1. Review of Accounting and Finance
Emerald Article: Apparent audit failures and value relevance of earnings
and book value
Li Dang, Kevin F. Brown, B.D. McCullough
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RAF
10,2 Apparent audit failures
and value relevance of
earnings and book value
134
Li Dang
Orfalea College of Business, California Polytechnic State University,
San Luis Obispo, California, USA
Kevin F. Brown
Department of Accountancy, Raj Soin College of Business,
Wright State University, Dayton, Ohio, USA, and
B.D. McCullough
Department of Decision Sciences, LeBow College of Business,
Drexel University, Philadelphia, Pennsylvania, USA
Abstract
Purpose – The purpose of this paper is to examine the value relevance of accounting information in
cases of apparent audit failures.
Design/methodology/approach – The authors adopt the bootstrapping technique and compare the
value relevance of key accounting information across samples of firms experiencing apparent audit
failures with matched non-audit failure firms.
Findings – Accounting information is found to be less value relevant for firms experiencing apparent
audit failures, regardless of auditor reputation.
Research limitations/implications – This study has limitations due to the ex ante research
approach adopted. Future research could address this issue by possibly incorporating an “intervening”
factor into the model to indicate how the market can differentiate audit failure firms from other firms.
Originality/value – The paper gives support to the assertion that the market appears to rely less on
accounting numbers when audit failures occur, even though formal allegations of audit failure may not
appear until years after their occurrence. In addition to contributing to value-relevance research by
providing empirical evidence for the market phenomenon around the time of material misstatements,
the paper demonstrates an innovative application of bootstrapping to test for differences in R 2.
Keywords Auditing, Accounting information, Earnings
Paper type Research paper
I. Introduction
DeAngelo (1981, p. 186) defines audit quality as “the market-assessed joint probability
that a given auditor will both (a) discover a breach in the client’s accounting system,
and (b) report the breach.” However, assessment of quality for particular audit
engagements remains somewhat murky, due to the nature of the audit process,
the reporting of audit outcomes, and users’ response to the auditor’s reputation (i.e. a Big
Review of Accounting and Finance vs non-Big dichotomous audit quality measurement). Previous studies have examined
Vol. 10 No. 2, 2011
pp. 134-154 the association between audit quality and earnings response coefficients (Teoh and
q Emerald Group Publishing Limited
1475-7702
Wong, 1993). The empirical results appear to support that the market responds
DOI 10.1108/14757701111129616 positively to reputable auditors.

3. However, whether auditor reputation could serve as a reliable proxy for audit Apparent
quality may be unknown due to the high-profile audit failures which occurred during the audit failures
downturn in the financial markets a decade ago. Many notorious firms, for instance,
Enron, WorldCom, and Tyco, were audited by Big five auditors with reputations similar
or even superior to those of the non-Big five. Thus, one might conclude that an auditor’s
reputation may actually hinder the market’s ability to assess the reliability of accounting
information. 135
The purpose of this paper is to explore whether auditor reputation affects the value
relevance of accounting information in cases of apparent audit failures. For the purpose
of this study, apparent audit failures are defined as instances that an auditor issues
an unqualified opinion on materially misstated financial statements. Since apparent
audit failures indicate poor audit quality, they offer an opportunity to explore the
association between stock prices and accounting information given low audit quality.
Therefore, this study compares ex ante value relevance of accounting information of
publicly held US firms experiencing apparent audit failures with a matched group
of firms not experiencing such failures. Ex ante value relevance refers to value relevance
of accounting information prior to the discovery of audit failures. Matching audit
failure firms with non-audit failure firms allows audit quality to be evaluated on a
“service-by-service” basis, consistent with the suggestion of Lam and Chang (1994).
This study differs from previous studies on market reactions to news of audit
failures, accounting scandals, or earnings restatements (Chaney and Philipich, 2002;
Dechow et al., 1996; Feroz et al., 1991). Instead of examining stock market reactions to the
news of audit failures, we focus on examining value relevance of accounting information
in the alleged periods of misstatement. Those alleged periods are the reporting periods
when audited financial statements contain material misstatements. We compare
firms experiencing apparent audit failures with matched firms without audit failures in
the same periods in order to find whether there is a difference in value relevance of
accounting information prior to audit failures becoming public. Therefore, this is an
exploratory study that aims to examine market reactions around the time when financial
statements contain misstatements. In addition, we divided our sample into sub-samples
classified by auditor types (i.e. Big 8/6/5/4 auditors vs non-Big 8/6/5/4 auditors) and
conduct the same comparisons to investigate whether such a difference is conditioned on
auditor reputation.
Several sources are used to identify apparent audit failures (in these cases, an auditor
issues an unqualified opinion on materially misstated financial statements). These
sources include the US Securities and Exchange Commission’s (SEC’s) Accounting and
Auditing Enforcement Releases (AAERs), financial statements restatements because of
past misstatements, and litigation against auditors due to audit failures.
Consistent with prior research (Collins et al., 1997, 1999; Rees, 1999; Rajgopal et al.,
2002), value relevance is measured by the explanatory power of contemporaneous
earnings and book values for stock prices (i.e. the regression of stock price on earnings
and book values). We apply a bootstrapping analysis to test data collected. The results of
this analysis indicate that the accounting information of firms experiencing apparent
audit failures is generally less value relevant than that of a matched group of firms not
experiencing such failures. Results of sub-sample comparisons further indicate that the
audit reputation may not be essential because the market appears to discount less
reliable accounting information. The remainder of this paper is organized as follows.

4. RAF Section II describes prior research pertaining to market perceptions of audit quality.
10,2 Section III contains the development of the research hypothesis. Section IV discusses
data collection and the research methodology. Section V presents statistical analysis and
empirical results. Section VI provides concluding remarks.
136 II. Prior research
Previous studies of audit failures have focused on market reactions to the
announcements of earnings restatements, fraud class actions, and SEC enforcement
actions (Liu et al., 2009; Akhigbe et al., 2005; Chaney and Philipich, 2002; Dechow et al.,
1996; Bhagat et al., 1994; Francis et al., 1994; Kellogg, 1984). Overall, these studies
have documented a negative market reaction to such announcements. For example,
Francis et al. (1994) find a negative market response to the corrective disclosure of firms
experiencing class action litigation. Feroz et al. (1991) document a 10 percent decline in
stock prices at announcements of accounting violations. Akhigbe et al. (2005) find that
earnings restatements lead to a negative market response and that the negative response
is conditioned on the content of earnings management. Liu et al. (2009) find that
shareholders are more likely to vote against the ratification of the auditor following an
audit failure. In spite of these findings, none of these studies examine market reactions
before accounting violations or audit failures became public knowledge.
Chaney and Philipich (2002) examine the reputation effect of the Enron audit failure.
Specifically, they investigate the impact of auditor reputation on the market prices of
Arthur Andersen’s clients around the Enron bankruptcy. After Andersen’s reputation
was damaged, its clients experienced a significant drop in their stock prices. The result
of Chaney and Philipich (2002) appears to indicate that market perception of reliability of
accounting information is conditioned on market perception of auditor reputation.
Recently, Griffin et al. (2004) investigate the impact of class action litigation of audit
failures on stock returns and earnings-returns relation in the alleged periods. They find
that the market responds positively to releases of misleading accounting information. As
Griffin et al. point out, the primary reason for this result is that the market initially accepts
the misstated information. Furthermore, the ex post negative reaction to class action lawsuit
announcement is conditioned on the returns-earnings relation in the alleged periods.
In summary, except for Griffin et al (2004), most previous studies have focused on
market reactions around news releases of audit failures instead of examining the market
reaction to misstated earnings in the alleged periods. To yield additional insights
for the literature, the purpose of this study is to explore value relevance of accounting
information when financial statements contain material misstatements. Specifically, we
examine the contemporaneous association between stock prices and misstated
accounting information in the periods with alleged misstatement. This study differs
from other audit failure studies in the following aspects:
.
Instead of focusing on ex post market reactions to audit failures, this study
examines the market perception of the information quality ex ante.
.
Instead of using an event study approach, this study examines the association
between stock prices and accounting information in a longer window.
.
This study also provides evidence on whether market perception of information
quality in the alleged periods is conditioned on auditor reputation.

5. III. Development of hypothesis Apparent
Because apparent audit failures are less ambiguous, they provide a unique way of audit failures
testing the market’s perception of accounting information quality ex ante driven by poor
audit quality. Using firms not experiencing apparent audit failures as benchmarks, this
study examines whether the value relevance of accounting information is different for
firms experiencing apparent audit failures from those of benchmarked firms. In this
study, value relevance indicates the extent to which accounting information explains the 137
variation of market prices. When financial statement users perceive higher information
quality, therefore more reliable financial statements, accounting information such as
earnings and book values should explain more of the variation in stock prices.
The Ohlson (1995) model is used to test the value relevance of accounting
information (Amir, 1996; Amir and Lev, 1996; Collins et al., 1997, 1999; Rees,
1999; Rajgopal et al., 2002). The Ohlson model relates a firm’s market value to its
contemporaneous accounting information. Specifically, this model provides a structure to
study the relationship between equity values and earnings, as well as its relationship with
book values (Stober, 1999). The degree of value relevance is measured by the R 2 of the
Ohlson model (Collins et al., 1997; Rees, 1999; Rajgopal et al., 2002). The R 2 measures
the degree of the variation in the dependent variable explained by independent variables.
If the perceived information quality (i.e. audit quality) is low, the association between stock
price and accounting information, hence the R 2 of the Ohlson model, should be weak
because accounting information will be viewed as less reliable.
Owing to the exploratory nature of this study, we state our hypothesis in its null
form:
H0. There is no difference between the explanatory power of earnings and book
values for stock prices of firms experiencing apparent audit failures and firms
not experiencing apparent audit failures.
IV. Data collection and methodology
Cases of apparent audit failures and the matched control group
Firms included in this study are selected from the SEC’s AAERs and restatements
of financial statements found in the Wall Street Journal Index and Lexis-Nexis
News Library for fiscal years ending between 1980 and 2000. The initial sample is
the combination of these two data sources. AAERs indicate firms whose financial
statements containing misstatements documented in the SEC sanctions against firms or
their auditors. We exclude AAER cases where quarterly misstatements are the focus of
the sanction because quarterly statements are only reviewed, but not audited,
by auditors. Restatements consist of firms that restated prior years’ financial statements
because of significant misstatements. These represent apparent audit failures because
auditors did not detect and/or report those material misstatements initially. We searched
keywords with the root “restat-” in Wall Street Journal Index and Lexis-Nexis News
Library. Quarterly restatements are also excluded from our sample. To ensure that the
search of the above two sources did not omit any audit failures; we also searched, as a
secondary source, news accounts of auditor litigation including the auditor litigation
database compiled by Palmrose (1999). Cases of litigation against auditors contain
allegations that auditors failed to detect and report material misstatements. Such cases
are included in this analysis due to possible lags in SEC scrutiny which would ordinarily
result in enforcement activity and/or restatements of prior financial statements.

6. RAF Accordingly, litigation cases that provide substantial evidence of audit failure are
10,2 included as apparent audit failures. While lawsuits seeking damages from an auditor are
not always indicative of the auditor’s failure to adhere to professional standards, such
suits may reveal audit failures before subsequent issuance of an AAER. A careful review
of the litigation news accounts helped to ensure that only apparent audit failures are
captured in the sample.
138 In order to perform hypothesis testing, all accounting data for the years of the alleged
audit failures that are necessary for testing the proposed relation are extracted from the
COMPUSTAT database. All stock price information is obtained from the Center for
Research in Security Prices (CRSP). The matched control group is selected based on
established matching criteria. These criteria include the year of financial statements,
auditor size (Big 8/6/5/4 vs non-Big 8/6/5/4), client industry, and client size. The required
accounting data and price information are compiled for the matched control group from
COMPUSTAT and CRSP, respectively.
Model specification
The model used to test the hypothesis is Ohlson’s (1995) valuation model (Amir, 1996;
Amir and Lev, 1996; Collins et al., 1997, 1999). The Ohlson model is expressed as follows:
P it ¼ a1 EPS it þ a2 BVPS it þ 1it ð1Þ
where:
Pit ¼ closing stock price of firm i’s equity three months after fiscal year
end t[1];
EPSit ¼ firm i’s reported earnings per share before extraordinary items for
period t;
BVPSit ¼ firm i’s book value of equity per share at time t;
1it ¼ random error term with mean 0 and variance 1; and
a1 and a2 ¼ the regression coefficients.
Testing of hypothesis
To test the hypothesis, comparisons are made between the apparent audit failure group of
firms and the matched control group of firms not experiencing audit failures. For each
comparison, two OLS regressions of the Ohlson model are performed: one regression for
the audit failure group, and the other for the matched control group. If the R 2 for the audit
failure group is statistically significantly lower than the R 2 for the matched control group,
it suggests that value relevance of accounting information is lower in the alleged period
and that the market appears to rely less on accounting information in the alleged
period when there are material misstatements. If the R 2 for the audit failure group is equal
to or higher than the R 2 for the matched control group, it suggests that the market may
be “fooled” in the alleged period. Moreover, we examine whether value relevance of
accounting information in the alleged period is conditioned on auditor reputation.
For example, there might be a greater difference in value relevance of accounting
information for firms audited by Big 8/6/5/4 auditors than those audited by non-Big
8/6/5/4 auditors. Comparisons we use to test the hypothesis are summarized in Table I.
Comparison 1 tests whether there is a difference in value relevance of accounting
information between the audit failure group and the matched group, in general.

7. Comparisons 2 and 3 test whether value relevance differs within Big 8/6/5/4 and Apparent
non-Big 8/6/5/4 groups. Comparisons 4 and 5 directly test whether the difference in value audit failures
relevance of accounting information between the audit failure group and the matched
group is conditioned on auditor size.
V. Analysis and results
Sample selection and characteristics 139
We searched both the SEC’s online archives and the Lexis-Nexis News Library for
AAERs. Between 1982 and 2000, AAERs No. 1 through No. 1357 were released. There
are 559 unique firms identified from those AAERs[2]. Of these 559 firms, 383 unique
firms are selected and the remaining 176 firms are deleted because the AAERs:
(1) do not indicate the years misstated;
(2) indicate the misstatement years are before 1980;
(3) pertain to quarterly financial statements; or
(4) indicate that the auditors are not responsible for the misstated financial
statements.
Our search for financial restatements in the Wall Street Journal Index and the
Lexis-Nexis News Library yields 462 unique firms with restatements. Of those 462 firms,
273 unique firms meet the requirements for our analysis. The other 189 firms are deleted
for reasons discussed in (1) through (4) above. We then combine the 383 firms sanctioned
in the AAERs with the 273 firms disclosing subsequent restatements and find that
42 firms exist in both data sources. Therefore, we identify 614 firms that experienced
apparent audit failures from these two data sources.
Since our analysis requires accounting data from COMPUSTAT, we first searched
for the ticker identification numbers for those 614 firms by their firm names
in COMPUSTAT. A total of 154 firms were deleted because they do not have
ticker identification numbers[3], leaving 460 firms remaining for further analysis.
An additional 297 firms that meet our data requirements are found in the auditor
litigation database compiled by Palmrose (1999)[4]. Since 74 of these firms are already
included in our 460-firm sample, our overall sample increased to 683 unique firms.
All these firms have materially misstated annual financial statements within the period
from 1980 to 2000, and their COMPUSTAT ticker identification numbers are available.
As this study defines apparent audit failures as cases in which auditors provide
unqualified opinions on financial statements that contain material misstatements,
a search of the COMPUSTAT database[5] for those firms receiving unqualified opinions
was performed. Of the 683 firms, 442 firms (848 firm/years) have audit opinion
Comparison Failure group (AF) auditors Non-failure group (NAF) auditors
1 Both Big 8/6/5/4 and non- Both Big 8/6/5/4 and non-Big 8/6/5/4 auditors
Big 8/6/5/4 auditors
2 Big 8/6/5/4 auditors Big 8/6/5/4 auditors
3 Non-Big 8/6/5/4 auditors Non-Big 8/6/5/4 auditors Table I.
4 Big 8/6/5/4 auditors Non-Big 8/6/5/4 auditors Comparisons used to test
5 Non-Big 8/6/5/4 auditors Big 8/6/5/4 auditors the hypothesis

8. RAF information available for the specified financial statement years. An additional 26 firms
10,2 (69 firm/years) were deleted because auditors issued qualified opinions to those firms.
Therefore, 416 firms (779 firm/years) are eligible for inclusion as apparent audit failures.
The sample for this study was reduced further because of the absence of other data
required to test the hypothesis. To test the hypothesis, earnings per share, book value of
stockholders’ equity, and stock price data must be available from the COMPUSTAT and
140 CRSP databases. Given these considerations, the resulting sample of apparent audit
failures is 346 firms (616 firm/years). Table II reports the sample size and the industry
distribution information of sample firms. Given the matched-pairs design used in this
study, each audit failure firm in the sample is matched with a control firm based on year of
misstated financial statements, industry (SIC code), company size (total assets), and auditor
type (Big 8/6/5/4 vs non-Big 8/6/5/4)[6]. To get the matched pairs, we searched for similar
size firms (with total assets within 10 percent) in the same industry (two- to four-digit SIC
code) in the alleged years[7]. If there is more than one similar size firm available, we select
the one with the closest SIC code. If there is no firm of similar size available in the same
industry, we select the firm with the closest total assets. In case there is no matched pair
available according to our matching criteria, we drop the audit failure observation.
Hypothesis testing
Prior accounting research has used a model’s R 2 statistic to measure the value relevance of
accounting information (Lang et al., 2003; Sami and Zhou, 2004; Francis and Schipper, 1999;
Nwaeze, 1998; Collins et al., 1997; Amir and Lev, 1996; Harris et al., 1994). These studies
have measured value relevance as the R 2 resulting from the regressions of stock prices on
per share values of earnings and book values of equity. These studies compare value
relevance measured by R 2 either over different time periods or across different samples.
In this study, R-squares are compared across audit failure and non-audit failure groups to
investigate whether there is a difference in value relevance between the group of firms
experiencing apparent audit failures and a matched group of firms that do not experience
Panel A: sample determination for apparent audit failure cases
Number of firms Number of firm/years
Audit failure cases 416 779
Less: observations without price data 70 163
Sample for testing 346 616
Panel B: industry distribution for apparent audit failure cases
Industry SIC code Number of observations Percentage
Agriculture, forestry, fishing 01-09 1 0.29
Mining 10-14 15 4.34
Construction 15-17 7 2.02
Manufacturing 20-39 127 36.71
Transportation and public utilities 40-49 31 8.96
Wholesale trade 50-51 12 3.47
Retail trade 52-59 38 10.98
Finance, insurance, real estate 60-67 28 8.09
Table II. Services 70-89 82 23.70
Sample determination Public administration 90-99 5 1.45
and industry distribution Total 346 100.00

9. apparent audit failures. Except in Harris et al. (1994), Lang et al. (2003) and Sami and Zhou Apparent
(2004), value-relevance studies have not included a formal test for the difference of R 2.
Harris et al. (1994), Lang et al. (2003) and Sami and Zhou (2004) use the procedure
audit failures
demonstrated in Cramer (1987) to obtain the mean and the variance of R 2. Then, they
conduct a z-test to compare the means of two R 2. A major problem in using the Cramer
method in this context of a z-test is that it depends on the assumption of the normal
distribution of R 2. However, the distribution of R 2 is not normal, even asymptotically 141
(Anderson, 2003, p. 155). As an example, Figure 1 shows an R 2 distribution given an R 2
of 0.9 for a regression model with ten independent variables (sample size ¼ 100). Visual
inspection of this figure reveals that the distribution of R 2 is not normal. An additional
complication is that calculation of the moments of R 2, which is required to apply
Cramer’s method, is prone to computational difficulties that may produce incorrect
results, even when the requisite formulae seem to be programmed correctly.
Because of the difficulty and possible unreliability of using the Cramer procedure, this
study uses the bootstrap method to create tests for the difference in R 2. Bootstrapping is
a resampling method that requires fewer assumptions than traditional methods.
In general, it also provides more accurate results. For example, the bootstrap method
does not require normality of the distribution of the R 2 and it can provide a faster
convergence to the expected value of the parameter of interest.
Since the models cannot be nested to formally test H0: R 2 ¼ R 2 against Ha: R 2 – R 2 ,
a b a b
we employ the usual approach of comparing the confidence interval for R 2 and the a
confidence interval for R 2 . Barr (1969) illustrates that the length of the confidence
b
intervals for the two-interval test must be constructed with the multiplier:
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
0 n1 þ n2
z ¼ pffiffiffiffiffi pffiffiffiffiffi z0:975
n1 þ n2
if the significance level of 0.05 is desired. When the sample sizes of the two samples are
the same, i.e. both have n observations, the multiplier becomes:
pffiffiffiffiffi pffiffiffi
0 2n 2
z ¼ pffiffiffi z0:975 ¼ z0:975 :
2 n 2
As a result, the percentage of the confidence intervals is 83.4 percent. Therefore,
comparing two 95 percent intervals is incorrect if a ¼ 0.05 is required[8].
20
15
10
5
Figure 1.
An example of an
R 2 distribution
0.86 0.88 0.92 0.94

10. RAF Descriptive statistics
10,2 The sample selection procedure yields 616 firm/year observations of apparent audit failures,
which are matched with a control group in order to test the hypothesis. In the audit failure
group, 502 (114) firm/years were audited by Big 8/6/5/4 auditors (non-Big 8/6/5/4 auditors).
Table III illustrates the observations included in each comparison[9].
Table IV presents descriptive statistics, including the mean, median, and standard
142 deviation for total assets (TA), stock prices (P), earnings per share (EPS), and book value
of equity per share (BVPS) for both groups in each comparison. Table IV also includes a
comparison of the means of these variables. Since the distributions of these variables
might not be normal, both a two-sample t-test and a nonparametric Wilcoxon test are
conducted. In general, the audit failure group and the matched control group are not
significantly different in total assets, which reflects a successful control for company
size. Because of the matching requirements, the audit failure and non-audit failure
groups exhibit different characteristics in firm size, as shown in comparison 4. As stated
earlier, in cases where there are no companies available in the same industries with
the similar firm sizes as audit failure observations, we select matched pairs with the
closest total assets. Consistent with the tendency of larger firms to have Big 8/6/5/4
auditors, comparison 4 shows that the average company size of the audit failure
group is significantly larger than that of the matched control group. In comparison 5,
the Wilcoxon test results show a significant difference in company size, while the
two-sample t-test does not indicate a significant difference. The control firms also appear
to earn more than the audit failure firms. With the exception of comparison 3, the
matched control groups have a higher EPS than the audit failure group.
Audit failure group Matched control group
Comparison 1
Number of observations 616 616
Big 8/6/5/4 502 502
Non-Big 8/6/5/4 114 114
Comparison 2
Number of observations 502 502
Big 8/6/5/4 502 502
Non-Big 8/6/5/4 0 0
Comparison 3
Number of observations 114 114
Big 8/6/5/4 0 0
Non-Big 8/6/5/4 114 114
Comparison 4
Number of observations 463 463
Big 8/6/5/4 463 0
Non-Big 8/6/5/4 0 463
Table III. Comparison 5
Number of firm/year Number of observations 114 114
observations in each Big 8/6/5/4 0 114
comparison Non-Big 8/6/5/4 114 0

11. Apparent
Auditor failure group Matched control group Compare meansa
Mean Median SD Mean Median SD t-test Wilcoxon test audit failures
Comparison 1
TA 2,421.1010 135.5100 8,309.7470 1,860.2880 114.5100 6,508.7950 0.1875 0.332
P 17.4288 12.2500 17.7024 18.3725 12.0630 22.4992 0.4135 0.9602
EPS 20.0065 0.3450 7.0179 0.7458 0.5000 5.7049 0.0392 0.0022 143
BVPS 7.6564 12.2500 9.7234 8.6550 5.7230 16.2675 0.1913 0.2421
Comparison 2
TA 2,958.4940 224.6000 9,121.2050 2,243.6640 182.2900 7,122.9120 0.1667 0.2256
P 19.4984 14.7500 17.9248 21.2554 15.2500 23.8012 0.1867 0.5052
EPS 0.01631 0.4900 7.7616 0.9306 0.7850 6.2850 0.0405 0.0007
BVPS 8.7918 6.2610 10.2356 9.9054 6.9360 17.6025 0.2208 0.3822
Comparison 3
TA 54.6836 16.7150 138.4634 172.0858 15.0150 1,447.4700 0.3904 0.9080
P 8.3157 4.0000 13.3492 5.6772 3.4380 6.6272 0.0605 0.2113
EPS 20.1072 2 0.0100 0.9703 20.0679 0.0200 1.0853 0.7734 0.8574
BVPS 3.1489 1.1916 4.3721 2.6570 1.7497 5.3825 0.4496 0.0890
Comparison 4
TA 3,008.3940 211.5500 9,391.1080 1,393.2470 64.5000 9,913.3340 0.0111 0.0000
P 19.6246 14.5000 18.3666 15.1947 8.8750 22.0622 0.0009 0.0000
EPS 20.0472 0.4700 8.0709 0.5576 0.3700 1.6394 0.1147 0.9536
BVPS 8.5995 6.1680 10.4589 7.8357 4.9530 13.6130 0.3386 0.0002
Comparison 5
TA 54.6836 16.7150 138.4634 46.9001 23.3100 70.6154 0.5932 0.0426
P 8.3157 4.0000 13.3492 10.3906 6.7500 14.9525 0.2691 0.0038
EPS 20.1072 2 0.0100 0.9703 0.1656 0.0900 1.3441 0.0796 0.0309
BVPS 3.1489 1.1916 4.3721 4.7300 3.3794 5.4881 0.0018 0.0000
Notes: All p-values less than or equal to 0.05 are shown in italics; aboth p-values of the two-sample t-test Table IV.
and the nonparametric two-sample Wilcoxon test are given; TA, total asset at the end of the fiscal year t; Descriptive statistics for
P, stock price, three month after the end of the fiscal year t; EPS, earnings per share excluding observations included in
extraordinary items for fiscal year t; BVPS, book value of equity per share at the end of the fiscal year t hypothesis testing
Hypothesis testing using bootstrap confidence intervals
For the purpose of the current study, 1,000 bootstrap resamples were created by
sampling with replacement from each of the original samples. Each bootstrap resample
is the same size as the original sample[10]. For each bootstrap resample, there is one R 2
generated from the bootstrap regression, which is called R 2 *. Therefore, the 1,000
bootstrap resamples generated 1,000 R 2 *s[11].
To test whether R 2 of the audit failure group and the matched control group differ, we
compare the 83.4 percent bootstrap percentile confidence intervals of R 2. Figures 2-6
show the histograms about the distribution of R 2 based on bootstrap resamples. The
histograms of R 2 *s sometimes indicate approximate normality, but other times show an
approximately normal central region with non-normality in the tail region. More
importantly, there are three cases where even the central region is obviously non-normal.
Of these three, two cases are the distributions of R 2 for the audit failure groups audited
by non-Big 8/6/5/4 auditors in comparisons 3 and 5, which exhibit an asymmetric
pattern because most R 2 from the bootstrap resamples are very close to zero. The third
case appears in comparison 5, where R 2 for the matched control group audited by Big

12. RAF 15
10,2
10
144
Density
5
0
0.10 0.15 0.20 0.25
R2 star
15
10
Density
5
0
0.20 0.25 0.30
0.35 0.40 0.45 0.50
Figure 2. R2 star
Notes: R2 *: audit failure group – both Big 8/6/5/4 and non-
Histograms for
Big 8/6/5/4; R2 *: matched control group – both Big 8/6/5/4
comparison 1
and non-Big 8/6/5/4
8/6/5/4 auditors appear to have a bimodal distribution, a probability distribution
characterized by two humps rather than the more common single hump that
characterizes the normal distribution. One hump is near 0.15 and the other is near 0.46.
Since we run bootstrap regressions by resampling the residuals from the original
regression, the bimodal distribution of R 2 appears to be driven by an outlier in the
residuals. Examining the regression output, we find one residual with an extreme value.
The bimodality exists when a highly influential point is included in some bootstrap
resamples but not in others. When we delete this outlier, the bimodal distribution of R 2
disappears. The histogram of R 2 after removing the outlier is shown in Figure 6.

13. 15 Apparent
audit failures
10
Density
145
5
0
0.10 0.15 0.20 0.25 0.30
R2 star
15
10
Density
5
0
0.25 0.30 0.35 0.40 0.45 0.50 0.55
R2 star Figure 3.
Notes: R2 *: audit failure group – Big 8/6/5/4; R2 *: matched
Histograms comparison 2
control failure group – Big 8/6/5/4
As stated earlier, one advantage of bootstrap methods is that they do not require
distributions to be normal. Hence, the above descriptions of a non-normal pattern are not
troublesome. To create 83.4 percent bootstrap percentile confidence intervals, the 1,000 R 2 *s
from bootstrap regressions were sorted in an ascending order. The lower value of the
confidence interval is the 83rd (0.083*1,000) R 2 * and the upper value is the 917th
(0.917*1,000) R 2 *. The results of the bootstrap percentile confidence intervals are shown in
Table V. Comparisons of bootstrap percentile confidence intervals indicate that there are
significant differences in R 2 for the audit failure groups and the matched control groups[12].
Hypothesis testing using the Cramer procedure
To be consistent with prior literature (Harris et al., 1994; Lang et al., 2003; Sami and Zhou,
2004), we perform the test using the Cramer procedure. Cramer (1987) provides a procedure

14. RAF 30
10,2
25
20
146
Density
15
10
5
0
0.0 0.1 0.2 0.3 0.4
R2 star
15
10
Density
5
0
0.1 0.2
0.3 0.4 0.5 0.6
Figure 4. R2 star
Notes: R2 *: audit failure group – non-Big 8/6/5/4; R2 *: matched
Histograms comparison 3
control group – non-Big 8/6/5/4
to calculate the first moment and second moment of R 2. Once we have the mean and
variance of R 2, we can calculate the z-statistic as in Harris et al. (1994) and Lang et al. (2003)
and compare means of R 2 across two samples. The expression of a z-test can be modified
as follows since the number of observations of R 2 is only one for each sample:
E R2 2 E R 2
1 2
z ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
V R 2 þ V R2
1 2

15. 15 Apparent
audit failures
10
147
Density
5
0
0.10 0.15 0.20 0.25 0.30
R2 star
15
10
Density
5
0
0.2 0.3 0.4 0.5 0.6
R2 star
Figure 5.
Notes: R2 *: audit failure group – Big 8/6/5/4; R2 *: matched
Histograms comparison 4
control group – non-Big 8/6/5/4
The results using the Cramer procedure and z-test are presented in Table VI. These results
are similar to those reported by employing the bootstrap method. Therefore, our results
are robust[13]. As discussed in McCullough and Vinod (1993), caution should be given
when using the Cramer procedure to calculate the mean and standard deviation of the R 2.
For instance, we encountered computer operation overflows, resulting in incorrect
outputs, before we modified our programming to resolve such problems.

16. RAF 30 15
10,2 25
20 10
Density
Density
15
148
10 5
5
0 0
0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.5 0.6
R2 star R2 star
15
10
Density
5
0
0.1 0.2 0.3 0.4 0.5 0.6
Figure 6. R2 star
Notes: R2 *: audit failure group – non-Big 8/6/5/4; R2 *: matched control group – Big 8/6/5/4; R2 *:
Histograms comparison 5
matched control group – Big 8/6/5/4; after removing the outlier
Sensitivity analysis
To ensure that our analyses are not affected by potentially confounding factors such
as negative earnings or financial distress, we perform several additional analyses.
First, as shown in the descriptive statistics, the audit failure group on average exhibits a
lower EPS compared with the matched control group. The frequency of losses in the
audit failure group is higher compared with the matched control group. Prior literature
(Collins et al., 1999; Burgstahler and Dichev, 1997; Hayn, 1995) has suggested that losses
are less informative for prices than profits. Therefore, the relation between stock
price and earnings and book values of firms in financial distress may be fundamentally
different from non-distressed firms. To ensure that our results are not driven by
the relatively higher frequency of losses or a greater degree of financial distress among
the firms in the audit failure group, we carry out two additional analyses. First,
we performed a second matching of non-audit failure firms to the audit failure group,
adding two additional matching criteria, return on assets (ROA) and leverage, to control
for the influence of financial distress. We also analyze the data after removing all these
observations with negative EPS and their pairs in the matched control group.

17. Apparent
Comparison Failure group Non-failure group
audit failures
1 Auditor Both Big and non-Big 8/6/ Both Big and non-Big 8/6/
5/4 5/4
83 percent bootstrap percentile (0.1344, 0.2234) (0.3555, 0.4825)
CI
2 Auditor Big 8/6/5/4 Big 8/6/5/4 149
83 percent bootstrap percentile (0.1175, 0.2072) (0.3520, 0.4877)
CI
3 Auditor Non-Big 8/6/5/4 Non-Big 8/6/5/4
83 percent bootstrap percentile (0.0068, 0.1161) (0.2640, 0.5030)
CI
4 Auditor Big 8/6/5/4 Non-Big 8/6/5/4
83 percent bootstrap percentile (0.1229, 0.2207) (0.3965, 0.6054)
CI
5 Auditor Non-Big 8/6/5/4 Big 8/6/5/4
83 percent bootstrap percentile (0.0068, 0.1161) (0.2045, 0.3894)
CI Table V.
Comparisons of bootstrap
Notes: All p-values less than or equal to 0.05 are shown in italics; regression model: P it ¼ a1 EPS it percentile confidence
þa2 BVPS it intervals for R 2
Also, in our original analyses, we include material misstatements that received both
“unqualified opinion” and “unqualified opinion with explanatory language” as audit
failure cases. Some of the “explanatory language” cases refer to an auditor’s substantial
doubts about a firm’s ability to continue as a going concern. Although the “going
concern” opinions are included in the audit failure group, insofar as the auditor’s opinion
on the fairness of the firm’s financial statements is not qualified, it is possible that
firms that receive going concern opinions are fundamentally different from the rest of
the firms in the sample. While we already control for financial distress factor by adding
ROA and financial leverage as two additional matching criteria, we perform additional
analyses after removing all firms with going concern opinions and their matched pairs in
the control group, in order to exclude the possibility that our results are driven by some
other confounding factor associated with the going concern opinion.
Additionally, we find that a few audit failure firms changed their auditors during the
period their financial statements were misstated. It is possible that these firms might be
different from other firms because they might be priced differently due to the change in
auditors. Previous research (Teoh, 1992) has documented that auditor changes might
affect stock prices and therefore the association between stock prices and accounting
information. Schwartz and Menon (1985) examine the motivations for auditor switching
and consider financial distress as a factor that affects auditor switching. Also, there are
other factors, such as audit opinion qualification (Chow and Rice, 1982) that might
trigger auditor switching. Therefore, in addition to controlling for financial distress, we
also analyze the data without those firms that changed auditors during their
misstatement years. The results of all these additional analyses are consistent with our
original analyses, which support our hypothesis[14].

18. RAF
Comparison Failure group Non-failure group
10,2
1 Auditor Both Big and non-Big 8/6/5/4 Both Big and non-Big 8/6/5/4
n 616 616
R2 0.1714 0.4179
Mean (R 2) 0.1735 0.4190
150 SD (R 2) 0.0264 0.0270
z-statistic 6.4977
2a Auditor Big 8/6/5/4 Big 8/6/5/4
n 502 502
R2 0.1555 0.4129
Mean (R 2) 0.1582 0.4142
SD (R 2) 0.0286 0.0210
z-statistic 6.1826
3b Auditor Non-Big 8/6/5/4 Non-Big 8/6/5/4
n 114 114
R2 0.0266 0.3555
Mean (R 2) 0.0353 0.3624
SD (R 2) 0.0369 0.0651
z-statistic 4.3668
4a Auditor Big 8/6/5/4 Non-Big 8/6/5/4
n 463 463
R2 0.1628 0.4827
Mean (R 2) 0.1656 0.4839
SD (R 2) 0.0301 0.0291
z-statistic 7.6015
5b Auditor Non-Big 8/6/5/4 Big 8/6/5/4
n 113c 113c
R2 0.0284 0.2862
Mean (R 2) 0.0381 0.2946
SD (R 2) 0.0369 0.0663
z-statistic 3.3805
Notes: All p-values less than or equal to 0.05 shown in italics; regression model:
P it ¼ a1 EPS it þ a2 BVPS it ; ain comparisons 2 and 4, audit failure group is the same group except
the number of observations differs; failure group in both comparisons represents firms audited by Big
auditors; in comparison 4, we dropped some failure group firms since we cannot find the matched pairs
audited by non-Big auditors; bin comparisons 3 and 5, audit failure group is the same group since it
Table VI. represents audit failure firms audited by non-Big auditors; cthe number of observations is due to
Comparison of R 2 deleting an outlier existing in the matched control group and its pair in the audit failure group; similar
using a z-test based to the bootstrap testing results, z-statistic is not significant before deleting the outlier because the large
on the Cramer procedure magnitude of the variance of the R 2 for the matched control group, 0.1939
VI. Limitations and conclusions
Limitations
This study has limitations due to the ex ante research approach adopted. Because of this ex
ante approach, our matching criteria have to rely on the “noisy” accounting measure, total
assets, in the alleged years. Therefore, the matching might not be effective in controlling
for size. Although our primary objective is to explore the market phenomenon around
the time of material misstatements, exploring why the market discounted accounting
information reported by audit failure firms even the news of these misstatements is also

19. an important question. Future research could address this issue by possibly incorporating Apparent
an “intervening” factor into the model to indicate how the market can differentiate audit audit failures
failure firms from other firms. Finally, our analysis is based on the pre-Sarbanes-Oxley
Act (SOX) period and therefore caution must be exercised when generalizing the findings
of this study to the post-SOX period.
Conclusions 151
In this study, we examine whether there is a difference in value relevance of accounting
information between firms experiencing apparent audit failures and firms that have not
experienced apparent audit failures. The bootstrap method is used to test for differences
in R 2 across samples. The results suggest that the value relevance of earnings and book
values of equity is lower for firms experiencing apparent audit failures than those for
firms that have not experienced apparent audit failures. Although the market may
exhibit a lower level of confidence in accounting information from firms audited by
non-Big 8/6/5/4 auditors, the results provide evidence that the market generally exhibits
lower confidence in firms experiencing audit failure, regardless of their auditors’ size.
These empirical results show that the explanatory power of accounting information
of firms experiencing apparent auditor failures is lower than that of firms that have not
experienced apparent auditor failures.
In our view, the market appears to rely less on accounting information reported by
firms experiencing apparent audit failures regardless of auditor size, even though an
apparent audit failure may not be confirmed by future actions and events until well after
its occurrence. Moreover, in comparison 4, accounting information provided by firms
experiencing audit failures and audited by Big 8/6/5/4 auditors is less value relevant
compared with firms audited by non-Big 8/6/5/4 auditors that have not experienced
audit failures. This result suggests the difference in value relevance between the
audit failure group and the matched groups is not conditioned on auditor size. Thus,
as illustrated in comparisons 1 through 5, the results indicate that there is a difference in
value relevance between the audit failure and the matched group ex ante.
In addition to providing evidence suggesting that auditor size does not impact the
value relevance of accounting information in a dysfunctional manner, this study
demonstrates an innovative application of bootstrapping to test for differences in R 2 in
the context of value-relevance research. We believe this method may allow researchers
to make more precise conclusions when conducting future value-relevance studies.
Notes
1. According to SEC requirements, before September 2002, Form 10-K had to be filed within
90 days after the end of the company’s fiscal year.
2. Several AAERs do not identify firm names. Further, some firms received multiple AAER
sanctions. There are also some cases where the alleged impropriety involves a governmental
entity.
3. Having ticker identification numbers does not necessarily mean that these firms have all
required data for specific years in question available in COMPUSTAT.
4. Palmrose’s (1999) database provides tickers for those companies that have ticker
identification numbers in COMPUSTAT. We exclude those cases of apparent audit failure
which do not have tickers in the Palmrose (1999) database.

20. RAF 5. COMPUSTAT data item no. 149 provides both auditor and audit opinion information.
However, it does not provide reasons why the qualified audit opinions were issued. We
10,2 include auditor opinions with a value 1 (unqualified opinion) and 4 (unqualified opinion with
explanatory language).
6. The matched-pairs control group also excludes firms with qualified audit opinions.
7. By selecting matched pairs according to such criteria, we control for confounding factors
152 such as size, industry, and time period. Since total assets reported by audit failure firms in
the alleged years could be noisy, control for size might not be very effective. However, we do
not expect that any discrepancy in matching would cause significant problems given that
“size” is matched within a ^10 percent range.
8. For example, if the sample size for two samples are both 300, the multiplier for the confidence
interval is:
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffi pffiffiffiffiffiffiffi pffiffiffi
0 2 £ 300 2 · 300 2
z ¼ pffiffiffiffiffiffiffi · z0:975 ¼ pffiffiffiffiffiffiffi · z0:975 ¼ · z0:975 ¼ 0:834:
2 300 2 300 2
9. Note that in comparison 4 in Table II, 39 of the 502 audit failure cases are dropped due to
inability to match these firm/years with those of a non-Big 8/6/5/4 control group.
10. For example, for the original sample size of 616 firm/years, the bootstrap resamples also will
contain 616 observations, but will not be identical to the set of observations in the original
sample.
11. See McCullough and Vinod (1993) for details on implementing the bootstrap procedure.
12. In comparison 5, the result is not significant before the outlier is removed. The confidence
interval (0.0822, 0.5369) is wide when the outlier is included in the sample because of the
bimodal distribution of R 2.
13. If the results are different using these two methods, the bootstrap method should be more
reliable since the z-test requires normality of the R 2 and the distribution of R 2 is not exactly
normal.
14. As a result of the more strict matching criteria, the sample size decreased in the second
match. We deleted 195, 128, and 61 firm/years with negative EPS, with explanatory
language and multiple auditors during the misstatement years, respectively. Results from
these additional analyses are not tabulated.
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About the authors
Li Dang is an Associated Professor at California Polytechnic State University-San Luis Obispo.
Her research interests and publications focus on audit quality, international accounting, and
accounting information systems. She teaches financial accounting and accounting information
systems courses. Li Dang is the corresponding author and can be contacted at: ldang@calpoly.edu
Kevin F. Brown is an Associate Professor in the Department of Accountancy of the Raj Soin
College of Business at Wright State University in Dayton, Ohio. He has published his research in
several scholarly and professional journals, including Issues in Accounting Education, Journal of
Accounting Education, and The CPA Journal. He has presented his research at numerous national
conferences, including Annual Meetings of the American Accounting Association. His primary
area of research is in auditing and his teaching areas include auditing and financial accounting.
B.D. McCullough is a Professor of Decision Sciences at Drexel University. Before he joined the
faculty at Drexel University, he was an Assistant Professor of Economics at Fordham University
from 1989 to 1994 and a Senior Economist at the Federal Communications Commission from
1995 to 2000. He is the Associate Editor for five journals and has authored or co-authored more
than 50 scholarly publications.
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