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Disclosure Timing and the Market Response to First-Time Going Concern
Modifications and Earnings Announcements
Linda A. Myers
University of Arkansas
[email protected]
Jonathan E. Shipman
University of Arkansas
[email protected]
Quinn T. Swanquist
Georgia State University
[email protected]
Robert L. Whited
University of Massachusetts – Amherst
[email protected]
June 2015
We are grateful to Ben Anderson, Douglas Ayres, Brian Blank, Cory Cassell, James Chyz, James Myers, Terry
Neal, Andy Puckett, and Lauren Reid for helpful suggestions and comments. We also thank workshop participants at
the University of Tennessee and conference participants at the 2013 AAA Audit Midyear Meeting and the
University of Arkansas 2014 Summer Research Conference for valuable input. Linda Myers gratefully
acknowledges financial support from the Garrison/Wilson Chair at the University of Arkansas.
Disclosure Timing and the Market Response to First-Time Going Concern Modifications
and Earnings Announcements
ABSTRACT: Auditing standards require the auditor to amend the audit report with a going
concern modification (GCM) if there is substantial doubt about the client’s ability to continue as
a going concern. Although GCMs are typically characterized as value relevant (DeFond and
Zhang 2014), prior research does not investigate whether they provide information beyond that
in concurrent disclosures. In this study, we find that 70 percent of first-time GCMs issued from
2004 through 2012 were issued concurrently with earnings announcements (EAs) which
generally reveal poor operating performance and often include management’s disclosure of
material information related to future operations. Although we document a negative market
reaction (consistent with prior research) and positive abnormal trading volume around the
issuance of first-time GCMs, we find no detectable market response to first-time GCMs that are
not released concurrently with EAs, suggesting that the market reaction is attributable to
information disclosed at the EA date rather than in the GCM. Furthermore, after controlling for
the news in EAs, we find no difference in the market response to EAs that are issued
concurrently with GCMs versus those that are not. Taken together, our findings strongly suggest
that GCMs do not convey incremental material information to investors in the current reporting
environment.
KEYWORDS: Going Concern Modifications; Market Reactions; Auditor Reporting
DATA AVAILABILITY: All data used are publicly available from sources cited in the text.
1. INTRODUCTION
The nature of audit services provides auditors with privileged insight into their clients’
operations and financial condition. As such, professional auditing standards require the auditor to
modify the audit report with an additional explanatory paragraph (or modification) if there is
‘substantial doubt’ about the client’s ability to continue as a going concern for a reasonable
period of time, not to exceed one year (AU Section 341). The going concern modification
(GCM) is one of the few auditor communications to investors outside of the auditor opinions on
the financial statements and related internal controls.
DeFond and Zhang (2014) suggest that GCM disclosures can be used to directly evaluate
whether audit report modifications are useful to investors. They review the archival auditing
literature to date and conclude that “while the exact timing of the reaction may be in dispute, the
research strongly suggests that market participants value the information communicated in GC
opinions” (DeFond and Zhang 2014, p. 293). Findings in prior research generally support this
belief.1
Notwithstanding the evidence above, there are several reasons why GCMs may not
provide useful information to investors. First, prior research notes the considerable number of
Type I and Type II errors associated with going concern reporting (Geiger and Rama 2006;
Menon and Williams 2010; Myers et al. 2014).2 Second, the informativeness of the GCM is
inherently limited by the vague nature of the term ‘substantial doubt’ and by significant variation
in how this term is interpreted by different groups of financial statement users (Ponemon and
1
Additionally, several recent studies investigate market mispricing following the issuance of a GCM (see, for
example, Taffler et al. (2004), Ogneva and Subramanyam (2007), and Kausar et al. (2009)).
2
Several studies use the terms ‘Type I’ and ‘Type II’ to describe errors in GCM reporting. When referring to these
studies, we use this terminology to be consistent with prior literature. We recognize, however, that AU Section 341
is explicit that the auditor is not responsible for predicting future conditions or events. As such, the inclusion
(exclusion) of a GCM in the audit is not a guarantee that the client will cease (continue) to exist.
1
Raghunandan 1994). Third, the current information (e.g., EDGAR) and regulatory (e.g.,
Regulation Fair Disclosure) environments make it likely that investors will be aware of the
circumstances giving rise to a GCM before the audit report is issued. Fourth, the trend towards
fair value accounting (e.g., under Statement of Financial Accounting Standards No. 142
Goodwill and Other Intangible Assets and other accounting standards) arguably provides
investors with better information about a company’s future prospects. Lastly, by their very
nature, GCMs are redundant to other important disclosures required by management. Consistent
with this, the auditor’s explanatory paragraph directs financial statement users to concurrent
management disclosures that elaborate on the circumstances giving rise to the GCM.
Furthermore, the auditor is required to assess the reasonableness of management’s disclosures
about material financial distress before issuing an unqualified audit opinion. Because companies
receiving GCMs are distressed, any concurrent information is likely to be negative. While this
increases the likelihood of a negative market reaction at the disclosure of a GCM, we posit that
this negative reaction may not be due to the disclosure of the GCM per se.
Consistent with Menon and Williams (2010), for a sample of more than 400 first-time
GCMs issued from 2004 through 2012, we document negative abnormal returns in the three-day
window beginning on the GCM disclosure date (i.e., on days [0, +2]).3 We also document
significant positive abnormal trading volume during this window. However, we find that GCM
disclosures are released concurrently with earnings announcements (EAs) (i.e., are
‘contaminated’) for 70 percent of our sample observations.4 Because the surprise in EAs affects
3
Our abnormal returns are also similar in magnitude to those in Menon and Williams (2010), at an average of -5.00
percent versus -6.28 percent, respectively.
4
For convenience, we follow Dopuch et al. (1986) and refer to GCMs released concurrently with EAs as
‘contaminated’ and to those released without concurrent EAs as ‘uncontaminated’. We acknowledge that
‘uncontaminated’ GCM disclosures are generally issued in the annual report (10-K) and thus are not free from
concurrent disclosures. However, information disclosed in the annual reports of distressed companies is likely to be
systematically negative and would thus bias against our findings.
2
stock prices (Kothari 2001), we suggest that market reactions around the majority of GCMs may
be attributable to earnings-related news (or other news released concurrently with earnings)
rather than information conveyed by the GCM. Consistent with this, when we isolate those
GCMs not released with EAs, we find no evidence of either a negative market reaction or
positive abnormal trading volume. To further test for information content of GCMs, we compare
the three-day market response at the EA date (i.e., in days [0, +2] relative to the EA) for
companies that concurrently announce GCMs with the market response at the EA date for
companies that announce GCMs separately (i.e., in an annual report following the EA). Here, we
find no difference in the market response, suggesting that the announcement of a GCM has no
discernable effect on the market response to earnings. Taken together, contrary to
characterizations in prior research, we find no evidence that investors respond to GCMs in the
current reporting environment. Furthermore, our evidence indicates that failure to control for
news in EAs results in overstated estimates of the market’s response to GCMs.
This study contributes to existing academic literature and active policy debates. In a
recent review of the going concern literature, Carson et al. (2013, p. 376) state that “future
research can examine how … disclosures by management and by auditors might be used and
interpreted differently by investors, lenders, and other financial statement users.” Furthermore,
the Center for Audit Quality is “interested in research that identifies issues with the current
auditor’s reporting under going concern” (CAQ 2012, p. 2). We address these calls for research
by taking advantage of variation in the timing of GCM disclosures relative to EAs to investigate
whether shareholders do in fact respond to auditors’ GCMs.
In addition, we contribute to the academic literature by carefully exploring stock price
reactions to the issuance of GCMs and to concurrent disclosures. Our findings suggest that
3
contrary to characterizations in prior literature, the negative stock price reactions associated with
GCMs are largely related to information in other disclosures. We also contribute to the extant
literature by investigating abnormal trading volume around the issuance of GCMs. Consistent
with results using stock prices, we document abnormal trading volume following the issuance of
GCMs but our analyses reveal that this abnormal trading volume is related to other news.
Finally, audit firm management should be interested in the results of our study. Previous
literature suggests that “missing” GCMs can expose auditors to additional litigation risk
(Carcello and Palmrose 1994; Kaplan and Williams 2013) and can adversely affect the auditorclient relationship (Carcello and Neal 2003). If GCMs are not materially informative to
investors, the costs of requiring GCMs could outweigh the benefits.
The remainder of the paper is organized as follows. The next section provides
background discussion, a review of prior literature, and our empirical predictions. We discuss
our sample selection, variable measurement, and research design in Section 3. Section 4 presents
our main results and Section 5 describes our additional analyses and robustness tests. We make
concluding remarks in Section 6.
2. BACKGROUND DISCUSSION, PRIOR LITERATURE, AND EMPIRICAL
PREDICTIONS
The standard audit report expresses the auditor’s opinion as to whether the client’s
financial statements and related disclosures are presented fairly, in all material respects. In
addition to this opinion, AU Section 341 requires that auditors assess the client’s ability to
continue as a going concern and issue a GCM when ‘substantial doubt’ exists. In this respect,
4
auditors are responsible for making subjective judgments about the future viability of their
clients.5
Prior research shows that GCMs, or lack thereof, influence many aspects of the auditorclient relationship. For example, Carcello and Neal (2003) suggest that GCMs influence auditor
retention decisions and Carcello and Palmrose (1994) and Kaplan and Williams (2013) find that
GCMs influence the likelihood and outcome of litigation against auditors. In addition, Blay and
Geiger (2013) find a negative relation between the issuance of a GCM and future fees generated
from the client. Given that GCM decisions influence a number of outcomes, it is important to
understand whether market participants value GCMs.
Recent research concludes that GCMs are informative because they are associated with
abnormal returns. For example, Menon and Williams (2010, p. 2013) document significant
negative returns at the disclosure of a first-time GCMs and conclude that “investors react to the
auditor’s assessment and adjust their valuations accordingly.” Similarly, Amin et al. (2014) find
that GCMs are associated with increases in the cost of equity capital, and Blay et al. (2011) find
that GCMs cause investors to adjust their perceptions of client value. Other studies condition on
the likelihood of receiving a GCM and find that unexpected GCMs are associated with a more
negative stock price reaction (Loudder et al. 1992; Fleak and Wilson 1994), while the absence of
an expected GCM is associated with positive returns (Jones 1996). In addition, Willenborg and
McKeown (2000) suggest that GCMs are value relevant because initial public offerings with
GCMs exhibit less first-day underpricing. Finally, Chen and Church (1996) and Holder-Webb
and Wilkins (2000) find that returns are significantly more negative around bankruptcy filings
Specifically, AU Section 341 states, “If the auditor concludes there is substantial doubt, he should (1) consider the
adequacy of disclosure about the entity’s possible inability to continue as a going concern for a reasonable period of
time, and (2) include an explanatory paragraph (following the opinion paragraph) in his audit report to reflect his
conclusion.”
5
5
when companies did not previously receive GCMs. They conclude that GCMs impact investors’
assessments of the likelihood of bankruptcy, leading to smaller reactions when bankruptcies are
disclosed following GCMs. Taken together, these studies suggest that going concern reporting
provides important information to investors.
As discussed above, however, there are several reasons why GCMs may not provide
investors with incremental information. First, prior research finds that a considerable number of
Type I and Type II going concern reporting errors occur (Geiger and Rama 2006; Menon and
Williams 2010; Myers et al. 2014). Second, there is a lack of consensus among user groups
regarding the probability of bankruptcy necessary for ‘substantial doubt’ to exist (Ponemon and
Raghunandan 1994), potentially limiting the informativeness of GCMs. Third, the issuance of a
GCM is generally preceded by important events (Elliott 1982; Dodd et al. 1984) that are likely to
be disclosed prior to the issuance of the 10-K (and so the disclosure of the GCM), possibly in the
EA. Fourth, EDGAR allows investors to immediately access all company filings and Reg FD
prohibits the selective disclosure of material information to parties outside of the company. Fifth,
recent trends in standard setting favor fair value accounting treatments that provide forwardlooking information to investors, potentially eroding the incremental value of the GCM. Lastly, a
GCM is likely to be redundant to existing management disclosures because professional
standards require the auditor to determine whether the financial statements and accompanying
footnotes are presented fairly, in all material respects, before issuing an unqualified opinion. This
determination requires that the auditor evaluate whether management has disclosed all material
information including that which relates to the going concern assumption.6 Therefore, the
PCAOB and SEC guidance is explicit that financial statements include “appropriate and prominent disclosure of
the financial difficulties giving rise to that uncertainty.” Refer to AU 341 paragraphs 10 and 14 and SEC Division of
Corporate Finance Financial Reporting Manual 4230.1b.
6
6
disclosure of a GCM is likely a product of ‘bad news’ disclosed by management rather than ‘bad
news’ itself. 7
Because the initial disclosure of ‘bad news’ events leading to the GCM can occur at the
same time as the GCM disclosure, GCMs can appear to convey negative information. In our
sample, all 478 GCMs refer financial statement users to specific management disclosures
relating to the going concern assumption and 70 percent of sample observations are disclosed
concurrently with fourth quarter earnings (which are typically disappointing for companies
receiving GCMs). In these cases, estimates of the market reaction to GCMs may be
‘contaminated’ by earnings news and concurrent disclosures.
In their studies of the market reaction to earnings, Francis et al. (2002a, 2002b)
demonstrate the importance of considering concurrent news.8 Our study follows their work in
that we demonstrate the importance of considering concurrent news when studying the market
reaction to GCMs. Because EAs and related disclosures can elicit market reactions, it is
important to consider the timing of earnings releases when investigating the market reaction to
GCMs.9
For the reasons outlined above, failure to control for the release of an EA concurrent with
a GCM will likely overstate the market reaction to the disclosure of a GCM. We address this
7
We note that the going concern evaluation performed by auditors may discipline management to be more
forthcoming in financial disclosures. However, professional standards require that if management omits material
disclosures, the auditor either withholds the opinion, issues a qualified/adverse opinion, or in extreme cases, resigns
from the audit engagement. In other words, the auditor is already required to modify the unqualified opinion if
management omits material disclosures. In this sense, the auditor’s GCM may not be needed to motivate
management to disclose bad news.
8 Specifically, EAs often include detailed financial statement data and other material client-specific information
(Francis et al. 2002b; D’Souza et al. 2010) that can affect stock prices.
9
We acknowledge that the market reaction at the EA is unlikely to be entirely attributable to earnings news (i.e.,
unexpected earnings per share) because EAs for troubled companies often disclose adverse events such as debt
covenant violations and/or goodwill impairments. However, the purpose of this study is to disentangle the reaction
to the GCM from the reaction to information disclosed in the EA (earnings or otherwise). In this sense, it is not
important which EA information investors are reacting to; it is only important that EAs be considered when
evaluating the market response to GCMs.
7
problem by taking advantage of variation in the relative timing of EA and GCM disclosures.
That is, we investigate the stock price and trading volume effects of GCMs while considering the
effect and timing of the earnings news. Because we expect GCMs to provide little incremental
information to investors (as explained previously), we predict that there will be no detectable
market response to the disclosure of ‘uncontaminated’ GCMs. We also predict that the market
response to earnings news will be unaffected by the concurrent disclosure of a GCM.
3. SAMPLE, MARKET RESPONSE MEASURES, AND RESEARCH METHODOLOGY
Sample
Sample Selection
We identify all audit opinions containing GCMs from 2000 through 2012 in the Audit
Analytics (AA) Opinions dataset. We manually verified that all opinions in our final dataset
included a GCM. Following prior research, we limit our sample to the first GCM issued for each
client (i.e., ‘first-time’ GCMs) during our sample period (from 2004 through 2012) so that our
sample includes only those GCMs that are less likely to be anticipated by investors. We begin
our sample period in 2004 to focus on investor reactions to GCMs following important
regulatory changes (i.e., the Sarbanes-Oxley Act of 2002 and Reg FD) and to allow for several
years of prior audit opinion data.10 Because we focus first-time GCMs, each company appears in
the sample only once.
10
The Audit Analytics dataset begins in 2000. While it is possible that a company received a GCM prior to 2000,
that GCM would have been issued at least four years prior. Thus, we consider any GCM in our sample a first-time
GCM.
8
We also require each sample observation to have the necessary data available from the
Center for Research in Security Prices (CRSP).11 Our initial sample includes 486 total first-time
GCM disclosures. For our multiple regression analyses, we obtain financial statement data from
Compustat, analyst forecast data from the Institutional Brokers’ Estimate System (I/B/E/S), and
institutional ownership data from Thompson Reuters. Because the data necessary to perform
some tests are lacking for some observations, we perform each analysis on the set of
observations with the requisite data for that test.
Sample Composition
Figure 1 describes the composition of the 486 first-time GCM sample observations.
Following Menon and Williams (2010), we began by separating GCMs disclosed concurrently
with the annual report and GCMs disclosed prior to the annual report date. To classify each
observation, we used the Securities and Exchange Commission (SEC) Analytics Suite to search
all 8-Ks filed with the SEC prior to the filing of the annual report for an indication that auditor
would be issuing a GCM in the audit report. This search resulted in the identification of 28
instances in which the GCM was disclosed prior to the annual report date. Although these early
disclosures may initially seem to be ‘clean’ GCM disclosures (i.e., GCM disclosures that are not
confounded by other disclosures in the annual report), further investigation reveals that all of
these early GCM disclosures were made with disclosures of other material events. Specifically,
of the 28 observations that disclosed the GCM early, 20 included the GCM in an EA and the
other 8 disclosed another significant event (e.g., an impairment, a restatement of prior financial
statements, a ‘cease and desist’ order from the Federal Deposit Insurance Corporation (FDIC), a
default and/or debt covenant violation, the inability to file a timely annual report, or a delisting).
11
Sample sizes for our abnormal volume tests are slightly lower than for our returns tests because we require a
longer time series of data to estimate normal trading volume.
9
The nature of each of these confounding events is described in detail in Appendix 1. Of the
remaining 458 observations that first disclosed the GCM in the annual report, 144 announced
earnings prior to filing the annual report and the remaining 314 concurrently disclosed the GCM
and EA (which provide the first indication of earnings related news to the market) in their annual
reports.12
(Insert Appendix 1 and Figure 1 here)
Figure 1 highlights the importance of considering the timing of confounding events when
studying the market reaction to GCMs. If confounding disclosures elicit systematically negative
market responses, failure to consider their timing can lead to overstated estimates of the market
response to GCMs. That is, the reaction to EAs and related events can be inadvertently attributed
to GCMs. We eliminate the 8 GCMs disclosed separately from either an EA or an annual report
(as summarized in Appendix 1) because each of these GCMs is disclosed concurrently with an
important negative event.13 Thus, our analyses are based on the remaining 478 observations. We
refer to the 334 GCM disclosures made concurrently with EAs as ‘contaminated’ GCMs and the
144 GCM disclosures made after EAs as ‘uncontaminated’ GCMs.
We define a GCM as ‘concurrent with’ or ‘contaminated by’ an EA if the GCM disclosure falls within the [0, +2]
window relative to the earnings announcement. For some observations, the EA is released 1 or 2 days prior to the
GCM. Because we cannot disentangle the market reaction to the EA from the market reaction to the GCM in these
cases, we treat these as concurrent disclosures. In untabulated analyses, we remove these observations and our
inferences are unchanged.
13
In untabulated analyses, we calculate the mean cumulative abnormal return in the three-day window starting at
these GCM disclosures and find that it is -16 percent. However, because each of these instances is confounded by
material information released concurrently with the GCM disclosure, we cannot attribute the negative market
reaction to the GCM.
12
10
Market Response Measures
Cumulative Abnormal Returns
Our first measure of the market response is the cumulative abnormal return (CAR) in the
three-day window starting with event date (i.e., days [0, +2]).14 Specifically, for each
observation, we calculate CARs in the three-day window starting at the EA date and in the threeday window starting at the GCM disclosure date by subtracting the size-decile portfolio’s daily
returns obtained from CRSP from the company’s raw daily returns and cumulating the excess
returns over the event window.15 We winsorize the CAR variable (CAR) at the 1st and 99th
percentiles to reduce the effects of outliers but our results are qualitatively unchanged if we do
not winsorize CAR.
Abnormal Trading Volume
Our second measure of the market response is the abnormal trading volume (AVOL) in
the three-day window starting at the release of the EA or GCM. Following Landsman et al.
(2012), we define abnormal trading volume as the natural log of actual trading volume scaled by
expected trading volume. We measure actual trading volume (the numerator) as the mean daily
trading volume in the event window (days [0, +2]), where daily trading volume is calculated as
the number of shares traded scaled by the number of shares outstanding, and we use two event
windows to estimate the expected trading volume (the denominator). We first follow Landsman
et al. (2012) and use days [-60, -10] relative to the EA date to calculate the mean expected daily
trading volume. Next, because an event window near the EA and GCM disclosures may be
For ‘contaminated’ observations, the EA and GCM dates are generally the same. However, for 43 observations,
these dates differ slightly (i.e., the EA is issued within two days of the GCM disclosure but not on the same day).
For our analyses, we calculate each response based on the event of interest (i.e., the EA or GCM disclosure). As
noted previously, our inferences are robust to the exclusion of these observations.
15
Our inferences remain unchanged if we use buy and hold abnormal returns instead of CARs or if we market-adjust
the company’s raw returns by the equally weighted market return rather than by returns to the size decile.
14
11
contaminated by the release of other information prior to the GCM, we also estimate expected
trading volume using a longer, earlier estimation period. Here, we use days [-224, -75] relative to
the release of the EA.16, 17 Similar to CARs, we estimate AVOL for both the EA and GCM event
windows.18 We winsorize the AVOL variable (AVOL) at the 1st and 99th percentiles to reduce the
effects of outliers but our results are qualitatively unchanged if we do not winsorize AVOL.
Research Methodology
Univariate Analyses
We begin by investigating whether there is a significant a market response (i.e., stock
price reaction and abnormal trading volume) in the three-day window starting at the issuance of
the GCM without considering whether the GCM is ‘contaminated’ by an earnings release. Next,
to examine whether the GCM provides information that is incremental to information in the EA,
we compare the market response in the three-day window starting at the GCM disclosure date for
‘contaminated’ versus ‘uncontaminated’ GCMs. We also separately calculate the market
response to EAs that are ‘uncontaminated’ by GCMs (i.e., those observations where the EA
window does not include the GCM disclosure). This allows us to disentangle the market
response to the EA from the market response to the GCM. Additionally, these ‘uncontaminated’
16
For the shorter, more recent estimation window, we require observations to have at least 20 days of trading
information available in CRSP. For the longer, earlier estimation window, we require at least 50 days of trading
information. There are five fewer observations with the requisite data available when using this longer estimation
window. If we perform all analyses using only those observations with the necessary data for both estimation
windows, all inferences are unchanged.
17
Throughout our analyses, we assume that when trading days are available in CRSP but are ‘missing’ volume data,
the trading volume is zero, and we treat trading days that are unavailable in CRSP as ‘missing’. However, if we
either treat both sets of observations as ‘missing’ or set the trading volume for both sets of observations to zero, our
inferences are unchanged. Furthermore, if we drop all observations with either a missing value or zero trading
volume, our inferences are also unchanged.
18
We use the same estimation window ([-60, -10] or [-224, -75] relative to the EA date) to calculate expected
trading volume for both the EA and GCM even when these dates differ so that our results cannot be attributed to
differences in the ‘expected trading volume’.
12
observations provide a control sample for the ‘contaminated’ sample of companies that make
GCM disclosures concurrently with their EAs.19
Multiple Regression Analyses
We also test whether the presence of a GCM influences the stock price reaction at the EA
date using multiple regression. We begin by estimating the following ordinary least squares
(OLS) model:
CAR = β0 + β1CONTAMINATED + β2(ΔEBIT or SURPRISE) + β3SIZE + β4CFO +
β5BIGN + β6ROA + β7LEVERAGE + β8INST_OWN + β9ZFC +
β10ΔLEVERAGE + ε
[1]
The dependent variable (CAR) is the CAR in the three-day window beginning on the EA date.
The coefficient of interest is the coefficient on an indicator variable (CONTAMINATED) equal to
one when the GCM is disclosed in the EA window, and zero otherwise. If the GCM provides an
additional distress signal for investors, then we expect the coefficient on CONTAMINATED to be
negative and significant. However, if the GCM conveys no incremental information, there will
be no difference in EA date returns for those companies that announce GCMs in the EA window
versus those that do not.
We use two proxies for earnings news. The first, ΔEBIT is defined as current year
earnings before interest and taxes less prior year earnings before interest and taxes, scaled by
prior year total assets. The second, SURPRISE, is a continuous variable equal to actual earnings
per share less the median consensus analyst forecast of earnings per share, scaled by the absolute
19
We do not attempt to identify a control group of non-GCM companies with similar traits to GCM companies
through propensity score matching (PSM) or similar methods. It is unlikely that matching GCM companies to nonGCM companies on observable characteristics would provide a valid test. Since many factors contributing to GCMs
are difficult to capture or measure (e.g., violation of debt contracts, supply chain issues, regulatory problems,
management’s remediation plans, ability to secure financing), any finding would be endogenous as these
(unmodeled) factors would contribute significantly to the decision to issue a GCM and likely any market reaction.
However, the variation in relative timing of disclosures allows for ideal treatment and control groups
(counterfactuals) because all companies in our sample are sufficiently distressed to receive a GCM but the relative
timing of market awareness of the GCM varies.
13
value of the median analyst forecasted earnings per share.20 We also include several companyspecific variables to adjust for potential differences in company characteristics including size,
distress, and profitability. Specifically, we control for the natural log of market value of equity
(SIZE), cash flows from operations scaled by lagged total assets (CFO), whether the company is
audited by a Big N auditor (BIGN), net income scaled by lagged total assets (ROA), the ratio of
total liabilities to total assets (LEVERAGE), the percentage of outstanding shares owned by
institutional investors (INST_OWN), the probability of bankruptcy (ZFC) from Zmijewski
(1984), and the change in leverage from the prior period (ΔLEVERAGE).
To test whether GCMs impact trading volume, we estimate the following OLS model:21
AVOL = β0 + β1CONTAMINATED + β2(|ΔEBIT| or |SURPRISE|) + β3SIZE +
β4LEVERAGE +β5LOSS + β6REPLAG_EA + β7NUMEST +
β8DISPERSION + ε
[2]
The dependent variable (AVOL) is the abnormal volume in the three-day window starting on the
EA date. We include the CONTAMINATED indicator variable to examine whether the
concurrent disclosure of a GCM influences trading volume. We control for |ΔEBIT| or
|SURPRISE|, which are the absolute values of the earnings news variables from Equation [1].
SIZE and LEVERAGE are as defined previously. LOSS is an indicator variable equal to one if net
income is negative, and zero otherwise. REPLAG_EA is the number of days between fiscal yearend and the EA date, NUMEST is the number of analysts following the company, and
DISPERSION is the standard deviation of analyst forecasts scaled by year-end stock price,
winsorized at one.22
20
We winsorize SURPRISE at the 1st and 99th percentiles to reduce the effect of outliers but our inferences are
unchanged if we do not winsorize SURPRISE. Furthermore, our inferences are unchanged if we calculate
SURPRISE scaling by stock price rather than the median consensus analyst forecast.
21
If we reperform analyses using all control variables in Equation [1], our inferences are unaffected.
22
Unless stated otherwise, we winsorize all continuous variables at the 1st and 99th percentiles. However, our
inferences are unaffected by winsorization.
14
4. RESULTS
Descriptive Statistics
Panel A of Table 1 presents descriptive statistics for the sample of first-time GCMs. As
expected, clients receiving GCMs are small, have poor operating performance, are highly
levered, and have a high probability of failure (i.e., a high ZFC score). Panel B displays
comparative descriptive statistics for companies where the disclosure of the GCM is
‘contaminated’ by an EA and for those where it is not. Consistent with our prediction that the
market reaction on the GCM date will be influenced by the EA, we find that abnormal returns are
significantly more negative and trading volume is significantly more positive in the GCM event
window when an EA is simultaneously disclosed. We also find no evidence that abnormal
returns or trading volume at the EA date differ when the GCM is simultaneously disclosed.
Collectively, these differences highlight the importance of disentangling confounding disclosures
when investigating the market response to GCMs.
(Insert Table 1 here)
In addition to observing differences in the stock price reaction and trading volume, we
find other differences between the ‘contaminated’ and ‘uncontaminated’ subsamples.
Specifically, companies with ‘uncontaminated’ GCMs tend to be larger, have less negative cash
flows and ROA, and are more likely to be audited by a Big N auditor. Importantly, we find no
evidence that the nature of the earnings information (i.e., ΔEBIT and SURPRISE) differs based
on the relative timing of the EA.
15
Panel C presents the sample composition by industry and year. First-time GCMs are
spread fairly evenly across industries and years, with the exception of a relatively higher
concentration in Fama-French 12 industry classifications 10 through 12 and fiscal year 2008.23
Analysis of Abnormal Returns to GCM Disclosures and EAs
We begin by examining the stock price reaction to the disclosure of a GCM in Table 2. In
Panel A, consistent with prior research, we document a significant negative market reaction (of
-5.00 percent for our sample) in the three-day window starting at the GCM disclosure. As shown
in Figure 1, however, 334 (70 percent) of these GCMs are disclosed concurrently with the
company’s EA (i.e., these are the ‘contaminated’ sample). For these observations, it is unclear
whether the negative stock price reaction was due to information in the EA or in the GCM. Thus,
to provide insight into the source of the negative reaction, we calculate the GCM window return
for the ‘contaminated’ and ‘uncontaminated’ samples separately. Here, we observe a significant
negative CAR (which averages -7.24 percent) for the ‘contaminated’ GCM subsample but we
find no detectable stock price reaction for the ‘uncontaminated’ GCM subsample. Furthermore,
the difference between the GCM disclosure returns for the ‘contaminated’ and ‘uncontaminated’
subsamples is highly significant, indicating that the estimated reaction to GCMs for the full
sample is likely to be attributable to confounding disclosures. Overall, these analyses suggest
that the -5.00 percent return for the full sample of GCMs is likely to be related to the EA news
rather than the GCM.
(Insert Table 2 here)
To further test whether GCMs convey additional information, we compare EA date
returns for ‘contaminated’ and ‘uncontaminated’ subsamples. The first sample (334
23
To ensure that our findings are not driven by any single industry or year, we re-perform all of our analyses
excluding each industry and year individually and find that our inferences are unchanged.
16
observations) is the ‘contaminated’ GCM subsample from the previous analysis.24 If GCMs
convey additional information that is not conveyed by EAs, then we should detect a more
negative stock price reaction for observations that also include a GCM disclosure in the EA
returns window. However, in Panel B, we find a significant negative EA date return (averaging
-6.07 percent) for those companies that do not announce a GCM with their EA and this is not
significantly different from the EA date return (which averages -7.60 percent) for companies that
announce a GCM with their EA. Again, these results suggest that the GCM does not convey
information which is incremental to that in the EA.
Taken together with the prior results, we find consistent evidence of negative stock price
reactions at the EA date for companies with first-time GCMs, but we find no evidence that the
GCMs themselves provide incremental information to investors.
Analysis of Abnormal Trading Volume around GCM Disclosures and EAs
The previous results suggest that the auditor’s GCM may not convey additional
information about company distress but even if no significant stock price reaction is observed
related to GCMs, GCMs could prompt a revision of investor beliefs and result in abnormal
trading volume. Thus, we re-estimate the tests in Table 2 using abnormal trading volume to
proxy for information content. The results are presented in Table 3.25
(Insert Table 3 here)
24
The estimated return is slightly different from that in Panel A because the EA date differs from the GCM date for
43 observations; these are the companies that issue earnings within two days of the GCM (which appears in the
annual report). Because the EA window includes the GCM, we include these observations in the ‘contaminated’
sample. However, if we exclude these 43 observations, our inferences are unchanged.
25
If GCMs result in a revision of investor beliefs (and as such, provide information), then the disclosure of a GCM
should be accompanied by positive abnormal trading volume. There are slightly fewer observations in this analysis
than in our stock price reaction analysis due to data requirements necessary to calculate abnormal trading volume. If
we re-perform our returns analysis using the subsample of companies with trading volume data, our inferences are
unchanged.
17
Results from the volume analyses are consistent with the CAR results in Table 2. In
particular, when we do not separately identify ‘contaminated’ GCMs in Panel A, we observe
positive AVOL in the GCM window using either abnormal trading volume estimation window,26
suggesting that GCMs have information content. However, when we distinguish between
‘contaminated’ and ‘uncontaminated’ GCMs, we find that abnormal trading volume is observed
only for the subsample of ‘contaminated’ GCMs. Specifically, we observe significantly positive
abnormal trading volume in the GCM window for ‘contaminated’ GCMs but find no evidence of
abnormal trading volume when GCMs are not accompanied by EAs, and the difference between
these two groups is statistically significant. This suggests that the abnormal trading observed
after GCMs is related to news released with the EAs rather than to the GCM disclosures per se.
Next, in Panel B, we compare AVOL in the EA window for ‘contaminated’ versus
‘uncontaminated’ GCMs. Consistent with prior research investigating market reactions to EAs,
AVOL is significantly positive around the EA date, and this occurs regardless of whether the EA
includes a GCM announcement. Furthermore, AVOL is not significantly different between the
subsamples, again supporting our conclusion from stock price reaction tests – that there is no
evidence that GCMs convey information once confounding disclosures are considered.
Multiple Regression Analysis of Returns and Abnormal Trading Volume
Abnormal Returns
We also use multiple regression to reduce the likelihood that our previous findings are
driven by differences in sample characteristics between ‘contaminated’ and ‘uncontaminated’
observations. To do this, we estimate returns models and include client-specific variables that
could relate to the timing of GCM disclosures and to returns following EAs. In both columns, the
26
This positive AVOL is significant at conventional levels in one window using a one-tailed test and in the other
using a two-tailed test.
18
dependent variable is the CAR in the three-day window starting at the EA disclosure date. For
these analyses, our variable of interest is an indicator variable, CONTAMINATED, set equal to
one if the EA is ‘contaminated’, and zero otherwise. In column 1, we include ΔEBIT to proxy
for unexpected earnings, and in column 2, we include the more restrictive unexpected earnings
variable, SURPRISE. The results from these tests are presented in Table 4.
(Insert Table 4 here)
The only significant control variables are those that proxy for unexpected earnings; here,
the decrease in stock prices is greater as the earnings surprise becomes more negative. More
importantly, consistent with findings from our univariate analyses, in both specifications, the
insignificant coefficient on CONTAMINATED suggests that the disclosure of a GCM with the
EA does not convey additional information to investors. Thus, in multiple regression tests, we
continue to find no evidence that GCMs contain value relevant information.
Abnormal Trading Volume
We also use multiple regression for tests of AVOL at the EA date. The results from these
tests are presented in Table 5. Consistent with the results from our abnormal returns analyses, we
find no evidence that GCMs result in increased abnormal trading volume.
(Insert Table 5 here)
5. ADDITIONAL ANALYSES
GCMs and Bankruptcy
Although our prior results suggest that investors do not respond to the disclosure of
GCMs, GCMs could still be helpful in anticipating bankruptcy. Therefore, we investigate
whether GCMs provide information about future bankruptcies that is incremental to that
19
provided in EAs and related disclosures. We obtain bankruptcy data from the Audit Analytics
Bankruptcy Notification Database and designate an issuer as ‘bankrupt’ if it files for bankruptcy
within two years of the EA date.27 We first compare EA date returns for those companies that file
for bankruptcy within two years of a first-time GCM with returns for those companies that do not
file for bankruptcy. We then distinguish between companies that do and do not disclose a GCM
with the EA. Results from these tests are presented in Table 6.
(Insert Table 6 here)
The results in Panel A indicate that EA returns are more negative for those companies
that eventually file for bankruptcy versus those that do not. This suggests that in the year of a
first-time GCM, information disclosed in the EA allows investors to differentiate between those
companies that will file for bankruptcy and those that will not. Next, in Panel B, we test whether
this differentiation varies with the disclosure of a GCM. We find that the stock price reaction at
the EA date is significantly more negative for those companies that file for bankruptcy (relative
to those that do not) whether or not the EA is accompanied by the disclosure of a GCM.
Furthermore, conditioning upon future bankruptcy status, we find no difference in the EA date
CARs for companies that disclose a GCM with their EA versus those that do not. These findings
also suggest that the GCM does not provide incremental information to investors once other
disclosures made with the EA are considered.28
EA Returns and Type II GCM Errors
Chen and Church (1996) find that the stock price reaction to bankruptcy announcements
is significantly more negative when companies do not receive GCMs prior to these
27
If we use windows of either one or three years, our inferences are unchanged.
In untabulated analyses, we also observe greater trading volume surrounding EAs that precede bankruptcy but
abnormal trading volume does not differ based on whether a GCM is disclosed with the EA.
28
20
announcements, and they interpret this as evidence that GCMs help investors anticipate
subsequent bankruptcy. Because results from our previous test suggest that the anticipation of
bankruptcy may be driven by information disclosed in the EA rather than by the disclosure of a
GCM, we compare the EA returns for companies that file for bankruptcy within two years of
receiving a first-time GCM but do not disclose the GCM with the EA (i.e., our ‘uncontaminated’
sample) with the EA returns for companies that do not receive a GCM prior to filing for
bankruptcy (i.e., companies where the auditor made Type II errors).29 In the first sample, market
participants do not receive any indication of the auditor’s intentions to include a GCM paragraph
in the annual report, and in the second sample, market participants never receive an indication
that the company may not continue as a going concern. The EA window abnormal returns for the
first sample (which includes 16 companies that eventually receive GCMs) average -18.19
percent (see Table 6) and the abnormal returns for the second sample (which includes 28
companies that do not receive GCMs but eventually file for bankruptcy) average -5.00 percent
(untabulated). Moreover, the EA window CARs are significantly more negative (p-value < 0.05)
for those companies that eventually receive GCMs than for those that never receive GCMs.
Importantly, this difference in returns cannot be attributed to the GCM because these companies
did not disclose the nature of the audit opinion with their EAs.30 Overall, these results suggest
29
We limit the second group to companies where earnings are announced prior to the audit report (i.e., where EAs
are ‘uncontaminated’) so that investors are unaware that the audit report will not include a GCM. Our inferences are
unchanged, however, if these omitted observations are included. For this group, we analyze abnormal returns at the
most recent EA date prior to bankruptcy. Because we use a two year window for bankrupt companies, we also
perform the analysis using the abnormal returns at the second most recent EA date prior to bankruptcy. Our
inferences are unchanged by this alternative specification.
30
Furthermore, for those companies that subsequently disclose GCMs with their annual reports, we find no evidence
of incremental negative returns following the GCM disclosures; this further supports our interpretation that GCMs
do not provide an additional signal of distress.
21
that differences in the ability of investors to anticipate subsequent bankruptcy stems from other
company-specific disclosures at the EA date rather than from GCMs.31
The Quality of the Information Environment and the Market Response to GCMs
It is possible that GCMs could provide useful information when companies operate in
low quality information environments. That is, in the absence of alternative sources of
information, investors may rely on the audit opinion for cues about the company’s ability to
continue as a going concern. To address this possibility, we separate sample companies with
analyst following from those without analyst following because analyst following can proxy for
the strength of the information environment (Frankel and Li 2004; Louis and Robinson 2005)
and we compare the CAR and AVOL for ‘uncontaminated’ GCMs in these subsamples. The
results from these analyses are presented in Table 7.
(Insert Table 7 here)
Regardless of whether analysts follow the company, we do not find significant abnormal
returns (in Panel A) or significant abnormal trading volume (in Panel B) related to the issuance
of ‘uncontaminated’ GCMs. Furthermore, these reactions do not differ between the two
subsamples, suggesting that GCMs do not provide value relevant information even for those
companies operating in weaker information environments.
Market Reactions to Clean Audit Opinions following GCMs
If GCMs provide information to market participants, we should observe a positive stock
price reaction when companies are expected to receive GCMs but do not. We identify a sample
of 405 companies that receive an audit report with no GCM in the year following an audit report
with a GCM (NoGCM). After limiting the sample to 153 observations that are uncontaminated
31
In untabulated analysis, we do not find that the stock price reaction to bankruptcy filings is more negative for
companies that never received a GCM.
22
by an EA, we examine the market response at the 10-K filing date (i.e., at the clean audit report
disclosure date). In untabulated analysis, we do not observe a positive stock price reaction or
abnormal trading volume response to the disclosure of these NoGCMs, again suggesting that
investors do not find GCMs informative.
We acknowledge that an inherent complication with this analysis is that the company’s
financial condition may have improved such that a GCM is no longer anticipated by investors.
Therefore, we further limit our sample to those companies with NoGCMs in the current year but
GCMs (again) in the subsequent year (i.e., these companies receive a GCM in year t, NoGCM in
year t+1, and a GCM in year t+2). For this set of 26 companies, sustainable improvements in
financial position were unlikely in the year of the NoGCM because the company received
another GCM in the following year. Thus, this sample represents a set of observations where the
absence of a GCM should be most surprising to investors. However, even for these companies,
we do not find a positive stock price reaction or abnormal trading volume response to the release
of a NoGCM (untabulated).
Management’s Influence on Disclosure Timing
Because the timing of GCM disclosures relative to EAs is subject to management
discretion, we consider whether management’s decision to announce earnings with the audit
report (i.e., the decision to issue a ‘contaminated’ GCM) is related to the content of the EA. In
other words, management can affect the timing of the EA and GCM disclosures and this decision
may be related to the content of these disclosures. To address this potential issue, we first
investigate whether companies change the relative timing of their EAs in the year of the firsttime GCM. We find that 74 percent of observations in our sample maintain the same relative
23
timing as in the prior year.32 Thus, the majority of sample observations do not change the timing
of their EAs, suggesting that our results are not likely to be the product of management’s
influence on disclosure timing. However, to ensure that the preceding results are not the product
of a change in the timing of disclosures, we replicate our prior analyses using only those
companies without changes in their reporting timing and all inferences are unchanged.
6. CONCLUSION
In this study, we examine whether there is a market reaction to GCMs after filtering out
the effects of confounding disclosures. Specifically, we identify first-time GCMs from 2004
through 2012 and find that 70 percent are disclosed simultaneously with earnings. After filtering
out the effects of earnings information, we find no evidence of a market reaction (i.e., abnormal
returns or abnormal trading volume) to GCMs. We do find a significant market reaction to
earnings, however, regardless of whether the EAs include the disclosure of GCMs. Importantly,
we find that the market response in the EA window does not differ based on whether the GCM is
disclosed concurrently. Taken together, we find no evidence that investors respond to the
disclosure of first-time GCMs. Although the lack of evidence of an association is not, in itself,
evidence of a lack of association (DeFond 2010), it is important to note that we do find
statistically significant evidence that, at minimum, the failure to control for news in the EA
results in overstated estimates of the market’s response to GCMs. Thus, these results challenge
what appears to be a widely held belief that auditor provided distress signals, in the form of
32
Companies that maintain their relative timing are those where the EA is made concurrently with the annual report
in each year (i.e., the ‘contaminated’ subsample) or those where the EA precedes the annual report in each year. For
the 123 companies that change their reporting timing, 83 percent disclosed earnings early in the prior year and
disclosed earnings with the audit report in the current year. Thus, financially distressed companies that will receive
GCMs are less likely to announce earnings early.
24
GCMs, provide value relevant information to investors. Specifically, although GCMs are
typically characterized as value relevant (DeFond and Zhang 2014), our findings support the
position that GCMs do not convey incremental information to investors in the current reporting
environment.
Our findings raise the question: Why are auditors required to make an assessment that
may influence audit work, fees, litigation risk, and auditor-client relationships while seemingly
doing little to inform investors? We suggest that regulators may wish to consider these results
when contemplating changes to the auditor’s role in going concern reporting. The Financial
Accounting Standards Board (FASB) recently adopted a standard requiring management
disclosure of going concern uncertainties beginning in 2016 (FASB 2014). In conjunction, the
Public Company Accounting Oversight Board (PCAOB) and the International Auditing and
Assurance Standards Board (IAASB) are evaluating revisions to the auditor’s responsibility for
evaluating and disclosing going concern uncertainties (IAASB 2013; PCAOB 2013, 2014).
Additionally, the PCAOB is contemplating increasing the amount of disclosure included in the
audit report (e.g., providing additional information about ‘critical auditing matters’) (PCAOB
2013). Our findings should inform these standard setting projects because they suggest that
carefully evaluating value relevance (or lack thereof) of auditor-provided disclosures should be
an important consideration in policy decisions. Specifically, if investors do not find GCMs
informative, regulators should consider the potential value in other auditor provided
communications (e.g., critical auditing matters), before adding requirements that could similarly
increase auditor work, fees, and litigation risk. In addition, our results suggest that revisions to
auditing standards could be more effective if they focused on assurance relating to
management’s disclosures, rather than increased auditor disclosure in the audit report.
25
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28
Appendix 1: 8-K Disclosures that include GCMs
Company
Company A
Other Disclosures in the 8-K
The company disclosed that the FDIC had issued a cease and desist order to
the company for “unsafe and unsound banking practices and violations of law
and/or regulations.” The cease and desist order requires the company to
“improve capital levels, develop a management plan, improve funds
management practices, reduce concentrations of credit, improve lending and
collection policies and requires the Board to increase its participation and
supervision of the Bank’s activities.”
Company B
The company disclosed a public offering of new common shares, issued a
prospectus, and disclosed that it would have to raise more additional funding
because “existing cash and cash equivalents and interest receivable will not be
sufficient to fund our operations for the next 12 months.”
Company C
The company disclosed 11 defaults and 6 additional anticipated defaults. The
company also disclosed entry into a forbearance agreement with its lender
relating to the defaults.
Company D
The company disclosed that it does not have sufficient cash on hand to fund
operations past the third quarter without raising additional funds but had no
intention of raising those funds. The company also disclosed a material
impairment.
Company E
The company disclosed that it was not in compliance with Nasdaq listing
requirements and would be delisted in 3 days.
Company F
The company disclosed that it would be unable to file its annual report on time.
It also disclosed a current material goodwill impairment and the possibility of
debt covenant non-compliance in the subsequent quarter.
Company G
The company disclosed that it had failed to comply with financial covenants
which gave one of its lenders the option of imposing a default interest rate that
was 5% greater than the current rate. The company also disclosed that it
expects to record significant non-cash goodwill and long-lived asset
impairment charges and that it would be unable to file its annual report on
time.
Company H
The company disclosed the intent to file restatements relating to 7 errors in
previously filed financial statements.
29
Appendix 2: Variable Definitions
Variable
AVOL
Variable Definition
Natural log of the average daily trading volume in the event window [0,
+2] scaled by the average estimation period daily trading volume [-60, 10] or [-224, -75]. Daily trading volume is calculated as the number of
shares traded scaled by the number of shares outstanding.
BIGN
An indicator variable equal to one if the company is audited by a Big N
firm, zero otherwise.
CAR
Cumulative abnormal daily return for a three-day window beginning on
the event date [0, +2] (company’s daily return minus the corresponding
size-decile portfolio’s daily return).
CONTAMINATED
An indicator variable equal to one when the GCM is disclosed in the EA
window, zero otherwise.
CFO
Cash flows from operations scaled by lagged total assets.
DISPERSION
Standard deviation of analyst forecasts scaled year-end stock price.
ΔEBIT
Current year earnings before interest and taxes less prior year earnings
before interest and taxes, scaled by lagged total assets.
INST_OWN
Percentage of outstanding shares owned by institutional investors.
LEVERAGE
Total liabilities scaled by total assets.
ΔLEVERAGE
Current LEVERAGE minus lagged LEVERAGE.
LOSS
An indicator variable equal to one if net income is negative, zero
otherwise.
NUMEST
Number of analysts following the company.
REPLAG_EA
Number of days between fiscal year-end and EA date.
ROA
Net income scaled by lagged total assets.
SIZE
Natural log of the market value of equity.
SURPRISE
Actual earnings per share less median consensus analyst forecast of
earnings per share, scaled by the absolute value of the median analyst
forecasted earnings per share.
30
ZFC
The probability of bankruptcy from Zmijewski (1984), calculated as: 4.336 + (-4.512 * ROA) + (5.679 * LEVERAGE) + (.004 * (current
assets scaled by current liabilities)).
31
Figure 1: Sample Selection
First-Time GCMs
n = 486
Annual Report
Disclosure of GCM
n = 458
Early EA
n = 144
Uncontaminated
Total
Uncontaminated
GCM Disclosures
n = 144
Early (pre-Annual Report)
Disclosure of GCM
n = 28
EA in Annual
Report
n = 314
Disclosed
with EA
n = 20
Contaminated
Contaminated
Total
Contaminated
GCM Disclosures
n = 334
Disclosed
without EA
n=8
Other
Total Other
GCM Disclosures
(Appendix 1)
n=8
Figure 1 outlines the process for classifying first-time GCMs as either contaminated or uncontaminated.
32
Table 1: First-Time GCM Descriptive Statistics
Panel A presents descriptive statistics for the full sample of observations. Panel B provides descriptive statistics for
the two subsamples of interest: CONTAMINATED = 0 and CONTAMINATED = 1. The last column of Panel B
presents the two-tailed p-values for tests of differences in means between the two subsamples. Panel C provides an
industry and year breakdown of sample observations. All variables are defined in Appendix 2. *, **, and ***
indicate significance at the 0.10, 0.05, and 0.01 levels, respectively (using two-tailed tests).
Panel A: Full Sample Descriptive Statistics
VARIABLES
CAR Analyses
CAR (GCM date)
CAR (EA date)
CONTAMINATED
ΔEBIT
SIZE
CFO
BIGN
ROA
LEVERAGE
INST_OWN
ZFC
ΔLEVERAGE
SURPRISE
n
Mean
SD
P25
Median
P75
478
478
478
451
451
451
451
451
451
451
451
451
217
-0.050
-0.071
0.699
-0.034
3.562
-0.230
0.583
-0.385
0.726
0.241
1.694
0.160
-0.345
0.174
0.174
0.459
0.202
1.483
0.481
0.494
0.480
0.491
0.247
4.402
0.393
0.607
-0.125
-0.148
0.000
-0.090
2.446
-0.362
0.000
-0.543
0.399
0.032
-0.492
0.022
-1.000
-0.036
-0.055
1.000
-0.021
3.430
-0.073
1.000
-0.288
0.686
0.151
1.128
0.083
-0.314
0.017
0.010
1.000
0.037
4.532
0.011
1.000
-0.062
0.953
0.394
2.508
0.249
0.071
AVOL Analyses
AVOL (GCM date) [-60, -10]
AVOL (EA date) [-60,-10]
AVOL (GCM date) [-224, -75]
AVOL (EA date) [-224,-75]
CONTAMINATED
|ΔEBIT|
SIZE
LEVERAGE
LOSS
REPLAG_EA
NUMEST
DISPERSION
|SURPRISE|
473
473
468
468
473
451
451
451
451
451
451
216
216
0.079
0.209
0.143
0.266
0.698
0.132
3.575
0.722
0.938
83.663
1.488
0.043
0.579
1.179
1.162
1.280
1.244
0.460
0.164
1.482
0.493
0.242
36.539
2.530
0.116
0.393
-0.604
-0.428
-0.634
-0.459
0.000
0.026
2.452
0.394
1.000
66.000
0.000
0.000
0.184
0.106
0.202
0.127
0.237
1.000
0.074
3.451
0.679
1.000
87.000
0.000
0.006
0.556
0.760
0.919
0.951
1.005
1.000
0.167
4.541
0.953
1.000
92.000
2.000
0.031
1.000
33
Panel B: Subsample Comparison Descriptive Statistics
CONTAMINATED = 0
VARIABLES
n
Mean
CAR Analyses
CAR (GCM date)
144
0.003
CAR (EA date)
144
-0.061
ΔEBIT
143
-0.047
SIZE
143
3.860
CFO
143
-0.168
BIGN
143
0.755
ROA
143
-0.331
LEVERAGE
143
0.724
INST_OWN
143
0.226
ZFC
143
1.281
ΔLEVERAGE
143
0.151
SURPRISE
80
-0.390
AVOL Analyses
AVOL (GCM date) [-60, -10]
AVOL (EA date) [-60,-10]
AVOL (GCM date) [-224, -75]
AVOL (EA date) [-224,-75]
|ΔEBIT|
SIZE
LEVERAGE
LOSS
REPLAG_EA
NUMEST
DISPERSION
|SURPRISE|
143
143
142
142
143
143
143
143
143
143
79
79
Panel C: Industry and Year Breakdown
Fama French 12 Industry
n
1-2 Consumer Goods
20
3 Manufacturing
26
4 Energy
19
5 Chemicals
10
6 Business Equipment
68
7 Telecommunications
14
8 Utilities
2
9 Wholesale/Retail
19
10 Healthcare
119
11 Finance
80
12 Miscellaneous
101
Total
478
-0.075
0.316
-0.011
0.378
0.112
3.876
0.720
0.923
62.741
1.727
0.038
0.580
CONTAMINATED = 1
n
Mean
Diff
p-value
334
334
308
308
308
308
308
308
308
308
308
137
-0.072
-0.076
-0.028
3.424
-0.259
0.503
-0.410
0.726
0.248
1.886
0.164
-0.319
-0.075
-0.015
0.019
-0.436
-0.091
-0.252
-0.079
0.002
0.022
0.605
0.013
0.071
0.000
0.364
0.341
0.004
0.024
0.000
0.063
0.962
0.377
0.100
0.695
0.403
***
330
330
326
326
308
308
308
308
308
308
137
137
0.146
0.162
0.211
0.217
0.141
3.435
0.722
0.945
93.377
1.377
0.046
0.578
0.221
-0.154
0.222
-0.161
0.029
-0.441
0.002
0.022
30.636
-0.350
0.008
0.002
0.045
0.149
0.068
0.155
0.065
0.004
0.958
0.402
0.000
0.177
0.646
0.973
**
Fiscal Year
2004
2005
2006
2007
2008
2009
2010
2011
2012
n
44
54
38
59
130
68
29
25
31
Total
478
***
**
***
*
*
*
***
***
34
Table 2: GCM and EA Date Cumulative Abnormal Returns
Panel A presents mean cumulative abnormal returns in the three-day window beginning on the GCM date (i.e., in
days [0, +2] relative to the GCM) for the full sample and for subsamples of GCMs contaminated and
uncontaminated by a concurrent EA. Panel B presents mean cumulative abnormal returns in the three-day window
beginning on the EA date (i.e., in days [0, +2] relative to the EA) for the full sample and for subsamples of EAs
disclosed and not disclosed with a GCM. Both panels present observation counts, mean CARs, and p-values (in
parentheses). *** indicates significance at the 0.01 level (using two-tailed tests).
Panel A: GCM Date Returns
All GCM Disclosures
n
478
CAR [0, +2]
-5.00%***
(0.000)
GCM with EA (contaminated)
n
334
GCM without EA (uncontaminated)
144
CAR [0, +2]
-7.24%***
(0.000)
0.35%
(0.788)
-7.59%***
(0.000)
Difference
Panel B: EA Date Returns
All EA Disclosures
n
478
CAR [0, +2]
-7.14%***
(0.000)
EA with GCM
n
334
EA without GCM
144
CAR [0, +2]
-7.60%***
(0.000)
-6.07%***
(0.000)
-1.53%
(0.377)
Difference
35
Table 3: GCM and EA Date Abnormal Trading Volume
Panel A presents mean abnormal trading volume in the three-day window beginning on the GCM date (i.e., in days
[0, +2] relative to the GCM) for the full sample and for subsamples of GCMs contaminated and uncontaminated by
a concurrent EA. Panel B presents mean abnormal trading volume in the three-day window beginning on the EA
date (i.e., in days [0, +2] relative to the EA) for the full sample and for subsamples of EAs disclosed and not
disclosed with a GCM. Both panels present observation counts, mean abnormal trading volume, and p-values (in
parentheses). *, **, and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively (using two-tailed
tests).
Panel A: GCM Date Abnormal Trading Volume
All GCM Disclosures
n
473, 468
GCM with EA (contaminated)
n
330, 326
GCM without EA (uncontaminated)
143, 142
Difference
AVOL [0,+2]
Estimation Period
[-60, -10]
[-224, -75]
0.0792
0.1435**
(0.145)
(0.016)
AVOL [0,+2]
Estimation Period
[-60, -10]
[-224, -75]
0.1462**
0.2108***
(0.032)
(0.005)
-0.0754
-0.0110
(0.385)
(0.910)
.02217**
0.2217*
(0.045)
(0.068)
Panel B: EA Date Abnormal Trading Volume
All EA Disclosures
n
473, 468
EA with GCM
n
330, 326
EA without GCM
143, 142
Difference
AVOL [0,+2]
Estimation Period
[-60, -10]
[-224, -75]
0.2086***
0.2656***
(0.000)
(0.000)
AVOL [0,+2]
Estimation Period
[-60, -10]
[-224, -75]
0.1620**
0.2166***
(0.017)
(0.003)
0.3162***
0.3779***
(0.000)
(0.000)
-0.1542
-0.1613
(0.149)
(0.155)
36
Table 4: Multiple Regression Abnormal Returns Analysis
Table 4 presents the results from estimating Equation [1]. CAR is the dependent variable in each regression. Column
(1) controls for the change in earnings before interest and taxes and column (2) controls for earnings surprise
relative to analyst expectations. Robust p-values are presented in parentheses below the coefficient estimates. All
variables are defined in Appendix 2. *** indicates significance at the 0.01 level (using two-tailed tests).
VARIABLES
INTERCEPT
CONTAMINATED
ΔEBIT
(1)
CAR [0, +2]
-0.0540
(0.215)
-0.0160
(0.403)
0.1079***
(0.005)
SURPRISE
SIZE
CFO
BIGN
ROA
LEVERAGE
INST_OWN
ZFC
ΔLEVERAGE
N
Adjusted R-squared
-0.0013
(0.834)
-0.0036
(0.919)
0.0213
(0.257)
-0.0094
(0.845)
-0.0115
(0.791)
-0.0555
(0.188)
-0.0002
(0.968)
0.0417
(0.297)
451
0.0100
(2)
CAR [0, +2]
-0.0439
(0.541)
0.0136
(0.552)
0.0835***
(0.001)
0.0004
(0.966)
0.0253
(0.543)
-0.0347
(0.253)
-0.0328
(0.634)
0.0001
(0.999)
-0.0356
(0.518)
0.0002
(0.987)
-0.0098
(0.824)
217
0.0411
37
Table 5: Multiple Regression Abnormal Trading Volume Analysis
Table 5 presents the results from estimating Equation [2]. AVOL is the dependent variable in each regression.
Columns (1) and (3) control for the absolute value of the change in earnings before interest and taxes and columns
(2) and (4) control for the absolute value of earnings surprise relative to analyst expectations. Robust p-values are
presented in parentheses below the coefficients. All variables are defined in Appendix 2. *, **, and *** indicate
significance at the 0.10, 0.05, and 0.01 levels, respectively (using two-tailed tests).
VARIABLES
INTERCEPT
CONTAMINATED
|ΔEBIT|
(1)
AVOL [0, +2]
[-60, -10]
(2)
AVOL [0, +2]
[-60, -10]
(3)
AVOL [0, +2]
[-224, -75]
(4)
AVOL [0, +2]
[-224, -75]
0.1549
(0.6611)
-0.1105
(0.3297)
-0.1439
(0.6194)
0.0129
(0.9779)
0.1455
(0.3179)
0.4279
(0.2700)
-0.0574
(0.6422)
-0.7108**
(0.0351)
0.5280
(0.2932)
0.1565
(0.3162)
|SURPRISE|
SIZE
LEVERAGE
LOSS
REPLAG_EA
NUMEST
-0.0390
(0.3461)
0.2275**
(0.0361)
-0.0237
(0.9215)
0.0013
(0.5042)
0.0615***
(0.0010)
DISPERSION
n
Adjusted R-squared
451
0.0150
0.3639**
(0.0490)
0.0290
(0.6380)
-0.0063
(0.9632)
-0.1947
(0.3683)
0.0002
(0.9473)
0.0360
(0.1271)
0.1596
(0.7114)
216
0.0081
-0.0772*
(0.0826)
0.2727**
(0.0132)
0.0787
(0.7800)
-0.0005
(0.8298)
0.0510***
(0.0091)
447
0.0258
0.0409
(0.8275)
0.0299
(0.6375)
0.0711
(0.5824)
-0.1570
(0.5612)
-0.0052
(0.1391)
0.0251
(0.3000)
0.3855
(0.4794)
215
-0.0101
38
Table 6: Analysis Considering Future Bankruptcy Status
Panel A presents mean cumulative abnormal returns in the three-day window beginning on the EA date (i.e., in days [0, +2] relative to the EA) for the full sample
and for subsample of firms that do and do not subsequently file bankruptcy. Panel B presents differences in EA returns based on both subsequent bankruptcy
status and whether or not the EA disclosure contained a GCM. Both panels present observations counts, means CARs, and p-values (in parentheses). ** and ***
indicate significance at the 0.05 and 0.01 levels, respectively (using two-tailed tests).
Panel A: EA CARs and Bankruptcy Status
All Observations
n
478
CAR [0, +2]
-7.14%***
(0.000)
Files Bankruptcy within 2 years
n
68
No Bankruptcy within 2 years
410
CAR [0, +2]
-14.43%***
(0.000)
-5.93%***
(0.000)
-8.50%***
(0.000)
Difference
Panel B: EA CARs and Bankruptcy Status for Contaminated and Uncontaminated Subsamples
EA without GCM
EA with GCM
Difference
No Bankruptcy within 2 Years
n
CAR [0, +2]
128
-4.55%
***
(0.001)
282
-6.55%
***
(0.000)
2.00%
(0.221)
Bankruptcy within 2 Years
n
CAR [0, +2]
16
-18.19%
***
(0.001)
52
-13.28%
***
(0.001)
-4.91%
(0.504)
Difference
-13.64%
(0.002)
-6.72%
(0.012)
***
**
39
Table 7: Information Environment and Market Responses to Uncontaminated GCMs
Panel A presents mean cumulative abnormal returns in the three-day window beginning on the GCM date (i.e., in
days [0, +2] relative to the GCM) for the full sample of uncontaminated observations and for subsamples with and
without analyst following. Panel B presents mean abnormal trading volume in the three-day window beginning on
the GCM date (i.e., in days [0, +2] relative to the GCM) for the full sample of uncontaminated observations and for
subsamples with and without analyst following. Both panels present observation counts, mean abnormal activity
(i.e., returns or trading volume) and two-tailed p-values (in parentheses).
Panel A: GCM Date Returns
All Uncontaminated GCMs
n
144
CAR [0, +2]
0.35%
(0.788)
No Analyst Following
n
61
Analyst Following
83
CAR [0, +2]
-1.45%
(0.309)
1.67%
(0.401)
-3.12%
(0.233)
Difference
Panel B: GCM Date Abnormal Trading Volume
All Uncontaminated GCMs
n
143, 142
No Analyst Following
n
61, 61
Analyst Following
82, 81
Difference
AVOL [0, +2]
Estimation Period
[-60, -10]
[-224, -75]
-0.0754
-0.0110
(0.385)
(0.910)
AVOL [0, +2]
Estimation Period
[-60, -10]
[-224, -75]
-0.1594
-0.1058
(0.247)
(0.507)
-0.0130
0.0605
(0.908)
(0.615)
-0.1465
-0.1663
(0.408)
(0.404)
40