going concern opinion in falling companies auditor dependence and opinion shopping
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going concern opinion in falling companies auditor dependence and opinion shoppingTRANSCRIPT
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Going-concern Opinions in Failing Companies:
Auditor Dependence and Opinion Shopping
Clive S. Lennox*
Economics Dept., University of Bristol.
Abstract:
Contrary to public expectations, companies usually receive clean audit
opinions shortly prior to failure. This study examines whether audit reports in
failing companies are affected by auditor dependence or opinion shopping. I
find audit fees, auditor size, auditor-client tenures and dominant directors are
not significantly associated with going-concern opinions. This suggests audit
reports are not affected by auditor dependence. I also find companies
strategically appoint auditors who are less likely to issue going concern
opinions. This suggests failing companies successfully engage in opinion
shopping.
* I would like to thank the Nuffield Foundation and the Centre for Market and Public Organisation for financial assistance.
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1. Introduction
The financial community and public expect auditors to disclose going-concern problems
before companies fail. Despite this, companies frequently fail after receiving clean audit
opinions. Companies may use auditor switching to avoid receiving going-concern opinions in
two ways (Teoh, 1992). First, auditors may face dismissal threats and may not disclose going-
concern problems if they lack independence. Second, even when auditors report
independently, companies may strategically dismiss (appoint) audit firms that are likely to
give going-concern (clean) audit opinions - I call such behavior ‘opinion shopping’. This
study examines whether failing companies avoid going-concern opinions because of auditor
dependence and opinion shopping.
Existing studies find audit opinions are not associated with proxies for auditor
dependence. Krishnan et al., (1996) find no significant association between audit reports and
switch probabilities, which suggests switch threats do not affect audit opinions. Since fees
might compromise independence, most EU countries ban the provision of some or all non-
audit services.1 However, researchers find no significant association between non-audit fees
and audit opinions in the UK and Australia, where non-audit services are not banned but fees
are publicly disclosed (Lennox, 1999a; Craswell, 1999). Since long auditor-client tenures may
increase auditor dependence, some countries require mandatory rotation of auditors.2 In the
US, AICPA and the SEC Practice Section have rejected calls for mandatory rotation of audit
firms (AICPA, 1978, 1992). Consistent with their position, Louwers (1998) finds no
significant relation between auditor-client tenures and audit opinions in the US.
Regulators respond to concerns about opinion shopping in a variety of ways. Several
EU countries require mandatory retention of audit firms to deter strategic switching.3 In
addition, most countries require communication between outgoing auditors, incoming
1 Belgium, France and Italy ban the provision of all non-audit services while other EU countries allow tax and financial advisoryservices. Bookkeeping services are banned in all EU countries except Denmark, Ireland, Luxembourg, Netherlands, Portugal,Sweden and the UK. Legal services are allowed in all EU countries except Belgium Denmark, France, Greece, Italy and Portugal.Corporate recovery services are allowed in all EU countries except Belgium, France, Italy and Portugal (Buijink et al., 1996).2 Listed companies in Italy are required to rotate audit firms every nine years . In the UK, the Cadbury Committee (1992)recommends rotation of individual audit partners but not the rotation of audit firms. The Australian Society of Certified PublicAccountants (1993) recommends partner rotation every seven years, but this is rejected by the Australian Joint StandingCommittee (1996). In the US, members of the SEC Practice Section are required to rotate partners every seven years.
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auditors and shareholders.4 The aim of such communication is to prevent companies
switching auditors to conceal unfavourable information. Evidence on opinion shopping is
conflicting because researchers employ different methodologies. Some studies conclude that
companies do not successfully engage in opinion shopping, because post-switch opinions are
not modified less often than pre-switch opinions (Chow and Rice, 1982; Smith, 1986;
Krishnan, 1994; Krishnan and Stephens, 1995). More recently Lennox (2000a, 2000b) shows
this conclusion is flawed, because it is necessary to compare the reports that companies would
receive under opposite switch decisions. He finds the evidence strongly supports successful
opinion shopping by UK and US companies.
In contrast to the above studies, this paper focuses exclusively on failing companies
for two reasons. First, auditor dependence may be more apparent in distressed companies
since managers may fear that going-concern opinions increase the probability of failure (the
‘self-fulfilling prophecy’ hypothesis). By sampling failing companies, I therefore expect to
increase the power of my tests for auditor dependence. Second, the regulatory implications of
auditor dependence and opinion shopping are clearer in failing than surviving companies.
Going-concern opinions issued to surviving companies and clean opinions issued to failing
companies are frequently classified as reporting ‘errors’ (e.g., Koh, 1991; Lennox, 1999b).
Therefore, the number of reporting errors is lower if surviving companies avoid going-
concern opinions and higher if failing companies avoid going-concern opinions. It follows
there is a stronger case for regulatory intervention if failing companies use switch threats or
opinion shopping to avoid going-concern opinions.
This is not the first study to examine going-concern opinions in failing companies,
but it is the first to consider auditor dependence and opinion shopping. Previous studies find
failing companies receive going-concern opinions more often when financial conditions are
3 There is mandatory retention of audit firms in France (six years), Portugal (four years), Belgium, Spain and Italy (three years),.4 In the US, the SEC must be informed of auditor changes within five business days and companies have to disclosedisagreements with auditors and modified opinions in the two years preceding auditor changes. To ensure compliance and toidentify companies who file late, the SEC Practice Section requires independent disclosure by audit firms as well as companies.The US Public Oversight Board (1994) recommends auditors meet with boards of directors and audit committees at least once ayear and auditors discuss the appropriateness of financial statements. Communication requirements are less stringent in the UKthan in the US (Dunn et al., 1994; Lennox, 2000b).
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clearly poor (Menon and Schwartz, 1987; McKeown et al., 1991; Mutchler et al., 1997;
Casterella et al., 1999). Consistent with these studies, I find auditors give clean opinions to
companies that appear healthy but which subsequently fail.
Section 2 explains how I test for auditor dependence and opinion shopping. Section 3
describes how I collect the sample and gives descriptive statistics. Section 4 tests for auditor
dependence and Section 5 tests for opinion shopping. Section 6 concludes with implications
for audit regulation.
2. Methodology
This section explains how I test for auditor dependence and opinion shopping and describes
the control variables shown to be important in previous studies.
2.1 Auditor dependence
2.1.1 Audit fees ( itAF )
When audit firms earn high fees, they may face economic pressures to give clean opinions in
order to deter clients from switching to other auditors. This suggests a negative association
between going-concern opinions and audit fees.5 However, auditors may give going-concern
opinions more often when audit fees are high. Auditors who exert more effort (and charge
higher fees) may be more likely to discover going-concern problems. Moreover, auditors may
exert more effort or charge higher risk premiums when companies face going-concern
problems. Therefore, the predicted relation between audit fees and going-concern opinions is
ambiguous. An important contribution of this study is that I use actual audit fees, which are
publicly disclosed by UK companies. Since US companies are not required to disclose fees,
previous studies use client characteristics such as size to capture the effects of fees (e.g.,
Louwers, 1998; McKeown et al., 1991).6
5 UK companies are only required to disclose non-audit fees since 1994. Since most sample companies fail prior to 1994, I donot test the association between non-audit fees and audit opinions.6 Threats to auditor independence may be more severe when an individual client’s fee is a large proportion of an audit firm’stotal income. Unfortunately, data on total fees are unavailable for small audit firms. However, this is unlikely to be a problem as
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2.1.2 Audit firm size ( itAUD )
I control for auditor size using a dummy variable ( itAUD ), which equals one if the audit firm
is one of the Big Five and zero otherwise. Reputation and deep pockets theories predict a
positive association between audit firm size and audit quality. DeAngelo (1981) argues large
audit firms have more incentive to avoid reputation-damaging criticism compared to small
audit firms. Dye (1993) suggests large audit firms are more likely to disclose problems
because they have more wealth at risk from litigation. In addition, individual clients
contribute proportionately less to the total incomes of large audit firms. These arguments
mean large audit firms have more incentive to detect and report going-concern problems. I
therefore expect a positive relation between audit firm size and going-concern opinions.
2.1.3 Auditor-client tenure ( itTEN )
My tenure variable ( itTEN ) equals the number of years that firms audit their clients. When
auditors have long relations with their clients, expected future rents may be higher and may
increase the threat to auditor independence. This suggests a negative association between
auditor-client tenures and going-concern opinions. However, an opposite positive association
is also possible. Long tenures may mean audit firms better understand clients’ financial
conditions and are more likely to detect going-concern difficulties. Therefore, the predicted
relation between tenures and audit opinions is ambiguous.
2.1.4 Dominant directors ( itDOMS , itDOMR )
Dominant directors may exert more pressure on auditors to issue clean opinions compared to
boards where control is exercised democratically.7 I measure board dominance using two
large audit firms have more highly diversified client portfolios and I control for the association between audit firm size andgoing-concern opinions.7 For example, Robert Maxwell held over 99% of the board’s shares in Maxwell Communications (MCC) and MCC receivedclean opinions prior to failure. The Financial Times writes (17th June, 1992), “Maxwell was able to seize money so quicklybecause the sweeping powers he had secured as chairman of his companies allowed him to move money with little reference toanyone else. His policy of divide and rule concealed what he was doing from many . . . the audit for the year to April 1991 nevertook place. One of the ways Maxwell avoided detection was by the simple device of extending the audit period. He simplydeclared that the next one would be in December 1991, a gap of 21 months.”
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variables - the proportion of board shares held by the director who owns most shares
( itDOMS ) and the proportion of board remuneration earned by the highest paid director
( itDOMR ). If dominant directors exert more pressure on auditors compared to democratic
boards, I expect a negative relation between director dominance and going-concern opinions.
To my knowledge, this is the first paper to examine whether dominant directors influence
audit opinions.
2.2 Control variables
2.2.1 Financial condition ( itBLAG , itR )
Previous studies find auditors disclose going-concern problems more often when companies
are close to failure. Consistent with previous studies, I include a bankruptcy lag variable
( itBLAG ) as an ex-post measure of financial condition (McKeown et al., 1991; Citron and
Taffler, 1992; Mutchler et al., 1997). The bankruptcy lag equals the number of calendar days
between audit opinions and failure. I expect a negative association between bankruptcy lag
and going-concern opinions since it is easier for audit firms to detect going-concern problems
when the bankruptcy horizon is short.8
Auditors may have difficulty in identifying going-concern problems when companies
fail with little warning. Therefore, an ex-ante measure of financial condition is also likely to
be correlated with audit opinions. Some studies use financial ratios or bankruptcy
probabilities predicted from accounting variables as ex-ante controls for financial condition
(Menon and Schwartz, 1987; McKeown et al., 1991; Mutchler et al., 1997; Casterella et al.,
1999).9 In contrast, I use stock returns ( itR ) for two reasons. First, predicted bankruptcy
probabilities likely do not capture all publicly available information about financial condition.
For example, the information content of financial ratios and predicted bankruptcy
8 Similarly, bankruptcy prediction models are more accurate over shorter bankruptcy horizons (e.g., Dambolena and Khoury,1980).9 Chen and Church (1996) show debt covenant violations significantly predict US going-concern opinions. Since UK companiesdo not generally disclose debt covenant violations, I am unable to test their association with audit opinions.
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probabilities may be reduced if distressed companies choose accounting policies that cover up
poor performance (Murphy and Zimmerman, 1993). In contrast, share prices reflect all
publicly available information if the semi-strong efficient markets hypothesis holds. Second,
going-concern opinions and share prices are forward looking variables, whereas financial
statements are primarily backward looking. Therefore, going-concern opinions are likely
more strongly associated with stock returns than accounting variables. Since going-concern
problems are more apparent in companies with poor stock returns, I expect a negative relation
between rates of return and going-concern opinions.
2.2.2 Audit lag ( itALAG )
The audit lag is defined as the number of calendar days between financial year-ends and audit
opinions. Research shows auditors give going-concern opinions more often when audit
opinions are delayed (McKeown et al., 1991; Louwers, 1998). There are several possible
explanations for this. First, auditors may discover problems more frequently when they carry
out more audit tests. Second, auditors may undertake more tests if they expect going-concern
problems. Third, managers and auditors may engage in prolonged negotiations when there are
going-concern uncertainties. Finally, auditors may delay issuing audit reports in the hope that
companies can resolve their problems and avoid going-concern opinions. I expect a positive
association between audit lag and going-concern opinions.
2.2.3 Prior audit opinions ( 1−itG )
The audit report dummy ( itG ) equals one when auditors issue going-concern opinions and
zero otherwise. Several studies find auditors issue going-concern opinions more often when
prior opinions disclose going-concern problems (e.g., Mutchler, 1985). In other words, there
is persistence in audit reporting. Therefore, I expect a positive relation between prior and
current going-concern opinions.
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2.3 Opinion shopping
I test for opinion shopping using the methodology employed by Lennox (2000a, 2000b). I use
an audit reporting model to predict the opinions that companies would receive if they switch
and if they do not switch. I include these predicted opinions in an auditor switching model to
test whether companies would receive going-concern opinions more often under opposite
switch decisions. Since auditor changes may be associated with financial distress, I control for
the effects of financial condition ( itBLAG , itR ) on auditor switching. I also include audit lag
( itALAG ) in the switching model, as newly appointed auditors may take longer to issue their
opinions compared to retained incumbent auditors.
3. The Data
3.1 Sample
My sample consists of UK companies traded on the London Stock Exchange (LSE), the
Unlisted Securities Market (USM) or the Alternative Investment Market (AIM). There are
355 companies that fail between 1980-99, where failure is defined as compulsory liquidation,
creditors’ voluntary liquidation, receivership or administration.10 I identify failed companies
using the “CGT Capital losses” publication. Fig. 1 shows the time-line for failing companies
and Table 1 describes how I obtain my final sample.
[INSERT TABLE 1 & FIG. 1 HERE]
I first require a minimum of two years’ accounts in order to identify auditor changes
and prior audit opinions. Where available, I collect final (t = -2) and penultimate (t = -3)
accounts from Companies House. There are 283 companies that meet this restriction. Second,
I require stock returns from Datastream and this results in the loss of a further 31 companies.11
10 I exclude voluntary liquidations by shareholders as these companies are typically non-distressed.11 In 9 companies there are insufficient data to calculate final annual returns because they become listed after the penultimateyear-end (t = -2). The remaining 22 companies are not listed by Datastream.
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Third, I use accounts prior to penultimate year-ends in order to identify auditor appointment
dates and auditor-client tenures. In 15 companies I am unable to identify tenures because
accounts are not available for a sufficient number of years. My final sample consists of 237
companies and wherever possible I collect data for the last three year-ends (t = -2, -3, -4).
Although the potential sample size is 711 company-year observations (711 = 3*237), 51
observations are lost due to companies becoming listed during the sample period. Therefore,
my final sample consists of 660 company-year observations. There are 237 observations at
final year-ends (t = -2), 218 at penultimate year-ends (t = -3) and 205 at t = -4. With the
exception of the bankruptcy lag and audit opinions, I measure all variables at year-ends rather
than audit opinion dates.12
3.2 Descriptive statistics
Table 2 reports current and prior audit opinions for switching and non-switching companies.
There are 57 (8.64%) auditor changes and 90 (13.64%) going-concern opinions. Auditor
changes occur more frequently when prior opinions disclose going-concern problems. There
are 12 (28.57%) auditor changes when prior opinions disclose going-concern problems, but
only 45 (7.28%) auditor changes when prior opinions are clean. The difference between these
frequencies (28.57% and 7.28%) is statistically significant at the 1% level.
Auditor changes are significantly associated with changes in audit opinions. There are
13 (22.81%) changes in opinions when companies change audit firms, but only 67 (11.11%)
opinion changes when companies retain audit firms. The difference between these frequencies
(22.81% and 11.11%) is statistically significant at the 5% level.
[INSERT TABLE 2 HERE]
12 There is evidence that the market reacts negatively when going-concern opinions are announced (Firth, 1978; Banks andKinney, 1982; Fleak and Wilson, 1994; Jones, 1996). This means there is a potential reverse causality problem from auditopinions to stock returns. I avoid the reverse causality problem by measuring stock returns at year-ends rather than audit opiniondates.
10
Consistent with extant research, post-switch reports do not disclose going-concern
problems less often than pre-switch reports. Going-concern opinions are issued in 13
(22.81%) post-switch reports and 12 (21.05%) pre-switch reports. This does not refute the
opinion shopping hypothesis, as we shall see that prior opinions poorly proxy the reports that
companies would receive if they made different switch decisions.
Table 3 summarises auditor changes, audit opinions, bankruptcy lags and share
returns prior to failure. The frequency of auditor changes does not change significantly as
bankruptcy draws near. In contrast, the frequency of going-concern opinions rises from 5.37%
three periods prior to failure (t = -4) to 25.74% in final reports (t = -2). The median
bankruptcy lag falls from 1076 to 291 days between the third and final audit opinions while
the median lag between suspension and bankruptcy is only 3 days. Median (mean) stock
returns fall from 8.69% (26.46%) three periods prior to failure to –26.89% (-7.49%) at final
year-ends. Median (mean) returns are -75.93% (-67.10%) between final year-ends and
suspension. This suggests going-concern difficulties often do not become apparent until
shortly prior to failure.
[INSERT TABLE 3 HERE]
Table 4 provides descriptive statistics on the auditor dependence and control
variables. Mean (median) audit fees are £96,450 (£43,000); large firms audit 58% of
companies; and, mean (median) auditor-client tenures are 7.33 (6) years. The mean (median)
proportions of board shares held by dominant directors are 67.61% (67.62%). The mean
(median) proportions of board remuneration earned by dominant directors are 32.41%
(29.48%).
Table 4 shows the audit fee ( itAF ), bankruptcy lag ( itBLAG ), returns ( itR ), and
audit lag ( itALAG ) variables have skewed distributions and contain outlying variables. To
account for this, I use log transformations ( )ln( ,)ln( ,)ln( ,)ln( itititit ALAGRBLAGAF ). In
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unreported results, I find my conclusions are robust to using either untransformed or log
variables although explanatory power is higher with the latter.
[INSERT TABLE 4 HERE]
Table 5 reports descriptive statistics for the explanatory variables across going-
concern and non-going-concern opinions. The univariate associations between opinions and
the auditor dependence variables are not statistically significant. In contrast, audit opinions
are significantly associated with financial conditions. Going-concern opinions are issued more
often when bankruptcy lags ( )ln( itBLAG ) are short and returns ( )ln( itR ) are low. In
addition, going-concern opinions are issued more often when audit lags ( )ln( itALAG ) are
long.
[INSERT TABLE 5 HERE]
4. Auditor dependence
4.1 Model specification
In this section, I test the multivariate associations between going-concern opinions and the
auditor dependence variables ( )ln( itAF , itAUD , itTEN , itDOMS , itDOMR ). As
explained in section 2.2, I also control for financial condition ( )ln( itBLAG , )ln( itR ), audit
lag ( )ln( itALAG ) and prior audit opinions ( 1−itG ). Eq. (1) is the model of going-concern
opinions:
itititit
itititititit
GALAGRBLAGDOMRDOMSTENAUDAFG
εαααααααααα
++++++++++=
− 19876
543210*
)ln()ln()ln()ln(
(1)
where:
.0 if 0
1 if 0*
*
=<=≥
itit
itit
GG
GG
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4.2 Estimation results
The probit estimation results for Eq. (1) are given in Table 6.13 Column (1) shows the
coefficients on audit fees ( )ln( itAF ), audit firm size ( itAUD ), tenure ( itTEN ) and director
dominance ( itDOMS , itDOMR ) are all statistically insignificant. Moreover, the coefficients
on audit firm size and director dominance have opposite signs to those predicted by the
auditor dependence hypothesis. Therefore, the auditor dependence variables do not explain
why failing companies avoid going-concern opinions.14
[INSERT TABLE 6 HERE]
Columns (1) and (2) show auditors disclose going-concern uncertainties more often
when financial conditions are poor. The significant negative coefficients on bankruptcy lag
( )ln( itBLAG ) indicate more frequent going-concern opinions when companies are close to
failure. The significant negative coefficients on returns ( )ln( itR ) show auditors issue going-
concern opinions more often when companies have poor market performance. The significant
positive coefficients on audit lag ( )ln( itALAG ) show auditors give going-concern opinions
more often when audit reports are delayed.15 The significant positive coefficients on prior
opinions ( 1−itG ) reveal strong persistence in audit reporting. Audit firms give going-concern
opinions more often when companies receive prior going-concern opinions. The explanatory
power of the reporting model compares favourably with previous studies. The pseudo R2 is
41.6% in Column (2), compared with 36.4% in Casterella et al., (1999), 30.6% in Mutchler et
al., (1997), 24.7% in McKeown et al., (1987), and 10.5% in McKeown et al., (1991).
13 In unreported estimations, I find probit and logit results are very similar.14 In unreported estimations, I find average audit fees ( ).ln( iAF ) and abnormal audit fees ( ).ln()ln( iAFitAF − ) are not
significantly correlated with going-concern opinions. I also find no evidence of a non-monotonic relation between tenure andgoing-concern opinions.
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5. Opinion shopping
5.1 Model specifications
In this section, I estimate an audit reporting model in order to predict the opinions companies
would receive if they switch or do not switch auditors. Eq. (2) omits the auditor dependence
variables as Section 4 shows them to be insignificant. It includes an auditor switch dummy
( itS ), which equals one if there is a change in audit firm and zero otherwise. Eq. (2) also
includes an interaction term between the switch dummy and prior opinions ( 1−ititGS ), to test
whether auditor changes are associated with less reporting persistence ( 06 <α ).
itititit
ititititit
GSSGALAGRBLAGG
εααααααα
+++++++=
−
−
165
143210* )ln()ln()ln(
(2)
where:
.0 if 0
1 if 0*
*
=<=≥
itit
itit
GG
GG
I use Eq. (2) to predict the opinions companies would receive if they switch ( *1̂itG ) or
do not switch ( *0̂itG ). My opinion shopping variable is the difference between these predicted
opinions ( *0*1 ˆˆitit GG − ). When *0*1 ˆˆ
itit GG − > 0, companies are more likely to receive going-
concern opinions if they switch auditors. When *0*1 ˆˆitit GG − < 0, companies are more likely to
receive going-concern opinions if they do not switch. If companies use auditor appointments
to avoid going-concern opinions, auditor changes occur when *0*1 ˆˆitit GG − < 0 and do not
occur when *0*1 ˆˆitit GG − > 0. I include the opinion shopping variable in Eq. (3) to test whether
companies receive going-concern opinions less often as a result of auditor switching. Under
the alternative hypothesis that companies successfully engage in opinion shopping, the
coefficient on *0*1 ˆˆitit GG − is negative ( 01 <θ ). Under the null hypothesis that companies do
15 In unreported results, I find the abnormal audit lag ( ).ln()ln( iALAGitALAG − ) is significantly associated with going-
concern opinions, but its association is weaker than the unadjusted audit lag ( )ln( itALAG ).
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not successfully engage in opinion shopping, 01 =θ . Eq. (3) also controls for financial
condition ( )ln( ,)ln( itit RBLAG ) and audit lag ( )ln( itALAG ).
ititititititit ALAGRBLAGGGS ωθθθθθ ++++−+= )ln()ln()ln()ˆˆ( 432*0*1
10* (3)
where:
.0 if 0
1 if 0*
*
=<=≥
itit
itit
SS
SS
Substituting Eq. (2) into Eq. (3) gives a reduced form model of auditor switching (Eq.
(4)):
itititititit GALAGRBLAGS ωαθθθθαθθ ++++++= − 161432510* ˆ)ln()ln()ln(ˆ (4)
where:
.0 if 0
1 if 0*
*
=<=≥
itit
itit
SS
SS
It is important to note that Eq. (3) includes the difference between predicted going-concern
opinions ( *0*1 ˆˆitit GG − ) while Eq. (4) includes prior audit opinions ( 1−itG ). Eq. (4) demonstrates
there is a positive association between prior going-concern opinions and auditor changes if
06̂1 >αθ . This condition holds if auditor changes are associated with less reporting
persistence ( 06̂ <α ) and companies successfully engage in opinion shopping ( 01 <θ ).
5.2 Estimation results
The probit estimation results for Eqs. (2)-(4) are given in Columns (1)-(3) of Table 7. Column
(1) shows the coefficient on the interaction term ( 1−ititGS ) is negative and statistically
significant. This means there is significantly less persistence in going-concern opinions when
there are auditor changes ( 06̂ <α ). In other words, audit opinions change more often when
there are changes in audit firms. The coefficients on the other variables are consistent with the
models in Table 6. Companies receive going-concern opinions more often when financial
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conditions ( )ln( ,)ln( itit RBLAG ) are poor, audit lags ( )ln( itALAG ) are long, and prior
opinions ( 1−itG ) disclose going-concern uncertainties.
[INSERT TABLE 7 HERE]
The results for the reporting model in Column (1) are used to predict going-concern
opinions if companies switch or do not switch auditors. When prior opinions disclose going-
concern problems, the mean predicted going-concern probabilities are 39.42% if companies
switch and 69.05% if companies do not switch. Therefore, companies are less likely to
receive repeat going-concern opinions if they change auditors. When prior opinions do not
disclose going-concern problems, the mean predicted going-concern probabilities are 12.21%
if companies switch and 10.01% if companies do not switch. Therefore, companies are more
likely to receive repeat clean opinions if they do not change auditors.
The results for the auditor switching models in Columns (2) and (3) are consistent
with successful opinion shopping. In Column (2), the significant negative coefficient ( 01 <θ )
on the opinion shopping variable ( *0*1 ˆˆitit GG − ) shows companies receive going-concern
opinions less often as a result of auditor changes. In Column (3), the significant positive
coefficient ( 06̂1 >αθ ) on prior reports ( 1−itG ) shows auditor changes occur more frequently
after companies receive going-concern opinions. This is what we would expect since Column
(1) shows that 06̂ <α . Auditor changes are not significantly associated with financial
condition ( )ln( ,)ln( itit RBLAG ). The significant positive coefficients on audit lag
( )ln( itALAG ) indicate newly appointed auditors take longer than retained incumbents to
issue their audit opinions.
6. Conclusions and implications for audit regulation
There are a number of regulatory measures that aim to strengthen auditor independence and
16
prevent opinion shopping. These include banning non-audit services, mandatory rotation,
mandatory retention and communication between outgoing auditors, incoming auditors and
shareholders. This paper investigates whether auditors give clean opinions to failing
companies due to auditor dependence or opinion shopping. I sample failing rather than
surviving companies in order to increase the power of my tests for auditor dependence and to
provide clearer recommendations for policy-makers.
Since bargaining between audit firms and clients occurs behind closed doors it is
arguably difficult to observe the effects of auditor dependence on audit opinions. Despite this,
I hypothesise managers are more likely to exert pressure and auditors are more likely to
acquiesce when: audit fees are high, audit firms are small, auditor-client tenures are long and
individual directors dominate boards. I find no significant association between these variables
and going-concern opinions, which suggests either failing companies do not pressurise
auditors or auditors report independently.
I test for opinion shopping by predicting the reports switching and non-switching
companies would receive under opposite switch decisions. My results suggest companies
would receive going-concern opinions significantly more often if auditor changes were
different to those actually observed. This suggests failing companies successfully engage in
opinion shopping.
My results indicate policy-makers should be more concerned with opinion shopping
than auditor dependence. Regulators might curb opinion shopping by introducing mandatory
retention of audit firms or by requiring greater disclosure about auditor changes. The need for
more communication between investors, outgoing and incoming auditors is particularly
relevant in the UK. Companies and auditors are not required to disclose auditor changes or
disagreements to the stock market and incoming auditors do not have access rights to the
working papers of outgoing auditors. However, policy recommendations such as increased
communication or mandatory retention carry an important caveat. If regulators deter opinion
shopping, the number of going-concern opinions is likely to increase in surviving as well as
failing companies.
17
t = -4 t = -3 t = -2 t = -1 t = 0
Third year-end Penultimate Final Suspension of Bankruptcyprior to failure year-end year-end share trading date
Fig. 1. The time-line for failing companies.
Table 1Derivation of the final sample.
Companies Company-yearobservations
Population of failing UK companies(1980-99).
355 .
Less: 72 companies where final andpenultimate accounts are unavailable.
(72) .
Less: 31 companies where stock returns areunavailable.
(31) .
Less: 15 companies where auditor-clienttenures are unavailable.
(15) .
Final sample of companies. 237 .
Less: 51 observations where companiesbecome listed during the sample period.
. (51)
Final sample of company-year observations. 237 660
Notes:Company-year observations are taken from the last three accounting year-ends (t = -4, -3, -2). See Fig. 1.
18
Table 2Current ( itG ) and prior ( 1−itG ) going-concern opinions.
Auditor retentions ( itS = 0)
itG = 1 itG = 0 Totals
1−itG = 1 20 10 30
1−itG = 0 57 516 573
Totals 77 526 603
Auditor changes ( itS = 1)
itG = 1 itG = 0 Totals
1−itG = 1 6 6 12
1−itG = 0 7 38 45
Totals 13 44 57
Notes:t = -4, -3, -2 at the last three year-ends prior to failure. See Fig. 1.
itS = 1 if company i changes audit firm at time t; 0 otherwise.
itG = 1 if company i receives a going-concern opinion at time t; 0 otherwise.
19
Table 3Auditor changes, audit opinions, and financial conditions prior to failure.
Time (t) -4 -3 -2 -1
No. (%) of auditor changes 16(7.80%)
21(9.63%)
20(8.44%)
.
.
No. (%) of going-concern opinions 11(5.37%)
18(8.26%)
61(25.74%)
.
.
Median (mean) bankruptcylag ( itBLAG )
1076(1098)
684(707)
291(321)
3(39)
Median (mean) returns ( itR ) 8.69%(26.46%)
-9.67%(6.58%)
-26.89%(-7.49%)
-75.93%(-67.10%)
Observations 205 218 237 237
Notes:t = -4, -3, -2 at the last three year-ends prior to failure and t = -1 at suspension. See Fig. 1.
itBLAG = Number of days between the audit opinion and the bankruptcy date.
1)1( −−−+= itSPitSPitDPSitSPitR
itSP = Share price of company i at time t.
itDPS = Dividend per share of company i at time t.
20
Table 4Descriptive statistics for the explanatory variables.
Mean Median Min MaxAuditor dependencevariables
itAF 96.45 43.00 0.10 3300
itAUD 0.58 1 0 1
itTEN 7.33 6 1 26
itDOMS 67.61 67.62 21.25 100
itDOMR 32.41 29.48 5.52 100
Control variables
itBLAG 690 673 17 2135
itR 7.70% -8.10% -98.86% 1622%
itALAG 135 122 27 548
Notes:t = -4, -3, -2 at the last three year-ends prior to failure. See Fig. 1. Observations = 660.
itAF = Audit fee (£000).
itAUD = 1 if audit firm is one of the Big Five; = 0 otherwise. An audit firm belongs to the ‘Big Five’ if it is
Ernst and Young, Price WaterhouseCoopers, Arthur Andersen, KPMG, Deloitte and Touche or a pre-mergerfirm (Arthur Young, Ernst & Whinney, Price Waterhouse, Coopers & Lybrand, Deloittes Haskins and Sells,Peat Marwick Thomson McLintock, Touche Ross, Spicer and Pegler, Spicer and Oppenheim).
itTEN = Number of years the audit firm has audited the client.
ngsshareholdi Boardngshareholdi sdirector' individualLargest *%100=itDOMS
onremunerati Boarddirector paidhighest of onRemunerati*%100=itDOMR
itBLAG = Number of days between the audit opinion and the bankruptcy date.
1)1( −−−+= itSPitSPitDPSitSPitR
itSP = Share price of company i at time t.
itDPS = Dividend per share of company i at time t.
itALAG = Number of days between the year-end and the audit opinion.
21
Table 5Descriptive statistics for going-concern and non-going-concern opinions.
Going-concernopinions ( itG = 1)
Non-going-concernopinions ( itG = 0)
Mean Median Mean MedianAuditor dependencevariables
)ln( itAF 10.75 10.67 10.69 10.66
itAUD 0.53 1 0.59 1
itTEN 7.46 5.50 7.32 6.00
itDOMS 72.67% 76.81% 66.81% 66.36%
itDOMR 34.16% 29.57% 32.14% 29.48%
Control variables)ln( itBLAG 5.76** 5.76** 6.43 6.58
)ln( itR -74.16%** -61.15%** -7.47% -3.90%
)ln( itALAG 5.24** 5.19** 4.75 4.76
Observations 90 570
Notes:** Significant difference between going-concern and non-going-concern opinions (1% level).t = -4, -3, -2 at the last three year-ends prior to failure. See Fig. 1.
itG = 1 if company i receives a going-concern opinion at time t; 0 otherwise.
itAF = Audit fee (£000).
itAUD = 1 if audit firm is one of the Big Five; = 0 otherwise. An audit firm belongs to the ‘Big Five’ if it is
Ernst and Young, Price WaterhouseCoopers, Arthur Andersen, KPMG, Deloitte and Touche or a pre-mergerfirm (Arthur Young, Ernst & Whinney, Price Waterhouse, Coopers & Lybrand, Deloittes Haskins and Sells,Peat Marwick Thomson McLintock, Touche Ross, Spicer and Pegler, Spicer and Oppenheim).
itTEN = Number of years the audit firm has audited the client.
ngsshareholdi Boardngshareholdi sdirector' individualLargest *%100=itDOMS
onremunerati Boarddirector paidhighest of onRemunerati*%100=itDOMR
itBLAG = Number of days between the audit opinion and the bankruptcy date.
)1)ln(()ln( −+= itSPitDPSitSPitR
itSP = Share price of company i at time t.
itDPS = Dividend per share of company i at time t.
itALAG = Number of days between the year-end and the audit opinion.
22
Table 6The association between going-concern opinions and auditor dependence.
Expectedsign
(1) (2)
Auditor dependencevariables
)ln( itAF ? -0.078(-1.098)
.
.
itAUD + -0.018(-0.107)
.
.
itTEN ? 0.015(0.999)
.
.
itDOMS - 0.004(1.177)
.
.
itDOMR - 0.001(0.176)
.
.Control variables
)ln( itBLAG - -0.370(-2.976)**
-0.352(-2.820)**
)ln( itR - -0.745(-6.066)**
-0.693(-5.684)**
)ln( itALAG + 1.526(6.092)**
1.543(6.534)**
1−itG + 1.710(6.037)**
1.729(6.082)**
CONSTANT ? -6.477(-3.644)**
-7.055(-5.126)**
Pseudo R2 42.36% 41.58%Notes:t = -4, -3, -2 at the last three year-ends prior to failure. See Fig. 1. Observations = 660.z-statistics reported in parentheses. ** Significant at the 1% level. * Significant at the 5% level.
itG = 1 if company i receives a going-concern opinion at time t; 0 otherwise.
itAF = Audit fee (£000).
itAUD = 1 if audit firm is one of the Big Five; = 0 otherwise. An audit firm belongs to the ‘Big Five’ if it is Ernst and
Young, Price WaterhouseCoopers, Arthur Andersen, KPMG, Deloitte and Touche or a pre-merger firm (Arthur Young,Ernst & Whinney, Price Waterhouse, Coopers & Lybrand, Deloittes Haskins and Sells, Peat Marwick ThomsonMcLintock, Touche Ross, Spicer and Pegler, Spicer and Oppenheim).
itTEN = Number of years the audit firm has audited the client.
ngsshareholdi Boardngshareholdi sdirector' individualLargest *%100=itDOMS
onremunerati Boarddirector paidhighest of onRemunerati*%100=itDOMR
itBLAG = Number of days between the audit opinion and the bankruptcy date.
)1)ln(()ln( −+= itSPitDPSitSPitR
itSP = Share price of company i at time t.
itDPS = Dividend per share of company i at time t.
itALAG = Number of days between the year-end and the audit opinion.
23
Table 7The association between going-concern opinions and opinion shopping.
Expectedsign
(1) Expectedsign
(2) (3)
Dependent variable *itG *
itS *itS
Opinion shopping variable*0*1 ˆˆ
itit GG − ..
- -0.555(-3.571)**
.
.Control variables
itS ? 0.174(0.522)
.
...
1−ititGS - -1.461(-2.447)*
.
...
)ln( itBLAG - -0.349(-2.773)**
- 0.080(0.776)
0.080(0.776)
)ln( itR - -0.709(-5.831)**
- -0.047(-0.423)
-0.047(-0.423)
)ln( itALAG + 1.608(6.428)**
+ 0.398(2.101)*
0.398(2.101)*
1−itG + 2.088(6.050)**
+ ..
0.811(3.571)**
CONSTANT ? -7.429(-5.233)**
? -3.803(-3.156)**
-3.899(-3.244)**
Pseudo R2 42.69% 5.50% 5.50%Notes:t = -4, -3, -2 at the last three year-ends prior to failure. See Fig. 1. Observations = 660.z-statistics reported in parentheses. ** Significant at the 1% level. * Significant at the 5% level.
itG = 1 if company i receives a going-concern opinion at time t; 0 otherwise.
.0 if 0*
;1 if 0* =<=≥ itGitGitGitG
firm.audit changes icompany if variableconcern-going Predicted*1̂ =itG
firm.audit changenot does icompany if variableconcern-going Predicted*0ˆ =itG
itS = 1 if company i changes audit firm at time t; 0 otherwise.
.0 if 0*
;1 if 0* =<=≥ itSitSitSitS
itBLAG = Number of days between the audit opinion and the bankruptcy date.
)1)ln(()ln( −+= itSPitDPSitSPitR
itSP = Share price of company i at time t.
itDPS = Dividend per share of company i at time t.
itALAG = Number of days between the year-end and the audit opinion.
24
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