognition, cost/expense,
restructuring and securities related restatements are positively related to abnormal returns
when restatements are not associated with mismanagement. Merger and acquisition and
reclassification restatements are negatively related to abnormal returns when they are
associated with mismanagement and have insignificant impact on returns otherwise. We
also find that market penalizes firms the restatements of which are initiated by company
and auditor. This is consistent with findings of Palmrose, Richardson and Scholz (2004),
but unlike them we do not find a negative reaction for more levered companies.
We also find that securities and restructuring restatements decrease the
idiosyncratic flow of information subsequent to restatement, while revenue recognition
restatements increase idiosyncratic flow of information subsequent to the announcement.
This result is consistent with out hypothesis that restatement of securities and
restructuring related items (estimation-based) that are inherently noisy improve the
precision of investors’ information set, while restatement of revenue items which are
mainly transactions-based increases the noisiness of investor’s information set. Merger
and acquisitions, reclassification and multiple reason restatements mostly increase the
idiosyncratic flow of information subsequent to the announcement. Companies,
restatements of which have a positive information effect (decrease the variance of
residual), are much larger and are more levered. Most of the wealth effects are preserved
after controlling for the information effect in the cross-sectional analysis.
Our results differ from those presented in previous studies. Palmrose, Richardson
and Scholz (2004) find no effect of the number of core accounts on the market reaction to
surrounding the announcement of the problem in a sample of 161 restatements that took place in 1997-1999.
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restatements2. Anderson and Yohn (2002) include dummy variables for restatements due
to revenue, restructuring and research items and find negative market reaction to revenue
recognition restatements; however, their analysis differs from ours. We use a finer
partition of restatements and examine the marginal impact of the reason for restatements
by allowing each reason to vary depending on the presence of mismanagement. In
addition, we consider a broader proxy for mismanagement than in previous literature.
We contribute to the literature in several ways. First, we examine a much broader
spectrum of reasons for restatements and their impact on the market then previous
studies. Such analysis provides evidence regarding marginal impact of different types of
accounts on investors’ information set. Second, we explicitly measure information effect
of different types of restatements and measure wealth effect conditional on the change in
variance.
The rest of the paper proceeds as follows. Section 2 presents the analysis of
different types of restatements and hypothesis development. Section 3 describes the data.
Section 4 presents wealth effect methodology and results. Information effect is examined
in section 5. Section 6 concludes the paper.
2. Hypotheses development
Multiple studies investigate the role of information in capital markets (e.g.
Diamond and Verrecchia (1981), Grossman and Stiglitz (1980), Grinblatt and Ross
(1985) etc.). Information is defined as a change in expectations about the outcome of an
event.3 Investors form expectations regarding a company’s future profitability in
accordance with their information set, which is shaped on the basis of available public
and private information. Diamond (1985) shows that the release of information by firms
benefits investors because it allows them to avoid gathering costly information and
improves risk sharing. Risk sharing is improved by making traders’ beliefs more
homogeneous and reducing the speculation activity of informed traders. Financial
statements are an important source of public information. Beaver (1968) shows that news
announcements revealed to investors prior to the earnings report do not fully preempt the
2 However, they did not decompose restatements by reason
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information content of earnings announcements. He suggests that investors rely on
earnings reports to form their expectations regarding a company’s future performance.
When firms make material mistakes in financial statements, they are required to
correct them by issuing restatements. Correction of an error related to a prior period
discovered after the issuance of financial statements for that period should be reported as
a prior period adjustment.4 Prior period adjustment is done both when 1) the information
was available at the time of the report but wasn’t properly used and when 2) new
information reveals that the previous report was inaccurate.5 If the restatement is made
because of the former, market should penalize the company more, because such
restatement suggests greater degree of inefficiency. Most restatement announcements
specify which item has been previously reported incorrectly (i.e. revenue, cost, in process
research and development) and in most instances only one item is restated. Thus,
restatements provide a unique opportunity to examine the marginal importance of
different items on a financial statement rather than their joint impact in earnings
announcements.
We examine how different types of restatements influence investors. Assume that
information available to investor yt at time t is defined as the expectation of the present
value of the future cash flows of the company, plus some error t6:
ttty~~~
,
where t
~
has precision 1/ >0. We hypothesize that financial restatements affect the
information set of investors in two ways. First, restatements influence the expectations of
investors regarding the NPV of future cash flows of the company, i.e. restatements
affectt
~
. If the restatement reveals that the company is doing better than before, i.e.
that1
~~
tt , then an investor will react positively to the announcement. We will observe
3 Henri Theil, Economics and Information Theory (Chicago and Amsterdam: Rand McNally and North Holland Publishing Company, 1967), Ch.1.4 In accordance with Accounting Principles Board Opinion No. 205 Sec. Act Rel. 6084, 17 SEC Dock. 1048, 1054 (1979): “there is a duty to correct statements made in any filing … if the statements either have become inaccurate by virtue of subsequent events, or are later discovered to have been false and misleading from the outset, and the issuer knows or should know that persons are continuing to rely on all or any material portion of the statement”.6 Variable can be also viewed as a return.
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positive abnormal returns if aggregate expectations of the investors change. This is the
wealth effect. Second, restatements influence the precision of the investors’ information
set 1/. An announcement of a restatement can lead to a resolution of uncertainty and
thus result in a positive market reaction even when t
~
is not affected. This is an
information effect. We hypothesize that the wealth and the information effects will differ
depending on the type of restatement.
In the reminder of this section we attempt to qualify heterogeneity. Different
items in financial statements involve different degrees of estimation. Revenue recognition
and cost and expense related items are mostly based on transactions data. We term these
items transaction-based. Restructuring, securities related, merger and acquisition, in
progress research and development related items involve a significant amount of
judgment. These are estimation-based items. Transaction-based items and estimation-
based items are associated with different level of noise, with transaction-based items
being more precise. Hence their correction should result in different market reaction. If
inherently noisy items are restated, it is likely that the restatement has been caused due to
arrival of new information rather than misusage of information used at the time initial
statement has been filed. Thus, restatements of noisier items improve precision of
investors’ information set, while restatements of precise items decrease the precision of
investors’ information set and signal greater management inefficiency. In the reminder of
this section we discuss predictions regarding transaction- and estimation-based
restatements in more detail.
Transaction based restatements
Elliott and Hanna (1996) provide evidence that markets react more strongly to
surprises in on-going operating income than to one-time special items. This suggests that
revenue recognition and cost/expense restatements should generate a more pronounced
market reaction. Palmrose and Scholz (2000) find that core restatements are positively
associated with the likelihood of shareholder litigation. Litigation decreases shareholder’s
wealth and signals the market that management is inefficient. We hypothesize that
restatements of revenue and cost/expense accounts will thus negatively affect investors’
perception regarding future profitability of the company. Furthermore, because revenue
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recognition and cost/expense restatements are transaction-based, the restatements of these
accounts might induce investors to believe that their information set is noisier. We
hypothesis that transaction based restatements will generate negative wealth effect and
negative information effect.
Estimation-based restatements
Several items on the financial statements are not based on factual data; rather they
are estimated by the management. For example, Huron Consulting Group LLC notes that
“balance-sheet reserves are established by companies for areas from bad debt to
restructuring, based on the estimates and judgments of management about everything
from how many accounts or loans receivable ultimately won’t be collected to how much
it will cost to close down outmoded factory.” Securities-related items include the
accounting for derivatives, warrants, stock options and other convertible derivatives. The
valuation of these items requires assumptions and estimation of model parameters by
management. Accounting for in process research and development (IPR&D) is also very
subjective and involves significant amount of judgment. In many instances a company
that conducts an acquisition writes off costs associated with the acquisition as in-process
research and development costs. Lucent wrote off $2.3 billion of IPR&D for its 1996 and
1997 acquisitions and IBM used the same technique to write off much of the cost of its
1995 acquisition of Lotus Development. Such practices became particularly popular
among high-tech companies.7 The company can restate restructuring-, IPR&D- and
securities-related items when observed information suggests that prior estimates have
been materially incorrect. More frequent reporting in such cases will benefit investors
because it reduces the noisiness of their information set. In the absence of restatement,
real results pertaining to the estimation-based items are never disclosed.8 In addition,
restatement of restructuring-, securities- and IPR&D-related items, which are non-cash,
can provide tax benefits even when the numbers are revised downwards. We hypothesize
that restatements of restructuring charges and IPR&D and securities related items
7 “Earnings hocus-pocus”, Financial Week, October, 1998.8 Lev Baruch, professor of accounting at NYU says that “such items as bad debt reserves, restructuring charges, pension plan rates-of-return, and contingencies (like warranties) are usually wrong, yet the real results are never disclosed after the year’s annual report is filed” Wall Street Journal, January 13, 2004.
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improve the precision of investors’ information set and thus result in positive market
reaction.
Mergers and acquisitions related items entail significant amount of judgment
because they involve treatment of intangible assets (goodwill). Even though merger and
acquisition related items are grouped as estimation-based, it is unlikely that they will
improve precision of investor’s information set because of the other negative signals that
they can potentially send. Restatements of such items can change market perception
about the value of the deal. Most of the M&A restatements in our sample are one of the
following 2 types First, some M&A restatements result from discovery of improper
accounting practices used by the acquired company that have been revealed during
acquisition process. Second, M&A related restatements are forced by SEC when
inappropriate method of accounting has been used to record the merger/acquisition. Both
of these types of M&A restatements are usually non-cash, however they can reveal that
either acquiring company overpaid or intentionally hid current expenses in one-time
M&A charges.9 It has also been shown in previous studies that corporations are most
likely to be sued if they engage in mergers, acquisitions and divestitures independently of
financial health, shareholders’ wealth and managerial entrenchment (Core (1997)). Thus,
the fact that a company restates mergers and acquisition items can proxy for litigation
risk. We expect that the M&A related restatements will have a negative wealth and
information effect.
Other types of restatements
Our sample includes three other types of restatements: reclassification, related
party transaction and restatements due to multiple reasons. Reclassification restatements
are due to improper classification of accounting items (for example debt payments being
classified as investments). Related-party transaction restatements take place when
revenues, expenses, debts or assets involving related parties are accounted for
improperly.10 All of these types might involve restatement of either transaction-based or
9 William Lerach “The alarming decline in the quality of financial reporting and upsurge in securities fraud.” Milberg Weiss Bershad Hynes @ Lerach LLP10 Our sample contains eight main reasons for restatements, which are described in Appendix A.
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estimation based items. The wealth and information effect of these types is to be
determined empirically.
Mismanagement
Restatements associated with mismanagement will result in negative market
reaction regardless of the reason for restatement. We use litigation around restatement as
a proxy for mismanagement. Previous research shows that companies that have been sued
in the past are likely to be sued in the future (Core, 1997). Litigation decreases the net
worth of a company due to direct costs involved in handling lawsuits as well as the
indirect costs associated with the need for better monitoring of inefficient management.
We suggest that restatements associated with mismanagement negatively affect investors’
perception regarding profitability of the company and result in negative abnormal returns
around announcement of restatements. Further, restatements associated with
mismanagement reduce the precision of investors’ information set 1/, because investors
rely less on the reports produced by inefficient management. Negative reaction to the
signal of management inefficiency will dominate the marginal effect of different reasons
for restatements. Within each reason for restatement we differentiate between those that
are associated with mismanagement and those that are not to preserve the marginal effect
of the reasons. Previous studies used the presence of fraudulent activity as a proxy for
mismanagement (Palmrose at el. (2004), Yohn and Stolz (2002)). We use litigation
because it captures a wider range of management inefficiencies and is an observable
variable. As pointed out by DeFond and Jiambalvo (1991) and Dechow and Skinner
(2000), the identification of fraud is very difficult because it is driven by similar motives
and generates the same market reaction as the usage of aggressive accounting. In
addition, previous literature suggests that the likelihood of costly litigation and
management change are the main reasons for the negative reaction to fraud related events
(Bonner et al., (1998) and Palmrose et al., (2004)). Palmrose and Scholz (2002) document
that only 18% of litigation in their sample of restatement related litigation are the result
of fraud. Yet, regardless of the reason for litigation, they are costly to shareholders. We
believe that litigation is a better proxy for mismanagement than using restatements
identified as results of fraudulent activity.
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3. Data
We analyze a sample of 919 companies that announced financial restatements
from January 1, 1997 to June 30, 2002. This sample has been assembles by Government
Accountability Office (GAO). It contains only restatements that occurred due to
aggressive accounting practices, intentional or unintentional misuse of facts, and
misinterpretation of accounting rules or fraud. It excludes restatements resulting from
accounting policy changes. The sample excludes restatements that followed the Sarbanes-
Oxley act, which avoids potential structural change.11 The dataset contains information
regarding the date, reason and the party that initiated restatement. We search for
information on litigation in Lexis-Nexis using keyword “litigation” and “lawsuit” around
the dates of restatement. We find the returns data on the CRSP tapes for 788 out of 919
companies and estimate abnormal returns for this sample. However, only 537 companies
in our sample have sufficient data on Compustat for cross-sectional analysis. We present
descriptive statistics for both the full sample and the sample used for cross-sectional
analysis.
4. Wealth effect
We are interested in short-term market reaction to restatement announcement. Our
sample doesn’t suffer from event clustering, therefore we employ a standard event study
methodology in computing abnormal returns. We estimate the returns using the following
market model:
Rit = i + i Rmt + it (1)
where Rit is the return on security i on day t, Rmt is the return on the market index on day
t, and it is a random error term. The model is estimated over 255 trading days, ending 46
days before the restatement announcement. The abnormal stock return for security i on
day t is defined as
ARit = Rit – (i + i Rmt) (2)
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The act was signed into law on July 30, 2002 and intends to increase accountability of corporate executives and enhances oversight of accounting practices.
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where alpha hat and beta hat are the ordinary least squares estimates of security i’s
market model parameters. We use a Patell Z and generalized sign tests in estimating the
significance of the abnormal returns. Patell Z test assumes cross-sectional independence
and estimates separate standard error for each security-event. The generalized sign test
uses normal approximation to binomial. The null hypothesis of this test is that the
fraction of positive returns is the same as in the estimated period.
We find a significant negative cumulative abnormal return (CAR) during the
two-day event period of –7.9% (Table 1). This suggests that restatement announcements
are not fully preempted by news regarding intentions to restate or market participants’
private information regarding the quality of financial statements. The most significant
market reaction is observed on the day of and the day following the announcement (Table
1, Figure 1). Palmrose, Richardson and Scholz (2004) found 9% negative average
abnormal returns around the same windows, however their sample was smaller and
spanned different years: 1995-1999. Both Patell Z test and generalized sign tests indicate
that restatements on average generate a negative market reaction at 0.1% significance
level. However, 31% of the abnormal returns are positive (Table 3).
In Table 2 we present mean and median cumulative abnormal returns on days (-2,
+3) by reason and for the sample of restatements with and without litigation. Table 3
shows the descriptive statistics for CARs on days zero and plus one. The reaction to
restatements due to different reasons differs in timing and magnitude. Markets have
insignificant reaction to securities related and IPR&D restatements on the day of the
announcement and small negative reaction on the day after. Related party transaction
restatement announcements result in statistically significant negative mean CARs of
-4.14% on the day of announcement and statistically significant positive mean CARs of
1.32%, 4.05% and 1.95% on days +1, +2 and +3 respectively. This suggests that the
market participants overreact to related party transaction restatements on the day of
announcement and correct in subsequent days.
Table 3 reveals that the distributions of returns differ across reasons for
restatements. Although both means and medians of cumulative abnormal returns are
negative for all types of restatements, the range of the values is large. Mean ranges from
–10.85% for multiple group to -2.78% for securities related restatements. Medians are
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less negative with the maximum value of -0.95% for restructuring related group. The
market has more negative reaction measured as mean CAR(0,+1) to multiple, mergers
and acquisitions and revenue recognition restatements. Cumulative abnormal returns are
negatively skewed, in particular cost and expense and restructuring groups. The average
standard deviation of returns is around 17.2% with multiple, restructuring and M&A
groups having the highest standard deviations. Although on average mean and median
returns are negative, approximately 31% of the cumulative abnormal returns in the
sample are positive.
Revenue recognition restatements have the second most pronounced negative
median CAR(0,+1) of -4.43%. Interestingly, the four largest positive abnormal returns in
our sample are revenue recognition restatements and 31% of abnormal returns for this
group are positive. Cost and expense restatements result in median CAR(0,+1) of
–0.51%, which is below the average. Forty five percent of securities related restatements
result in positive abnormal returns, with the least negative median among all groups of
restatements of -0.95%. This result is consistent with our hypothesis that securities
related restatements reduce the noise of investors’ information set. The evidence with
regards to the restructuring group of restatements is not as clear. The distribution of
returns is skewed, with a large negative mean of -7.50% and small (in absolute terms)
median of –1.14%.
Tables 2 and 3 suggest that there is heterogeneity in signals sent by restatements
due to different reasons even when samples for each reason include restatements with and
without mismanagement. Table 2 shows that market reacts much more negatively to
restatements associated with mismanagement than to restatements without
mismanagement. This suggests negative market reaction to mismanagement related
restatements for all groups of restatements. In results not shown we calculate CARs for
each reason group separately for subsamples with and without litigation. The results for
each group are qualitatively similar to full sample results.
Cross sectional analysis of abnormal returns
In this section we analyze whether wealth effect persists after controlling for other
factors. Abnormal returns are regressed on explanatory variables designed to decompose
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restatements into different types in accordance with the signals they send. Several of the
variables used in the regression were obtained from Compustat and weren’t available for
all companies. As a result our sample reduced to 537 companies. The regression
specification includes an indicator variable for each reason of restatement. Within each
reason, we differentiate between restatements with and without mismanagement by
interacting a dummy variable for litigation with an indicator variable for reason. We also
include an indicator variable for the prompter of the restatement. As shown in Table 4,
59.3% of restatements are initiated by the company, with a few prompted by SEC and
auditor (24.6% and 11% respectively). Restatements that are initiated internally suggest
that the company has better internal controls. On the other hand, outside party initiated
restatements can be better anticipated by the market due to news regarding SEC and other
agencies investigations of the company’s accounting practices. Previous studies have
shown that these variables are important in explaining CARs around restatements
(Palmrose, Richardson and Scholz (2004), Anderson and Yohn (2002)).
Three other control variables: size, leverage and a NASDAQ dummy are
included. Size is estimated as natural logarithm of total assets in the quarter prior to
restatement and leverage is calculated as the ratio of the long-term debt to total assets in
the quarter prior to restatement. We do not have any prediction regarding the direction of
the relationship. Previous literature suggests that the relationship can be ambiguous.
Kinney and McDaniel (1989) find that firms that issue restatements are typically smaller
and more highly leveraged than other firms in the same industry. They attribute this to
weaker control mechanisms of smaller companies and thus a higher probability of
restatement. Hence, there may be a penalty for restatements of larger companies with
better control and monitoring in place. El-Gazzar (1998) and Bamaber and Cheon (1995)
find that the market is less sensitive to earnings announcements made by large firms.
They conclude that this is due to lower levels of asymmetric information for large firms
relative to small firms. It is probable that information in earnings restatements can be
better preempted for larger companies. Therefore larger companies will have a small
market reaction to the restatement announcement. We add a NASDAQ indicator variable
to control for industry effect. In summary, our cross-sectional regression is as follows:
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CAR = +11Rev +12RevLitig + 21Cost + 22CostLitig+31Restr + 32RestrLitig
+ 41Sec + 42SecLitig + 51M&A + 52M&ALitig +61Reclass + 62ReclassLitig
+ 71Research + 72ResearchLitig + 81Trans + 81TranLitig +91Mult + 91MultLitig
+ 10Company + 11Auditor + 12SEC + 14size + 15Leverage + 16 Nasdaq + (3)
As shown in Table 2, abnormal returns are significant on the day of (day 0) and
the day following the announcement (day +1) for the majority of restatements. That is
why we use CAR(0,+1) as the dependent variable in the main regression12. Descriptive
statistics for the variables used in the regression are presented in Tables 4 and 5. The
results of this regression are presented in Table 6. Regression results and findings are
discussed in the next section.13
Results of cross-sectional analysis of abnormal returns
We find that the market reacts negatively to all types of restatements when they
are associated with mismanagement. The coefficients for all reasons interacted with
litigation are negative suggesting that such restatements send negative signals to the
market about the quality of management. Although the signal is predominantly negative,
it is not homogeneous across different reasons for restatements. Notably, all reasons for
restatements have positive coefficients on corresponding dummies when they are not
interacted with litigation, yet the magnitudes and statistical significance vary. We discuss
the results in more detail by reason.
Revenue recognition and cost/expense restatements are negatively related to
abnormal returns if they are associated with litigation. The coefficients on the
Revenue*Litigation and Cost*Litigation dummies are negative and statistically
significant: -0.0950 (at 1% level) and -0.1157 (at 1% level), respectively. Interestingly,
coefficients on Revenue and Cost are positive and significant at 10% level with values of
0.0473 and 0.0597, respectively. Hence, market reacts negatively to these restatements
only when they are associated with mismanagement. The positive sign on Revenue and
Cost dummies is quite puzzling, especially considering that 58% of the revenue
12 Model 1 in Table 6.13 Discussion in sections 4.1-4.6 pertain to Table 6, regression (1)
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recognition and 72% of cost and expense restatements are not associated with litigation
(Table 4, Panel A). Previous studies documented a negative reaction to revenue
recognition restatements in different samples and model specifications. Anderson and
Yohn (2002) and Wu (2002) found a negative reaction to revenue recognition
restatements. Palmrose et al. (2004), reports that in a sample of 492 restatements, core
accounts have statistically insignificant effect on abnormal returns. This puzzling result
can be due to the fact that our revenue recognition sample contains restatements due to
the adoption of SAB No 101. Such restatements are not due to error, unlike those in the
reminder of the sample.14
Securities related restatements have a negative but statistically insignificant effect
on abnormal returns when associated with litigation and a positive and significant effect
otherwise.15 The securities dummy has the highest positive coefficient of 0.0846 among
all reasons for restatements that is significant at a 5% level. Restructuring restatements
also have a positive and statistically significant coefficient when they are not associated
with mismanagement and have a negative and significant coefficient otherwise.
Restatements related to mergers and acquisitions that are associated with
litigation send the most negative signal. The absolute value of the coefficient of such
restatements is equal to -0.2019 (significant at 1%) and is the lowest value among all
other groups. However the coefficient on M&A is positive but not significant. The
univariate analysis shows that M&A restatements have the highest percent of negative
CARS (76%) and have second lowest mean return (Table 3). The fact that the reaction is
so pronounced might suggest that such restatements signal to the market that
management wasn’t efficient in handling mergers and acquisitions and possibly overpaid
for the acquisition.
The coefficients on IPR&D are indistinguishable from zero. Coefficients on
related party transaction, reclassification and multiple reason restatements are
insignificant, despite the fact that it encompasses many reasons for restatements that by
themselves have strong impact on returns. A possible explanation for this result is the
14 In the future drafts we plan to exclude this restatements from our sample.15 Only 2 restatements in securities related group are associated with litigation, however.
17
difficulty faced by investors in interpreting signals sent by restatements with multiple
reasons. Some offsetting influences can also be the causes for the observed result.
Coefficient on NASDAQ dummy is negative but insignificant. Neither size no
leverage significantly contributes to the explanation of cumulative abnormal returns.
Coefficient on Auditor and Company is negative and significant while the coefficient on
SEC is not different from zero. This result is surprising. One would expect negative
reaction to any external party initiated restatement (in this case negative coefficients on
SEC and Auditor) because such restatements will signal poor internal control.
Insignificant coefficient on SEC dummy can result from better anticipation of the market
of such restatements due to better coverage prior to restatements. A penalty for
restatement initiated by company is consistent with Palmrose at el (2004), however is not
well explained in the literature.
Our approach and results extend previous literature on the wealth effect of
restatements. We have used a finer partition of reasons than Palmrose at el. (2004).
Palmrose at. el. (2004) group restatements only into core and non-core reasons, where
core includes revenue, cost/expense and other operating income adjustments and non-
core restatements include the rest. They find that market reaction to core restatements is
not significantly different to the reaction of non-core accounts. Anderson and Yohn
(2002) find that markets react more negatively to revenue recognition restatements.
However, their study is different from ours. They use Fraud as a proxy for
mismanagement, while we use Litigation. We believe that litigation includes a much
greater range of management inefficiencies that are costly to shareholders. Anderson and
Yohn (2002) did not differentiate between restatements that are associated with
mismanagement and those that are not, which, as our study shows, is important in
analyzing the market reaction to restatements. In addition, Anderson and Yohn (2002)
used a much smaller sample that contained only 4 frauds and did not control for company
specific characteristics in conducting their analysis.
5. Information effect
In this section we examine the information effect of the restatements. Ross (1989)
shows that increase in the rate of flow of idiosyncratic information manifests itself not in
18
the wealth effect, but in the increase in idiosyncratic stock price volatility. Hence, a
regular even study methodology might misclassify increases in the rate of idiosyncratic
information flow as the wealth effect. We used generalized sign test to draw the inference
from the event study. Giaccotto and Sfiridis (1996) show that this test is robust to
changes in information flow during a financial event.
In addition to controlling for the immediate change in the flow of information, we
examine a short run impact of restatements on the information flow. We estimate
residuals from the market model for 3 months periods before and after the restatement.
We use the following two windows: (-94, -4) and (4, +94), where days are numbered with
regard to the day of the restatement announcement. The ratio of the estimated variances,
22 / ab SS (where 2bS is the sample variance prior to the restatement and 2
aS is the sample
variance after the restatement) is distributed as F91,91. Several companies didn’t have
returns for full period ((-94,-4) or (4,+94)). If the company had returns for windows (-25,-
4) and (4,+25) or better, we retained it in the sample and used appropriate degrees of
freedom. Otherwise the company has been excluded from analysis in this section. Figure
2 details results. Out of 511 firms in our sample, we found 189 (37%) firms to have
higher variance after restatements at 10% significance level as compared to 158 (31%)
with lower variance. Figure 3 presents the change in variance by reason. Restatements of
restructuring and securities items decreased the variance of returns as hypothesized.
Revenue recognition, M&A, reclassification, IPR&D and multiple restatements increased
the variance, suggesting a temporary increase in the flow of idiosyncratic information and
riskiness of the firm. Companies, restatements of which have a positive information
effect (decrease the variance of the residual), are much larger and are more levered
(Table 7).
We reexamine the cross-sectional analysis of cumulative abnormal returns
conditional on information effect in model 2, Table 6 by including two dummy variables:
increase in variance and decrease in variance. These variables are based on the F
statistic presented in Figures 2 and 3. Increase in variance equals 1 if the increase in
variance subsequent to restatement (measured over windows ((-94,-4) and (+4, 94)) was
significant at 10% level and zero otherwise. Decrease in variance equals 1 if the decrease
in variance subsequent to restatement (measured over windows ((-94,-4) and (+4, 94))
19
was significant at 10% level and zero otherwise. Restatements that increase variance are
associated with more negative CARS. After controlling for the information effect, most
of the results are preserved and the explanatory power of the model is improved.
6. Conclusion
The study examined whether financial statement restatements produce different
wealth effect (measured by market model adjusted abnormal returns around
announcement dates of restatements) and information effect (measured by the change in
variance of returns) depending on the reason for restatement (revenue, cost, securities
related, M&A, reclassification, IPR&D, transaction, multiple, other) and the presence of
mismanagement as proxied by litigation. Our decomposition of signals sent by financial
restatements allowed us to provide new evidence pertaining to market reaction to
financial restatements. Our main result is that restatements do send a variety of signals.
Market reaction to different reasons for restatements differs both in timing and
magnitude. The mean of cumulative abnormal returns over two-day window ranges from
-10.85% to –2.78%. Although the most significant market reaction for most reasons for
restatement is observed on days zero and plus one, markets start reacting to securities
related and reclassification restatements only on the day after the announcement.
We observe significant difference in the information conveyed by different types
of restatements. Mismanagement as proxied by litigation has a negative effect on the
market response to the reasons for restatement for all reasons. The effect is significantly
negative for revenue, cost, M&A, and restructuring. Restructuring and securities related
restatements that are not associated with mismanagement are positively related to market
reaction. This is consistent with our hypothesis that such restatements reduce the
noisiness of investors’ information set by revealing more accurate numbers in place of
estimates. However, we also find that revenue and cost restatements are positively related
to market reaction when they are not associated with mismanagement. This result is
counter to our prediction of negative reaction to revision of transaction based items.
We also find that securities and restructuring restatements decrease the
idiosyncratic flow of information subsequent to restatement, while revenue recognition
restatements increase idiosyncratic flow of information subsequent to the announcement.
20
This result is consistent with out hypothesis that restatement of securities and
restructuring related items (estimation-based) that are inherently noisy improve the
precision of investors’ information set, while restatement of revenue items, which are
mainly transactions-based, increases the noisiness of investor’s information set. Merger
and acquisitions, reclassification and multiple reason restatements mostly increase the
idiosyncratic flow of information subsequent to the announcement. Companies,
restatements of which have a positive information effect (decrease the variance of
residual), are much larger and are more levered.
Our results suggest substantial heterogeneity in market reaction to the correction
of different items on financial statements. Results are mainly consistent with our
hypothesis that restatements of inherently noisy items improve the precision of investors’
information set, while restatement of precise figures increases the noisiness of investor’s
information set.
21
References:
Anderson, K., and T. Yohn (2002), “The effect of 10-K restatements on firm value,
information asymmetries, and investors’ reliance on earnings,” Working paper.
Ayers, B., Lefanowicz C., and Robinson J., (1998) “Do firms purchase the pooling
method? Working paper, The University of Georgia, Michigan State University,
University of Taxes, Austin.
Bamber, L.; Y., Cheon, 1995 “Differential Price and Volume Reactions to Accounting
Earnings Announcements” The Accounting Review, Vol. 70, No. 3. (Jul., 1995), pp.
417-441.
Beaver, W., “The information content of annual earnings announcements,” Empirical
Studies in Accounting: Selected Studies, 1968. Supplement to Journal of Accounting
Research 10:1-38.
Chan, S., J. Martin, and J. Kensinger, 1990, “Corporate research and development
expenditures and share value”, Journal of Financial Economics 26, 255-76.
Core, J.E. (1997). “On the Corporate Demand for Directors’ and Officers’ Insurance.”
Journal of Risk and Insurance, 64: 63-87.
Cowan, Arnold, 2002, Eventus 7 User’s Guide, revised edition, Cowan Research LC,
Ames, Iowa.
Davis, M. (1990). “Differential market reaction to pooling and purchase methods.” The
Accounting Review 65, no. 3 (July): 696-709.
Diamond, D. and R. Verrecchia, "Information Aggregation in a Noisy Rational
Expectations Economy," Journal of Financial Economics, (1981), 221-35.
Diamond, D., 1985 “Optimal Release of Information By Firms” The Journal of Finance,
Vol. XL, No 4
22
El-Gazzar, S.M. “Predisclosure information and institutional ownership: a cross-sectional
examination of market revaluations during earnings announcement periods.” The
Accounting Review (January 1998): 119-129.
Elliott, J. and J., Hanna. “Repeated accounting write-offs and the informational content of
earnings.” Journal of Accounting Research (Supplement, 1996): 135-156.
General Accounting Office (GAO), 2002, Financial statement restatements: trends,
market impacts, regulatory responses, and remaining challenges, GAO-03-138.
Giaccotto, C. and Sfiridis, J. (1996), “Hypothesis testing in event studies: the case of
variance changes.” Journal of Economics and Business, 48-349-370.
Grinblatt, M. and S. Ross, "Market Power in a Securities Market with Endogenous
Information," Quarterly Journal of Economics, (1985), 1143-67.
Grossman, S. and J. Stiglitz, "On the Impossibility of Informationally Efficient Markets,"
American Economic Review (1980), 393-408.
Henri Theil, Economics and Information Theory (Chicago and Amsterdam: Rand
McNally and North Holland Publishing Company, 1967), Ch.1.
Hong, H., R. S. Kaplan, and G. Mandelker. 1978. “Pooling vs. purchase: The effects of
accounting for mergers on stock prices.” The Accounting Review 53, no. 1 (January):
31-47.
Kasznik, R. and M. Williams (2000) “Purchase versus pooling in stock-for-stock
acquisitions: Why do firms care?” Journal of Accounting and Economics 29: 261-286.
Kinney, W. Jr., and L., McDaniel. “Characteristics of firms correcting previously
reported quarterly earnings.” Journal of Accounting and Economics (February 1989):
71:93.
Martinez-Jerez, F. A. 2000. “Impact of Accounting Method on Market Assessment of
Business Combinations.” Working paper, Harvard Business School.
23
Nathan, K. 1988. “Do firms pay to pool? Some empirical evidence.” Journal of
Accounting and Public Policy 7: 185-200.
Owers, J., C. Lin, and R. Rogers, 2002 “The informational content and valuation
ramifications of earnings restatements,” International Business and Economics Journal,
2002, V.1, No. 5, pp 71-84
Palmrose Z.V., and S., Scholz “Restated financial statements and auditor litigation.”
Working paper, University of Southern California (2000).
Palmrose Z.V., Richardson V.J., Scholz S., “Determinants of Market Reaction to
Restatement Announcement.” Journal of Accounting and Economics, 37. (2004)
Robinson, J. and P. Shane. 1990. “Acquisition accounting method and bid premia for
target firms.” The Accounting Review 55, no. 1, (January): 25-48.
Woolridge, R. and C. Snow 1990, “Stock market reaction to strategic investment
decisions”, Strategic Management Journal 11, 353-63.
Wu, M. 2002 “Earnings restatements: A capital market examination.” New York
University working paper.
24
Appendix A: Categories of Restatements and Category Description
Categories of Restatements Category Description
Acquisitions and mergers Restatements of acquisitions or mergers that were improperly accounted for or not accounted for at all. These include instances in which the wrong accounting method was used or losses or gains related to the acquisition were understated or overstated. This category does not include in-process research and development or restatements for mergers, acquisitions, and discontinued operations when appropriate accounting methods were employed.
Cost or expense Restatements due to improper cost accounting. This category includes instances of improperly recognizing costs or expenses, improperly capitalizing expenditures, or any other number of mistakes or improprieties that led to misreported costs. It also includes restatements due to improper treatment of tax liabilities, income tax reserves, and other tax-related items.
In-process research and development
Restatements resulting from instances in which improper accounting methodologies were used to value in-process research and development at the time of an acquisition.
Other+ Loan-loss and tax Any restatement not covered by the listed categories. Cases included in this category include restatements due to inadequate loan-loss reserves, delinquent loans, loan write-offs, improper accounting for bad loans and restatements due to fraud, and accounting irregularities that were left unspecified.
Reclassification Restatements due to improperly classified accounting items. These include restatements due to improprieties such as debt payments being classified as investments.
Related-party transactions Restatements due to inadequate disclosure or improper accounting of revenues, expenses, debts, or assets involving transactions or relationships with related parties. This category includes those involving special-purpose entities.
Restructuring, assets, or inventory Restatements due to asset impairment, errors relating to accounting treatment of investments, timing of asset write-downs, goodwill, restructuring activity and inventory valuation, and inventory quantity issues.
Revenue recognition Restatements due to improper revenue accounting. This category includes instances in which revenue was improperly recognized, questionable revenues were recognized, or any other number of mistakes or improprieties were made that led to misreported revenue.
Securities related Restatements due to improper accounting for derivatives, warrants, stock options, and other convertible securities.
Source: GAO.Note: We excluded announcements involving stock splits and changes in accounting principles, as well as other financial statement restatements that were not made to correct mistakes in the application of accounting standard.
25
Figure 1: Mean Abnormal Returns around Restatements
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30
Day
Ret
urn
The following market model is estimated: Rit = i + i Rmt + it, where Rit is the return on security i on day
t, Rmt is the return on the market index on day t, and it is a random error term. The model is estimated over
255 trading days, ending 46 days before the restatement announcement. The abnormal stock return for
security i on day t is defined as ARit = Rit – (i + i Rmt), where alpha hat and beta hat are the ordinary
least squares estimates of security i’s market model parameters.
26
Figure 2: F statistic
96
37 3831
24
46
28 30
19 16 14 13 10
109
0
20
40
60
80
100
120
0.5
0.6
0.7
0.8
0.9 1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
Mor
e
Nu
mb
er o
f F
irm
s
Histogram of 511 F statistics 22 / ab SS (where 2bS is the sample variance prior to the
restatement and 2aS is the sample variance after the restatement) testing the hypothesis
that the variance of stock returns changes after restatement. Lower and upper critical values of the F91,91. are the following: at 10% - (0.7635, 1.3097), at 5% - (0.7070, 1.4143), at 1% - (0.6117, 1.6348).
27
Figure 3: Change in variance by reason
51
1915
117
15
9
22
65
22
12 106 5 5 6
18
67
20
12
6
13
510 10
28
0
10
20
30
40
50
60
70
80
Reven
ueCos
t
Restruc
turin
g
Secur
ities
MA
transa
ct
recla
ss RD
mult
iple
decrease no change increase
Change in variance is estimated as 22 / ab SS , where 2bS is the sample variance prior to the
restatement and 2aS is the sample variance after the restatement. Lower and upper critical
values of the F91,91. are the following at 10% - (0.7635, 1.3097) are used to determine statistically significant increase and decrease in variance.
29
Table 1: Abnormal returns around restatement announcements using market model
Abnormal returns are calculated using market model, parameters, which are estimated for the period of 255 days ending 46 days prior to the announcement. CRSP weighted average index is used as a proxy for the return on the market. Abnormal returns are computed for the sample of 788 restatements announced in 1997-2002
Date Sample SizeMean
Abnormal ReturnMedian
Abnormal ReturnsPositive : Negative
Mean Abnormal Returns-2 784 -0.56% -0.24% 356:428-1 782 -0.32% -0.17% 360:4220 769 -3.53%*** -1.13%*** 307:462
+1 757 -4.47%*** -1.48%*** 275:482+2 757 -0.13% -0.26% 358:399
***Denotes statistical significance at the 0.1% level using 2-tail generalized sign test
Generalized sign test is used in estimating the significance of the abnormal returns. The test uses normal approximation to binomial. The null hypothesis of this test is that the fraction of positive returns is the same as in the estimated period.
Patell Z test of significance yields similar results: returns on days 0 and +1 are significant at 0.1%. In addition, returns on day -2 are significant at 0.1% and on day +2 at 10%. Patell Z test assumes cross-sectional independence and estimates separate standard error for each security-event.
30
Table 2: Mean and Median Abnormal Returns by Reason
Abnormal returns are calculated using market model, parameters, which are estimated for the period of 255 days ending 46 days prior to the announcement. CRSP weighted average index is used as a proxy for the return on the market. Abnormal returns are computed for the sample of 788 restatements announced in 1997-2002.
Panel A: Mean Abnormal Returns by Reason and With/Without Litigation
Reason Mean Abnormal Return on Event Day:
-2 -1 0 +1 +2 +3Number
of observ.
Revenue recognition -0.59% -0.61%$ -4.00%*** -4.32%*** -0.07% 0.46% 267
Cost and Expense -1.39%** 0.08% -3.64%*** -3.49%*** -0.11% 0.91%$ 91
Restructuring -0.53%* 0.14% -2.32%*** -5.36%*** -0.75% 2.59%*** 71
Securities related 0.98% -0.79% -0.62% -2.22%** -2.27% -0.11% 38
Mergers and acquisitions -0.27% 0.38% -4.87%*** -5.70%*** 1.25% 0.44% 42
Reclassification -0.53% 0.02% -1.90%** -1.85%** -2.12%* -0.88% 28
IPR&D -0.91% 0.36% -1.45% -2.86%*** 0.37% -0.41% 33
Related party transactions -1.91% 0.50% -4.14%*** 1.32%** 4.05%** 1.95%$ 15
Multiple -0.75%** -0.78%$ -5.70%*** -5.50%*** 0.92%* 0.06% 118
Other 0.42 -0.20% -2.12%*** -5.87%*** -0.92%*** -0.45% 85
788
With litigation -1.28%*** -0.66%** -6.46%*** -7.67%*** -0.53% 0.93%** 335
No litigation -0.01% -0.12% -1.41%*** -2.27%*** 0.15% 0.11% 443
778
Panel B: Median Abnormal Returns by Reason and With/Without Litigation
Reason Median Abnormal Return on Event Day:
-2 -1 0 +1 +2 +3Number
of observ.
Revenue recognition -0.09% -0.32%$ -1.71%*** -1.36%*** -0.21% -0.10% 267
Cost and Expense -0.43%** -0.14% -1.15%*** -1.13%*** -0.58% 0.11%$ 91
Restructuring -0.24%* 0.07% -0.66%*** -1.37%*** -0.36% -0.10% 71
Securities related -0.05% -0.28% 0.18% -0.41%** -0.62% -0.25% 38
Mergers and acquisitions -0.34% 0.10% -0.50%*** -2.44%*** 0.73% -0.42% 42
Reclassification 0.20% -0.24% -0.79%** -0.61%** -1.28% -0.60% 28
IPR&D -1.04% 0.35% -1.24% -2.65%*** -0.11% -1.29% 33
Related party transaction -1.12% 0.60% -2.27%*** 0.83%** 1.68%** 1.32%$ 15
Multiple -0.61%** -0.43%$ -2.27%*** -2.08%*** -0.11%*** 0.51% 118
Other -0.29% -0.39% -0.16%*** -2.76%*** -0.46%*** -0.81% 85
788
With litigation -0.61%*** -0.60%** -2.16%*** -3.13%*** -0.46% -0.37%** 335
No litigation -0.11% 0.09% -0.66%*** -0.0103*** -0.11% -0.19% 443
778
$, *, **, *** indicates statistical significance at 10%, 5%, 1% and 0.1% respectively using Patell test.
31
Table 3: Univariate Statistics for Cumulative Abnormal Returns by Reason
Abnormal returns are calculated using market model, parameters of which are estimated for the period of 255 days ending 46 days prior to the announcement. CRSP weighted average index is used as a proxy for the return on the market. Abnormal returns are computed for the sample of 788 restatements announced in 1997-2002
CAR(0,+1)
Reason for restatement Mean median min max St. dev skewness
Number of restatements with positive CARs
as %
Number of restatements with negative CARs
as %
# of observa-tions in event window
Total number of restate-ments by reason
Revenue recognition -8.24%*** -4.43% -91.37% 41.63% 17.03% -1.3254 84 31% 180 67% 264 267
Cost or expense -7.05%*** -2.51% -77.22% 20.35% 13.28% -2.2427 25 27% 65 71% 90 91
Restructuring -7.50%*** -1.14% -92.50% 29.22% 19.65% -2.8927 25 35% 46 65% 71 71
Securities related -2.78%*** -0.95% -41.30% 18.19% 12.25% -1.5530 17 45% 21 55% 38 38Mergers and acquisitions -10.45%*** -4.32% -64.44% 13.83% 18.19% -1.4376 10 24% 32 76% 42 42
Reclassification -3.76%*** -1.77% -27.06% 12.51% 9.52% -0.3750 10 36% 18 64% 28 28
IPR&D -4.30%*** -2.69% -64.42% 20.20% 15.84% -1.6712 9 27% 24 73% 33 33Related-party transactions -2.81%*** -1.53% -25.84% 5.70% 7.92% -1.8954 5 33% 9 60% 14 15
Multiple -10.85%*** -4.50% -2.07% 21.56% 21.95% -1.7046 36 31% 76 64% 112 118
Other -7.89%*** -3.89% -68.49% 38.62% 15.54% -2.5620 27 32% 55 65% 82 85
Full Sample -7.88%*** -3.19%*** -91.37% 41.63% 17.20% -1.8343 248 31% 526 67% 774 788
$, *, **, *** indicates statistical significance at 10%, 5%, 1% and 0.1% respectively using Patell test.
32
Table 4: Litigation and Prompter Summary Statistics
Panel A: Litigation
Reason With litigation % No litigation % Total
Revenue 80 42.3% 109 57.7% 189
Cost 18 27.7% 47 72.3% 65
Restructuring 13 30.2% 30 69.8% 43
Securities 2 6.9% 27 93.1% 29
M&A 12 42.9% 16 57.1% 28
Reclassification 8 40.0% 12 60.0% 20
IPR&D 12 46.2% 14 53.8% 26
Related party transaction 3 27.3% 8 72.7% 11
Multiple 43 59.7% 29 40.3% 72
Other 16 29.6% 38 70.4% 54
Total 207 38.5% 330 61.5% 537
Panel B: Party Initiated Restatement
Full Sample Subsample of 537Prompter Number % of known Number % of known
Company 312 61.7% 188 59.3%
Auditor 56 11.1% 35 11.0%
SEC 104 20.6% 78 24.6%
Multiple parties 34 6.7% 16 5.0%
Number of restatements with known prompter 506 64.2% 317 59.0%
Unknown 282 35.8% 220 39.6%
Total 788 537
33
Table 5: Company Characteristics
This table is computed for the sample of 537 companies used in multivariate analysis
Abnormal returns are calculated using market model, parameters of which are estimated for the period of 255 days ending 46 days prior to the announcement. CRSP weighted average index is used as a proxy for the return on the market. Abnormal returns are computed for the sample of 788 restatements announced in 1997-2002. Size is estimated as natural logarithm of total assets in the quarter prior to restatement and leverage is calculated as the ratio of the long-term debt to total assets in the quarter prior to restatement.
Characteristics With litigation No litigation Full sample
Mean CAR(0,+1) -13.29% -3.51% -7.17%
Mean CAR(-1,+3) -12.62% -3.44% -6.88%
Median CAR(0,+1) -9.31% -1.76% -3.07%
Median CAR(-1,+3) -7.26% -1.89% -3.55%
Leverage 19.00% 32.78% 27.62%
Total assets 4,166,466,328
2,607,599,149 2,607,599,149
Number of observations 201 336 537
34
Table 6: Announcement return regressions
Dependent variable is cumulative abnormal return for a window (0,+1).
Model 1 Model 2
Dependent VariablesEstimated coefficients P-value
Estimated coefficients P-value
Revenue 0.0473* (0.062) 0.0422* (0.095)Revenue*Litigation -0.0950*** (0.000) -0.100*** (0.000)
Cost and Expense 0.0597* (0.052) 0.0561* (0.067)Cost*Litigation -0.1157*** (0.007) -0.078* (0.079)
Restructuring 0.0635* (0.068) 0.0553 (0.112)Restructuring*Litigation -0.0816* (0.097) -0.0872* (0.098)
Securities related 0.0846** (0.019) 0.0779** (0.032)Securities*Litigation -0.1003 (0.369) -0.0846 (0.437)
Mergers and acquisitions 0.0354 (0.416) 0.0393 (0.382)M&A*Litigation -0.2019*** (0.001) -0.2097*** (0.000)
Reclassification 0.0729 (0.133) 0.0748 (0.116)Reclassification*Litigation -0.1051 (0.129) -0.1092 (0.105)
In progress R&D 0.0295 (0.535) 0.0210 (0.659)IPR&D*Litigation -0.0129 (0.832) -0.0221 (0.711)
Transaction 0.0792 (0.171) 0.0782 (0.166)Transaction*Litigation -0.0800 (0.436) -0.0777 (0.437)
Multiple 0.0201 (0.542) 0.0166 (0.618)Multiple*Litigation -0.0399 (0.267) -0.0368 (0.310)
Company -0.0294* (0.053) -0.0354** (0.019)Auditor -0.0674** (0.019) -0.0849*** (0.005)SEC 0.0182 (0.417) -0.0114 (0.607)
Decrease in variance -0.0059 (0.721)Increase in variance -0.0273* (0.087)
Size 0.0052 (0.129) 0.0064* (0.077)Debt ratio 0.0019 (0.553) -0.0020 (0.269)NASDAQ -0.0226 (0.153) -0.0197 (0.213)Constant -0.1635** (0.035) -0.0875 (0.330)
Number of observations 537 510R squared 15% 17%Adjusted R squared 11% 12%
* Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level
35
Table 7: Change in variance for different types of restatements
Change in variance is assessed using an F test. F= {variance of market model residuals estimated prior to restatement (-94, -4)} / {variance of market model residuals estimated subsequent to restatement (+4, +94)}. If returns for full period ((-94,-4) or (4,+94)) were missing, we retained company with for windows (-25,-4) and (4,+25) or better and used appropriate degrees of freedom. Otherwise the company has been excluded from analysis in this section. Increase in variance equals 1 if the increase in variance subsequent to restatement (measured over windows ((-94,-4) and (+4, 94)) was significant at 10% level and zero otherwise. Decrease in variance equals 1 if the decrease in variance subsequent to restatement (measured over windows ((-94,-4) and (+4, 94)) was significant at 10% level and zero otherwise. All other restatements belong to group "no change in variance. Debt ratio is calculated as the ratio of long-term debt to total assets in the quarter prior to restatement.
Impact on the variance of market model residuals
Number of firms
% of total
sample Mean Car(0,+1)
Mean Size in Dollars
Mean Debt ratio Auditor Company SEC Litigation NASDAQ
Decrease in variance 158 31% -0.0680 $3,983,975,589 18.3% 12 55 22 64 86
No change in variance 164 32% -0.0511 $3,194,637,194 19.3% 7 55 26 55 97
Increase in variance 189 37% -0.0878 $2,791,598,598 5.9% 10 68 28 72 115
Total 511