non-audit service fees and audit quality

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DOI: 10.1111/j.1475-679X.2007.00266.x

Journal of Accounting Research Vol. 46 No. 1 March 2008

Printed in U.S.A.

Non-audit Service Fees and Audit Quality: The Impact of Auditor

Specialization

C H E E - Y E O W L I M ∗ A N D H U N - T O N G T A N ∗

Received 26 January 2006; accepted 9 May 2007

ABSTRACT

We posit that the effect of non-audit fees on audit quality is conditional

on auditor industry specialization. Industry specialist auditors are more likely than nonspecialists to be concerned about reputation losses and litigationexposure, and to benefit from knowledge spillovers from the provision of non-audit services. We find evidence that audit quality measured by increasedpropensity to issue going-concern opinion, increased propensity to miss ana-lysts’ forecasts, as well as higher earnings-response coefficients increases withthe level of non-audit services acquired from industry specialist auditors com-pared to nonspecialist auditors.

1. Introduction

In this study, we investigate whether the relation between the provisionof non-audit services and the impairment of auditor quality is conditionalon auditor specialization. We posit and provide evidence that impairment of audit quality is contingent on auditor specialization—audit quality isless likely to be impaired with the provision of non-audit services in thecase of specialists compared to nonspecialists. In doing so, we add to priorresearch that documents mixed findings on the relation between non-audit service provision and audit quality (e.g., Defond, Raghunandan, andSubramanyam [2002], Frankel, Johnson, and Nelson [2002], Ashbaugh,

∗Nanyang Technological University. We appreciate the helpful comments of the anonymousreferee, Merle Erickson (editor), Mahmud Hossain, and Yen-Hee Tong.



200 C.- Y . LIM AND H.-T. TAN

Lafond, and Mayhew [2003], Krishnan, Sami, and Zhang [2005], Francisand Ke [2006]) by showing that the effects of non-audit services on audit quality are not readily apparent without also jointly accounting for the ef-fects of auditor specialization.

Regulators’ concerns that the provision of non-audit services impairs au-ditor independence (Levitt [1998], SEC [2000]) gave rise to several stud-ies that examine whether the provision of non-audit services impairs audit quality. These studies report seemingly conflicting results depending on theproxy of audit quality used. For example, notwithstanding earlier evidenceby Frankel, Johnson, and Nelson [2002], recent evidence indicates that pro-

vision of non-audit services is not associated with the incidence of higherdiscretionary accruals and the propensity to meet earnings benchmarks(Ashbaugh, Lafond, and Mayhew [2003], Chung and Kallapur [2003]).Similarly, there is no evidence of an association between the provision of

non-audit services and a reduced proclivity to issue going-concern opinionsfor financially distressed firms (Defond, Raghunandan, and Subramanyam[2002]). Kinney, Palmrose, and Scholz [2004] examine restatements of pre-

viously issued financial statements and find either no or negative associationbetween restatements and major classes of non-audit services, and a positiveassociation only for a small class of unspecified non-audit services (compris-ing 4.6% of total fees in their sample). In contrast, Francis and Ke [2006]document that market response to quarterly earnings surprises is signifi-cantly lower for firms with higher (vs. lower) non-audit fees. Krishnan, Sami,and Zhang [2005] also find a negative association between non-audit fees

and earnings response coefficients in each of the three quarters followingthe proxy statement public disclosure of fee information.

We view higher discretionary accruals, greater (lower) propensity to meet (miss) earnings benchmarks, lower propensity to issue going-concern opin-ions, and higher incidence of restatements to be proxies for impairment of auditor independence in fact. Discretionary accruals are subject to moremeasurement error than the other measures (Dechow, Sloan, and Sweeney [1995], Defond, Raghunandan, and Subramanyam [2002], Kinney andLibby [2002]). The strength of stock market responses to earnings surprisesdue to non-audit fees proxies for the market’s perceptions of auditor in-

dependence in appearance. Given this assessment, one interpretation of cumulative research to-date is that there is some evidence that provision of non-audit services impairs independence in appearance, but has either a

weak or no effect on independence in fact.1

1 Onepossibleinterpretationis that themarket hasbeen systematically wrong in itsresponses(in terms of earnings response coefficients) in that it may have overreacted in its responsesto the possibility of impairment of audit (and financial reporting) quality when in actual fact,this is either not the case or is restricted to a small subset of firms/non-audit fee classes. Yet another interpretation of prior studies that find no statistical association between non-audit fees and various proxies for audit quality (going-concern opinions and discretionary accruals)

is that these proxies lack power (e.g., see Defond, Raghunandan, and Subramanyam [2002,p. 1250]).



NON- AUDIT SERVICE FEES AND AUDIT QUALITY 201

Prior research indicates that the provision of non-audit services cre-ates economic bonds that weaken an auditor’s independence, and there-fore, audit quality (DeAngelo [1981], Simunic [1984], Beck, Frecka, andSolomon [1988]). However, research also indicates that reputation con-cerns (Benston [1975], Watts and Zimmerman [1983]), litigation exposure(Palmrose [1988], Shu [2000]), and knowledge spillovers (Simunic [1984])serve to counter incentives for auditors to lose independence and compro-mise audit quality. If auditors are to maintain independence and preserveaudit quality even when they provide non-audit services, concerns about rep-utation losses, litigation exposure, and knowledge spillover benefits must besufficient to overwhelm incentives arising from provision of non-audit ser-

vices. Prior studies (e.g., Defond, Raghunandan, and Subramanyam [2002])use this argument to interpret the absence of an association between non-audit service provision fees and audit quality. However, disentanglement of

the effects of non-audit service provision and other mitigating factors (rep-utation concerns, litigation exposure, and knowledge spillover) requiresseparate measurement or proxies for these mitigating factors.

We follow prior literature that argues auditors/audit firms that special-ize in particular industries build expertise in these specific areas and makegreater specific investments in building up a reputation of good quality.

We posit that concerns about reputation and litigation exposure, as well asbenefits from knowledge spillover, are heightened with auditor industry spe-cialization (O’Keefe, Kin, and Gaver [1994], Craswell, Francis, and Taylor[1995], Solomon, Shields, and Whittington [1999], Owhoso, Messier, and

Lynch [2002], Carcello andNagy [2004]). More specifically, we posit that theassociation between provision of non-audit services and impairment of audi-tor quality is moderated by auditor industry specialization, and that failure toaccount for this moderating role of auditor industry specialization can maskthe relation between the provision of non-audit services and auditor quality.

Empirical measures for our theoretical constructs—audit quality, non-audit fees, and auditor specialization—can be noisy, and there is little con-sensus on the most appropriate proxy. Hence, we conduct our empiricaltests using multiple proxies of audit quality that are used by prior studies.

We infer improved audit quality from: (1) a higher propensity for audi-

tors to issue a going-concern opinion to financially distressed firms, (2) alower level of discretionary current accruals associated with the firm, (3) areduced (heightened) propensity for firms to just meet (miss) analyst fore-casts, and (d) a stronger market response to quarterly earnings surprises(i.e., earnings-returns coefficients [ERCs]). The first three measures proxy for actual (as apposed to perceived) audit quality, while the last measureproxies for investors’ perception of audit quality.

We proxy auditor industry specialization based on the market shareof the Big 5 auditors, and test the sensitivity of our results using otheroperationalizations of market share. We proxy the economic bond with

the client created through the provision of non-audit services both by themagnitude of the non-audit fee and by client importance, measured in terms




of non-audit fees provided to the client relative to the firm’s other clients. Toprovide a more complete coverage of the total economic bonding betweenauditor and client, we also include total fees that include both audit andnon-audit fees.2

Our results provide some evidence that audit quality is higher when clientspurchase more non-audit services from industry specialists. For our first proxy of audit quality, going-concern opinions, we use a sample of 1,692financially distressed firm-year observations for the period 2000–2001. Wefind that, consistent with Defond, Raghunandan, and Subramanyam [2002],the provision of non-audit services (measured using the natural log of non-audit fees and percentile rank of a client’s non-audit fees) is not associated

with a reduced propensity to issue going-concernopinions. However, we finda positive association between the issuance of going-concern opinions andthe natural log of total fees. More importantly, all three fee measures interact

significantly with audit specialization in explaining auditors’ propensity toissue going-concern opinions. Specifically, we find that an increased level of non-audit services is positively and significantly associated (not associated)

with the incidence of going-concern opinions issued for clients audited by specialists (nonspecialists), suggesting that audit specialists are more likely than nonspecialists to issue going-concern opinions to financially distressedfirms when they provide non-audit services.

For our second proxy of audit quality relating to discretionary current accruals, we detect weak or no association between provision of non-audit services and the absolute level of discretionary current accruals for our sam-

ple of 4,943 firm-year observations for the 2000–2001 period, consistent with findings by Frankel, Johnson, and Nelson [2002], Ashbaugh, Lafond,and Mayhew [2003], and Chung and Kallapur [2003]. Unlike the going-concern analysis, we find that auditor specialization does not moderate therelation between provision of non-audit services and (signed and unsigned)discretionary current accruals.

Our third proxy of audit quality relates to firms’ propensity to meet oravoid missing analysts’ forecasts. We analyze a sample of 3,498 firm-yearobservations from 2000 to 2001, and find that non-audit fees are not as-sociated with firms’ propensity to just meet analysts’ forecasts (coded as

the tendency for actual earnings minus analysts’ forecasts to be within zeroto positive one cent). By comparison, prior research either finds no suchassociation (Ashbaugh, Lafond, and Mayhew [2002]) or finds a positive as-sociation (Frankel, Johnson, and Nelson [2002]). We find no interactionbetween the provision of non-audit services and industry specialization forfirms’ propensity to just meet analysts’ forecasts. In contrast, we find that firms’ propensity to avoid missing analysts’ forecasts (coded as the tendency for actual earnings minus analysts’ forecasts to be within negative two cents)

2 For brevity, we refer to all three measures as proxies for non-audit fees, although total fees(which include non-audit fees) are more related to total economic bonding.




is negatively associated with the provision of non-audit services as proxiedby percentile rank of a client’s non-audit fees (but not with the other two feemeasures). For all three fee measures, we find that compared to specialist auditors, clients audited by nonspecialists are less likely to just miss analysts’forecasts when the public accounting firms provide non-audit services. Fi-nally, in terms of the market’s reaction to earnings surprises, we find that ERCs for a set of 2,935 firm-year observations during the period 2000–2001are significantly lower for firms that purchase non-audit services (using allthree fee measures) from non-audit specialists relative to firms that do sofrom audit specialists.

Our paper contributes to the literature on the effect of non-audit service provision on audit quality by providing the first empirical evidence that this effect is conditional on auditor specialization. Prior research presentsseemingly conflicting results on the effects of non-audit service provision on

actual audit quality (e.g., no effects on going-concern opinions, negative ef-fects on propensity to avoid missing analysts’ forecasts) and perceived audit quality (effects on ERCs). We provide triangulation with prior research find-ings by examining multiple proxies of audit quality in the same study. Acrossa variety of audit quality proxies, we generally obtain consistent evidencethat specialists provide higher audit quality as non-audit services increase

while nonspecialist auditors provide lower audit quality as non-audit feesincrease. Our results suggest the important role of auditor specialization inaddressing the regulatory and academic communities’ concerns about theappropriateness of accounting firms providing non-audit services.

Our study also contributes to the literature on auditor industry special-ization. Prior studies (e.g., Krishnan [2003], Balsam, Krishnan, and Yang[2003]) generally show that audit quality, as measured by ERCs and discre-tionary accruals, is higher for firms audited by specialists. There have not been any studies that examine the association between audit specializationand audit quality proxied by going-concern opinions and the propensity tomeet or miss analysts’forecasts, nor the interaction between industry special-ization and the provision of non-audit services in determining audit quality.Our results show that industry specialization interacts with the provision of non-audit services in influencing audit quality in terms of going-concern

opinions, the propensity to avoid missing analysts’ forecasts, and ERCs.The remainder of this paper is organized as follows. We discuss prior lit-

erature and develop our hypotheses in section 2. We present our researchdesign, including the sample characteristics, in section 3, and report empir-ical results in section 4. We offer some concluding remarks in section 5.

2. Background and Hypothesis Development

Following regulators’ concern about the lack of auditor independencethrough the provision of non-audit services (e.g., Levitt [1998]), the Securi-

ties and Exchange Commission (SEC) revised auditors’ independence rulesin 2000, narrowing the scope of non-audit services and requiring disclosure




of both audit fees and fees derived from different components of non-audit services (SEC [2000]). The Sarbanes Oxley Act passed in 2002 went a stepfurther, and effectively banned auditors from performing certain types of non-audit services. The assumption made by regulators is that the provi-sion of non-audit services impairs auditor independence both in fact and inappearance.

Research indicates that auditors’ provision of non-audit services createseconomic bonds on the auditor and may potentially cause the auditor to befinancially reliant on the client (DeAngelo [1981], Simunic [1984], Beck,Frecka, and Solomon [1988]) and lose objectivity. In addition, auditors may be less objective when they audit operations or transactions that they (ormembers of the certified public accounting firm) had previously providedadvice on (Plumlee [1985]). However, prior research also indicates that several factors counter these incentives that dilute auditors’ objectivity—

reputation concerns (Benston [1975], Watts and Zimmerman [1983]), liti-gation exposure (Palmrose [1988], Shu [2000]), and knowledge spillovers(Simunic [1984], Beck, Frecka, and Solomon. [1988]). Whether auditors’independence and audit quality are impaired when they provide non-audit services is a function of the net balance of the economic dependency arisingfrom non-audit service provision, and the mitigating factors that promoteauditor independence. However, without a proxy for these mitigating fac-tors, it is difficult to disentangle their effects.

In this study, we attempt to measure and disentangle some of these factorsthat have been discussed in the literature on non-audit services and audi-

tor independence. We posit that the effects of these mitigating factors aremagnified with auditor specialization. Auditors with industry specializations

who make investments in developing a reputation for performing auditsin particular industries are particularly concerned about preserving theirreputational capital, and avoiding reputation damage through litigation ex-posure. Similarly, at the firm level, audit firms that make strategic choicesand invest organizational resources in developing intellectual capital in par-ticular industries likely have greater concerns about reputation preserva-tion, and are less likely to cave in to client pressures and lose objectivity.Consistent with this argument, prior research shows that industry-specialist

auditors are more likely to comply with auditing standards (O’Keefe, Kin,and Gaver [1994]), and have clients that are less likely to be associated withSEC enforcement actions (Carcello and Nagy [2004]), lower discretionary accruals, and higher ERCs (Balsam, Krishnan, and Yang [2003], Krishnan[2003]).

In addition, knowledge spillover, the incremental knowledge generatedfrom providing non-audit services (Simunic [1984], Beck, Frecka, andSolomon [1988]), is also likely associated with auditor specialization. 3 In

3 Simunic [1984]and Beck, Frecka, and Solomon [1988] argue that knowledge that auditorsacquire while performing non-audit services can transfer to the performance of an audit and




recent years, audit firms have moved to a business-risk audit methodology (Bell, Peecher, and Solomon [2005]) centered on understanding of theclient’s risk andoperations. Knowledge spillover from provision of non-audit services can enhance the auditor’s understanding of the client and its risks.Prior research shows that auditors with industry specialization have supe-rior knowledge and performance relative to nonspecialists (e.g., Solomon,Shields, and Whittington [1999], Owhoso, Messier, and Lynch [2002]). Thissuggests that industry-specialist auditors (vs. non–industry specialist audi-tors) have the background knowledge both to more effectively perform thenon-audit services of a client from a specialized industry and to acquire andleverage on the knowledge spillover from performing non-audit services toperform a more effective and efficient audit.4 Finally, auditor expertise aris-ing from industry specialization can improve audit quality, in and of itself.Ceteris paribus, two auditors may have similar incentives to meet clients’

preferences, but the overall quality of the auditor with greater industry spe-cialization will still be higher than the one without.

In summary, our discussion above suggests that the provision of non-audit services is less likely to impair audit quality of industry specialists than non–industry specialists. We test the following hypothesis (stated in alternativeform):

H1: The association between the level of non-audit services and audit quality is conditional on whether or not the audit firm is an industry specialist.

This hypothesis is tested using four proxies for audit quality: going-concern opinions, discretionary current accruals, the propensity to meet (avoid missing) analysts’ forecasts, and the ERC.

3. Data and Research Design

3.1 SAMPLE

Our initial sample consists of 9,501 firm-years with fee data available fromthe Compustat database for fiscal years 2000–2001. We do not include year2002 because that year is associated both with the demise of Arthur Andersen

and the effective banning of auditors from performing various kinds of non-audit services by the Sarbanes-Oxley Act. These two events may have undue

thus generate production efficiencies. A caveat to this knowledge spillover effect is that tests of this effect yield mixed results (see discussion by Solomon [1990]). In fact, using internal billingdata from a public accounting firm, Davis, Ricchiute, and Trompeter [1993] provide evidencethat questions the knowledge spillover argument. However, consistent with our arguments inthis paper, it is possible that knowledge spillovereffects aremore apparent forspecialist auditorsbecause these specialist auditors can better leverage on their expertise (Solomon, Shields, and

Whittington [1999]) to generate these production efficiencies.4 Note that, unlike the reputation preservation factor, the knowledge-spillover and expertise

effects are more directly related to enhancement of audit quality, and less with the motivationto withstand client pressure.




influences on the firms, and the audit and stock market during that year. 5

We restrict our study to clients of Big 5 auditors to control for brand name(Craswell, Francis, and Taylor [1995], Chung and Kallapur [2003]). Accord-ingly, we remove 1,269 observations that are not audited by Big 5 auditors.6

We further remove 1,852 financial firms (Standard Industrial Classification[SIC] codes 6000–6999), leaving a remaining sample of 6,380 firm-year ob-servations. We winsorize each of the continuous control variables used in theregression at the top and bottom 1% to remove extreme values. For our first proxy of audit quality, going-concern opinions, we select those firms that are subject to financial difficulties. Following prior studies (e.g., Reynoldsand Francis [2000], Defond, Raghunandan, and Subramanyam [2002]),

we define financially distressed firms to be firms that report either nega-tive earnings or operating cash flows during the current fiscal year. Thereare a total of 1,692 firm-years that meet these criteria and have all availablefinancial information for the control variables used in the going-concernopinion study. Of these firm-year observations, a total of 120 firms receivegoing-concern opinions for the first time during 2000–2001.7

For the discretionary accruals test, a total of 4,943 firm-years are available with all necessary financial information in Compustat. For the analysts’ fore-casts benchmark test, we obtain analyst data from I/B/E/S detailed files,of which a total of 3,498 firm-years with complete information are avail-able. Finally, for the earnings-returns regression test, we have 2,935 firm-

year observations with complete information from Compustat, I/B/E/S de-tailed files, and the Center for Research in Security Prices (CRSP) databases.

Table 1, panels A and B report the distribution of sample firms by year andindustry, respectively, for the four sets of data used for the going-concernopinion, discretionary current accruals, analysts’ forecasts benchmark, andearnings-returns regression tests.

3.2 NON- AUDIT FEES

Following prior studies (e.g., Defond, Raghunandan, and Subramanyam[2002], Chung and Kallapur [2003]), we use the following three measuresto capture the economic bonding between the clients and auditor through

the provision of non-audit services: (1) the natural log of non-audit fees(LNAU ), which captures the level of economic bonding resulting from the

5 For instance, clients may deliberately reduce the purchase of non-audit services simply toavoid attracting public and regulatory attention. In addition, we find a big decrease in fee ratioin 2002: the mean (median) fee ratio is 0.52 (0.54) for the year 2000, 0.45 (0.45) for the year2001, and 0.28 (0.26) for the year 2002.

6 The Big 5 public accounting firms command a premium in audit fees compared to theother smaller firms. Of the 1,269 firms not audited by Big 5 auditors, only 650 firms have theirauditors’ names identified in Compustat, of which two-thirds are audited by BDO Seidman andGrant Thornton. We re-run our analyses with these firms, and our results remain unchanged.

7 Compustat does not provide the nature of the modified opinion. Hence, we hand collect the going-concern opinions from firms’ annual reports stored in the SEC Edgar database.




T A B L E

1

S a m p l e S i z e a n

d I n d u s t r y D e s c r i p t i o n

P a n e l A : D i s t r i b u t i o n o f s a m p l e fi r m s b

y y e a r

O p i n i o n M o d e l

A c c r u a l s M o d e l

A n a l y s t s ’ F o r e c a s t M o d e l

E R C M o d e l

Y E A R

N

P e r c e n t

N

P e r c e n t

N

P e r c e n t

N

P e r c e n t

2 0 0 0

6 4 9

3 8 . 3

6

2 , 0 2 7

4 1 . 0 0

1 , 5 3 5

4 3 . 8 8

1 , 2 1 9

4 1 . 5 3

2 0 0 1

1 , 0 4 3

6 1 . 6

4

2 , 9 1 6

5 9 . 0 0

1 , 9 6 3

5 6 . 1 2

1 , 7 1 6

5 8 . 4 7

T o t a l

1 , 6 9 2

1 0 0 . 0

0

4 , 9 4 3

1 0 0 . 0 0

3 , 4 9 8

1 0 0 . 0 0

2 , 9 3 5

1 0 0 . 0 0

P a n e l B : D i s t r i b u t i o n o f s a m p l e fi r m s b

y i n d u s t r y

O p i n i o n M o d e l A c c r u a l s M o d e l A n a l y s t s ’ F o r e c a s t M o d e l

E

R C M o d e l

S I C

N

P e r c e n t

N

P e r c

e n t

N

P e r c e n t

N

P e r c e n t

7 3

B u s i n e s s s e r v i c e s , i n c l u d i n g s o f t w a r e

4 7 4

2 8 . 0 1

9 7 9

1 9

. 8 1

6 0 2

1 7 . 2 1

3

6 9

1 2 . 5 7

2 8

C h e m i c a l a n d a l l i e d p r o d u c t s

2 6 2

1 5 . 4 8

5 8 1

1 1

. 7 5

3 6 6

1 0 . 4 6

2

9 1

9 . 9 1

3 6

E l e c t r o n i c / o t h e r e l e c t r i c e q u i p m e n t

2 2 4

1 3 . 2 4

5 1 2

1 0

. 3 6

3 3 7

9 . 6 3

2

9 6

1 0 . 0 9

3 5

I n d u s t r i a l m a c h i n e r y / e q u i p m e n

t

1 2 5

7 . 3 9

3 8 6

7

. 8 1

2 5 0

7 . 1 5

2

1 0

7 . 1 6

3 8

I n s t r u m e n t s a n d r e l a t e d p r o d u c t s

1 5 9

9 . 4 0

3 3 6

6

. 8 0

2 7 5

7 . 8 6

2

3 0

7 . 8 4

4 8

C o m m u n i c a t i o n s

8 6

5 . 0 8

2 1 2

4

. 2 9

1 1 3

3 . 2 3

8 9

3 . 0 3

1 3

O i l a n d g a s e x t r a c t i o n

2 0

1 . 1 8

1 8 0

3

. 6 4

1 0 7

3 . 0 6

1

0 3

3 . 5 1

8 7

E n g i n e e r i n g , a c c o u n t i n g , r e s e a r

c h , m a n a g e m e n t , a n d r e l a t e d s e r v i c e s

5 9

3 . 4 9

1 5 0

3

. 0 3

9 3

2 . 6 6

7 4

2 . 5 2

4 9

E l e c t r i c / g a s / s a n i t a r y s e r v i c e s

9

0 . 5 3

1 4 7

2

. 9 7

8 4

2 . 4 0

1

5 7

5 . 3 5

2 0

F o o d a n d k i n d r e d p r o d u c t s

1 2

0 . 7 1

1 2 8

2

. 5 9

6 6

1 . 8 9

6 4

2 . 1 8

5 0

D u r a b l e g o o d s — w h o l e s a l e

3 8

2 . 2 5

1 2 5

2

. 5 3

6 1

1 . 7 4

5 6

1 . 9 1

3 7

T r a n s p o r t a t i o n e q u i p m e n t

2 3

1 . 3 6

1 2 4

2

. 5 1

8 8

2 . 5 2

8 5

2 . 9 0

5 9

M i s c e l l a n e o u s r e t a i l

2 8

1 . 6 5

1 2 1

2

. 4 5

8 0

2 . 2 9

5 8

1 . 9 8

O t h e r s ( 4 5 i n d u s t r i e s )

1 7 3

1 0 . 2 2

9 6 2

1 9

. 4 6

9 7 6

2 7 . 9 0

8

5 3

2 9 . 0 5

T o t a l

1 , 6 9 2

1 0 0 . 0 0

4 , 9 4 3

1 0 0

. 0 0

3 , 4 9 8

1 0 0 . 0 0

2 , 9

3 5

1 0 0 . 0 0

T h e s a m p l e p e r i o d i s fi s c a l y e a r s 2 0 0 0 – 2 0 0 1 , a n d c o n s i s t s o f n o n fi n a n c i a l fi r m s

a u d i t e d b y B i g 5 p u b l i c a c c o u n t i n g fi r m s . F o r t h e g o i n g - c o n c e r n o p i n i o n s t u d y , t h e s a m p l e

c o n s i s t s o f 1 , 6 9 2 fi n a n c i a l l y d i s t r e s s e d fi r m s t h a t r e p o r t e i t h e r n e g a t i v e e a r n i n g s o r o p

e r a t i n g c a s h fl o w s d u r i n g t h e c u r r e n t fi s c a l y e a r o v e r t h e p e r i o d 2 0 0 0 – 2 0 0 1 . O f t h e s e fi r m - y e a r

o b s e r v a t i o n s , a t o t a l o f 1 2 0 fi r m s r e c e i v e g o i n g

- c o n c e r n o p i n i o n f o r t h e fi r s t t i m e d u r i n

g t h e s a m p l e p e r i o d . F o r t h e a c c r u a l s m o d e l , t h e s a m p l e c o n s i s t s o f 4 , 9 4 3 fi r m - y e a r o b s e r v a t i o n s

f o r t h e p e r i o d 2 0 0 0 – 2 0 0 1 t h a t h a v e c o m p l e t e fi n a n c i a l i n f o r m a t i o n i n t h e C o m p u s t a t d a t a b a s e . A t o t a l o f 3 , 4 9 8 fi r m - y e a r o b s e r v a t i o n s i s a v a i l a b l e f o r t h e a n a l y s t s ’ f o r e c a s t m o d e l

f o r t h e p e r i o d 2 0 0 0 – 2 0 0 1 . A n a l y s t f o r e c a s t d a t a a r e f r o m t h e d e t a i l e d I / B / E / S fi l e . T h e s a m p l e f o r t h e e a r n i n g s - r e t u r n s r e g r e s s i o n m o d e l i s 2 , 9 3 5 fi r m - y e a r o b s e r v a t i o n s , w i t h a l l

i n f o r m a t i o n a v a i l a b l e f r o m C o m p u s t a t , I / B / E / S d e t a i l e d fi l e s , a n d C R S P .




purchase of non-audit services; (2) the percentile rank of a particular client’snon-audit fees given all total fees received by the audit firm (PRNAU ), whichcaptures the relative significance of client non-audit fees to the total fees rev-enue received by the auditor;8 and (3) the natural log of total fees (LTOT ),

which captures the total economic bonding of the client to the auditorcreated by the provision of both non-audit and audit services.9

3.3 AUDITOR INDUSTRY SPECIALIZATION

We use client sales to estimate industry market share of the Big 5 auditors(Krishnan [2003], Balsam, Krishnan, and Yang [2003], Dunn and Mayhew [2004]), defined as follows:10

ADTR MS ik =

J ik j =1

SALES ijk

I k i =1

J ik j =1

SALES ijk

(1)

For brevity, we do not denote the subscript denoting a specific year. The variable SALES denotes the client’s sales revenue. The numerator is thesum of the sales of all J ik clients (reported in Compustat) of Big 5 audit firm i in industry k . The denominator in equation (1) is the sales of all J ik

clients in industry k reported in Compustat, summed over all I k audit firms(including both Big 5 firms and other audit firms auditing in the industry).To estimate industry market share for the Big 5 auditors in a given industry for a particular year, we require a minimum of 20 clients in the industry (using the two-digit SIC classification).

Consistent with prior literature (Lys and Watts [1994], Chung andKallapur [2003]), we define the auditor with the largest industry market share (SPEC ) as the specialist.11 As a robustness check, we also use another

8 Chung and Kallapur [2003] use client importance measured by non-audit fees relative tototal revenue received by the auditor. However, the measure for client importance is highly skewed and non-normal (skewness = 55.89; kurtosis = 3338) for the overall sample. Hence, wetransform it using ranks, such that firms are assigned a rank of 1 (100) for those in the lowest

(highest) percentile (skewness = −0.06, kurtosis = −1.16).9 Prior research indicates that audit and non-audit fee are jointly determined (Whisenant,

Sankaraguruswamy, and Raghunandan [2003]). To assess whether audit fees affect the associa-tion between non-audit fees and audit quality, we reanalyze our results by including audit feesas an additional control variable (along with LNAU and PRNAU , but not LTOT ). We obtainsimilar results with our main analyses for all tests with the exception that for the ERC test, thefee by specialization interaction is no longer significant when fee is measured by LNAU ( p =

0.16). This nonresult is likely driven by the significant positive correlation between audit andnon-audit fees (correlation coefficient = 0.75).

10 We do not use the actual audit fees to compute market share of each auditor because theCompustat database provides audit fees details for only 50% of all the listed firms.

11 We also measure SPEC using the number of clients as the base. Using number of clients

as the base avoids the bias toward larger clients that is implied by using sales as the base. Ourresults are similar with this alternative measure.




three alternative measures of audit specialization: (1) we measure industry specialization using a continuous measure of market share, (2) we define anauditor to be a specialist when it has the largest market share and its market share is at least 10% higher than the second largest auditor (e.g., Mayhew and Wilkins [2003]), and (3) we designate any auditor with a market shareof 24% or more as a specialist (e.g., Neal and Riley [2004]). 12 We obtainsimilar results as our main analyses using the first two alternative measures of specialization. For the last measure (market share cutoff of 24%), we obtainsimilar results as our main analyses for the various measures of audit quality

with the following exceptions: for the going-concern measure, significant effects are obtained only when fees are measured using LNAU , and for thepropensity to avoid missing test, significant effects are obtained only whenfees are measured using PRNAU .13 In the interest of parsimony, we only report results based on SPEC .

4. Research Design and Empirical Results

4.1 GOING-CONCERN OPINIONS

4.1.1. Empirical Model. To test the association between non-audit feesand going-concern opinion, we estimate the following logistic regressionmodel:

OPIN = βo + β1 FEE + β2ZSCORE + β3BETA + β4RETURN + β5VOL

+β6LEV + β7CLEV + β8LLOSS + β9OCF + β10REPLAG

+β11ASSET + β12INVM + β13AGE + β14 FFIN + β15SPEC

+β16Y 00 + β17 FEE ∗ SPEC + e (2)

where

OPIN = 1 if the firm receives a going-concern opinion, and 0 otherwise; FEE = fee metrics, LNAU , PRNAU , and LTOT , as defined earlier;

ZSCORE = Altman’s [1968] Z -score reported by Compustat; it is coded 2if the score is less than 1.81, 1 if the score is between 1.81 and3, and 0 if the score is more than 3; 14

BETA = systematic risk over the fiscal year;

12 Following Neal and Riley [2004], the appropriate cutoff for the market share is given by (1/N )∗1.2. Hence, when N = 5, an auditor holding more than 24% market share is consideredas a specialist.

13 In particular, the consistency of our results using SPEC and the continuous measureprovides assurance that our results are not driven by some arbitrary cutoff point in identifyingspecialists versus nonspecialists.

14 We do not use the bankruptcy score based on Zmijewski [1984] because the distributionof that variable is not normal (skewness = −7.43, kurtosis = 141.63). Instead, we use the Altman

[1968] Z -score provided by Compustat, which has a normal distribution (skewness = −0.16,kurtosis = −1.98).




RETURN = the firm’s stock return over the fiscal year;VOL = the variance of the residual from the market model over the

fiscal year;LEV = debt-to-capital ratio;

CLEV = change in LEV during the year;LLOSS = 1 if the firm reports a loss for the previous year, and 0

otherwise;OCF = operating cash flows divided by total assets at fiscal year-end;

REPLAG = number of days between the fiscal year-end and earnings an-nouncement date;

ASSET = natural log of total assets at fiscal year-end;INVM = cash, cash equivalents, and short- and long-term investment

securities deflated by total assets at fiscal year-end;AGE = natural log of the number of years since the company was listed

on a stock exchange; FFIN = an indicator variable, equals 1 when the firm issues equity or

debt in the following year;SPEC = 1 if the auditor has the largest market share in the industry,

and 0 otherwise;Y 00 = year dummy.

To enable comparability with Defond, Raghunandan, and Subramanyam[2002], we use a set of control variables (listed above) that is similar to thoseused in their study.

4.1.2. Empirical Results. Table 2 presents the descriptive statistics for thegoing-concern opinion results. Twenty-six percent of the financially dis-tressed firms are audited by specialist auditors. Additionally, about 7% of these financially distressed firms receive a going-concern opinion from theauditors, which is comparable with that reported in prior studies (Defond,Raghunandan, and Subramanyam [2002] and Reynolds and Francis [2000]report values of 9% and 8%, respectively). We also compare the fees betweenfirms audited by specialists and nonspecialists. LNAU and LTOT (but not PRNAU ) are significantly greater for firms audited by specialists than those

audited by nonspecialists. This finding is consistent with specialist auditorsbeing able to provide higher-quality non-audit (and audit) services becauseof their superior expertise, and clients’ preference for specialist auditorsto perform non-audit services. The descriptive statistics for other control

variables and the correlation coefficients are reported in panels A and B of table 2, respectively.

We report the results of the logistic regression in table 3. For each inde-pendent variable, we report the regression coefficient, followed by the Waldstatistic in parentheses, and the marginal effect (in percent) in the squarebrackets. The marginal effect indicates the change in the probability of a

firm receiving a going-concern opinion per standard deviation change ineach respective independent variable (holding other independent variables




T A B L E

2

D e s c r i p t i v e S t a t i s t i c s a n d C o r r e l a t i o n b e t w e e n

V a r i a b l e s U s e d i n t h e G o i n g - C o n c e r n O

p i n i o n M o d e l

P a n e l A : D e s c r i p t i v e s t a t i s t i c s

M e a n

M e d i a n

1 s t Q u a r t i l e

3 r d Q u a r t i l e

S t d . D e v .

F e e m e t r i c s

T o t a l a u d i t f e e s

3 1 7

1 7 4

1 0 5

3 1 5

5 4 2

T o t a l n o n - a u d i t f e e s

5 7 8

1 5 3

5 3

4 5 5

2 , 1 3 1

T o t a l f e e s

8 0 5

3 6 0

1 8 5

7 8 2

2 , 4 9 3

F e e r a t i o

0 . 4 6

0 . 4 7

0 . 2 8

0 . 6 5

0 . 2 4

A u d i t o r s p e c i a l i z a t i o n

S P E C

0 . 2 6

0 . 0 0

0 . 0 0

1 . 0 0

0 . 4 4

C o n t r o l v a r i a b l e s u s e d i n o p i n i o n m o d e l

O P I N

0 . 0 7

0 . 0 0

0 . 0 0

0 . 0 0

0 . 2 6

Z S C O

R E

1 . 0 8

2 . 0 0

0 . 0 0

2 . 0 0

1 . 0 0

B E T A

2 . 2 2

1 . 9 9

0 . 9 5

3 . 2 2

1 . 8 7

R E T U R N

− 0 . 1 6

− 0 . 3 6

− 0 . 6 6

0 . 0 6

0 . 9 3

V O L

0 . 0 8

0 . 0 5

0 . 0 3

0 . 0 9

0 . 1 0

L E V

0 . 2 5

0 . 0 6

0 . 0 0

0 . 3 9

1 . 0 4

C L E V

− 0 . 0 5

0 . 0 0

− 0 . 0 2

0 . 0 6

1 . 8 5

L L O S S

0 . 7 0

1 . 0 0

0 . 0 0

1 . 0 0

0 . 4 6

O C F

− 0 . 1 6

− 0 . 0 6

− 0 . 2 2

0 . 0 3

0 . 3 4

R E P L A G

5 7

4 8

3 2

8 2

3 7

A S S E

T

4 . 8 5

4 . 7 0

3 . 8 0

5 . 8 2

1 . 6 0

I N V M

0 . 5 1

0 . 3 8

0 . 1 0

0 . 8 2

0 . 4 6

A G E

1 . 4 5

1 . 5 8

0 . 5 6

2 . 2 8

1 . 1 8

F F I N

0 . 6 3

1 . 0 0

0 . 0 0

1 . 0 0

0 . 4 8

S p e c i a l i s t s

( N =

4 4 7 )

N

o n s p e c i a l i s t s ( N =

1 , 2 4 5 )

D i f f e r e n c e

M e a n

M e d i a n

M e a n

M e d i a n

t - s t a t i s t i c

z - s t a t i s t i c

L N A U

5 . 1 5

5 . 2 2

4 . 9

1

4 . 9 6

2 . 3 2 ∗

2 . 8 7 ∗ ∗

P R N A U

4 4

4 2

4 3

4 2

0 . 7 7

0 . 6 1

L T O T

6 . 1 7

5 . 9 9

5 . 9

3

5 . 8 4

3 . 7 7 ∗ ∗

3 . 1 8 ∗ ∗

( C o n t i n u e d )




constant), given a base-rate probability of 7% of receiving a going-concernopinion.15

In a model with SPEC alone (without fee measures), the coefficient es-timate for SPEC is positive but insignificant. In a model with fee mea-sures alone (without SPEC ), consistent with Defond, Raghunandan, andSubramanyam’s [2002] finding of no association between the provision of non-audit services and going-concern opinion, we find that LNAU andPRNAU are not associated with OPIN . However, in contrast to Defond,Raghunandan, and Subramanyam [2002], we find that LTOT is positively and significantly associated with OPIN . For the set of control variables, ourresults indicate that firms with higher bankruptcy risk, leverage, stock return

volatility, poor stock market performance, losses in prior years, and smallerassets, as well as poor operating cash flow and liquidity, are more likely toreceive going-concern opinions.

We next examine the interaction effect between the fee metrics and audi-tor specialization on OPIN . The coefficient for FEE ∗ SPEC (β17) shows theincremental effect of FEE on OPIN when a firm is audited by a specialist rather than a nonspecialist auditor. We expect this coefficient to be positivebased on our hypothesis. Consistent with our prediction, the coefficient es-timate for FEE ∗ SPEC is positive and statistically significant for all the threefee variables, which suggests that firms audited by audit specialists are morelikely to receive going-concern opinions when non-audit services increase.The impact of considering this interaction is nontrivial. The marginal effect associated with this interaction effect (see table 3) indicates that, depending

on the fee proxy, every standard deviation change in FEE ∗ SPEC increases afirm’s likelihood of receiving a going-concern opinion by 2.97% to 17.41%(i.e., the likelihood increases from 7% to a range of 10% to 24% dependingon the fee metric used).

To assess the nature of the interaction, we further analyze the coefficient of FEE (β1), which represents the effect of non-audit fees on the issuanceof going-concern opinions when firms are audited by nonspecialists (i.e.,

when SPEC is coded zero). To the extent that non-audit services providedby nonspecialists impair auditor independence, we expect these auditorsto be less likely to issue going-concern reports for the financially distressed

firms, implying a negative β1. The coefficient estimate for FEE is not statisti-cally significant across all three measures of fees. The sum of the coefficientsof FEE + FEE ∗ SPEC (β1 + β17) represents the effect of FEE on OPIN whenfirms are audited by specialists. If specialist auditors provide high-quality au-dits, we expect the sum of the coefficients of (β 1 + β17) to be non-negative(in contrast to a negative association in the case of nonspecialists). We usechi-square statistics to test whether the sum of the two regression coefficients

15 The marginal effect per standard deviation (SD) change for a variable is computed as p ×

(1 − p ) × β × SD, where p is the base rate (0.07) and β is the estimated coefficient from thelogistic regression (Liao [1994]).




differs from zero. The results in table 3 indicate that the sum of these coef-ficients is positive and statistically significant at either 1% or 5% for all ourthree fee variables.

As a robustness check, we use an alternative definition for financially distressed firms. Following previous studies (McKeown, Mutchler, andHopwood [1991], Geiger and Rama [2003]), we classify a company as beingin financial stress if at least one of the following financial stress signals is met:negative working capital at the end of the fiscal year, negative retained earn-ings at the end of the fiscal year, or loss for the fiscal year. There are 2,071firms that meet the criteria. The unreported results using this alternativedefinition do not vary with those reported in table 3.

Overall, there is evidence that specialist auditors, but not nonspecialists,are more likely to issue qualified going-concern opinions when they receivehigher non-audit fees from clients. To the extent that a greater propensity to

issue qualified going-concern opinions to financially distressed firms is asso-ciated with higher audit quality, this result implies that specialists are morelikely than nonspecialists to provide higher quality audits with increasedprovision of non-audit services to clients.

4.2 DISCRETIONARY CURRENT ACCRUALS

4.2.1. Empirical Model. Following Ashbaugh, Lafond, and Mayhew [2003], we compute performance-adjusted discretionary current accrualsbased on the cross-sectional modified Jones [1991] model for all firms

recorded in Compustat. We define current accruals (CA) as income beforeextraordinary items plus depreciation and amortization minus operatingcash flows. To obtain the discretionary current accruals (DCA) in a given

year, we regress the following:

CA i ,t

TA i ,t −1= λ1

1

TA i ,t −1

+ λ2

REV i ,t

TA i ,t −1

+ λ3

IB i ,t −1

TA i ,t −1

+ εi ,t (3)

where CA i ,t is the current accruals for firm i in fiscal year t , TA i ,t − 1 is thetotal assets for firm i in fiscal year t −1, REV i ,t measures the change inrevenues for firm i in year t less revenues in t −1, IB t −1 is the income before

extraordinary items in year t −

1, andε

i ,t is the random residual term. Similarto previous studies, we estimate equation (3) cross-sectionally on all firmsrecorded in Compustat with the same two-digit SIC industry code. DCA arethen estimated as:

DCA i ,t =

CA i ,t

TA i ,t −1

− λ̂1

1

TA i ,t −1

− λ̂2

(REV − TR )i ,t

TA i ,t −1

− λ̂3

IB t −1

TA t −1

(4)

where λ̂i is the estimated parameters from equation (3) and TR i ,t is the

change in trade receivables for firm i in year t less the trade receivables inthe previous year.




T A B L E

3

G o i n g C o n c e r n O p i n i o n M o d e l : F e e M e t r i c s a n d A u d i t e r S p e c i a l i z a t i o n

L N A U

P R N A U

L T O T

I n t e r

c e p t

β 0

− 3 . 3 8 6

− 3 . 5 9 1

− 3 . 1 4 1

− 3 . 3 7 3

−

3 . 1 5 9

− 5 . 1 1 2

− 4 . 0 4 1

( 2 0 . 9 5 ) ∗ ∗ ∗

( 2 2 . 6 8 ) ∗ ∗ ∗

( 1 5 . 7 9 )

∗ ∗ ∗

( 2 0 . 6 3 ) ∗ ∗ ∗

( 1

7 . 3 4 ) ∗ ∗ ∗

( 2 8 . 6 9 ) ∗ ∗ ∗

( 1 3 . 8 4 ) ∗ ∗ ∗

F E E

β 1

0 . 1 3 1

0 . 0 3 6

0 . 0 0 7

0 . 0 0 3

0 . 5 0 3

0 . 3 1 3

( 2 . 3 3 )

( 0 . 1 3 )

( 1 . 6 1 )

(

0 . 2 3 )

( 8 . 3 1 ) ∗ ∗ ∗

( 2 . 5 2 )

[ 1 . 4 6 % ]

[ 0 . 4 0 %

]

[ 1 . 2 5 % ]

[

0 . 5 5 % ]

[ 3 . 6 0 % ]

[ 2 . 2 4 % ]

Z S C O

R E

β 2

0 . 8 1 2

0 . 8 2 4

0 . 8 1 6

0 . 8 2 7

0 . 8 2 1

0 . 8 1 3

0 . 8 1 2

( 1 6 . 8 9 ) ∗ ∗ ∗

( 1 3 . 3 7 ) ∗ ∗ ∗

( 1 6 . 7 9 )

∗ ∗ ∗

( 1 7 . 4 8 ) ∗ ∗ ∗

( 1

6 . 9 4 ) ∗ ∗ ∗

( 1 6 . 7 5 ) ∗ ∗ ∗

( 1 6 . 3 1 ) ∗ ∗ ∗

[ 5 . 2 7 % ]

[ 5 . 3 5 % ]

[ 5 . 3 0 %

]

[ 5 . 3 7 % ]

[

5 . 3 3 % ]

[ 5 . 2 8 % ]

[ 5 . 2 7 % ]

B E T A

β 3

− 0 . 0 9 0

− 0 . 0 9 7

− 0 . 1 0 9

− 0 . 0 9 6

−

0 . 1 0 7

− 0 . 1 1 1

− 0 . 1 2 0

( 1 . 6 6 )

( 1 . 9 5 )

( 2 . 4 3 )

( 1 . 9 0 )

(

2 . 3 2 )

( 2 . 4 8 )

( 2 . 8 5 ) ∗

[ − 1 . 1 0 % ]

[ − 1 . 1 8 % ]

[ − 1 . 3 3 %

]

[ − 1 . 1 6 % ]

[ − 1 . 3 0 % ]

[ − 1 . 3 4 % ]

[ − 1 . 4 5 % ]

R E T U R N

β 4

− 0 . 4 3 9

− 0 . 4 4 1

− 0 . 4 7 3

− 0 . 4 3 3

−

0 . 4 6 2

− 0 . 3 8 4

− 0 . 4 2 2

( 5 . 1 3 ) ∗ ∗

( 5 . 0 9 ) ∗ ∗

( 5 . 6 7 )

∗ ∗

( 4 . 9 3 ) ∗ ∗

(

5 . 4 4 ) ∗ ∗

( 3 . 8 9 ) ∗ ∗

( 4 . 5 2 ) ∗ ∗

[ − 2 . 6 7 % ]

[ − 2 . 6 8 % ]

[ − 2 . 8 7 %

]

[ − 2 . 6 3 % ]

[ − 2 . 8 1 % ]

[ − 2 . 3 3 % ]

[ − 2 . 5 6 % ]

V O L

β 5

1 . 8 6 1

1 . 8 9 2

2 . 0 1 2

1 . 8 6 6

1 . 9 8

1 . 6 5 6

1 . 8 1 8

( 3 . 4 0 ) ∗

( 3 . 5 8 ) ∗

( 3 . 9 9 )

∗ ∗

( 3 . 4 7 ) ∗

(

3 . 8 8 ) ∗ ∗

( 2 . 6 6 ) ∗

( 3 . 2 0 ) ∗

[ 1 . 2 2 % ]

[ 1 . 2 4 % ]

[ 1 . 3 2 %

]

[ 1 . 2 2 % ]

[

1 . 3 0 % ]

[ 1 . 0 9 % ]

[ 1 . 1 9 % ]

L E V

β 6

0 . 1 4 5

0 . 1 5 9

0 . 1 6 2

0 . 1 5 6

0 . 1 5 8

0 . 1 6 2

0 . 1 6 4

( 2 . 7 4 ) ∗

( 3 . 2 6 ) ∗

( 3 . 3 1 )

∗

( 3 . 1 5 ) ∗

(

3 . 1 9 ) ∗

( 3 . 2 8 ) ∗

( 3 . 3 0 ) ∗

[ 0 . 9 7 % ]

[ 1 . 0 7 % ]

[ 1 . 0 9 %

]

[ 1 . 0 5 % ]

[

1 . 0 7 % ]

[ 1 . 0 9 % ]

[ 1 . 1 0 % ]

C L E V

β 7

− 0 . 0 7 5

− 0 . 0 7 9

− 0 . 0 8 1

− 0 . 0 7 7

−

0 . 0 7 9

− 0 . 0 7 9

− 0 . 0 7 9

( 2 . 3 7 )

( 2 . 5 4 )

( 2 . 6 8 )

( 2 . 4 4 )

(

2 . 5 9 )

( 2 . 5 6 )

( 2 . 6 3 )

[ − 0 . 9 0 % ]

[ − 0 . 9 5 % ]

[ − 0 . 9 8 %

]

[ − 0 . 9 3 % ]

[ − 0 . 9 5 % ]

[ − 0 . 9 5 % ]

[ − 0 . 9 5 % ]

L L O S S

β 8

1 . 3 4 2

1 . 3 6 2

1 . 4 1 1

1 . 3 5 5

1 . 3 9 0

1 . 3 5 4

1 . 3 9 3

( 1 5 . 1 9 ) ∗ ∗ ∗

( 1 5 . 5 5 ) ∗ ∗ ∗

( 1 6 . 2 7 )

∗ ∗ ∗

( 1 5 . 4 0 ) ∗ ∗ ∗

( 1

5 . 8 7 ) ∗ ∗ ∗

( 1 5 . 3 1 ) ∗ ∗ ∗

( 1 5 . 6 2 ) ∗ ∗ ∗

[ 4 . 0 1 % ]

[ 4 . 0 6 % ]

[ 4 . 2 1 %

]

[ 4 . 0 4 % ]

[

4 . 1 5 % ]

[ 4 . 0 4 % ]

[ 4 . 1 6 % ]

O C F

β 9

− 2 . 1 4 4

− 2 . 0 2 0

− 2 . 0 2 1

− 2 . 0 5 1

−

2 . 0 4 5

− 1 . 9 3 6

− 1 . 9 1 6

( 4 2 . 6 1 ) ∗ ∗ ∗

( 3 7 . 1 3 ) ∗ ∗ ∗

( 3 6 . 8 7 )

∗ ∗ ∗

( 3 8 . 6 9 ) ∗ ∗ ∗

( 3

8 . 1 9 ) ∗ ∗ ∗

( 3 4 . 3 7 ) ∗ ∗ ∗

( 3 3 . 3 9 ) ∗ ∗ ∗

[ − 4 . 7 4 % ]

[ − 4 . 4 6 % ]

[ − 4 . 4 7 %

]

[ − 4 . 5 3 % ]

[ − 4 . 5 3 % ]

[ − 4 . 2 8 % ]

[ − 4 . 2 3 % ]





T A B L E

3 — C o n t i n u e d

L N A U

P R N A

U

L T O T

R E P L

A G

β 1 0

− 0 . 0 0 8

− 0 . 0 0 8

− 0 . 0 0 8

− 0 . 0 0 7

− 0 . 0 0 8

− 0 . 0 0 7

− 0 . 0 0 7

( 3 . 9 7 ) ∗ ∗

( 3 . 6 1 ) ∗

( 3 . 9 1 ) ∗ ∗

( 3 . 6 5 ) ∗

( 3 . 8 9 ) ∗ ∗

( 3 . 6 9 ) ∗ ∗

( 3 . 5 9 ) ∗

[ − 1 . 8 0 % ]

[ − 1 . 7 2 % ]

( − 1 . 8 1 % )

[ − 1 . 7 3 % ]

[ − 1 . 8 0 % ]

[ − 1 . 7 6 % ]

[ − 1 . 7 5 % ]

A S S E T

β 1 1

− 0 . 1 9 4

− 0 . 2 8 1

− 0 . 2 9 9

− 0 . 2 6 9

− 0 . 2 9 7

− 0 . 4 5 8

− 0 . 4 6 6

( 5 . 8 2 ) ∗ ∗ ∗

( 7 . 7 3 ) ∗ ∗ ∗

( 8 . 5 5 ) ∗ ∗ ∗

( 6 . 7 9 ) ∗ ∗ ∗

( 7 . 8 8 ) ∗ ∗ ∗

( 1 3 . 4 6 ) ∗ ∗ ∗

( 1 3 . 4 4 ) ∗ ∗ ∗

[ − 2 . 0 3 % ]

[ − 2 . 9 3 % ]

[ − 3 . 1 2 % ]

[ − 2 . 8 1 % ]

[ − 3 . 1 1 % ]

[ − 4 . 7 8 % ]

[ − 4 . 8 6 % ]

I N V M

β 1 2

− 3 . 2 7 7

− 3 . 2 2 3

− 3 . 2 8 0

− 3 . 2 1 2

− 3 . 2 4 5

− 3 . 1 2 8

− 3 . 1 5 9

( 4 0 . 8 5 ) ∗ ∗ ∗

( 3 9 . 2 4 ) ∗ ∗ ∗

( 3 9 . 8 6 ) ∗ ∗ ∗

( 3 8 . 9 8 ) ∗ ∗ ∗

( 3 9 . 1 7 ) ∗ ∗ ∗

( 3 5 . 9 5 ) ∗ ∗ ∗

( 3 6 . 2 3 ) ∗ ∗ ∗

[ − 9 . 9 2 % ]

[ − 9 . 7 6 % ]

[ − 9 . 9 3 % ]

[ − 9 . 7 2 % ]

[ − 9 . 8 2 % ]

[ − 9 . 4 7 % ]

[ − 9 . 5 6 % ]

A G E

β 1 3

0 . 1 4 3

0 . 1 7 4

0 . 1 7 1

0 . 1 7 4

0 . 1 7 1

0 . 1 8 1

0 . 1 6 7

( 1 . 4 2 )

( 2 . 0 5 )

( 1 . 9 5 )

( 2 . 0 3 )

( 1 . 9 4 )

( 2 . 2 6 )

( 1 . 8 8 )

[ 1 . 1 0 % ]

[ 1 . 3 3 % ]

[ 1 . 3 1 % ]

[ 1 . 3 3 % ]

[ 1 . 3 1 % ]

[ 1 . 3 9 % ]

[ 1 . 2 8 % ]

F F I N

β 1 4

− 0 . 1 1 5

− 0 . 1 2 7

− 0 . 1 5 5

− 0 . 1 1 7

− 0 . 1 4 1

− 0 . 1 3 9

− 0 . 1 7 4

( 0 . 2 5 )

( 0 . 3 0 )

( 0 . 4 5 )

( 0 . 2 6 )

( 0 . 3 7 )

( 0 . 3 6 )

( 0 . 5 6 )

[ − 0 . 3 6 % ]

[ − 0 . 4 0 % ]

[ − 0 . 4 9 % ]

[ − 0 . 3 7 % ]

[ − 0 . 4 4 % ]

[ − 0 . 4 4 % ]

[ − 0 . 5 5 % ]

S P E C

β 1 5

0 . 3 5 0

− 0 . 8 8 4

− 0 . 2 2 2

− 2 . 3 9 1

( 2 . 1 5 )

( 1 . 6 7 )

( 0 . 2 4 )

( 3 . 4 0 ) ∗

[ 1 . 0 0 % ]

[ − 2 . 5 4 % ]

[ − 0 . 6 4 % ]

[ − 6 . 8 7 % ]

Y 0 0

β 1 6

− 0 . 1 6 9

− 0 . 1 7 6

− 0 . 2 4 5

− 0 . 0 7 9

− 0 . 1 3 3

− 0 . 1 3 2

− 0 . 1 8 1

( 0 . 4 4 )

( 0 . 4 8 )

( 0 . 9 0 )

( 0 . 0 9 )

( 0 . 2 6 )

( 0 . 2 7 )

( 0 . 4 9 )

F E E ∗ S P E C

β 1 7

0 . 2 6 1

0 . 0 1 5

0 . 4 4 9

( 4 . 0 4 ) ∗ ∗

( 2 . 7 0 ) ∗

( 4 . 7 0 ) ∗ ∗

[ 6 . 3 4 % ]

[ 2 . 9 7 % ]

[ 1 7 . 4 1 % ]

χ 2 t e s t : F E E +

F E E ∗ S P E C

β 1 +

β 1 7

0 . 2 2 5

0 . 0 1 8

0 . 7 6 2

[ 6 . 3 6 ] ∗ ∗ ∗

[ 4 . 8 3 ] ∗ ∗

[ 1 3 . 2 9 ] ∗ ∗ ∗

N

1 , 6 9 2

1 , 6 9 2

1 , 6 9 2

1 , 6 9 2

1 , 6 9 2

1 , 6 9 2

1 , 6 9 2

P s e u d o R 2

3 7 . 0 %

3 7 . 0 %

3 8 . 0 %

3 6 . 9 %

3 7 . 7 %

3 7 . 8 %

3 8 . 7 %

χ 2 s t a t i s t i c

1 4 3 . 4 3 ∗ ∗ ∗

1 4 4 . 4 4 ∗ ∗ ∗

1 4 4 . 2 8 ∗ ∗ ∗

1 4 3 . 9 3 ∗ ∗ ∗

1 4 3 . 6 2 ∗ ∗ ∗

1 4 7 . 0 1 ∗ ∗ ∗

1 4 7 . 1 5 ∗ ∗ ∗

T h e g o i n g c o n c e r n o p i n i o n l o g i s t i c m o d e l i s :

O P I N =

β o +

β 1 F E E +

β 2 Z S C O R E +

β 3 B E T A +

β 4 R

E T U R N +

β 5 V O L +

β 6 L E V +

β 7 C L E V +

β 8 L L O S S +

β 9 O C F

+

β 1 0 R E P L A G +

β 1 1 A S S E T +

β 1 2 I N V M +

β 1 3 A G E +

β 1 4 F F I N +

β 1 5 S P E C +

β 1 6 Y 0 0 +

β 1 7 F E E ∗ S P E C +

ε

T h e s a m p l e i n c l u d e s a s e t o f fi n a n c i a l l y d i s t r e s s e d fi r m s . W e d e fi n e fi n a n c i a l l y d i s t r e s s e d fi r m s a s fi r m s t h a t r e p o r t e i t h e r

n e g a t i v e e a r n i n g s o r o p e r a t i n g c a s h fl o

w s d u r i n g t h e

c u r r e

n t fi s c a l y e a r . T h e v a r i a b l e s u s e d i n t h e

l o g i s t i c r e g r e s s i o n m o d e l a r e a s d e fi n e d i n t h e f o o t n o t e s o f t a b l e 2 . Y 0 0 i s y e a

r d u m m y . F o r e a c h v a r i a b l e , w e r e p o r t

t h e r e g r e s s i o n

c o e f fi

c i e n t , f o l l o w e d b y t h e W a l d s t a t i s t i c i n p a r e n t h e s e s , a n d t h e m a r g i n a l e f f e c t ( i n p

e r c e n t ) i n t h e s q u a r e b r a c k e t s . T h e m a r

g i n a l e f f e c t i n d i c a t e s t h e c h a n g e i n t h e p r o b a b i l i t y o f a

fi r m r e c e i v i n g a g o i n g - c o n c e r n o p i n i o n p e r s t a n d a r d d e v i a t i o n c h a n g e i n e a c h r e s p e c t i v e i n d e p e n d e n t v a r i a b l e ( h o l d i n g o t h e r i n d e p e n d e n t v a r i a b l e s c o n s t a n t ) , g i v e n t h e b a s e - r a t e

p r o b a b i l i t y o f t h e g o i n g c o n c e r n o p i n i o n ( 7 % ) . W e u s e χ 2 s t a t i s t i c s t o t e s t w h e t h e r

t h e s u m o f t h e c o e f fi c i e n t s ( β 1 +

β 1 7 )

d i f f e r s f r o m z e r o a n d r e p o r t t h e χ 2 s t a t i s t i c i n s q u a r e

b r a c k

e t s . ∗ , ∗

∗ , a n d ∗ ∗ ∗

d e n o t e s i g n i fi c a n c e a t

t h e 1 0 % , 5 % , a n d 1 % l e v e l s ( t w o - t a i l e d )

, r e s p e c t i v e l y .




Consistent with Frankel, Johnson, and Nelson [2002] and Ashbaugh,Lafond, and Mayhew [2003], we run the following model to test the as-sociation between non-audit fees and discretionary current accruals:

ADCA = ω0 + ω1 FEE + ω2TENU + ω3CFO + ϕ4LEV + ω5LITIG + ω6MB

+ω7MV + ω8LOSS + ω9 FIN + ω10LCA + ω11SPEC + ω12Y 00

+ω13 FEE ∗ SPEC + ε (5)

where

ADCA = absolute value of discretionary current accruals estimated withlagged return on assets (ROA) in the cross-sectional Jones [1991]model;

FEE = fee metrics, LNAU , PRNAU , and LTOT , as defined earlier;TENU = number of years that the auditor has audited the firm’s financial

statements;CFO = cash flow from operations scaled by total assets at the beginning

of the fiscal year;LEV = debt-to-capital ratio;

LITIG = 1 if the firm operates in a high-litigation industry, and 0 otherwise;high-litigation industries are industries with SIC codes 2833–2836,3570–3577, 3600–3674, 5200–5961, and 7370–7374;

MB = market-to-book ratio;MV = natural log of market value;

LOSS = 1 if a firm reports a loss, and 0 otherwise; FIN = 1 if the firm issued securities or acquired another company, and 0

otherwise;LCA = lag of absolute current accruals in the previous year;

SPEC = 1 if the auditor has the largest market share in the industry, and 0otherwise;

Y 00 = year dummy.

4.2.2. Empirical Results. We report the descriptive statistics and the corre-lation coefficients for the fee metrics and variables used in the discretionary

current accruals model in table 4. The mean (median) absolute value fordiscretionary current accruals (ADCA ) is 0.13 (0.08). On average, 27% of thefirms are audited by industry specialists. All the three fee variables are signif-icantly higher for firms audited by specialists than nonspecialists.

We report the regression results for the absolute discretionary current accruals in table 5, panel A. The adjusted R 2 is around 16%, comparedto the adjusted R 2 of 18% to 21% reported in Ashbaugh, Lafond, andMayhew [2003]. In a model with SPEC alone (without fee measures), wefind no significant association between ADCA and SPEC . In contrast, Krish-

nan [2003] and Balsam, Krishnan, and Yang [2003] document a negativerelation between auditor industry specialization and absolute discretionary




T A B L E

4

D e s c r i p t i v e S t a t i s t i c s a n d C o r r e l a t i o n b e t w e e n V a

r i a b l e s U s e d i n t h e D i s c r e t i o n a r y C u r r e n t A c c r u a l M o d e l


M e a n

M e d i a n



S t d . D e v .

F e e m e t r i c s


6

2 8

2 2 3

1 2 5

5 0 0

1 , 6 8 2


1 , 3 5 9

2 4 1

7 4

7 4 7

4 , 9 3 8

T o t a l f e e s

1 , 9 8 7

4 9 8

2 2 5

1 , 2 5 7

6 , 1 9 1

F e e r a t i o

0 . 4 8

0 . 5 0

0 . 3 1

0 . 6 7

0 . 2 3


S P E C

0 . 2 7

0 . 0 0

0 . 0 0

1 . 0 0

0 . 4 4

C o n t r o l v a r i a b l e s u s e d i n d i s c r e t i o n a r y c u r r e n t a c c r u a l s m o d e l

A D C A

0 . 1 3

0 . 0 8

0 . 0 3

0 . 2 2

0 . 5 2

T E N U

8 . 8 8

6 . 0 0

4 . 0 0

1 2 . 0 0

7 . 5 0

C F O

0 . 0 0

0 . 0 6

− 0 . 0 5

0 . 1 3

0 . 2 6

L E V

0 . 3 4

0 . 2 2

0 . 0 1

0 . 5 1

0 . 7 7

L I T I G

0 . 4 2

0 . 0 0

0 . 0 0

1 . 0 0

0 . 4 9

M B

3 . 0 7

2 . 0 0

1 . 0 6

3 . 7 0

8 . 1 2

M V

5 . 6 6

5 . 6 1

4 . 2 4

6 . 9 5

2 . 0 8

L O S S

0 . 4 6

0 . 0 0

0 . 0 0

1 . 0 0

0 . 5 0

F I N

0 . 2 3

0 . 0 0

0 . 0 0

0 . 0 0

0 . 4 2

L C A

0 . 3 3

0 . 0 2

0 . 0 0

0 . 0 9

3 . 6 4

S p e c i a l i s t s ( N =

1 , 3 3 7 )

N


3 , 6 0 6 )

D i f f e r e n c e

M e a n

M e d i a n

M e a n

M e d i a n



L N A U

5 . 7 1

5 . 7 3

5 . 3

5

5 . 3 9

5 . 6 3 ∗ ∗

5 . 9 8 ∗ ∗

P R N A U

5 2

5 3

5 0

5 0

2 . 4 9 ∗

2 . 5 3 ∗

L T O T

6 . 6 2

6 . 4 1

6 . 3

1

6 . 1 4

7 . 0 1 ∗ ∗

6 . 3 3 ∗ ∗





T A B L E


P a n e l B : P e a r s o n c o r r e l a t i o n m a t r i x

A D C A

L N A U

P R N

A U

L T O T

T E N U

L E

V

L I T I G

M B

M

V

L O S S

F I N

L C A

S P E C

A D C A

1 . 0 0

L N A U

− 0 . 0 8 ∗ ∗

1 . 0 0 ∗ ∗

P R N A U

− 0 . 0 8 ∗ ∗

0 . 9 3 ∗ ∗

1 . 0 0

L T O T

− 0 . 1 0 ∗ ∗

0 . 9 0 ∗ ∗

0 . 8 9 ∗ ∗

1 . 0 0

T E N U

− 0 . 0 7 ∗ ∗

0 . 2 2 ∗ ∗

0 . 2 3 ∗ ∗

0 . 2 6 ∗ ∗

1 . 0 0

L E V

− 0 . 0 5 ∗ ∗

0 . 0 8 ∗ ∗

0 . 0 9 ∗ ∗

0 . 1 2 ∗ ∗

0 . 0 6 ∗ ∗

1 . 0 0

L I T I G

− 0 . 0 8 ∗ ∗

− 0 . 0 9 ∗ ∗

− 0 . 1 0 ∗ ∗

− 0 . 1 4 ∗ ∗

− 0 . 1 8 ∗ ∗

− 0 . 1 6 ∗ ∗

1 . 0 0

M B

0 . 0 4 ∗

0 . 0 1

0 . 0 0

0 . 0 0

0 . 0 1

− 0 . 0 4 ∗ ∗

0 . 0 7 ∗ ∗

1 . 0 0

M V

− 0 . 1 4 ∗ ∗

0 . 6 6 ∗ ∗

0 . 6 3 ∗ ∗

0 . 7 2 ∗ ∗

0 . 2 3 ∗ ∗

− 0 . 0 3 ∗

− 0 . 0 5 ∗ ∗

0 . 1 3 ∗ ∗

1 . 0 0

L O S S

0 . 1 0 ∗ ∗

− 0 . 1 8 ∗ ∗

− 0 . 1 7 ∗ ∗

− 0 . 2 1 ∗ ∗

− 0 . 2 5 ∗ ∗

− 0 . 0 1

0 . 2 7 ∗ ∗

0 . 0 1

− 0 . 3 3 ∗ ∗

1 . 0 0

F I N

0 . 0 3 ∗

− 0 . 0 3

− 0 . 0 5 ∗ ∗

− 0 . 0 6 ∗ ∗

− 0 . 1 0 ∗ ∗

0 . 0 0

0 . 0 6 ∗ ∗

0 . 0 8 ∗ ∗

− 0 . 0 1

0 . 1 3 ∗ ∗

1 . 0 0

L C A

0 . 1 2 ∗ ∗

− 0 . 0 7 ∗ ∗

− 0 . 0 7 ∗ ∗

− 0 . 0 8 ∗ ∗

− 0 . 0 5 ∗ ∗

− 0 . 0 2

0 . 0 3 ∗

0 . 0 4 ∗ ∗

− 0 . 1 0 ∗ ∗

0 . 0 7 ∗ ∗

0 . 0 6 ∗ ∗

1 . 0 0

S P E C

− 0 . 0 1

0 . 0 8 ∗ ∗

0 . 0 4 ∗

0 . 1 0 ∗ ∗

− 0 . 0 4 ∗ ∗

0 . 0 1

− 0 . 0 6 ∗ ∗

− 0 . 0 3 ∗

0 . 0 7 ∗ ∗

− 0 . 0 4 ∗ ∗

0 . 0 1

0 . 0 0

1 . 0 0

T h e t a b l e r e p o r t s t h e d e s c r i p t i v e s t a t i s t i c s f o r t h e f e e m e t r i c s a n d t h e c o r r e l a t i o n b e t w e e n v a r i a b l e s u s e d i n t h e d i s c r e t i o n a r y c u r r e n t a c c r u a l s m o d e l . S e e t a b l e 2 f o r t h e

d e fi n

i t i o n s o f f e e m e t r i c s a n d a u d i t o r s p e c i a l i z a t i o n . D e fi n i t i o n s o f o t h e r v a r i a b l e s

a r e a s f o l l o w s : A D C A i s t h e a b s o l u t e v

a l u e o f d i s c r e t i o n a r y c u r r e n t a c c r u a l s e s t i m a t e d w i t h

l a g g e

d R O A i n t h e c r o s s - s e c t i o n a l J o n e s [ 1 9 9 1 ] m o d e l .

L N A U i s t h e n a t u r a l l o g o

f n o n - a u d i t f e e s .

P R N A U i s t h e p e r c e n

t i l e r a n k o f a p a r t i c u l a r c l i e n t ’ s n o n - a u d i t f e e s g i v e n

a l l n o n - a u d i t f e e s r e c e i v e d b y t h e a u d i t fi r m

. L T O T i s t h e n a t u r a l l o g o f t o t a l f e e s .

T E N U i s t h e n u m b e r o f y e a r s t h a t t h e a u d i t o r h a s a u d i t e d t h e fi r m ’ s fi n a n c

i a l s t a t e m e n t s .

C F O

i s c a s h fl o w f r o m o p e r a t i o n s s c a l e d b y

t o t a l a s s e t s a t t h e b e g i n n i n g o f t h e fi s c a l y e a r .

L E V i s d e b t - t o - c a p i t a l r a t i o . L I T I G e q u a l s 1 i f t h e fi r m o p e r a t e s i n a

h i g h - l i t i g a t i o n

i n d u s t r y , a n d 0 o t h e r w i s e . H i g h - l i t i g a t i o n i n

d u s t r i e s a r e i n d u s t r i e s w i t h S I C c o d e s

2 8 3 3 – 2 8 3 6 , 3 5 7 0 – 3 5 7 7 , 3 6 0 0 – 3 6 7 4 , 5 2 0

0 – 5 9 6 1 , a n d 7 3 7 0 – 7 3 7 4 . M B i s m a r k e t - t o - b o o k r a t i o .

M V i s t h e n a t u r a l l o g o f m a r k e t v a l u e . L O S S e q u a l s 1 i f a fi r m r e p o r t s a l o s s , a n d

0 o t h e r w i s e . F I N e q u a l s 1 i f t h e fi r m i s s u e d s e c u r i t i e s o r a c q u i r e d a n o t h e r c o

m p a n y , a n d 0

o t h e r w i s e . L C A i s t h e a b s o l u t e v a l u e o f c u r r e n t a c c r u a l s i n t h e p r i o r y e a r .

S P E C e q u

a l s 1 i f t h e a u d i t o r h a s t h e l a r g e s t m a r k e t s h a r e i n t h e i n d u s t r y , a n d 0 o t h e r w i s e . M e a n ( m e -

d i a n ) d i f f e r e n c e s i n f e e s b e t w e e n s p e c i a l i s t s a n

d n o n s p e c i a l i s t s a r e b a s e d o n t - t e s t s ( M a n n - W h i t n e y t e s t s ) . ∗ a n d ∗ ∗

d e n o t e s i g n i fi

c a n c e a t t h e 5 % a n d 1 % l e v e l s ( t w o - t a i l e d

) , r e s p e c t i v e l y .




accruals. In a model with fee measures alone (without SPEC ), we find that ADCA is positively and significantly associated with LNAU , but not withPRNAU and LTOT . Prior studies document either a positive or no associa-tion between ADCA and non-audit fees, depending on the measure of non-audit fees used (Frankel, Johnson, and Nelson [2002], Ashbaugh, Lafond,and Mayhew [2003], Chung and Kallapur [2003]). Contrary to our predic-tion in H1, the coefficient estimate for the interaction term ( FEE ∗ SPEC )is not statistically significant for all three measures of fee variables. We doobserve that when fees are proxied by LNAU , the sum of the coefficients FEE + FEE ∗ SPEC (ω1 + ω13) is positive and significant at the 5% level, sug-gesting that firms audited by specialists are associated with higher absolutelevels of discretionary current accruals as non-audit fees increase. As shownbelow, this association is primarily driven by negative discretionary accruals.

We next partition the sample based on the sign of discretionary accruals

and report the results for positive and negative discretionary accruals intable 5, panels B and C, respectively. We find no association between SPEC and signed discretionary accrual measures in models that contain SPEC but

without the fee measures. In a model with fee alone (without SPEC ), only LTOT (but not LNAU and PRNAU ) is negatively and significantly associ-ated with positive (or income increasing) discretionary current accruals. Incontrast, all three fee variables are negatively and significantly associated

with negative (or income decreasing) discretionary current accruals; that is, firms report more income-decreasing accruals as fees increase. For bothsigned discretionary accrual measures, estimates for the interaction term

( FEE ∗ SPEC ) are not statistically significant across all three measures of fee variables.16 Further analysis of the model with the interaction term (table 5,panel C) reveals that the coefficients of FEE (ω1) and the summation FEE +

FEE ∗ SPEC (ω1 + ω13) are both negative and statistically significant forall fee measures, suggesting that firms audited by both nonspecialists andspecialists report more income-decreasing accruals as fees increase.17 Thisfinding is consistent with both nonspecialist and specialist auditors beingeither more conservative or more tolerant of income-decreasing earningsmanagement as fees increase.

We also compute an alternative measure for discretionary current accruals

based on a portfolio approach, as in Ashbaugh, Lafond, and Mayhew [2003]. We partition firms within each two-digit SIC code into deciles based on theirprior year’s ROA. Performance-adjusted discretionary current accruals are

16 Chung and Kallapur [2003] also examine the interaction between audit specializationand fees on discretionary accruals. They also report insignificant results for the interactionterm.

17 In comparison, Ashbaugh, Lafond, and Mayhew [2003] find no association between non-audit services provision and positive discretionary current accruals, and some evidence of anegative association for negative discretionary accruals. Frankel, Johnson, and Nelson [2002]

report a significant positive (negative) association between non-audit fees and positive (nega-tive) discretionary accruals.




T A B L E

5

D i s c r e t i o n a r y C u r r e n t A c c r u a l s M o d e l : F e e M e t r i c s a n d A u d i t o r S p e c i a l i z a t i o n

P a n e

l A : A b s o l u t e d i s c r e t i o n a r y c u r r e n t a c c r u a l s

L N A U

P R N A U

L T O T

I n t e r c e p t

ω 0

0 . 4 9 5

0 . 4 7 8

0 . 4 9 7

0 . 4 9 4

0 . 4 9 9

0 . 4 7 5

0 . 4 9 6

(

1 7 . 8 2 ) ∗ ∗ ∗

( 1 6 . 6 7 ) ∗ ∗ ∗

( 1 5 . 4 4 ) ∗ ∗ ∗

( 1 7 . 9 9 ) ∗ ∗ ∗

( 1 6 . 9 8 ) ∗ ∗ ∗

( 1 1 . 9 8 ) ∗ ∗ ∗

( 1 0 . 7 4 ) ∗ ∗ ∗

F E E

ω 1

0 . 0 1 0

0 . 0 0 7

0 . 0 4 8

0 . 0 4 3

0 . 0 0 5

0 . 0 0 2

( 1 . 9 6 ) ∗ ∗

( 1 . 2 7 )

( 1 . 4 0 )

( 1 . 1 5 )

( 0 . 6 5 )

( 0 . 2 7 )

T E N U

ω 2

− 0 . 0 0 2

− 0 . 0 0 3

− 0 . 0 0 3

− 0 . 0 0 3

− 0 . 0 0 3

− 0 . 0 0 2

− 0 . 0 0 3

( − 2 . 3 7 ) ∗ ∗ ∗

( − 2 . 5 3 ) ∗ ∗ ∗

( − 2 . 5 5 ) ∗ ∗ ∗

( − 2 . 4 8 ) ∗ ∗

( − 2 . 4 9 ) ∗ ∗

( − 2 . 4 2 ) ∗ ∗

( − 2 . 4 4 ) ∗ ∗

C F O

ω 3

− 0 . 1 8 1

− 0 . 1 8 1

− 0 . 1 8 0

− 0 . 1 8 2

− 0 . 1 8 1

− 0 . 1 8 2

− 0 . 1 8 1

( − 5 . 0 9 ) ∗ ∗ ∗

( − 5 . 0 9 ) ∗ ∗ ∗

( − 5 . 0 6 ) ∗ ∗ ∗

( − 5 . 1 0 ) ∗ ∗ ∗

( − 5 . 1 0 ) ∗ ∗ ∗

( − 5 . 1 0 ) ∗ ∗ ∗

( − 5 . 0 8 ) ∗ ∗ ∗

L E V

ω 4

− 0 . 0 4 8

− 0 . 0 5 1

− 0 . 0 5 1

− 0 . 0 5 0

− 0 . 0 5 0

− 0 . 0 4 9

− 0 . 0 4 9

( − 5 . 0 5 ) ∗ ∗ ∗

( − 5 . 2 6 ) ∗ ∗ ∗

( − 5 . 2 8 ) ∗ ∗ ∗

( − 5 . 2 0 ) ∗ ∗ ∗

( − 5 . 2 0 ) ∗ ∗ ∗

( − 5 . 0 9 ) ∗ ∗ ∗

( − 5 . 1 0 ) ∗ ∗ ∗

L I T I G

ω 5

− 0 . 1 5 1

− 0 . 1 4 9

− 0 . 1 5 0

− 0 . 1 5 0

− 0 . 1 5 0

− 0 . 1 5 0

− 0 . 1 5 0

( − 9 . 7 0 ) ∗ ∗ ∗

( − 9 . 6 0 ) ∗ ∗ ∗

( − 9 . 6 0 ) ∗ ∗ ∗

( − 9 . 6 0 ) ∗ ∗ ∗

( − 9 . 6 0 ) ∗ ∗ ∗

( − 9 . 5 7 ) ∗ ∗ ∗

( − 9 . 5 7 ) ∗ ∗ ∗

M B

ω 6

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

( 3 . 6 1 ) ∗ ∗ ∗

( 3 . 8 0 ) ∗ ∗ ∗

( 3 . 7 8 ) ∗ ∗ ∗

( 3 . 7 4 ) ∗ ∗ ∗

( 3 . 7 3 ) ∗ ∗ ∗

( 3 . 6 8 ) ∗ ∗ ∗

( 3 . 6 6 ) ∗ ∗ ∗

M V

ω 7

− 0 . 0 2 5

− 0 . 0 3 1

− 0 . 0 3 2

− 0 . 0 2 9

− 0 . 0 2 9

− 0 . 0 2 8

− 0 . 0 2 8

( − 6 . 5 1 ) ∗ ∗ ∗

( − 6 . 3 1 ) ∗ ∗ ∗

( − 6 . 3 6 ) ∗ ∗ ∗

( − 5 . 9 9 ) ∗ ∗ ∗

( − 5 . 9 7 ) ∗ ∗ ∗

( − 5 . 1 0 ) ∗ ∗ ∗

( − 5 . 1 2 ) ∗ ∗ ∗

L O S S

ω 8

0 . 0 3 5

0 . 0 3 2

0 . 0 3 2

0 . 0 3 3

0 . 0 3 3

0 . 0 3 4

0 . 0 3 3

( 1 . 9 2 ) ∗

( 1 . 7 6 ) ∗

( 1 . 7 3 ) ∗

( 1 . 8 0 ) ∗

( 1 . 7 9 ) ∗

( 1 . 8 4 ) ∗

( 1 . 8 1 ) ∗

F I N

ω 9

0 . 0 0 6

0 . 0 0 7

0 . 0 0 7

0 . 0 0 7

0 . 0 0 7

0 . 0 0 7

0 . 0 0 7

( 0 . 3 6 )

( 0 . 4 1 )

( 0 . 4 1 )

( 0 . 4 1 )

( 0 . 4 1 )

( 0 . 4 0 )

( 0 . 4 0 )

L C A

ω 1 0

1 . 2 7 9

1 . 2 7 9

1 . 2 7 7

1 . 2 7 9

1 . 2 7 8

1 . 2 7 8

1 . 2 7 8

( 6 . 3 3 ) ∗ ∗ ∗

( 6 . 3 4 ) ∗ ∗ ∗

( 6 . 3 3 ) ∗ ∗ ∗

( 6 . 3 3 ) ∗ ∗ ∗

( 6 . 3 3 ) ∗ ∗ ∗

( 6 . 3 3 ) ∗ ∗ ∗

( 6 . 3 3 ) ∗ ∗ ∗

S P E C

ω 1 1

− 0 . 0 0 5

− 0 . 0 6 4

− 0 . 0 1 4

− 0 . 0 7 2

( − 0 . 2 8 )

( − 1 . 3 1 )

( − 0 . 4 3 )

( − 0 . 9 3 )

Y 0 0

ω 1 2

− 0 . 0 5 0

− 0 . 0 5 2

− 0 . 0 5 2

− 0 . 0 4 7

− 0 . 0 4 7

− 0 . 0 5 0

− 0 . 0 5 0

( − 3 . 2 9 ) ∗ ∗ ∗

( − 3 . 4 5 ) ∗ ∗ ∗

( − 3 . 4 5 ) ∗ ∗ ∗

( − 3 . 0 8 ) ∗ ∗ ∗

( − 3 . 0 8 ) ∗ ∗ ∗

( − 3 . 3 0 ) ∗ ∗ ∗

( − 3 . 3 0 ) ∗ ∗ ∗

F E E ∗ S P E C

ω 1 3

0 . 0 1 0

0 . 0 2 0

0 . 0 1 0

( 1 . 2 6 )

( 0 . 3 4 )

( 0 . 8 8 )

F E E +

F E E ∗ S P E C

ω 1 +

ω 1 3

0 . 0 1 7

0 . 0 6 3

0 . 0 1 2

[ 5 . 0 4 ] ∗ ∗

[ 1 . 3 2 ]

[ 1 . 2 1 ]

N

4 , 9 4 3

4 , 9 4 3

4 , 9 4 3

4 , 9 4 3

4 , 9 4 3

4 , 9 4 3

4 , 9 4 3

F - s t a t i s t i c

2 9 . 6 3 ∗ ∗ ∗

2 9 . 9 9 ∗ ∗ ∗

2 5 . 5 1 ∗ ∗ ∗

2 9 . 8 1 ∗ ∗ ∗

2 5 . 2 3 ∗ ∗ ∗

2 9 . 6 6 ∗ ∗ ∗

2 5 . 1 6 ∗ ∗ ∗

A d j . R 2

1 5 . 9 9 %

1 6 . 0 6 %

1 6 . 0 6 %

1 6 . 0 3 %

1 5 . 9 9 %

1 6 . 0 0 %

1 5 . 9 8 %





T A B L E


P a n e

l B : P o s i t i v e d i s c r e t i o n a r y c u r r e n t a c c r u a l s

L N A U

P R N A U

L T O T

I n t e r c e p t

ω 0

0 . 5 8 8

0 . 5 9 0

0 . 6

1 6

0 . 5 9 0

0 . 5 9 6

0 . 7 1 2

0 . 7 5 4

( 1 2 . 2 6 ) ∗ ∗ ∗

( 1 1 . 9 1 ) ∗ ∗ ∗

( 1 1 . 2

2 ) ∗ ∗ ∗

( 1 2 . 4 6 ) ∗ ∗ ∗

( 1 1 . 8 7 ) ∗ ∗ ∗

( 9 . 9 3 ) ∗ ∗ ∗

( 9 . 2 9 ) ∗ ∗ ∗

F E E

ω 1

0 . 0 0 1

− 0 . 0

0 4

− 0 . 0 4 9

− 0 . 0 6 4

− 0 . 0 3 4

− 0 . 0 4 1

( 0 . 0 7 )

( − 0 . 4

3 )

( − 0 . 8 2 )

( − 0 . 9 7 )

( − 2 . 2 4 ) ∗ ∗

( − 2 . 5 4 ) ∗ ∗ ∗

T E N U

ω 2

− 0 . 0 0 3

− 0 . 0 0 3

− 0 . 0

0 3

− 0 . 0 0 3

− 0 . 0 0 3

− 0 . 0 0 2

− 0 . 0 0 2

( − 1 . 4 4 )

( − 1 . 4 9 )

( − 1 . 4

0 )

( − 1 . 4 3 )

( − 1 . 3 8 )

( − 1 . 2 6 )

( − 1 . 1 7 )

C F O

ω 3

− 0 . 1 5 1

− 0 . 1 5 1

− 0 . 1

4 8

− 0 . 1 4 7

− 0 . 1 4 7

− 0 . 1 4 0

− 0 . 1 3 7

( − 2 . 3 7 ) ∗ ∗

( − 2 . 3 7 ) ∗ ∗

( − 2 . 3

3 ) ∗ ∗

( − 2 . 3 2 ) ∗ ∗

( − 2 . 3 0 ) ∗ ∗

( − 2 . 2 0 ) ∗ ∗

( − 2 . 1 5 ) ∗ ∗

L E V

ω 4

− 0 . 0 8 8

− 0 . 0 8 8

− 0 . 0

8 8

− 0 . 0 8 6

− 0 . 0 8 6

− 0 . 0 8 0

− 0 . 0 8 0

( − 5 . 0 5 ) ∗ ∗ ∗

( − 5 . 0 0 ) ∗ ∗ ∗

( − 5 . 0

0 ) ∗ ∗ ∗

( − 4 . 8 6 ) ∗ ∗ ∗

( − 4 . 8 7 ) ∗ ∗ ∗

( − 4 . 4 9 ) ∗ ∗ ∗

( − 4 . 4 9 ) ∗ ∗ ∗

L I T I G

ω 5

− 0 . 3 0 0

− 0 . 3 0 1

− 0 . 3

0 0

− 0 . 3 0 2

− 0 . 3 0 1

− 0 . 3 0 8

− 0 . 3 0 8

( − 1 0 . 9 9 ) ∗ ∗

∗

( − 1 1 . 0 3 ) ∗ ∗ ∗

( − 1 0 . 9

7 ) ∗ ∗ ∗

( − 1 1 . 0 6 ) ∗ ∗ ∗

( − 1 1 . 0 1 ) ∗ ∗ ∗

( − 1 1 . 2 4 ) ∗ ∗ ∗

( − 1 1 . 1 9 ) ∗ ∗ ∗

M B

ω 6

0 . 0 0 6

0 . 0 0 6

0 . 0

0 6

0 . 0 0 6

0 . 0 0 6

0 . 0 0 5

0 . 0 0 5

( 3 . 4 3 ) ∗ ∗

∗

( 3 . 4 0 ) ∗ ∗ ∗

( 3 . 4

0 ) ∗ ∗ ∗

( 3 . 3 2 ) ∗ ∗ ∗

( 3 . 3 3 ) ∗ ∗ ∗

( 3 . 0 6 ) ∗ ∗ ∗

( 3 . 0 7 ) ∗ ∗ ∗

M V

ω 7

− 0 . 0 2 7

− 0 . 0 2 7

− 0 . 0

2 8

− 0 . 0 2 2

− 0 . 0 2 3

− 0 . 0 1 1

− 0 . 0 1 1

( − 3 . 8 9 ) ∗ ∗ ∗

( − 3 . 0 1 ) ∗ ∗ ∗

( − 3 . 1

1 ) ∗ ∗ ∗

( − 2 . 4 6 ) ∗ ∗

( − 2 . 5 2 ) ∗ ∗

( − 1 . 0 7 )

( − 1 . 1 4 )

L O S S

ω 8

0 . 0 0 4

0 . 0 0 3

0 . 0

0 3

0 . 0 0 4

0 . 0 0 4

0 . 0 0 5

0 . 0 0 5

( 0 . 1 1 )

( 0 . 1 0 )

( 0 . 1

0 )

( 0 . 1 0 )

( 0 . 1 2 )

( 0 . 1 4 )

( 0 . 1 5 )

F I N

ω 9

0 . 0 4 9

0 . 0 4 9

0 . 0

5 0

0 . 0 4 8

0 . 0 4 9

0 . 0 4 7

0 . 0 4 7

( 1 . 5 8 )

( 1 . 5 8 )

( 1 . 6

1 )

( 1 . 5 8 )

( 1 . 5 9 )

( 1 . 5 2 )

( 1 . 5 5 )

L C A

ω 1 0

1 . 1 5 5

1 . 1 5 7

1 . 1

5 6

1 . 1 6 0

1 . 1 5 6

1 . 1 6 8

1 . 1 7 0

( 4 . 3 8 ) ∗ ∗

∗

( 4 . 3 9 ) ∗ ∗ ∗

( 4 . 3

8 ) ∗ ∗ ∗

( 4 . 4 0 ) ∗ ∗ ∗

( 4 . 3 8 ) ∗ ∗ ∗

( 4 . 4 4 ) ∗ ∗ ∗

( 4 . 4 4 ) ∗ ∗ ∗

S P E C

ω 1 1

0 . 0 1 2

− 0 . 0

9 3

− 0 . 0 1 9

− 0 . 1 4 7

( 0 . 4 2 )

( − 1 . 0

7 )

( 0 . 3 3 )

( − 1 . 0 5 )

Y 0 0

ω 1 2

− 0 . 0 3 2

− 0 . 0 3 2

− 0 . 0

3 1

− 0 . 0 3 5

− 0 . 0 3 4

− 0 . 0 3 0

− 0 . 0 2 9

( − 1 . 2 0 )

( − 1 . 2 1 )

( − 1 . 1

6 )

( − 1 . 3 2 )

( − 1 . 2 7 )

( − 1 . 1 4 )

( − 1 . 0 8 )

F E E ∗ S P E C

ω 1 3

0 . 0

1 9

0 . 0 6 3

0 . 0 2 5

( 1 . 3

6 )

( 0 . 6 1 )

( 1 . 1 9 )

F E E +

ω 1 +

0 . 0

1 5

− 0 . 0 0 1

− 0 . 0 1 6

F E E ∗ S P E C

ω 1 3

[ 1 . 0

7 ]

[ 0 . 0 0 ]

[ 0 . 5 3 ]

N

2 , 1 9 6

2 , 1 9 6

2 , 1 9 6

2 , 1 9 6

2 , 1 9 6

2 , 1 9 6

2 , 1 9 6

F - s t a t i s t i c

1 9 . 1 7 ∗ ∗ ∗

1 9 . 1 6 ∗ ∗ ∗

1 6 . 3

5 ∗ ∗ ∗

1 9 . 2 2 ∗ ∗ ∗

1 6 . 3 0 ∗ ∗ ∗

1 9 . 6 6 ∗ ∗ ∗

1 6 . 7 6 ∗ ∗ ∗

A d j . R 2

1 8 . 3 5 %

1 8 . 3 4 %

1 8 . 3

3 %

1 8 . 3 7 %

1 8 . 3 1 %

1 8 . 5 5 %

1 8 . 5 4 %





T A B L E


P a n e l C : N e g a t i v e d i s c r e t i o n a r y c u r r e n t a c c r u a l s

L N A U

P R N A U

L T O T

I n t e r c e p t

ω 0

− 0 . 3 9 3

− 0 . 3 6 1

− 0 . 3 7 1

− 0 . 3 9 1

− 0 . 3 9 3

− 0 . 2 8 5

− 0 . 2 8 4

( V

1 2 . 7 0 ) ∗ ∗ ∗

( − 1 1 . 3 1 ) ∗ ∗ ∗

( − 1 0 . 2 6 ) ∗ ∗ ∗

( − 1 2 . 7 9 ) ∗ ∗ ∗

( − 1 1 . 9 2 ) ∗ ∗ ∗

( − 6 . 6 4 ) ∗ ∗ ∗

( − 5 . 5 8 ) ∗ ∗ ∗

F E E

ω 1

− 0 . 0 1 8

− 0 . 0 1 7

− 0 . 1 2 7

− 0 . 1 2 9

− 0 . 0 3 0

− 0 . 0 3 1

( − 3 . 2 6 ) ∗ ∗ ∗

( − 2 . 7 7 ) ∗ ∗ ∗

( − 3 . 4 7 ) ∗ ∗ ∗

( − 3 . 2 0 ) ∗ ∗ ∗

( − 3 . 5 1 ) ∗ ∗ ∗

( − 3 . 2 8 ) ∗ ∗ ∗

T E N U

ω 2

0 . 0 0 2

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

( 2 . 0 3 ) ∗ ∗

( 2 . 3 9 ) ∗ ∗

( 2 . 4 6 ) ∗ ∗

( 2 . 4 1 ) ∗ ∗

( 2 . 4 3 ) ∗ ∗

( 2 . 5 3 ) ∗ ∗ ∗

( 2 . 6 0 ) ∗ ∗ ∗

C F O

ω 3

0 . 1 6 3

0 . 1 5 3

0 . 1 5 3

0 . 1 5 4

0 . 1 5 5

0 . 1 5 5

0 . 1 5 5

( 4 . 0 9 ) ∗ ∗ ∗

( 3 . 8 5 ) ∗ ∗ ∗

( 3 . 8 5 ) ∗ ∗ ∗

( 3 . 8 8 ) ∗ ∗ ∗

( 3 . 8 9 ) ∗ ∗ ∗

( 3 . 9 0 ) ∗ ∗ ∗

( 3 . 9 1 ) ∗ ∗ ∗

L E V

ω 4

0 . 0 1 5

0 . 0 1 9

0 . 0 1 9

0 . 0 1 9

0 . 0 1 9

0 . 0 2 1

0 . 0 2 1

( 1 . 4 9 )

( 1 . 8 4 ) ∗

( 1 . 8 5 ) ∗

( 1 . 8 6 ) ∗

( 1 . 8 6 ) ∗

( 2 . 0 4 ) ∗ ∗

( 2 . 0 6 ) ∗ ∗

L I T I G

ω 5

0 . 0 3 3

0 . 0 2 8

0 . 0 2 9

0 . 0 2 7

0 . 0 2 8

0 . 0 2 5

0 . 0 2 6

( 1 . 9 5 ) ∗ ∗

( 1 . 6 5 ) ∗

( 1 . 6 9 ) ∗

( 1 . 5 8 )

( 1 . 6 1 )

( 1 . 4 7 )

( 1 . 5 1 )

M B

ω 6

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

( − 1 . 7 3 ) ∗

( − 2 . 0 4 ) ∗ ∗

( − 2 . 0 2 ) ∗ ∗

( − 2 . 0 5 ) ∗ ∗

( − 2 . 0 2 ) ∗ ∗

( − 2 . 1 4 ) ∗ ∗

( − 2 . 1 2 ) ∗ ∗

M V

ω 7

0 . 0 2 2

0 . 0 3 3

0 . 0 3 3

0 . 0 3 4

0 . 0 3 3

0 . 0 3 7

0 . 0 3 7

( 5 . 2 9 ) ∗ ∗ ∗

( 6 . 2 3 ) ∗ ∗ ∗

( 6 . 2 2 ) ∗ ∗ ∗

( 6 . 3 7 ) ∗ ∗ ∗

( 6 . 2 9 ) ∗ ∗ ∗

( 6 . 3 0 ) ∗ ∗ ∗

( 6 . 3 0 ) ∗ ∗ ∗

L O S S

ω 8

− 0 . 0 6 2

− 0 . 0 5 4

− 0 . 0 5 4

− 0 . 0 5 3

− 0 . 0 5 3

− 0 . 0 5 1

− 0 . 0 5 0

( − 3 . 1 1 ) ∗ ∗ ∗

( − 2 . 7 2 ) ∗ ∗ ∗

( − 2 . 6 9 ) ∗ ∗ ∗

( − 2 . 6 6 ) ∗ ∗ ∗

( − 2 . 6 5 ) ∗ ∗ ∗

( − 2 . 5 4 ) ∗ ∗ ∗

( − 2 . 5 0 ) ∗ ∗ ∗

F I N

ω 9

0 . 0 2 0

0 . 0 1 8

0 . 0 1 7

0 . 0 1 6

0 . 0 1 6

0 . 0 1 5

0 . 0 1 4

( 1 . 0 2 )

( 0 . 9 0 )

( 0 . 8 8 )

( 0 . 8 3 )

( 0 . 8 0 )

( 0 . 7 5 )

( 0 . 7 1 )

L C A

ω 1 0

− 1 . 8 4 8

− 1 . 8 4 8

− 1 . 8 3 5

− 1 . 8 5 1

− 1 . 8 4 7

− 1 . 8 4 9

− 1 . 8 4 2

( − 4 . 6 9 ) ∗ ∗ ∗

( − 4 . 7 0 ) ∗ ∗ ∗

( − 4 . 6 6 ) ∗ ∗ ∗

( − 4 . 7 0 ) ∗ ∗ ∗

( − 4 . 6 9 ) ∗ ∗ ∗

( − 4 . 7 0 ) ∗ ∗ ∗

( − 4 . 6 8 ) ∗ ∗ ∗

S P E C

ω 1 1

0 . 0 1 2

0 . 0 3 7

0 . 0 0 8

0 . 0 1 2

( 0 . 6 9 )

( 0 . 7 1 )

( 0 . 2 1 )

( 0 . 1 5 )

Y 0 0

ω 1 2

0 . 0 6 8

0 . 0 7 3

0 . 0 7 4

0 . 0 6 2

0 . 0 6 2

0 . 0 6 9

0 . 0 6 9

( 4 . 1 1 ) ∗ ∗ ∗

( 4 . 4 0 ) ∗ ∗ ∗

( 4 . 4 2 ) ∗ ∗ ∗

( 3 . 6 9 ) ∗ ∗ ∗

( 3 . 6 9 ) ∗ ∗ ∗

( 4 . 1 7 ) ∗ ∗ ∗

( 4 . 1 8 ) ∗ ∗ ∗

F E E ∗ S P E C

ω 1 3

− 0 . 0 0 4

0 . 0 0 7

0 . 0 0 1

( − 0 . 4 4 )

( 0 . 1 2 )

( 0 . 8 8 )

F E E +

F E E ∗ S P E C

ω 1 +

ω 1 3

− 0 . 0 2 1

− 0 . 1 2 2

− 0 . 0 3 0

[ 6 . 5 4 ] ∗ ∗ ∗

[ 4 . 5 1 ] ∗ ∗

[ 6 . 5 2 ] ∗ ∗ ∗

N

2 , 7 4 7

2 , 7 4 7

2 , 7 4 7

2 , 7 4 7

2 , 7 4 7

2 , 7 4 7

2 , 7 4 7

F - s t a t

i s t i c

1 8 . 8 8 ∗ ∗ ∗

1 9 . 8 8 ∗ ∗ ∗

1 6 . 8 9 ∗ ∗ ∗

2 0 . 0 2 ∗ ∗ ∗

1 6 . 9 6 ∗ ∗ ∗

2 0 . 0 4 ∗ ∗ ∗

1 7 . 0 3 ∗ ∗ ∗

A d j . R

2

1 6 . 6 9 %

1 7 . 0 3 %

1 7 . 0 0 %

1 7 . 0 8 %

1 7 . 0 3 %

1 7 . 0 9 %

1 7 . 0 6 %

T h e s a m p l e f o r t h e a c c r u a l s t e s t c o n s i s t s o f 4 , 9 4 3 fi r m - y e a r o b s e r v a t i o n s f o r t h e p e r i o d 2 0 0 0 – 2 0 0 1 t h a t h a v e c o m p l e t e fi n a n c

i a l i n f o r m a t i o n i n C o m p u s t a t . T h e r e g r e

s s i o n m o d e l i s :

A D C A =

ω 0 +

ω 1 F E E +

ω 2 T E N U +

ω 3 C F O +

ϕ 4 L E V +

ω 5 L I T I G +

ω 6 M B +

ω 7 M V +

ω 8 L O S S +

ω 9 F I N +

ω 1 0 L C A

+

ω 1 1

S P E C +

ω 1 2 Y 0 0 +

ω 1 3 F E E ∗ S P E C +

ε

S e e t a b l e 4 f o r t h e d e fi n i t i o n s o f t h e v a r i a b l e s u s e d i n t h e r e g r e s s i o n . N o t e t h a t

t h e d e p e n d e n t v a r i a b l e s i n p a n e l s B a n

d C a r e s i g n e d w h i l e p a n e l A i s t h e a b s o l u t e v a l u e o f

d i s c r e t i o n a r y a c c r u a l s . Y 0 0 i s y e a r d u m m y . W e u s e F - s t a t i s t i c s t o t e s t w h e t h e r t h e s u m

o f t h e c o e f fi c i e n t s ( ω 1 +

ω 1 3 ) d i f f e r s f r o m z e r o a n d r e p o r t t h e F - s t a t i s t i c i n s q u a r e b r a c k e t s . ∗ ,

∗ ∗ , a n

d ∗ ∗ ∗

d e n o t e s i g n i fi c a n c e a t t h e 1 0 % , 5 % , a n d 1 % l e v e l s ( t w o - t a i l e d ) , r e s p e c t i v e l y .




calculated as the difference between a sample firm’s discretionary current accruals and the median discretionary current accruals for each ROA decileexcluding the sample firm. We repeat the analyses and the untabulatedresults are similar to those reported in table 5.

Overall, our results document no moderating effect of auditor specializa-tion on the association between the various fee measures and discretionary accruals.

4.3 BENCHMARK TESTS

4.3.1. Meeting or Missing Analysts’ Forecasts. We also use firms’ propensity to meet or avoid missing analysts’ forecasts to infer audit quality. Prior re-search suggests that the market appears to reward firms that meet analysts’forecasts and punish those that miss analysts’ forecasts (Bartov, Givoly, andHayn [2002], Kasznik and McNichols [2002], Lopez and Rees [2002]). Fol-

lowing prior studies (e.g., Frankel, Johnson, and Nelson [2002], Ashbaugh,Lafond, and Mayhew [2003]), we compute actual earnings per share (EPS)minus the last available median consensus analysts’ forecasts prior to theannouncement of annual earnings, with MEET equal to one for firms re-porting an earnings surprise of zero to positive one cent, and zero other-

wise.18 Consistent with Frankel, Johnson, and Nelson [2002], we computeMISS for firms that just fall short of meeting analysts’ consensus forecasts,

with MISS equal to one for firms that miss the forecasts by two cents, andzero otherwise.

We run the following logistic regression model:

MEET or MISS = ϕ0 + ϕ1 FEE + ϕ2TENU + ϕ3LITIG + ϕ4MB + ϕ5MV

+ϕ6LOSS + ϕ7CFO + ϕ8 FIN + ϕ9ROA + ϕ10 DCA

+ϕ11SPEC + ϕ12Y 00 + ϕ13 FEE*SPEC + ε (6) where

MEET = 1 when a firm’s actual EPS minus the consensus analysts’ forecast is within zero to one cent (both inclusive), and zero otherwise;

MISS = 1 when a firm’s actual EPS minus the consensus analysts’ forecast falls between 0 cents (exclusive) and −2 cents (inclusive), andzero otherwise;

ROA = returns on assets;DCA = discretionary current accruals estimated with lagged ROA in the

cross-sectional Jones [1991] model;

All other variables are as previously defined.

18 We define the consensus analyst forecast as the median EPS forecast computed over theset of the analysts providing forecasts for the firm. We use the most recent forecasts that are noearlier than two months before the earnings release date. This procedure avoids the problemof stale analyst forecasts. We use the unadjusted I/B/E/S forecasts so that we do not havethe problem of losing precision in the decimal places of the forecasts because of I/B/E/S

adjustments of prior forecasts for subsequent stock splits (Payne and Thomas [2003]). To beconsistent, we measure the actual earnings using I/B/E/S as well.




4.3.2. Empirical Results. Table 6 reports the descriptive statistics and cor-relation matrix for the fees andvariables used in the analysts’ forecast model.On average, 17% of the firms just meet analysts’ forecasts by one cent, and7% of firms just miss analysts’ forecasts by two cents. The fee variables mea-sured by LNAU and LTOT (but not PRNAU ) are significantly greater foraudit specialists than non-audit specialists.

We report the results for the logistic regression for meeting and missingthe analysts’ forecasts in panels A and B of table 7. For each independent

variable, we report the regression coefficient, followed by the Wald statisticin parentheses, and the marginal effect (in percent) in the square brackets.

We find no association between SPEC and either MEET or MISS in mod-els that only include SPEC but not the fee measures. In a model with feemeasures alone (without SPEC ), we find that MEET is not significantly as-sociated with all three fee variables and MISS is negatively associated with

PRNAU (but not LNAU and LTOT ). In comparison, Frankel, Johnson, andNelson [2002] find that non-audit fees have a positive association with MEET and a negative association with MISS , while Ashbaugh, Lafond, and Mayhew [2003] do not test MISS and find no such association with MEET .

When we include the interaction term ( FEE ∗ SPEC ) in the MEET model,the coefficient for the interaction term (φ13) for all three fee variables is not statistically significant, inconsistent with the prediction in H1. However, forthe MISS model, the coefficient φ13 for the interaction term ( FEE ∗ SPEC )is positive and statistically significant for all the three fee variables. The sig-nificant positive interaction term ( FEE ∗ SPEC ) suggests that firms audited

by audit specialists rather than nonspecialists are more likely to miss ana-lysts’ forecasts when non-audit services increase. Analysis of the marginaleffects associated with this interaction term (reported in table 7, panel B)shows that, depending on the fee proxy, every standard deviation change in FEE ∗ SPEC increases the firm’s likelihood of missing the earnings bench-mark by 1.94% to 5.06% (i.e., the likelihood increases to about 9% to 12%,starting from a base rate of 7%). For MISS , the coefficient estimate for FEE (φ1) is significantly negative, suggesting that firms audited by nonspecialistsare less likely to miss analysts’ forecasts as non-audit services increase. Thesum of the coefficient estimates (φ1 +φ13) for all three fee variables does not

vary significantly from zero, indicating a lack of evidence that firms auditedby specialists are likely to avoid missing analysts’ forecasts. 19

Overall, the results indicate that firms that purchase more non-audit services from nonspecialists are less likely to miss analysts’ forecasts, relative tothose that purchase more non-audit services from specialists. To the extent that a firm’s enhanced ability to avoid missing analysts’ forecasts is achieved

19 As additional analyses, we also code the dependent variable MEET MISS to be one if afirm’s actual EPS minus the consensus analysts’ forecast is within zero to one cent, and 0 if a firm’s actual EPS minus the consensus analysts’ forecast is within negative two cents (see

footnote 13 of Frankel, Johnson, and Nelson [2002]). We fail to find a significant fee by specialization interaction for this measure.




T A B L E

6

D e

s c r i p t i v e S t a t i s t i c s a n d C o r r e l a t i o n b e t w

e e n V a r i a b l e s U s e d i n t h e A n a l y s t s ’ F o r e

c a s t M o d e l


M e a n

M e d i a n



S t d . D e v .

F e e m e t r i c s


5 9 1

2 5 9

1 4 5

5 5 5

1 , 3 6 9


1 , 1 9 5

3 0 4

1 0 4

8 6 9

3 , 8 7 6

T o t a l f e e s

1 , 7 8 6

6 0 4

2 8 8

1 , 4 2 7

4 , 9 0 3

F e e r a t i o

0 . 5 1

0 . 5 3

0 . 3 4

0 . 6 9

0 . 2 3


S P E C

0 . 2 4

0 . 0 0

0 . 0 0

1 . 0 0

0 . 4 3

C o n t r o l v a r i a b l e s u s e d i n a n a l y s t s ’ f o r e c a s t m o d e l

M E E

T

0 . 1 7

0 . 0 0

0 . 0 0

0 . 0 0

0 . 3 8

M I S S

0 . 0 7

0 . 0 0

0 . 0 0

0 . 0 0

0 . 2 6

T E N U

9 . 1 4

6 . 0 0

4 . 0 0

1 2 . 0 0

7 . 6 2

L I T I G

0 . 4 1

0 . 0 0

0 . 0 0

1 . 0 0

0 . 4 9

M B

3 . 0 0

2 . 1 5

1 . 2 6

3 . 7 9

2 . 9 2

M V

6 . 1 5

6 . 0 4

4 . 9 3

7 . 1 9

1 . 6 5

L O S S

0 . 4 0

0 . 0 0

0 . 0 0

1 . 0 0

0 . 4 9

C F O

0 . 0 3

0 . 0 7

− 0 . 0 2

0 . 1 3

0 . 1 7

F I N

0 . 2 2

0 . 0 0

0 . 0 0

0 . 0 0

0 . 4 2

R O A

− 0 . 0 8

0 . 0 2

− 0 . 0 9

0 . 0 7

0 . 3 9

D C A

0 . 0 3

− 0 . 0 1

− 0 . 0 9

0 . 0 5

1 . 0 0

S p e c i a l i s t s (

N =

8 2 7 )

N


2 , 6 7 1 )

D i f f e r e n c e

M e a n

M e d i a n

M e a

n

M e d i a n



L N A U

5 . 8 4

5 . 8 3

5 . 6 3

5 . 6 8

3 . 0 9 ∗ ∗

3 . 1 0 ∗ ∗

P R N A U

5 4

5 6

5 4

5 7

0 . 1 3

0 . 0 9

L T O T

6 . 6 5

6 . 4 5

6 . 5 1

6 . 3 8

2 . 9 5 ∗ ∗

2 . 4 6 ∗





T A B L E



M E E T

M I S S

L N A U

P R N A U

L T O T

T E N U

L I T

I G

M B

M V

L O S S

C F O

F I N

R O A

D C

A

S P E C

M E E

T

1 . 0 0

M I S S

− 0 . 1 3 ∗ ∗

1 . 0 0

L N A U

0 . 0 9 ∗ ∗

0 . 0 6 ∗ ∗

1 . 0 0

P R N A U

0 . 1 1 ∗ ∗

0 . 0 5 ∗ ∗

0 . 9 3 ∗ ∗

1 . 0 0

L T O T

0 . 1 0 ∗ ∗

0 . 0 8 ∗ ∗

0 . 8 9 ∗ ∗

0 . 8 8 ∗ ∗

1 . 0 0

T E N U

0 . 0 7 ∗ ∗

0 . 0 7 ∗ ∗

0 . 2 4 ∗ ∗

0 . 2 5 ∗ ∗

0 . 3 1 ∗ ∗

1 . 0 0

L I T I G

− 0 . 0 5 ∗ ∗

− 0 . 0 2

− 0 . 0 7 ∗ ∗

− 0 . 0 8 ∗ ∗

− 0 . 1 4 ∗ ∗

− 0 . 1 9 ∗ ∗

1 . 0

0

M B

0 . 0 8 ∗ ∗

0 . 0 2

0 . 0 1

− 0 . 0 1

− 0 . 0 4 ∗

− 0 . 0 4 ∗ ∗

0 . 1

6 ∗ ∗

1 . 0 0

M V

0 . 1 8 ∗ ∗

0 . 1 2 ∗ ∗

0 . 5 8 ∗ ∗

0 . 5 7 ∗ ∗

0 . 6 6 ∗ ∗

0 . 3 0 ∗ ∗

− 0 . 0 4 ∗

0 . 3 4 ∗ ∗

1 . 0 0

L O S S

− 0 . 1 6 ∗ ∗

− 0 . 0 8 ∗ ∗

− 0 . 1 4 ∗ ∗

− 0 . 1 3 ∗ ∗

− 0 . 1 7 ∗ ∗

− 0 . 2 5 ∗ ∗

0 . 2

7 ∗ ∗

0 . 0 1

− 0 . 3 1 ∗ ∗

1 . 0 0

C F O

0 . 1 1 ∗ ∗

0 . 0 1

0 . 1 8 ∗ ∗

0 . 2 0 ∗ ∗

0 . 2 3 ∗ ∗

0 . 2 3 ∗ ∗

− 0 . 2

4 ∗ ∗

− 0 . 0 7 ∗ ∗

0 . 3 5 ∗ ∗

− 0 . 5 8 ∗ ∗

1 . 0 0

F I N

− 0 . 0 4 ∗ ∗

− 0 . 0 2

− 0 . 0 3

− 0 . 0 5 ∗ ∗

− 0 . 0 8 ∗ ∗

− 0 . 1 0 ∗ ∗

0 . 0

5 ∗ ∗

0 . 1 3 ∗ ∗

− 0 . 0 3

0 . 1 5 ∗ ∗

− 0 . 2 0 ∗ ∗

1 . 0 0

R O A

0 . 0 9 ∗ ∗

0 . 0 3

0 . 0 8 ∗ ∗

0 . 0 6 ∗ ∗

0 . 1 0 ∗ ∗

0 . 1 8 ∗ ∗

− 0 . 2

1 ∗ ∗

− 0 . 0 1

0 . 2 3 ∗ ∗

− 0 . 4 7 ∗ ∗

0 . 6 0 ∗ ∗

− 0 . 0 7 ∗ ∗

1 . 0 0

D C A

0 . 0 0

− 0 . 0 2

− 0 . 0 1

− 0 . 0 3 ∗

− 0 . 0 3 ∗

0 . 0 0

− 0 . 0

9 ∗ ∗

0 . 0 4 ∗

0 . 0 1

− 0 . 0 8 ∗ ∗

− 0 . 0 1

0 . 0 3 ∗

0 . 2 5 ∗ ∗

1 . 0

0

0 . 0 1

S P E C

− 0 . 0 1

− 0 . 0 1

0 . 0 5 ∗ ∗

0 . 0 0

0 . 0 5 ∗ ∗

− 0 . 0 6 ∗ ∗

− 0 . 0

3

0 . 0 1

0 . 0 4 ∗

0 . 0 1

0 . 0 1

0 . 0 1

0 . 0 2

0 . 0

1

1 . 0 0

T h e t a b l e r e p o r t s d e s c r i p t i v e s t a t i s t i c s f o r t h e f e e m e t r i c s a n d t h e c o r r e l a t i o n b e t w

e e n v a r i a b l e s u s e d i n t h e a n a l y s t s ’ f o r e c a s t m o d e l . S e e t a b l e 2 f o r t h e d e fi n i t i o n s o f f e e m e t r i c s

a n d a u d i t o r s p e c i a l i z a t i o n . D e fi n i t i o n s o f o t h

e r v a r i a b l e s a r e a s f o l l o w s : M E E T e q u a l s 1 w h e n a fi r m ’ s a c t u a l E P S m i n u s c o n s

e n s u s a n a l y s t s ’ f o r e c a s t i s w i t h i n z e r o t o

o n e c e n t , a n d

0 o t h

e r w i s e . M I S S e q u a l s 1 w h e n a fi r m ’ s a c t u a l E P S m i n u s c o n s e n s u s a n a l y s t s ’ f o r e c a s t i s w i t h i n n e g a t i v e t w o c e n t s , a n d 0 o t h e r w i s e . T E N U i s t h e n u m b e r o f y e a r s t h a t t h e a u d i t o r

h a s a u d i t e d t h e fi r m ’ s fi n a n c i a l s t a t e m e n t s . L

I T I G e q u a l s 1 i f t h e fi r m o p e r a t e s i n a h i g h - l i t i g a t i o n i n d u s t r y , a n d 0 o t h e r w i s e . H i g h - l i t i g a t i o n i n d u s t r i e s a r e i n d u s t r i e s w i t h S I C c o d e s

2 8 3 3 – 2 8 3 6 , 3 5 7 0 – 3 5 7 7 , 3 6 0 0 – 3 6 7 4 , 5 2 0 0 – 5 9 6 1 , a n d 7 3 7 0 – 7 3 7 4 . M B i s m a r k e t - t o - b o o k r a t i o .

M V i s t h e n a t u r a l l o g o f m a r k e t v a l u e . L O S S e q u a l s 1 i f a fi r m r e p o r t s a l o s s , a n d 0

o t h e r w i s e . C F O i s c a s h fl o w f r o m o p e r a t i o n s s c a l e d b y t o t a l a s s e t s a t t h e b e g i n n i n g o f t h e fi s c a l y e a r . F

I N e q u a l s 1 i f t h e fi r m

i s s u e d s e c u r i t i e s o r a c q u i r e d a n o t h e r c o m p a n y , a n d 0

o t h e r w i s e . R O A i s t h e r e t u r n s o n a s s e t s . D

C A i s t h e d i s c r e t i o n a r y c u r r e n t a c c r u a l s e s t i m a t e d w i t h l a g g e d R O A i n t h e c r o s s - s e c t i o n a l J o n e s [ 1 9 9 1 ] m o d e l . S

P E C e q u a l s 1 i f t h e a u d i t o r

h a s t h e l a r g e s t m a r k e t s h a r e i n t h e i n d u s t r y , a n d 0 o t h e r w i s e . M e a n ( m e d i a n ) d i f f e r e n c e s i n f e e s b e t w e e n s p e c i a l i s t a n d n o n

s p e c i a l i s t s a r e b a s e d o n t - t e s t s ( M a n n - W

h i t n e y t e s t s ) . ∗

a n d ∗ ∗

d e n o t e s i g n i fi c a n c e a t t h e 5 % a n d 1 %

l e v e l s ( t w o - t a i l e d ) , r e s p e c t i v e l y .




T A B L E

7

F e e M e t r i c s , A u d i t o r S p e c

i a l i z a t i o n , a n d A n a l y s t s ’ F o r e c a s t

P a n e l A : M e e t i n g a n a l y s t s ’ f o r e c a s t s

L N A U

P R N A U

L T O T

I n t e r c e p t

ϕ 0

− 2 . 4 2 5

− 2 . 4 5 6

− 2 . 3 9 0

− 2 . 4 2 3

− 2 . 3 3 8

− 2 . 3 5 1

− 2 . 1 6 4

( 1 2 9 . 7 7 ) ∗ ∗ ∗

( 1 2 9 . 1 7 ) ∗ ∗ ∗

( 1 0 1 . 9 2 )

∗ ∗ ∗

( 1 2 9 . 8 1 ) ∗ ∗ ∗

( 1 0 9 . 0 0 ) ∗ ∗ ∗

( 7 6 . 7 7 ) ∗ ∗ ∗

( 5 0 . 0 6 ) ∗ ∗ ∗

F E E

ϕ 1

0 . 0 1 3

0 . 0 0 8

0 . 0 0 3

0 . 0 0 2

− 0 . 0 3 0

− 0 . 0 5 1

( 0 . 1 4 )

( 0 . 0 5 )

( 1 . 2 6 )

( 0 . 4 7 )

( 0 . 3 0 )

( 0 . 7 8 )

[ 0 . 3 1 % ]

[ 0 . 1 9 %

]

[ 0 . 9 5 % ]

[ 0 . 6 2 % ]

[ − 0 . 5 2 % ]

[ − 0 . 5 2 % ]

T E N U

ϕ 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 1

− 0 . 0 0 1

( 0 . 0 9 )

( 0 . 0 6 )

( 0 . 1 1 )

( 0 . 0 9 )

( 0 . 1 4 )

( 0 . 0 3 )

( 0 . 0 6 )

[ − 0 . 2 0 % ]

[ − 0 . 1 7 % ]

[ − 0 . 2 2 %

]

[ − 0 . 2 0 % ]

[ − 0 . 2 5 % ]

[ − 0 . 1 1 % ]

[ − 0 . 1 6 % ]

L I T I G

ϕ 3

− 0 . 0 9 8

− 0 . 0 9 3

− 0 . 0 9 5

− 0 . 0 9 0

− 0 . 0 9 2

− 0 . 1 0 2

− 0 . 1 0 4

( 0 . 9 4 )

( 0 . 8 4 )

( 0 . 8 8 )

( 0 . 7 9 )

( 0 . 8 2 )

( 1 . 0 0 )

( 1 . 0 3 )

[ − 0 . 6 8 % ]

[ − 0 . 6 5 % ]

[ − 0 . 6 6 %

]

[ − 0 . 6 2 % ]

[ − 0 . 6 4 % ]

[ − 0 . 7 1 % ]

[ − 0 . 7 2 % ]

M B

ϕ 4

0 . 0 3 8

0 . 0 3 9

0 . 0 3 9

0 . 0 4 2

0 . 0 4 2

0 . 0 3 5

0 . 0 3 4

( 5 . 0 8 ) ∗ ∗

( 5 . 2 7 ) ∗ ∗

( 5 . 2 7 )

∗ ∗

( 6 . 1 1 ) ∗ ∗

( 6 . 0 7 ) ∗ ∗ ∗

( 3 . 7 5 ) ∗ ∗

( 3 . 7 0 ) ∗ ∗

[ 1 . 5 5 % ]

[ 1 . 6 2 % ]

[ 1 . 6 2 %

]

[ 1 . 7 5 % ]

[ 1 . 7 4 % ]

[ 1 . 4 2 % ]

[ 1 . 4 1 % ]

M V

ϕ 5

0 . 2 1 1

0 . 1 9 9

0 . 1 9 9

0 . 1 8 2

0 . 1 8 1

0 . 2 2 7

0 . 2 2 5

( 4 0 . 6 2 ) ∗ ∗ ∗

( 2 3 . 2 2 ) ∗ ∗ ∗

( 2 2 . 8 8 ) ∗ ∗ ∗

( 1 9 . 7 4 ) ∗ ∗ ∗

( 1 9 . 4 2 ) ∗ ∗ ∗

( 2 3 . 5 0 ) ∗ ∗ ∗

( 2 3 . 0 2 ) ∗ ∗ ∗

[ 4 . 9 3 % ]

[ 4 . 6 5 % ]

[ 4 . 6 4 %

]

[ 4 . 2 4 % ]

[ 4 . 2 3 % ]

[ 5 . 3 0 % ]

[ 5 . 2 5 % ]

L O S S

ϕ 6

− 0 . 8 5 0

− 0 . 8 5 4

− 0 . 8 5 4

− 0 . 8 5 6

− 0 . 8 5 7

− 0 . 8 4 8

− 0 . 8 5 1

( 3 8 . 1 9 ) ∗ ∗ ∗

( 3 8 . 5 3 ) ∗ ∗ ∗

( 3 8 . 5 3 ) ∗ ∗ ∗

( 3 8 . 8 9 ) ∗ ∗ ∗

( 3 8 . 9 8 ) ∗ ∗ ∗

( 3 7 . 9 5 ) ∗ ∗ ∗

( 3 8 . 2 0 ) ∗ ∗ ∗

[ − 5 . 8 7 % ]

[ − 5 . 9 0 % ]

[ − 5 . 9 0 %

]

[ − 5 . 9 1 % ]

[ − 5 . 9 2 % ]

[ − 5 . 8 6 % ]

[ − 5 . 8 8 % ]

C F O

ϕ 7

− 0 . 5 9 9

− 0 . 5 8 8

− 0 . 5 8 5

− 0 . 5 8 8

− 0 . 5 8 0

− 0 . 6 0 8

− 0 . 5 9 0

( 1 . 7 6 )

( 1 . 6 9 )

( 1 . 6 7 )

( 1 . 6 8 )

( 1 . 6 3 )

( 1 . 8 1 )

( 1 . 6 9 )

[ − 1 . 4 3 % ]

[ − 1 . 4 1 % ]

[ − 1 . 4 0 %

]

[ − 1 . 4 1 % ]

[ − 1 . 3 9 % ]

[ − 1 . 4 6 % ]

[ − 1 . 4 1 % ]





T A B L E


P a n e l A : M e e t i n g a n a l y s t s ’ f o r e c a s t s

L N A U

P R N A U

L T O T

F I N

ϕ 8

− 0 . 1 5 8

− 0 . 1 6 0

− 0 . 1 5 7

− 0 . 1 5 9

− 0 . 1 5 7

− 0 . 1 6 2

− 0 . 1 5 8

( 1 . 7 5 )

( 1 . 7 8 )

( 1 . 7 2 )

( 1 . 7 7 )

( 1 . 7 2 )

( 1 . 8 3 )

( 1 . 7 3 )

[ − 0 . 9 3 % ]

[ − 0 . 9 4 % ]

[

− 0 . 9 3 % ]

[ − 0 . 9 4 % ]

[ − 0 . 9 2 % ]

[ − 0 . 9 5 % ]

[ − 0 . 9 3 % ]

R O A

ϕ 9

0 . 0 0 2

0 . 0 0 2

0 . 0 0 2

0 . 0 0 2

0 . 0 0 2

0 . 0 0 2

0 . 0 0 2

( 0 . 8 6 )

( 0 . 8 5 )

( 0 . 8 7 )

( 0 . 9 3 )

( 0 . 9 2 )

( 0 . 7 8 )

( 0 . 7 6 )

[ 1 . 1 2 % ]

[ 1 . 1 2 % ]

[ 1 . 1 3 % ]

[ 1 . 1 8 % ]

[ 1 . 1 7 % ]

[ 1 . 0 7 % ]

[ 1 . 0 6 % ]

D C A

ϕ 1 0

− 0 . 0 4 7

− 0 . 0 4 7

− 0 . 0 4 7

− 0 . 0 4 7

− 0 . 0 4 7

− 0 . 0 4 8

− 0 . 0 4 8

( 0 . 7 7 )

( 0 . 7 8 )

( 0 . 7 7 )

( 0 . 7 9 )

( 0 . 7 6 )

( 0 . 8 1 )

( 0 . 7 9 )

[ − 0 . 6 7 % ]

[ − 0 . 6 7 % ]

[

− 0 . 6 7 % ]

[ − 0 . 6 7 % ]

[ − 0 . 6 6 % ]

[ − 0 . 6 8 % ]

[ − 0 . 6 8 % ]

S P E C

ϕ 1 1

− 0 . 1 0 9

− 0 . 3 1 4

− 0 . 3 5 1

− 0 . 7 9 1

( 0 . 9 8 )

( 0 . 6 6 )

( 1 . 6 5 )

( 1 . 8 6 )

[ − 0 . 6 5 % ]

[

− 1 . 8 8 % ]

[ − 2 . 1 1 % ]

[ − 4 . 7 4 % ]

Y 0 0

ϕ 1 2

− 0 . 4 2 6

− 0 . 4 2 8

− 0 . 4 3 2

− 0 . 4 1 3

− 0 . 4 1 5

− 0 . 4 2 1

− 0 . 4 2 4

( 1 9 . 8 6 ) ∗ ∗ ∗

( 1 9 . 8 1 ) ∗ ∗ ∗

( 2 0 . 0 8 ) ∗ ∗ ∗

( 1 8 . 4 4 ) ∗ ∗ ∗

( 1 8 . 6 1 ) ∗ ∗ ∗

( 1 9 . 3 4 ) ∗ ∗ ∗

( 1 9 . 5 7 ) ∗ ∗ ∗

F E E ∗ S P E C

ϕ 1 3

0 . 0 3 3

0 . 0 0 4

0 . 1 0 0

( 0 . 2 9 )

( 0 . 9 8 )

( 1 . 4 5 )

[ 1 . 2 3 % ]

[ 1 . 5 3 % ]

[ 4 . 0 7 % ]

F E E +

F E E ∗ S P E C

ϕ 1 +

ϕ 1 3

0 . 0 4 1

0 . 0 0 6

0 . 0 4 9

[ 0 . 4 6 ]

[ 2 . 0 3 ]

[ 0 . 3 4 ]

N

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8


1 7 5 . 0 1 ∗ ∗ ∗

1 7 4 . 6 0 ∗ ∗ ∗

1 7 5 . 5 5 ∗ ∗ ∗

1 7 5 . 6 9 ∗ ∗ ∗

1 7 6 . 8 7 ∗ ∗ ∗

1 7 4 . 1 9 ∗ ∗ ∗

1 7 6 . 0 0 ∗ ∗ ∗

P s e u

d o R 2

9 . 0 %

8 . 9 %

9 . 0 %

9 . 0 %

9 . 1 %

9 . 0 %

9 . 1 %





T A B L E


P a n e l B : M i s s i n g a n a l y s t s ’ f o r e c a s t

L N A U

P R N A U

L T O T

I n t e r c e p t

ϕ 0

− 3 . 9 9 0

− 3 . 9 3 5

− 3 . 6 5 0

− 4 . 0 5 9

− 3 . 8 5 2

− 3 . 7 6 7

− 3 . 2 8 1

( 1 6 7 . 0 0 ) ∗ ∗ ∗

( 1 5 9 . 2 5 ) ∗ ∗ ∗

( 1 1 7 . 6 4 )

∗ ∗ ∗

( 1 7 1 . 7 2 ) ∗ ∗ ∗

( 1 4 2 . 1 2 ) ∗ ∗ ∗

( 9 8 . 1 0 ) ∗ ∗ ∗

( 5 8 . 0 9 ) ∗ ∗ ∗

F E E

ϕ 1

− 0 . 0 7 1

− 0 . 1 0 0

− 0 . 0 0 5

− 0 . 0 0 7

− 0 . 0 8 3

− 0 . 1 4 1

( 2 . 3 0 )

( 4 . 1 9 )

∗ ∗

( 2 . 8 4 ) ∗

( 5 . 0 4 ) ∗ ∗

( 1 . 2 0 )

( 3 . 0 6 ) ∗

[ − 0 . 8 0 % ]

[ − 1 . 1 3 %

]

[ − 0 . 9 3 % ]

[ − 1 . 2 9 % ]

[ − 0 . 6 6 % ]

[ − 1 . 1 2 % ]

T E N U

ϕ 2

0 . 0 0 9

0 . 0 1 1

0 . 0 1 0

0 . 0 1 1

0 . 0 1 0

0 . 0 1 1

0 . 0 1 0

( 1 . 1 1 )

( 1 . 5 6 )

( 1 . 3 8 )

( 1 . 5 9 )

( 1 . 3 9 )

( 1 . 5 6 )

( 1 . 3 2 )

[ 0 . 4 4 % ]

[ 0 . 5 3 % ]

[ 0 . 5 0 %

]

[ 0 . 5 3 % ]

[ 0 . 5 0 % ]

[ 0 . 5 3 % ]

[ 0 . 4 9 % ]

L I T I G

ϕ 3

− 0 . 0 6 2

− 0 . 0 6 6

− 0 . 0 6 6

− 0 . 0 6 4

− 0 . 0 6 8

− 0 . 0 7 4

− 0 . 0 8 0

( 0 . 1 8 )

( 0 . 2 1 )

( 0 . 2 1 )

( 0 . 2 0 )

( 0 . 2 2 )

( 0 . 2 6 )

( 0 . 3 0 )

[ − 0 . 2 0 % ]

[ − 0 . 2 1 % ]

[ − 0 . 2 1 %

]

[ − 0 . 2 1 % ]

[ − 0 . 2 2 % ]

[ − 0 . 2 4 % ]

[ − 0 . 2 6 % ]

M B

ϕ 4

− 0 . 0 3 5

− 0 . 0 4 4

− 0 . 0 4 4

− 0 . 0 4 5

− 0 . 0 4 5

− 0 . 0 4 5

− 0 . 0 4 7

( 2 . 0 1 )

( 2 . 9 4 ) ∗

( 2 . 9 8 )

∗

( 3 . 0 8 ) ∗

( 3 . 1 7 ) ∗

( 2 . 8 6 ) ∗

( 3 . 0 5 ) ∗

[ − 0 . 6 6 % ]

[ − 0 . 8 3 % ]

[ − 0 . 8 4 %

]

[ − 0 . 8 5 % ]

[ − 0 . 8 6 % ]

[ − 0 . 8 5 % ]

[ − 0 . 8 8 % ]

M V

ϕ 5

0 . 2 9 0

0 . 3 3 9

0 . 3 2 8

0 . 3 4 4

0 . 3 3 8

0 . 3 3 7

0 . 3 3 1

( 3 8 . 6 5 ) ∗ ∗ ∗

( 3 3 . 5 7 ) ∗ ∗ ∗

( 3 1 . 2 1 ) ∗ ∗ ∗

( 3 5 . 1 8 ) ∗ ∗ ∗

( 3 3 . 6 5 ) ∗ ∗ ∗

( 2 5 . 9 0 ) ∗ ∗ ∗

( 2 4 . 8 5 ) ∗ ∗ ∗

[ 3 . 1 2 % ]

[ 3 . 6 5 % ]

[ 3 . 5 3 %

]

[ 3 . 7 0 % ]

[ 3 . 6 4 % ]

[ 3 . 6 3 % ]

[ 3 . 5 6 % ]

L O S S

ϕ 6

− 0 . 6 6 6

− 0 . 6 5 8

− 0 . 6 6 7

− 0 . 6 6 2

− 0 . 6 6 9

− 0 . 6 6 2

− 0 . 6 7 4

( 1 1 . 9 6 ) ∗ ∗ ∗

( 1 1 . 5 9 ) ∗ ∗ ∗

( 1 1 . 9 0 ) ∗ ∗ ∗

( 1 1 . 7 2 ) ∗ ∗ ∗

( 1 1 . 9 6 ) ∗ ∗ ∗

( 1 1 . 7 5 ) ∗ ∗ ∗

( 1 2 . 1 3 ) ∗ ∗ ∗

[ − 2 . 1 2 % ]

[ − 2 . 1 0 % ]

[ − 2 . 1 2 %

]

[ − 2 . 1 1 % ]

[ − 2 . 1 3 % ]

[ − 2 . 1 1 % ]

[ − 2 . 1 5 % ]

C F O

ϕ 7

− 2 . 4 5 8

− 2 . 4 8 5

− 2 . 4 6 8

− 2 . 4 5 7

− 2 . 4 4 9

− 2 . 4 8 6

− 2 . 4 4 2

( 1 7 . 2 5 ) ∗ ∗ ∗

( 1 7 . 6 5 ) ∗ ∗ ∗

( 1 7 . 2 7 ) ∗ ∗ ∗

( 1 7 . 3 5 ) ∗ ∗ ∗

( 1 7 . 1 3 ) ∗ ∗ ∗

( 1 7 . 6 9 ) ∗ ∗ ∗

( 1 6 . 9 0 ) ∗ ∗ ∗

[ − 2 . 7 1 % ]

[ − 2 . 7 4 % ]

[ − 2 . 7 3 %

]

[ − 2 . 7 1 % ]

[ − 2 . 7 1 % ]

[ − 2 . 7 5 % ]

[ − 2 . 7 0 % ]

F I N

ϕ 8

− 0 . 1 5 7

− 0 . 1 6 1

− 0 . 1 5 3

− 0 . 1 6 3

− 0 . 1 5 9

− 0 . 1 6 7

− 0 . 1 5 8

( 0 . 8 7 )

( 0 . 9 1 )

( 0 . 8 2 )

( 0 . 9 3 )

( 0 . 8 8 )

( 0 . 9 7 )

( 0 . 8 8 )

[ − 0 . 4 3 % ]

[ − 0 . 4 4 % ]

[ − 0 . 4 2 %

]

[ − 0 . 4 4 % ]

[ − 0 . 4 3 % ]

[ − 0 . 4 5 % ]

[ − 0 . 4 3 % ]





T A B L E


P a n e l B : M i s s i n g a n a l y s t s ’ f o r e c a s t

L N A U

P R N A U

L T O T

R O A

ϕ 9

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

0 . 0 0 3

( 1 . 2 3 )

( 1 . 0 7 )

( 1 . 0 5 )

( 0 . 9 8 )

( 0 . 9 5 )

( 1 . 0 7 )

( 0 . 9 9 )

[ 0 . 7 4 % ]

[ 0 . 6 9 % ]

[ 0 . 6 9 % ]

[ 0 . 6 6 % ]

[ 0 . 6 5 % ]

[ 0 . 6 9 % ]

[ 0 . 6 6 % ]

D C A

ϕ 1 0

− 0 . 1 5 5

− 0 . 1 5 7

− 0 . 1 5 6

− 0 . 1 5 7

− 0 . 1 5 5

− 0 . 1 5 7

− 0 . 1 5 6

( 4 . 6 2 ) ∗ ∗

( 4 . 7 2 ) ∗ ∗

( 4 . 5 4 ) ∗ ∗

( 4 . 7 1 ) ∗ ∗

( 4 . 4 8 ) ∗ ∗

( 4 . 7 7 ) ∗ ∗

( 4 . 6 0 ) ∗ ∗

[ − 1 . 0 1 % ]

[ − 1 . 0 3 % ]

[ − 1 . 0 2 % ]

[ − 1 . 0 2 % ]

[ − 1 . 0 1 % ]

[ − 1 . 0 2 % ]

[ − 1 . 0 2 % ]

S P E C

ϕ 1 1

− 0 . 1 5 7

− 1 . 2 6 4

− 0 . 8 5 1

− 2 . 0 5 1

( 0 . 9 8 )

( 4 . 5 3 ) ∗ ∗

( 4 . 3 5 ) ∗ ∗

( 6 . 0 2 ) ∗ ∗ ∗

[ − 0 . 4 3 % ]

[ − 3 . 5 0 % ]

[ − 2 . 3 5 % ]

[ − 5 . 6 7 % ]

Y 0 0

ϕ 1 2

0 . 9 8 1

0 . 1 1 0

0 . 1 0 4

0 . 0 6 4

0 . 0 6 0

0 . 0 9 4

0 . 0 9 1

( 0 . 3 8 )

( 0 . 6 7 )

( 0 . 6 0 )

( 0 . 2 3 )

( 0 . 2 0 )

( 0 . 5 0 )

( 0 . 4 6 )

F E E ∗ S P E C

ϕ 1 3

0 . 1 7 7

0 . 0 1 1

0 . 2 6 9

( 4 . 0 0 ) ∗ ∗

( 3 . 6 1 ) ∗

( 5 . 5 4 ) ∗ ∗

[ 3 . 0 2 % ]

[ 1 . 9 4 % ]

[ 5 . 0 6 % ]

F E E +

F E E ∗ S P E C

ϕ 1 +

ϕ 1 3

0 . 0 7 7

0 . 0 0 4

0 . 1 2 8

[ 0 . 7 7 ]

[ 0 . 4 4 ]

[ 1 . 2 2 ]

N

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8

3 , 4 9 8


7 8 . 9 2 ∗ ∗ ∗

7 9 . 4 7 ∗ ∗ ∗

8 3 . 7 0 ∗ ∗ ∗

8 0 . 2 5 ∗ ∗ ∗

8 3 . 9 5 ∗ ∗ ∗

7 8 . 5 4 ∗ ∗ ∗

8 4 . 0 0 ∗ ∗ ∗

P s e u

d o R 2

5 . 6 %

5 . 7 %

6 . 0 %

5 . 7 %

6 . 0 %

5 . 6 %

6 . 0 %

A

t o t a l o f 3 , 4 9 8 fi r m - y e a r o b s e r v a t i o n s i s a v a i l a b l e f o r t h e a n a l y s t s ’ f o r e c a s t m o d e l f o r t h e p e r i o d 2 0 0 0 – 2 0 0 1 . A n a l y s t f o r e c a

s t d a t a a r e f r o m t h e d e t a i l e d I / B / E / S fi l e . T h e l o g i s t i c

r e g r e

s s i o n i s a s f o l l o w s :

M E E T o r

M I S S =

ϕ 0 +

ϕ 1 F E E +

ϕ 2 T E N U +

ϕ 3 L I T

I G +

ϕ 4 M B +

ϕ 5 M V +

ϕ 6 L O S S +

ϕ 7 C F O

+

ϕ 8 F I N +

ϕ 9 R O A

+

ϕ 1 0 D C A +

ϕ 1 1 S P E C +

ϕ 1 2 Y 0 0

+

ϕ 1 3 F E E ∗ S P E C +

ε

O t h e

r v a r i a b l e s a r e a s d e fi n e d i n t h e f o o t n o t e s o f t a b l e 6 . Y 0 0 i s y e a r d u m m y . F o r e a

c h v a r i a b l e , w e r e p o r t t h e r e g r e s s i o n c o e f fi c i e n t , f o l l o w e d b y t h e W a l d s t a t i s t i c i n p a r e n t h e s e s ,

a n d t h e m a r g i n a l e f f e c t ( i n p e r c e n t ) i n s q u

a r e b r a c k e t s . T h e m a r g i n a l e f f e c t i n d i c

a t e s t h e c h a n g e i n t h e p r o b a b i l i t y o f a

fi r m m e e t i n g ( m i s s i n g ) a n e a r n i n g s b

e n c h m a r k p e r

s t a n d

a r d d e v i a t i o n c h a n g e i n e a c h r e s p e c t i v e i n d e p e n d e n t v a r i a b l e ( h o l d i n g o t h e r i n d

e p e n d e n t v a r i a b l e s c o n s t a n t ) , g i v e n t h e

b a s e - r a t e p r o b a b i l i t y o f m e e t i n g ( m i s s i n g ) t h e e a r n i n g s

b e n c h m a r k o f 1 7 % ( 7 % ) . W e u s e χ 2 s t a t i s t i c

s t o t e s t w h e t h e r t h e s u m o f t h e c o e f fi c i e n t s ( ϕ 1 +

ϕ 1 3 ) d i f f e r s f r o m z e r o a n d r e p o r t t h e χ 2 s t a t i s t i c i n s q u a r e b r a c k e t s . ∗ ,

∗ ∗ , a n d ∗ ∗ ∗

d e n o

t e s i g n i fi c a n c e a t t h e 1 0 % , 5 % , a n d 1 % l e v e l s ( t w o - t a i l e d ) , r e s p e c t i v e l y .




through lower audit and financial reporting quality, these results suggest that when an auditor’s provision of non-audit services to a firm increases,audit quality is more likely to be impaired for nonspecialist auditors thanspecialist auditors.

4.4 EARNINGS-RETURNS RELATION

4.4.1. Empirical Model. We use the ERC from earnings-returns regressionsas a proxy for investor perceptions of earnings quality. Following prior stud-ies (e.g.,Krishnan, Sami, andZhang [2005], Francis and Ke [2006]), we mea-sure three-day cumulative abnormal returns for days −1, 0, and 1, where day 0 is the day of the first quarterly earnings announcement after fee disclosurein the proxy statements, where abnormal returns is defined as the differencebetween a firm’s returns and CRSP value-weighted market returns.20

To ensure comparability with prior studies, we use a set of control variables

similar to those used in Francis and Ke [2006]. Specifically, we run thefollowing regression:

CAR = α0 + α1(UE ) + α2( FEE ) + α3(UE*FEE ) + α4(GRW ) + α5(UE*GRW )

+α6(VOL ) + α7(UE*VOL ) + α8(LEV ) + α9(UE*LEV ) + α10(MV )

+α11(UE*MV ) + α12(LOSS ) + α13(UE*LOSS ) + α14(RESTR )

+α15(UE*RESTR ) + α16SPEC + α17(UE*SPEC ) + α18Y 00

+α19(UE*Y 00) + α20( FEE*SPEC ) + α21(UE*FEE*SPEC ) + ε (7)

where

CAR = three-day cumulative abnormal returns around the first quarterly earnings announcement after the fee discourse in the proxy state-ments;

UE = earnings surprise, measured by the difference between actual EPSand the most recent median earnings forecast for the quarterimmediately after the disclosure of fee information in the proxy statement, scaled by stock price at the beginning of the quarter;both actual and forecasted EPS are from I/B/E/S detailed files;

FEE = fee metrics, LNAU , PRNAU , and LTOT , as defined earlier;GRW = sum of the market value of equity and book value of debt scaled

by book value of assets at the end of the quarter;VOL = standard deviation of daily stock returns over a 90-day window

ending seven days prior to the earnings announcement date;LEV = debt-to-capital ratio at the end of the quarter;MV = natural log of market capitalization at the end of the quarter;

20 Firms must file proxy statements with fee information to the SEC, which is typically threeto four months after the fiscal year-end. We track the date of these proxy statements indicated

in the Audit Analytics database to ensure that the fee information is available to investors whenfirms make the first quarterly earnings announcements after the fiscal year-end.




LOSS = 1 if the current quarter’s earnings is negative, and 0 otherwise;RESTR = 1 if the special item as a percentage of total assets in the quarter

is less than or equal to −5%, and 0 otherwise;SPEC = 1 if the auditor has the largest market share in the industry, and

0 otherwise;Y 00 = year dummy.

4.4.2. Empirical Results. Descriptive statistics for the fee data and variablesused in the earnings-returns regression model are reported in table 8. Themean (median) CAR is 1% (0.5%). The mean (median) earnings surprise is0.2% (0.1%) of the stock price at the beginning of the quarter. All three fee

variables are significantly higher for the audit specialists than the non-audit specialists.

Table 9, panel A reports the results of the impact of non-audit fees and au-

ditorspecializationon the earnings-returns relation. Consistent withBalsam,Krishnan, and Yang [2003], we find that auditor specialization (in the model

with SPEC but without FEE ) is associated with a higher ERC. When we assessthe effects of fees (without SPEC ), consistent with prior studies (Krishnan,Sami, and Zhang [2005], Francis and Ke [2006]), we find that non-audit ser-

vice fees are associated with a lower ERC, indicating that investors perceiveearnings quality to be lower with high non-audit fees. 21

We next include the interaction term UE ∗ FEE ∗ SPEC (α21) in themodel. Consistent with our prediction in H1, the coefficient estimate forUE ∗ FEE ∗ SPEC is positive and statistically significant for all three fee mea-

sures, suggesting that when auditors provide non-audit services to clients,earnings quality, and presumably audit quality, is perceived to be higherfor specialist auditors than for nonspecialist auditors. To investigate thenature of this three-way interaction, we split the sample into two groups,firms audited by specialist and nonspecialist auditors, and assess whetherthe UE ∗ FEE interaction varies by auditor specialization. Results are shownin table 9, panel B. The UE ∗ FEE interaction tests whether the earnings-returns relation varies at different levels of fees. For firms audited by spe-cialists, the coefficient estimate for UE ∗ FEE (α3) is positive and significant at the 1% level, which is consistent with the notion that investors perceive

earnings quality and presumably audit quality to be higher as specialists provide more non-audit services to their clients. In contrast, for firms audited by nonspecialists, the coefficient estimate for UE ∗ FEE (α3) is negative and sig-nificant at the 1% level. This suggests that investors perceive earnings quality to be eroded when non-audit specialists provide more non-audit services.

21 Krishnan, Sami, and Zhang [2005] find that total fees are associated with lower ERConly in the first quarter (but not the second and third quarters) following the release of fee information in the proxy statements. However, they find that unexpected total fees areassociated with lower ERC in the second and third quarters following the proxy releases. In

conclusion, they interpret their results as consistent with the notion of investors perceiving theprovision of non-audit services as impairing auditor independence.




T A B L E

8

D e s c r i p t i v e S t a t i s t i c s a n d C o r r e l a t i o n b e t w e e n V a r i a b l e s U s e d i n t h e E a r n i n g s - R e t u r n s R e g r e s s i o n


M

e a n

M e d i a n



S t d . D e v .

F e e m e t r i c s


8 5 7

3 3 2

1 6 4

7 5 8

2 , 0 5 3

T o t a l N o n - a u d i t f e e s

1

, 9 4 3

4 2 0

1 3 9

1 , 3 0 0

5 , 8 1 4

T o t a l f e e s

2

, 8 0 0

8 1 3

3 5 1

2 , 1 0 4

7 , 3 6 2

F e e r a t i o

0

. 5 3

0 . 5 6

0 . 3 8

0 . 7 0

0 . 2 2


S P E C

0

. 2 5

0 . 0 0

0 . 0 0

0 . 0 0

0 . 4 3

V a r i a b l e s u s e d i n e a r n i n g s - r e t u r n s r e g r e

s s i o n m o d e l

C A R

0

. 0 1 0

0 . 0 0 5

− 0 . 0 3 4

0 . 0 5 0

0 . 0 9 6

U E

0

. 0 0 2

0 . 0 0 1

0 . 0 0 0

0 . 0 0 2

0 . 0 2 1

G R W

1

. 9 7 5

1 . 3 9 9

0 . 9 3 4

2 . 3 2 8

1 . 8 2 4

V O L

0

. 0 3 9

0 . 0 3 3

0 . 0 2 3

0 . 0 4 9

0 . 0 2 2

L E V

0

. 5 9 8

0 . 3 6 9

0 . 0 1 7

0 . 9 8 6

4 . 8 6 5

M V

6

. 6 8 6

6 . 5 6 8

5 . 4 6 8

7 . 7 1 8

1 . 7 0 6

L O S S

0

. 2 8 0

0 . 0 0 0

0 . 0 0 0

1 . 0 0 0

0 . 4 4 9

R E S T R

0

. 0 2 0

0 . 0 0 0

0 . 0 0 0

0 . 0 0 0

0 . 1 3 8

S p e c i a l i s t s ( N =

7 3 3 )

N o n s p e c i a l i s t s ( N =

2 , 2 0 2 )

D i f f e r e n c e

M e a n

M e d i a n

M e a n

M e d i a n



L N A U

6 . 3 6

6 . 2 7

5 . 9 5

5 . 9 5

5 . 1 0 ∗ ∗

5 . 1 0 ∗ ∗

P R N A U

6 0

6 4

5 8

6 0

2 . 3 7 ∗

2 . 6 9 ∗ ∗

L T O T

7 . 1 0

6 . 8 9

6 . 7 5

6 . 6 3

5 . 5 4 ∗ ∗

4 . 7 5 ∗ ∗





T A B L E



C A R

U E

L N A U

P R N A U

L T O T

G R W

V O L

L E V

M V

L O S S

R E S T R

S P E C

C A R

1 . 0 0

U E

0 . 0 8 ∗

1 . 0 0

L N A U

0 . 0 0

− 0 . 0 1

1 . 0 0

P R N A U

− 0 . 0 2

0 . 0 0

0 . 9 4 ∗ ∗

1 . 0 0

L T O T

− 0 . 0 1

0 . 0 0

0 . 9 2 ∗ ∗

0 . 8 8 ∗ ∗

1 . 0 0

G R W

− 0 . 0 2

− 0 . 0 3

−

0 . 1 3 ∗ ∗

− 0 . 1 4 ∗ ∗

− 0 . 1 7 ∗ ∗

1 . 0 0

V O L

0 . 0 8 ∗ ∗

0 . 0 7 ∗ ∗

−

0 . 2 0 ∗ ∗

− 0 . 2 6 ∗ ∗

− 0 . 2 8 ∗ ∗

0 . 1 0 ∗ ∗

1 . 0 0

L E V

0 . 0 0

− 0 . 0 3

0 . 0 5 ∗ ∗

0 . 0 5 ∗

0 . 0 6 ∗ ∗

− 0 . 0 6 ∗ ∗

− 0 . 0 6 ∗ ∗

1 . 0 0

M V

− 0 . 0 1

− 0 . 0 8 ∗ ∗

0 . 6 4 ∗ ∗

0 . 6 1 ∗ ∗

0 . 7 2 ∗ ∗

0 . 1 7 ∗ ∗

− 0 . 4 1 ∗ ∗

0 . 0 4 ∗

1 . 0 0

L O S S

− 0 . 1 0 ∗ ∗

− 0 . 0 2

−

0 . 1 8 ∗ ∗

− 0 . 1 8 ∗ ∗

− 0 . 2 2 ∗ ∗

0 . 0 3

0 . 4 2 ∗ ∗

− 0 . 0 1

− 0 . 3 3 ∗ ∗

1 . 0 0

R E S T R

− 0 . 0 2

0 . 0 3

0 . 0 6 ∗ ∗

0 . 0 5 ∗ ∗

0 . 0 5 ∗ ∗

− 0 . 0 4

0 . 1 5 ∗ ∗

0 . 0 2

− 0 . 0 2

0 . 0 9 ∗ ∗

1 . 0 0

S P E C

− 0 . 0 3

− 0 . 0 3

0 . 1 0 ∗ ∗

0 . 0 4 ∗

0 . 1 1 ∗ ∗

− 0 . 0 2

0 . 0 0

− 0 . 0 2

0 . 0 7 ∗ ∗

0 . 0 1

0 . 0 4 ∗

1 . 0 0

T h e s a m p l e f o r t h e e a r n i n g s - r e t u r n s r e g r e s s i o n m o d e l i s 2 , 9 3 5 fi r m - y e a r o b s e r v a t i o n s , w i t h a l l i n f o r m a t i o n a v a i l a b l e f r o m C o m p u s t a t , I / B / E / S d e t a i l e d fi l e s , a n d C R

S P . S e e t a b l e 2

f o r d e fi n i t i o n s o f f e e m e t r i c s a n d a u d i t o r s p e c

i a l i z a t i o n . D e fi n i t i o n s o f o t h e r v a r i a b l e s a r e a s f o l l o w s : C A R i s t h r e e - d a y c u m u l a t i v e a b n o r m a l r e t u r n s a r o u n d t h e fi r s t q u a

r t e r l y e a r n i n g s

a n n o

u n c e m e n t a f t e r t h e f e e d i s c o u r s e i n t h e p r o x y s t a t e m e n t s . U

E i s e a r n i n g s s u r p r i s e

, m e a s u r e d b y t h e d i f f e r e n c e b e t w e e n a c t u a l E P S a n d m o s t r e c e n t m e d i a n e a r n i n

g s f o r e c a s t f o r

t h e q

u a r t e r i m m e d i a t e l y a f t e r t h e d i s c l o s u r e o

f f e e i n f o r m a t i o n i n t h e p r o x y s t a t e m e n t , s c a l e d b y s t o c k p r i c e a t t h e b e g i n n i n g o f t h e q u a r t e r . B o t h a c t u a l a n d f o r e c a s t e d E P S a r e f r o m

I / B / E / S d e t a i l e d fi l e s . L N A U i s t h e n a t u r a l l o

g o f n o n - a u d i t f e e s . P

R N A U i s t h e p e r c e n

t i l e r a n k o f a p a r t i c u l a r c l i e n t ’ s n o n - a u d i t f e e s g i v e n a l l n o n - a u d i t f e e s r e c e i v e d b y

t h e a u d i t fi r m .

L T O T

i s t h e n a t u r a l l o g o f t o t a l f e e s . G R W i s t h e s u m o f t h e m a r k e t v a l u e o f e q u i t y a n d

b o o k v a l u e o f d e b t s c a l e d b y b o o k v a l u e o f a s s e t s a t e n d o f q u a r t e r . V O L i s t h e s t a n

d a r d d e v i a t i o n

o f d a

i l y s t o c k r e t u r n s o v e r a 9 0 - d a y w i n d o w e n d i n g s e v e n d a y s p r i o r t o t h e e a r n i n g s a n n o u n c e m e n t d a t e . L E V i s t h e d e b t - t o - c a p i t a l r a t i o a t t h e e n d o f t h e q u a r t e r . M V

i s t h e n a t u r a l

l o g o f m a r k e t c a p i t a l i z a t i o n a t t h e e n d o f t h e

q u a r t e r . L O S S e q u a l s 1 i f t h e c u r r e n t q u a r t e r ’ s e a r n i n g s i s n e g a t i v e , a n d 0 o t h e r w

i s e . R E S T R e q u a l s 1 i f t h e s p e c i a l i t e m a

s a p e r c e n t a g e

o f t o t a l a s s e t s i n t h e q u a r t e r i s l e s s t h a n o r e q u a l t o − 5 % , a n d 0 o t h e r w i s e . S P E C e q u a l s 1 i f t h e a u d i t o r h a s t h e l a r g e s t m a r

k e t s h a r e i n t h e i n d u s t r y , a n d 0 o t h e r w i s e . M e a n ( m e -

d i a n ) d i f f e r e n c e s i n f e e s b e t w e e n s p e c i a l i s t s a n

d n o n s p e c i a l i s t s a r e b a s e d o n t - t e s t s ( M a n n - W h i t n e y t e s t s ) . ∗ a n d ∗ ∗ d e n o t e s i g n i fi c a n c e a t t h e 5 % , a n d 1 % l e v e l s ( t w o - t a i l e d

) , r e s p e c t i v e l y .




T A B L E

9

F e e M e t r i c s , A u d i t o r S p e c i a l i z a t i o n , a n d E a r n i n g s - R e t u r n s R e l a t i o n

P a n e l A : I n t e r a c t i o n e f f e c t b e t w e e n f e e

m e t r i c s a n d a u d i t o r s p e c i a l i z a t i o n o n e a r n i n g s - r e t u r n s r e l a t i o n

L N A U

P R N A U

L T O T

I n t e r c e p t

α 0

− 0 . 0 0 7

− 0 . 0 0 9

− 0 . 0 0 4

− 0 . 0 1 0

−

0 . 0 0 7

− 0 . 0 0 5

0 . 0 0 4

( 0 . 7 6 )

( − 0 . 9 1 )

( − 0 . 4 2 )

( − 1 . 0 5 )

( − 0 . 6 7 )

( − 0 . 4 0 )

( 0 . 3 5 )

U E

α 1

− 2 . 9 6 0

− 0 . 8 1 5

− 0 . 0 9 3

− 1 . 4 1 1

−

0 . 7 3 0

0 . 5 6 5

1 . 2 4 4

( − 4 . 8 6 ) ∗ ∗ ∗

( − 0 . 8 2 )

( − 0 . 0 9 )

( − 1 . 6 7 ) ∗

( − 0 . 8 5 )

( 0 . 4 3 )

( 0 . 9 3 )

F E E

α 2

− 0 . 0 0 1

− 0 . 0 0 2

− 0 . 0 0 6

−

0 . 0 1 1

− 0 . 0 0 2

− 0 . 0 0 3

( − 0 . 7 3 )

( − 1 . 0 5 )

( − 0 . 6 4 )

( − 1 . 1 2 )

( − 0 . 9 7 )

( − 1 . 4 2 )

U E ∗ F E E

α 3

− 0 . 3 2 3

− 0 . 3 6 2

− 0 . 0 1 8

−

0 . 0 2 0

− 0 . 4 5 0

− 0 . 4 7 7

( − 2 . 9 5 ) ∗ ∗ ∗

( − 3 . 2 6 ) ∗ ∗ ∗

( − 2 . 9 2 ) ∗ ∗ ∗

( − 3 . 2 2 ) ∗ ∗ ∗

( − 3 . 1 6 ) ∗ ∗ ∗

( − 3 . 3 3 ) ∗ ∗ ∗

G R W

α 4

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

−

0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 2

( − 1 . 5 9 )

( − 1 . 6 9 ) ∗

( − 1 . 7 3 ) ∗

( − 1 . 6 4 ) ∗

( − 1 . 7 4 ) ∗

( − 1 . 7 8 ) ∗

( − 1 . 8 5 ) ∗

U E ∗ G R W

α 5

0 . 4 0 8

0 . 2 9 2

0 . 3 1 9

0 . 2 6 6

0 . 3 2 1

0 . 2 4 2

0 . 2 7 8

( 1 . 7 0 )

( 1 . 1 9 )

( 1 . 2 9 )

( 1 . 0 7 )

( 1 . 2 8 )

( 0 . 9 7 )

( 1 . 1 1 )

V O L

α 6

0 . 5 6 8

0 . 5 8 7

0 . 5 8 0

0 . 5 8 6

0 . 5 8 3

0 . 5 8 6

0 . 5 8 3

( 5 . 5 1 ) ∗ ∗ ∗

( 5 . 6 8 ) ∗ ∗ ∗

( 5 . 6 1 ) ∗ ∗ ∗

( 5 . 6 7 ) ∗ ∗ ∗

( 5 . 6 5 ) ∗ ∗ ∗

( 5 . 6 8 ) ∗ ∗ ∗

( 5 . 6 6 ) ∗ ∗ ∗

U E ∗ V O L

α 7

− 7 . 3 7 5

− 1 9 . 6 7 4

− 2 1 . 1 2 9

− 2 1 . 6 4 6

− 2 3 . 2 8 3

− 2 1 . 7 9 3

− 2 2 . 7 5 9

( − 1 . 4 1 )

( − 3 . 0 3 ) ∗ ∗ ∗

( − 3 . 2 1 ) ∗ ∗ ∗

( − 3 . 1 3 ) ∗ ∗ ∗

( − 3 . 3 3 ) ∗ ∗ ∗

( − 3 . 2 4 ) ∗ ∗ ∗

( − 3 . 3 4 ) ∗ ∗ ∗

L E V

α 8

− 0 . 0 6 0

− 0 . 0 4 7

− 0 . 0 4 4

− 0 . 0 4 7

−

0 . 0 4 2

− 0 . 0 4 4

− 0 . 0 4 2

( − 1 . 4 9 )

( − 1 . 1 7 )

( − 1 . 0 8 )

( − 1 . 1 6 )

( − 1 . 0 4 )

( − 1 . 1 0 )

( − 1 . 0 4 )

U E ∗ L E V

α 9

0 . 0 8 4

0 . 0 6 6

0 . 0 5 8

0 . 0 6 6

0 . 0 5 7

0 . 0 6 3

0 . 0 5 6

( 3 . 9 0 ) ∗ ∗ ∗

( 2 . 9 4 ) ∗ ∗ ∗

( 2 . 5 3 ) ∗ ∗

( 2 . 9 0 ) ∗ ∗ ∗

( 2 . 4 8 ) ∗ ∗

( 2 . 7 8 ) ∗ ∗ ∗

( 2 . 4 4 ) ∗ ∗

M V

α 1 0

0 . 0 0 8

0 . 0 8 4

0 . 0 9 1

0 . 0 6 7

0 . 0 8 3

0 . 1 4 2

0 . 1 5 3

( 0 . 0 7 )

( 0 . 5 4 )

( 0 . 5 8 )

( 0 . 4 4 )

( 0 . 5 5 )

( 0 . 8 0 )

( 0 . 8 6 )





T A B L E


P a n e l A : I n t e r a c t i o n e f f e c t b e t w e e n f e e

m e t r i c s a n d a u d i t o r s p e c i a l i z a t i o n o n e a r n i n g s - r e t u r n s r e l a t i o n

L N A U

P R N A U

L T O T

U E ∗ M V

α 1 1

1 . 0 0 3

1 . 1 7 8

1 . 0 4 6

1 . 1 4 4

0 . 9 8 7

1 . 1 7 6

1 . 0 3 5

( 7 . 2 0 ) ∗ ∗ ∗

( 8 . 2 6 ) ∗ ∗ ∗

( 7 . 0 9 ) ∗ ∗ ∗

( 8 . 1 7 ) ∗ ∗ ∗

( 6 . 7 8 ) ∗ ∗ ∗

( 8 . 3 0 ) ∗ ∗ ∗

( 7 . 0 0 ) ∗ ∗ ∗

L O S S

α 1 2

− 0 . 0 2 7

− 0 . 0 2 6

− 0 . 0 2 6

− 0 . 0 2 6

− 0 . 0 2 6

− 0 . 0 2 6

− 0 . 0 2 6

( − 6 . 0 2 ) ∗ ∗ ∗

( − 5 . 9 3 ) ∗ ∗ ∗

( − 5 . 8 4 ) ∗ ∗ ∗

( − 5 . 9 7 ) ∗ ∗ ∗

( − 5 . 8 8 ) ∗ ∗ ∗

( − 5 . 8 8 ) ∗ ∗ ∗

( − 5 . 8 2 ) ∗ ∗ ∗

U E ∗ L O S S

α 1 3

− 0 . 8 9 2

− 1 . 0 9 0

− 1 . 0 9 7

− 0 . 9 6 3

− 0 . 9 6 3

− 1 . 1 4 4

− 1 . 1 3 3

( − 2 . 8 2 ) ∗ ∗ ∗

( − 3 . 3 0 ) ∗ ∗ ∗

( − 3 . 2 8 ) ∗ ∗ ∗

( − 3 . 0 1 ) ∗ ∗ ∗

( − 2 . 9 8 ) ∗ ∗ ∗

( − 3 . 4 4 ) ∗ ∗ ∗

( − 3 . 3 6 ) ∗ ∗ ∗

R E S T R

α 1 4

− 0 . 0 2 0

− 0 . 0 2 0

− 0 . 0 2 0

− 0 . 0 2 0

− 0 . 0 1 9

− 0 . 0 2 0

− 0 . 0 1 9

( − 1 . 5 3 )

( − 1 . 5 9 )

( − 1 . 5 5 )

( − 1 . 5 8 )

( − 1 . 5 2 )

( − 1 . 5 5 )

( − 1 . 5 0 )

U E ∗ R E S T R

α 1 5

− 0 . 5 4 8

− 0 . 0 0 2

0 . 1 0 8

− 0 . 0 2 4

0 . 0 3 9

− 0 . 0 9 9

− 0 . 0 3 2

( − 1 . 1 7 )

( − 0 . 0 0 )

( 0 . 2 1 )

( − 0 . 0 5 )

( 0 . 0 8 )

( − 0 . 2 0 )

( − 0 . 0 7 )

S P E C

α 1 6

− 0 . 0 0 6

− 0 . 0 1 8

− 0 . 0 1 5

− 0 . 0 3 1

( − 1 . 5 1 )

( − 1 . 3 0 )

( − 1 . 6 1 )

( − 1 . 5 9 )

U E ∗ S P E C

α 1 7

1 . 3 8 7

− 3 . 8 7 8

− 2 . 3 8 2

− 7 . 0 1 5

( 2 . 4 5 ) ∗ ∗

( − 1 . 7 7 ) ∗

( − 1 . 8 4 ) ∗

( − 2 . 0 1 ) ∗ ∗

Y 0 0

α 1 8

0 . 0 1 2

0 . 0 1 2

0 . 0 1 2

0 . 0 1 2

0 . 0 1 2

0 . 0 1 2

0 . 0 1 2

( 3 . 0 9 ) ∗ ∗ ∗

( 3 . 2 2 ) ∗ ∗ ∗

( 3 . 2 9 ) ∗ ∗ ∗

( 3 . 0 4 ) ∗ ∗ ∗

( 3 . 1 0 ) ∗ ∗ ∗

( 3 . 2 1 ) ∗ ∗ ∗

( 3 . 2 4 ) ∗ ∗ ∗

U E ∗ Y 0 0

α 1 9

0 . 2 8 7

0 . 3 5 0

0 . 3 8 8

0 . 2 3 3

0 . 3 4 6

0 . 3 7 6

0 . 4 1 7

( 0 . 7 2 )

( 0 . 8 7 )

( 0 . 9 7 )

( 0 . 5 8 )

( 0 . 8 6 )

( 0 . 9 4 )

( 1 . 0 4 )

F E E ∗ S P E C

α 2 0

0 . 0 0 2

0 . 0 1 5

0 . 0 0 4

( 0 . 8 9 )

( 1 . 0 3 )

( 1 . 3 1 )

U E ∗ F E E ∗ S P E C

α 2 1

0 . 9 0 1

0 . 0 6 9

1 . 2 5 3

( 2 . 4 7 ) ∗ ∗

( 3 . 1 8 ) ∗ ∗ ∗

( 2 . 4 2 ) ∗ ∗

N

2 , 9 3 5

2 , 9 3 5

2 , 9 3 5

2 , 9 3 5

2 , 9 3 5

2 , 9 3 5

2 , 9 3 5

F - s t a

t i s t i c

1 4 . 3 0

∗ ∗ ∗

1 4 . 4 1 ∗ ∗ ∗

1 2 . 4 0 ∗ ∗ ∗

1 4 . 3 9 ∗ ∗ ∗

1 2 . 6 2 ∗ ∗ ∗

1 4 . 5 2 ∗ ∗ ∗

1 2 . 5 2 ∗ ∗ ∗

A d j .

R 2

7 . 1 5 %

7 . 2 1 %

7 . 5 5 %

7 . 2 0 %

7 . 6 8 %

7 . 2 7 %

7 . 6 2 %





T A B L E


P a n e l B : E f f e c t o f f e e m e t r i c s o n e a r n i n

g s - r e t u r n s r e l a t i o n b a s e d o n a u d i t s p e c i a l i z a t i o n


N o n s p e c i a l i s t s

L N A

U

P R N A U

L T O T

L N A U

P R N A U

L T O T

I n t e r c e p t

α 0

− 0 . 0 3 7

− 0 . 0 3 9

− 0 . 0 3 6

− 0 . 0 0 3

− 0 . 0 0 5

0 . 0 0 7

( − 1 . 8 6 ) ∗

( − 1 . 9 4 ) ∗

( − 1 . 6 1 )

( − 0 . 2 4 )

( − 0 . 4 5 )

( 0 . 4 9 )

U E

α 1

3 . 8 0 3

6 . 4 9 4

− 3 . 1 1 3

− 0 . 2 5 0

− 0 . 8 6 1

1 . 0 8 7

( 1 . 0 5 )

( 1 . 9 1 ) ∗

( − 0 . 6 7 )

( − 0 . 2 4 )

( − 0 . 9 9 )

( 0 . 7 9 )

F E E

α 2

− 0 . 0 0 2

− 0 . 0 0 5

− 0 . 0 7 0

− 0 . 1 3 4

− 0 . 0 1 1

− 0 . 3 6 9

( − 0 . 7 7 )

( − 0 . 2 7 )

( − 0 . 1 6 )

( − 0 . 8 8 )

( − 1 . 0 8 )

( − 1 . 5 4 )

U E ∗ F E E

α 3

1 . 6 6 7

0 . 1 1 5

2 . 6 7 8

− 0 . 3 8 1

− 0 . 0 2 2

− 0 . 4 9 6

( 3 . 1 9 ) ∗ ∗ ∗

( 3 . 8 2 ) ∗ ∗ ∗

( 3 . 3 9 ) ∗ ∗ ∗

( − 3 . 3 7 ) ∗ ∗ ∗

( − 3 . 4 9 ) ∗ ∗ ∗

( − 3 . 3 6 ) ∗ ∗ ∗

G R W

α 4

− 0 . 0 0 1

0 . 0 0 1

0 . 0 0 1

− 0 . 0 0 2

− 0 . 0 0 2

− 0 . 0 0 3

( − 0 . 0 8 )

( 0 . 0 5 )

( 0 . 1 4 )

( − 2 . 1 0 ) ∗ ∗

( − 2 . 1 2 ) ∗ ∗

( − 2 . 3 5 ) ∗ ∗

U E ∗ G R W

α 5

0 . 4 7 0

0 . 6 1 7

0 . 5 2 2

0 . 4 1 1

0 . 3 6 3

0 . 3 6 0

( 0 . 8 4 )

( 1 . 1 0 )

( 0 . 9 3 )

( 1 . 4 8 )

( 1 . 2 9 )

( 1 . 2 7 )

V O L

α 6

0 . 6 2 7

0 . 6 2 3

0 . 6 4 3

0 . 5 5 3

0 . 5 5 7

0 . 5 5 6

( 3 . 0 8 ) ∗ ∗ ∗

( 3 . 0 6 ) ∗ ∗ ∗

( 3 . 1 5 ) ∗ ∗ ∗

( 4 . 6 3 )

∗ ∗ ∗

( 4 . 6 7 ) ∗ ∗ ∗

( 4 . 6 8 ) ∗ ∗ ∗

U E ∗ V O L

α 7

− 6 . 9 9 0

2 . 3 4 8

− 9 . 7 9 0

− 2 4 . 1 4 1

− 2 7 . 2 5 1

− 2 5 . 6 4 9

( − 0 . 2 6 )

( 0 . 0 9 )

( − 0 . 3 6 )

( − 3 . 5 9 ) ∗ ∗ ∗

( − 3 . 7 9 ) ∗ ∗ ∗

( − 3 . 6 8 ) ∗ ∗ ∗

L E V

α 8

0 . 0 9 1

0 . 0 9 2

0 . 0 8 5

− 0 . 1 0 9

− 0 . 1 0 7

− 0 . 1 0 5

( 1 . 3 0 )

( 1 . 3 3 )

( 1 . 2 2 )

( − 2 . 1 5 ) ∗ ∗

( − 2 . 1 1 ) ∗ ∗

( − 2 . 0 7 ) ∗ ∗

U E ∗ L E V

α 9

− 0 . 1 1 3

− 0 . 0 8 1

− 0 . 0 8 1

0 . 0 7 5

0 . 0 7 3

0 . 0 7 3

( − 0 . 8 1 )

( − 0 . 5 9 )

( − 0 . 5 8 )

( 3 . 1 5 )

∗ ∗ ∗

( 3 . 0 7 ) ∗ ∗ ∗

( 3 . 0 5 ) ∗ ∗ ∗

M V

α 1 0

0 . 4 2 2

0 . 2 8 8

0 . 2 6 0

0 . 0 7 6

0 . 0 8 8

0 . 2 0 5

( 1 . 2 3 )

( 0 . 8 9 )

( 0 . 6 6 )

( 0 . 4 4 )

( 0 . 5 2 )

( 1 . 0 3 )





T A B L E


P a n e l B : E f f e c t o f f e e m e t r i c s o n e a r n i n

g s - r e t u r n s r e l a t i o n b a s e d o n a u d i t s p e c i a l i z a t i o n


N o n s p e c i a l i s t s

L N A U

P R N A U

L T O T

L N A U

P R N A U

L T O T

U E ∗ M V

α 1 1

− 1 . 1 0 4

− 1 . 1 4 0

− 1 . 3 7 7

1 . 1 5 8

1 . 1 2 7

1 . 1 5 3

( − 1 . 8 3 ) ∗

( − 2 . 0 0 ) ∗ ∗

( − 2 . 1 4 ) ∗ ∗

( 7 . 5 6 )

∗ ∗ ∗

( 7 . 4 7 ) ∗ ∗ ∗

( 7 . 5 4 ) ∗ ∗ ∗

L O S S

α 1 2

− 0 . 0 2 5

− 0 . 0 2 5

− 0 . 0 2 6

− 0 . 0 2 3

− 0 . 0 2 3

− 0 . 0 2 3

( − 2 . 6 7 ) ∗ ∗ ∗

( − 2 . 7 2 ) ∗ ∗ ∗

( − 2 . 7 4 ) ∗ ∗ ∗

( − 4 . 6 2 ) ∗ ∗ ∗

( − 4 . 6 4 ) ∗ ∗ ∗

( − 4 . 5 8 ) ∗ ∗ ∗

U E ∗ L O S S

α 1 3

− 5 . 6 0 8

− 5 . 6 6 3

− 5 . 2 4 4

− 1 . 0 4 6

− 0 . 9 0 4

− 1 . 0 8 8

( − 4 . 0 9 ) ∗ ∗ ∗

( − 4 . 2 1 ) ∗ ∗ ∗

( − 3 . 7 7 ) ∗ ∗ ∗

( − 3 . 0 0 ) ∗ ∗ ∗

( − 2 . 6 7 ) ∗ ∗ ∗

( − 3 . 0 9 ) ∗ ∗ ∗

R E S T R

α 1 4

0 . 0 0 8

0 . 0 0 8

0 . 0 0 9

− 0 . 0 3 6

− 0 . 0 3 5

− 0 . 0 3 5

( 0 . 3 7 )

( 0 . 3 5 )

( 0 . 3 9 )

( − 2 . 2 8 ) ∗ ∗

( − 2 . 2 4 ) ∗ ∗

( − 2 . 2 0 ) ∗ ∗

U E ∗ R E S T R

α 1 5

− 2 . 4 9 7

− 3 . 1 5 5

− 2 . 6 0 5

0 . 3 1 7

0 . 3 1 1

0 . 1 4 9

( − 1 . 0 6 )

( − 1 . 3 3 )

( − 1 . 1 1 )

( 0 . 6 1 )

( 0 . 6 0 )

( 0 . 3 0 )

Y 0 0

α 1 8

0 . 0 1 6

0 . 0 1 5

0 . 0 1 4

0 . 0 1 2

0 . 0 1 1

0 . 0 1 2

( 1 . 9 3 ) ∗

( 1 . 8 3 ) ∗

( 1 . 7 8 ) ∗

( 2 . 8 7 )

∗ ∗ ∗

( 2 . 6 1 ) ∗ ∗ ∗

( 2 . 8 4 ) ∗ ∗ ∗

U E ∗ Y 0 0

α 1 9

− 1 . 3 0 7

− 1 . 3 0 6

− 1 . 1 8 8

0 . 3 1 8

0 . 1 8 2

0 . 3 5 2

( − 0 . 9 5 )

( − 0 . 2 2 )

( − 0 . 8 7 )

( 0 . 7 6 )

( 0 . 4 4 )

( 0 . 8 4 )

N

7 3

3

7 3 3

7 3 3

2 , 2 0 2

2 , 2 0 2

2 , 2 0 2

F - s t a

t i s t i c

6 . 2 3 ∗ ∗ ∗

6 . 5 1 ∗ ∗ ∗

6 . 3 1 ∗ ∗ ∗

1 1 . 9 4

∗

∗ ∗

1 2 . 0 3 ∗ ∗ ∗

1 2 . 0 5 ∗ ∗ ∗

A d j .

R 2

1 0 . 8 2 %

1 1 . 3 5 %

1 0 . 9 7 %

7 . 7 9 %

7 . 8 5 %

7 . 8 6 %

T h e e a r n i n g s - r e t u r n s r e g r e s s i o n m o d e l i s :

C A R =

α 0 +

α 1 ( U E )

+

α 2 ( F E E ) +

α 3 ( U E ∗ F E E ) +

α 4 ( G R W )

+

α 5 ( U E ∗ G R W ) +

α 6 ( V O L ) +

α 7 ( U E ∗ V O L ) +

α 8 ( L E V )

+

α 9 ( U E ∗ L E

V ) +

α 1 0 ( M V ) +

α 1 1 ( U E ∗ M V ) +

α 1 2 ( L

O S S ) +

α 1 3 ( U E ∗ L O S S ) +

α 1 4 ( R E S T R ) +

α 1 5 ( U E ∗ R E S T R ) +

α 1 6 S P E C

+

α 1 7 ( U E ∗ S P E C ) +

α 1 8 Y 0 0 +

α 1 9 ( U E ∗ Y 0 0 ) +

α 2 0 ( F

E E ∗ S P E C ) +

α 2 1 ( U E ∗ F E E ∗ S P E C ) +

ε

T h e v a r i a b l e s a r e a s d e fi n e d i n t h e f o o t n o t e s o f t a b l e 8 . Y 0 0 i s a y e a r d u m m y . ∗ , ∗ ∗ , a n d ∗ ∗ ∗

d e n o t e s i g n i fi c a n c e a t t h e 1 0 % , 5 % , a n d 1 % l e v e l s ( t w o - t a i l e d ) , r e s p e c t i v e l y .




As a robustness check, we include industry dummies that interact withearnings surprises in the ERC model to assess whether our results are drivenby industry effects (Balsam, Krishnan, and Yang [2003]). Our results remainrobust even after the inclusion of industry dummies. We also measure re-turns over [−1,0] or [−1,1] around the quarterly announcement date, withor without adjusting for market returns, and obtain similar results.

Overall, the evidence indicates that when auditors provide non-audit services to clients, earnings quality, and therefore audit quality, is perceived tobe higher for specialist auditors than for nonspecialist auditors.

4.5 YEAR -BY - YEAR ANALYSES

In additional analyses, we analyze the models for each year separately.For the going-concern opinion model, the coefficient estimate for the in-

teraction term ( FEE ∗ SPEC ) is positive and significant at the 10% level foreach year only for LTOT , but, inconsistent with our main findings, is not significant in either year for LNAU and PRNAU . One possible reason is that the power of the test is reduced when we split the sample by year. There are649 firms in 2000, with only 33 firms receiving going-concern opinions, and1,043 firms in 2001, with 87 firms with going-concern opinions.

For the discretionary accruals test, similar to those reported in table 5, we do not find any statistically significant FEE ∗ SPEC interaction. For theMEET test, the interaction term is not statistically significant for all three fee

variables, similar to our main analyses.For the MISS test, theinteraction term

( FEE ∗ SPEC ) is positive andstatistically significant only for the year 2001, but not 2000, for all three fee measures (whereas interaction terms for all threefee proxies are statistically significant when pooled, as reported in the mainanalyses). Finally, for the ERC test, we find that firms audited by specialistsare associated (not associated) with a higher ERC in year 2000 (2001). Incomparison, firms audited by nonspecialists are associated with a lower ERConly in the year 2001, but not in the year 2000. In the pooled analyses, wefind that firms audited by specialists (nonspecialists) are associated with ahigher (lower) ERC.

Overall, our results are generally weaker using year-by-year data than thosefound in the main analyses using pooled data, likely due to the lower powerassociated with smaller sample sizes.

4.6 SIZE EFFECT

It is possible that our measures of auditor specialization and fees areproxying for some size effect—that is, larger firms likely have more complexoperations and attract larger litigation risks, and are therefore more likely to employ specialized auditors for their expertise and pay higher fees. Inall our analyses, we control for firm size, which somewhat alleviates thisconcern. We further explore this issue here. We find that SPEC has low

correlations with firm size (between 0.04 and 0.08) and the various feemeasures (between 0.00 and 0.11); see panel B of various tables. Thus, SPEC




has good discriminant validity from other constructs such as firm size andfees.

On the other hand, the correlation between size and fee measures is high(between 0.57 and 0.79), which raises the possibility that fees and firm sizearesubstitutesandnotdistinctconstructs.Ifthisisthecase,itimpliesthatourfindings of a two-way interaction between SPEC and FEE should replicate

when we replace our fee measures with firm size. On the other hand, if they are distinct constructs, these results may not replicate. In untabulatedresults, for models that exclude the fee measures but include SPEC , firmsize (using total assets as a proxy for size), and their interaction, we fail tofind significant interactions between SPEC and firm size for any of the audit quality measures. In contrast, we find a SPEC by FEE interaction for thegoing-concern, MISS , and ERC measures. These findings suggest that feesand firm size are different constructs.

5. Conclusion

Recent studies on whether the provision of non-audit services impairs au-ditor independence and audit quality have found mixed results, dependingon the proxy for audit quality used. We posit and provide some evidencethat the effect of non-audit fees on audit quality is conditional on auditorindustry specialization. Table 10 summarizes our primary results, and key differences between findings in our study and those of prior studies. We findthat audit quality as measured by the increased propensity to issue going-

concern opinions, reduced propensity to avoid missing analysts’ forecasts,and higher ERCs, is generally enhanced for firms that acquire non-audit ser-

vices from specialist auditors compared to those that acquire non-audit services from nonspecialist auditors. We fail to detect a non-audit fee by auditorspecialization interaction for both the propensities to record higher discre-tionary current accruals and to just meet analysts’ forecasts. Taken together,our results provide some evidence, using going-concern opinions, propen-sity to avoid missing analysts’ forecasts, and ERCs, that industry-specializedauditors are more likely than nonspecialists to provide higher audit quality

when they provide non-audit services to clients. This higher audit quality

associated with industry specialists (relative to nonspecialists) when they provide non-audit services can be attributed to their greater independenceboth in fact and in appearance, and/or their greater ability to benefit fromknowledge spillovers.

Our failure to detect an interaction between non-audit service provisionand auditor specialization for discretionary accruals and the propensity to

just meet analysts’ forecasts raises questions on the measurement error as-sociated with some of these measures (e.g., see Kinney and Libby’s [2002]comment on the reliability of discretionary accruals) and on the sensitivity of benchmark tests to how they are operationalized (e.g., in terms of avoid-

ing missing or just meeting analysts’ forecasts). It is also possible that theprovision of non-audit fees has no association with some proxies of audit




T A

B L E

1 0

S u m m

a r y o f R e s u l t s

G o i n g - c o n c e r n o p i n i o n s t e s t D e f o n d , R a g h u n a n d a n ,

a n d S u b r a m a n

y a m [ 2 0 0 2 ]

O u r S t u d y

M o d e r a t i n g R o l e o f

N o n - a u d i t F e e s

T o t a l F e e s

N o n - A u d i t F e e s

C l i e n t I m p o r t a n c e

T o t a l F e e s

A u d i t o r S p e c i a l i z a t i o n

G C o

p i n i o n s

N o a s s o c i a t i o n




P o s i t i v e a s s o c i a t i o n

Y e s

D i s c

r e t i o n a r y a c c r u a l s t e s t

F r a n k e l , J o h n s o n ,

A s h b a u g h , L a f o n d , a n d

C h u n g a n d

a n d N e l s o n [ 2 0 0 2 ]

M a y h e w [ 2 0 0 3 ]

K a l l a p u r [ 2 0 0 3 ]

O u r S t u d y

M o d

e r a t i n g R o l e

N o n - A u d i t

T o t a l

N o n - A u d i t

C l i e n t

N o n - A u d i t

C l i e n t

o

f A u d i t o r

F e e s

F e e s

F e e s

T o t a l F e e s

I m p o r t a n c e

F e e s

I m p o r t a n c e

T o t a l F e e s

S p e c i a l i z a t i o n

A b s o

l u t e

P o s i t i v e

N o

P o s i t i v e / n o

N o

N o

P o s i t i v e

N o

N o

N o

a c c r u a l s

a s s o c i a t i o n


a s s o c i a t i o n ∗






P o s i t i v e

P o s i t i v e

P o s i t i v e

N o

N o

N o t t e s t e d

N o

N o

N e g a t i v e

N o

a c c r u a l s








N e g a t i v e

N e g a t i v e

N o

N e g a t i v e / n o

N o

N o t t e s t e d

N e g a t i v e

N e g a t i v e

N e g a t i v e

N o

a c c r u a l s



a s s o c i a t i o n ∗









T A B L E

1 0 — C o n t i n u e d

P r o p

e n s i t y t o m e e t a n a l y s t s ’ f o r e c a s t s

F r a n k e l , J o h n s o n ,

A s h b a u g h , L a f o n d , a n

d

a n d N e l s o n [ 2 0 0 2 ]

M a y h e w [ 2 0 0 3 ]

O u r S t u d y

N o n - A u d i t

N o n - A u d i t

N o n - A u d i t

C l i e n t


F e e s

T o t a l F

e e s

F e e s

T o t a l F e

e s

F e e s

I m p o r t a n c e

T o t a l F e e s

A u d i t S p e c i a l i z a t i o n

M E E

T

P o s i t i v e a s s o c i a t i o n

N o a s s o c

i a t i o n


N o a s s o c i a

t i o n




N o

M I S S

N e g a t i v e a s s o c i a t i o n

N o t r e p o r t e d

N o t t e s t e d

N o t t e s t e d


N e g a t i v e a s s o c i a t i o n


Y e s

E R C

t e s t

F r a n c i s a n d K e

K r i s h n a n , S a m i , a n d

[ 2 0 0 6 ]

Z h a n g [ 2 0 0 5 ]

O u r S t u d y

N o n - A u d i t

N o n - A u d i t

N o n - A u d i t

C l i e n t


F e e s

F e e s

T o t a l F e e s

F e e s

I m p o r t a n c e

T o t a l F e e s

A u d i t S p e c i a l i z a t i o n

E R C

N e g a t i v e

N

e g a t i v e

N e g a t i v e

N e g a t i v e

N e g a t i v e

N e g a t i v e

Y e s







T h e t a b l e s u m m a r i z e s a n d c o m p a r e s o u r p r i m a r y r e s u l t s a n d t h o s e o f p r i o r s t u d i e s . S t a t e d a s s o c i a t i o n s a r e s t a t i s t i c a l l y s i g n

i fi c a n t a t t h e 1 0 % l e v e l .

∗ D

e p e n d i n g o n t h e p r o x i e s o f a c c r u a l s u s e d .




quality to begin with, that there are limits to the extent auditor specializa-tion moderates the influence of non-audit services, and/or that the relationbetween auditor specialization and provision of non-audit fees is more com-plex and involves other moderators not investigated in this study. Futureresearch can explore these possibilities.

In conclusion, concerns about the impairment of auditors’ independencethrough the provision of non-audit services to their audit clients have ledto a series of recent actions by regulators, beginning with the requirement for SEC registrants to disclose audit fees in their proxy statements, followedby restrictions of certain categories of non-audit services that auditors canprovide. These measures apply to all public accounting firms and their listedclients. Our findings suggest that not all public accounting firms are nec-essarily tainted by the provision of non-audit services. Our results suggest that there is some evidence consistent with industry specialist auditors re-

taining their independence both in fact and appearance even when they provide non-audit services to their audit clients. In addition, our findingsindicate that auditor industry specialization, or more broadly speaking, au-ditor expertise, can play an important role in mitigating the regulatory andacademic communities’ concerns about the appropriateness of accountingfirms providing non-audit services.

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