When auditors say “no”, does the market listen?
Abstract
Previous research on whether the market responds to auditors’ opinions has provided mixed
results. We revisit this issue in China, where the stock market is dominated by individual
investors who are less sophisticated in assimilating value-relevant information such as modified
audit opinions (MAOs). In addition, China permits audit modifications triggered by violations of
the generally accepted accounting principles (GAAP) or disclosure rules (GAAP/DISC MAOs),
which are subtler than going concern opinions (GCOs) in their value implications and thus are
more likely to be mispriced. The Chinese stock market thus gives us the best chance of detecting
MAO mispricing. We find that MAOs predict firms’ future financial performance, and that themarket reaction during the short window around MAO disclosure is also consistent with suchpredictive power of MAOs. Importantly, MAO disclosure is not followed by negative long-term
stock returns, suggesting that stock price adjustments to MAOs are speedy and unbiased. Thesefindings hold for both GCOs and GAAP/DISC MAOs. Together, our findings support the
informativeness of audit opinions and cast doubt on the argument that investors inefficiently use
this important information in pricing securities due to information processing bias.
Keywords: audit modifications; information content; capital market efficiency
JEL Classifications: G14; M42

1.

Introduction
In theory, audit opinions should be a useful input in valuing securities. From the information

perspective, a modified audit opinion (MAO) would indicate greater uncertainty associated with theprospect of the auditee. From the contracting perspective, an MAO would imply the possible breachof contractual arrangements and the transfer of wealth from shareholders to other parties. The releaseof MAOs should therefore be accompanied by a decline in stock prices. Two streams of studies
examine market response to MAOs in general and going-concern opinions (GCOs) in particular.
While event studies attempt to infer the informativeness of audit opinions by observing market
reaction around the disclosure of audit qualifications (e.g., Elliot 1982; Dodd et al. 1984; Loudder etal. 1992; Frost 1994; Menon and Williams 2010; Myers et al. 2015), long-window studies examine
12-month abnormal returns following GCOs to assess whether the market fully absorbs their negative
information content (e.g., Taffler et al. 2004; Ogneva and Subramanyam, 2007; Kausar et al. 2009;
Kausar et al. 2013). Overall, both streams of research provide mixed evidence. As Healy and Palepu(2001, p. 406) note, “While theory suggests that auditors enhance the credibility of financial reports,
empirical research has provided surprisingly little evidence to substantiate it.”We revisit this issue by taking advantage of a more powerful research setting in China to test
mispricing of MAOs if any. The emerging Chinese stock market is dominated by small individual
investors who are likely to be less sophisticated. As argued by Taffler et al. (2004) and Kausar et al.
(2013), market inefficiency with respect to audit opinions can be attributed to investors’, particularly
small retail investors’, tendency to deny the bad news conveyed by audit reports. China is thus asetting where value relevant information contained in audit opinions is most likely to be ignored andmispriced. Second and more interestingly, unlike the U.S. SEC, the China Securities Regulatory
Commission (CSRC) does not bar audited financial statements with a qualified opinion due toviolations of GAAP or disclosure rules (GAAP/DISC MAOs). These two types of audit modifications
differ in nature, with the latter reflecting auditors’ concerns about the integrity of clients’ accounting
or disclosure transparency rather than financial viability. The value implications of GAAP/DISC
MAOs thus cannot be inferred from the previous GCO-based studies. Compared with GCOs,
GAAP/DISC MAOs should have a subtler implication in equity valuation. If audit opinion mispricing
exists, it should be more identifiable in GAAP/DISC MAOs than in GCOs. Thus, investigating-1-

GAAP/DISC MAOs increases the power of mispricing tests (Bradshaw et al. 2001). Finally, theshort-selling constraint in China’s stock market may also inhibit the incorporation of negative
information into stock prices, further exacerbating the mispricing of MAO stocks. In sum, China’s
experimental setting gives mispricing of audit opinions a better chance of being detected if it does
exist. Evidence from such a setting represents the lower-bound estimate of the effectiveness ofinvestors’ information processing and assimilating ability.
Methodologically, we adopt a more comprehensive approach, different from prior studies in two
important ways. First, previous studies have assumed that audits are of high quality and thatmodifications predict the financial distress of client firms. However, auditors can “misreport” (Denget al. 2012; Fogel-Yaari and Zhang 2013; DeFond and Zhang 2014) so that audit modifications do notnecessarily convey meaningful information to investors. As Richardson et al. (2010) articulate, toexamine how a given accounting variable is associated with future returns, one must first establish theability of this variable in forecasting future fundamentals. We therefore investigate the association
between audit modifications and future financial performance before assessing the usefulness of audit
opinions in pricing securities. Second, much of the evidence for MAO information content is based on
short-window market reaction around MAO disclosure. Menon and Williams (2010) is a recent
example. However, contemporaneous price response evidence alone is not sufficient to conclude theinformativeness of audit opinions as investors can over- or under-react. To fully understand MAO
mispricing, we extend the measurement window to further assess post-MAO stock returns.
In sum, we ask the following two questions in this study. Are audit opinions in China relevant inpredicting client firms’ future performance and thus useful in valuation? If so, do Chinese investors
rationally use the information contained in audit reports? To answer the first question, we investigate
how audit opinions predict firms’ future financial performance. We find that MAOs strongly predictrecipients’ one-year-ahead underperformance relative to non-MAO firms. Moreover, the mean
reversion pattern of MAO firms’ financial performance differs significantly from that of other firms inthat poor performers have a significantly slower recovery rate (i.e., poor performance is more
persistent for MAO recipients) while good performers have a much faster decay rate in their financial
performance (i.e., MAO firms’ good performance is largely transitory). To answer the second
question, we first analyze market reaction to MAO disclosure by the event study approach and-2-observe that the market reacts negatively. Moreover, the negative reaction is stronger when MAO
recipients exhibit poorer performance in the current year, which is consistent with the greater
persistence of MAO firms’ poor financial performance. We then test whether the short-term negative
market reaction fully reflects MAOs’ information content by examining post-MAO stock returns.
Inconsistent with the mispricing argument, we find no evidence on negative 12-month stock returns
following MAO announcements. This result is robust to various measurements of abnormal returns,
return computation methods (buy-and-hold vs. cumulative), and data analysis approaches (univariate
portfolio vs. multivariate regression analysis and pooled vs. Fama-MacBeth (1973) type time-seriesdata). Taken together, our evidence supports the usefulness of audit reports in a powerful test setting.
Our contributions to accounting research and practice are three-fold. First, we provide consistentevidence for the informativeness of modified audit opinions in a more powerful research setting.
Second, by examining audit modifications triggered by violations of GAAP or disclosure rules (i.e.,
GAAP/DISC MAOs), we add new evidence to the capital market effect of disclosing problematic
accounting practices (Dechow et al. 2010). Previous studies have documented negative stock pricereactions to the ex post disclosure of earnings manipulation alleged by the regulators (Feroz et al.
1991; Dechow et al. 1996) and firms’ restatements of financials (Wu 2003; Palmrose et al. 2004;
Gleason et al. 2008). We document how the market assimilates problematic accounting practices thatare explicitly challenged by the auditor. This type of evidence helps to answer the question of whether“investors are able to unwind incentives and to incorporate an expectation of earnings management
into their pricing” (Dechow et al. 2010, p. 380). Finally, our research is of implications for the recentchanges in audit reports’ form and content, as proposed by the Public Company Accounting Oversight
Board (PCAOB 2013) and the International Auditing and Assurance Standards Board (IAASB 2011).
All these proposed changes to improve the structure and wording of the auditor’s report are based onan underlying assumption that audit-related information is used effectively in assessing firms’
financial condition, performance, and value. Our findings suggest that investors in China use thevalue-relevant audit information in pricing stocks in an unbiased manner, and thus support theregulatory initiative that additional information should be provided to users through audit reports.

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2.

Background of Research

2.1. The literature
Under the event study approach, researchers infer the usefulness of audit opinions by observing
the market reactions to audit qualifications. Early studies (Elliot 1982; Dodd et al. 1984) failed to findany systematic response. Because audit opinions could be released to the market through several
possible channels in the U.S., one explanation for the lack of evidence is the difficulty in identifying
the precise dates when investors first learn the audit opinions from public sources (Dodd et al. 1984).
However, by improving the methodology of identifying the precise disclosure dates of audit
qualifications, subsequent studies continue to find that the negative price effects of audit
qualifications are specific to qualifications that receive media attention (Dopuch et al. 1986) or arevery weak economically (Loudder et al. 1992; Frost 1994). More recently, Menon and Williams (2010)
find economically large negative excess returns when a going concern audit report is disclosed,
especially when the report refers to a financing problem. Nevertheless, the debate goes on: Myers et al.
(2015) show that there is no detectable market response to going concern modification once
concurrently disclosed material information is controlled for.
Market response evidence alone, however, is not sufficient to establish the informativeness ofaudit opinions. As the market could over- or under-react, the observation of negative market reactions
to qualified audit opinions does not necessarily mean such information being incorporated into
security prices in a rational way.1 This can also be illustrated by the evolution of the literature on thevalue relevance of accounting accruals. Without finding any difference in the contemporaneous stock
price responses to the cash flow and accrual components of earnings, early studies conjecture that the
information content of cash flows may not be different from that of accruals (e.g., Bernard and Stober1987). The seminal study of Sloan (1996) demonstrates that cash flows are significantly more

1

Using a sample of firms listed on the Shanghai Stock Exchange from 1995 to 1997, Chen et al. (2000)

document significantly negative market returns during the short windows surrounding the disclosure of MAOs.
However, they note that due to the lack of previous experience, Chinese investors may interpret MAOs as anindication of bankruptcy, which could lead to the negative market reaction. In a similar vein, the market may
under-estimate the negative implications of MAOs and thus under-react, even if researchers observe thenegative responses when MAOs are disclosed.
-4-persistent in predicting future earnings than accruals; however, the market fails to fully recognize thisdifference until the realization of future earnings unravels it. Therefore, to fully understand theinformativeness of audit opinions, it is necessary to extend the measurement windows forward toallow for a better assessment of the content of relevant information (Aboody et al. 2002).
A number of studies investigate the 12-month stock returns following GCO disclosure.
Consistent with the literature on the anomalous market under-reaction to bad news events, Taffler etal. (2004) document significantly negative 12-month abnormal returns following the first-time GCOs
in the U.K. However, Ogneva and Subramanyam (2007) find no such evidence in the Australianmarket, and report that the negative post-GCO abnormal returns are sensitive to choices of expected
returns in the U.S. Interestingly, Kausar et al. (2009) re-examine the U.S. market and are able to show
significantly negative post-GCO stock returns. They attribute the difference between their findings
and those of Ogneva and Subramanyam (2007) to several methodological issues, including the data
source for identifying GCO observations, the computation of delisting returns, and the treatment ofoutliers. Although Taffler et al. (2004), Kausar et al. (2009), and Kausar et al. (2013) argue thatinvestors tend to deny the bad news conveyed by GCOs, as recognized by these authors andemphasized by Basu (2004), an alternative explanation for the “anomalous” results is that hightransaction costs impede rational investors from exploiting the anomaly. Adding to the debate over themarket efficiency regarding audit opinions, recent field evidence suggests that financial statement
users take auditor opinions into account (Gray et al. 2011).2 In sum, how the market uses GCO
information in pricing stocks remains inconclusive [see also the discussion by Church et al. (2008)
and Mock et al. (2013) in their syntheses of audit report research].

2.2. Stock market and audit reporting in China
Although the Chinese stock market was established only in the early 1990s, by the end of 2012,in terms of market capitalization, China has overtaken Japan to become the world’s second-largest

2

In particular, Gray et al. (2011, p. 672) report that “There was a general consensus among participants that agoing-concern opinion provides important information to nancial statement users. User groups assumed that if
the auditor’s report does not include a going-concern comment, the auditors performed an adequate
going-concern analysis and concluded going-concern was not an issue.”-5-

stock market, exceeded only by the U.S. 3 In October 2002, the Chinese Securities Regulatory
Commission (CSRC), the equivalent of the Securities and Exchange Commission in the U.S., issued
the first Sino-foreign fund management license. Since then, foreign money managers have increased
their stakes invested in China. By the end of March 2013, China had granted a combined USD41.745
billion in Qualified Foreign Institutional Investors (QFII) quotas to 197 foreign institutions (Reuters2013).
Despite China’s significant strides in developing its stock market, small individual investors are
predominant in China’s stock market. At the end of 2002, less than 10% of the accounts on the
Shanghai Stock Exchange had a portfolio value of RMB100,000 (or approximately USD12,000) or
more, which indicates that large investors, including institutional investors, do not dominate themarket (Security Times, January 4, 2003). Although recent years have observed the development ofChina’s institutional investor community, trading by individual investors in 2013 still accounts for82.24% of the Shanghai market’s total trading volume (Shanghai Stock Exchange, 2014). Smallinvestors overall tend to be less sophisticated and are more likely to neglect the implications ofvalue-relevant information such as audit opinions than institutional investors (Cohen et al. 2002;
Barber and Odean 2008). Ogneva and Subramanyam (2007, p. 440) argue that, “Ceteris paribus, if
the principal source of the GC opinion anomaly is human information processing bias, one would
expect to observe similar inef ciency in other markets (countries).” Following this reasoning, we
expect such an anomaly to be even more pronounced in China, if investor behavior bias indeed leads
to the mispricing of audit opinions.
Three other characteristics of China’s stock market also make it a desirable setting for testing thepossible mispricing of audit opinions. First, the short-selling of stocks is generally not allowed in
China.4 Because short-selling speeds up the incorporation of negative news into stock prices (Chen etal. 2002; Chang et al. 2007), its constraint in China should magnify the mispricing of problematic
firms’ stocks, including those of MAO firms. Second, China’s stock market is highly liquid, as
evidenced by their high turnover ratio. During our sample period, the average monthly turnover ratio
3

Source: http://data.worldbank.org/indicator/CM.MKT.LCAP.CD.

4

In October 2008, the CSRC announced a pilot scheme of short selling, and the bans on short selling andmargin trading were lifted at the end of March 2010. However, only 6 brokerages were qualified to engage insecurities lending and margin financing, and only 90 designated stocks were available to investors for shorting.
-6-

for the A-share stocks was 38.06%, ranking China top among the 31 emerging markets investigatedby Rouwenhorst (1999).5 As we show later, the high liquidity also holds for the MAO stocks in our
sample. Given that the stocks are actively traded, we can, to a great extent, exclude friction in trading
as an explanation for mispricing (Basu 2004). Finally, as Kausar et al. (2009) observe, returns to GCO
firms are highly sensitive to the computation of delisting returns. For Chinese data, this complicationis less serious, as only a very small number of stocks in the MAO portfolios are delisted.
In tandem with the rapid development of stock market, the Chinese audit industry has grownphenomenally. In 2013, total industry revenues reached RMB56.3 billion (XinHua Net, May 2014),
which represents a growth of 511.8% relative to 2002 and ranks the Chinese audit market among the
major audit markets in the world. According to China’s Independent Auditing Standards (CIAS),
there are four types of audit opinions: unqualified, qualified, disclaimer, and adverse. The CIAS also
stipulates that explanatory notes can be used with unqualified opinions where necessary. Chen et al.
(2001) note that an unqualified opinion with explanatory notes is often issued in place of a qualified
one in China and that the Chinese regulators also treat this type of reports as non-clean opinions indisclosure requirements or delisting decisions. Therefore, consistent with previous studies on China’s
audit market (e.g., Chen et al. 2000), we consider this type of audit opinion as one form of
qualifications.
We use the term of “GAAP/DISC” to be as comprehensive as possible, because the reasons forsuch modifications include both direct violations of GAAP and issues that are of a disclosure nature.6
In our sample, more than half of MAOs are issued under such reasons. Unlike previous research
focusing on GCOs, we are thus able to study the effects of auditors’ concerns about the application of
GAAP or disclosure issues on securities pricing. Intuitively, the interpretation of GCOs should be
more straightforward than the GAAP/DISC-type opinions in the context of equity valuation; that is,

5

Consistent with Rouwenhorst (1999), we first compute the median turnover by month across firms and thentake the time series average of these monthly medians. Although Rouwenhorst (1999) did not include China,
Taiwan and Korea have the highest median monthly share turnover ratios, at 30.22% and 8.13%, respectively, in
his sample.
6

For example, auditors may cast doubt about firms’ material contingencies/commitments, or emphasizetransactions with related parties or questionable business transactions. See Chen et al. (2001, Table 2) for moredetails on typical reasons underlying MAOs in China.
-7-the former is the red flag raised by auditors over the firms’ financial health and the latter represents
auditors’ doubts over the accounting for or disclosure of economic transactions/activities and could be
issued to firms that do not exhibit signs of financial distress. We therefore expect that investors are
more likely to misinterpret the value implications of GAAP/DISC MAOs than of GCOs. As Bradshaw
et al. (2001) argue, mispricing should be more likely to occur for more complex matters, and lack ofevidence on mispricing could be due to the Type II errors in statistical inferences. To the extent thatthe market is more likely to misprice GAAP/DISC-type audit opinions than GCOs, our researchsetting has an added advantage of increased statistical power.

3.

Research Methodology

3.1. Audit opinions and future financial performance
To establish the usefulness of audit opinion information, a necessary first step is to examine
whether audit opinion modifications predict firms’ future financial performance. Following Sloan(1996), we investigate the link between current audit opinions and one-year-ahead financial
performance. Consistent with Piotroski (2000) and Mohanram (2005), we measure firms’ financial
performance by their profitability, financial risk, and operating efficiency. Specifically, the following
variables are used to measure these aspects:
ROA = Operating income, adjusted for net interest expenses, divided by the average of
beginning and ending total assets.7
OCF = Operating cash flow divided by the average of beginning and ending total assets.8
Loss = 1 if operating income (adjusted for net interest expenses) is negative, and 0

7

Under the Chinese GAAP, financial expenses are reported above operating income in income statements. We

adjust operating income by adding financial expenses back to reported operating income so that the operating
income variable is free of firms’ financing activities.
8

For years before 1998, when cash flow statement data are not available in China, operating cash flow isdefined as the difference between operating income (adjusted for net interest expenses) and total accruals, andtotal accruals are estimated by the balance sheet approach as:
Current asset
liability

Cash

Short-term investments

Short-term borrowings

and amortization expense, where

Current portion of long-term investments)

Current portion of long-term debt

Dividends payable)

denotes the change between the current and previous year.

For year 1998 and onwards, operating cash flow data are obtained from cash flow statements.
-8-( Current

Depreciation

otherwise.
Negative OCF = 1 if operating cash flow is negative, and 0 otherwise.
Leverage = Total borrowings divided by total assets at the end of the year.
Liquidity = Current assets divided by current liabilities at the end of the year.
Gross Margin = Gross margin divided by sales.
Turnover = Sales divided by the average of beginning and ending total assets.
Among these fundamentals, ROA and OCF measure firms’ operating performance. We consider
both accrual- and cash-flow-based measures because the accrual components of earnings can be
managed by firms, as the longstanding earnings management literature documents. Moreover, we use
two indicator variables, Loss and Negative OCF, because the ability to generate positive earnings orcash flow is critical to firms’ long-term survival and a loss-status is often interpreted as a warning
signal in firms’ performance evaluation. We measure firms’ financial risk using Leverage andLiquidity. Higher Leverage or lower Liquidity indicates difficulty in meeting debt service obligations.
Finally, Gross Margin and Turnover capture the efficiency of firms’ operations. We aggregate thesemeasures by running a by-year factor analysis of the abovementioned eight fundamental variables.
The variable of F-Score is the first factor score obtained from the factor analysis. Because factor
scores are standardized to have a zero mean and unit variance, F-Score itself is its unexpectedcomponent [i.e., E(F-Score) = 0]. For our data, a higher F-Score indicates stronger performance.9
We expect GC in year t to be negatively correlated with F-Score in t+1 because GCOs represent
the auditors’ adverse opinions regarding firms’ ability to continue as a going concern. Although
GAAP/DISC MAOs may not have a direct bearing on firms’ future financial viability, the violation of
GAAP or disclosure rules per se indicates misrepresentation in financial reporting. Because audit
failure costs are asymmetrically higher for overstatements than for understatements, auditors are moreconcerned with performance overstatements (St. Pierre and Anderson 1984). Consistent with this9

The F-Score is negatively correlated with Loss, Negative OCF, and Leverage and is positively correlated withthe other fundamentals) (p < 0.001 for all correlation coefficients). With annual mean eigenvalues of 2.630, theF-Score is able to explain 32.9% of the variability in the fundamental variables. We do not consider the second
factor because it provides conflicting signals about firms’ performance. For example, this second factor ispositively (negatively) correlated with Negative OCF, Liquidity, and Gross Margin (the other fundamentals) andis uncorrelated with ROA ( = 0.007, p = 0.263).
-9-

explanation, clients that manage earnings upwards to meet regulatory benchmarks are significantly
more likely to receive MAOs (Chen et al. 2001). Prior studies have documented that overstatedperformance is likely to be followed by weaker future performance (Teoh et al. 1998b; Xie 2001;
Richardson et al. 2005). We therefore predict GAAP/DISC MAOs in year t to be negatively related to
F-Score in year t+1.

3.2. The abnormal stock return measures
3.1.1. The benchmark-based abnormal returns
To test whether audit opinions are mispriced and thus predict future stock returns, we compute12-month abnormal returns beginning in May of year t+1. All Chinese firms use calendar fiscal years
and should disclose audited financial statements within four months after the fiscal-year end.
Therefore, by the end of April of year t+1, audit opinion types and related financials are known to themarket and portfolios can be formed by audit opinions. In light of the debate on the accuracy oflong-term stock return measurement (Barber and Lyon 1997; Kothari and Warner 1997; Fama 1998),
we compute both buy-and-hold and cumulative abnormal returns, and use a variety of metrics ofabnormal stock returns, as follows:(1) The market-adjusted returns are defined as stock returns minus the equal-weighted market return
over the same period.
(2) The portfolio-adjusted returns are measured as stock returns minus the equal-weighted return of acomparable Size and B/M portfolio return over the same period. Size is defined as the market
value of tradable shares.10 B/M is defined as the book value of equity per share divided by themarket value per share. At the beginning of each May, all stocks are sorted into quintile groups
by Size and B/M independently, and then 25 Size-B/M portfolios are formed.11
10

Before the split-share structure reform launched in 2005, listed Chinese firms had two classes of commonshares: the tradable shares were freely floating on the stock exchanges and non-tradable shares could only betransferred through negotiation between designated parties. Our results are not sensitive to measuring Size as
total market value (= total number of shares outstanding, tradable or non-tradable, times share price per share).
11

Fama and French (1992) demonstrate that size and B/M are two important determinants of cross-sectionalvariation in average stock returns in the U.S. This finding also holds in Japan (Chan et al. 1991; Daniel et al.
2001) and other countries (Fama and French 1998). Our untabulated analysis suggests that both Size and B/M
also explain the average monthly stock returns in China’s stock market. A number of papers, e.g., Chan et al.
- 10 -

(3) The Size-B/M matched control returns are calculated as stock returns minus the return of a control
stock matched by size and B/M over the same period. We rst identify all non-MAO recipientswith a market value of equity between 70 and 130% of the market value of equity of the MAO
recipient at the end of April in year t+1. From this set of firms, we select the firm with the closest
book-to-market ratio to the MAO recipient at the end of April in year t+1 as its match.
(4) The B/M-Size matched control returns are defined similarly to those of Size-B/M matched control
returns, except that we match MAO and non-MAO recipients first by B/M and then by Size.
(5) The F-Score matched control returns are measured as stock returns minus the return of a control
stock matched by F-Scoret over the same period. For each MAO recipient, we select a non-MAO
observation in the same year with the closest F-Scoret to the MAO recipient as its match.
The above metrics (1)–(3), namely, market-adjusted, portfolio-adjusted, and Size-B/M matched
control returns, are close to those used in Ogneva and Subramanyam (2007) and Kausar et al. (2009).
For metric (4), the B/M-Size matched control returns, we reverse the order of Size and B/M in defining
comparable stocks, as compared with metric (3), the Size-B/M matched control returns. We do so
because there is a lack of theoretical guidance on the matching order of the Size and B/M variables.
Using the Size-B/M match method, an MAO stock would be closely matched with a non-MAO stockby B/M but may not be perfectly matched by Size (the caliper allowed is ±30%). Changing the orderof matching ensures a close match by Size but at the cost of precision of matching by B/M. We
therefore use both to ensure that the results are not biased due to the matching order. The rationale for
metric (5), i.e., F-Score matched control returns, is that MAO recipients are significantly different
from non-MAO recipients in F-Scoret, as we will show later. Being correlated with financial distressrisk, F-Scoret could be a relevant risk factor not completely captured by Size or B/M variables.
For returns to stock that are delisted during the holding period of t+1, we compute thepost-delisting returns as follows. For the market-adjusted returns measurement, returns earned bydelisted firms are represented by the equivalent monthly market returns, i.e., assuming that proceedsfrom selling delisted stocks are reinvested in the market portfolio. For the portfolio-adjusted returns or
the matched control returns, we replace returns to delisted firms with those to the Size and B/M(Footnote continued.)(2004), Fan et al. (2007), and Jiang et al. (2010), control for these two variables in studying stock price behavior
in China.
- 11 -

portfolio to which the delisted stocks belong, i.e., assuming the reinvestment of delisted stocks in
portfolios that are comparable to the delisted stocks in the Size and B/M characteristics. As we discusslater in section 6.1, delisted stocks in China are actively traded even in the final month before
delisting, thus the above reinvestment assumption may not be unreasonable. We also examine whether
results are sensitive to setting delisting returns equal to –100% in that section.
For all of the above return metrics, we report results on both pooled and annual samples. The
annual sample mean is the simple average of the annual mean abnormal returns, and the t-statistics are
based on the time-series variation of the annual mean returns (Fama and MacBeth 1973). This
procedure adjusts the cross-sectional dependence of stock returns among observations because theinferences are based on the yearly mean returns. More importantly, the time-series results help toassess whether MAO stocks are consistently associated with future abnormal returns during thesample period. If “abnormal” returns are actually due to some unspecified risk factors, the frequencyof positive and negative yearly returns should be roughly the same, although the average from thepooled sample could be non-zero. The evidence based on the annual samples is thus particularly
useful considering the possibility that some risk factors could be unknown and thus are not adequately
controlled for by researchers (Bernard et al. 1997).
3.1.2. The factor model-based abnormal returns
Both Ogneva and Subramanyam (2007) and Kausar et al. (2009) use the Fama and French (1993)
three-factor model and Carhart (1997) four-factor model to estimate expected returns. A pre-event
estimation period (e.g., up to 60 months before the receipt of GCOs in Kausar et al. 2009) should be
used to estimate the factor model coefficients to be applied to the holding period. Because many of
the firms in our sample have a short listing history, this method could lead to substantial sample
attrition and is thus not feasible. As an alternative, we use the calendar-time portfolio approachadvocated by Fama (1998). Specifically, portfolios are formed each month from May 1996 throughApril 2013 by sorting all firms according to the types of audit opinions received in the previous fiscal
year. We then regress the portfolio excess monthly returns on the Fama and French (1993) model
factors or Carhart (1997) model factors as follows:
Rp, t – Rf, t =p+p(Rm, t – Rf, t) + spSMBt + hpHMLt +Rp, t – Rf, t =p+p(Rm, t – Rf, t) + spSMBt + hpHMLt + upUMDt +- 12 -

,

(1)
,

(2)

where Rp is portfolio raw returns, Rf is risk-free monthly returns, Rm is the monthly market returns,
and SMB, HML, and UMD are the returns on zero-investment factor-mimicking portfolios for size,
book-to-market, and momentum factors, respectively. We strictly follow Fama and French (1993) toconstruct the SMBt and HMLt factors and Carhart (1997) to construct the UMDt factor, but using
Chinese data.12 To the extent that factor-mimicking portfolio returns capture returns to risk, timeseries variation in risk is controlled for in regression models (1) and (2), and the intercepts,p,represent the abnormal returns to the portfolios. This calendar-time portfolio approach also corrects
for the potential cross-sectional dependence in returns across firms due to the clustering of similar
events in calendar time (Mitchell and Stafford 2000).

4.

SampleThe sample period is from 1995 to 2011. Modified audit opinions (MAOs) were rare in China

before 1995. In December 1995, the Chinese Institute of Certified Public Accountants (CICPA) issued
the first batch of CIAS, which specified the responsibility of auditors and the content and format ofaudit reports. As a result, MAOs increased substantially in that year (DeFond et al. 2000). Financial
statement and stock price data are retrieved from the China Stock Market and Accounting Research
Database (CSMAR). Data for the types of audit opinions are collected from audit reports published in
the companies’ annual reports. We determine the underlying reasons for the MAOs by reading theseaudit reports. If the auditors explicitly mention issues related to firms’ financial troubles or operatingproblems, we then code the audit opinions as a GCO. Other MAOs are treated as violations of GAAP
or disclosure rules, i.e., GAAP/DISC-type modification.
We begin with the whole population of Chinese firms listed in the A-share markets between 1995
and 2011 (n = 21,471) on CSMAR’s financial statement database. We then delete: (a) 249
observations in the financial industry; (b) 629 observations with missing current financial statement or
stock market data; and (c) 159 observations with missing one-year-ahead financial statement or stock
market data. The final sample comprises 20,434 firm-year observations for 2,383 unique firms.
Because we use one-year-ahead data, the actual financial statement (stock return) data used in thisstudy run from 1995 to 2012 (January 1996 to April 2013).

12

See Jiang et al. (2010) for a similar application of the Fama and French (1993) three-factor model in China.
- 13 -

We drop firms with missing one-year-ahead data, which is mainly due to the delisting or
suspension from trading of their stocks by the stock exchanges. During our sample period, 77
companies’ stocks were delisted. Among these, 29 were delisted due to merger and acquisition
activities or the listing of their parent firms’ stocks, and the remaining 48 were delisted due to poor
financial performance. For these 48 companies, all but one had received MAOs in the final year
before the delisting. The delisting of these 48 companies, however, does not materially affect our
results. According to the Shanghai and Shenzhen Stock Exchanges’ trading rules, a firm’s shares are
suspended from trading when it has reported losses for three consecutive years (which is the condition
for delisting). Most of the delisted firms’ shares are not traded after they publish the final set offinancial statements before delisting, and are thus excluded from the portfolios formed at the
beginning of May of year t+1. Only 11 delisted firms’ shares are traded between the portfolio
formation date and the delisting month, and are included in the MAO portfolios.13 In section 6.1, we
further consider how returns to these delisted stocks influence our findings.
Table 1 shows the distribution of our sample by time and types of audit opinions. The sample
size increases almost monotonically from 282 in 1995 to 2,269 in 2011, reflecting the rapid growth ofstock markets in China. For the whole sample, 1,470 firm-years, or 7.19% of all observations received
GAAP/DISC-type audit modifications, and auditors issued GC-type modifications to 712 or 3.48% of
firm-year observations.14(Insert Table 1)

5.

Empirical results

5.1. Audit opinion modifications and financial performance
Table 2 displays the mean values of F-Score and its components in both t and t+1 by three groupsof firms formed by audit opinion type. In Panel A, both the GAAP/DISC and GC groups
13

Also note that excluding delisted stocks from the MAO portfolios if stocks are not traded after the publicationof final set of financial statements is entirely consistent with Kausar et al. (2009: p. 215); that is, they exclude
firms that “are delisted in the GC month” from the sample.
14

The numbers of observations under the “Clean”, “GAAP/DISC”, and “GC” columns do not add up to thetotal sample size because 289 observations’ audit reports are modified by their auditors for both GAAP/DISCand GC-related reasons.
- 14 -

underperform the Clean group in F-Score and all its components in year t. The differences are
significant in the t-tests at the 1% level. This suggests that firms that have received GAAP/DISC or
GC modifications from their auditors have worse current financial performance than those in the
Clean group. In Panel B, we examine whether audit modifications help to predict future financial
performance. We find that both the GAAP/DISC and GC groups continue to underperform the Clean
group in year t+1. Again, the differences between the GAAP/DISC or GC and Clean groups in themeans of F-Score and all of its components are significant in the t-tests.
(Insert Table 2)
Table 2 also reveals that F-Score follows the well-documented mean reversion pattern offinancial performance (Nissim and Penman 2001). For example, the Clean group’s mean F-Score
declines from 0.136 in t to 0.052 in t+1, whereas the GAAP/DISC (GC) group’s mean F-Score
improves from –0.925 (–1.775) in t to 0.822 in t+1 (–1.449). Generally, such a mean reverting
tendency can also be observed in the individual components of F-Score. It is important to examine
whether the mean reversion of F_Score differs between MAO and non-MAO firms. We test this in
Table 3, where for each year, we first sort firms into deciles by F-Score in year t and then track their
F-Score in year t+1. To determine the mean reversion rate, in Panel A, we compute the statistics
of %Reversal as: (F-Scoret+1 – F-Scoret) / | F-Scoret |. We omit this statistic for middle decile (4–7)
groups because | F-Scoret | for these groups are close to zero and the statistics are subject to thesmall-denominator problem. The columns headed by “n” show the number of observations in eachdecile group. As one may expect, there are relatively more (less) observations in the lower (upper)
half of the panel for the Clean group, and this pattern is reversed for the GAAP/DISC and GC groups.
For the Clean group, the mean reversion rate, as evaluated by the %Reversal statistics, is higher in theupper half than in the lower half of the panel, i.e., mean reversion is faster when non-MAO firms’
F-Score is low. This is consistent with Fama and French’s (2000) finding that mean reversion is faster
when profitability is below the average. More importantly, the mean reversion pattern is quite
different for the MAO firms. In the upper half of the panel, the GAAP/DISC and GC groups,
compared with the Clean group, have a much slower mean reversion rate. In fact, starting from the 3rd
and 2nd deciles, respectively, of the GAAP/DISC and GC groups, the %Reversal statistics are negative,i.e., performance deteriorates in t+1. This indicates that MAO firms in year t+1 may not recover from- 15 -

poor performance in year t; and if they do, the recovery rate is slower than that of non-MAO firms.
Turning to the lower half of the panel, we observe the opposite in the magnitude of %Reversal
statistics: good performers (if any, given the small n) of GAAP/DISC and GC groups have a much
faster decay rate in F-Score than the Clean group. Together, we conclude that the financial
performance of MAO firms is highly persistent when the firms exhibit poor performance in thecurrent year. In contrast, MAO firms’ performance is highly transitory when they perform well in thecurrent year.
(Insert Table 3)
In the above analysis, we control for the effect of current performance by sorting firms into
decile groups. However, one may argue that the control is not adequate because within each decile,
MAO firms still display worse performance than non-MAO firms (e.g., the mean F-Scoret in decile
group 1 is –1.531, –2.131, and –2.312, respectively, for the Clean, GAAP/DISC, and GC groups).
Because the level of financial performance matters in predicting future performance, as the data inPanel A suggest, we adopt a more rigorous exact match design in Panel B to control for the differencein F-Scoret between the MAO and non-MAO firms. Specifically, for each MAO recipient, we select a
non-MAO observation in the same year with the closest F-Scoret to the MAO recipient as its match.
With this exact match, MAO and non-MAO firms have close F-Scoret. For example, for the
GAAP/DISC (GC) group, the mean F-Scoret is –0.925 (–1.775), whereas their matched Clean firms
have a mean F-Scoret of –0.922 (–1.769). The inference from the match design is consistent with thatfrom Panel A: %Reversal (or the recovery rate from poor performance) for either GAAP/DISC or GC
firms is far lower than that for their respective matched Clean firms.
The Type I/II error rates in audit reporting are often used to assess audit quality.15 Carson et al.
(2013) show that there are about 0.448% of firms file for bankruptcy in the U.S. from 2000 to 2010
and 60.1% of them receive GCOs one year prior to bankruptcy filing, suggesting a Type II error rateof 39.9%; the Type I error rate is about 15.7% as auditors have issued GCOs to this percentage of
non-bankruptcy firms. Because no Chinese public firm has ever gone bankruptcy within our sample

15

Here a Type I error means false rejection, i.e. issuing a modified opinion to a client that does not becomebankrupt later, and a Type II error means false acceptance, i.e., issuing a clean opinion to a client that
subsequently goes into bankruptcy.
- 16 -

period, we use the ex post realization of F-Scoret+1 to assess the MAO accuracy. To approximate the0.448% bankruptcy incidence rate in Carson et al.’s (2013), we sort firms into 250 quantile groups by
F-Scoret+1 (so that 0.4% of observations are grouped into each quantile group). The percentages of
GAAP/DISC, GC, and total MAOs for each group are plotted in Figure 1. In the far left quantile
group (where F-Scoret+1 is lowest), 78.1% of observations received MAOs from their auditors in year
t, indicating a Type II error rate of 21.9%. The percentages of MAOs drop dramatically as one moves
to the higher F-Scoret+1 quantiles. Starting from the 50th quantile, the MAO rates are generally below
10% and approach zero in the far right quantile group. Thus, the Type I error rates decline when
F-Scoret+1 increases. This pattern is similar for both GAAP/DISC and GC. We conclude from thisexercise that Chinese auditors exhibit a reasonable degree of accuracy in issuing MAOs if
benchmarked against clients’ future realization of financial performance.
(Insert Figure 1)
Taken together, the evidence from Figure 1, Table 2, and Table 3 supports the intuition that audit
opinions predict firms’ future financial performance. MAO recipients have significantly worse
financial performance in year t+1 than non-MAO recipients. Moreover, the poor performance of
MAO firms in year t+1 can be at least partly attributed to their distinct time-series mean reversion offinancial performance; that is, they tend to recover very slowly from poor performance in year t but
have a much faster decay rate when they exhibit above-average performance in year t. These results
suggest that audit opinions have the power to predict future performance, probably because auditors
have private information about the operations and financial health of their clients. We next study how
the market exploits the information contained in audit reports.

5.2. Market reaction to audit opinion modifications
Although market reaction to audit opinions does not constitute sufficient evidence of the rational
pricing of audit qualifications, as discussed earlier, we first examine this issue to reconcile withprevious research. Figure 2 plots the mean buy-and-hold market-adjusted returns around theannouncements of annual financial reports, with a solid line for the Clean group and round and squaredotted lines for the GAAP/DISC and GC groups, respectively, from trading days –10 to +10, where- 17 -

day 0 represents the days when annual financial reports are announced.16 The buy-and-hold returns to
the Clean group during the announcement window are largely flat, although there is a small pricerun-up before day –1 and a subsequent drop around day 0. As for the GAAP/DISC and GC groups,
there is a strong negative reaction by the market up to day +2, after which cumulative returns largely
level off. Untabulated statistics suggest that the means of buy-and-hold market-adjusted returns up to
day +2 are 0.25%, –1.98%, and –3.21%, respectively, for the Clean, GAAP/DISC, and GC groups.
Figure 2 thus suggests that investors respond negatively to both types of MAOs.17(Insert Figure 2)
As previously shown, MAOs are associated with poor financial performance captured by
F-Scoret. To examine whether information in audit opinions is incremental to F-Scoret, we estimate
the following multivariate regression:
BHMAR[0, +1] =0+

1GAAP/DISCt +2GCt +

1F-Scoret +2LagRETt +,(3)

where BHMAR [0, +1] is the buy-and-hold market-adjusted returns from day 0 to +1, with day 0 being
the annual report announcement day; GAAP/DISC and GC are indicators for firms that receive
GAAP/disclosure- and GC-related modifications from auditors, respectively; F-Scoret is as previously
defined; LagRETt is the buy-and-hold market-adjusted returns from one week after earnings
announcement in year t-1 to day –1; andis the regression error term. We control the fundamental

variable, F-Scoret, which covaries with audit qualifications and likely influences stock prices
concurrently. Following Brown et al. (1987), we also include LagRETt as a control for information
released before the [0, +1] window and possible measurement errors in the independent variables. Its

16

Not surprisingly, the results are similar if we use Size-B/M reference portfolio adjusted returns. For shortwindows, the daily expected returns are close to zero and the choice of expected returns does not have a big
effect on inferences (Fama 1998).
17

Figure 2 also suggests that the negative market reaction to GAAP/DISC (GC) occurs as early as day –7 (–8).

The auditor’s opinion is classified as inside information according to China’s Securities Law and the specific
type of audit opinions are unknown to the market before the formal announcements. We confirm such a practicewith a senior audit partner in China and an official at the CSRC. However, MAO firms usually have a longer
reporting lag than other firms (Haw et al. 2003). The absence of disclosing financial reports on time implies
forthcoming audit modifications, allowing investors to infer the type of audit opinions even before the actual
announcements.
- 18 -

coefficient,

2, is expected to be negative.

18

The regression results are presented in Table 4. In Model (1), before controlling for F-Scoret, thecoefficients on GAAP/DISCt and GCt are both negative and statistically significant at the 10 and 1%
levels, respectively. The inclusion of F-Scoret in the regression in Model (2) renders the coefficient on
GAAP/DISC insignificant, although the coefficient on GC remains significantly negative. Thus, partof the negative market reaction to the GAAP/DISC-type MAOs is driven by the poor performance of
this group of firms. In light of the different persistence patterns of F-Score between MAO andnon-MAO firms in Table 3, we allow the slope coefficient on F-Scoret to vary by types of audit
opinions by adding two interaction terms, F-Scoret×GAAP/DISCt and F-Scoret×GCt, to Model (3).
With this specification, neither GAAP/DISCt nor GCt loads with a statistical significant coefficient.
However, both coefficients on F-Scoret×GAAP/DISCt and F-Scoret×GCt are significantly positive.
Because MAO firms have significantly worse F-Scoret than non-MAO firms, the negative reaction to
MAOs in Figure 2 or Models (1) or (2) is conditional on the F-Scoret variable. To see this, the slope
coefficient on the GAAP/DISC group is 0.006 (= 0.001 + 0.004, allowing for rounding error) and its
mean of F-Scoret is –0.925. Thus, the total average effect of earnings announcements on stock pricesof GAAP/DISC firms during the [0, +1] window is estimated as: –1.13% (= –0.008 + 0.002 +0.006×–0.925, where –0.008 is the regression intercept and 0.002 is the coefficient on the main effectof GAAP/DISCt). Likewise, the average firms in the GC group experience a drop in market value by –1.80% mainly due to its poor F-Scoret. Moreover, in Table 3 we have observed that MAO firms’
F-Score is more persistent than that of non-MAO firms when F-Score is below its average (and the
majority of MAO firms have below-average F-Score). The differential slopes on F-Scoret between
MAO and non-MAO firms are consistent with the notion that investors’ response to information
depends on its persistence (Kormendi and Lipe 1987).

18

Because part of information innovation in audit opinions and fundamentals could be released to the market

through other channels before the annual report announcements, variables GAAP/DISCt, GCt and F-Scoretmeasure surprise to the market with errors. To the extent that LagRETt is positively correlated with information
innovation not captured by independent variables in equation (3) but uncorrelated with the dependent variable
BHMAR[0, +1] (due to the fact that stock returns are serially uncorrelated), including the LagRETt variable reduces
the effects of measurement errors in estimating the effects of audit opinion and fundamental variables on
short-term market reactions.
- 19 -

(Insert Table 4)
To summarize, the analysis of market reaction to audit opinions suggests that MAOs have anegative effect on share prices. Moreover, it appears that Chinese investors are sophisticated enoughto understand the implications of MAOs for the persistence of fundamental signals as captured by
F-Score and use them accordingly in their investment decisions.

5.3. Post-MAO stock returns
5.3.1. Univariate analysis
Although the market reacts negatively to MAOs, it remains possible that the market assigns asmaller valuation coefficient to audit modifications relative to their actual predictability for
performance in the subsequent year. If so, the stock prices of MAO firms are likely to be overvalued.
When realized fundamentals in subsequent periods differ from those expected by the investors, themarket corrects the mispricing, but with a delay. This suggests that part of the future returns can bepredicted by the information that is contained in current audit reports. Therefore, we perform theportfolio test to examine the predictability of future returns by audit opinions.
Table 5 displays the 12-month post-announcement abnormal returns, using five metrics, to thethree portfolios formed by the audit opinion types. The results based on buy-and-hold and cumulative
returns are shown in Panels A and B, respectively. In each panel, we report results based on thepooled and annual samples. Recall that annual sample tests are based on the time-series of the yearlymean returns. This corrects the cross-sectional dependence of stock returns among observations(Fama and MacBeth 1973) and helps to evaluate whether some unspecified risk factors influence theresults (Bernard et al. 1997).
(Insert Table 5)
The overall evidence in Panels A and B of Table 5 does not suggest a negative relation between
MAOs and stock returns in year t+1. The strongest evidence of negative returns to MAOs is from thereference portfolio approach in column (2) of Panel A for the GAAP/DISC group, when returns arecomputed as buy-and-hold returns; that is, the mean returns are –0.050 (t = –1.79) and –0.055 (t = –1.56), respectively, for the pooled and annual samples. However, the magnitude of these returns
declines when cumulative returns are used in Panel B, suggesting that part of the results in Panel A- 20 -

are caused by more negative returns during the early months of the holding period.19 The returns to
the GC portfolios are more variant compared with those to the GAAP/DISC portfolios. GC portfolios
actually realize significantly positive returns when abnormal returns are computed as market-adjusted,
Size-B/M, or F-Score matched control returns for the pooled sample. Nevertheless, the magnitudes ofthese abnormal returns decline and they are not statistically significant when the inferences are madeby the time-series of the annual sample. Therefore, positive returns to GC stocks are observed in asmall number of years, probably representing compensation for the higher risks associated with suchstocks.20
5.3.2. Multivariate regression analysis
To facilitate the control of other variables that are correlated with both stock returns and audit
modifications, we estimate multivariate regressions to examine the relation between audit
modifications and future stock returns based on the following model:
BHARt+1 =0+

1GAAP/DISCt +2GCt +

1R(B/Mt) +2R(Sizet) +3R(*ARETt)+ 4F-Scoret + 5EQOt + 6Delistt + 7RP Loant + 8Aget +

t+

k+

(4)

.

The dependent variable BHARt+1 stands for buy-and-hold stock returns. We control for the effects
of book-to-market ratio and firm size on stock returns by B/M and Size. Because MAO firms haveexperienced negative returns in the current year and the momentum anomaly literature suggests thatnegative stock returns could persist into the future (Jegadeesh and Titman 1993), we also consider the
12-month market- or portfolio-adjusted stock returns beginning in May of year t (MARETt or PARETt)
as a control variable. In light of the poor financial performance of MAO firms in the current year andthe possible effect of current financial performance on future returns (Bernard and Thomas 1989), we

19

Harvey et al. (2016) note that extensive data mining in financial economics makes the usual criteria forestablishing signi cance less useful and suggest a t-statistic greater than 3.0 as a hurdle for signi cance level.
By this standard, the negative mean returns for the GAAP/DISC group are far from being statisticallysignificant.
20

In columns 1 and 2 of Table 5, we use the equally-weighted market or portfolio returns, respectively. We also

try the value-weighted market or portfolio returns. We find that the abnormal returns to MAO firms are
generally more positive than those reported because the average returns to large stocks are lower than those tosmall stocks, and therefore, value-weighted market or portfolio returns are smaller than corresponding
equally-weighted measures.
- 21 -

include the F-Scoret variable, as defined before. Chen and Yuan (2004) and Haw et al. (2005) findthat Chinese firms often manipulate earnings to qualify for stock rights offerings, and Chen et al.
(2001) find that auditors are more likely to issue MAOs to such earnings manipulators. Evidence fromthe U.S. indicates that equity offerings are followed by the under-performance of stock returns(Loughran and Ritter 1995; Spiess and Affleck-Graves 1995; Teoh et al. 1998a). We therefore include
an indicator, EQOt, for observations that have equity offerings in year t to capture this effect. The
stocks of a listed firm that has incurred losses in the previous three years should be delisted by thestock exchange, according to China’s Company Law and related regulations. To warn investors about
the delisting risk, stock exchanges assign the Special Treatment mark to a firm that has had twoconsecutive annual losses or a negative book value of equity. As firms that are close to being delisted
are more risky, auditors are more likely to issue MAOs to such clients and their expected stock returns
may not be captured by conventional measures. We use a dummy variable, Delistt, to indicate firmsthat have reported losses in both years t and t-1, or their shareholders’ equity is negative at the end ofyear t. Loans to related parties are often used by Chinese firms to channel resources from public firms
to related parties. Jiang et al. (2010) find that the existence of related-party loans triggers the issuanceof MAOs by the auditor and is negatively associated with future stock returns. We use variable RP
Loant, defined as the balance of loans to related parties, scaled by total assets, at the end of year t, tocontrol for this effect. Chen et al. (2001) find that auditors are more likely to issue MAOs to firms
with a longer listing age. Meanwhile, firms with a longer listing age are also more mature, suggestinga lower risk and thus lower expected returns. We add Aget, defined as the number of years a company
has been listed by end of year t, as another covariate. Finally, we include year and industry indicators
to control for any possible time or industry effects on stock returns, as indicated by

t and

k.

Although we only report results for the buy-and-hold stock returns, the results based on
cumulative returns yield qualitatively the same inferences. Similar to the analysis in the previous
section, we use five different stock return metrics. When BHARt+1 is the portfolio-adjusted returns, we
use PARETt, i.e., portfolio-adjusted returns in t, and MARETt, i.e., market-adjusted returns in t,otherwise. In the above model, B/Mt, Sizet, and MARETt or PARETt are transformed to their annual
decile rankings of 0 to 9, and then scaled by 9. The transformation, as denoted by the R(•) operator,
reduces the possible undue influences of extreme values and accommodates a monotonic non-linear- 22 -

relationship between future returns and these variables. As the scaled rankings are between 0 and 1,
the coefficients on these ranked variables can be directly interpreted as the effect of changing fromaverage observations in the bottom decile to those in the top decile on stock returns. We winsorize thetwo continuous variables, F-Scoret and RP Loant, at the bottom and top percentiles of their respective
annual distributions. The regressions are estimated on both the pooled and annual samples. For thepooled regressions, we cluster the standard errors at the firm level to correct for biased standard errors
due to cross-correlated residuals in asset pricing studies (Petersen 2009). For the annual sample
regressions, statistical inferences are based on the Fama and MacBeth (1973) procedure; that is,
statistical tests are based on the time-series variation of the annual coefficients. As noted, thisprocedure adjusts the dependence of residuals among observations, and the time-series pattern ofregression coefficients also helps to assess whether the variables are consistently related to futureabnormal returns over time (Bernard et al. 1997).21
Table 6 shows the descriptive statistics for independent variables used in model (4). Comparedwith the Clean group, the GAAP/DISC or GC group has lower B/M, smaller Size, and inferior stockand financial performance in the current year as indicated by the lower MARET, PARET, and F-Score.
In addition, it has a higher Delist risk and is less likely to conduct equity offerings (EQO) and more
likely to lend to related parties (RP Loan). The differences in the abovementioned variables are all
significant at the 1% level in both the mean and median values. Finally, GC firms are significantly
older than the Clean firms in terms of their listing Age, but the difference between the GAAP/DISC
and Clean groups in Age is insignificant. The differences between MAO and non-MAO firms in theabove aspects necessitate the control of these variables in regressions.
(Insert Table 6)
The regression results are reported in Table 7. For the pooled regressions in Panel A, none of thecoefficients on GAAP/DISC are statistically significant, and their magnitude suggests that the
post-MAO abnormal returns to GAAP/DISC firms are between –0.035 and –0.024, which are not21

In our data, there is no GC-type MAO in 1995 and 1996, and no observation takes a value of 1 in the Delist

variable in 1995. The RP Loant variable is based on the disclosure of related-party transactions, starting in 1997.
Therefore, in the Fama and MacBeth (1973) regressions, the coefficient estimates and t-statistics for GCt, Delistt,and RP Loant variables are based on 15, 16, and 15 annual regressions, respectively, rather than 17 annual
regressions.
- 23 -

economically significant. The coefficients on the GC variable are uniformly positive and only the one
in the market-adjusted return regression is statistically significant. The coefficients on the control
variables of B/M, Size, and Delist are significant with expected signs, suggesting that these areimportant determinants of stock returns in China.
(Insert Table 7)
Turning to the annual regressions estimated by the Fama and MacBeth (1973) approach in PanelB, we find that GAAP/DISC or GC never loads with statistically significant coefficients across thecolumns. The most negative coefficient on GAAP/DISC (GC) is –0.039 (–0.034) when B/M-Sizematched control (F-Score matched control) returns are used. These estimates are hardly significant
from an economic standpoint. With respect to the control variables, Size and Delist are significant at
the 5% or lower levels in four out of five regressions, whereas B/M is significant at the 10% level in
only one regression. This evidence suggests that the B/M effect, to a certain extent, reflects risk
premiums for stocks with a high B/M ratio, and therefore higher returns to B/M stocks do not show up
every year.
We check whether multicollinearity is the culprit for the insignificance of GAAP/DISC and GC
variables. In the pooled regressions, the highest variance inflation factor (VIF) is 1.630 for the GC
variable when the regression dependent variable is Size-B/M matched control returns. This VIF value
is well below the typical cutoff value of 10 for the possible presence of a multicollinearity problem.
5.3.3. Factor model approach
In the above analysis, the effects of market returns, Size, and B/M on stock returns are controlled
for by comparing returns to MAO stocks with returns to reference portfolios or matched control firms,
or by including these variables in regressions. These approaches are based on the assumption that B/M
and Size effects are similar between MAO and non-MAO firms. Fama and French (1995) documentthat Size and B/M also signal persistent poor earnings performance and small stocks are particularly
sensitive to the size factor. It is possible that the sensitivity of MAO stock returns to the risk factors
differs from that of non-MAO stock returns. In Panel A of Table 8, we fit the Fama and French (1993)
three-factor model for three calendar-time portfolios formed by audit opinions. This approach allows
sensitivity of MAO stock returns to the market, size, and book-to-market ratio factor returns to varyacross portfolios. In Panel B, we estimate the Carhart (1997) four-factor model with momentum as- 24 -

one additional factor. For both the GAAP/DISC and GC portfolios, the regression intercepts are notsignificantly different from zero in either the three- or four-factor model. Because the intercept
estimates represent portfolios’ monthly abnormal returns, we find no abnormal post-MAO returns.
The annualized abnormal returns to the GAAP/DISC portfolio are –1.02% and –0.14%, respectively,
using the intercept estimates from the Fama and French (1993) and the Carhart (1997) models. The
corresponding estimates for the GC portfolio are 0.86% and 3.38%. In an economic sense, themagnitudes of these estimates suggest that abnormal returns to the GAAP/DISC or GC portfolio arepuny. In the four rightmost columns of Table 8, we also estimate the factor models for the hedge
portfolios formed by buying stocks with clean audit opinions and shorting stocks with MAOs. Again,
we find that the intercept estimates of these hedge portfolios are indistinguishable from zero in astatistical or economic sense. Therefore, allowing for the heterogeneity in return covariation with risk
factors between MAO and non-MAO stock does not change the tenor of our earlier results.
(Insert Table 8)6.

Additional analysis

6.1. The delisting returns
As Kausar et al. (2009) stress, returns to GC firms in the U.S. are highly sensitive to thecomputation of delisting returns. For Chinese data, this complication is less serious because only 11
delisted stocks are included in portfolios formed by audit opinions, as explained in Section 4. On
average, these stocks are traded for 8.36 months in year t+1 before their eventual delisting. In the
results reported above, we measure returns to delisted stocks as returns to the market or comparable
portfolios. This approach assumes that investors sell delisted stocks and reinvest the proceeds in themarket index or comparable portfolios. In the final trading month, the mean (median) turnover ratio,
defined as the number of shares traded in the month divided by the number of outstanding shares atthe beginning of the month, is 0.478 (0.300). By comparison, the mean (median) of monthly turnoverof all stocks in the sample, excluding the final month of delisted stocks, is 0.439 (0.293). By any
standard, the delisted stocks are actively traded and thus the above reinvestment assumption appears
to be reasonable.
We also apply the most extreme assumption to computing delisting returns; that is, returns to- 25 -

delisting stocks in the year when they are delisted are –100%. This assumes that stocks are virtuallyworthless after delisting, and should eliminate any possible upside bias for returns to the two MAO
portfolios. With such estimation, we continue to find that the 12-month returns to both GAAP and GC
portfolios are close to those reported in Tables 5 and 7. In the multivariate regressions, none of thecoefficients on GAAP/DISCt or GCt are significantly smaller than zero. Actually, the regressions
coefficients change only at the third decimal place, and the largest changes occur at themarket-adjusted returns when regressions are estimated by the Fama and MacBeth (1973) approach:the coefficients (t-values) for GAAP/DISCt and GCt are –0.018 (–0.891) and 0.014 (0.258),
respectively.

6.2. Unobservable firm characteristics and stock returns
We use various normal stock return metrics and control for firm attributes that are correlated with
MAOs and stock returns in the above analyses. Yet, there may still be some unknown correlated andomitted firm characteristics. Therefore, we adopt the fixed-effect model, which is a common methodof controlling for omitted variables in a panel data set (Hausman and Taylor 1981). Specifically, we
replace the dependent variable in Model (4) by raw buy-and-hold returns in year t+1 and add firm
fixed effects to the model. Because the effects of unobservable firm characteristics on stock returns
are captured by the firm fixed effects, the average realized stock returns of the firm are effectively
used as its own expected returns in this fixed-effect framework. Therefore, the coefficients on our
variables of interest, GAAP/DISCt and GCt, estimate abnormal returns specific to the years when thefirm has received MAOs from the auditor.
Because the fixed effects only capture time-invariant firm characteristics that affect stock returns,
we estimate the fixed-effect models over a five-year rolling window to ensure that firm characteristics
are relatively stable in each window. We require that a firm have at least four observations for eachperiod, so that there are sufficient data points to estimate its fixed effects. Table 9 displays the resultsof the fixed-effect models for the 13 five-year rolling periods. To save space, we only show thecoefficient estimates and t-values of the key variables. For GAAP/DISCt, its coefficients are
significantly negative (positive) at the conventional level only in two (one) periods. Thus, after
controlling for firm-specific fixed characteristics that are associated with realized returns, we find noabnormal returns to GAAP/DISC stocks. As for GCt, the coefficient estimates are only negative in- 26 -

three periods and none of them is statistically significant. In fact, the GCt variable is loaded withsignificantly positive coefficients for six periods. This means that the average realized returns to GC
stocks are positive during these periods, which is contrary to the underreaction interpretation of
investor behavior.22(Insert Table 9)
As noted, the fixed-effect model does not control for time-variant effects, and we use a five-year
window to mitigate this concern. It remains possible that during the period when firms receive MAOsfrom auditors, some unobservable firm characteristics have different effects on stock returns,
rendering the coefficients on our explanatory variables positively biased. Subject to this caveat, ourfindings from the firm fixed-effect model do not support the argument that investors underweight thenegative information in MAOs.

6.3. Two- and three-year-ahead financial and stock return performance
In the above analyses, we examine how MAOs are related to one-year-ahead financial
performance and whether MAOs predict one-year-ahead stock returns. It is interesting to examine
whether MAO recipients differ from other firms in the long term. An untabulated analysis indicatesthat the mean values of F-Scoret+2 (F-Scoret+3) for the Clean, GAAP/DISC, and GC groups are –0.016,–0.628, and –1.056 (–0.060, –0.525, and –0.750), respectively. Linking this result to those presented
in Table 2, we conclude that the differences in financial performance scores between MAO andnon-MAO firms gradually converge to zero over time. This is consistent with the long-term mean
reversion of profitability and other financial ratios (Nissim and Penman 2001). However, thedifferences in the mean of F-Scoret+2 or F-Scoret+3 between the Clean and GAAP/DISC or GC groups
remain highly significant (p < 0.01). Therefore, MAOs’ predictive power of future financial
performance persists into year t+3.
With respect to the stock returns in years t+2 or t+3, the results are in line with those for year t+1.
After changing the dependent variables in model (4) to BHARt+2 or BHARt+3, we find that none of the

22

Note that the periods examined in Table 9 are not independent of each other due to the use of the rolling

windows. An untabulated analysis suggests that the significantly positive coefficients on CGt for the six periods
are primarily driven by large positive stock returns realized in 2005 and 2006.
- 27 -

regression coefficients on GAAP/DISC or GC are significantly lower than zero. The most negative
coefficient is observed in the GC variable estimated by the annual sample when the dependent
variable is portfolio-adjusted returns in t+2: the coefficient is estimated at –0.043, with a t-value of –1.283 (p = 0.221).23 Therefore, although financial performance after t+1 is reliably correlated with
MAOs, there is no evidence on delayed market response to MAOs in the longer holding periods.

7.

Conclusion
Motivated by the recent debate on market efficiency with respect to audit opinions, we revisit thisissue using a sample of 20,434 firm-year observations from China between 1995 and 2011. Several of
the characteristics of the Chinese stock market make it particularly suitable for examining the pricing
of audit opinions. First, this market is dominated by small individual investors, a group of financial
statement users who are most likely to misunderstand and even neglect the value implications of audit
opinions. Second, audit modifications due to violations of GAAP or disclosure rules are permissible
in China. Given its subtle nature in the equity valuation context, tests based on this type of audit
modifications are more powerful. Finally, the short-selling constraint in China also magnifies themispricing of MAO stocks. We thus have the best chance of detecting the mispricing of audit opinionsin China, if this phenomenon is driven by human information processing bias as previous studies
conjecture. Moreover, the Chinese stock market is highly liquid in that stocks are actively traded, andthe computation of stock returns is less affected by stocks’ delisting. These characteristics help us to
exclude friction in trading as a potential explanation for mispricing and avoid the complication arisingfrom the computation of delisting returns.
We examine two research questions: one is whether audit opinion is informative of firms’ futurefinancial performance, and the other is whether investors use such information effectively. For bothquestions, our answer is affirmative. MAO recipients significantly underperform other firms in theyear following MAOs. Our financial performance persistence analysis shows that MAO firms’ decline
in future financial performance is due to their slower recovery rate from current poor performance or
faster decay rate if their current performance is above the average. An examination of market reaction
to MAO announcements suggests that investors react negatively to MAOs, and more importantly, that23

The corresponding coefficient estimated by the pooled sample is –0.017 (t = –0.457).
- 28 -

the pattern of market reaction is consistent with the persistence pattern of MAO firms’ financial
performance. Using a battery of abnormal return metrics and different return computation methods
and data analysis approaches, we find that the post-MAO 12-month returns are indistinguishable fromzero, statistically or economically. This evidence suggests that there is no delayed response to theinformation contained in MAOs. Finally, our findings hold for both the GAAP/DISC and GC-type
MAOs.
Although we choose China, a setting in which the mispricing of value-relevant audit information
is most likely to manifest itself, we find no compelling evidence suggesting long-term post-MAO
negative returns. Instead, our evidence suggests that the stock price adjustments to MAOs are speedy
and unbiased. It appears that even Chinese investors are sophisticated enough to understand theeconomic meaning of audit opinions, and incorporate this important information into pricing. In fact,
they respond not only to auditors’ doubt about firms’ financial viability as reflected in GCOs, but also
to auditors’ warnings about problematic accounting/disclosure practices via GAAP/DISC MAOs. The
evidence from GAAP/DISC MAOs is particularly intriguing in that Chinese investors are able to undo
the effects of earnings management or disclosure opacity on valuation. Given the more implicit value
implications of GAAP/DISC MAOs and higher likelihood of being mispriced, it is difficult to simply
ascribe the lack of evidence on mispricing of MAOs to the low power of tests.
Collectively, our evidence casts doubt on the behavior-bias-based mispricing interpretation of
GC audit modifications in the mature markets. Due to the differences in the data used, it is notpossible for us to reconcile our results with those of Taffler et al. (2004) and Kausar et al. (2009). As
Basu (2004) notes, one possible reason for the post-GCO negative returns is the high transaction costs
that impede arbitrage. The literature also documents great information uncertainty associated with
GCO recipients, likely reflecting excessively conservative audit reporting to avoid litigation in the
U.S. (DeFond and Zhang 2014). For example, Fogel-Yaari and Zhang (2013) show that GCOs in the
U.S. contain substantial Type I errors, i.e., a GCO is issued to a client that does not actually fail.
Future research may investigate whether frictions due to transaction costs or information uncertainty
contribute to the anomalous post-GCO returns in the U.S. or the U.K. For practitioners, the message
from this study is that investors do incorporate the information contained in auditor opinions into
stock prices in a rational and timely manner. It is therefore meaningful for regulators and auditing- 29 -

standard setters to continue their efforts in improving the content and structure of auditors’ reports(PCAOB 2013; IAASB 2011).

- 30 -

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Figure 1

Audit opinions and the realization of future financial performance
GAAP/DISC

GC

MAO

90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0

25

50

75

100

125

150

175

200

225

250

F-Scoret+1 quantile
Observations are sorted into 250 quantile groups by F-Scoret+1, from low to high. The figure presents thepercentages of GAAP/DISC, GC, and total MAOs in each quantile group.

- 35 -

Figure 2
Stock price behavior around the announcements of audit opinions

Clean

GAAP/DISC

GC

0.02
0.01
0.00
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
(0.01)(0.02)(0.03)(0.04)
The figure is based on the pooled sample of 20,135 firm-year observations between 1995 and 2011. Day 0 is theannouncement days of firms’ annual reports, and the returns are computed as buy-and-hold returns.

- 36 -

Table 1
Sample distribution
Year

Clean

1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Total

243
256
420
678
731
880
977
1,034
1,154
1,196
1,173
1,264
1,392
1,437
1,592
1,938
2,176
18,541MAOs
GAAP/DISC
39
49
87
139
178
169
139
136
67
95
95
66
48
47
39
37
40
1,470

GC
n/a
n/a
3
22
36
29
33
42
45
61
78
60
52
63
69
56
63
712

Total sample
282
305
508
822
917
1,051
1,124
1,188
1,243
1,326
1,320
1,371
1,477
1,526
1,682
2,023
2,269
20,434

MAOs include unqualified opinions with explanatory notes, qualified opinions, disclaimers, andadverse opinions.
The GC-type MAOs include cases where the auditors explicitly mention issues involving firms’
financial troubles or operating problems. Other MAOs are due to the violations of GAAP ordisclosure rules, and are classified as GAAP/DISC-type modification.

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Table 2
The F-Score and its component by type of audit opinion
Variables
F-Score
ROA
OCF
Loss
Negative OCF
Leverage
Liquidity
Gross Margin
Turnover
F-Score
ROA
OCF
Loss
Negative OCF
Leverage
Liquidity
Gross Margin
TurnoverClean(n = 18,541)

GAAP/DISC(n = 1,470)

Panel A: F-Score and its component in year t
0.136–0.925†0.062–0.018†0.050
0.013†
0.077
0.423†
0.227
0.408†
0.217
0.340†
2.097
1.374†
0.263
0.204†
0.687
0.440†
Panel B: F-Score and its component in year t+1
0.052–0.822†0.056–0.013†0.050
0.028†
0.102
0.397†
0.228
0.354†
0.222
0.349†
1.967
1.277†
0.255
0.210†
0.693
0.450†

GC(n = 712)–1.775‡–0.097‡0.001‡
0.798‡
0.473‡
0.458‡
0.714‡
0.157‡
0.354‡–1.449‡–0.063‡0.014‡
0.653‡
0.430‡
0.437‡
0.762‡
0.189‡
0.395‡

F-Score = The first factor score obtained from a by-year factor analysis of the following eight
fundamental variables.
ROA = Operating income, adjusted for net interest expenses, divided by the average of beginningand ending total assets.
OCF = Operating cash flow divided by the average of beginning and ending total assets.
Loss = 1 if operating income (adjusted for net interest expenses) is negative, and 0 otherwise.
Negative OCF = 1 if operating cash flow is negative, and 0 otherwise.
Leverage = Total borrowings divided by total assets at the end of the year.
Liquidity = Current assets divided by current liabilities at the end of the year.
Gross Margin = Gross margin divided by sales.
Turnover = Sales divided by the average of beginning and ending total assets.
For years before 1998, when cash flow statement data are not available, operating cash flow is defined as thedifference between operating income (adjusted for net interest expenses) and total accruals, and total accruals
are estimated by the balance sheet approach as:
Current asset
Cash
Short-term investments
Current portion of long-term investments)
Current liability
Short-term borrowings
Current portion of long-term debt
Dividends payable)
Depreciation and amortization expense, where denotes the change between the current and previous year.
For year 1998 and onwards, operating cash flow data are obtained from cash flow statements.
†and ‡ denote that the differences between the Clean and GAAP/DISC and GC groups, respectively, in themean values are significant at the 1% level or beyond in the two-tailed t-test.

- 38 -

Table 3
Audit opinions and the persistence of financial performance
Panel A: The F-Score in t and t+1 by decile groups formed by F-ScoretDecile groups

Clean

GAAP/DISC

GC

n

t

t+1

%Reversal

n

t

t+1

%Reversal

n

t

t+1

%Reversal

1 (Lowest)

1,405–1.531–0.74451.4%

589–2.131–1.57626.0%

531–2.312–1.893

18.1%

2

1,786

–0.811–0.40849.7%

217–0.859–0.67221.8%

84–0.912–1.103–20.8%

3

1,837–0.405–0.26833.7%

165–0.406–0.471–16.0%

31–0.403–0.471–17.0%

4

1,862–0.135–0.154140–0.152–0.24918–0.127–0.955

5

1,919

0.059

–0.01581

0.040–0.21614

0.007–1.086

6

1,935

0.236

0.133

71

0.221

–0.1168

0.243–0.350

7

1,938

0.417

0.288

65

0.415

0.174

6

0.398

–0.716

8

1,939

0.622

0.457

–26.6%

62

0.619

0.502

–18.8%

6

0.530–1.014–291.4%

9

1,959

0.908

0.747

–17.7%

45

0.925

0.592

–36.1%

7

0.913

0.433

–52.6%

10 (Highest)

1,961

1.448

1.229

–15.1%

35

1.439

1.041

–27.7%

7

1.735–0.918–152.9%Panel B: The F-Score in t and t+1 of MAO firms and their Clean pairs matched by F-ScoretGroups

GAAP/DISC

GC

n

t

t+1

%Reversal

n

t

t+1

%Reversal

Clean

1,470

–0.922–0.51644.1%

712–1.769–0.901

49.1%

MAO

1,470

–0.925–0.822

11.2%

712–1.775–1.44918.3%

In Panel A, for each year, firms are sorted into decile groups by F-Score in year t. In Panel B, for each MAO recipient, we select a non-MAO observation in the same yearwith the closest F-Scoret to the MAO recipient as its match. See Table 2 for the definition of F-Score.
The numbers in the column headed by “n” are the number of observations in each decile group. The numbers in the column labeled “t” and “t+1” are the mean values of
F-Score in year t and t+1, respectively. %Reversal is computed as: (F-Scoret+1 – F-Scoret) / | F-Scoret |.

- 39 -

Table 4
Market’s response to modified audit opinions
Model (2)Model (1)
Variables

Coeff.

t-stat.
***Intercept–0.008–4.730GAAP/DISCt–0.003–1.934*

Coeff.

Model (3)t-stat.

–0.008–4.604–0.002–1.101***

Coeff.

t-stat.

–0.008–4.703***

0.002

0.989

–3.854***–0.001–0.258–0.005–4.799***–0.004–4.777***0.002

3.951***

0.001

2.467**

F-Scoret×GAAPt

0.004

3.066***

F-Scoret×GCt

0.004

1.798*

GCt–0.012

LagRETt–0.004–5.220***–4.145***

F-Scoret

Year/Industry
indicators
N2

Adj. R–0.009Included

Included

Included

20,135

20,135

20,135

0.97%

1.07%

1.12%

The dependent variable is BHMAR[0, +1], the buy-and-hold market-adjusted returns from day 0 to +1,
where day 0 is the announcement day of annual reports.
GAAP/DISCt = 1 if the firm has received a GAAP/disclosure-related MAO from its auditor, and 0
otherwise.
GCt = 1 if the firm has received a GC-related MAO from its auditor, and 0 otherwise.
LagRETt = The buy-and-hold market-adjusted returns from one week after the announcement
day of annual reports in year t-1 to day -1.
F-Scoret = The fundamental score in year t (see Table 2 for detailed definition).
The t-statistics are based on standard errors clustered at the firm level. *, **, and *** denote two-tailed
significance at the 10%, 5%, and 1% level, respectively.

- 40 -

Table 5
Univariate analysis of stock returns in year t+1 to portfolios formed by types of audit opinions

Statistics(1) Market adjusted returns(2) Portfolio adjusted returns

Clean

Clean

GAAP/DISCGC

GAAP/DISC

GC(3) Size-B/M matched(4) B/M-Size matched(5) F-Score matched

control returns

control returns

control returns

GAAP/DISCGC

GAAP/DISCGC

GAAP/DISC

GC

0.112

–0.0270.078–0.0020.164

Panel A: Buy-and-hold returns
A1. Pooled sample
Mean–0.024

0.001

0.131
***

–0.016–0.050*–0.015–0.035****(t-stat.)(1.25)(4.01 )(–1.79 )(0.04)(–1.67 )(3.29 )(–1.08)(1.51)(–0.09)(3.74***)[p-value][0.210][0.000][0.074][0.969][0.095][0.001][0.279][0.133][0.932][0.000]A2. Annual sample
Mean–0.018–0.0020.058(t-stat.)(0.31)[p-value][0.759]–0.009–0.055–0.050–0.0240.048–0.038

0.030

0.017

0.086

(0.89)(–1.56)(–0.94)(–0.83)(0.61)(–0.60)(0.39)(0.66)(1.14)[0.387][0.135][0.361][0.417][0.551][0.554][0.705][0.516][0.274]

Panel B: Cumulative returns
B1. Pooled sample
Mean–0.006–0.002

0.082
***

0.000

–0.025**0.018–0.0380.044–0.0240.028–0.005

0.092

***

**

(t-stat.)(0.26)(3.46 )(–1.98 )(0.72)(–3.13 )(2.01 )(–1.57)(0.75)(–0.35)(3.38***)[p-value][0.797][0.001][0.048][0.469][0.002][0.045][0.116][0.454][0.724][0.001]B2. Annual sample
Mean–0.002

0.004

0.039

(t-stat.)(0.17)[p-value][0.867]0.006–0.022–0.016–0.0300.011–0.020–0.004

0.014

0.033

(0.69)(–1.29)(–0.52)(–1.47)(0.23)(–0.69)(–0.07)(0.62)(0.59)[0.503][0.213][0.611][0.161][0.819][0.499][0.948][0.544][0.568](The table continues on the next page.)- 41 -

Table 5 (Continued)
The abnormal stock returns are defined as follows.
(1) The market-adjusted returns are defined as stock returns minus the equal-weighted market return over the same period.
(2) The portfolio-adjusted returns are defined as stock returns minus the equal-weighted return of a comparable size and B/M portfolio return over the same period.
(3) The Size-B/M matched control returns are defined as stock returns minus the return of a control stock matched by size and B/M over the same period. We rst identify
all non-MAO recipients with a market value of equity between 70% and 130% of the market value of equity of the MAO recipient at the end of April in year t+1. From
this set of firms, we select the firm with the closest book-to-market ratio to the MAO recipient at the end of April in year t+1 as its match.
(4) The B/M-Size matched control returns are defined similarly to those of Size-B/M matched control returns, except that we match MAO and non-MAO recipients first by
B/M and then by market value of equity.
(5) The F-Score matched control returns in year t+1 are defined as stock returns minus the return of a control stock matched by F-Scoret over the same period. For each
MAO recipient, we select a non-MAO observation in the same year with the closest F-Scoret to the MAO recipient as its match.
For all return metrics, we compute both buy-and-hold and cumulative returns over the 12-month period beginning in May of year t+1.
In Panels A1 and B1, we pool all observations during the sample period. In the annual sample analysis of Panels A2 and B2, the mean value is the simple average of theannual mean abnormal returns, and statistics are based on the empirical distribution of the annual mean returns. For the market/portfolio-adjusted returns in Columns (1) and(2), the t-statistics are obtained from t-tests that compare the mean values between the Clean and MAO groups. For Size-B/M or F-score matched control returns, thet-statistics are obtained from t-tests that examine whether the mean values are different from zero.

- 42 -

Table 6
Descriptive statistics for variables used in multivariate regressions

Variables

Clean

GAAP/DISC

GC(n = 18,541)(n = 1,470)(n = 712)

MeanMedianMeanMedianMeanMedianR(B/Mt)

0.522

0.556

0.393†

0.333†

0.146‡

0.000‡

R(Sizet)

0.515

0.556

0.352†

0.333†

0.174‡

0.111‡

R(MARETt)

0.509

0.556

0.409†

0.333†

0.389‡

0.333‡

R(PARETt)

0.510

0.556

0.395†

0.333†

0.351‡

0.222‡

F-Scoret

0.136

0.192

–0.925†–0.779†–1.775‡–2.000‡

EQOt

0.176

0.000

0.071†

0.000†

0.001‡

0.000‡

Delistt

0.018

0.000

0.222†

0.000†

0.607‡

1.000‡RP Loant

0.018

0.001

0.095†

0.028†

0.112‡

0.015‡

Aget

7.543

6.839

7.583

6.832

10.896‡10.716‡

Definitions of variables:
R(B/Mt) = The decile ranking of book-to-market ratio of equity at the end of April of year t+1.
R(Sizet) = The decile ranking of market value of equity at the end of April of year t+1.
R(MARETt) = The decile ranking of 12-month market-adjusted stock returns beginning in May ofyear t.
R(PARETt) = The decile ranking of 12-month portfolio-adjusted stock returns beginning in May ofyear t.
F-Scoret = The fundamental score in year t.
EQOt = 1if the firm has equity offerings in year t, and 0 otherwise.
Delistt = 1 if the firm has reported losses in both year t and t-1 or its shareholders’ equity is
negative at the end of year t, and 0 otherwise.
RP Loant = The balance of loans to related parties, scaled by total assets, at the end of year t.
Aget = The number of years a company has been listed by end of year t.
†and ‡ denote that the differences between the Clean and GAAP/DISC and GC groups, respectively,in the mean/median values are significant at the 1% level or beyond in the two-tailedt-/Wilcoxon-test.

- 43 -

Table 7
Multivariate regression analysis of stock returns in year t+1 to portfolios formed by types of audit opinions
Panel A: Pooled regressions(1) Market adjusted(2) Portfolio adjusted(3) Size-B/M matched(4) B/M-Size matched(5) F-Score matchedreturns

returns

control returns

control returns

control returns

Variables

Coeff.

t-stat.

Intercept

0.008

0.281

GAAP/DISCt–0.026–1.298

GCt

0.101

2.604

***

***

Coeff.

t-stat.

–0.053*–1.846

0.157

2.672

–0.033–1.618–0.031–1.520

0.019

0.486

0.032

0.964

***

Coeff.

t-stat.
***

0.142

4.563

***

Coeff.

t-stat.

Coeff.

t-stat.

0.079

1.243

0.049

0.453

–0.035–1.552–0.024–0.746

0.020

0.517

0.056

1.357

0.167

4.837

***

0.173

3.446***

R(B/Mt)0.138

10.426

0.077

5.487

R(Sizet)–0.212–16.479***–0.027–1.979**–0.251–7.838***–0.266–7.522***–0.310–5.848***R(MARETt) or R(PARETt)

0.026

1.949*

0.010

0.734

–0.045–1.403

0.008

0.242

–0.041–1.026

0.011

0.946

0.004

0.334

0.034

1.124

0.026

0.872

0.102

2.479**

F-Scoret

0.023

4.302

EQOt

0.001

0.150

Delistt

0.157

4.512

RP Loant–0.013–0.122

Aget

0.001

***

***

1.677

*

0.019

3.448

–0.003–0.382

0.099

2.791

–0.071–0.665

0.002

2.007

***

***

**

0.021

2.001

0.026

0.918

**

0.144

4.099

***

***

0.146

3.619

–0.177–1.828**–0.117–1.015–0.119–0.567

0.001

0.304

0.002

0.670

0.009

2.395**

Year/Industry indicators

Yes

Yes

Yes

Yes

Yes

# of obs

20,434

20,434

3,716

3,154

3,786

2

2.13%

0.49%

70.41%

69.83%

65.35%

Adj. R(The table continues on the next page.)- 44 -

Table 7 (Continued)
Panel B: The annual regressions(1) Market adjusted(2) Portfolio adjusted(3) Size-B/M matched(4) B/M-Size matched(5) F-Score matchedreturns

returns

control returns

control returns

control returns

Variables

Coeff.

t-stat.

Coeff.

t-stat.

Coeff.

t-stat.

Coeff.

t-stat.
**Coeff.

t-stat.

0.275

1.503

Intercept

0.009

0.140

–0.053–1.057

0.283

1.600

0.294

1.803

GAAP/DISCt–0.016–0.810–0.025–1.406–0.001–0.028–0.039–1.241–0.012–0.975

GCt

0.016

0.296

–0.014–0.335–0.013–0.209–0.025–0.480–0.034–0.845

0.175

1.585

0.151

1.469

0.177

1.460

R(B/Mt)

0.151

1.632

0.104
***

1.871

**

R(Sizet)–0.220–3.612–0.056–0.947–0.216–2.994–0.247–3.598–0.270–2.972***

R(MARETt) or R(PARETt)

0.005

0.108

0.023

0.552

–0.074–1.698–0.042–0.718–0.053–0.920F-Scoret

0.025

1.085

0.025

1.293

0.015

1.018

0.017

0.926

–0.013–0.756

EQOt

0.002

0.052

–0.003–0.083–0.011–0.118

0.011

0.110

0.009

0.248

0.078

1.260

0.073

2.740**

Delistt

0.095

2.823

RP Loant–0.051

Aget

0.003

**

0.072

2.656

–0.606–0.060

0.894

0.002

**

***

**

***

0.105

2.355

–0.787–0.049–0.317

0.102

0.455

0.035

0.251

0.725

0.005

1.108

0.006

1.030

0.013

1.585

Industry indicators

Yes

Yes

Yes

Yes

Yes

Mean # of obs

1,202

1,202

218.59185.53222.71

2

14.11%

7.07%

12.03%

13.80%

15.53%

Mean Adj. R

The dependent variables are 12-month buy-and-hold stock returns in year t+1. See Table 5 for the definitions of the stock return variables. The independent variables aredefined in Table 6.
In Panel A, the regressions are estimated on the pooled sample. The t-statistics are based on standard errors clustered at the firm level. In Panel B, the regressions are
estimated by year and the reported coefficient estimates are the mean values of the 17 annul regression coefficients, except that GCt, Delistt, and RP Loant coefficients are
based on 15, 16, and 15 annul regressions, respectively. The t-statistics are calculated as the mean values of the annual coefficients divided by their standard errors.
For regressions in Columns (1), (3), (4), and (5), variable R(MARETt) is included as one predictor variable, and for regressions in Column (2), variable R(PARETt) is used.
, , and *** denote two-tailed significance at the 10%, 5%, and 1% level, respectively.

* **- 45 -

Table 8
Estimates from the factor models
Variables

Intercept
RM – RFSMB
HML# of obs
Adj. R2Intercept
RM – RF
SMB
HML
UMD# of obs
Adj. R2

Clean portfolio

GAAP/DISC portfolio

Coeff.

Coeff.

0.001
1.028
0.568
–0.105t-stat.
0.934
75.971***
20.958***–3.369***204
97.05%
0.001
1.028
0.566
–0.105–0.0050.943
75.374***19.116***–3.341***–0.14520497.03%

t-stat.

GC portfolio
Coeff.

t-stat.

Hedge portfolio:
Clean – GAAP/DISC
Coeff.
t-stat.

Panel A: The Fama-French (1993) three-factor model–0.001–0.340
0.001
0.142
0.002
0.865
***
***
1.048
37.3841.016
17.701–0.020–0.792
***
***
0.947
16.8681.283
11.318–0.379–7.373***–0.129–2.000**–0.286–1.900*
0.024
0.408
204
180
204
90.08%
73.65%
20.97%
Panel B: Carhart (1997) four-factor model–0.000–0.049
0.003
0.592
***
1.060
39.1061.027
18.947***0.850
14.433***1.041
8.781***–0.170–2.710***–0.462–3.148***–0.267–4.162***–0.634–4.746***20418090.83%
76.52%

0.001–0.032–0.284
0.065
0.263

0.577
–1.299–5.290***1.131
4.491***

204
27.89%

Hedge portfolio:
Clean – GC
Coeff.
t-stat.
0.001
0.004–0.692
0.071

0.132
0.077
–6.057***0.469
18016.01%–0.001–0.006–0.4780.227
0.560–0.244–0.103–3.941***1.512
4.106***
180
22.95%In Panels A and B, we estimate the Fama-French (1993) three-factor model and Carhart (1997) four-factor model, respectively, on calendar-time portfolios formed eachmonth from May 1996 through April 2013 according to firms’ audit opinions received in the previous fiscal year. The hedge portfolios are formed by buying stocks withclean audit opinions and shorting stocks with MAOs.
* **, , and *** denote two-tailed statistical significance at the 10%, 5%, and 1% level, respectively.

- 46 -

Table 9
Summary of fixed-effect model results
Period1995-1999
1996-2000

GAAP/DISCt

GCt

Coeff.

t-Stat.

Coeff.

t-Stat.

–0.064–1.228–0.068

0.004

0.028

**

0.076

1.068

**

–2.385

1997-2001–0.046–2.032–0.021–0.395

1998-2002–0.012–0.601–0.057–1.327

1999-2003

0.001

0.081

–0.044–1.6392000-2004

0.001

0.074

0.008

0.256

2001-2005

0.019

0.362

0.346

4.096***

2002-2006–0.001–0.0140.442

5.149***

2003-2007

0.024

0.374

0.390

4.571***

2004-2008

0.079

1.286

0.289

3.550***

2005-2009

0.175

2.844***

0.280

3.660***

2006-2010–0.024–0.7240.090

2.243**

2007-2011

0.006

0.193

0.039

1.105

The table summarizes the coefficient estimates of GAAP/DISCt and GCt variables fromfixed-effect models estimated on the five-year rolling windows between 1995 and 2011. The
dependent variable is raw buy-and-hold returns over the 12-month period beginning in May
of year t+1, and a firm should have at least four valid observations to be included in thesamples for fitting the fixed-effect model.

- 47 -