chang et al - 2010 - market reaction to auditor switching from big 4 to third-tier small accounting firms

Bạn đang xem bản rút gọn của tài liệu. Xem và tải ngay bản đầy đủ của tài liệu tại đây (243.19 KB, 32 trang )

Market Reaction to Auditor Switching from
Big 4 to Third-Tier Small Accounting Firms
Hsihui Chang, C. S. Agnes Cheng, and Kenneth J. Reichelt
SUMMARY: After the demise of Arthur Andersen, the public accounting industry has
witnessed a signiﬁcant migration of public clients to second-tier ͑Grant Thornton and
BDO Seidman͒ and smaller third-tier accounting ﬁrms. While prior literature documents
that smaller auditors are perceived by the stock market as an inferior substitute for a
Big 4 auditor, this perception appears to have changed in recent years. In this paper, we
analyze market responses to auditor switching from Big 4 to smaller accounting ﬁrms
during 2002 to 2006. We break our sample period into two separate periods ͑Periods 1
and 2͒ based on when regulatory changes occurred. These changes included
Sarbanes-Oxley ͑SOX͒ 404 implementation, Public Company Accounting Oversight
Board ͑PCAOB͒ inspections, and a tightened Form 8-K ﬁling deadline. We ﬁnd a rela-
tively more positive stock market reaction to clients switching from a Big 4 to a smaller
third-tier auditor in Period 2. This relatively more positive reaction in Period 2 reﬂects
companies seeking better services rather than a lower audit fee, when an audit quality
drop is less likely. Overall, our results suggest that companies and investors have
become more receptive to smaller accounting ﬁrms.
Keywords: market reaction; auditor switching; Big 4; small accounting ﬁrms; audit
quality
.
Data Availability: Data are publicly available from the sources identiﬁed in the paper.
INTRODUCTION
A
fter the failure of Arthur Andersen, regulators around the world were concerned about the
degree of concentration in the audit profession by the Big 4. In a speech at the American
Institute of Certiﬁed Public Accountants ͑AICPA͒ annual national conference on Decem-
ber 11, 2006, Mark Olson, Chairman of the Public Company Accounting Oversight Board, ex-
Hsihui Chang is a Professor at Drexel University, and C. S. Agnes Cheng is a Professor and Kenneth J.
Reichelt is an Assistant Professor, both at Louisiana State University.
We gratefully acknowledge the comments and suggestions of Dan Simunic ͑the editor͒, two anonymous reviewers, Steven

Lin, Kannan Raghunandan, Gordon Richardson, Sam Tiras, Angela Woodland, and workshop participants at Florida
International University, Louisiana State University, National Cheng Kung University, the 2007 International Research
Conference for Accounting Educators in Mexico City, and the 2008 Joint Journal of Contemporary Accounting and
Economics and Auditing: A Journal of Practice and Theory Symposium, and in particular the discussant at that Sympo-
sium, Clive Lennox. We thank Menghistu Sallehu for his excellent research assistance, and directEDGAR for the use of
their data and software.
Editor’s note: Accepted by Dan Simunic.
Auditing: A Journal of Practice & Theory American Accounting Association
Vol. 29, No. 2 DOI: 10.2308/aud.2010.29.2.83
November 2010
pp. 83–114
Submitted: September 2007
Accepted: December 2009
Published Online: October 2010
83
pressed his concerns of audit ﬁrm concentration, as well as his hope for improvement from the
growth in the size and skill of smaller accounting ﬁrms ͑PCAOB 2006͒. Similar conﬁdence had
also been expressed by Christopher Cox, Chairman of the U.S. Securities and Exchange Commis-
sion, on December 5, 2005 ͑SEC 2005͒, and by PCAOB Chairman William McDonough on
October 17, 2005 ͑Civils 2005͒. Both ofﬁcials encouraged smaller public companies to consider
hiring smaller accounting ﬁrms.
Despite the regulators’ encouragement, and their belief that many smaller accounting ﬁrms
have improved their competence, the audit market continues to be dominated by the Big 4 audi-
tors. This is partly because investors have perceived that brand name auditors, i.e., the Big N ͑Big
8/6/5/4͒, provide higher quality audits than smaller accounting ﬁrms, as suggested by prior litera-
ture ͑DeAngelo 1981; Dopuch and Simunic 1982; Nichols and Smith 1983; Teoh and Wong 1993͒.
Consequently, the stock market has perceived switches from a Big N auditor to a non-Big N as a
“red ﬂag” ͑Eichenseher et al. 1989; Dunn et al. 1999; Knechel et al. 2007͒.
Prior to 2002, some auditor switches, regardless of auditor type, involved companies that
were experiencing going-concern and/or auditor disagreements. These companies were considered

riskier, evident from poorer economic performance ͑Dhaliwal et al. 1993͒, higher auditor litigation
risk ͑Krishnan and Krishnan 1997; Shu 2000͒, greater likelihood of violating debt-covenants
͑DeFond and Jiambalvo 1993͒, and greater likelihood of a going-concern audit opinion ͑Krishnan
and Krishnan 1997; Geiger et al. 1998͒. From a risk perspective, some of these studies suggest and
show that the stock market responded negatively to the announcement of an auditor switch ͑e.g.,
Dhaliwal et al. 1993; Shu 2000͒.
From a beneﬁcial reason perspective, recent studies ͑e.g., Ettredge et al. 2007; Sankaragu-
ruswamy and Whisenant 2004͒ document that auditor switches are undertaken for seeking audit
fee savings and/or better services. Under such circumstances, a nonnegative or even a positive
market reaction could be expected for a switch from a Big 4 to a non-Big 4 auditor. Speciﬁcally,
if ͑1͒ investors can justify a net economic beneﬁt from a tradeoff between a perceived downgrade
in audit quality and a combined lower audit fee and better personalized services, or ͑2͒ the market
has changed its perception about the lower audit quality of non-Big 4 auditors. If the ﬁrst case is
true, the market reaction will: ͑1͒ neither be negative nor positive when the market perceives an
equivalent trade-off between higher audit quality and a combined lower audit fee and improved
personalized services, and ͑2͒ be positive when the net economic beneﬁt is signiﬁcantly positive.
If the second case is true, however, it is likely that we will observe a positive market reaction due
to a perceived positive net economic beneﬁt arising from a combined lower audit fee and better
services, without a compromise in audit quality.
We believe the market’s perception of switching from a Big 4 to a Small auditor has changed
in recent years ͑2004–2006͒ for the following three main reasons.
Increase in Seeking Better Services and a Lower Audit Fee from Smaller Auditors
After the demise of Andersen and the enactment of the Sarbanes-Oxley Act ͑SOX͒, a tempo-
rary capacity constraint resulted. This exogenous shock provided an opportunity for Big 4 auditors
to rebalance their client portfolios toward better-aligned former Andersen clients, evident from
increased sensitivity to client mismatch ͑Landsman et al. 2009͒. Consequently, for smaller clients
Big 4 audit services may have deteriorated and audit fees may have increased so much that smaller
accounting ﬁrms appeared more attractive for better services and lower audit fees. Consistent with
this notion is a Wall Street Journal article that reports that after SOX, more IPOs are relying on
84 Chang, Cheng, and Reichelt

Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
smaller accounting ﬁrms and investors are more receptive to smaller accounting ﬁrms ͑Reilly
2006͒.
1
Decline in Perceived Audit Quality Differences between Big N and Non-Big N Auditors
After the Big N’s involvement in a series of accounting scandals in the early 2000s, investors
viewed a smaller difference in audit quality between Big N and non-Big N auditors. This state-
ment is consistent with the analysis by Choi et al. ͑2008͒ that the audit quality of the Big N and
non-Big N is expected to converge as the legal and regulatory regime becomes more onerous.
During 2004, more onerous legal and regulatory changes were introduced, including SOX 404
audit requirements, PCAOB inspections of auditors, and a tighter 8-K ﬁling deadline. Evidently,
the initial PCAOB inspections of the Big 4 reported disappointing news of the Big 4 audit quality,
since 21 of 64 clients examined eventually restated their ﬁnancial statements.
2
Thus, a non-Big 4
auditor should be an optimal choice in order to avoid paying a brand-name audit fee premium,
especially if the brand-name auditor does not provide higher audit quality.
Lack of Evidence of Opinion Shopping by Switching to a Small Auditor
Prior studies indicate that opinion shopping is generally unsuccessful ͑e.g., Chow and Rice
1982; Krishnan 1994; Krishnan and Stephens 1995; Geiger et al. 1998͒; nonetheless, companies
are likely to switch to a non-Big 4 auditor if they believe it will be successful. Since PCAOB
inspections create quality pressures for non-Big 4 auditors, Small auditors are especially cautious
to not provide a low-quality audit. As a result, in recent years it is less likely that opinion shopping
is the motivation for a switch from a Big 4 to a Small auditor.
While these three reasons are intuitively appealing, whether investors have actually changed
their perception remains an empirical issue. If switching from a Big 4 to a Small auditor is indeed
for beneﬁcial reasons and the stock market does not perceive a signiﬁcant drop in audit quality, we
should not see a negative market response. Furthermore, assuming public companies can ﬁnd a
competent Small audit ﬁrm, and the company’s management believes that the market will not react

negatively, we should see an increasing trend of switching from Big 4 to Small audit ﬁrms. In a
recent report, Grant Thornton provides empirical evidence that the market does not respond
negatively to auditor switches from the Big 4 to Grant Thornton ͑Whisenant 2006͒. However, the
Grant Thornton report does not examine Small audit ﬁrms, which are different from the Big 4 and
Medium 2 in many respects; namely, Small audit ﬁrms are better adapted to local markets, are
traditionally viewed as inferior, and are substantially smaller.
3
Thus, the report’s ﬁndings may not
be applicable to switches from the Big 4 to Small audit ﬁrms. Therefore, our study focuses on
auditor switches from Big 4 to Small audit ﬁrms. We seek to empirically document the market
response to these auditor switches and assess whether the evidence supports the regulators’ belief
in the competence of Small audit ﬁrms.
Analyzing data on auditor changes for the period 2002–2006, we ﬁnd that the ratio of
switches to Small audit ﬁrms out of all the switches from the Big 4 has been increasing. More
importantly, the market response to clients switching from a Big 4 to a Small audit ﬁrm ͑BtS͒ is
nonnegative and is relatively more positive for the period after August 23, 2004. This relatively
1
On average, we believe Big 4 auditors should provide better audit quality than smaller auditors. However, if a Big 4
auditor does not warrant higher audit quality ͑e.g., nonexpertise͒, then a switch to a smaller auditor may not be
perceived by the market as a bad move.
2
Most of these restatements arose from the balance sheet classiﬁcation of borrowings from revolving credit agreements
͑PCAOB 2004a, 2004b, 2004c, 2004d͒.
3
Based on audit fees ﬁled in 2006, the Big 4 occupy about 92.6 percent of the total market share, followed by BDO
Seidman ͑1.7 percent͒ and Grant Thornton ͑1.6 percent͒. The rest ͑about 643 ﬁrms reported in the audit fee data set from
Audit Analytics͒ are Small audit ﬁrms, the highest occupying only 0.2 percent.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 85
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association

more positive market response occurs when the predecessor auditor’s quality is lower ͑the prede-
cessor is a nonspecialist and accruals quality is more inferior͒, suggesting that there is less like-
lihood for audit quality to drop from switching to a Small auditor. We ﬁnd the relatively more
positive response to a BtS switch is driven by better services,
4
not by a lower audit fee, when there
is less likelihood for audit quality to drop.
Our study extends the literature in several ways. We ﬁnd a relatively more positive market
response to clients switching from a low-quality Big 4 auditor to a Small auditor, particularly
when the Small auditor provides better services, thus extending Sankaraguruswamy and
Whisenant ͑2004͒, who ﬁnd a relatively more positive response to service and fee reasons without
examining the quality of the auditor switch. We document a nonnegative market reaction to
switches from a Big 4 auditor to a Small auditor, as well as with switches from a Big 4 to a
Medium 2 and to another Big 4 in the post-SOX 404/PCAOB inspection era, thus extending
Knechel et al. ͑2007͒, who ﬁnd a negative response to switches from a Big 4 to a non-Big 4 in the
period 2000 to 2003. In addition, after intended improvements in ﬁnancial reporting quality and
audit quality ͑e.g., SOX 404 and PCAOB inspections͒ in recent years, we ﬁnd that the market does
not perceive an audit quality drop when companies switched from a “low-quality” Big 4 auditor to
a Small auditor. Speciﬁcally, we ﬁnd that the market responded relatively more positive ͑i.e., less
negatively͒ to switches from a Big 4 to a Small auditor, especially when the Small auditor is
considered a specialist and when the client experienced low audit quality from the Big 4 auditor.
This ﬁnding is consistent with our conjecture that the market is starting to perceive that Small
auditors do not necessarily deliver inferior audit quality. Small auditors may even be able to
improve audit quality ͑possibly as a specialist providing more personal attention͒ when the Big 4
predecessor did not provide high audit quality.
The remainder of our paper is structured as follows. The next section reviews prior literature
of the market reaction to auditor switches and articulates our prediction of the market response to
Big 4 to Small auditor switches. The third section presents and discusses our empirical results and
the fourth section concludes the paper.
PRIOR LITERATURE

In order to gain perspective as to the overall changes in investor attitudes regarding auditor
switches, we must examine the results from both before and after the SOX/Andersen period. Early
studies of stock market reactions to auditor switches provide mixed results. Fried and Schiff
͑1981͒ ﬁnd a negative market reaction to auditor switches between 1972 and 1975 for companies
that switched, based on a 21-week window. However, Nichols and Smith ͑1983͒ ﬁnd when using
a shorter eight-week window, that there was no signiﬁcant reaction to auditor switches between
1973 and 1979. Similarly, Johnson and Lys ͑1990͒ fail to ﬁnd a signiﬁcant stock price reaction for
auditor switches from 1973 to 1982 using a three-day window around the time of the auditor
change announcement.
In contrast, later studies tend to document a negative market reaction to auditor switches.
Smith ͑1988͒ examines auditor switches between 1975 and 1982, using a one-week event window,
and ﬁnds a negative and signiﬁcant reaction when a new auditor has not yet been appointed.
Eichenseher et al. ͑1989͒ ﬁnd a signiﬁcant negative market reaction to auditor switches from Big
8 to non-Big 8 ﬁrms by OTC companies between 1980 to 1982, using a ﬁve-week event window.
DeFond et al. ͑1997͒ ﬁnd a signiﬁcant negative market reaction to auditor resignations between
1982 and 1987, using three event windows: from the auditor resignation date to the 8-K ﬁling
receipt date, from the receipt of the 8-K ﬁling through the following four business days, and the
4
We use a relative measure of Small auditors’ expertise to reﬂect better services.
86 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
sum of these two periods. Wells and Loudder ͑1997͒ ﬁnd a signiﬁcant negative market reaction to
auditor resignations between 1988 and 1991, using a two-day window. Similarly, Dunn et al.
͑1999͒ ﬁnd a negative market reaction to auditor resignations between 1988 and 1993, on the date
of the resignation letter, and the reaction is more negative from the loss of a Big 6 auditor. In
addition, Shu ͑2000͒ ﬁnds a signiﬁcant negative market reaction to auditor resignations between
1985 and 1996, using a three-day window. Whisenant et al. ͑2003͒ ﬁnd a signiﬁcant negative
market response to auditor switches arising from weaknesses in internal controls and problems
related to the reliability of management representations and/or ﬁnancial statement reliability be-

tween 1993 and 1996, and their results are robust using a three-day window and a seven-day
window. Beneish et al. ͑2005͒ extend Whisenant et al. ͑2003͒ by controlling for confounding
events around the time of the auditor change announcement ͑e.g., earnings and management
changes͒ and by limiting the analysis to resignations. Beneish et al.’s ͑2005͒ study uses a three-day
window from 1994 to 1998 and only ﬁnds a negative market response to resignation announce-
ments where a reason was provided for the change ͑disagreements over accounting treatment or
over the adequacy of internal controls͒. Resignation announcements without a reason have an
insigniﬁcant market response. Recently, Knechel et al. ͑2007͒ report that Big 4 clients who
switched to a non-Big 4 auditor during the period 2000 to 2003 experienced a negative abnormal
return.
While prior market studies have generally found a negative response to auditor change an-
nouncements, we have seen no studies that document a positive market reaction to the recent
migration of clients from Big 4 to smaller third-tier auditors. Although Sullivan ͑2006͒ examines
the trends and determinants of auditor switching behavior to explain that the migration arises from
higher costs from the SOX Section 404 requirements, the analysis does not include an event study.
Louis ͑2005͒ provides indirect evidence from market reactions to merger announcements, ﬁnding
that acquiring companies audited by non-Big 4 ﬁrms outperform the market reaction to those
audited by Big 4 ﬁrms. Louis ͑2005͒ suggests that non-Big 4 audit ﬁrms have superior knowledge
of local markets and they have a better relation with smaller clients. However, since the time
period studied ͑1980–2002͒ predates the SOX/Andersen era and the study does not examine
auditor change announcements, the ﬁndings are less relevant to the more recent potential changes
in market attitudes. Sankaraguruswamy and Whisenant ͑2004͒ provide some evidence that the
market response is relatively more positive for service and fee reasons, using a seven-day window,
albeit the time period examined, 1993 to 1996, predates the SOX/Andersen era. Whisenant ͑2006͒
examines whether the market responds negatively to auditor switches from a Big 4 or Arthur
Andersen to Grant Thornton, during the period 2002 to 2006. The author ﬁnds a signiﬁcant
positive market response ͑p ϭ 0.08͒, with an 11-day window, for auditor switches from a Big 4
͑
excluding Arthur Andersen͒ to Grant Thornton. However, the study is restricted to switches to
Grant Thornton clients, so it does not include switches to Small ͑third-tier͒ ﬁrms, and it uses raw

returns without controlling for the market movement and risk.
Recent literature suggests a nonnegative or even a positive market response to a switch from
a Big 4 to a Small auditor may be imminent when the Big 4 predecessor does not provide high
audit quality. In recent years, anecdotal evidence in the ﬁnancial press ͑e.g., Reilly 2006͒ suggests
that investors are more receptive to companies using smaller auditors. Evidence of a higher audit
fee demanded by Big 4 over Small auditors ͑Ho and Wang 2007͒ and service-related reasons
͑Louis 2005͒ suggest that Small accounting ﬁrms may be an attractive audit alternative. When the
switch is not likely to reduce audit quality, the market response should be nonnegative.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 87
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
EMPIRICAL RESULTS
Frequency and Trend of Switching from Big 4 to Small Accounting Firms
Based on 8-K ﬁlings compiled by Audit Analytics, we ﬁrst analyze the frequency and trend of
auditor switching for the years 2000 through 2006. Since this study focuses on switches from a
Big 4 to a Small accounting ﬁrm, we consider only clients that already have an auditor before the
change and exclude initial public offerings. Table 1 summarizes the auditor switching frequency
and trend.
5
Panel A presents the number of switches between size categories of accounting ﬁrms.
Panel B reports the number of switches by year, across 2000 to 2006, and the number of switches
from Big N to Small auditors. Similar to Panel B, Panel C reports for publicly traded companies
that have ﬁled an auditor change.
Table 1, Panel A, reports that more auditor switches are from Small auditors ͑48 percent͒ than
from Big 4 auditors ͑33 percent͒, and more switches are to Small auditors ͑62 percent͒ than to Big
4 auditors ͑30 percent͒. Panels B and C indicate that the total number of auditor switches was
highest in 2002, when Arthur Andersen failed. However, the number increased from 2003 to 2004,
when the SOX Section 404 requirements became effective, and it further increased from 2004 to
2005 for publicly traded companies. Panel C also reports that in 2006 most Big 4 departures are
to a Small auditor ͑52 percent͒, and the percentage has been increasing since 2002.

Final Sample with Return Data
To evaluate the market reaction to auditor switches, we collect daily returns from the Center
for Research in Security Prices ͑CRSP͒ for each case of an auditor change ﬁled after the demise
of Arthur Andersen ͑January 2002͒.
6
Our sample period contains two separate periods: Period 1 is
prior to August 23, 2004, and Period 2 is post-August 23, 2004. Our choice of these two periods
is motivated by three regulatory events: the implementation of the SOX Section 404 requirements
on auditing of internal controls over ﬁnancial reporting ͑ICOFR͒, the ﬁrst PCAOB inspection
reports, and the change in the Form 8-K ﬁling deadline.
Auditing Standard No. 2 ͑for SOX 404 audits͒ was issued by the PCAOB and approved by the
SEC on June 17, 2004. Accelerated ﬁlers ͑those in excess of $75M market capitalization͒ are
required to engage an auditor to audit their ICOFR, beginning with ﬁscal years ending November
15, 2004. Since auditing of ICOFR is very costly, clients may switch auditors. Hence, the reason
for switching may differ when ﬁrms consider the Section 404 requirements.
The ﬁrst PCAOB inspection reports were announced August 26, 2004. These inspections are
required by Section 101 of the SOX to ensure a minimum ͑and a likely higher͒ level of audit
quality. The PCAOB is required to conduct inspections of ﬁrms with over 100 clients every year
and inspections of ﬁrms with 100 or fewer clients every three years. Hence, the market perception
of audit quality of Small auditors may have changed due to the awareness that Small auditors are
subject to the PCAOB’s tight inspections.
7
Effective August 23, 2004, the SEC tightened its 8-K ﬁling deadline from ﬁve to four business
days for notifying an auditor change and several other events. Studies have shown that the com-
pliance rate was low under the ﬁve-day deadline requirement ͑Schwartz and Soo 1996; Carter and
Soo 1999͒. After the SOX Section 404 requirements took effect, we suspect that the compliance
rate improved, since auditors may consider compliance with ﬁling deadlines part of ICOFR, and
5
Data are from Audit Analytics as of January 2007.
6

Grant Thornton’s study by Whisenant ͑2006͒ uses observations from January 1, 2002, to May 23, 2006. Our sample
period is similar except we exclude January ﬁlings since they include switches to Arthur Andersen, and we extend our
period to December 31, 2006.
7
We do not claim that the PCAOB has improved the audit quality of Small auditors. We simply suggest that there may
be a potential change in the market perception.
88 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
penalties for noncompliance have increased. Using August 23, 2004, as the cut-off date to separate
our sample into two periods roughly coincides with the legislative events discussed in this and the
two previous paragraphs. To keep our observations homogenous, we use August 23, 2004, as our
cut-off date.
TABLE 1
Analysis of Auditor Switches
Panel A: Number of Auditor Switches between Size Categories of Accounting Firms
Years 2000–2006 Engaged
%
DepartedDeparted AA Small Medium 2 Big 4 Total
AA — 184 108 1,544 1,836 14%
Small 32 5,720 171 342 6,265 48%
Medium 2 10 534 26 98 668 5%
Big 4 105 1,651 635 1,887 4,278 33%
Total 147 8,089 940 3,871 13,047
% Engaged 1% 62% 7% 30% 100%
Panel B: Companies Filing an Auditor Change
Year
Total
Changes
Switches from Big 4 to Small

Departed Engaged Ratio
2000 961 460 83 18.0%
2001 1,553 573 171 29.8%
2002 2,947 456 175 38.4%
2003 1,615 599 244 40.7%
2004 2,070 790 338 42.8%
2005 2,033 775 348 44.9%
2006 1,868 625 292 46.7%
Total 13,047 4,278 1,651 38.6%
Panel C: Publicly Traded Companies Filing an Auditor Change
Year
Total
Changes
Switches from Big 4 to Small
Departed Engaged Ratio
2000 343 139 13 9.4%
2001 632 256 32 12.5%
2002 1,495 186 — 0.0%
2003 914 349 131 37.5%
2004 1,274 480 207 43.1%
2005 1,356 505 246 48.7%
2006 1,290 418 219 52.4%
Total 7,304 2,333 848 36.3%
Panel A reports the number of auditor switches between size categories of accounting ﬁrms for companies ﬁling an auditor
change. Panels B and C report the trend of all auditor switches ͑Total Changes͒ to those departing from a Big 4 accounting
ﬁrm ͑Departed͒, and those departing from a Big 4 and engaging a Small accounting ﬁrm ͑Engaged͒. Big 4 accounting ﬁrms
are Deloitte, Ernst & Young, KPMG, and PricewaterhouseCoopers. Small accounting ﬁrms exclude Big 4, Medium 2
͑BDO Seidman and Grant Thornton͒, and Arthur Andersen ͑AA͒.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 89
Auditing: A Journal of Practice & Theory November 2010

American Accounting Association
Our ﬁnal sample requires observations to have both departed and engaged auditors, a ﬁling
date, a return, and market value data.
8
This limitation generates a total of 1,824 observations for
our ﬁnal sample. This sample is used for our multiple regression analyses and is not limited to
switches from a Big 4 accounting ﬁrm, but includes all other categories of audit switches. Table 2
reports the number of switches by period, industry, and auditor switching category. For Period 1,
there are 1,121 switches; ignoring 666 mandatory switches from Arthur Andersen, there are only
455 switches. For Period 2, there are 703 switches.
9
From Table 2, Panel A, we observe that durable manufacturers, ﬁnancial, and computer-
related ﬁrms account for approximately 50 percent of the ﬁnal sample in both periods. Panel B
indicates that switches from the Big 4 constitute the largest number of switches: 378 out of 455
non-Arthur Andersen switches in Period 1, and 571 out of 703 switches in Period 2. Among the
switches from Big 4 auditors, the majority of the switches are from a Big 4 to another Big 4
auditor in Period 1. However, in Period 2, switches to a Small auditor outnumber switches to a
Medium 2 and a Big 4 auditor.
Univariate Analysis of Market Return during the Event Window
To measure whether a company’s stock market return resulted from a triggering event and not
from a change in the aggregate stock market level, we use the market-adjusted return
10
for our
univariate analysis. Market-adjusted return is deﬁned as the buy and hold daily return less the
value-weighted market daily return, accumulated over the event window. We focus our analyses of
market responses using a ﬁve-day event window ͑Ϫ4,0͒, where day 0 is the ﬁling date. Ideally, we
should identify the ﬁrst day that the market is aware of the auditor change ͑e.g., news announce-
ment͒ and then design an event window surrounding the news announcement date ͑e.g., Ϫ1toϩ1
day͒. We searched the Form 8-K auditor change ﬁlings and ﬁnd that a small number ͑n ϭ 152, or
8 percent͒ had ﬁled a press release. We ﬁnd that the mean ﬁling date is 1.47 days after the news

announcement date ͑standard deviation ϭ 2.48͒. Our event window ͑4,0͒ is based on the fact that
four days after the news announcement falls within one standard deviation, and that the SEC
requires that the 8-K should be ﬁled within four business days of the auditor change.
11,12
Table 3 reports market responses to auditor switches from Big 4 to Small auditors ͑BtS͒, Big
4 to Medium 2 ͑BtM͒, and Big 4 to Big 4 ͑BtB͒ for the two periods. For each switch group, Table
3 provides the number of observations, the mean daily return, the mean market-adjusted return,
and their respective p-values.
Panel A indicates that in Period 1 the market responded nonnegatively to switches from BtS,
BtM, and BtB. The mean daily return and the mean market-adjusted return are not signiﬁcant at
conventional levels, except for the BtB mean daily return ͑signiﬁcant at 10 percent, one-tailed͒.
Panel B indicates a nonnegative market response in Period 2. BtS auditor switches earn a 0.8
percent mean daily return ͑signiﬁcant at a 10 percent level, one-tailed͒, BtM earn 0.7 percent ͑not
signiﬁcant͒, and BtB earn 0.5 percent ͑signiﬁcant at a 10 percent level, one-tailed͒; however, the
mean market-adjusted return is not signiﬁcant at conventional levels for all three groups. As well,
the BtS switch group reports a higher mean daily return and a higher mean market-adjusted return
8
We deleted one observation with the most extreme positive return.
9
When we drop switches from Arthur Andersen, our main conclusions do not change.
10
We also use size-adjusted and market model-adjusted abnormal returns; our conclusions are not altered.
11
For robustness purposes, we use a longer event window ͑Ϫ11,0͒, and we ﬁnd our results are stronger for Period 1 but
weaker for Period 2. These ﬁndings are consistent with our conjecture that there is improved compliance in Period 2,
which deserves further investigation.
12
When the news announcement date is substituted for the ﬁling date of a smaller sample of Big 4 to Small auditor
switches ͑n ϭ 23͒, daily returns and market-adjusted returns are virtually unchanged.
90 Chang, Cheng, and Reichelt

Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
TABLE 2
Analysis of Final Sample
Panel A: Number of Observations by Industry and Period
Industry Group Description
a
Period 1 % Period 2 %
1. Agriculture 5 0.4% 2 0.3%
2. Mining and Construction 23 2.1% 15 2.1%
3. Food 9 0.8% 8 1.2%
4. Textiles and Printing 49 4.4% 19 2.7%
5. Chemicals 18 1.6% 19 2.7%
6. Pharmaceuticals 55 4.9% 43 6.1%
7. Extractive 70 6.2% 22 3.1%
8. Durable Manufacturers 209 18.6% 121 17.2%
9. Transportation 75 6.7% 41 5.8%
10. Utilities 56 5.0% 20 2.9%
11. Retail 97 8.7% 45 6.4%
12. Financial 202 18.0% 187 26.6%
13. Services 107 9.5% 59 8.4%
14. Computers 141 12.6% 97 13.8%
15. Other 5 0.5% 5 0.7%
Total Number of Observations 1,121 100.0% 703 100.0%
Panel B: Number of Observations by Switching Category and Period
Switching Category Period 1 % Period 2 %
Big 4 to Small 71 6.3% 243 34.6%
Big 4 to Medium 2 81 7.2% 148 21.0%
Big 4 to Big 4 226 20.2% 180 25.6%
Medium 2 to Small 14 1.2% 39 5.5%

Medium 2 to Medium 2 1 0.1% 2 0.3%
Medium 2 to Big 4 10 0.9% 19 2.7%
Small to Small 20 1.8% 45 6.4%
Small to Medium 2 9 0.8% 11 1.6%
Small to Big 4 23 2.1% 16 2.3%
Andersen to Small 4 0.4% — —
Andersen to Medium 2 17 1.5% — —
Andersen to Big 4 645 57.5% — —
Total Number of Observations 1,121 100% 703 100%
a
Industry groups are based on the following SIC codes: Agriculture ͑0100–0999͒, Mining and Construction ͑1000–1999͒,
Food ͑2000–2111͒, Textiles and Printing ͑2200–2799͒, Chemicals ͑2800–2824, 2840–2899͒, Pharmaceuticals ͑2830–
2836͒, Extractive ͑1300–1399, 2900–2999͒, Durable Manufacturers ͑3000–3999͒, Transportation ͑4000–4899͒, Utilities
͑4900–4999͒, Retail ͑5000–5999͒, Financial ͑6000–6999͒, Services ͑7000–8999͒, Computers ͑3570–3579, 3670–3679,
7370–7379͒, and Other is all other SIC groups.
Period 1 is February 1, 2002, to August 23, 2004. Period 2 is August 24, 2004, to December 31, 2006.
Big 4 accounting ﬁrms are Deloitte, Ernst & Young, KPMG, and PricewaterhouseCoopers. Medium 2 are BDO Seidman
and Grant Thornton. Small accounting ﬁrms are all other ﬁrms except Andersen.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 91
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
than the other groups, but the differences are not signiﬁcant. Results in Table 3 suggest that the
market response to switches from Big 4 to other audit ﬁrms is nonnegative in our sample periods.
Our univariate analysis is restricted to a small sample and does not control for the underlying
reasons for auditor switching. We conduct multiple regression analyses by using the whole sample
after we examine the underlying reasons and ﬁrm characteristics for the switches in Periods 1 and
2.
Comparison of Reasons and Firm Characteristics
Since most company 8-K auditor change ﬁlings do not report the auditor change reasons,
13

we
employ observable measures as surrogates for our major reasons of interest ͑i.e., audit fee reduc-
tion and better services͒. We also select reported 8-K reasons that may imply opinion shopping if
we ﬁnd a signiﬁcant number of observations ͑Ͼ5 percent͒.
We propose that the market will not respond negatively to an auditor switch if it is not likely
to imply a decrease in future audit quality. Under this situation, the market may respond relatively
more positive to BtS switches if the switch is associated with an audit fee reduction or better
services.
13
We ﬁnd that 22 percent ͑Period 1: 13 percent, Period 2: 38 percent͒ of our sample observations report either client-
related reasons ͑e.g., going-concern, internal controls, ﬁnancial restatements, audit opinion concerns͒, auditor-related
reasons ͑e.g., scope limitations, lack of independence, and auditor merger͒, or fee-related reasons in the auditor change
8-K ﬁlings.
TABLE 3
Market Responses to Auditor Switches—Univariate Analysis
Event Window (؊4, 0)
Panel A: Period 1 (February 1, 2002 to August 23, 2004)
BtS BtM BtB
Number of Observations 71 81 226
Mean Daily Return 0.002 0.010 0.006
p-value 0.77 0.37 0.19
Mean Market-Adjusted Return 0.000 0.008 0.004
p-value 0.98 0.46 0.43
Panel B: Period 2 (August 24, 2004 to December 31, 2006)
BtS BtM BtB
Number of Observations 243 148 180
Mean Daily Return 0.008 0.007 0.005
p-value 0.18 0.23 0.18
Mean Market-Adjusted Return 0.006 0.004 0.003
p-value 0.31 0.46 0.45

Event window ͑Ϫ4, 0͒ indicates the return starts to accumulate at four days prior to the 8-K ﬁling and ends on the ﬁling
date.
BtS, BtM, and BtB indicate the switch from Big 4 to Small, Big 4 to Medium 2, and Big 4 to Big 4 ﬁrms, respectively.
Mean Daily Return is the mean buy-and-hold return cumulated over the event window.
Mean Market-Adjusted Return is the mean Daily Return less the mean CRSP value-weighted market return.
p-values are two-tailed tests of no difference from 0.
92 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
To assess if a switch is not likely to imply a decrease in audit quality, we examine the audit
inferiority of the predecessor auditor. We presume that if the audit quality of the predecessor is
low, then there is little room for the successor to lower the quality. There may even be greater
room for the successor to improve audit quality. Consequently, we suggest that predecessor audit
inferiority is consistent with a low likelihood of a drop in future audit quality. We use two
variables to measure predecessor audit inferiority: nonexpertise and accruals inferiority.
To examine if the market response to a BtS switch is relatively more positive due to a lower
audit fee or better services, given a low likelihood of an audit quality drop, we condition the
market response to an audit fee reduction or better services on accruals inferiority and nonexper-
tise of the predecessor. Below we discuss these measures.
Nonexpertise of Predecessor
Studies suggest that Big N auditors have higher audit quality than non-Big 4 auditors ͑see
Francis 2004, 352–354, for a summary͒; hence, a switch from a Big 4 to a Small auditor implies
an audit quality drop. However, previous literature also suggests that auditor expertise is associ-
ated with audit quality ͑e.g., Craswell et al. 1995; Balsam et al. 2003; Ferguson et al. 2003;
Krishnan 2003; Francis et al. 2005b͒: a Big 4 nonspecialist should have lower audit quality than
a Big 4 specialist. This implies that if a client switches from a Big 4 nonspecialist to a Small
auditor, it is likely that the loss of audit quality ͑if any͒ will be lower than a switch from a Big 4
specialist to a smaller auditor. Hence, we use nonexpertise of the predecessor auditor to surrogate
for the low likelihood of an audit quality drop after the switch.
We deﬁne an industry specialist as the audit ﬁrm with the greatest market share in audit fees

for a particular city, two-digit SIC and year, based on Francis et al. ͑2005b͒. Non_Specialist equals
1 if the predecessor auditor is not an industry specialist, 0 otherwise. We use the city of the auditor
stated on the auditor’s report and the audit fee, from Audit Analytics, to construct this variable.
Table 4 provides some evidence that BtS switchers are less likely to reﬂect an audit quality
drop than BtM and BtB switchers in Period 2. BtS switchers have a relatively higher ratio of
nonspecialist predecessors in Period 2 and the smallest decrease over the two periods. In Period 2,
BtS have the largest ratio of nonspecialist predecessors ͑0.407͒, which is signiﬁcantly greater than
for BtB ͑0.300͒. Comparing Period 2 to Period 1 for BtS, the Non_Specialist ratio decreases by
0.030 ͑not signiﬁcant͒, which is less than the decrease for BtM ͑0.107͒ and BtB ͑0.129͒.
Accruals Inferiority Prior to Audit Switch
A second variable to measure the low likelihood of an audit quality drop after a switch is the
client’s accruals inferiority. Numerous studies have used accruals quality to measure audit quality
͑e.g., Becker et al. 1998; Francis et al. 1999; Chung and Kallapur 2003; Srinidhi and Gul 2007͒.
These studies assume that the client’s accruals quality is associated with audit quality since
accruals are audited. However, accruals may measure audit quality with error. For example, a
client with poor accruals quality may hire a brand-name Big 4 auditor to convey that their earnings
quality is high ͑Francis et al. 1999͒. If the Big 4 auditor fails to detect accruals-based earnings
management when the client has a higher propensity to manage earnings ͑Chung and Kallapur
2003͒, we may observe higher reported accruals. Thus, the propensity to manage earnings creates
measurement error for accruals quality in measuring audit quality.
There is little debate that auditor expertise measures auditor quality, hence, audit quality, so
we use the Non_Specialist variable as one surrogate of audit quality. While prior studies show that
accruals quality is associated with auditor expertise
͑e.g., Balsam et al. 2003; Krishnan 2003͒, the
client’s propensity to manage earnings may reduce accruals quality, thus creating measurement
error in the Non_Specialist variable to measure audit quality. We believe that both a nonspecialist
variable and an accruals inferiority variable measure audit quality with error, so we propose to use
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 93
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association

TABLE 4
Comparison of Selected Mean Reasons and Firm Characteristics between Big 4 Switching
Groups
Panel A: Period 1 Means
BtS
(n ؍ 71)
BtM
(n ؍ 81)
BtB
(n ؍ 226) BtS less BtM BtS less BtB
Non_Specialist 0.437 0.506 0.429 Ϫ0.070 0.007
Accruals_Inferiority 0.007 0.003 Ϫ0.002 0.003 0.009
Innate_Accruals Inferiority 0.038 0.054 0.044 Ϫ0.016
** Ϫ0.006*
Abs_Acc 0.084 0.110 0.106 Ϫ0.026*** Ϫ0.022**
Fee_Decrease 0.183 0.074 0.093 0.109** 0.090**
Small_Auditor_Expert 0.437 NA NA NA NA
GCAO 0.099 0.086 0.027 0.012 0.072
***
AAIC 0.099 0.210 0.119 Ϫ0.111 * Ϫ0.021
AAFR 0.028 0.025 0.066 0.003 Ϫ0.038
Size 1.861 0.407 3.712 1.455 Ϫ1.850
***
SG 1.128 1.283 1.219 Ϫ0.155 Ϫ0.091**
BP 0.699 0.624 0.556 0.075 0.142
Segment 0.525 0.534 0.617 Ϫ0.009 Ϫ0.092
FORSA 0.133 0.186 0.158 Ϫ0.053 Ϫ0.025
Leverage 0.151 0.139 0.200 0.012 Ϫ0.049
∆Financing 0.008 0.012 Ϫ0.011 Ϫ0.004 0.018
Loss 0.268 0.469 0.283 Ϫ0.202

** Ϫ0.016
ROA Ϫ0.117 Ϫ0.106 Ϫ0.033 Ϫ0.011 Ϫ0.085
***
Panel B: Period 2 Means
BtS
(n ؍ 243)
BtM
(n ؍ 148)
BtB
(n ؍ 180) BtS less BtM BtS less BtB
Non_Specialist 0.407 0.399 0.300 0.009 0.107
**
Accruals_Inferiority Ϫ0.007 Ϫ0.008 0.002 0.000 Ϫ0.009
Innate_Accruals Inferiority 0.052 0.069 0.044 Ϫ0.017
*** 0.008
Abs_Acc 0.060 0.088 0.077 Ϫ0.028
*** Ϫ0.017**
Fee_Decrease 0.185 0.169 0.067 0.016 0.119***
Small_Auditor_Expert 0.350 NA NA NA NA
GCAO 0.103 0.122 0.156 Ϫ0.019 Ϫ0.053
AAIC 0.247 0.378 0.328 Ϫ0.131
*** Ϫ0.081*
AAFR 0.091 0.122 0.156 Ϫ0.031 Ϫ0.065**
Size 1.962 0.915 2.552 1.048 Ϫ0.590***
SG 1.198 1.184 1.217 0.014 Ϫ0.019*
BP 0.502 0.483 0.445 0.019 0.057*
Segment 0.574 0.481 0.717 0.093 Ϫ0.143**
FORSA 0.156 0.181 0.179 Ϫ0.025 Ϫ0.023
Leverage 0.156 0.141 0.203 0.015
** Ϫ0.046*

∆Financing Ϫ0.028 Ϫ0.005 0.027 Ϫ0.023 Ϫ0.055***
Loss 0.288 0.331 0.239 Ϫ0.043 0.049
ROA Ϫ0.054 Ϫ0.018 Ϫ0.021 Ϫ0.036 Ϫ0.033
***
*, **, *** Signiﬁcant at 0.10, 0.05, and 0.01, respectively. All tests are two-tailed.
See the Appendix for variable deﬁnitions.
94 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
an interaction variable ͑i.e., Non_Specialist ء Accruals_Inferiority͒. Our results are stronger using
this interaction variable and our main regression analysis only reports results using this interaction
variable to measure the low likelihood of an audit quality drop.
Our accruals inferiority measure derives from Dechow and Dichev ͑2002; hereafter DD͒.In
their model, accruals quality is the extent to which accruals map into operating cash ﬂow realiza-
tions. This measure has been used to assess audit quality ͑e.g., Srinidhi and Gul 2007; Chen et al.
2008͒. Audit quality reﬂects the auditor’s role in reducing estimation errors of accruals. One
problem with this measure is if the variation contained in DD’s accruals quality measure stems
from client operational characteristics, then it will reﬂect the ﬁrm’s underlying economics and will
not measure accruals quality properly. Francis et al. ͑2005a; hereafter FLOS͒ point out that the
DD-based accruals quality measure consists of two components: an innate accruals quality com-
ponent resulting from client operational characteristics, and a discretionary accruals quality com-
ponent resulting from managerial earnings opportunism ͑and noise͒. We use their method and base
our audit inferiority measure on the portion of accruals inferiority that is less likely from the
client’s operational characteristics.
Following FLOS, Accruals_Inferiority is deﬁned as the residual of annually estimating the
DD-based accruals quality measure ͑AQ͒ from the following OLS model ͑ﬁrm subscripts are not
shown for brevity͒:
AQ
t
= ␭

0
+ ␭
1
lAssets
t
+ ␭
2
␴
͑CFO͒
t
+ ␭
3
␴
͑Sales͒
t
+ ␭
4
OperCycle
t
+ ␭
5
NegEarn
t
+ u
t
͑1͒
where:
14
lAssets
t

ϭ the natural logarithm of total assets ͑#6͒ in year t;
␴
͑CFO͒
t
ϭ the standard deviation of operating cash ﬂow ͑#308͒ divided by average assets
over years t−9 to t ͑with at least ﬁve years͒;
␴
͑Sales͒
t
ϭ the standard deviation of sales revenue ͑#12͒ divided by average assets over
years t−9 to t ͑with a least ﬁve years͒;
OperCycle
t
ϭ the natural logarithm of the operating cycle length ͑sum of days receivables, and
days inventory͒ in year t;
NegEarn
t
ϭ the number of years out of the past ten in year t where net income before
extraordinary items ͑#123͒ Ͻ 0;
u
t
ϭ the error term;
AQ
t
ϭ accruals quality in year t, the standard deviation of the residual from estimating
the following Equation ͑2͒ by year t and two-digit SIC:
TACC
t
= b
0

+ b
1
CFO
t−1
+ b
2
CFO
t
+ b
3
CFO
t+1
+ b
4
⌬REV
t
+ b
5
PPE
t
+ ␧
t
͑2͒
where:
TACC
t
ϭ total accruals ͑net income before extraordinary items ͑#123͒ less operating cash
ﬂow ͑#308͒͒ in year t divided by average total assets ͑average of current and
prior year #6͒ in year t;
CFO

t
ϭ operating cash ﬂow ͑#308͒ in year t divided by total assets in year t;
⌬REV
t
ϭ the change in sales revenue ͑#12͒ between year t−1 and t divided by total assets
in year t;
14
The Compustat Annual variable number is in parentheses.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 95
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
PPE
t
ϭ gross value of PPE ͑#7͒ in year t divided by total assets in year t; and
␧
t
ϭ the error term.
AQ
t
is the standard deviation of the residuals from Equation ͑2͒ from years t−4 to t and is
computed for each ﬁrm in year t ͑with at least three observations available͒. Year t is the last ﬁscal
year audited by the predecessor auditor.
A larger standard deviation of residuals ͑AQ
t
͒ from Equation ͑2͒ indicates poorer accruals
quality, since there is greater uncertainty of total accruals mapping into operating cash ﬂow
realizations. Innate_Accruals_Inferiority is the predicted value of accruals quality from Equation
͑1͒, the accruals quality predicted by client operational characteristics. Accruals_Inferiority is the
residual from Equation ͑1͒, the unpredicted accruals quality potentially due to managerial earnings
opportunism.

Table 4, Panels A and B, report that BtS switchers have similar Accruals_Inferiority to BtM
switchers, and to BtB switchers in both Periods 1 and 2. There is no signiﬁcant difference in mean
accruals inferiority in both periods.
15
Audit Fee Decrease
Prior literature has shown that client characteristics affect the size of the audit fee ͑e.g., Hay
et al. 2006; Ettredge et al. 2007͒. Typical determinants of audit fees are client size, complexity, and
audit risk. We construct an audit fee decrease variable based on the residual of an audit fee
estimation model ͑Ettredge et al. 2007͒. Speciﬁcally, we estimate the audit fee by year t from the
following model ͑ﬁrm subscripts are not shown for brevity͒:
laf
t
=
␤
0
+
␤
1
lta
t
+
␤
2
segment
t
+
␤
3
for_sales_pc
t

+
␤
4
rec
t
+
␤
5
in
v
t
+
␤
6
roa
t
+
␤
7
loss
t
+
␤
8
opinion
t
+
␤
9
ltd

t
+
␤
10
dec
t
+
␤
11
opinion_lag
t
+ industry dummies
t
+ e
t
͑3͒
where:
laf
t
ϭ the natural logarithm of audit fee in year t;
lta
t
ϭ the natural logarithm of total assets ͑#6͒ in year t;
segment
t
ϭ the natural logarithm of the number of business segments in year t;
for_sales_pc
t
ϭ foreign sales as a percentage of total sales in year t;
rec

t
ϭ total receivables ͑#2͒ divided by total assets in year t;
in
v
t
ϭ inventory ͑#3͒ divided by total assets in year t;
roa
t
ϭ earnings before extraordinary items ͑#18͒ divided by total assets in year t;
loss
t
ϭ 1 if net income ͑#172͒ in year t Ͻ 0, 0 otherwise;
opinion
t
ϭ 1 if the auditor issues a going-concern opinion in year t, 0 otherwise;
ltd
t
ϭ long-term debt ͑#9͒ divided by total assets in year t;
dec
t
ϭ 1 if the ﬁscal year t ends December 31, 0 otherwise;
opinion_lag
t
ϭ the number of days between the ﬁscal year-end t and the audit report date;
industry
dummies
t
ϭ indicator variables from two-digit SIC categories in year t; and
e
t

ϭ the error term.
15
Innate accruals inferiority does signiﬁcantly differ between switching groups ͑see Table 4͒, since client operational
characteristics vary.
96 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
Audit fee, audit opinion, and audit report date are obtained from Audit Analytics, and all other
variables are from Compustat Annual and Compustat Annual Segment data. Following prior
studies, client size is estimated by lta, complexity is estimated by segment, for_sales_pc, and dec,
and risk is measured by the remaining variables. Fee_Decrease equals 1 if the actual audit fee in
the last year of the predecessor auditor is greater than model ͑3͒ predicts and if the actual audit fee
in the ﬁrst year of the successor auditor is less than model ͑3͒ predicts, 0 otherwise.
16
Table 4 reports that BtS switchers have signiﬁcantly more fee decreases than BtB switchers in
both Periods 1 and 2.
Better Services
Prior literature ﬁnds that clients switch from larger auditors to seek better services ͑Sankara-
guruswamy and Whisenant 2004͒. Big 4 auditors are reputed to favor larger and more lucrative
clients, and consequently neglect smaller clients ͑Louis 2005͒.AWall Street Journal article ͑Ber-
ton 1994͒ provides insight from complaints by former small clients of Big N ﬁrms, which include
slow/delayed responses to clients’ questions, less experienced and less cooperative audit staff,
higher turnover of audit staff, and poorer tax advice.
Compared to a Big N auditor, a Small auditor has fewer layers of management, allowing
prompter client advice. For publicly traded clients, Small auditors are more likely to staff the
engagement with more experienced staff that pay more personal attention. These factors create a
better working relationship, improving the auditor’s knowledge of the client, building the auditor’s
expertise, and improving audit quality.
17
We develop a measure of better services based on the Small auditor’s expertise. Different

from the measure of expertise for the Big 4, we measure expertise based on the Small auditor’s
website and industry concentration of the number of switches to the same Small auditor. This
measure cannot be compared directly to the expertise measure we use for the Big 4 ͑i.e., Small
auditor expertise does not necessarily imply better expertise than a Big 4 nonspecialist͒;itisa
measure to distinguish among Small auditors who will more likely provide better services.
We examine the auditor’s website to determine whether the auditor indicates that they have
expertise in the client’s industry. If there is not enough information on the website, we contacted
the auditor’s ofﬁce by telephone. For Small auditors with six or more BtS client switches, we
examine whether there is industry concentration among the switches to a particular Small auditor.
The rationale is that if more switches come from clients in the same industry, then it is likely that
the Small audit ﬁrm is an industry specialist. We use different benchmark concentration ratios for
Small auditors with different numbers of switches to determine if the auditor is likely an industry
specialist. For Small auditors with six to ten BtS switches, we require at least a 50 percent industry
ratio ͑i.e., at least three to ﬁve switches are from clients in the same industry͒; for auditors with 11
to 20 BtS switches, we require at least a 40 percent industry ratio; and for auditors with more than
20 BtS switches, we require at least a 35 percent industry ratio.
18
The variable Small_Auditor_Ex-
pert equals 1 if the successor Small auditor is identiﬁed as an industry specialist for BtS switches,
0 otherwise. Table 4, Panels A and B, report the mean Small_Auditor_Expert ratio is 0.437 and
0.350 for all BtS switches for Periods 1 and 2, respectively.
16
Our results are qualitatively similar when we use a continuous measure instead of an indicator variable of fee decrease.
17
Informal interviews with several CFOs from publicly traded companies who switched from a Big 4 auditor to a small
auditor conﬁrm that their working relationship signiﬁcantly improved with the smaller auditor, and the improved
working relationship will likely improve audit quality.
18
Our design of the benchmark ratio is based on the absolute number of clients in the same industry rather than a strict
ratio. We also vary our benchmark ratio, and our results get weaker when our benchmark ratio is too low.

Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 97
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
8-K Reported Reasons
Previous research ͑e.g., Geiger et al. 1998͒ suggests that clients attempt to switch auditors in
order to opinion shop, albeit unsuccessfully. We previously discussed that accruals inferiority
measures the low likelihood of an audit quality drop. Accruals inferiority may also reﬂect opinion
shopping: clients with higher accruals inferiority may seek a more tolerant auditor. If a switch
suggests opinion shopping, then the market should respond negatively. Table 4 reports that accru-
als inferiority decreased from Period 1 to Period 2, for the BtS switchers and for the BtM
switchers, possibly reﬂecting less opinion shopping intention. There is no signiﬁcant change for
the BtB switchers.
In addition to the accruals inferiority measure, we also extract the 8-K reasons that may relate
to opinion shopping ͑qualiﬁed audit opinions, internal controls, and ﬁnancial restatements͒ and
which also have a signiﬁcant number of observations in our sample ͑at least 5 percent͒.
19
These
8-K reasons are required disclosures for audit switches. Based on Audit Analytics-coded 8-K
reasons, we identiﬁed three opinion-shopping measures: GCAO, AAIC, and AAFR.
20
GCAO equals
1 if there was not an unqualiﬁed audit opinion on the ﬁnancial statements ͑adverse, disclaimer,
qualiﬁed, or modiﬁed, including going-concern͒ in the past two ﬁscal years, 0 otherwise. AAIC
equals 1 if there was an internal control issue, 0 otherwise. AAFR equals 1 if a restatement of the
ﬁnancial statements occurred or will occur, 0 otherwise. Table 4 reports that the percentage of
AAIC and AAFR reasons has increased from Period 1 to Period 2, likely due to the coincident
change in the audit environment ͑i.e., PCAOB inspections and SOX 404 audits͒. These increases
are statistically signiﬁcant for all audit switch groups. GCAO has increased but not signiﬁcantly
for BtS and BtM groups. However, the increases for these three variables are the lowest for the
BtS group.

Firm Characteristics
We evaluate various ﬁrm characteristics that previous studies ͑e.g., Johnson and Lys 1990;
Shu 2000͒ have employed to help explain the underlying reasons for auditor switches. These
characteristics are size, growth, complexity, need for external ﬁnancing, and proﬁtability. Clients
are more likely to switch to a smaller ͑larger͒ auditor if they are smaller ͑larger͒ in size, have
lower ͑higher͒ growth, less ͑more͒ complexity, are in less ͑more͒ need of external ﬁnancing, and
have poorer ͑better͒ proﬁtability. We follow previous studies to measure these characteristics. For
size, we include market capitalization. For growth, we include the average past three years’ sales
growth, and the book-to-price ratio. For complexity, we include the natural logarithm of the
number of business segments and the foreign sales ratio. For need for ﬁnancing, we include
leverage and the change in ﬁnancing after the auditor switch. For proﬁtability, we add a loss
indicator and ROA in the last year of the predecessor auditor. The variables are deﬁned as follows,
where year t is the last ﬁscal year audited by the predecessor auditor:
Size ϭ market capitalization ͑$billions͒ during the auditor switch, the stock price per
share times the number of shares outstanding ͑in billions͒;
SG ϭ average sales growth for years t−2 to t ͑sales
t
/ sales
t−1
+ sales
t−1
/ sales
t−2
+ sales
t−2
/ sales
t−3
͒ / 3 where sales
t
is sales revenue ͑#12͒ in year t;

BP ϭ the book to price ratio, book value ͑#216͒/market value ͑#199 ء #25͒, in year t;
Segment ϭ the natural logarithm of the number of business segments in year t;
FORSA ϭ the ratio of foreign sales to total sales revenue in year t;
19
We obtained the 8-K reasons from Audit Analytics and veriﬁed them against our hand-collected source.
20
We excluded redundant reasons, such as “Reportable Conditions,” or those which are too infrequent, such as “Consulted
with Incoming Auditor.”
98 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
Leverage ϭ long-term debt ͑#9͒ divided by total assets ͑#6͒ in year t;
∆Financing ϭ the change in total ﬁnancing in year t, ͑͑equity issues
t+1
͑#108͒
+ debt issues
t+1
͑#111͒͒ / total assets
t+1
͒ minus ͑͑equity issues
t
+ debt issues
t
͒ / total assets
t
͒;
Loss ϭ 1 if net income before extraordinary items ͑#123͒ in year t Ͻ 0, 0 otherwise; and
ROA ϭ net income before extraordinary items divided by total assets in year t.
Table 4, Panel B, reports in Period 2 that BtS switchers are likely better suited for a Small
auditor, compared to BtB switchers, since on average they are smaller, have less sales growth, are

less complex, are less proﬁtable, and require less ﬁnancing. BtS have a smaller mean Size, less
mean sales growth ͑SG͒ than BtB, a higher mean book-to-price ratio ͑BP͒, a lower mean Segment,
and a lower mean ROA. Mean change in ﬁnancing is lower for BtS than BtB, and signiﬁcantly
decreased from Period 1 to Period 2 for BtS, but not for BtM and BtB.
We also present correlation coefﬁcients of key variables in Table 5 for the whole sample. For
expertise and total accruals, we ﬁnd that Non_Specialist is positively correlated with the absolute
value of total accruals ͑Abs_Acc͒: 0.068 for the Pearson coefﬁcient, consistent with the conjecture
that industry expertise reduces the magnitude of total accruals. However, we do not ﬁnd signiﬁcant
correlations ͑at a 1 percent level͒ between Accruals_Inferiority and Non_Specialist or Small_Au-
ditor_Expert. This lack of correlation implies that our accruals inferiority variable measures client
quality that is not captured by expertise. Moreover, we ﬁnd Size is positively and signiﬁcantly
correlated with Accruals_Inferiority, implying that larger ﬁrms tend to have more earnings
opportunism.
21
Our 8-K reasons of GCAO, AAIC, and AAFR are positively and signiﬁcantly
correlated among each other.
Multiple Regressions—Basic Analysis
Our multiple regression analysis of event window returns starts by ﬁrst focusing on the
indicator variables of the switching groups ͑BtS, BtM, and BtB͒. We then add reasons that we
derive from publicly available sources, including Fee_Decrease, Non_Specialist, Small_Auditor-
_Expert, and Accruals_Inferiority ͑tercile ranked to reduce extreme values͒, followed by the 8-K
reported reasons ͑GCAO, AAIC, and AAFR͒ and Size ͑log͒.
22
These results are reported in Table 6.
The ﬁrst major column is for Period 1 ͑February 1, 2002, to August 23, 2004͒ which has a total of
1,121 observations; the second major column is for Period 2 ͑August 24, 2004, to December 31,
2006͒ which has a total of 703 observations.
We ﬁrst discuss results without controlling for reasons and ﬁrm characteristics. Starting with
Period 1, Table 6 reports a negative intercept ͑Ϫ0.006͒͑signiﬁcant at 10 percent͒, and positive
͑but insigniﬁcant͒ coefﬁcients on BtS ͑0.006͒, BtM ͑0.014͒, and BtB ͑0.010͒, respectively. The

intercept represents the average market reaction to other types of switches. The coefﬁcient on each
switch group represents the incremental market reaction. The sum of the intercept and the coef-
ﬁcient on each switch group represents the average market reaction for each switch group: 0.000,
0.008, and 0.004 for BtS, BtM, and BtB, respectively, which are not signiﬁcantly different from
0.
23
Similarly, for Period 2, Table 6 reports a negative intercept ͑Ϫ0.012͒ and positive coefﬁcients
͑all signiﬁcant͒ on BtS ͑0.017͒, BtM ͑0.014͒, and BtB ͑0.014͒, suggesting a relatively more positive
21
To control for the high correlation between size and our discretionary accruals quality variable, we add size and
interaction variables for size in our multiple regression model, and our main conclusion is unchanged.
22
In addition to size, we add other ﬁrm characteristics variables, described in the previous section, and our conclusions do
not change. To keep our paper short, we do not report these results.
23
Results for Period 1 are basically unchanged if switches from Arthur Andersen are dropped from the sample.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 99
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
TABLE 5
Correlation Analysis of Key Variables
(n ؍ 1,824)
Daily_
Return
Market_
Return
Non_
Specialist
Accruals
Inferiority Abs_Acc

Fee_
Decrease
Small_
Auditor_
Expert GCAO AAIC AAFR Size
Daily_Return 0.269 Ϫ0.001 Ϫ0.016 Ϫ0.016 Ϫ0.021 0.051 0.012 0.062 0.035 Ϫ0.004
Market_Return 0.366 Ϫ0.037 0.017 0.001 Ϫ0.015 0.026 0.045 0.084 0.052 Ϫ0.001
Non_Specialist Ϫ0.049 Ϫ0.040 0.035 0.068 Ϫ0.032 0.026 0.003 Ϫ0.027 Ϫ0.016 Ϫ0.054
Accruals_Inferiority Ϫ0.004 0.003 0.018 0.222 0.004 Ϫ0.025 0.044 0.016 Ϫ0.026 0.077
Abs_Acc Ϫ0.014 Ϫ0.037 Ϫ0.006 Ϫ0.001 Ϫ0.047 ؊0.082 0.143 0.044 Ϫ0.005 Ϫ0.035
Fee_Decrease Ϫ0.027 Ϫ0.014 Ϫ0.032 Ϫ0.036 Ϫ0.042 0.053 0.018 0.009 0.020 Ϫ0.024
Small_Auditor_Expert 0.025 0.040 0.026 0.000 ؊0.148 0.053 0.015 Ϫ0.010 Ϫ0.002
Ϫ0.022
GCAO Ϫ0.007 0.061 0.003 0.007 0.065 0.018 0.015 0.394 0.665 Ϫ0.029
AAIC 0.043 0.120 Ϫ0.027 Ϫ0.050 0.019 0.009 Ϫ0.010 0.394 0.472 Ϫ0.042
AAFR 0.038 0.069 Ϫ0.016 Ϫ0.045 Ϫ0.027 0.020 Ϫ0.002 0.665 0.472 Ϫ0.022
Size 0.087 0.029 ؊0.155 0.103 ؊0.079 Ϫ0.024 0.003 ؊0.103 ؊0.103 Ϫ0.037
Bold indicates signiﬁcance at 1 percent level.
Left lower corner reports Spearman correlation coefﬁcients, upper right corner reports Pearson correlation coefﬁcients.
See the Appendix for variable deﬁnitions.
100 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
͑i.e., less negative͒ market response for these switch groups. The sum of the intercept and the
coefﬁcient is 0.005, 0.002, and 0.002 for BtS, BtM, and BtB, and are not signiﬁcantly different
from 0. Consistent with univariate results reported in Table 3, these multiple regression results
show a nonnegative market response to BtS, BtM, and BtB switches. Without investigating the
reasons, our results only reﬂect the mean effect of the switch.
In the second column of each Period, we add Fee_Decrease, Non_Specialist, Small_Auditor-
_Expert, and Accruals_Inferiority. These variables are not signiﬁcant in Period 1 but the coefﬁ-

cient on Small_Auditor_Expert becomes positive and signiﬁcant in Period 2. Note that the coef-
ﬁcient on BtS in Period 2 is still positive ͑0.011, reduced from 0.017͒ but is no longer signiﬁcant,
suggesting that the relatively more positive market response to BtS switches is partly explained by
better services of the Small successor auditor.
24
In the third column, the 8-K reasons that may imply opinion shopping, and size are added.
Only the GCAO coefﬁcient is signiﬁcant and negative ͑Ϫ0.029͒ in Period 1, implying that the
24
If we do not include the Small_Auditor_Expert variable but include Fee_Decrease, Non_Specialist, and Accruals_Infe-
riority, then the coefﬁcient on BtS is positive ͑0.016͒ and signiﬁcant ͑p ϭ 0.04͒ in Period 2.
TABLE 6
Regression Analysis without Interaction of Reason Variables
Period 1
(n ؍ 1,121)
Period 2
(n ؍ 703)
Adjusted R
2
0.075 0.073 0.075 0.068 0.068 0.069
Intercept Ϫ0.006
* Ϫ0.011** Ϫ0.022** Ϫ0.012* Ϫ0.011 Ϫ0.031**
Market_Return 0.795*** 0.787*** 0.784*** 1.379*** 1.389*** 1.316***
BtS ͑Big4toSmall͒ 0.006 0.004 0.008 0.017** 0.011 0.009
BtM ͑Big 4 to Medium͒ 0.014 0.013 0.016
* 0.014* 0.015* 0.013
BtB ͑Big 4 to Big 4͒ 0.010 0.010 0.009 0.014
* 0.015* 0.011
Fee_Decrease Ϫ0.007 Ϫ0.007 Ϫ0.003 Ϫ0.003
Non_Specialist 0.001 0.002 0.002 0.003
Small_Auditor_Expert 0.000 Ϫ0.001 0.017

* 0.018*
Accruals_Inferiority 0.002 0.002 Ϫ0.005 Ϫ0.005
GCAO Ϫ0.029
** 0.019
AAIC 0.002 0.010
AAFR 0.011 Ϫ0.019
Size ͑log͒ 0.002 0.003
F-test: Intercept ϩ
␤
ϭ 0
Intercept ϩ BtS 0.000 Ϫ0.007 Ϫ0.014 0.005 0.000 Ϫ0.022
Intercept ϩ BtM 0.008 0.002 Ϫ0.006 0.002 0.004 Ϫ0.018
Intercept ϩ BtB 0.004 Ϫ0.001 Ϫ0.013 0.002 0.004 Ϫ0.020
*, **, *** Signiﬁcant at 0.10, 0.05, and 0.01, respectively. All tests are two-tailed, except the F-test of intercept ϩ
␤
ϭ
0. All F-tests of intercept ϩ
␤
ϭ 0 are not signiﬁcant at a 10 percent level.
Period 1 is from February 1, 2002, to August 23, 2004. Period 2 is from August 24, 2004, to December 31, 2006.
Dependent variable is Daily_Return ͑see the Appendix͒. Size ͑log͒ is the natural logarithm of market capitalization.
Accruals_Inferiority is the tercile rank of Accruals_Inferiority, deﬁned in the Appendix. Please see the Appendix for all
other variable deﬁnitions. Coefﬁcients for missing Fee_Decrease and missing Accruals_Inferiority indicator variables are
not reported for brevity. Results are quantitatively similar without these indicator variables.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 101
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
market responds negatively to companies with a going-concern or other audit opinion concern.
However, the coefﬁcient sign changes and becomes insigniﬁcant in Period 2. Also note that the
intercept in both periods is signiﬁcant and ranges in magnitude in Period 2 from Ϫ0.012 to

Ϫ0.031.
25
If a switch from a Big 4 has a low likelihood of an audit quality drop, ceteris paribus, then the
response should be relatively more positive when compared to other switch groups. For BtS
switches, if the market perceives that the client also intends to seek an audit fee decrease without
sacriﬁcing audit quality or seek better services to improve audit quality, then the market should
respond relatively more positive. To test these hypotheses, we ﬁrst interact the BtS, BtM, and BtB
variables with a variable that surrogates a low likelihood of an audit quality drop ͑Non_Specialist
ء Accruals_Inferiority͒. We then condition Fee_Decrease on these interaction variables. For BtS
switches, we further condition Small_Auditor_Expert on Non_Specialist ء Accruals_Inferiority.
Equation ͑4͒ below describes the model ͑ﬁrm subscripts are omitted for brevity͒:
R=
␤
0
+
␤
1
Market_ Return +
␤
2
BtS +
␤
3
BtM +
␤
4
BtB +
␤
5
Fee_Decrease +

␤
6
Non_Specialist
+
␤
7
Small_Auditor_Expert +
␤
8
Accruals_Inferiority +
␤
9
GCAO +
␤
10
AAIC +
␤
11
AAFR
+
␤
12
Size͑log͒ +
␤
13
Non_Specialist ء Accruals_Inferiority
+
␤
14
BtS ء Non_Specialist ء Accruals_Inferiority

+
␤
15
BtM ء Non_Specialist ء Accruals_Inferiority
+
␤
16
BtB ء Non_Specialist ء Accruals_Inferiority
+
␤
17
BtS ء Fee_Decrease ء Non_Specialist ء Accruals_Inferiority
+
␤
18
BtM ء Fee_Decrease ء Non_Specialist ء Accruals_Inferiority
+
␤
19
BtB ء Fee_Decrease ء Non_Specialist ء Accruals_Inferiority
+
␤
20
Small_Auditor_Expert ء Non_Specialist ء Accruals_Inferiority + ␧ ͑4͒
where R is the daily return culminated over the event window ͑Ϫ4,0͒. We also add Market_Re-
turn, the CRSP value-weighted return culminated over the ͑Ϫ4,0͒ event window, to control for
market movement.
26
Table 7 reports the results.
To show whether BtS, BtM, or BtB switches generate a relatively more positive market

response when the likelihood of an audit quality drop is low, Table 7, Panel A, ﬁrst ignores the
conditioning effects from an audit fee decrease and small auditor expertise. Panel A reports that
the coefﬁcient on BtS ء Non_Specialist ء Accruals_Inferiority is positive in Period 2 ͑0.037͒ and
signiﬁcant at the conventional level.
27,28
Recall that the coefﬁcient on BtS is 0.017, as previously
25
The signiﬁcantly negative intercept in Period 2 is explained by clients switching from Medium 2 to Small auditors
͑MtS͒. Compared to Big to Small switchers, MtS switchers are smaller, have more nonspecialist predecessor auditors,
higher accruals magnitude, and greater accruals inferiority. Further investigation of the market response is left to future
research. We also ﬁnd that the intercept decreases with the addition of variables with positive coefﬁcient signs: Smal-
l_Auditor_Expert, AAIC, and Size ͑log͒.
26
Instead of using a market-adjusted return which restricts the market beta ͑i.e., the correlation between ﬁrm return and
market return͒ to 1, we include market return in the regression model. For robustness, we also use the Fama-French
three-factor return, size-adjusted return, and market-model beta risk-adjusted return, and our results are similar.
27
Note that the coefﬁcients on the corresponding interaction term for both BtM and BtB are not signiﬁcant at conventional
levels in Period 2. Our presumption is that BtM and BtS successors are not specialists, but for BtB successors this
presumption may not hold. We test and do not ﬁnd that BtB switches with a successor specialist generate a positive
market response, suggesting that seeking expertise is not important for BtB clients.
28
We also drop the intercept terms Non_Specialist, Accruals_Inferiority,andNon_Specialist ء Accruals_Inferiority;we
ﬁnd that the coefﬁcient on BtS ء Non_Specialist ء Accruals_Inferiority ͑0.035͒ is still signiﬁcant.
102 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
TABLE 7
Regression Analysis
Interaction of Reason Variables

Panel A: Interaction of Audit Quality Variables
Period 1
(n ؍ 1,121)
Period 2
(n ؍ 703)
Adjusted R
2
0.078 0.093
Intercept Ϫ0.020
** Ϫ0.023*
Market_Return 0.793*** 1.345***
BtS ͑Big4toSmall͒ 0.011 Ϫ0.002
BtM ͑Big 4 to Medium 2͒ 0.001 0.009
BtB ͑Big 4 to Big 4͒ 0.015
** 0.006
Fee_Decrease Ϫ0.006 0.000
Non_Specialist 0.001 Ϫ0.005
Accruals_Inferiority 0.001 Ϫ0.012
***
Non_Specialist ء Accruals_Inferiority 0.005 0.007
BtS ء Non_Specialist ء Accruals_Inferiority Ϫ0.017 0.037
***
BtM ء Non_Specialist ء Accruals_Inferiority 0.022** 0.000
BtB ء Non_Specialist ء Accruals_Inferiority Ϫ0.016
* Ϫ0.007
Small_Auditor_Expert 0.000 0.027
***
GCAO Ϫ0.026** 0.007
AAIC 0.001 0.007
AAFR 0.010 Ϫ0.007

Size ͑log͒ 0.001 0.004
**
Panel B: Interaction of Audit Quality, Fee Decrease and Service Variables
Period 1
(n ؍ 1,121)
Period 2
(n ؍ 703)
Adjusted R
2
0.076 0.123
Intercept Ϫ0.021
** Ϫ0.023**
Market_Return 0.791*** 1.304***
BtS ͑Big4toSmall͒ 0.012 0.005
BtM ͑Big 4 to Medium 2͒ 0.001 0.009
BtB ͑Big 4 to Big 4͒ 0.015
** 0.006
Non_Specialist 0.001 Ϫ0.003
Accruals_Inferiority 0.001 Ϫ0.011
***
Non_Specialist ء Accruals_Inferiority 0.005 0.006
BtS ء Non_Specialist ء Accruals_Inferiority Ϫ0.018 0.020
*
BtM ء Non_Specialist ء Accruals_Inferiority 0.022** 0.000
BtB ء Non_Specialist ء Accruals_Inferiority Ϫ0.015
* Ϫ0.007
Fee_Decrease Ϫ0.004 0.001
BtS ء Fee_Decrease ء Non_Specialist ء Accruals_Inferiority Ϫ0.026 Ϫ0.023
BtM ء Fee_Decrease ء Non_Specialist ء Accruals_Inferiority 0.000 0.010
BtB ء Fee_Decrease ء Non_Specialist ءAccruals_Inferiority Ϫ0.006 Ϫ0.005

Small_Auditor_Expert Ϫ0.004 0.003
Small_Auditor_Expert ء Non_Specialist ء Accruals_Inferiority 0.010 0.097
***
GCAO Ϫ0.025** 0.004
AAIC 0.000 0.008
AAFR 0.009 Ϫ0.003
Size ͑log͒ 0.001 0.004
**
(continued on next page)
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 103
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
reported in Table 6, and in Table 7 the coefﬁcient on BtS is reduced to Ϫ0.002 ͑insigniﬁcant͒.
These results imply that the relatively more positive market response to BtS switches is explained
by a low likelihood of an audit quality drop. In other words, the market views switches from a Big
4 auditor to a Small auditor more favorably if they have a low likelihood of an audit quality drop.
Note that the Accruals_Inferiority coefﬁcient is negative ͑Ϫ0.012͒ and signiﬁcant at conventional
levels.
29
The negative coefﬁcient implies that the market reacts more negatively in Period 2 to
switches from a non-Big 4 auditor with higher accruals inferiority, possibly suggesting opinion
shopping for those switches.
30
To examine whether the relatively more positive market response to BtS switches, when the
likelihood of an audit quality drop is low, reported in Table 7, Panel A, is due to clients seeking an
audit fee decrease and/or better services from Small auditors, Table 7, Panel B, reports the esti-
mation of Equation ͑4͒. Equation ͑4͒ further interacts Fee_Decrease and Small_Auditor_Expert
with BtS ء Non_Specialist ء Accruals_Inferiority. With these additional interaction variables, the
coefﬁcient on BtS ء Non_Specialist ء Accruals_Inferiority is reduced from 0.037 to 0.020 ͑still
signiﬁcant͒. This decrease is explained by Small_Auditor_Expert ͑0.097 and signiﬁcant͒ but not by

Fee_Decrease ͑Ϫ0.023 but not signiﬁcant͒. It is interesting to note that the coefﬁcient on BtS ء
Non_Specialist ء Accruals_Inferiority is still positive, which implies that reasons other than better
expertise explain the relatively more positive response. If we have controlled for an audit fee
decrease and small auditor expertise adequately, this relatively more positive effect may be attrib-
uted to better services affects other than small auditor expertise.
31
To check if our ﬁndings are robust to missing ﬁrm characteristics, we add the following
variables: SG, BP, Segment, FORSA, Leverage, ∆Financing, Loss, and ROA, and also interact
them with each switching category ͑BtS, BtM, and BtB͒. In untabulated analysis, we ﬁnd that our
main results still hold, that is, the coefﬁcient on Small_Auditor_Expert ء Non_Specialist ء Accru-
als_Inferiority is still positive. We conclude that in Period 2, the market responds relatively more
positively to clients switching from a Big 4 to a Small auditor, who expect better services when
the predecessor’s audit quality is low.
Robustness Checks
This section provides a discussion of various robustness checks.
29
The coefﬁcient on Accruals_Inferiority from Table 6 in Period 2 becomes signiﬁcant in Table 7 once we control for the
low likelihood of an audit quality drop ͑i.e., add Non_Specialist ء Accruals_Inferiority͒.
30
The negative coefﬁcient on Accruals_Inferiority in Table 7 is due to switches from a non-Big 4 auditor.
31
To conﬁrm this conjecture, we add another variable to surrogate for better services: Capacity. Small auditors with more
capacity can focus greater personal attention on publicly traded clients. Using the number of BtS switches to measure
Capacity,weﬁndCapacity ء Non_Specialist ء Accruals_Inferiority is signiﬁcantly positive; however, the coefﬁcient on
BtS ء Non_Specialist ء Accruals_Inferiority is still signiﬁcantly positive, implying that either our measures of better
services are weak or there are other positive reasons. We leave this question to future research.
*, **, *** Signiﬁcant at 0.10, 0.05, and 0.01, respectively. All tests are one-tailed.
Period 1 is from February 1, 2002 to August 23, 2004. Period 2 is from August 24, 2004 to December 31, 2006. Dependent
variable is Daily_Return ͑see the Appendix͒. Size ͑log͒ is the natural logarithm of market capitalization. Accruals_Inferi-
ority is the tercile rank of Accruals_Inferiority, deﬁned in the Appendix. Please see the Appendix for all other variable

deﬁnitions. Coefﬁcients for missing Fee_Decrease, missing Accruals_Inferiority indicator variables, and slope interactions
between ﬁnancial services and Non_Specialist, and among switching categories, ﬁnancial services, and Non_Specialist are
not reported for brevity. Results are quantitatively similar without these indicator variables.
104 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
Dismissals versus Resignations
Prior research suggests that the market responds negatively when auditors resign ͑e.g., Shu
2000͒. If this is the case in our sample period ͑2002–2006͒, we should expect a relatively more
positive market response for dismissals than for resignations. To investigate if there are differences
in market responses between dismissals and resignations, we add to Equation ͑4͒ an indicator
variable for dismissals along with interactions of the Big 4 to Small accounting ﬁrm switching
category ͑BtS_Dismiss͒ and the other two switching categories. We do not ﬁnd that the coefﬁcients
on BtS ء Dismiss, BtM ء Dismiss, and BtB ء Dismiss are signiﬁcantly positive. We conclude that
our main results are not affected by whether the BtS switches are resignations or dismissals.
We conjecture two reasons. First, prior literature indicates that auditors resign due to client
risk ͑e.g., Krishnan and Krishnan 1997; Shu 2000͒, client misalignment ͑Shu 2000͒, and a low
audit fee ͑Hackenbrack and Hogan 2005͒. However, Landsman et al. ͑2009͒ suggest that in the
post-SOX era, the Andersen supply and the SOX 404 demand shocks reduced the sensitivity of
Big N switches to client risk reasons and increased the sensitivity to client misalignment. In
untabulated analysis, we ﬁnd that the BtS rate of resignations increased from 17 percent of total
switches in Period 1 to 25 percent in Period 2. The increased rate of BtS resignations suggests that
the SOX 404 demand shocks provided an opportunity to improve client alignment, and hence, no
negative market response. Second, many of the dismissals may in principle be resignations ͑e.g.,
the auditor forced the dismissal by asking too high an audit fee for next year’s engagement͒.
Control for Departure Return
Many auditor resignation cases, and some dismissal cases, are announced so far before the
successor auditor’s engagement date that our ﬁve-day event window may not include the market
return of the resignation or dismissal. Upon closer examination, resignations ͑n ϭ 288͒ on average
are 35.45 days apart, from the departure date to the engagement date, and dismissals ͑n ϭ 1,536͒

are 2.47 days apart. Consequently, our return variable may be biased since it may omit the market
“correction” ͑i.e., a departure return͒ for the previous resignation and dismissal return that is
outside our ͑Ϫ4,0͒ return window surrounding the engagement of the new auditor.
To control for the omitted departure return, we compute the departure return for any auditor
switch which occurs outside the ͑Ϫ4,0͒ event window, and we add it to our regressions. The
departure return is the ﬁve-day market-adjusted return ͑0,4͒ estimated from the date of the depar-
ture through to the following four business days. We ﬁnd our main results are robust to controlling
for the departure return.
Period Cut-Off
As discussed before, we chose August 23, 2004, as the cut-off point between the two periods
due to three regulatory events. We further examine the market response for the period after
October 17, 2005, when the SEC and the PCAOB encouraged companies to switch from a Big 4
accounting ﬁrm. Then-PCAOB Chairman William McDonough expressed views in a Wall Street
Journal article published on October 17, 2005, stating that smaller clients are better off with a
smaller auditor that ﬁts, not necessarily a Big 4 ͑Civils 2005͒. To examine whether the stock
market response to Big 4 to Small accounting ﬁrm switches is relatively more positive after
October 17, 2005, we use this date to separate Period 2 into two sub-periods.
We ﬁnd that the mean market-adjusted return after October 17, 2005, is 0.008 ͑p ϭ 0.39͒,
higher than 0.006 reported in Table 3. Similarly, regressing returns on market returns and the three
switching groups, BtS, BtM, and BtB, reveals a higher coefﬁcient on BtS ͑0.023, p ϭ 0.06͒,
compared to 0.017 reported in Table 6. These results suggest that the relatively more positive
market response to companies switching from a Big 4 to a Small accounting ﬁrm occurred after
the SEC and the PCAOB encouraged companies to switch.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 105
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
Different Return Deﬁnitions
Recall that the coefﬁcient on market returns reported in Table 6 differs between Period 1
͑0.795͒ and Period 2 ͑1.379͒, suggesting that there may be systematic differences in the risk of the
sample ﬁrms between the two periods. To control for differences in risk for the sample ﬁrms, we

employ the Fama and French ͑1993͒ three-factor abnormal return in our main results. Table 8
reports the results of estimating Equation ͑4͒ by replacing the dependent variable with the Fama
and French ͑1993͒ abnormal return and dropping the market return as an explanatory variable. In
Period 2, the coefﬁcient on Small_Auditor_Expert ء Non_Specialist ء Accruals_Inferiority is
positive ͑0.101 versus 0.097 reported in Table 7, Panel B͒ and signiﬁcant at the conventional level.
For further robustness purposes, we also use the market model-adjusted abnormal return and
the size-adjusted return, and our results still hold.
TABLE 8
Regression Analysis—Interaction of Reason Variables—Dependent Variable is Abnormal
Return from Fama-French Three-Factor Model
Interaction of Audit Quality, Fee Decrease, and Service Variables
Period 1
(n ؍ 1,121)
Period 2
(n ؍ 703)
Adjusted R
2
0.008 0.057
Intercept Ϫ0.022
** Ϫ0.022*
BtS ͑Big4toSmall͒ 0.012 0.011
BtM ͑Big 4 to Medium 2͒ Ϫ0.009 0.007
BtB ͑Big 4 to Big 4͒ 0.013
** 0.004
Non_Specialist 0.003 Ϫ0.003
Accruals_Inferiority Ϫ0.001 Ϫ0.010
**
Non_Specialist ء Accruals_Inferiority 0.005 0.004
BtS ء Non_Specialist ء Accruals_Inferiority Ϫ0.017 0.019
*

BtM ء Non_Specialist ء Accruals_Inferiority 0.039*** 0.007
BtB ء Non_Specialist ء Accruals_Inferiority Ϫ0.012 0.000
Fee_Decrease Ϫ0.002 0.001
BtS ء Fee_Decrease ء Non_Specialist ء Accruals_Inferiority Ϫ0.031 Ϫ0.026
BtM ء Fee_Decrease ء Non_Specialist ء Accruals_Inferiority 0.000 0.001
BtB ء Fee_Decrease ء Non_Specialist ء Accruals_Inferiority Ϫ0.013 Ϫ0.006
Small_Auditor_Expert Ϫ0.005 Ϫ0.005
Small_Auditor_Expert ء Non_Specialist ء Accruals_Inferiority 0.002 0.101
***
GCAO Ϫ0.015 0.015
AAIC 0.001 0.008
AAFR 0.012 Ϫ0.011
Size ͑log͒ 0.002 0.003
*
*, **, *** Signiﬁcant at 0.10, 0.05, and 0.01, respectively. All tests are one-tailed.
Period 1 is from February 1, 2002, to August 23, 2004. Period 2 is from August 24, 2004, to December 31, 2006.
Dependent variable is the abnormal return from the Fama and French ͑1993͒ three-factor model. Event Window is from
four days before the 8-K ﬁling date to the ﬁling date ͑Ϫ4, 0͒. Size ͑log͒ is the natural logarithm of market capitalization.
Accruals_Inferiority is the tercile rank of Accruals_Inferiority deﬁned in the Appendix. Please see the Appendix for other
variable deﬁnitions. Coefﬁcients for missing Fee_Decrease, missing Accruals_Inferiority indicator variables, and slope
interactions between ﬁnancial services and Non_Specialist, and among switching categories, ﬁnancial services, and Non-
_Specialist are not reported for brevity. Results are quantitatively similar without these indicator variables.
106 Chang, Cheng, and Reichelt
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association
Confounding Events
Our results may be inﬂuenced by confounding events around the time of the 8-K ﬁling
announcement. To control for these events, we add indicator variables for individual confounding
event categories to the regression analysis. Confounding events were searched from LexisNexis 9
and Factiva nine days surrounding the 8-K ﬁling date. Knechel et al. ͑2007͒ used a seven-day

search window, but our ﬁve-day return window is greater than their three-day return window.
Search terms were based on Thompson et al. ͑1987, Appendix A͒ and Knechel et al. ͑2007͒.
Search terms are: earnings, revis, restat, correct, dividend, repurchase, offer, issue, acquire, dis-
pose, takeover, tender, r&d, capital expenditure, joint venture, forecast, analysis, analyst, enforce-
ment, distress, litigation, settle, and SEC. Total Confounding equals 1 if a confounding event was
found nine days surrounding the 8-K ﬁling date, 0 otherwise. Individual confounding event cat-
egories and indicator variables are as follows. Asset Change is an acquisition of a subsidiary, plant,
or major asset; Distress is a company reorganization or restructuring of debt; Dividend is a
declaration of a cash or stock dividend; Earnings is an earnings announcement for the year or
quarter; Forecast is a sales forecast; Management Related is hiring, termination, or promotion of
senior management ͑CEO, COO, or CFO͒; Other includes lawsuits, product recalls, plant closure,
or risk of expropriation; Ownership Change is transactions related to stock offerings and stock
repurchases; and Product Related is a product launch, licensing, patent grant, and R&D spending.
Table 9 reports the results of adding these indicator variables. Our results, controlling for con-
founding events, still hold.
32
Other Tests
We employ the following robustness tests and we ﬁnd our main results from Equation ͑4͒ still
hold. These tests include using event windows ͑Ϫ4,1͒ and ͑Ϫ11,0͒, measuring accruals inferiority
with abnormal accruals estimated from the performance-adjusted modiﬁed Jones model ͑Kothari
et al. 2005͒, restricting the audit quality measure exclusively to either Accruals_Inferiority or
Non_Specialist, and adding a size interaction variable for each of the three switching groups ͑BtS,
BtM, BtB͒.
CONCLUSION
We evaluate the market reaction to auditor switching from Big 4 to Small accounting ﬁrms in
the post-Arthur Andersen era. Using data on auditor changes from Audit Analytics for the period
2002 to 2006, we ﬁnd in the second period ͑after August 2004͒ that the market responded non-
negatively to auditor switches from Big 4 to Small ͑third-tier͒ accounting ﬁrms ͑BtS͒, as well as
to switches from Big 4 to Medium 2 and Big 4 to Big 4, while the market reacted negatively to
other switches. We ﬁnd this relatively more positive market reaction to BtS switches occurred

when the Big 4 predecessor did not warrant high audit quality ͑implying a low likelihood of an
audit quality drop͒. We propose that seeking an audit fee decrease and/or seeking better services,
when the likelihood of an audit quality drop is low, should induce a relatively more positive
market response to a switch from a Big 4 to a Small audit ﬁrm after August 2004. We ﬁnd that the
relatively more positive stock market response to a BtS switch is not explained by an audit fee
decrease, but rather by a Small successor auditor who is likely to provide better services. Results
are robust to several tests such as confounding events, various market return measures, controls for
ﬁrm characteristics of complexity, and need for external ﬁnancing and proﬁtability.
Our paper has important policy and managerial implications. Our results suggest that the
market has conﬁdence in companies choosing Small audit ﬁrms to enhance the economic beneﬁt
32
Our basic test results with interaction terms and reason variables ͑Equation ͑4͒͒ are unchanged by the addition of the
confounding event indicator variables.
Market Reaction to Auditor Switching from Big 4 to Third-Tier Small Accounting Firms 107
Auditing: A Journal of Practice & Theory November 2010
American Accounting Association

chang et al - 2010 - market reaction to auditor switching from big 4 to third-tier small accounting firms

Tài liệu liên quan

Tài liệu bạn tìm kiếm đã sẵn sàng tải về