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 auditor- client 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 auditor- client 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

5 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 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 forward- looking 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

6 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 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.

12 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.

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 three- day 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

14 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.

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 non- GCM 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 company- specific 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 year- end 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. 28 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.

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 first- time 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 References

Amin, K., J. Krishnan, and J. S. Yang. 2014. Going Concern Opinion and Cost of Equity. Auditing: A Journal of Practice & Theory 33 (4): 1-39. Blay, A. D., and M. A. Geiger. 2013. Auditor Fees and Auditor Independence: Evidence from Going Concern Reporting Decisions. Contemporary Accounting Research 30 (2): 579- 606. ———, ———, and D. S. North. 2011. The Auditor’s Going-Concern Opinion as a Communication of Risk. Auditing: A Journal of Practice & Theory 30 (2): 77-102. Carcello, J. V., and T. L. Neal. 2003. Audit Committee Characteristics and Auditor Dismissals Following “New” Going-Concern Reports. The Accounting Review 78 (1): 95-117. ———, and Z.-V. Palmrose. 1994. Auditor Litigation and Modified Reporting on Bankrupt Clients. Journal of Accounting Research 32 (3): 1-30. Carson, E., N. L. Fargher, M. A. Geiger, C. S. Lennox, K. Raghunandan, and M. Willekens. 2013. Audit Reporting for Going-Concern Uncertainty: A Research Synthesis. Auditing: A Journal of Practice & Theory 32: 353-384. Center for Audit Quality (CAQ). 2012. Request for Proposals for Academic Research in Auditing. http://www.thecaq.org/docs/research-advisory- board/proposalsforacademicresearchdecember2012.pdf?sfvrsn=2 Chen, K. C. W., and B. K. Church. 1996. Going Concern Opinions and the Market’s Reaction to Bankruptcy Filings. The Accounting Review 71 (1): 117-128. D’Souza, J., K. Ramesh, and M. Shen. 2010. Disclosure of GAAP Line Items in Earnings Announcements. Review of Accounting Studies 15 (1): 179-219. DeFond, M. 2010. How Should the Auditors be Audited? Comparing the PCAOB Inspections with the AICPA Peer Reviews. Journal of Accounting & Economics 49 (1): 104-108. DeFond, M., and J. Zhang. 2014. A Review of Archival Auditing Research. Journal of Accounting & Economics 58 (2): 275-326. Dodd, P., N. Dopuch, R. Holthausen, and R. Leftwich. 1984. Qualified Audit Opinions and Stock-Prices – Information Content, Announcement Dates, and Concurrent Disclosures. Journal of Accounting & Economics 6 (1): 3-38. Dopuch, N., R. W. Holthausen, and R. W. Leftwich. 1986. Abnormal Stock Returns Associated with Media Disclosures of ‘Subject to’ Qualified Audit Opinions. Journal of Accounting & Economics 8 (2): 93-117. Elliott, J. A. 1982. “Subject to” Audit Opinions and Abnormal Security Returns – Outcomes and Ambiguities. Journal of Accounting Research 20 (2): 617-638. Financial Accounting Standards Board (FASB). 2014. Disclosure of Uncertainties about an Entity’s Ability to Continue as a Going Concern. Fleak, S. K., and E. R. Wilson. 1994. The Incremental Information Content of the Going- Concern Audit Opinion. Journal of Accounting, Auditing & Finance 9 (1): 149-166.

26 Francis, J., K. Schipper, and L. Vincent. 2002a. Earnings Announcements and Competing Information. Journal of Accounting & Economics 33 (3): 313-342. ———, ———, and ———. 2002b. Expanded Disclosures and the Increased Usefulness of Earnings Announcements. The Accounting Review 77 (3): 515-546. Frankel, R., and X. Li. 2004. Characteristics of a Firm’s Information Environment and the Information Asymmetry between Insiders and Outsiders. Journal of Accounting & Economics 37 (2): 229-259. Geiger, M. A., and D. V. Rama. 2006. Audit Firm Size and Going-Concern Reporting Accuracy. Accounting Horizons 20 (1): 1-17. Holder-Webb, L. M., and M. S. Wilkins. 2000. The Incremental Information Content of SAS No. 59 Going-Concern Opinions. Journal of Accounting Research 38 (1): 209-219. International Auditing and Assurance Standards Board (IAASB). 2013. Reporting on Audited Financial Statements: Proposed New and Revised International Standards on Auditing. Jones, F. L. 1996. The Information Content of the Auditor’s Going Concern Evaluation. Journal of Accounting and Public Policy 15 (1): 1-27. Kaplan, S. E., and D. D. Williams. 2013. Do Going Concern Audit Reports Protect Auditors from Litigation? A Simultaneous Equations Approach. The Accounting Review 88 (1): 199-232. Kausar, A., R. J. Taffler, and C. Tan. 2009. The Going-Concern Market Anomaly. Journal of Accounting Research 47 (1): 213-239. Kothari, S. P. 2001. Capital Markets Research in Accounting. Journal of Accounting & Economics 31 (1-3): 105-231. Landsman, W. R., E. L. Maydew, and J. R. Thornock. 2012. The Information Content of Annual Earnings Announcements and Mandatory Adoption of IFRS. Journal of Accounting & Economics 53 (1-2): 34-54. Loudder, M. L., I. K. Khurana, R. B. Sawyers, C. Cordery, C. Johnson, J. Lowe, and R. Wunderle. 1992. The Information Content of Audit Qualifications. Auditing: A Journal of Practice & Theory 11 (1): 69-82. Louis, H., and D. Robinson. 2005. Do Managers Credibly Use Accruals to Signal Private Information? Evidence from the Pricing of Discretionary Accruals Around Stock Splits. Journal of Accounting & Economics 39 (2): 361-380. Menon, K., and D. D. Williams. 2010. Investor Reaction to Going Concern Audit Reports. The Accounting Review 85 (6): 2075-2105. Myers, L. A., J. Schmidt, and M. Wilkins. 2014. An Investigation of Recent Changes in Going Concern Reporting Decisions among Big N and Non-Big N Auditors. Review of Quantitative Finance & Accounting 43 (1): 155-172. Ogneva, M., and K. R. Subramanyam. 2007. Does the Stock Market Underreact to Going Concern Opinions? Evidence from the U.S. and Australia. Journal of Accounting & Economics 43 (2/3): 439-452.

27 Ponemon, L. A., and K. Raghunandan. 1994. What is “Substantial Doubt”? Accounting Horizons 8 (2): 44-54. Public Company Accounting Oversight Board (PCAOB). 2013. Proposed Auditing Standards – The Auditor’s Report on an Audit of Financial Statements When the Auditor Expresses an Unqualified Opinion. ———. 2014. Standard Setting Agenda. http://pcaobus.org/Standards/Documents/201406_ standard_setting_agenda.pdf. Taffler, R. J., J. Lu, and A. Kausar. 2004. In Denial? Stock Market Underreaction to Going- Concern Audit Report Disclosures. Journal of Accounting & Economics 38 (1-3): 263- 296. Willenborg, M., and J. C. McKeown. 2000. Going-Concern Initial Public Offerings. Journal of Accounting & Economics 30 (3): 279-313. Zmijewski, M. E. 1984. Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 22: 59-82.

28 Appendix 1: 8-K Disclosures that include GCMs

Company Other Disclosures in the 8-K Company A 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 Variable Definition AVOL 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 Early (pre-Annual Report) Disclosure of GCM Disclosure of GCM n = 458 n = 28

Early EA EA in Annual Disclosed Disclosed n = 144 Report with EA without EA

n = 314 n = 20 n = 8

Uncontaminated Contaminated Contaminated Other

Total Total Total Other Uncontaminated Contaminated GCM Disclosures GCM Disclosures GCM Disclosures (Appendix 1) n = 144 n = 334 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 n Mean SD P25 Median P75 CAR Analyses CAR (GCM date) 478 -0.050 0.174 -0.125 -0.036 0.017 CAR (EA date) 478 -0.071 0.174 -0.148 -0.055 0.010 CONTAMINATED 478 0.699 0.459 0.000 1.000 1.000 ΔEBIT 451 -0.034 0.202 -0.090 -0.021 0.037 SIZE 451 3.562 1.483 2.446 3.430 4.532 CFO 451 -0.230 0.481 -0.362 -0.073 0.011 BIGN 451 0.583 0.494 0.000 1.000 1.000 ROA 451 -0.385 0.480 -0.543 -0.288 -0.062 LEVERAGE 451 0.726 0.491 0.399 0.686 0.953 INST_OWN 451 0.241 0.247 0.032 0.151 0.394 ZFC 451 1.694 4.402 -0.492 1.128 2.508 ΔLEVERAGE 451 0.160 0.393 0.022 0.083 0.249 SURPRISE 217 -0.345 0.607 -1.000 -0.314 0.071

AVOL Analyses AVOL (GCM date) [-60, -10] 473 0.079 1.179 -0.604 0.106 0.760 AVOL (EA date) [-60,-10] 473 0.209 1.162 -0.428 0.202 0.919 AVOL (GCM date) [-224, -75] 468 0.143 1.280 -0.634 0.127 0.951 AVOL (EA date) [-224,-75] 468 0.266 1.244 -0.459 0.237 1.005 CONTAMINATED 473 0.698 0.460 0.000 1.000 1.000 |ΔEBIT| 451 0.132 0.164 0.026 0.074 0.167 SIZE 451 3.575 1.482 2.452 3.451 4.541 LEVERAGE 451 0.722 0.493 0.394 0.679 0.953 LOSS 451 0.938 0.242 1.000 1.000 1.000 REPLAG_EA 451 83.663 36.539 66.000 87.000 92.000 NUMEST 451 1.488 2.530 0.000 0.000 2.000 DISPERSION 216 0.043 0.116 0.000 0.006 0.031 |SURPRISE| 216 0.579 0.393 0.184 0.556 1.000

33 Panel B: Subsample Comparison Descriptive Statistics CONTAMINATED = 0 CONTAMINATED = 1 VARIABLES n Mean n Mean Diff p-value CAR Analyses CAR (GCM date) 144 0.003 334 -0.072 -0.075 0.000 *** CAR (EA date) 144 -0.061 334 -0.076 -0.015 0.364 ΔEBIT 143 -0.047 308 -0.028 0.019 0.341 SIZE 143 3.860 308 3.424 -0.436 0.004 *** CFO 143 -0.168 308 -0.259 -0.091 0.024 ** BIGN 143 0.755 308 0.503 -0.252 0.000 *** ROA 143 -0.331 308 -0.410 -0.079 0.063 * LEVERAGE 143 0.724 308 0.726 0.002 0.962 INST_OWN 143 0.226 308 0.248 0.022 0.377 ZFC 143 1.281 308 1.886 0.605 0.100 ΔLEVERAGE 143 0.151 308 0.164 0.013 0.695 SURPRISE 80 -0.390 137 -0.319 0.071 0.403

AVOL Analyses AVOL (GCM date) [-60, -10] 143 -0.075 330 0.146 0.221 0.045 ** AVOL (EA date) [-60,-10] 143 0.316 330 0.162 -0.154 0.149 AVOL (GCM date) [-224, -75] 142 -0.011 326 0.211 0.222 0.068 * AVOL (EA date) [-224,-75] 142 0.378 326 0.217 -0.161 0.155 |ΔEBIT| 143 0.112 308 0.141 0.029 0.065 * SIZE 143 3.876 308 3.435 -0.441 0.004 *** LEVERAGE 143 0.720 308 0.722 0.002 0.958 LOSS 143 0.923 308 0.945 0.022 0.402 REPLAG_EA 143 62.741 308 93.377 30.636 0.000 *** NUMEST 143 1.727 308 1.377 -0.350 0.177 DISPERSION 79 0.038 137 0.046 0.008 0.646 |SURPRISE| 79 0.580 137 0.578 0.002 0.973

Panel C: Industry and Year Breakdown Fama French 12 Industry n Fiscal Year n 1-2 Consumer Goods 20 2004 44 3 Manufacturing 26 2005 54 4 Energy 19 2006 38 5 Chemicals 10 2007 59 6 Business Equipment 68 2008 130 7 Telecommunications 14 2009 68 8 Utilities 2 2010 29 9 Wholesale/Retail 19 2011 25 10 Healthcare 119 2012 31 11 Finance 80 12 Miscellaneous 101 Total 478 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

n CAR [0, +2] All GCM Disclosures 478 -5.00%*** (0.000)

n CAR [0, +2] GCM with EA (contaminated) 334 -7.24%*** (0.000) GCM without EA (uncontaminated) 144 0.35% (0.788) Difference -7.59%*** (0.000)

Panel B: EA Date Returns

n CAR [0, +2] All EA Disclosures 478 -7.14%*** (0.000)

n CAR [0, +2] EA with GCM 334 -7.60%*** (0.000) EA without GCM 144 -6.07%*** (0.000) Difference -1.53% (0.377)

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 AVOL [0,+2] Estimation Period n [-60, -10] [-224, -75] All GCM Disclosures 473, 468 0.0792 0.1435** (0.145) (0.016)

AVOL [0,+2] Estimation Period n [-60, -10] [-224, -75] GCM with EA (contaminated) 330, 326 0.1462** 0.2108*** (0.032) (0.005) GCM without EA (uncontaminated) 143, 142 -0.0754 -0.0110 (0.385) (0.910) Difference .02217** 0.2217* (0.045) (0.068)

Panel B: EA Date Abnormal Trading Volume AVOL [0,+2] Estimation Period n [-60, -10] [-224, -75] All EA Disclosures 473, 468 0.2086*** 0.2656*** (0.000) (0.000)

AVOL [0,+2] Estimation Period n [-60, -10] [-224, -75] EA with GCM 330, 326 0.1620** 0.2166*** (0.017) (0.003) EA without GCM 143, 142 0.3162*** 0.3779*** (0.000) (0.000) Difference -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).

(1) (2) VARIABLES CAR [0, +2] CAR [0, +2]

INTERCEPT -0.0540 -0.0439 (0.215) (0.541) CONTAMINATED -0.0160 0.0136 (0.403) (0.552) ΔEBIT 0.1079*** (0.005) SURPRISE 0.0835*** (0.001) SIZE -0.0013 0.0004 (0.834) (0.966) CFO -0.0036 0.0253 (0.919) (0.543) BIGN 0.0213 -0.0347 (0.257) (0.253) ROA -0.0094 -0.0328 (0.845) (0.634) LEVERAGE -0.0115 0.0001 (0.791) (0.999) INST_OWN -0.0555 -0.0356 (0.188) (0.518) ZFC -0.0002 0.0002 (0.968) (0.987) ΔLEVERAGE 0.0417 -0.0098 (0.297) (0.824)

N 451 217 Adjusted R-squared 0.0100 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).

(1) (2) (3) (4) AVOL [0, +2] AVOL [0, +2] AVOL [0, +2] AVOL [0, +2] VARIABLES [-60, -10] [-60, -10] [-224, -75] [-224, -75]

INTERCEPT 0.1549 0.0129 0.4279 0.5280 (0.6611) (0.9779) (0.2700) (0.2932) CONTAMINATED -0.1105 0.1455 -0.0574 0.1565 (0.3297) (0.3179) (0.6422) (0.3162) |ΔEBIT| -0.1439 -0.7108** (0.6194) (0.0351) |SURPRISE| 0.3639** 0.0409 (0.0490) (0.8275) SIZE -0.0390 0.0290 -0.0772* 0.0299 (0.3461) (0.6380) (0.0826) (0.6375) LEVERAGE 0.2275** -0.0063 0.2727** 0.0711 (0.0361) (0.9632) (0.0132) (0.5824) LOSS -0.0237 -0.1947 0.0787 -0.1570 (0.9215) (0.3683) (0.7800) (0.5612) REPLAG_EA 0.0013 0.0002 -0.0005 -0.0052 (0.5042) (0.9473) (0.8298) (0.1391) NUMEST 0.0615*** 0.0360 0.0510*** 0.0251 (0.0010) (0.1271) (0.0091) (0.3000) DISPERSION 0.1596 0.3855 (0.7114) (0.4794) n 451 216 447 215 Adjusted R-squared 0.0150 0.0081 0.0258 -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 n CAR [0, +2] All Observations 478 -7.14%*** (0.000)

n CAR [0, +2] Files Bankruptcy within 2 years 68 -14.43%*** (0.000) No Bankruptcy within 2 years 410 -5.93%*** (0.000) Difference -8.50%*** (0.000)

Panel B: EA CARs and Bankruptcy Status for Contaminated and Uncontaminated Subsamples

No Bankruptcy within 2 Years Bankruptcy within 2 Years n CAR [0, +2] n CAR [0, +2] Difference EA without GCM 128 -4.55% *** 16 -18.19% *** -13.64% *** (0.001) (0.001) (0.002) EA with GCM 282 -6.55% *** 52 -13.28% *** -6.72% ** (0.000) (0.001) (0.012) Difference 2.00% -4.91% (0.221) (0.504)

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

n CAR [0, +2] All Uncontaminated GCMs 144 0.35% (0.788)

n CAR [0, +2] No Analyst Following 61 -1.45% (0.309) Analyst Following 83 1.67% (0.401) Difference -3.12% (0.233)

Panel B: GCM Date Abnormal Trading Volume

AVOL [0, +2] Estimation Period n [-60, -10] [-224, -75] All Uncontaminated GCMs 143, 142 -0.0754 -0.0110 (0.385) (0.910)

AVOL [0, +2] Estimation Period n [-60, -10] [-224, -75] No Analyst Following 61, 61 -0.1594 -0.1058 (0.247) (0.507) Analyst Following 82, 81 -0.0130 0.0605 (0.908) (0.615) Difference -0.1465 -0.1663 (0.408) (0.404)

40