Managerial Auditing JournalEmerald Article: Auditor tenure, auditor specialization, and information asymmetryAli R. Almutairi, Kimberly A. Dunn, Terrance Skantz

Article information:

To cite this document: Ali R. Almutairi, Kimberly A. Dunn, Terrance Skantz, (2009),"Auditor tenure, auditor specialization, and information asymmetry", Managerial Auditing Journal, Vol. 24 Iss: 7 pp. 600 - 623

Permanent link to this document: http://dx.doi.org/10.1108/02686900910975341

Downloaded on: 17-10-2012

References: This document contains references to 50 other documents

Citations: This document has been cited by 2 other documents

To copy this document: permissions@emeraldinsight.com

Users who downloaded this Article also downloaded: *

Hui Chen, Miguel Baptista Nunes, Lihong Zhou, Guo Chao Peng, (2011),"Expanding the concept of requirements traceability: The role of electronic records management in gathering evidence of crucial communications and negotiations", Aslib Proceedings, Vol. 63 Iss: 2 pp. 168 - 187http://dx.doi.org/10.1108/00012531111135646

Charles Inskip, Andy MacFarlane, Pauline Rafferty, (2010),"Organising music for movies", Aslib Proceedings, Vol. 62 Iss: 4 pp. 489 - 501http://dx.doi.org/10.1108/00012531011074726

Aryati Bakri, Peter Willett, (2011),"Computer science research in Malaysia: a bibliometric analysis", Aslib Proceedings, Vol. 63 Iss: 2 pp. 321 - 335http://dx.doi.org/10.1108/00012531111135727

Access to this document was granted through an Emerald subscription provided by KUWAIT UNIVERSITY For Authors: If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service. Information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.

About Emerald www.emeraldinsight.comWith over forty years' experience, Emerald Group Publishing is a leading independent publisher of global research with impact in business, society, public policy and education. In total, Emerald publishes over 275 journals and more than 130 book series, as well as an extensive range of online products and services. Emerald is both COUNTER 3 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

*Related content and download information correct at time of download.

Auditor tenure, auditorspecialization, and information

asymmetryAli R. Almutairi

Accounting Department, College of Business Administration,Kuwait University, Kuwait City, Kuwait, and

Kimberly A. Dunn and Terrance SkantzSchool of Accounting, Florida Atlantic University, Boca Raton, Florida, USA

Abstract

Purpose – The purpose of this paper is to examine the relation between a company’s bid-ask spread,a proxy for information asymmetry, and auditor tenure and specialization.

Design/methodology/approach – The tests use clustered regression for a sample of 31,689company-years from 1992 to 2001 and control for factors known to impact bid-ask spread incross-section.

Findings – The findings suggest that the market’s perception of disclosure quality is higher andprivate information search opportunities are fewer for companies engaging industry specialistauditors. In addition, the paper finds that information asymmetry has a U-shaped relation to auditortenure. This U-shaped relation holds for both specialists and non-specialists; however, the bid-askspread for specialists tends to fall below that of non-specialists at all tenure intervals.

Research limitations/implications – The findings may directly result from auditor tenure andspecialization or it may be that those auditor-related characteristics are a subset of concurrent choicesmade by the company that impacts disclosure quality.

Practical implications – Companies have incentives to lower information asymmetry and thefindings document that the choice of a specialist auditor and the length of the auditor relationship canpotentially influence this objective.

Originality/value – The paper provides information to academics, regulators, companies, andauditors concerning the effect of auditor-client relationships on the level of information asymmetry. Inaddition, it shows the importance of industry specialization and audit firm tenure on audit quality.

Keywords Auditing, Auditors, Information strategy, Disclosure, Bid offer spreads

Paper type Research paper

I. IntroductionA large body of research examines whether audit quality varies with auditor tenureand industry specialization[1]. Extant research generally finds that cost of capital islower, earnings response coefficients (ERCs) are larger, discretionary accruals aresmaller, and debt ratings are better when companies employ specialists and retain their

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0268-6902.htm

Data availability: The data used in this paper are publicly available from the sources indicated inthe text.

The authors appreciate comments received on earlier versions of this paper from Julia Higgs,Jayanthi Krishnan, Mark Kohlbeck, and workshop participants at Florida Atlantic University.

MAJ24,7

600

Received 22 July 2008Revised 25 February 2009Accepted 20 March 2009

Managerial Auditing JournalVol. 24 No. 7, 2009pp. 600-623q Emerald Group Publishing Limited0268-6902DOI 10.1108/02686900910975341

auditors for a number of years. These findings are interpreted as evidence thatspecialization and longer tenure improve audit quality.

Information asymmetry is a critical link that justifies studies examining the relationbetween audit quality (proxied by specialization and tenure) and cost of capital, ERCs,and debt ratings. Those studies can be interpreted as exploring whether specializationand tenure are beneficial to the client company. Our paper complements prior studiesby examining how the bid-ask spread is associated with specialization and tenure.Because the bid-ask spread provides a reasonably direct measure of the market’sperception of information asymmetry, this paper examines whether a reduction in themarket’s perception of information asymmetry is a mechanism from which benefits ofspecialization and tenure documented in prior research might flow.

Our tests use a sample of 31,689 company-years from 1992 to 2001 and control forfactors known to impact bid-ask spread in cross-section. Results indicate that clientswith specialist auditors have lower bid-ask spreads than clients of non-specialistauditors in the approximately 48 trading-days following the disclosure of auditedfinancial information. The results are robust across three different measures ofspecialization and also robust to whether the three specialization measures areincluded in the model as indictor or continuous measures[2].

Our results also show that the bid-ask spread has a U-shaped relation to tenure.After controlling for the first year of the audit engagement, bid-ask spread issignificantly lower in the second and third year of the engagement than in latersub-periods (four to nine years, and longer than nine years). In addition, we find nodifference in the bid-ask spread between medium (four to nine years) and long tenure(longer than nine years). The results suggest that the market views informationasymmetry as relatively high in the first year of an engagement, decreasing in the earlyyears of the engagement and then increasing later in the engagement. In general, thereduced bid-ask spread in the early years of an engagement is stronger for clients ofspecialist auditors.

Our findings are consistent with a market that associates two important auditorcharacteristics with audit quality and the market’s perception of informationasymmetry. However, it is unlikely that these two characteristics alone determine themarket’s perception of a company’s financial reporting quality. One conclusion is that acompany’s auditor-related choices are part of a portfolio of concurrent reporting anddisclosure choices that affect the opportunity (need) for private information search.

Our results are important because understanding the effect of auditor tenure andindustry specialization on the market’s perception of information asymmetry mayassist client companies in making auditor-related choices consistent with their overalldisclosure strategy, assist auditors in making strategic and marketing decisions tobetter serve their clients, and guide regulators in setting policies consistent withincreasing market transparency.

The remainder of this paper is structured as follows. Section II justifies our choice ofbid-ask spread as a dependent variable, reviews the literature on informationasymmetry and audit quality, discusses the findings of prior research with respectto specialization and tenure, and develops our three hypotheses. Research design,sample selection and variable measurement are discussed in Section III. Empiricalresults, supplemental analysis and robustness tests are presented in Section IV.Summary and concluding remarks are presented in Section V of the paper.

Auditor tenure,specialization

and asymmetry

601

II. Development of hypothesesInformation asymmetry and the bid-ask spreadThe market’s perception of a company’s auditing, reporting, and disclosure quality willaffect the market’s perception of information asymmetry and opportunities forprofitable private information-search activities. The beneficial effects of auditsdocumented in prior research (e.g. lower cost of capital, larger ERCs, and better debtratings) may result from a reduction in information asymmetry and privateinformation production. For example, Pittman and Fortin (2004) state that big sixauditors can enhance the credibility of financial statements and obviate the need forlenders “[. . .] to conduct costly information production and monitoring usingalternative sources [. . .].” In other words, audits are efficient ways to reduce agencycosts and private information production. Accordingly, audits of higher quality shouldbe associated with lower levels of information asymmetry and private informationproduction among investors.

One way to characterize information asymmetry is the extent to which managersknow more about the firm than investors as a group. A second characterization is theextent to which the amount of information regarding the firm varies from one group ofinvestors to another (Watts and Zimmerman, 1986). Among investors in publicmarkets, information asymmetry is predicated on the existence of uninformed(liquidity) traders and informed traders. Informed traders have an incentive to trade onprivate information that is expected to become public.

Bid-ask spread is the difference between the bid price a dealer is willing to pay for asecurity and the higher ask price at which the dealer is willing to sell a security. Anincrease in information asymmetry increases the risk that a market maker will tradewith an informed investor and will be reflected in a higher bid-ask spread for a security(Callahan et al., 1997). After controlling for inventory and transactions costcomponents, bid-ask spreads provide a reasonably direct measure of the market’sperception of information asymmetry (Kim and Verrechia 1994, 2001; Leuz andVerrecchia, 2000).

After an earnings announcement, information asymmetry among investors willreflect the extent to which financial statements resolve or fail to resolve uncertaintyabout company value[3]. As audit quality and the credibility of a firm’s financialdisclosures increase, earnings announcements will better resolve uncertainty aboutcompany value, reduce the level of information asymmetry and result in lower levels ofbid-ask spread[4].

Audits, auditor industry specialization, and audit qualityAudits are one way to reduce information asymmetry and associated agency costs.Since higher quality audits are more likely to detect and avoid accounting errors andmisstatements than lower quality audits, higher quality audits should reduceinformation asymmetry more than lower quality audits.

Both experimental and archival studies find a positive relation between industryspecialization and various direct and indirect measures of audit quality. Experimentalresearch finds that industry specialization improves performance on a variety of audittasks. Specialists in the banking industry are more confident than non-specialists inassessing inherent risk (Taylor, 2000); audit managers and senior auditors are better atdetecting errors when they conduct audit tasks in industries within their specialization

MAJ24,7

602

(Owhoso et al., 2002); and, specialist auditors are more adept than non-specialists atinterpreting an incomplete pattern that suggests a misstatement (Hammersley, 2006).

Archival studies have examined the relation between industry specialization andmeasures of earnings quality. Relative to clients of non-specialist auditors, clients ofspecialists have significantly lower absolute discretionary accruals (Krishnan, 2003),and larger ERCs at earnings announcement dates (Balsam et al., 2003). Dunn andMayhew (2004) find a positive relation between the employment of an industryspecialist auditor and analysts’ perceptions of disclosure quality.

In addition, there is evidence that specialists receive a fee premium from clients intheir industry of expertise (Craswell et al., 1995; DeFond et al., 2000) and thatspecialization may lead to scale economies (Cairney and Young, 2006; Houghton et al.,2005; Mayhew and Wilkins, 2002). Fee premiums and production efficiencies couldimply that specialists are more profitable than non-specialists and may have morereputation capital at stake. In that case, specialists would be less likely to compromisetheir independence and more likely to report generally accepted accounting principles(GAAP) violations[5].

Taken together, prior research suggests a positive relation between audit firmspecialization and both financial reporting quality and audit quality. This researchprovides indirect evidence about the relation between industry specialization and themarket’s perception of a company’s disclosure quality. If the market shares the viewthat industry specialist auditors ensure more complete, relevant, and reliableinformation, we would expect companies with specialist auditors to exhibit lowerlevels of information asymmetry as reflected in a lower bid-ask spread. On the otherhand, the market may view specialization primarily as a means through which auditfirms increase the profitability of audit engagements (as a result of increased efficiency,reduced competition, and higher audit prices) but with little or no effect on audit quality.Similarly, the market may anticipate that specialists will enforce strict adherence toGAAP which could be viewed as enhancing reporting quality or as blocking thecommunication of inside information. Thus, while we state a directional hypothesis,findings in the literature do not resolve how the market perceives the opportunities forprivate information search for clients of specialist versus non-specialist auditors:

H1. The level of information asymmetry as measured by the bid-ask spread islower for clients of specialist than non-specialist auditor.

Audit firm tenure and audit qualityAs discussed fully in Carcello and Nagy (2004) and Myers et al. (2003), the effect oftenure on audit quality is controversial. Proponents of mandatory audit firm rotationargue that longer tenure can lead to reduced auditor independence, increasedcomplacency and reduced objectivity. On the other side are those who contend thataudit quality increases with tenure because with experience the auditor becomes morefamiliar with the client’s business operations and reporting issues.

Most studies find evidence consistent with a positive association between auditquality and auditor tenure. Libby and Frederick (1990) find that experience auditorsexhibit better understanding of financial errors and have lower error frequency rates.Myers et al. (2003) find that absolute discretionary and current accruals are decreasingin tenure, and Carcello and Nagy (2004) conclude that the incidence of fraudulentreporting decreases with tenure. Similarly, Mansi et al. (2004) find the cost of debt is

Auditor tenure,specialization

and asymmetry

603

decreasing with auditor tenure; and Ghosh and Moon (2005) report that ERCs areincreasing with tenure. These studies suggest that longer tenure is associated withincreased earnings quality and reduced cost of capital. Conversely, Myers et al. (2005)find that income-increasing misstatements are more likely as tenure increases.

Most, but not all, prior studies suggest a positive relation between auditor tenureand proxies for audit quality, and are generally inconsistent with the position thatlonger auditor-client relationships compromise independence-in-fact and/orappearance. However, the evidence does not directly address how the market viewstenure. The market could perceive longer tenure as enhancing the economic bondbetween the auditor and client, or as increasing the expertise of the auditor. In thesecond case, and consistent with most academic research, we expect to find the bid-askspread is decreasing with auditor tenure. With that in mind, our H2 is:

H2. The level of information asymmetry as measured by the bid-ask spreaddecreases with audit firm tenure.

Specialization and tenure interaction effectsOur H3 predicts that the association between tenure and bid-ask spread will differ forspecialist and non-specialist auditors. The predicted interaction effects are based onresearch that finds the relation between tenure and audit quality varies betweenspecialist and non-specialist auditors. For example, Fung et al. (2007) find higherdiscretionary accruals for short tenure auditors when they are non-specialists, but findno relation between tenure and discretionary accruals for specialists. In addition,Myers et al. (2005) find that income-increasing financial statement misstatements aremore likely as tenure increases, but only for non-specialist auditors.

There are theoretical reasons to expect tenure to have different effects on auditquality for specialists and non-specialists but the direction of the effect is not clear. Iftenure provides non-specialists with expertise similar to that possessed by specialists,information asymmetry may be decreasing with tenure for non-specialists auditors butthat association may be absent or smaller for specialists. Additionally, the (presumed)economic rents earned by specialists may be viewed by the market as increasing thelikelihood that specialist auditors will acquiesce to a client’s questionable reportingchoices and that the likelihood that such acquiescence has and will continue to occur isincreasing with tenure. Alternatively, the market may expect that specialist auditorswill protect their reputation capital and the associated rents by being more willing towithdraw from an engagement over disputes with the client. If longer tenure reducesaudit quality due to impaired independence, specialists may be at least partiallyimmune because they earn higher economic rents from fee premiums or scaleeconomies than non-specialists and thus have more to lose in the event of audit failure.Given the competing theories, we do not make a directional prediction:

H3. The association between tenure and bid-ask spread differs for specialist andnon-specialist auditors.

III. Research designInformation asymmetry model specificationThe effect of auditor tenure and specialization on information asymmetry will be due,at least in part, to the market’s assessment of the quality of the audited annual report.

MAJ24,7

604

As a result, and as discussed above, we examine bid-ask spread in the period followingthe announcement of annual earnings and prior to the release of the subsequent firstquarter earnings.

For each company-year during 1992-2001, we measure bid-ask spread and certaincontrol variables over the interval starting seven days after the annual earningsannouncement for fiscal year t and ending seven days before the first quarterlyearnings announcement for fiscal year t þ 1 (Ertimur, 2004). This time interval is usedto capture the level of information asymmetry conditional on disclosures in the auditedannual report while avoiding the short-term increase in information asymmetry knownto accompany earnings announcements[6]. Our selection of a relatively shortmeasurement window (approximately 48 trading days) should increase the power ofour tests. A long measurement window increases the likelihood that the numberof information events will vary across companies, yielding a noisy measure ofcross-sectional differences in the level of information asymmetry following auditedearnings announcements. Muller and Riedl (2002), for example, measure bid-askspread over a seven-month period and fail to find a link between spread and auditorquality (i.e. big six auditors).

Variables. Bid-ask spread. We use relative bid-ask spread (SPREAD) as our proxyfor information asymmetry and limit the sample to National Association of SecuritiesDealers Automated Quotations (NASDAQ) listed companies to avoid different tradingenvironments between dealer (NASDAQ) and auction (NYSE) markets. The SPREADis found for each day during our post earnings announcement measurement windowand is calculated as (b 2 a)/m, where b is the closing bid price, a is the closing askprice, and m is the mid-point between b and a. The Center for Research in SecurityPrices (CRSP) database is used as the source of the bid-ask spread. SPREAD for eachcompany-year observation is measured as the median of the daily SPREAD over themeasurement window[7].

Industry specialization. Although auditor industry specialization is a growing areaof interest in academic research, a single measure of specialization has not emerged(Neal and Riley, 2004). The two primary ways of identifying industry specialization areindustry market share (Balsam et al., 2003; Dunn and Mayhew, 2004) and auditorportfolio share (Krishnan, 2003). In addition, Neal and Riley (2004) propose a newcomposite measure that is a function of both market share and portfolio share. Weinclude all three measures of specialization in our analysis[8].

The market share measure of specialization assumes that a firm that audits asufficiently large percentage of companies in a particular industry is a specialist in thatindustry. For each year t, our first measure of industry specialization is based on anauditor’s market share for industry k. Suppressing the t subscript, audit firm i’s marketshare in industry k for year t is MKTSHRik and is found as follows:

MKTSHRik ¼

PJ ikj¼1SALESijkPI k

i¼1

PJ ikj¼1SALESijk

ð1Þ

where i, an index of auditors (i ¼ 1, 2, 3, 4, 5, 6); j, an index of client companies; k, an indexof audited industries; Ik, the number of auditors in industry k; Jik, the number of clientsaudited by auditor i in industry k; and SALESijk, sales revenue for auditor i’s client j.

Auditor tenure,specialization

and asymmetry

605

We use both the continuous market share measure and an indicator variable in ouranalysis. The indicator variable is defined following Balsam et al. (2003) by classifyingan auditor as a specialist in industry k if they have the highest market share in industryk and if that share exceeds the share of the auditor with the second highest marketshare by at least 10 percent.

Our second measure of industry specialization is based on how important eachindustry is to an auditor’s total client portfolio (Krishnan, 2003). Our portfolio sharemeasure of industry specialization is based on the proportion of audit firm i’s portfoliorepresented by industry k in year t. Suppressing the t subscript, portfolio share is foundas follows:

PORTSHRik ¼

PJ ikj¼1SALESijkPK

k¼1

PJ ikj¼1SALESijk

ð2Þ

where SALES and all indices are defined as in equation (1).We use both the continuous portfolio share measure and an indicator variable in our

analysis. For the indicator variable, audit firm i is classified as a specialist in industry kif the portfolio share for industry k is in the top 10 percent of audit firm i’s portfolioshare for all K industries in year t. The audit firm is classified as a non-specialist in allother industries for that year.

Our third measure of industry specialization is based on a composite measureproposed by Neal and Riley (2004). Their measure incorporates both the audit firm’sindustry market share and the industry’s share of the auditor’s portfolio[9]. For ourcontinuous composite variable, we include the product of market share and portfolioshare from equations (1) and (2). For our indicator variable, the product of MKTSHRik

and PORTSHRik from equations (1) and (2) is compared to the weighted market sharecut-off (WMSCO), where:

WMSCO ¼1

N audit firms£ 1:2

� �£

1

N industries

� �� �ð3Þ

If the product, MKTSHRik £ PORTSHRik, for year t is greater than the weightedcut-off, audit firm i is classified as a specialist in industry k for year t. Otherwise, theaudit firm is classified as a non-specialist.

Tenure. Auditor tenure is based on the length of the auditor-client relationship asreported by Compustat. Following prior research, we use both continuous (Myers et al.,2003; Ghosh and Moon, 2005) and indicator variables (Carcello and Nagy, 2004) fortenure in our analysis. Our continuous measure for tenure is the number of consecutiveyears of the auditor-client relationship as reported on Compustat[10]. FollowingCarcello and Nagy (2004), we use tenure to create indicator variables. We classify thefirst year of the auditor-client relationship as CHANGE, the second and third year asSHORT, and any tenure that is ten years or longer as LONG[11]. Medium tenure (fromfour to nine years) is the benchmark group in our regression models using indicatorvariables.

Control variables in bid-ask spread regressions. A market maker or dealer functionsto increase the liquidity in shares by making shares of a stock immediately available(Demetz, 1968). The bid-ask spread is the price a dealer charges to recover the costs of

MAJ24,7

606

his or her services. The dealer faces three types of costs – inventory holding costs,order processing costs, and the cost of adverse selection. The variables in ourregression equation that control for inventory holding costs and order processing costsare based on Stoll’s (1978) model.

Holding costs are a function of the risk aversion and equity of the dealer, pricevolatility, holding period, and value of each transaction. As a dealer’s risk aversion(equity wealth) increases, holding costs increase (decrease). Dealer risk aversion andwealth are not observable and are thus assumed to be constant across dealers;therefore, we do not include proxies for these variables in our analysis.

Price volatility affects holding costs because higher volatility increases the risk ofadverse price changes for the market maker. As price volatility increases, an order atthe bid exposes the market maker to the risk of price decline and an order at the askexposes the market maker to the risk of price increases. The variance of return is usedto measure price volatility. Thus, higher stock price variability is associated with ahigher bid-ask spread. Price volatility risk is exacerbated as the probability of dealingwith informed traders increases. VOLATILITY is measured as the standard deviationof daily security returns during our measurement window.

Longer holding periods increase holding costs. Trading volume is a proxy for theholding period since higher trading volume increases the ease with which a dealer canreverse a position. Market makers can reduce inventory levels for firms with highertrading volume because arrival of buy and sell orders are more predictable (McInishand Wood, 1992). Higher volume also provides market makers with more opportunitiesto recover losses to informed traders through trades with liquidity traders (Roulstone,2003). Consistent with Stoll’s model, Gregoriou et al. (2005) find the bid-ask spread ispositively associated with volatility and negative associated with trading volume. Tocontrol for the expected negative relation between trading volume and bid-ask spread,we first find daily turnover computed as the number of shares traded each day duringour measurement window scaled by the number of shares outstanding that day. Foreach company-year, TURNOVER is defined as the median daily turnover during themeasurement window.

The larger the transaction size, the larger the holding costs. Share price serves as aproxy for the value of each transaction since price quotes are for standard lot sizes (100shares). In addition, price is related to order processing costs; processing costs are afixed amount per trade and are decreasing with the value of the transaction. In Stoll’smodel, the association between relative spread and price would be positive if holdingcosts dominate and negative if order processing costs dominate. Additionally, therealso is a mechanical link between relative spread and stock price that is expected tolead to a negative association between the two (Roulstone, 2003). Thus, stock pricehandles a number of roles in our model and the coefficient should reflect the averageeffect of different factors. The variable PRICE for any company-year is the medianclosing daily stock price during the measurement window.

In addition to variables to control for holding and order costs, we include variablesthat affect a market maker’s adverse selection risk. Research demonstrates a positiverelation between company size and the amount of publicly available information; thusmarket makers face a lower risk of adverse selection for larger companies (Atiase,1985). To control for the negative relation between company size and bid-ask spread,we include a variable for market value (Gregoriou et al., 2005). We calculate the market

Auditor tenure,specialization

and asymmetry

607

value of common equity for each day during the measurement window. MKTVAL foreach company-year is then defined as the median daily market value of common equityover the measurement window.

The amount of public information is expected to increase with the number ofanalysts following a company, thereby reducing the expected returns to privateinformation search. While evidence is mixed (Roulstone, 2003), recent studies find thatmarket liquidity is increasing (i.e. information asymmetry is decreasing) as the numberof analysts increase (Muller and Riedl, 2002; Roulstone, 2003; Yohn, 1998). We includethe variable ANALYSTS in our model, found as the number of analyst issuing aforecast for annual earnings per share for year t. Companies not in the InstitutionalBrokers’ Estimate System (I/B/E/S) database are treated as having no analystcoverage[12].

Finally, we include company age to control for the positive association betweenaudit firm tenure and company age (Myers et al., 2003; Carcello and Nagy, 2004; Ghoshand Moon, 2005). AGE is found as the number of years since the company’s annualfinancial statement was first available on Compustat[13].

Regression models. We estimate two regression models to test for cross-sectionaldifferences in bid-ask spreads for clients of specialist versus non-specialist auditorsand clients of shorter versus longer-tenure auditors. Both include the same controlvariables and both have an indicator variable (CHANGE) to capture the effect of newaudit engagements on bid-ask spread. The first model, equation (4), uses indicatorvariables for specialization and tenure; the second model uses continuous variablesfor both:

SPREAD ¼ V0 þV1TURNOVER þV2VOLATILITY þV3MKTVAL

þV4PRICE þV5ANALYSTS þV6AGE þ SV7215YEAR

þV16SPECI þV17CHANGE þV18SHORT þV19LONG þ 1

ð4Þ

SPREAD ¼ b0 þ b1TURNOVER þ b2VOLATILITY þ b3MKTVAL

þ b4PRICE þ b5ANALYSTS þ b6AGE þ Sb7215YEAR

þ b16SPECC þ b17CHANGE þ b18TENURE þ 1

ð5Þ

where:

SPREAD – is the median of the relative daily bid-ask spread over themeasurement window where relative daily bid-ask spread isdefined as (BID 2 ASK)/{(BID 2 ASK)/2}, BID is the dailyclosing bid price, and ASK is the daily closing ask price.

TURNOVER – is the median scaled trading volume over the measurementwindow where scaled trading volume is defined as[VOLUME/SHARES] where VOLUME and SHARES are thedaily trading volume and the daily number of shares outstanding,respectively.

VOLATILTY – is the standard deviation of daily returns over the measurementwindow.

MAJ24,7

608

MKTVAL – is the median market value over the measurement window wheremarket value is defined as (PRC*SHARES), where PRC is dailyclosing price.

PRICE – is the median closing daily share price over the measurement window.

ANALYSTS – is the number of analysts issuing a forecast for annual earnings pershare for year t.

AGE – is the number of years since annual financial statements were firstavailable on Compustat.

YEAR – is an indicator variable to control for fixed year effects.

SPECI – is one of three indicator specialization variables (market share,portfolio share, or composite measure as defined in the sectiondescribing industry specialization variables).

SPECC – is one of three continuous specialization variables (market share,portfolio share, or composite measure as defined in the sectiondescribing industry specialization variables).

TENURE – is the number of consecutive years of the auditor-client relationship.

CHANGE – is equal to 1 if TENURE ¼ 1, and 0 otherwise.

SHORT – is equal to 1 if 2 # TENURE # 3, and 0 otherwise.

LONG – is equal to 1 if TENURE $ 10, and 0 otherwise.(MEDIUM where 4 # TENURE # 9 is not represented in equation(4) because medium tenure audit engagements serve as thebenchmark in the dichotomous tenure equation).

Because the sample is drawn from panel data, we expect serial autocorrelation of theindependent variables and the error term within companies. As discussed by Petersen(2009) and shown through his simulation results, t-statistics based on averageregression coefficients from year-by-year regressions using the methodology of Famaand MacBeth (1973) are biased upwards and potentially quite severely in situationswhere within company correlation exist. By contrast, clustered regression (clusteringby company) corrects for the serial correlation in panel data and provides unbiasedt-statistics. Additionally, there is evidence of a systematic decline in the bid-ask spreadover the time period in this study[14], possibly due to changes in tick sizes (from 1/8 to1/16 and finally to decimals) and changes in order handling rules (Barclay et al., 1999)during our sample period. Thus, we use a fixed-effects model with indicator variablesfor years to control for year-to-year changes in the bid-ask spread.

Sample selectionOur sample is restricted to publicly traded companies in the USA that were audited bybig N auditors for fiscal years ending 1992-2001 and that are traded through NASDAQ.This time period is chosen to limit the number of mergers that occurred among the bigN audit firms and to avoid the audit market changes that resulted from the demise ofArthur Andersen LLP[15].

Auditor tenure,specialization

and asymmetry

609

The sample is limited to companies audited by big N audit firms for two reasons.First, these large audit firms audit nearly 90 percent of publicly held companies in theUSA (Wallace, 1998). Second, there is substantial evidence that larger audit firmsdeliver higher quality audits in part because of product differentiation between big Nand non-big N audit firms (Palmrose, 1986; Menon and Williams, 1991). Clients oflarger audit firms have higher ERCs (Teoh and Wong, 1993), lower discretionaryaccruals (Becker et al., 1998), and lower cost of debt (Wallace, 1981; Mansi et al., 2004).As a result, this paper includes only the companies employing one of the big N auditfirms to avoid any differences in bid-ask spread due to audit firm size effect.

Our analysis requires daily closing bid, closing ask, number of shares traded (forvolume), daily returns (for volatility), number of shares outstanding and closing pricefrom the CRSP database. In addition, we require total assets, audit firm, and earningsrelease dates from the Compustat database, and analyst following from I/B/E/S.Consistent with Neal and Riley (2004) we exclude from the analysis in year t anyindustry with fewer than 20 companies audited by big N auditors[16]. Our datarequirements resulted in a final sample of 31,689 usable company-year observations.The number of observations in a single year varies from a low of 1,820 in 1992 to a highof 3,795 in 1999.

IV. Empirical resultsDescriptive statisticsTable I shows the number of company-year observations in our sample by industry. Atotal of 55 industries are represented with the number of company year observations inan industry ranging from 12 (0.04 percent of the sample) in leather and leather products(Standard Industrial Classification – SIC 31) to 4,507 (14.2 percent of the sample) inbusiness services (SIC 73). No other single industry represents more than 10 percent ofthe company-year observations.

Table II shows definitions for all variables reported in the descriptive statistics andused in the analysis. Table III shows descriptive statistics for the pooled sample.Approximately, 18.5 percent of sample companies are audited by an industry specialistwhen specialization is defined by our market share measure. The percentageof the companies in our sample that are audited by a specialist drops to 16.8 percentwhen specialization is defined by our portfolio measure and increases to 43.6 percent whenspecialization is defined by our composite measure. Slightly less than 25 percent of thecompany-year observations have an auditor-client relationship of two to three years,49 percent have an auditor-client relationship of four to nine years, and 22 percent have anauditor-client relationship of ten years or longer. Approximately, 5 percent ofcompany-years represent auditors in their first year with a client.

The mean (median) market value of the companies included in our sample is $1.20($0.15) billion with the 25th (75th) percentile equal to $0.05 billion ($0.55 billion).Accordingly, the companies in our sample represent a wide range of company sizeswith a skewed distribution. The mean (median) share price is $17.27 (11.88) with the25th (75th) percentile equal to $5.49 ($23.25).

Pearson and Spearman correlation coefficients are shown in Table IV. Allcorrelations between our specialization measures are statistically significant( p , 0.001). There is much stronger correlation between the composite measure andboth the market share (0.715) and portfolio share measures (0.918). In general, longer

MAJ24,7

610

Two-digit SIC code Name of industry Number of company-years

10 Metal mining 24613 Oil and gas extraction 1,11915 General building contractors 21316 Heavy construction contractors 7417 Construction special trade 3220 Food and kindred products 52722 Textile mill products 19923 Apparel and other finished products 28624 Lumber and wood products 12625 Furniture and fixtures 17326 Paper and allied products 24227 Printing publishing and allied 33828 Chemicals and allied products 2,55129 Petroleum and coal products 8230 Rubber and miscellaneous plastics products 36731 Leather and leather products 1232 Stone, clay, glass, and concrete products 16133 Primary metal industries 45634 Fabricated metal products 39735 Industrial machinery and equipment 2,12036 Electrical and electronic equipment 2,63237 Transportation equipment 59038 Instruments and related products 2,24339 Miscellaneous manufacturing industries 34040 Railroad transportation 8042 Motor freight transportation and warehousing 33944 Water transportation 11745 Transportation by air 18647 Transportation services 9648 Communications 1,06549 Electric, gas, and sanitary services 1,19850 Durable goods wholesale 74451 Nondurable goods wholesale 396

52Building materials, hardware, garden supply, andmobile 42

53 General merchandise stores 17854 Food stores 18455 Automotive dealers and gasoline service stations 13156 Apparel and accessory stores 34557 Furniture, home furnishings and equipment stores 17158 Eating and drinking places 55459 Miscellaneous retail 62961 Non-depository credit institutions 41862 Security and commodity 42063 Insurance carriers 1,31764 Insurance agents, brokers, and services 19765 Real estate 20767 Holding and other investment offices 24870 Hotels, rooming houses, and other lodging places 12072 Personal services 43

(continued )

Table I.Distribution of sample

company-years byindustry

Auditor tenure,specialization

and asymmetry

611

tenure auditors are more likely to be specialists, but there is little evidence that tenureand specialization are linked in an economically meaningful way. AGE and TENUREare strongly positively correlated (Pearson correlation ¼ 0.59). Thus, controlling forcompany age in our regression models will provide cleaner measures of the associationbetween tenure and bid-ask spread.

Regression resultsTable V shows regression results for H1 and H2. SPREAD is our proxy forinformation asymmetry. Regression results are presented separately for each of ourthree specialization measures.

In Panel A, we report results using indicator variables for specialization along withcategorical variables for tenure length (SHORT and LONG). We also include theindicator variable, CHANGE, to capture any first-year auditor effects. We refer to theseas our indicator variables models.

Two-digit SIC code Name of industry Number of company-years

73 Business services 4,50778 Motion pictures 24079 Amusement and recreational services 35480 Health services 75582 Educational services 11183 Social services 5287 Engineering and management services 719Table I.

SPREAD The median of the relative daily bid-ask spread over the measurement window whererelative daily bid-ask spread is defined as (bid-ask)/{(bid-ask)/2}, bid is the dailyclosing bid price, and ask is the daily closing ask price

SPECC One of three continuous variables for specialization (market share, portfolio share, ora composite measure) as defined fully in the body of the paper

SPECI One of three indicator variables for specialization (market share, portfolio share, or acomposite measure) as defined fully in the body of the paper

TENURE The consecutive number of years of the auditor-client relationshipCHANGE Equal to 1 if TENURE ¼ 1 (the first year of the auditor-client pair), and 0 otherwiseSHORT Equal to 1 if 2 # TENURE # 3, and 0 otherwiseMEDIUM Equal to 1 if 4 # TENURE # 9, and 0 otherwiseLONG Equal to 1 if TENURE $ 10, and 0 otherwiseTURNOVER The median scaled trading volume over the measurement window where scaled

trading volume is defined as (volume/number of shares outstanding) where volumeand number of shares outstanding are the daily trading volume and the daily numberof shares outstanding

VOLATILTY The standard deviation of daily returns over the measurement windowMKTVAL The median market value over the measurement window where market value is

defined as (price £ number of shares outstanding), where price and number of sharesoutstanding are daily closing prices and daily number of shares outstanding

PRICE The median daily share price over the measurement windowANALYSTS The number of analyst issuing a forecast for annual earnings per share for year tAGE The number of years since annual financial statements were first available on

Compustat

Table II.Variable definitions

MAJ24,7

612

In Panel B, we use continuous measures for each of the three specialization variablesand a continuous measure for tenure (TENURE); the variable CHANGE is againincluded. We refer to these as our continuous variables models. Although we use afixed effect model, Table V does not report year-by-year fixed effects for parsimony.Each of the fixed effects is significant ( p-value , 0.001) and decreases almostmonotonically from 2.77 for 1992 to 0.526 for 2001[17].

All control variables are statistically significant and, except for company size(MKTVAL), each carries the sign that is expected based on prior empirical studies.Bid-ask spread is increasing in volatility and company size (MKTVAL) anddiminishing in turnover and stock price. Because of the unexpected relation betweenbid-ask and MKTVAL and the high correlation between MKTVAL and stock price(0.814) and analyst following (0.649), we estimated the regression equation withoutPRICE and ANALYSTS. The result of this analysis, show the expected negative( p , 0.01) relation between bid-ask spread and MKTVAL. We also find that bid-askspread is increasing reliably with company age suggesting that the market perceivesmore opportunities associated with private information search for older companies.

The coefficient on specialization is negative and statistically significant for each ofthe three measures. Our continuous market share measure of specialization is the leastsignificant (two-tailed p-value ¼ 0.031); all other coefficients are significant at p-valuesless than or equal to 0.003. These results are consistent with a market that associateshigher audit quality with companies choosing an industry specialist auditor. As agauge of the economic significance, consider results for our indicator measures ofspecialization. For companies audited by specialists, the bid-ask spread is lower thanthe sample average by as much as 10 percent (20.363 coefficient for the composite

Variablea N Mean Median 25th percentile 75th percentile

SPREAD 31,689 3.514 2.353 1.207 4.416SPECI

Industry share 31,689 0.185 0 0 0Portfolio share 31,689 0.168 0 0 0Composite 31,689 0.436 0 0 1.000

SPECCIndustry share 31,689 0.198 0.161 0.106 0.252Portfolio share 31,689 0.033 0.022 0.008 0.046Composite 31,689 0.009 0.003 0.001 0.009

CHANGE 31,689 0.052 0 0 0SHORT 31,689 0.246 0 0 0MEDIUM 31,689 0.487 0 0 1.000LONG 31,689 0.215 0 0 0TENURE 31,689 6.323 5.000 3.000 9.000TURNOVER 31,689 0.447 0.240 0.097 0.531MKTVAL ($billions) 31,689 1.196 0.152 0.049 0.552VOLATILITY 31,689 0.042 0.035 0.024 0.052PRICE 31,689 17.271 11.875 5.490 23.250ANALYSTS 31,689 5.043 3.000 1.000 7.000AGE 31,689 11.897 8.000 6.000 14.000

Note: aSee Table II for variable definitionsTable III.

Descriptive statistics

Auditor tenure,specialization

and asymmetry

613

SP

EC

C

Var

iab

lea

SP

RE

AD

Ind

ust

rysh

are

Por

tfol

iosh

are

Com

pos

ite

TE

NU

RE

TU

RN

OV

ER

MK

TV

AL

($b

illi

ons)

VO

LA

TIL

ITY

PR

ICE

AN

AL

YS

TS

AG

E

SP

RE

AD

1.00

02

0.10

8*

20.

163

*2

0.16

7*

20.

104

*2

0.54

6*

20.

744

*0.

262

*2

0.66

3*

20.

519

*2

0.05

6*

SP

EC

CIn

du

stry

shar

e2

0.06

8*

1.00

00.

416

*0.

715

*0.

039

*0.

034

*0.

115

*2

0.03

8*

0.08

0*

0.10

9*

0.03

7*

Por

tfol

iosh

are

20.

095

*0.

412

*1.

000

0.91

8*

0.00

20.

121

*0.

154

*0.

018

*0.

115

*0.

093

*0.

023

*

Com

pos

ite

20.

074

*0.

595

*0.

917

*1.

000

0.01

5*

0.10

9*

0.16

1*

0.00

40.

114

*0.

115

*0.

028

*

TE

NU

RE

20.

072

*0.

044

*0.

042

*0.

049

*1.

000

20.

023

*2

0.12

1*

20.

174

*0.

089

*0.

134

*0.

590

*

TU

RN

OV

ER

20.

271

*0.

014

0.04

1*

0.02

2*

20.

030

*1.

000

0.39

0*

0.23

4*

0.31

1*

0.41

4*

20.

147

*

MK

TV

AL

20.

087

*0.

051

*0.

075

*0.

066

*0.

091

*0.

046

*1.

000

20.

397

*0.

814

*0.

649

*0.

100

*

VO

LA

TIL

ITY

0.29

5*

20.

013

20.

039

*2

0.03

8*

20.

154

*0.

270

*2

0.06

0*

1.00

02

0.50

3*

20.

227

*2

0.28

6*

PR

ICE

20.

361

*0.

061

*0.

113

*0.

087

*0.

090

*0.

283

*0.

292

*2

0.23

0*

1.00

00.

507

*0.

084

*

AN

AL

YS

TS

20.

319

*0.

105

*0.

099

*0.

084

*0.

173

*0.

236

*0.

370

*2

0.19

0*

0.44

5*

1.00

00.

043

*

AG

E2

0.05

9*

0.05

7*

0.13

3*

0.12

4*

0.44

9*

20.

127

*0.

110

*2

0.25

6*

0.15

9*

0.17

7*

1.00

0

Notes:

* Sig

nifi

can

ceat

the

1p

erce

nt

lev

el;

ase

eT

able

IIfo

rv

aria

ble

defi

nit

ion

s;p

ears

onco

rrel

atio

nco

effi

cien

tsap

pea

rb

elow

the

dia

gon

al;

spea

rman

corr

elat

ion

coef

fici

ents

app

ear

abov

eth

ed

iag

onal

Table IV.Pearson and Spearmancorrelation coefficients

MAJ24,7

614

specialization measure, in Panel A column (c), divided by the 3.514 mean spread inTable III).

The findings do not allow us to conclude that specialists “cause” their clients toreport “better” information or that improved disclosures are limited to audited financialreports. However, we can infer that specialist auditors are one element of the reportingand disclosure choices made by a company that – post the annual reportannouncement – is related to lower information asymmetry as reflected in marketmakers’ quoted bid-ask spreads.

As shown in Panel A, auditors in their second and third years with a client (SHORT)have significantly lower bid-ask spreads than MEDIUM auditors in their fourth toninth year with a client[18]. We find that auditors with tenure of ten or more years

a b cVariablea Predicted sign Industry share Portfolio share Composite

Panel A: indicator variables for tenure and specializationSPREAD ¼ V0 þV1TURNOVER þV2VOLATILITY þV3MKTVALþV4PRICE

þV5ANALYSTS þV6AGE þ SV7215YEAR þV16SPECI þV17CHANGE þV18SHORTþV19LONGþ 1

Coefficient estimates (p-values) for the indicator specialization measuresIntercept ? 1.371 (,0.001) 1.360 (,0.001) 1.452 (,0.001)TURNOVER 2 21.581 (,0.001) 21.579 (,0.001) 21.574 (,0.001)VOLATILITY þ 53.462 (,0.001) 53.348 (,0.001) 53.490 (,0.001)MKTVAL 2 0.027 (,0.001) 0.0272 (,0.001) 0.027 (,0.001)PRICE ? 20.035 (,0.001) 20.035 (,0.001) 20.034 (,0.001)ANALYSTS 2 20.087 (,0.001) 20.087 (,0.001) 20.086 (,0.001)AGE ? 0.017 (,0.001) 0.018 (,0.001) 0.018 (,0.001)SPECI ? 20.178 (0.003) 20.272 (,0.001) 20.363 (,0.001)CHANGE ? 0.021 (0.836) 0.022 (0.823) 0.029 (0.772)SHORT ? 20.256 (,0.001) 20.256 (,0.001) 20.245 (,0.001)LONG ? 20.032 (0.649) 20.034 (0.624) 20.037 (0.594)R 2 0.319 0.319 0.320Panel B: continuous variables for tenure and specialization

SPREAD ¼ b0 þ b1TURNOVER þ b2VOLATILITY þ b3MKTVALþ b4PRICEþb5ANALYSTS þ b6AGE þ Sb7215YEAR þ b16SPECC þ b17CHANGE þ b18TENURE þ 1

Coefficient estimates (p-values) for the continuous specialization measuresIntercept ? 1.267 (,0.001) 1.309 (,0.001) 1.235 (,0.001)TURNOVER 2 21.581 (,0.001) 21.570 (,0.001) 21.576 (,0.001)VOLATILITY þ 53.369 (,0.001) 53.415 (,0.001) 53.357 (,0.001)MKTVAL 2 0.027 (,0.001) 0.027 (,0.001) 0.0270 (,0.001)PRICE ? 20.035 (,0.001) 20.034 (,0.001) 20.035 (,0.001)ANALYSTS þ 20.086 (,0.001) 20.085 (,0.001) 20.086 (,0.001)AGE ? 0.017 (,0.001) 0.019 (,0.001) 0.018 (,0.001)SPECC ? 20.448 (0.031) 25.357 (,0.001) 29.914 (,0.001)CHANGE ? 0.184 (0.070) 0.175 (0.086) 0.177 (0.082)TENURE ? 0.016 (0.057) 0.014 (0.088) 0.015 (0.069)R 2 0.318 0.320 0.319

Notes: aSee Table II for variable definitions; þ , positive relation between independent and dependentvariable predicted; 2 , negative relation between independent and dependent variable predicted;?, no relation between independent and dependent variable predicted

Table V.Multivariate models

explaining bid-ask spread

Auditor tenure,specialization

and asymmetry

615

(LONG) have a bid-ask spread that is not distinguishable statistically from the bid-askspread of auditor with MEDIUM tenure. In further testing (not tabulated here) we findthat SHORT tenure auditors have significantly lower bid-ask spreads than LONG termauditors[19]. We also find that the bid-ask spread for tenure years two and three issignificantly smaller than the first year bid-ask spread[20]. Thus, there is a U-shapedrelation between bid-ask spread and tenure with a relatively high bid-ask spread in theyear of an auditor change, a significant decrease in the second and third years of anengagement, and a subsequent reversal of that decrease. This is consistent with amarket that perceives the selection of a new auditor as a commitment to and a deliveryof higher quality financial reporting (and potentially better quality disclosures moregenerally) in the short run but with a later perception that financial reporting qualitygains vanish. The results suggest that early in an auditor’s tenure, market makersperceive a lower probability that any given trader has superior private information andthat perception changes later in the auditor’s tenure. We conclude that the bid-askspread is higher in the first year of the audit, lower in early years, and higher againafter the auditor-client relationship exceeds some critical number of years.

The coefficient on the continuous variable TENURE is positive and marginallystatistically significant (0.057 , p-values , 0.088 in Panel B). The observed level ofsignificance may be the result of nonlinearity in the association between bid-ask spreadand tenure shown in Table V, Panel A.

Table VI shows results of our regression models for H3. Because we found that theeffect of tenure is nonlinear in Table V, we use our categorical tenure variables in allanalyses shown in Table VI. In addition, we use all indicator and continuous measuresof specialization shown in Table V. Results are shown for each of those measures ascategorical (Panel A) and continuous (Panel B). The model uses the bid-ask spread ofmedium tenure non-specialists auditors as a benchmark and controls for other factorsexpected to impact bid-ask spread as in Table V.

First, note that four of our six proxies for specialization are significant at two-tailedp-values of 0.07 or less. This means that, in general, medium tenure specialists have asmaller bid-ask spread than medium tenure non-specialists. Second, except for Panel Bcolumn (a), the categorical tenure variable SHORT is negative and significant at atwo-tailed p-value of 0.07 or less. Thus, the favorable bid-ask spread we observe inTable V for short tenure auditors (which is the average for clients of both specialist andnon-specialist auditors) holds for the subset of companies audited by non-specialistsalone. Specifically, the negative and significant coefficient on SHORT indicates that thebid-ask spread for short tenure non-specialist auditors is lower than for medium tenurenon-specialists.

Next, our interaction terms allow us to examine if the tenure effects differ betweenspecialists and non-specialists. The interaction between short tenure and specialization(SPEC £ SHORT) is significant and negative (two-tailed p-values of 0.029 or less) forfour of our six specialization proxies. Although mixed, the evidence suggeststhe favorable bid-ask spread effect of short tenure auditors is more pronounced forspecialists than for non-specialists. In other words, the decrease in the bid-ask effectfor short tenure auditors is more pronounced for specialists than non-specialists.Further, the reversal of that favorable effect as tenure increases to the medium term isless pronounced for specialists than non-specialists. Other interaction terms are largelyinsignificant.

MAJ24,7

616

The effect of tenure on bid-ask spread differs for specialists and non-specialists in away that suggests two conclusions. First, short tenure auditors are generally perceivedby the market as a signal that a company is committed to more accurate, relevant andreliable disclosure and reporting choices. Second, disclosure choices by clients ofshort-tenure specialist auditors reduce the market’s perception of the likelihood of

a b cVariablea Predicted sign Industry share Portfolio share Composite

Panel A: indicator variables for tenure and specializationSPREAD ¼ V0 þV1TURNOVER þV2VOLATILITY þV3MKTVALþV4PRICE

þV5ANALYSTS þV6AGE þ SV7215YEAR þV16SPECI þV17CHANGE þV18SHORTþ V19LONGþV20SPEC £ CHANGE þV21SPEC £ SHORT þV22SPEC £ LONGþ 1

Coefficient estimates (p-values) for the indicator specialization measuresIntercept ? 1.343 (,0.001) 1.332435 (,0.001) 1.435 (,0.001)TURNOVER 2 21.581 (,0.001) 21.580 (,0.001) 21.573 (,0.001)VOLATILITY þ 53.502 (,0.001) 53.369 (,0.001) 53.492 (,0.001)MKTVAL 2 0.027 (,0.001) 0.027 (,0.001) 0.027 (,0.001)PRICE ? 20.035 (,0.001) 20.035 (,0.001) 20.034 (,0.001)ANALYSTS 2 20.087 (,0.001) 20.087 (,0.001) 20.086 (,0.001)AGE ? 0.0171 (,0.001) 0.019 (,0.001) 0.018 (,0.001)SPECI ? 20.055 (0.519) 20.146 (0.070) 20.334 (,0.001)CHANGE ? 0.043 (0.709) 0.079 (0.491) 0.032 (0.816)SHORT ? 20.198 (0.002) 20.236 (0.002) 20.246 (0.002)LONG ? 0.003 (0.971) 0.024 (0.752) 0.022 (0.817)SPEC £ CHANGE ? 20.119 (0.567) 20.353 (0.066) 20.008 (0.969)SPEC £ SHORT ? 20.327 (0.011) 20.117 (0.352) 0.004 (0.968)SPEC £ LONG ? 20.183 (0.169) 20.334 (0.006) 20.137 (0.241)R 2 0.319 0.319 0.320Panel B: continuous variables

SPREAD ¼ V0 þV1TURNOVER þV2VOLATILITY þV3MKTVALþV4PRICEþV5ANALYSTS þV6AGE þ SV7215YEAR þV16SPECC þV17CHANGE þV18SHORT

þV19LONGþV20SPEC £ CHANGE þV21SPEC £ SHORT þV22SPEC £ LONGþ 1Coefficient estimates (p-values) for the continuous specialization measures

Intercept ? 1.362 (,0.001) 1.410 (,0.001) 1.355 (,0.001)TURNOVER 2 21.581 (,0.001) 21.572 (,0.001) 21.577 (,0.001)VOLATILITY þ 53.473 (,0.001) 53.487 (,0.001) 53.435 (,0.001)MKTVAL 2 0.027 (,0.001) 0.028 (,0.001) 0.028 (,0.001)PRICE ? 20.035 (,0.001) 20.034 (,0.001) 20.035 (,0.001)ANALYSTS 2 20.087 (,0.001) 20.087 (,0.001) 20.087 (,0.001)AGE ? 0.017 (,0.001) 0.0185 (,0.001) 0.019 (,0.001)SPECC ? 20.146 (0.615) 24.006 (,0.001) 25.811 (0.008)CHANGE ? 0.147 (0.491) 0.137 (0.328) 0.080 (0.505)SHORT ? 20.074 (0.482) 20.144 (0.069) 20.161 (0.016)LONG ? 0.012 (0.920) 0.027 (0.766) 0.0129 (0.872)SPEC £ CHANGE ? 20.642 (0.360) 23.502 (0.128) 27.547 (0.173)SPEC £ SHORT ? 20.951 (0.029) 23.108 (0.018) 211.334 (0.0003)SPEC £ LONG ? 20.215 (0.629) 21.925 (0.144) 25.485 (0.090)R 2 0.319 0.320 0.319

Notes: aSee Table II for variable definitions; þ , positive relation between independent and dependentvariable predicted; 2 , negative relation between independent and dependent variable predicted;?, no relation between independent and dependent variable predicted

Table VI.Multivariate models

explaining bid-ask spread

Auditor tenure,specialization

and asymmetry

617

private information search opportunities more than the disclosure choices byshort-term non-specialist auditors. Third, sufficiently long auditor-client tenure isgenerally associated with a market perception that disclosure quality is deteriorating,but that this deterioration is less likely for companies with specialists.

Robustness tests to control for biases in specialization measuresThe auditor portfolio share measure of specialization contains an inherent industrybias. The bias occurs because total industry sales vary across two-digit SIC industriesin any year; thus, the continuous portfolio measure (which for any auditor is the samefor all clients in a particular industry-year) is positively correlated with industry-yeartotal sales. As a result, cross-sectional differences in auditor portfolio share for anycompany-year will be biased upward (downward) for companies in industries thathave larger (smaller) than average total sales. Thus, observed information asymmetryeffects may be due to lower information asymmetry in larger industries. To control forthis potential industry bias, for each industry in each year we include industry salesdivided by total sales in regression equations (4) and (5). With the percentage ofindustry-year sales included in the analysis, the coefficient on the auditorportfolio-based measure of specialization is no longer significant at conventionallevels. This suggests that the portfolio measure of specialization may be a proxy forindustry size rather than firm specialization.

We next devised a control for our market-based measure of specialization, whichcontains a bias related to the degree of concentration and the size of companies withinany industry. To better understand this control, assume that certain industries aredominated by a few large companies, while other industries have companies ofrelatively equal size. Moreover, assume companies are randomly assigned to auditors.Cross-sectional comparisons of market share for a particular auditor will partiallyreflect industry concentration. Also, in industries with heavy concentration, we expectgreater variation in market share than in industries with less concentration. To controlfor industry concentration and any effects that may have on industry-specificinformation asymmetry, we include the standard deviation of industry-year sales as acontrol variable; the results in Table V for our market-based measures of specializationremains negative and statistically significant ( p-value , 0.05).

V. Summary and conclusionsPrior research has documented a positive relation between audit quality (as measuredby industry specialization and audit firm tenure) and cost of capital, ERCs and debtratings. Our study differs from prior research in that we use bid-ask spread, arelatively direct measure of information asymmetry, to examine the market’sperception of the role of industry specialization and tenure on audit quality. Our resultssupport the notion that the beneficial effects of audit quality documented in priorresearch can be explained, at least in part, by a reduction in information asymmetry.

Our results indicate a negative relation between specialization and informationasymmetry when specialization is measured using market share, portfolio share, and acomposite measure. The results are robust to indicator and continuous specificationsfor our specialization measures. Although our findings for the portfolio measure aresensitive to inclusion of industry size, our results are consistent with a market thatperceives fewer opportunities for private information search when a company is

MAJ24,7

618

audited by a specialist auditor. A reduction in private information search opportunitiesis expected as disclosure and audit quality improve.

Our results reveal a U-shaped relation between the length of the auditor-clientrelationship and bid-ask spread. The findings for tenure are consistent with a marketthat perceives auditor changes as a resolution by a firm to provide more relevant andreliable disclosures. Specifically, the bid-ask spread is higher in the first year of theaudit engagement, declines in the second and third year of the engagement andincreases in the later engagement years[21]. The association between bid-ask spreadand tenure is different for specialist and non-specialists. The market’s perception of thereduction in information asymmetry for short tenure auditors is more pronounced forspecialists and less of those gains dissipate as tenure increases.

Our findings have implications for academics, regulators, companies, and auditors.First, future researchers exploring information asymmetry issues should be cognizantof the effect of auditor-client relationships on the level of information asymmetry.Second, regulators should be aware that the market takes both industry specializationand audit firm tenure into consideration when evaluating audit quality. However, it isunlikely that those evaluations are independent of other factors that are important informing the market’s perception of a company’s disclosure and reporting quality.Although most academic research has shown a positive relation between tenure andaudit quality, our study provides some evidence that the market perceives lower auditquality with longer auditor-client relationships. Finally, companies and auditorsshould be aware of the market’s perception of industry specialization and tenure onperceived audit quality. Companies have incentives to lower information asymmetryand our findings document that the choice of a specialist auditor and the length ofauditor relationship can potentially influence this objective. We cannot, however, becertain that it is auditor specialization and tenure alone that affect the market’sperception of information quality. It is possible that a company’s portfolio of importantdisclosure choices varies concurrently with auditor tenure and specialization.

Notes

1. We adopt DeAngelo’s (1981, p. 186) definition of audit quality: “[. . .] the market-assessedjoint probability that a given auditor will both (a) discover a breach in the client’s accountingsystem, and (b) report the breach.”

2. The three measures are: the audit firm’s industry market share, the industry of the client as aproportion of the audit firm’s portfolio, and a composite measure based on market andportfolio share.

3. The incentive for private information search and the level of information asymmetry amonginvestors is expected to be particularly high prior to informative, scheduled announcements(Kim and Verrecchia, 1994). We expect abnormally high levels of information asymmetry inthe period just prior to scheduled earnings announcements and during the subsequent(short) period when that asymmetry is resolved (Kim and Verrechia, 1994) and investorsform new estimates of company value. In the period surrounding the annual earningsrelease, bid-ask spread will increase as dealers protect themselves from adverse selection. Asdiscussed by Chae (2005), we would expect an increase in bid-ask spread for informative,scheduled announcements if we use a measurement window starting several days before andending several days after an announcement.

4. After the annual earnings announcement, informed traders will concentrate their tradingactivity in stocks where information asymmetry remains the highest and liquidity traders

Auditor tenure,specialization

and asymmetry

619

will tend to avoid those same stocks resulting in relatively high adverse selection costs forthe market maker and relatively high bid-ask spreads.

5. The specialist fee premium is not apparent in more recent periods (Ferguson and Stokes,2002), suggesting that any production efficiencies from specialization might result in lowerfees and may not increase audit quality.

6. The use of a seven-day window before and seven-day after the announcement should avoidannouncement related bid-ask changes. Krinsky and Lee (1996), for example, use a two-daywindow before and two-day window after the earnings announcement to measure the effectof earnings announcements on components of the bid-ask spread.

7. There are a number of ways to measure the adverse selection component of the bid-askspread using intra-day trade data such as NYSE’s Trade and Quote (TAQ) database. Theprimary disadvantage of the use of intra-day data for the present study is that computationalcosts are quite high and thus the period of analysis is necessarily limited. For example,Danielsen et al. (2007) examine the association between audit and non-audit fees and anumber of adverse selection measures derived from TAQ data; their study is limited toAugust 2001 (and 741 companies). We use the closing bid-ask spread as a proxy forinformation asymmetry (controlling for inventory holding and transaction costs componentsof the spread) because information asymmetry measures derived from intra-day data are notfeasible for our panel data set. Moreover, van Ness et al. (2001, p. 77) compare five adverseselection models that use intra-day data and conclude that the models “measure adverseselection weakly at best”; thus, it is not clear that those other information asymmetrymeasures are superior to our proxy at least in the context of this study.

8. All specialization proxies are calculated separately for each year. An audit firm may beclassified as a specialist in industry k in year t but not in year t þ 1. Although we allow forfirms to be reclassified each year, 68 percent of our sample is consistently classified as eithera specialist or non-specialist throughout our sample period. An additional 26 percent of oursample is reclassified once (i.e. moves from specialist to non-specialist or non-specialist tospecialist) during our sample period.

9. Refer to Neal and Riley (2004) for a more detailed explanation of this composite measure ofspecialization.

10. Compustat began reporting auditor codes in 1974; therefore, we are not able to determine thelength of the auditor-client relationship when the initial engagement began prior to 1974.Since our sample period begins in 1992, auditor-client relationships exceeding 18 years arenot reliable and we exclude these firm-year observations from our analysis.

11. Carcello and Nagy (2004) include the first year of the auditor-client relationship in theSHORT category.

12. Other variables have been shown to affect the bid-ask spread including number of competingdealers (Tinic and West, 1972) and institutional holdings (Muller and Riedl, 2002). Thesedata are not readily available and are not included in our study.

13. Compustat data beginning in 1950 was used to calculate FIRMAGE.

14. The mean annual SPREAD from 1992 to 2001 is 5.14, 4.66, 4.49, 4.00, 4.13, 2.79, 3.46, 3.03,2.99, and 1.67, respectively. We do not report those fixed effects in regression tables.

15. Companies in our sample are audited by Arthur Andersen LLP, Coopers & Lybrand LLP,Deloitte & Touche LLP, Ernst&Young LLP, KPMG Peat Marwick LLP, or Price WaterhouseLLP. In addition, companies in our sample may have been audited by PriceWaterhouseCoopers LLP, the firm created by the merger between Coopers & Lybrand LLP and PriceWaterhouse LLP in 1998.

16. In addition we delete firms whose stock price is less than $1 and we also delete BerkshireHathaway Inc. whose stock price exceeded $50,000 during our sample period.

MAJ24,7

620

17. The coefficients for each year from 1992 to 2000 are, respectively, 2.77, 2.47, 2.55, 2.20, 2.25,1.22, 1.32, 0.68, and 0.53 for Table V, Panel A column (a). Results for fixed effect in othercolumns are almost identical.

18. MEDIUM is not represented in the model as it is the benchmark group.

19. To determine this we made LONG our benchmark group by removing LONG and includingMEDIUM in the model. SHORT was significantly smaller than LONG.

20. To make this determination, we included indicator variables for each tenure year, except thefirst year thus allowing first year engagement auditors to be the benchmark.

21. We do not examine the reason for the change (resignation or dismissal) or nature of thechange (switch from (to) specialist).

References

Atiase, R.K. (1985), “Predisclosure information, firm capitalization, and security price behavioraround earnings announcements”, Journal of Accounting Research, Vol. 23, pp. 21-36.

Balsam, S., Krishnan, J. and Yang, J.S. (2003), “Auditor industry specialization and earningsquality”, Auditing: A Journal of Practice & Theory, Vol. 22, September, pp. 71-97.

Barclay, M., Christie, W., Harris, J., Kandel, E. and Schultz, P. (1999), “Effects of market reform on thetrading costs and depths of NASDAQ stock”, The Journal of Finance, Vol. 54 No. 1, pp. 1-34.

Becker, C., DeFond, M., Jiambalvo, J. and Subramanyam, K. (1998), “The effect of audit quality onearnings management”, Journal of Accounting Research, Vol. 15 No. 1, pp. 1-24.

Cairney, T.D. and Young, G.R. (2006), “Homogenous industries and auditor specialization:an indication of production economies”, Auditing: A Journal of Practice & Theory, Vol. 25No. 1, pp. 49-67.

Callahan, C., Lee, C. and Yohn, T. (1997), “Accounting information and bid-ask spreads”,Accounting Horizons, Vol. 11, December, pp. 50-60.

Carcello, J.V. and Nagy, A.L. (2004), “Audit firm tenure and fraudulent financial reporting”,Auditing: A Journal of Practice & Theory, Vol. 23 No. 2, pp. 55-69.

Chae, J. (2005), “Trading volume, information asymmetry, and timing information”, The Journalof Finance, Vol. 60 No. 1, pp. 413-42.

Craswell, A.T., Francis, J.R. and Taylor, S.L. (1995), “Auditor brand name reputations and industryspecialization”, Journal of Accounting and Economics, Vol. 20, December, pp. 297-322.

Danielsen, B.R., van Ness, R.A. and Warr, R.S. (2007), “Auditor fees, market microstructure, andfirm transparency”, Journal of Business Finance & Accounting, Vol. 34 Nos 1/2, pp. 202-21.

DeAngelo, L.E. (1981), “Auditor size and audit quality”, Journal of Accounting and Economics,Vol. 3 No. 3, pp. 183-99.

DeFond, M.L., Francis, J.R. and Wong, T.J. (2000), “Auditor industry specialization and marketsegmentation: evidence from Hong Kong”, Auditing: A Journal of Practice & Theory,Vol. 19 No. 1, pp. 49-66.

Demetz, H. (1968), “The cost of transacting”, Quarterly Journal of Economics, Vol. 82 No. 1, pp. 33-53.

Dunn, K. and Mayhew, B.W. (2004), “Audit firm industry specialization and client disclosurequality”, Review of Accounting Studies, Vol. 9 No. 1, pp. 35-58.

Ertimur, Y. (2004), “Accounting numbers and information asymmetry: evidence from loss firms”,working paper, Stanford University, Stanford, CA.

Fama, E.F. and MacBeth, J.D. (1973), “Risk, return, and equilibrium: empirical tests”, Journal ofPolitical Economy, Vol. 81 No. 3, pp. 607-36.

Auditor tenure,specialization

and asymmetry

621

Ferguson, A. and Stokes, D. (2002), “Brand name audit pricing, industry specialization, andleadership premiums post-big 8 and big 6 mergers”, Contemporary Accounting Research,Vol. 19 No. 1, pp. 77-110.

Fung, S.Y.K., Gul, F. and Jaggi, B. (2007), “Auditor tenure, auditor specialization and earningsquality”, paper presented at 2007 Auditing Section Midyear Conference, Charleston, SC,January 11-13.

Ghosh, A. and Moon, D. (2005), “Audit tenure and perceptions of audit quality”, The AccountingReview, Vol. 80, April, pp. 585-612.

Gregoriou, A., Ioannidis, C. and Skerratt, L. (2005), “Information asymmetry and the bid-ask spread:evidence from the UK”, Journal of Business Finance & Accounting, November, pp. 1801-25.

Hammersley, J.S. (2006), “Pattern identification and industry-specialist auditors”, The AccountingReview, No. 81, pp. 309-36.

Houghton, K., Dolley, C., Jubb, C., Monroe, G. and Hay, D. (2005), “Auditor industry specializationand quality status: do both ‘premium’ and ‘discount’ suppliers exist?”, paper presented atAAA Auditing Section Midyear Conference, Austin, TX.

Kim, O. and Verrecchia, R. (1994), “Market liquidity and volume around earningsannouncements”, Journal of Accounting and Economics, Vol. 17, pp. 14-67.

Kim, O. and Verrecchia, R. (2001), “The relation among disclosure, returns, and trading volumeinformation”, The Accounting Review, Vol. 76 No. 4, pp. 633-54.

Krinsky, I. and Lee, J. (1996), “Earnings announcements and the components of the bid-askspread”, The Journal of Finance, Vol. 51 No. 4, pp. 1523-35.

Krishnan, G. (2003), “Does auditors’ industry expertise constrain earnings management?”,Accounting Horizons, Vol. 17, Supplement, pp. 1-16.

Leuz, C. and Verrecchia, R.E. (2000), “The economic consequences of increased disclosure”,Journal of Accounting Research, Vol. 38, Supplement, pp. 91-124.

Libby, R. and Frederick, D. (1990), “Experience and the ability to explain audit findings”, Journalof Accounting Research, Vol. 28 No. 2, pp. 348-67.

McInish, T.H. and Wood, R.A. (1992), “An analysis of intraday patterns in bid/ask spreads forNYSE stocks”, The Journal of Finance, Vol. 47 No. 2, pp. 753-64.

Mansi, A.S., Maxwell, W.F. and Miller, D.P. (2004), “Does auditor quality and tenure matter toinvestors? Evidence from the bond market”, Journal of Accounting Research, Vol. 42 No. 4,pp. 755-93.

Mayhew, B. and Wilkins, M. (2002), “Audit firm industry specialization as a differentiationstrategy: evidence from fees charged to firms going public”, Auditing: A Journal ofPractice & Theory, Vol. 22 No. 2, pp. 33-52.

Menon, K. and Williams, D.D. (1991), “Auditor credibility and initial public offerings”,The Accounting Review, Vol. 66 No. 2, pp. 313-32.

Muller, K. III and Riedl, E. (2002), “External monitoring of property appraisal estimates andinformation asymmetry”, Journal of Accounting Research, Vol. 38, June, pp. 865-81.

Myers, J.N., Myers, L.A. and Omer, T. (2003), “Exploring the term of the auditor-clientrelationship”, The Accounting Review, Vol. 78, July, pp. 779-99.

Myers, J.N., Myers, L.A., Palmrose, Z. and Scholz, S. (2005), “The length of auditor-clientrelationships and financial statement restatements”, working paper, University ofWisconsin, Madison, WI.

Neal, T.L. and Riley, R.R. Jr (2004), “Auditor industry specialist research design”, Auditing:A Journal of Practice & Theory, Vol. 23 No. 2, pp. 169-77.

MAJ24,7

622

Owhoso, E.V., Messier, W.F. Jr and Lynch, J.G. Jr (2002), “Error detection by industry-specialized teamsduring sequential audit review”, Journal of Accounting Research, Vol. 40, June, pp. 883-900.

Palmrose, Z. (1986), “Audit fees and auditor size: further evidence”, Journal of AccountingResearch, Vol. 24, pp. 97-110.

Petersen, M.A. (2009), “Estimating standard errors in finance panel data sets: comparingapproaches”, Review of Financial Studies, Vol. 22 No. 1, pp. 435-80.

Pittman, J.A. and Fortin, S. (2004), “Auditor choice and the cost of debt capital for newly publicfirms”, Journal of Accounting and Economics, Vol. 37 No. 1, pp. 113-36.

Roulstone, D.T. (2003), “Analyst following and market liquidity”, Contemporary AccountingResearch, Vol. 20 No. 3, pp. 551-78.

Stoll, H. (1978), “The pricing of security dealer services: an empirical analysis of NASDAQstocks”, The Journal of Finance, Vol. 33, pp. 1153-72.

Taylor, M.H. (2000), “The effects of industry specialization on auditors’ inherent risk assessmentsand confidence judgments”, Contemporary Accounting Research, Vol. 17 No. 4, pp. 693-712.

Teoh, S.H. and Wong, T.J. (1993), “Perceived auditor quality and earnings response coefficient”,The Accounting Review, Vol. 68, pp. 346-66.

Tinic, S. and West, R. (1972), “Competition and the pricing of dealer services in the over-the-countermarket”, Journal of Financial and Quantitative Analysis, Vol. 7, pp. 1707-27.

van Ness, B.F., van Ness, R.A. and Warr, R.S. (2001), “How well do adverse selection componentsmeasure adverse selection?”, Financial Management, Vol. 30 No. 3, pp. 77-98.

Wallace, A.W. (1981), “The association between municipal market measures and selectedfinancial reported practices”, Journal of Accounting Research, Vol. 19 No. 2, pp. 502-20.

Wallace, A.W. (1998), “Instructional case: is auditor selection associated with cost of capital?”,Issues in Accounting Education, Vol. 13, pp. 693-714.

Watts, R. and Zimmerman, J.L. (1986), Positive Accounting Theory, Prentice-Hall, EnglewoodCliffs, NJ.

Yohn, T. (1998), “Information asymmetry around earnings announcements”, Review ofQuantitative Finance and Accounting, Vol. 11, pp. 165-82.

Corresponding authorKimberly A. Dunn can be contacted at: kdunn@fau.edu

Auditor tenure,specialization

and asymmetry

623

To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints