audit quality attributes, client size and cost of capital

Electronic copy available at: http://ssrn.com/abstract=817286

Audit Quality Attributes, Client Size and Cost of Capital

Guy D. Fernando* Assistant Professor

Department of Accounting & Law University at Albany

1400, Washington Ave Albany, NY 12222

[email protected]

Randal J. Elder Professor

Joseph I. Lubin School of Accounting Whitman School of Management

Syracuse University 721 University Avenue

Syracuse, NY 13244-2450 [email protected]

Ahmed M. Abdel-Meguid Assistant Professor

Accounting & Auditing Department Faculty of Commerce, Ain Shams University

Cairo, Egypt

First Draft: September 2005 This version: April 2008

*Corresponding author. We acknowledge the comments made by the seminar participants at Syracuse University and at the AAA Annual meeting 2006. We thank Thomson Financial for providing earnings per share forecast data, available through the Institutional Brokers Estimate System at an academic rate. These data have been provided as part of a broad academic program to encourage earnings expectations research. All errors are our own.

Electronic copy available at: http://ssrn.com/abstract=817286

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Abstract

We investigate the effects of audit quality attributes related to the auditor and the auditor-client relationship on the firm’s cost of capital. We further examine whether these effects differ according to client size. We focus on two auditor characteristics; auditor

size and auditor industry specialization and two auditor-client relationship characteristics; auditor tenure and the auditor’s opinion. We use the client firm’s cost of capital as a proxy for the degree to which the market values these quality attributes.

Auditor size, industry specialization, tenure and type of auditor opinion are important determinants of perceived audit quality. The first three characteristics are negatively related to the cost of capital. We also find that that the client firm’s cost of capital increases if the auditor issues any opinion other than a clean opinion. These results are driven by small clients, suggesting audit quality attributes are highly appreciated for smaller clients.

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I. Introduction

We investigate the effects of audit quality attributes related to the auditor and the

auditor-client relationship on the firm’s cost of capital. We further examine whether these

effects differ according to client size. We focus on two auditor characteristics; auditor

size and auditor industry specialization and two auditor-client relationship

characteristics; auditor tenure and the auditor’s opinion. We use the client firm’s cost of

capital as a proxy for the degree to which the market values these quality attributes.

The auditor provides “reasonable assurance” that the financial statements are

free from “material misstatements”. Auditing dilutes the adverse effects of the separation

of ownership and control (Jensen and Meckling 1976) by reducing the information

asymmetry between users of financial statements (e.g. investors) and its preparers. Thus

auditing is a means of reducing information risk for users of financial statements. This

risk reduction should be matched by a reduction in the cost of capital for the firm (Leuz

and Verrecchia 2005). The demand for auditing in capital markets can be analyzed from

three different perspectives (i.e. auditing roles); a monitoring role, an information role

and an insurance role (Wallace 1980). How the auditor fulfils these roles determines the

level of audit quality.

DeAngelo (1981) posits that BigX1 auditors provide higher audit quality than

non-BigX firms. Audit quality is defined as the joint probability that the auditor will (1)

detect a “breach” in the client’s accounting system, and (2) is willing to report the breach.

It is widely accepted in the literature that BigX auditors provide, or are perceived to

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provide, higher levels of audit quality (e.g., Teoh and Wong 1993; Becker, Defond,

Jiambalvo and Subramanyam 1998; Francis, Maydew and Sparks 1999).

Khurana and Raman (2004) (hereafter KR) show that clients of BigX auditors

have significantly lower cost of capital compared to clients of non-BigX auditors in the

US but not in other Anglo-American countries. They argue that the perception of BigX

performing higher quality audits than non-BigX is a function of the litigation

environment. Their conclusion is that the threat of litigation is a stronger driver than

reputation behind perceived audit quality, proxied by cost of capital. This conclusion

implies that the investing public primarily perceives audit quality in terms of the BigX

auditor’s “deep pockets”. Thus their paper primarily studies the firm size audit quality

attribute from an insurance role perspective. This role is magnified in a more litigious

environment like the US, but may be of less importance to investors in other

environments (i.e., UK, Australia or Canada).

The main purpose of the multi-country analysis used by KR was to examine how

litigation environments moderate the effect of auditor size on perceived audit quality. KR

analyzed audit quality from a litigation exposure versus reputation perspective. We

extend their work and attempt to disentangle insurance from non-insurance roles of

auditing. We argue that the monitoring and information roles of auditing are related to the

technical aspects of auditing. These technical aspects correspond to both the auditor’s

competence and independence as described by DeAngelo (1981). On the other hand the

insurance role is related to the compensatory aspects of the audit. Thus the latter role is

merely a function of the financial resources of the auditor, regardless of technical

qualities.

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We also analyze the type of firms that may benefit from having a higher quality

audit. The effect of firm size in the available information environment of the firm is well

documented (e.g., Atiase 1985; Bamber 1987; Llorente, Michaely; Saar and Wang 2002).

These papers show that larger firms have a better information environment compared to

smaller firms. Hence, the incremental value of audit quality will be greater for small

clients than for large clients. We analyze the KR results to examine whether client size

plays a role in the effect of audit quality on cost of capital.

There are two aspects of this study that are intended to bring the monitoring and

information roles of auditing to the forefront. First, the sample is restricted to US clients

only and thus the litigation environment is held constant. Second, three auditing

characteristics are used which unlike auditor size have no direct insurance effect; industry

specialization, auditor tenure and auditor opinion.

Our study generates two sets of results. First, auditor size, industry specialization,

tenure, and type of auditor opinion are important determinants of perceived audit quality.

The first three characteristics are negatively related to the cost of capital. We also find

that that the client firm’s cost of capital increases if the auditor issues any opinion other

than a clean opinion. Second, we find that these results are driven by small clients,

suggesting audit quality attributes are highly appreciated for smaller clients.

Thus our paper contributes to the literature in four ways. First, we show that the

effect of audit quality attributes on cost of capital of client firms is limited to small firms.

This suggests that the market perceives audit quality to be more important for smaller

firms than for bigger ones. Second, we provide empirical evidence that quality

differentials among BigX auditors based on specialization impact the cost of capital.

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Third, we highlight the non-insurance roles of auditing; information and monitoring and

their effects on audit quality. Fourth, we examine the effects of several quality attributes

(i.e. industry specialization, tenure, and type of opinion) on audit quality within a new

setting, the cost of capital.

The remainder of the paper is organized as follows. Section II presents the

literature review and hypotheses development. Section III describes the data and

research design. Section IV reports the empirical results. Section V is the summary and

conclusion.

II. Literature Review and Hypotheses Development.

The separation of ownership and control of the firm leads to information

asymmetries between owners and managers of the firm (Jensen and Meckling 1976). In

addition rational expectation theory and agency theory suggest that the principals

(investors) and agents (managers) have divergent interests resulting in a moral hazard

problem. Wallace (1980) argues that investors price-protect their investments resulting in

a reduced stock price, which implies a higher cost of capital. Auditing curtails the extent

of such price protection by playing three roles - monitoring, information and insurance

(Wallace 1980).

The Monitoring Role of Auditing and Cost of Capital

The first role for auditing suggests that auditing will ensure better use of resources

entrusted to the agent by the principal. Jensen and Meckling (1976) state that one

component of the agency cost is the cost of monitoring the managers. Chow (1982)

conducted a study on publicly traded companies in 1926.2 He finds that the probability of

engaging an auditor is increasing in the degree of conflict among the stakeholders of the

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company. In addition, Francis and Wilson (1988) show that the demand for quality

differentiated audits, proxied by auditing firm size, is positively related to the company’s

agency costs. Thus it is evident that auditing is used to dilute agency problems by

assuming the role of a monitor. The perceived effectiveness of the monitoring role of

auditing would be reflected in the client’s cost of capital.

The Informational Role of Auditing and Cost of Capital

As previously mentioned, one of the adverse effects of the separation of

ownership and control is information asymmetries between managers and investors. As

information asymmetries are reduced via more transparent and reliable information, the

risk assumed by investors should decline. This would ultimately lead to less price

protection.

Barry and Brown (1985) suggest that better information can reduce the rate of

return demanded by investors by reducing estimation risk. Merton (1987) suggests that

better information reduces cost of capital by improving risk sharing. Leuz and Verrecchia

(2005) state that better information will lead to lower cost of capital due to better

alignment between the firm’s investment opportunities and its investment choices. Easley

and O’Hara (2004) develop a multi-asset rational expectations model which shows that

investors will demand higher returns to hold stock in firms with greater information

asymmetry, thus increasing the cost of capital to such firms. The model also shows that

cost of capital decreases with the quality and quantity of information available. Botosan

(1997), Botosan, Plumlee and Xie (2004) and Francis et al. (2004) provide empirical

evidence to show that better information results in lower cost of capital.

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The effectiveness of the auditor in reducing information asymmetries and the

associated risks is a function of audit quality. Teoh and Wong (1993) show that clients of

BigX auditors have higher earnings response coefficients (ERCs) than those other

auditors. More recently, Balsam, Krishnan and Yang (2003) find that clients which

engage industry specialists have higher ERCs than those engaging non-specialists. This

empirical evidence suggests that investors’ reaction to information (i.e. earnings) is

stronger when the client is associated with a higher quality auditor (i.e. BigX, industry

specialist). Thus the information role of the audit indicates that auditing will reduce risk

through better quality information. In this study the reduction of risk is manifested in a

lower cost of capital.

The Insurance Role of Auditing and Cost of Capital

The insurance role postulates that risk to investors will be reduced because the

auditor provides another source of compensation in the event of failure of the firm.

Menon & Williams (1994) argue that this insurance factor is built into the share price of

the client firm. They examined the impact of the bankruptcy of Laventhol & Horwath

(L&H) on the stock prices of the audit firm’s clients. They document a significant

negative effect on client stock prices and attribute this to the deterioration of the

insurance capacity of L&H. Within the context of cost of capital, KR document a

significant negative relation between engaging a Big4 auditor and the cost of capital of

the client. This negative relation was only found in the US but not in other Anglo-

American countries. They attribute this finding to differences in litigation exposure

between the US and other countries.

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The first two roles of the audit indicate that higher audit quality will lead to lower

cost of capital for the client firm (technical aspect of auditing). The third role of the audit

indicates that the more financial resources the auditor has, the lower the cost of capital of

the client firm (compensatory aspect of auditing). Thus we examine how the three roles

of auditing suggested by Wallace (1980) are captured the previously mentioned audit

quality attributes.

The three roles put forward by Wallace (1980), combined with literature

establishing the connection between information quality and risk, indicate that investors

will value a higher quality audit over a lower quality audit. Balvers, McDonald and

Miller (1988) and Beatty (1989) document a negative relation between underpricing in

IPOs and engaging a BigX auditor. Pitman and Fortin (2004) show that clients of BigX

auditors have a lower cost of interest immediately after an IPO compared to clients of

non BigX auditors. Big X auditors have the financial resources to better fulfill their

monitoring, information and insurance roles than non-Big X auditors. In the context of

this study this better fulfillment of these roles is expected to be reflected in a reduced cost

of capital.

Auditor Quality Attributes

We examine the effects of two auditor characteristics; auditor size and auditor

industry specialization on the client’s cost of capital.

Auditor Size:

DeAngelo (1981) argues that BigX auditors provide better quality audits than

non-BigX auditors, which is supported by extensive subsequent empirical research. Teoh

and Wong (1993) find higher ERCs for clients audited by BigX firms compared to those

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audited by non-BigX firms. Becker et al. (1998) and Francis et al. (1999) demonstrate

that BigX auditors are better at constraining client earnings management compared to

non-BigX auditors; they find that clients of non-BigX auditors have higher levels of

discretionary accruals. Elder, Zhou and Chen (2004) show the same results in the context

of commercial banks. Thus consistent with the results of KR we develop our first

hypothesis (stated in the alternative form):

HYPOTHESIS 1: There is a negative relation between the auditor’s size and the client’s

cost of capital.

Auditor Industry Specialization:

The cost of capital of a client audited by a BigX auditor could be lower due to the

monitoring role, the information role, the insurance role or a combination of all three

roles. Disentangling the effects of such roles is a difficult task, which requires very

unique circumstances. For example, Menon and Williams (1994) were able to isolate the

insurance role of auditing within the context of an auditor’s bankruptcy. Another

approach would be to identify an audit quality attribute that is unlikely to be related to a

specific auditing role. We argue that industry specialization is not related to the insurance

capacity of the auditor. On the other hand, specialization would be related to the

monitoring and information roles of the auditor.

Casterella, Francis, Lewis and Walker (2004) describe auditor industry

specialization as “A differentiation strategy whose purpose is to provide auditors with a

sustainable competitive advantage over nonspecialists.” Krishnan (2003) and Balsam et

al. (2003) find that there is less earnings management in clients of specialist BigX

auditors compared to non-specialist BigX auditors by analyzing the discretionary accruals

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of client firms. Dunn and Mayhew (2004) find that clients of specialist BigX auditors

have significantly better AIMR (Association for Investment Management and Research)

rankings than clients of non-specialist BigX auditors, signifying that the former have

better quality financial reports.

There is also empirical evidence from the governmental sector that supports the

argument that industry specialization is an important audit quality attribute. Deis and

Giroux (1992) document a negative relationship between auditor specialization and

quality control review outcomes. In a similar study, O’Keefe, King and Gaver (1994) find

a negative relation between auditor specialization and Generally Accepted Auditing

Standards (GAAS) violations.

The auditor’s industry specialization implies extensive knowledge of the client’s

business environment, its industry accounting practices and potential illegitimate

accounting practices. Such knowledge and expertise would be perceived by investors as

an information risk reducing factor. Therefore, according to Leuz and Verrecchia (2005)

it should ultimately result in lower cost of capital for the clients of specialist BigX

auditors, compared to non-specialist BigX auditors.

Audit firm size, and not industry specialization, determines the audit firm’s ability

to pay compensation in case of a client failure. On the other hand, a negative relation

between engaging a specialist BigX auditor and the client’s cost of capital is indicative of

non-insurance roles. Therefore we state our second hypothesis (in the alternative form)

as:

HYPOTHESIS 2: There is a negative relation between the auditor’s industry specialization

and the client’s cost of capital.

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Auditor-Client Relationship Quality Attributes

We also examine the effects of the auditor-client relationship on the client’s cost

of capital. The two variables examined are auditor tenure and the auditor’s opinion.

Auditor Tenure:

There has been a perception among regulatory authorities that auditors, over time,

will develop stronger relationships with clients, resulting in a deterioration of audit

quality. This has lead to the imposition of mandatory auditor retirement in some countries

(See Geiger and Raghunandan (2002) for a discussion on this issue). However, academic

research into this area finds contrary results. Research has found that there are more audit

failures in the early years of the auditor-client relationship (Geiger and Raghunandan

2002) and shorter audit tenure is associated with lower earnings quality (Johnson,

Khurana and Reynolds 2002; Myers, Myers, and Omer 2003). Investors, too apparently

acknowledge this fact and reward long auditor-client relationships with lower cost of debt

(Mansi, Maxwell and Miller 2004) and higher earnings response coefficients (Ghosh and

Moon 2005).

If longer audit tenure results in a higher quality audit, this should also reduce

information risk and result in a lower cost of capital. Our third hypothesis is stated as (in

the alternative form):

HYPOTHESIS 3: There is a negative relation between the auditor’s tenure and the client’s

cost of capital

Auditor’s Opinion:

Statement on Auditing Standards (SAS) No. 59, "`The Auditor's Consideration of

an Entity's Ability to Continue as a Going Concern"', requires auditors to evaluate

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whether substantial doubt exists about an audit client's ability to continue as a going

concern. It stresses that this information is an essential signal for users of financial

statements. Prior research has indicated that the issuance of a going concern opinion by

the auditor is likely to be preceded by increasing probability of bankruptcy (McKeown,

Mutchler and Hopwood 1991).

Chappell, Ota, Berryman, Elo, Preston and Jones (1996) compares 68 audit

reports that disclose going concern uncertainties during the period 1979 to 1988 with 86

similar financially distressed firms that receive unqualified opinions during the same

period. He finds that firms that receive a going concern opinion had negative abnormal

returns whereas the financially distressed firms that did not receive a going concern

report had positive abnormal returns over a 5 day window around the release of the going

concern opinion. Carlson, Glezen and Benefield (1998) confirm these results.

Furthermore, Firth (1980) documents that a going concern opinion impairs the client’s

credit rating. Existing research indicates that a going concern opinion increases the risk to

the investors in the client firm, which should result in increased cost of capital. Geiger

and Raghunadhan (2001) explicitly speculate that going concern opinion might result in

increased cost of capital to the client. Hence, we state our fourth hypothesis as (in the

alternative form):

HYPOTHESIS 4: There is a positive relation between the auditor’s issuance of a going

concern opinion and the client’s cost of capital.

The Effect of Client Size on the Monitoring and Informational Roles of Auditing

Small firms have poorer information environments compared to large firms

(Atiase 1985; Bamber 1987; Llorente et al. 2002). Larger firms have higher analyst

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following (Christensen, Smith and Stuerke 2004; O’Brien and Bhushan 1990) and higher

percentages of institutional ownership (O’Brien and Bhushan 1990). Furthermore, there

is more media attention to larger firms. Thus smaller firms are less visible to their

stakeholders, implying less information and weaker monitoring. Such a setting is

conducive to a more pronounced role for the information and monitor roles of auditing.

Hence, the marginal effects of higher audit quality will be greater for small firms.

Casterella et al. (2004) show that Big 6 specialist auditors charge a fee premium

to small clients, while large clients enjoy a fee discount. They justify this result by

arguing that larger clients have stronger bargaining power over their auditors than smaller

clients. However we interpret these results from a different perspective. First, industry

specialization, unlike auditor size, has no “deep pockets” effect. In other words,

specialization is not a manifestation of the insurance role of auditing. Thus only the

monitoring and informational roles are driving the results. Second, this fee differential

could be driven by the value differential of specialized auditors for small versus large

clients. Put simply, specialization, as an audit quality attribute, is more important for

smaller clients than for larger ones.

We extend the above reasoning and make the general argument that the

monitoring and informational roles of auditing manifested in observable audit quality

attributes are more appreciated by the market if they are perceived to play a greater

value-adding role (i.e. for smaller audit clients).

Although the three roles of auditing; monitoring, informational, and insurance, are

reflected in auditor size, we develop HYPOTHESIS 5A (in the alternative form) as a further

extension of KR.

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HYPOTHESIS 5A:The negative relation between auditor size and the client’s cost of capital

is stronger for smaller clients.

Finally we develop HYPOTHESES 5B, 5C, and 5D (in the alternative forms) based on

our arguments regarding the general effect of client size on the relation between the

remaining audit quality attributes and cost of capital.

HYPOTHESIS 5B: The negative relation between auditor industry specialization and the

client’s cost of capital is stronger for smaller clients.

HYPOTHESIS 5C: The negative relation between auditor tenure and the client’s cost of

capital is stronger for smaller clients.

HYPOTHESIS 5D: The positive relation between the auditor’s issuance of a going concern

opinion and the client’s cost of capital is stronger for smaller clients.

III. Data & Research Design

Data

We use the data available on Compustat, CRSP and IBES databases for the years

1990– 2004. The data for all firms from the 3 data bases are merged together. Financial

firms (SIC code 6000 – 6999) are omitted. If a firm year lacks analyst forecast data for

year 1, that firm year is omitted. If the analyst forecast for year 2 is missing, we use the

forecast for year 1 and the consensus analyst growth forecast to calculate the forecast for

year 2. Furthermore, in accordance with the PEG approach of calculating the ex ante cost

of capital (Easton 2004), if the analyst forecast for year 2 is less than that for year 1, we

omit such data. Finally, to eliminate the undue influence of outliers we winsorize the 1st

and the 99th percentiles of all the variables (excluding those variables that are transformed

into their natural log values). This leaves us with a total of 18,955 firm years of data, of

which 18,116 are for BigX clients, and 839 are for non-BigX clients. Of these

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observations, 9,472 firm years are for large clients and 9,483 firm years are for small

clients (see table 1 for more details of the data distribution).

Cost of Capital

We use the methodology adopted by KR in our analysis. As per KR, we calculate the

ex-ante cost of capital using the PEG approach developed by Easton (2004).

0

12

P

epsepsre

−=

re = ex-ante cost of capital eps1 = one year ahead mean analyst forecast per share eps2 = two year ahead mean analyst forecast per share P0 = fiscal year end price per share

Industry Specialization

We calculate specialization based on the percentage of total client sales an auditor

audits in a particular industry during a particular year (Krishnan 2003). Hence

specialization is measured as:

∑∑

∑

= =

=

=k ik

ik

I

i

J

j

ijk

J

j

ijk

sClientSale

sClientSale

tionSpecializa

1 1

1

where:

ClientSales – Client sales revenue i – denotes audit firm j – denotes client firms k – denotes industry category Jik – number of clients of the ith auditor in the kth industry. Ik – number of audit firms in the kth industry.

Audit Tenure

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Audit tenure is calculated from a Compustat database containing data as far back

as 1970. Therefore, 1970 is treated as Year1 and the tenure counter is set to 1 in 1970 for

all firms. If the auditor is unchanged in the next year, the counter increases to 2 and in the

third year to 3 and so on. If there is an auditor change, the counter is reset to 1. This

follows the method used by Ghosh and Moon (2005) although their start date is 1982. If

longer tenure results in a lower cost of capital, the coefficient for Tenure should be

significantly negative.

Audit Opinion

Auditor opinion is obtained from Compustat data149. The Compustat database

lists 6 types of auditor opinions numbered 0 – 5 of which opinion 1 is unqualified.

Following the methodology outlined in Larcker and Richardson (2004) we define the

variable OP which denotes auditor opinion. If auditor opinion = 1 (unqualified opinion)

in the Compustat database, OP is coded as 0 and 1 otherwise.3 Since we expect firms

receiving any opinion other than an unqualified opinion to have a higher cost of capital,

we expect OP to have a positive coefficient.

Large Firms and Small Firms

We calculate the median market value of equity (MVE) for each year, and firms,

which have a MVE for the year that is lower than the median MVE for that year are

classified as small firms. Firms with an MVE for the year that is larger than the median

MVE are classified as large firms.

Regression Models

We use regression model (1) to examine HYPOTHESES 1 – 4. This model uses the

same control variables as KR. The independent variable is AQ (AQ is a set containing the

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elements BIGX, SPX, TENURE, and OP) a measure of audit quality.4 We run regression

model (1) four times, with AQ representing OP, Tenure, BigX and SPX respectively. We

expect BIGX, SPX, TENURE, and OP to have negative, negative, negative and positive

coefficients respectively.

Model (1):

εαα

αααααααα

+++

+++++++=

INDYR

AQSTDEVGRWLNBMLNSIZELNLEVBETACoEC

ji

76543210

Where:

CoEC = ex ante Cost of equity capital calculated using the PEG approach specified

by Easton (2004).

BETA = Stock beta calculated over 36 months ending in the month of issue of

forecast, representing systematic risk.

LNLEV = Natural log of debt to assets ratio calculated as

((Data # 34 + Data # 9) / Data # 6).

LNSIZE = Natural log of market value of equity. (data # 25 * data # 199)

LNBM = Natural log of book-to-market ratio (data # 60 / data # 25 * data # 199)

GRW = Annual growth calculated as the difference between the Year 2 forecast

and the Year 1 forecast scaled by the Year 1 forecast = (eps2 - eps1) / eps1

STDEV = Standard deviation of analyst forecasts (obtained from IBES).

AQ = Audit Quality Attribute:

BIGX: Categorical variable where BigX = 1 if the auditor is a BigX firm

and 0 otherwise.

SPX is a dichotomous variable, which is equal to 1 if Specialization > 20

percent, and 0 otherwise.

TENURE: Audit Tenure obtained from Compustat.

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OP : Categorical variable where OP = 1 if there is a modified opinion and

0 otherwise were a modified opinion is any opinion coded other than 1 on

Compustat data149 (Larcker and Richardson 2004). .

YR = Dummy variables indicating the year from 1990 to 2004.

IND = Dummy variable indicating the industry category of the client firm. We

use the classification according to Barth, Beaver, Hand and Landsman

(1999).

We run regressions for the overall sample first. Then, we isolate the effect of

client size by running the regressions for large firms and small firms separately, as

specified in Casterella et al (2004). This enables us to test HYPOTHESES 5A – 5D.

Next, we run regression model (2) below, on the overall sample with all four audit

variables. This enables us to examine if there any confounding interactions within the

four audit variables, that might dilute the effect of one or all of the variables. This further

enables us to examine if specialization has an effect on cost of capital of client firms,

over and above that of the BigX effect. The regressions are run for the overall sample

first (to test HYPOTHESES 1 – 4), and then for the large firms and small firms separately

(to test HYPOTHESES 5A – 5D).

Model (2):

εαααααα

ααααααα

+++++++

++++++=

INDYROPTENURESPXBIGX

STDEVGRWLNBMLNSIZELNLEVBETACoEC

ji10987

6543210

The control variables we use are the same as those used in KR. Consistent with

KR, we control for other explanatory factors as follows. We include controls for year and

industry because cost of capital can change with the year and industry. Cost of capital is

expected to be positively associated with systematic risk, proxied by beta. As leverage

increases, the risk associated with the firm increases, so we include leverage and expect a

19

positive relationship with the cost of capital. Size is expected to have a negative

relationship to risk; hence we include size in our model and expect a negative relationship

with cost of capital. As the book-to-market ratio increases, we expect risk to increase.

Hence, we expect a positive relationship between book-to-market ratio and cost of

capital. Expected earnings due to growth are supposed to be riskier than steady state

earnings. Hence, we expect a positive relationship between growth and cost of capital As

the standard deviation of analyst forecasts increase, the information environment

associated with the firm decreases, and therefore cost of capital will increase..

IV. Empirical Results.

Descriptive statistics for the variables used in our analysis are given in Table 1.

Table 1 Panel A compares the cost of capital of BigX firms with non-BigX firms and

specialist firms with non-specialist BigX firms across the three data samples (i.e. overall

sample of firms, large firms only and small firms only). Amongst the overall sample of

firms, the mean of the cost of capital of 13.25 percent for firms audited by non-BigX

auditors is significantly higher than the mean cost of capital for firms audited by BigX

auditors of 11.11 percent (the difference of 2.14 percent is significant at t=7.79). This

univariate result supports our first hypothesis, and is consistent with KR who find cost of

capital to be 13.2 percent and 11.9 percent for BigX and non-BigX firms respectively.

The same pattern can be observed for the cost of capital of clients of specialist auditors

versus clients of non-specialist auditors. Specialist auditor clients have a cost of capital of

10.87 percent which is significantly less than that of non-specialist auditor clients at

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11.33 percent. The difference of 0.46 percent is significant with a t-statistic of 4.91,

supporting our second hypothesis.

The univariate statistics of Panel A also indicate that large clients of non-BigX

firms have a lower cost of capital compared to clients of BigX firms, although the

difference is not significant. Large clients of non-specialist auditors have a lower cost of

capital compared to specialist clients; the difference is marginally significant. However,

the results for small firms show that small BigX clients have a significantly lower cost of

capital compared to small non-BigX clients (t-statistic of 4.11) and small specialist BigX

clients have a significantly lower cost of capital compared to small non-specialist BigX

clients (t-statistic of 2.77). This provides support for hypotheses 5A and 5B and shows that

audit quality as measured by auditor size or auditor industry specialization is more

important for small firms.

Table 1 panel B provides descriptive statistics for the dependent variable and

independent variables. The mean cost of capital for the entire sample is 11.2 percent. The

control variables are similar to the figures obtained in KR. Tenure, which is not measured

in KR has a mean of 10.63 years and Opinion has a mean of 0.291. Since Opinion is a

categorical variable, this indicates that 29.1 percent of the firms receive an opinion other

than a standard unqualified opinion as defined by Larcker and Richardson (2004).

Table 2 presents the correlation matrix for variables used in the model. Both

Pearson correlations and Spearman Rank correlations are run on the entire sample,

containing both BigX clients and non-BigX clients.

Insert Table 1 ere

21

The variable of interest, cost of capital (CoEC) is significantly related to all the

explanatory variables, and has the expected signs. It is negatively correlated with size and

positively correlated with the remaining control variables. CoEC is positively and

significantly correlated to OP and negatively and significantly correlated to Tenure, BigX

and SPX. These results provide further support for HYPOTHESES 1 – 4. The results are

robust for both Pearson correlation coefficients and Spearman rank correlation

coefficients.

Table 3 presents the regression of the cost of capital against controls and the audit

quality (AQ) variable of interest BIGX as shown in model (1) We do not report the YR

(year) and IND (industry) dummies for brevity.

Panel A of Table 3 is a replication of the KR results. All explanatory variables are

significant and have the expected signs. As evidence of hypothesis 1, we test if the

inclusion of a categorical variable signifying that the auditor is a BigX firm has any effect

on the cost of capital. The categorical variable BigX has a coefficient of -0.0068, which is

highly significant with a t-stat of -2.77 (p-value < 0.01). This result is consistent with KR

and hypothesis 1, and indicates that the client’s cost of capital is significantly lower if the

client uses a BigX audit firm.

However, Panels B and C of Table 3 show how the above results are driven by

firm size. When the sample is split into large client firms (Panel B) and small client firms

Insert Table 2 ere

Insert Table 3 ere

22

(Panel C), the behavior of the BigX variable show opposite effects. For large firms, Panel

B shows that the BigX variable is positive, although insignificant. For small firms, BigX

is negative and significant with a coefficient of -0.0057 and a t-statistic of -1.97. These

two results are consistent with HYPOTHESIS 5A, and support our argument that the

marginal impact of audit quality attributes is significant for smaller firms due to their

inherently poorer information environment.

In our second hypothesis we argue that engaging a specialist auditor will result in

a further reduction of the cost of capital; in addition to the reduction associated with

using a BigX auditor. We expect that the market will recognize the industry specific

expertise of the specialist auditor, and expect a higher quality audit, resulting in a lower

ex-ante cost of capital for the client. Hence, we expect SPX to be significant and

negative. To test this hypothesis, we run regression model given by model (1) where AQ

is SPX. The results are shown in Table 4.

Table 4 – Panel A shows the results for the entire sample of firms, Panel B shows

the results for the sub-sample of large firms, and Panel C shows the results for the sub-

sample of small firms. For all 3 panels, the control variables have the expected signs and

significance. Consistent with HYPOTHESIS 2, Panel A shows that for the entire sample,

SPX is negative and significant with a coefficient of -0.0015 and a t-statistic of -1.60

(one-tailed p-value of 0.055). However, this result masks a variation within the sample,

which is shown by the results in Panels B and C. Panel B shows that SPX variable is

insignificant for large firms, albeit with the expected sign. On the other hand, the SPX

Insert Table 4 ere

23

variable is negative and significant for small firms. The coefficient for SPX is -0.003 (t-

stat = -2.06; one-tailed p-value = 0.0197). This shows that small firms can further reduce

their cost of capital by selecting a specialist BigX auditor.5 These results are consistent

with HYPOTHESIS 5b.

KR asserts that the reduction in cost of capital by employing a BigX auditor is

due to litigation exposure. However, our results indicate that in addition to the insurance

role, the other two roles of auditing also have an effect. If the reduction in cost of capital

was a result of insurance due to the larger financial resources of the BigX auditors, there

should not be a variation between the BigX auditors. However our results show that even

amongst BigX auditors, where the insurance effect should be constant, use of a specialist

auditor results in a lower cost of capital.

We test HYPOTHESIS 3 by running regression model (1) where AQ represents

Tenure. Tenure represents the length of the auditor-client relationship. The results are

shown in Table 5.

Panel A show the results for the entire sample, Panel B show the results for large

firms and Panel C show the results for small firms. Panel A show that as expected in

Hypothesis 3, Tenure has a coefficient of -0.0003 with a t-statistic of -4.39 which is

highly significant. This indicates that as the length of the auditor client relationship

increases, the cost of capital to the firm declines, consistent with HYPOTHESIS 3.

However, Panels B and C show that the results in Panel A are driven by the small firms

in the sample. The results for large firms shown in Panel B demonstrate that auditor

Insert Table 5

24

tenure does not have a significant impact on cost of capital. As reported in Panel C, for

small firms the coefficient for tenure is -0.0006 with a t-statistic of -5.42. This shows that

increasing auditor tenure results in a decline in the cost of capital only for small firms,

consistent with Hypothesis 5c. All the control variables have the expected signs and

significances.

To test HYPOTHESIS 4, we run regression model (1) with AQ representing auditor

opinion as defined by OP. The results are shown in Table 6.

The results follow a pattern similar to Tables 3, 4 and 5. Panel A shows the results

for the whole sample, and show that OP is positive and significant (coefficient = 0.0033;

t-statistic = 3.60), demonstrating that in accordance with HYPOTHESIS 4, the cost of

capital increases for firms which receive an opinion other than a standard unqualified

opinion. However, these results are driven by small firms in the sample. Panel B shows

that OP is insignificant for large firms, while it is positive and significant for small firms

(coefficient = 0.0047; t-statistic = 2.95). This supports HYPOTHESIS 5d, and indicates that

the audit opinion impacts the cost of capital for small firms more than it does for big

firms. The control variables again have the expected signs.

Robustness Checks

Dilution effects when all independent variables are included together.

We test if the independent variables denoting either audit quality or the auditor-

client relationships are independent of each other. To test this we run model (2), which

Insert Table 6

25

includes all the independent variables, BIGX, SPX, TENURE, and OP simultaneously.

The results are given in table 7.

Panel A of Table 7 shows the results for the entire sample. BIGX, TENURE, and

OP, have negative, negative, and positive coefficients respectively. All coefficients are

significant, confirming the results shown in Tables 3, 5 and 6. SPX has a negative

coefficient confirming the results of Table 4, but the t-statistic of -1.27 is only marginally

significant. Confirming the results of Tables 3 – 6, Panel B of Table 7 shows that for

large firms, all the audit quality variables are insignificant. Panel C of table 7 show

results consistent with Tables 3 – 6. However, for small firms, SPX is significantly

negative while the BigX coefficient, although negative, is no longer significant,

indicating that investors value the industry specialization of BigX auditors.

The average market value of equity for the entire sample of small firms is $

265,195,000.6 This indicates that by choosing a non-specialist BigX auditor, the average

small firm will save (265,195,000 x 0.00347) $901,663 annually. If the same firm chooses

a specialist BigX auditor, it will save an additional (265,195,000 x 0.00288) $742,546

annually in reduced cost of capital. We conclude that these savings accrue as a result of

the market perception of the greater quality of audit that is provided by BigX auditors,

and even higher quality of audit provided by specialist BigX auditors.

SPX cut-off levels

We test whether the results for the dichotomous specialist variable (SPX) are

sensitive to the cutoff level. We find that the results hold for cutoffs of 10 percent, 15

Insert Table 7

26

percent, 25 percent and 30 percent, and are marginally significant using a 35 percent

cutoff.

Different measure of Audit Opinion

As explained in endnote 3, OP is a noisy measure. We obtain a different measure

of the auditor’s opinion from the AuditAnalytics database. This database is available

from the year 2000 onwards and indicates whether the company received a going concern

opinion. Combining the AuditAnalytics database with the data from COMPUSTAT,

CRSP and IBES for the years 2000 – 2005 yields a dataset with 5,533 firm years of data.

Of the 5,533 data items, the auditors express a going concern for 33 data items.

Untabulated results show that using going concern opinions does not qualitatively change

the results observed in Tables 6 and 7.

Alternative Size Classifications

We test alternative size cutoffs for classifying firms as small firms. We originally

classified firms as small if they are below the median market value of equity (MVE) for

each year. When we classify small firms as those in the lowest third of market value of

equity for each year, the SPX variable is not quite marginally significant, and the

significance of the BIGX, TENURE, and OP variables remains unchanged. However, all

variables have the expected signs. When we class small firms as those in the lowest

quartile of market value of equity, OP and Tenure remain significant, but BigX and SPX

are no longer significant.9

V. Summary and Conclusion

Khurana and Raman (2004) examine the effect of engaging a BigX auditor on the

cost of capital. They find a significant negative relation between audit firm size and cost

27

of capital in the highly litigious US environment, but not in less litigious environments

(i.e., UK, Australia and Canada). This result is indicative primarily of the insurance role

of auditing.

We extend the work of KR in several ways. First we examine how the market

perceives the monitoring and informational roles of auditing. These roles are manifested

in observable audit quality attributes. We use two set of attributes; characteristics of the

auditor (auditor size and auditor industry specialization) and characteristics of the

auditor-client relationship (auditor tenure and auditor opinion). The market perception is

captured via the client’s cost of capital.

We are able to replicate the KR findings regarding the effect of audit firm size

and cost of capital; BigX audit clients enjoy a lower cost of capital compared to non-

BigX clients. We highlight the monitoring and information roles of the audit. We find a

significantly negative relation between the auditor’s industry specialization and the firm’s

cost of capital. For the small firms in our sample, we find that employing a specialist

BigX auditor will save the average firm $ 742,546 in cost of capital, compared to

employing a non-specialist BigX auditor. We also find the auditor tenure and type of

audit opinion affect cost of capital. Since specialization, tenure, and opinion type are

unrelated to the insurance role of auditing, we argue that these audit quality attributes are

manifestations of the monitoring and informational roles of auditing.

Finally we empirically show that the market puts more weight on the above

mentioned audit quality attributes, including auditor size, for smaller firms rather than

larger ones. This suggests that the market perceives that the insurance, monitoring, and

informational roles of auditing are more pronounced for smaller, less visible firms.

28

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32

Table 1

Descriptive Statistics

Panel A – Differences in CoEC means across sample partitions (a)

(a) T-values are presented between brackets [ ] while P-values are presented between parentheses ( ). CoEC: ex ante Cost of equity Capital calculated using

the PEG approach specified by Easton (2004). BIGX: Categorical variable = 1 if a client of an BigX auditor and 0 otherwise SPX: Categorical variable = 1 if the company is a client of a BigX auditor which audits more than 20% of the sales of the client’s industry, and 0 otherwise.

Partition

Overall Sample Large Firms Small Firms

BigX Non-BigX CoEC Mean Difference



Number of Observations

18116

839

-0.0214 [-7.79] (0.00)

9260

212

0.0021 [0.74] (0.23)

8856

627

-0.0138 [-4.11] (0.00)

Mean 0.1111 0.1325 0.0892

0.0870

0.1341

0.1479

Partition SPX

Non-SPX

-0.0046 [-4.91] (0.00)

SPX

Non-SPX

0.0013 [1.41] ( 0.08)

SPX

Non-SPX

-0.0042 [-2.77] (0.00)

Number of Observations

8502

9614

4675

4585

3827

5029

Mean 0.1087

0.1133

0.0898

0.0885

0.1317

0.1359

33

Panel B – Descriptive statistics for the overall sample (N=18,955)

Variable Mean Standard Deviation

Lower Quartile

Median Upper Quartile

CoEC 0.112 0.065 0.068 0.097

0.139

BETA 0.963

1.201

0.478

0.932

1.434

LEVER 0.247

0.165

0.112

0.240

0.360

MVE 3361.455

8784.841

197.922

658.490

2236.436

BM 1.534

0.312

1.299

1.474

1.706

GRW 0.391

0.684

0.132

0.197

0.342

STDEV 0.078

0.103

0.020

0.040

0.090

TENURE 10.630

7.298

5.000

9.000

16.000

OP 0.291

0.454

0

0

1

CoEC: ex ante Cost of equity Capital calculated using the PEG approach specified by Easton (2004). BETA: Stock beta calculated over 36 months ending in the month of issue of forecast, representing systematic risk. LEVER: Ratio or Debt to Assets ratio calculated as [(Data # 34 + Data # 9) / Data # 6]. MVE: Market value of equity calculated as (data # 25 * data # 199). BM: Book to Market Ratio calculated as [(data # 60 / (data # 25 * data # 199)]. GRW: Annual growth calculated as the difference between the Year 2 forecast and the Year 1 forecast scaled by the Year 1 forecast. STDEV: Standard deviation of analyst forecasts obtained from IBES. TENURE: Time period the auditor was retained by its client (Data #149) OP: Categorical variable where equals 1 if there is a modified opinion and 0 otherwise where a modified opinion is any opinion coded other than 1 on Compustat data149 (Larcker and Richardson 2004).

34

Table 2

Correlation Matrix (a)

(a) Pearson (Spearman) Correlations below (above) the Diagonal. All Correlation coefficients are significant at the 5% level or less. CoEC: ex ante Cost of equity Capital calculated using the PEG approach specified by Easton (2004). BETA: Stock beta calculated over 36 months ending in the month of issue of forecast, representing systematic risk. LNLEV: Natural log of Ratio or Debt to Assets ratio calculated as [(Data # 34 + Data # 9) ÷ Data # 6]. LNSIZE: Natural log of the market-value of equity (data # 25 × data # 199). LNBM: Natural log of book-to-market ratio (data # 60 / data # 25 * data # 199).GRW: Annual growth calculated as the difference between the Year 2 forecast and the Year 1 forecast scaled by the Year 1 forecast. STDEV: Standard deviation of analyst forecasts obtained from IBES. BIGX: Categorical variable = 1 if a client of an BigX auditor and 0 otherwise SPX: Categorical variable = 1 if the company is a client of an auditor which audits more than 20% of the sales of the client’s industry, and 0 otherwise. TENURE: Time period the auditor was retained by its client (Data #149) OP: Categorical variable where equals 1 if there is a modified opinion and 0 otherwise where a modified opinion is any opinion coded other than 1 on Compustat data149 (Larcker and Richardson 2004).

Variable CoEC BETA LNLEV LNSIZE LNBM GRW STDEV BIGX SPX TENURE OP

CoEC 1 0.0797 0.0678

-0.4337

0.4547

0.4773

0.2473

-0.06805 -0.04753 -0.12513 0.0238

BETA 0.1161 1 -0.1163 0.0476 -0.0499 0.1256 0.0404 -0.00111 0.005401 -0.06326 -0.0055

LNLEV 0.1036 -0.1417 1 0.0491 0.1409 -0.0431 0.1242 0.052898 0.045846 0.088224 0.0871

LNSIZE -0.4260 0.0201 0.0232 1 -0.4830 -0.1353 0.1196 0.129151 0.155643 0.239282 0.0895

LNBM 0.4133 -0.0532 0.1535 -0.4737 1 0.0534 0.1426 -0.02219 -0.00871 -0.04757 0.0324

GRW 0.6210 0.2302 -0.0769 -0.2215 0.0159 1 0.1566 -0.0097 -0.00269 -0.07952 0.0300

STDEV 0.2382 0.0473 0.1543 0.1736 0.1638 0.0524 1 0.034788 0.032445 0.083474 0.0747

BIGX -0.0653 -0.0239 0.0432 0.1296 -0.0197 -0.0500 0.0588 1 0.194084 0.113381 -0.03665

SPX -0.0428 -0.0261 0.0675 0.1500 -0.0009 -0.0521 0.0303 0.1941 1 -0.01677 -0.06624

TENURE -0.1310 -0.0855 0.0436 0.2116 -0.0239 -0.1602 0.0867 0.1232 -0.0230 1 -0.021

OP 0.0221 -0.0173 0.0895 0.0965 0.0418 0.0018 0.0805 -0.0367 -0.0662 -0.0192 1

35

Table 3

The Effect of Auditor's Size (BIGX) on CoEC (a), (b)

εαααααααααα ++++++++++= INDYRBIGXSTDEVGRWLNBMLNSIZELNLEVBETACoEC ji76543210

Parameter

Predicted

Sign

Panel A

Full Sample Panel B

Large Firms

Panel C

Small Firms

+/- Estimate t-value Estimate t-value Estimate t-value

Intercept ? 0.2655 29.68*** 0.1454 14.32*** 0.4469 21.69***

BETA + 0.0024 5.67*** 0.0045 5.07*** 0.0024 5.06***

LNLEV + 0.0035 11.32*** 0.0030 8.02*** 0.0035 7.80***

LNSIZE - -0.0105 -27.76*** -0.0052 -12.23*** -0.0204 -19.84***

LNBM + 0.0762 25.94*** 0.0697 16.36*** 0.0667 17.42***

GRW + 0.0352 32.93*** 0.0268 15.37*** 0.0379 29.67***

STDEV + 0.1199 19.52*** 0.1231 16.03*** 0.1404 14.77***

BIGX - -0.0068 -2.77*** 0.0024 0.79 -0.0057 -1.97**

Year Dummies YES YES YES Industry Dummies YES YES YES Adjusted R2 51.81% 46.82% 47.64% N 18,955 9,472 9,483

(a) All t-statistics are Newey-West (1987) corrected. (b) ***, **, and * indicate significance at 1 percent, 5 percent and, 10 percent levels respectively (one- tailed where signs are predicted, two-tailed otherwise). CoEC: ex ante Cost of equity Capital calculated using the PEG approach specified by Easton (2004). BETA: Stock beta calculated over 36 months ending in the month of issue of forecast, representing systematic risk. LNLEV: Natural log of Ratio or Debt to Assets ratio calculated as [(Data # 34 + Data # 9) ÷ Data # 6]. LNSIZE: Natural log of the market-value of equity (data # 25 × data # 199). LNBM: Natural log of book-to-market ratio (data # 60 / data # 25 * data # 199).GRW: Annual growth calculated as the difference between the Year 2 forecast and the Year 1 forecast scaled by the Year 1 forecast. STDEV: Standard deviation of analyst forecasts obtained from IBES. BIGX: Categorical variable = 1 if a client of a BigX auditor and 0 otherwise. YR: Year dummies from dummy variables indicating the years from 1990 to 2004. IND: Industry dummies indicating the industry category of the client firm based on Barth et al (1999).

36

Table 4

The Effect of Auditor's Industry Specialization (SPX) on CoEC (a), (b)

εαααααααααα ++++++++++= INDYRSPXSTDEVGRWLNBMLNSIZELNLEVBETACoEC ji76543210

Parameter

Predicted

Sign

Panel A

Full Sample Panel B

Large Firms

Panel C

Small Firms


Intercept ? 0.2607 29.72*** 0.1472 15.29*** 0.4444 21.43***

BETA + 0.0024 5.66*** 0.0045 5.08*** 0.0024 5.04***

LNLEV + 0.0035 11.19*** 0.0030 8.08*** 0.0035 7.76***

LNSIZE - -0.0106 -27.48*** -0.0051 -12.11*** -0.0206 -19.42***

LNBM + 0.0761 25.84*** 0.0697 16.38*** 0.0665 17.27***

GRW + 0.0351 32.85*** 0.0268 15.37*** 0.0379 29.60***

STDEV + 0.1199 19.59*** 0.1231 16.05*** 0.1406 14.77***

SPX - -0.0015 -1.60* -0.0004 -0.41 -0.0030 -2.06**


(a) All t-statistics are Newey-West (1987) corrected. (b) ***, **, and * indicate significance at 1 percent, 5 percent and, 10 percent levels respectively (one-

tailed where signs are predicted, two-tailed otherwise). CoEC: ex ante Cost of equity Capital calculated using the PEG approach specified by Easton (2004). BETA: Stock beta calculated over 36 months ending in the month of issue of forecast, representing systematic risk. LNLEV: Natural log of Ratio or Debt to Assets ratio calculated as [(Data # 34 + Data # 9) ÷ Data # 6]. LNSIZE: Natural log of the market-value of equity (data # 25 × data # 199). LNBM: Natural log of book-to-market ratio (data # 60 / data # 25 * data # 199).GRW: Annual growth calculated as the difference between the Year 2 forecast and the Year 1 forecast scaled by the Year 1 forecast. STDEV: Standard deviation of analyst forecasts obtained from IBES. SPX: Categorical variable = 1 if the company is a client of a BigX auditor which audits more than 20% of the sales of the client’s industry, and 0 otherwise. YR: Year dummies from dummy variables indicating the years from 1990 to 2004. IND: Industry dummies indicating the industry category of the client firm based on Barth et al (1999).

37

Table 5

The Effect of Auditor's Tenure (TENURE) on CoEC (a), (b)

εαααααααααα ++++++++++= INDYRTENURESTDEVGRWLNBMLNSIZELNLEVBETACOEC ji76543210

Parameter

Predicted

Sign

Panel A

Full Sample Panel B

Large Firms

Panel C

Small Firms


Intercept ? 0.2571 29.53*** 0.1467 15.18*** 0.4381 21.21***

BETA + 0.0023 5.53*** 0.0044 5.01*** 0.0023 4.88***

LNLEV + 0.0035 11.35*** 0.0030 8.11*** 0.0036 7.93***

LNSIZE - -0.0103 -26.87*** -0.0051 -11.80*** -0.0200 -19.03***

LNBM + 0.0767 26.19*** 0.0699 16.40*** 0.0675 17.63***

GRW + 0.0351 32.82*** 0.0268 15.35*** 0.0377 29.61***

STDEV + 0.1202 19.69*** 0.1231 16.09*** 0.1410 14.83***

TENURE - -0.0003 -4.39*** -0.0001 -0.78 -0.0006 -5.42***


(a) All t-statistics are Newey-West (1987) corrected. (b) ***, **, and * indicate significance at 1 percent, 5 percent and, 10 percent levels respectively (one- tailed where signs are predicted, two-tailed otherwise). CoEC: ex ante Cost of equity Capital calculated using the PEG approach specified by Easton (2004). BETA: Stock beta calculated over 36 months ending in the month of issue of forecast, representing systematic risk. LNLEV: Natural log of Ratio or Debt to Assets ratio calculated as [(Data # 34 + Data # 9) ÷ Data # 6]. LNSIZE: Natural log of the market-value of equity (data # 25 × data # 199). LNBM: Natural log of book-to-market ratio (data # 60 / data # 25 * data # 199).GRW: Annual growth calculated as the difference between the Year 2 forecast and the Year 1 forecast scaled by the Year 1 forecast. STDEV: Standard deviation of analyst forecasts obtained from IBES.TENURE: Time period the auditor was retained by its client (Data #149).. YR: Year dummies from dummy variables indicating the years from 1990 to 2004. IND: Industry dummies indicating the industry category of the client firm based on Barth et al (1999).

38

Table 6

The Effect of Auditor's Opinion (OP) on CoEC (a), (b)

εαααααααααα ++++++++++= INDYROPSTDEVGRWLNBMLNSIZELNLEVBETACoEC ji76543210

Parameter

Predicted

Sign

Panel A

Full Sample Panel B

Large Firms

Panel C

Small Firms


Intercept ? 0.2628 29.90*** 0.1480 15.34*** 0.4471 21.79***

BETA + 0.0024 5.65*** 0.0045 5.07*** 0.0024 5.02***

LNLEV + 0.0034 10.93*** 0.0030 8.04*** 0.0033 7.48***

LNSIZE - -0.0107 -27.86*** -0.0052 -12.25*** -0.0208 -19.89***

LNBM + 0.0755 25.60*** 0.0695 16.34*** 0.0657 17.17***

GRW + 0.0351 32.81*** 0.0268 15.37*** 0.0378 29.47***

STDEV + 0.1193 19.43*** 0.1228 15.92*** 0.1402 14.77***

OP + 0.0033 3.60*** 0.0010 0.98 0.0047 2.95***


(a) All t-statistics are Newey-West (1987) corrected. (b) ***, **, and * indicate significance at 1 percent, 5 percent and, 10 percent levels respectively (one-

tailed where signs are predicted, two-tailed otherwise). CoEC: ex ante Cost of equity Capital calculated using the PEG approach specified by Easton (2004). BETA: Stock beta calculated over 36 months ending in the month of issue of forecast, representing systematic risk. LNLEV: Natural log of Ratio or Debt to Assets ratio calculated as [(Data # 34 + Data # 9) ÷ Data # 6]. LNSIZE: Natural log of the market-value of equity (data # 25 × data # 199). LNBM: Natural log of book-to-market ratio (data # 60 / data # 25 * data # 199).GRW: Annual growth calculated as the difference between the Year 2 forecast and the Year 1 forecast scaled by the Year 1 forecast. STDEV: Standard deviation of analyst forecasts obtained from IBES. OP: Categorical variable where equals 1 if there is a modified opinion and 0 otherwise where a modified opinion is any opinion coded other than 1 on Compustat data #149 (Larcker and Richardson 2004). YR: Year dummies from dummy variables indicating the years from 1990 to 2004. IND: Industry dummies indicating the industry category of the client firm based on Barth et al (1999).

39

Table 7

The Combined Effects of Auditor's Size (BIGX), Auditor’s Industry Specialization (SPX), Auditor's Tenure (TENURE), and

Auditor's Opinion (OP) on CoEC (a), (b)

εααααααααααααα +++++++++++++= INDYROPTENURESPXBIGXSTDEVGRWLNBMLNSIZELNLEVBETACoEC ji109876543210

Parameter

Predicted

Sign

Panel A

Full Sample Panel B

Large Firms

Panel C

Small Firms


Intercept ? 0.2647 28.67*** 0.1451 14.06*** 0.4420 21.01***

BETA + 0.0023 5.55*** 0.0044 5.018*** 0.0023 4.87***

LNLEV + 0.0035 11.23*** 0.0030 8.03*** 0.0034 7.70***

LNSIZE - -0.0102 -26.51*** -0.0051 -11.83*** -0.0198 -18.99***

LNBM + 0.0765 26.08*** 0.0698 16.37*** 0.0676 17.71***

GRW + 0.0351 32.91*** 0.0268 15.36*** 0.0377 29.58***

STDEV + 0.1196 19.52*** 0.1229 15.98*** 0.1404 14.66***

BIGX - -0.0057 -2.25** 0.0029 0.93 -0.0034 -1.12

SPX - -0.0012 -1.27* -0.0006 -0.58 -0.0028 -1.81**

TENURE - -0.0003 -4.26*** -0.0001 -0.87 -0.0006 -5.33***

OP + 0.0035 3.76*** 0.0010 0.98 0.0050 3.22***


(a) All t-statistics are Newey-West (1987) corrected. (b) ***, **, and * indicate significance at 1 percent, 5 percent and, 10 percent levels respectively (one- tailed where signs are predicted, two-tailed otherwise). CoEC: ex ante Cost of equity Capital calculated using the PEG approach specified by Easton (2004). BETA: Stock beta calculated over 36 months ending in the month of issue of forecast, representing systematic risk. LNLEV: Natural log of Ratio or Debt to Assets ratio calculated as [(Data # 34 + Data # 9) ÷ Data # 6]. LNSIZE: Natural log of the market-value of equity (data # 25 × data # 199). LNBM: Natural log of book-to-market ratio (data # 60 / data # 25 * data # 199).GRW: Annual growth calculated as the difference between the Year 2 forecast and the Year 1 forecast scaled by the Year 1 forecast. STDEV: Standard deviation of analyst forecasts obtained from IBES. BIGX: Categorical variable = 1 if a client of an BigX auditor and 0 otherwise SPX: Categorical variable = 1 if the company is a client of a BigX auditor which audits more than 20% of the sales of the client’s industry, and 0 otherwise. TENURE: Time period the auditor was retained by its client (Data #149) OP: Categorical variable where equals 1 if there is a modified opinion and 0 otherwise where a modified opinion is any opinion coded other than 1 on Compustat data149 (Larcker and Richardson 2004). YR: Year dummies from dummy variables indicating the years from 1990 to 2004. IND: Industry dummies indicating the industry category of the client firm based on Barth et al (1999).

40

Endnotes

1. We use the term BigX to refer to the large public accounting firms that perform most of the audits for publicly traded firms. Our sample period begins in 1990 when there were six large firms, and ends in 2005 when there were four such firms, following the demise of Andersen.

2. The use of a sample from 1926 controls for the mandatory engagement of an auditor that was later required by the 1933 and 1934 Securities acts.

3. A limitation of this methodology is that it groups auditor opinion 4 (unqualified opinion with explanatory language) with other than unqualified opinions. The explanatory language may merely be for a change in accounting treatment. Further, these unqualified with explanatory paragraph opinions include auditor going concern opinions. The going concern opinion is strongly linked to financial distress and hence, may proxy for the impact of financial distress on cost of capital.

4. All regressions used in this paper are corrected for heteroskedasticity and autocorrelation using the Newey-West correction (1987).

5. A subsequent study by Ahmed et al. (2007) uses a self-selection approach to model the choice of a specialist auditor and the cost of capital. They find that specialists are associated with a significantly lower cost of capital for both large and small firms, and that the effect is greater for small firms. When we control for self-selection, the specialization variable increases in significance, and is becomes significant for large firms.

6. Untabulated Results. 7. Coefficient of BigX in Panel C of table 7. 8. Coefficient of SPX in Panel C of table 7. 9. When analyzing the lowest quartile both BigX and SPX have the expected signs,

and although not significant at conventional probability levels, have significance at 0.179 and 0.173 levels of probability.

audit quality attributes, client size and cost of capital

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