AUDIT QUALITY AND THE MARKET VALUATION OF BANKS’ ALLOWANCE
FOR LOAN LOSSES
Kiridaran Kanagaretnam
McMaster University and
Nanyang Technological University
E-mail: [email protected]
Gopal V. Krishnan
College of Business and Economics
Lehigh University
E-mail: [email protected]
Gerald J. Lobo
C. T. Bauer College of Business
University of Houston
E-mail: [email protected]
Robert Mathieu
School of Business and Economics
Wilfrid Laurier University
E-mail: [email protected]
April 2009
Kanagaretnam, Lobo and Mathieu thank the Social Sciences and Humanities Research Council
of Canada (SSHRC) for its financial support.
AUDIT QUALITY AND THE MARKET VALUATION OF BANKS’ ALLOWANCE FOR
LOAN LOSSES
Abstract
The recent banking crisis has led market participants to focus on the adequacy and quality of
banks’ balance sheet items such as the allowance for loan losses. Beaver and Engel (1996)
document that the capital market prices the nondiscretionary component of loan loss allowance
negatively and the discretionary component less negatively. Using three measures of audit
quality, auditor type (i.e., Big 5 vs non-Big 5), auditor industry specialization/expertise, and audit
and nonaudit fees paid to auditors, we examine the effect of audit quality on the market valuation
of the discretionary component of the allowance for loan losses. We find that, relative to the
nondiscretionary component, the market valuation of the discretionary component of loan loss
allowance is higher for banks audited by Big 5 auditors than for banks audited by non-Big 5
auditors. This result holds even after controlling for self-selection, i.e., the possibility that large
banks and banks with more complex operations are more likely to hire Big 5 auditors. We also
find that the relative market valuation of the discretionary component is increasing in auditor
expertise. Our findings regarding the impact of fees paid to auditors indicate that banks paying
higher audit fees have higher relative market valuation for the discretionary component of the
allowance for loan losses, but banks that pay higher nonaudit fees do not.
JEL classification: G14; G21; M41; M42
Key words:
Banks; Auditor type; Auditor expertise; Audit Fees; Audit quality; Loan loss
Allowance; Market valuation; Signaling
AUDIT QUALITY AND THE MARKET VALUATION OF BANKS’
ALLOWANCE FOR LOAN LOSSES
I. Introduction
This study examines the effects of audit quality on the market valuation of the
discretionary and nondiscretionary components of banks’ allowance for loan losses.
Beaver and Engel (1996) document that the capital market prices the nondiscretionary
component of the loan loss allowance negatively and the discretionary component less
negatively. This evidence suggests that the discretionary component of the loan loss
allowance conveys favorable information that is incrementally positively priced relative
to the nondiscretionary component. The uncertainty associated with estimating the loan
loss allowance and the manner in which it is defined under GAAP give management
considerable discretion in reporting the allowance. How management uses that discretion
has implications for the incremental market valuation of the discretionary allowance for
loan losses. If management uses its discretion to communicate its private information, the
discretionary loan loss allowance will likely include a relatively larger information
component. Alternatively, if management uses its discretion opportunistically, then the
discretionary loan loss allowance will likely include a relatively larger noninformation
component. The higher the audit quality, the higher the proportion of the information
component relative to the noninformation component of the discretionary loan loss
allowance and, therefore, . the higher the market valuation of discretionary loan loss
allowance relative to the market valuation of nondiscretionary loan loss allowance.
The allowance for loan losses provides an appealing context for studying the
impact of audit quality because it constitutes a significant percentage of stockholders’
equity (9.5% of book value of equity for our sample). In its May 2006 report on large
firm Public Company Accounting Oversight Board (PCAOB) inspection deficiency
analysis, the American Institute of Certified Public Accountant’s (AICPA 2006) Center
for Public Company Audit Firms finds that banks’ loan loss allowance ranks number one
among the various deficiencies found by inspectors. This finding indicates that auditing
the loan loss allowance is a challenging task for auditors in general.
This study extends the research on the stock market valuation of the allowance for
loan losses by examining the role of auditing in enhancing the informational value of the
discretionary component of the allowance.
Audit quality is defined as the joint
probability of detecting and reporting material financial statement errors (DeAngelo
1981). Specifically, this study examines three aspects of audit quality.
First, it
investigates the implications of auditor type (Big 5 vs. non-Big 5 auditors) for the market
valuation of discretionary loan loss allowance.
A large body of empirical research
documents that higher audit quality is associated with Big 5 auditors.1 Relative to nonBig 5 auditors, Big 5 auditors have greater expertise, resources, and more importantly,
market-based incentives (e.g., mitigating the risk of litigation and protecting the auditor’s
reputation capital) to constrain the tendency of their audit clients to engage in aggressive
reporting. Consequently, we predict that the discretionary component of allowance for
loan losses will be more informative for banks audited by Big 5 auditors.
Second, this study examines the effect of auditor industry expertise on the market
valuation of discretionary loan loss allowance. Auditors who are specialists in the
banking industry can better assess the adequacy of the loan loss allowance than nonspecialist auditors.
Prior research documents that auditor industry specialization
1
We refer to the brand-name auditors as Big 5 (currently Big 4 after the demise of Arthur Andersen)
auditors throughout the paper for simplicity.
3
enhances financial reporting quality and mitigates fraudulent financial reporting (Johnson
et al. 1991; Carcello and Nagy 2003; Krishnan 2003b; and 2005). Third, this study
examines the effects of audit and nonaudit fees on the market valuation of discretionary
loan loss allowance. Audit fees are more likely to reflect effort since the audit market is
closely regulated and opportunities to earn rents are limited (Srinidhi and Gul 2007).
Therefore, higher audit fees should reduce estimation error in the allowance for loan
losses and improve its decomposition into discretionary and nondiscretionary
components. If audit fees enhance audit quality, then we should observe a higher market
valuation of the discretionary loan loss allowance for firms with higher audit fees.
Nonaudit services, on the other hand, are likely to impair auditor independence (Frankel
et al. 2002; Srinidhi and Gul 2007). Therefore, we should observe a lower market
valuation of the discretionary loan loss allowance for firms with higher nonaudit fees.
An examination of whether and how auditing enhances the market valuation of
the allowance for loan losses is important for several reasons. First, financial statements
are a joint product of management’s representations and the auditor’s assurance to
outsiders about the validity of those representations, including the adequacy of the
allowance for loan losses. Yet, prior research has not directly addressed the role of
auditing in the market valuation of the allowance for loan losses. The allowance for loan
losses includes a nondiscretionary and a discretionary component. While the
discretionary loan loss allowance could be driven by managerial opportunism, it could
also be informative because it could communicate managers’ private, inside information
to the capital markets. Since the discretionary loan loss allowance contains a
noninformation component as well as an information component that are conditional on
4
managerial discretionary behavior, audit quality could enhance its informational value,
and thus contribute to firm value (Krishnan 2003a).
Second, an examination of the role of external monitoring mechanisms in the
market’s valuation of the discretionary allowance for loan losses opens up a new layer in
the linkages between a bank’s accounting information and valuation. Beaver and Engle
(1996) in their concluding remarks state that “Establishment of the differential pricing of
the components by the capital market provides a basis for future research to refine the
estimates of the discretionary component and disentangle the bewildering arrays of
motives, proxies, and functional forms, in a manner that is consistent with the observed
differential pricing by the market.” Our study can be viewed as a response to Beaver and
Engel’s (1996) call for additional research. It adds to the body of research on the stock
market valuation of loan loss allowance by examining whether audit quality enhances the
information value of discretionary loan loss allowance. By doing so, our study opens up a
new layer in the linkages between a bank’s accounting information and firm valuation.2
Finally, a study of the valuation implications of audit quality is interesting in its
own right in the wake of the many high-profile scandals that have undermined the
credibility of the financial reporting system. Further, specific to the banking industry, the
estimation and adequacy of balance sheet items such as the allowance for loan losses is
under intense scrutiny following the recent, massive loan write-offs by banks around the
world. This crisis has re-focused the attention of market participants on the quality and
2
Related research by Kanagaretnam et al. (2008) examines whether the market valuation of loan loss
provisions is conditional on auditor reputation. Our study differs from that study in several important ways.
First, we focus on loan loss allowance, a balance sheet item, instead of loan loss provisions. As discussed
earlier, there is evidence that auditing loan loss allowance is a challenging task in general. Second, we use a
broader definition of audit quality, includinges auditor type, auditor industry specialization and audit fees
instead of the narrower audit specialist definition used by Kanagaretnam et al. (2008). Finally, our sample
covers more recent periods and is more than twice their sample size.
5
strength of a bank’s balance sheet. In this regard, linking audit quality to the market
valuation of the discretionary and nondiscretionary components of loan loss allowance is
relevant to capital market participants.
Our sample consists of 1,796 bank-year observations representing years 20002006. Following Beaver and Engel (1996), we estimate discretionary loan loss allowance
as residuals from a regression of the allowance for loan losses on the reciprocal of book
value of common equity, total loans outstanding, net loan write-offs, nonperforming
loans, and other bank monitoring related variables. Next, we estimate a valuation model
which includes allowance for loan losses, discretionary allowance for loan losses, and
nonperforming loans, and permits the coefficients on allowance for loan losses and
discretionary allowance for loan losses to differ across Big 5 and non-Big 5 auditors.
Similar to Beaver and Engel (1996) we find that the nondiscretionary allowance for loan
losses is negatively priced by the market and the discretionary allowance for loan losses
is incrementally positively priced.
More importantly, we find that the incremental
positive pricing of the discretionary allowance is significantly greater for banks audited
by Big 5 auditors than for banks audited by non-Big 5 auditors. We also consider the
possibility that banks that hire a Big 5 auditor could systematically differ from banks that
hire a non-Big 5 auditor (i.e., self-selection). Therefore, we employ the Heckman (1979)
two-stage procedure to control for this potential self-selection bias. We find consistent
results after controlling for self-selection; the incremental positive pricing of the
discretionary allowance for loan losses is significantly greater for banks audited by Big 5
auditors than for banks audited by non-Big 5 auditors.
We consider a classification of auditor industry expertise following GAO (2003).
6
The GAO analysis identifies KPMG and PwC as the top-two market leaders in the
banking industry after 1997. 3 We find that the incremental positive pricing of the
discretionary allowance for loan losses over the nondiscretionary loan loss allowance is
greater for banks audited by KPMG/PwC relative to banks audited by other auditors. This
result suggests that the market values KPMG/PwC’s leadership in the banking industry
and therefore perceives the discretionary allowance of banks audited by KPMG/PwC to
be of higher quality relative to the discretionary allowance of non-KPMG/PwC clients.
We also investigate the impact of audit and nonaudit fees on the market valuation
of the allowance for loan losses. First, we divide our sample firms into two groups each
year based on whether their audit (nonaudit) fees are above or below the annual median
fee, and examine whether the incremental market valuation of the discretionary
component of the allowance is greater for the high-audit-fee (high-nonaudit-fee) group.
Second, we adopt the methodology used by Fields et al. (2004) where we estimate
abnormal fees and then use these abnormal fees to assess the incremental market
valuation of the discretionary loan loss allowance conditional on abnormal audit and
nonaudit fees. Our results indicate that the higher market valuation of the discretionary
component of the allowance for loan losses relative to the nondiscretionary loan loss
allowance is related to audit fees but not to nonaudit fees. Overall, our results indicate
that the incremental market valuation of the discretionary allowance for loan losses over
the nondiscretionary allowance is conditioned on audit quality, i.e., auditor type, auditor
industry specialization and audit fees enhance the relative informativeness of the
discretionary component of the allowance for loan losses.
3
According to GAO (2003), KPMG and PwC together had a market share based on total assets audited of
58% in 1997 and 71% in 2002.
7
The rest of this paper is organized as follows. The next section develops the
hypotheses. Section three explains the empirical models used to estimate the
discretionary component of the allowance for loan losses and the valuation model.
Section four describes the sample selection, section five discusses the results, and section
six contains the conclusions of the study.
II. Hypotheses
Using economic theory DeAngelo (1981) argues that auditor size (i.e., Big 5
auditors vs. non-Big 5 auditors) is a proxy for audit quality. She reasons that brand-name
auditors (i.e., Big 5 auditors) have greater ability to detect material misstatements in
financial statements (expertise) and greater willingness to report what they find
(independence) than smaller auditors (i.e., non-Big 5 auditors).
Higher expertise is
associated with the Big 5 auditors because they not only have more resources but also
devote more resources to specialized staff training, peer reviews, and investment in
information technology relative to the non-Big 5 auditors (Dopuch and Simunic 1982;
Craswell et al. 1995). Similarly, higher independence is associated with the Big 5
auditors because they have higher reputation capital (brand name) at stake relative to the
non-Big 5 auditors. Loss of reputation, as Arthur Andersen learned the hard way, could
put a Big 5 auditor out of business. Litigation risk also motivates Big 5 auditors to
maintain a high level of independence.4 In short, higher audit quality is associated with
the Big 5 auditors.
Indeed, there is a large body of empirical research that documents that higher
audit quality is associated with the Big 5 auditors. Teoh and Wong (1993) find that
4
In the wake of the Enron scandal, PricewaterhouseCoopers, Deloitte & Touche, and Ernst & Young have
resigned from more than 1,200 clients to mitigate the risk of litigation (Hindo 2003).
8
earnings response coefficients are higher for clients of Big 5 auditors relative to the
clients of non-Big 5 auditors, consistent with the notion that investors perceive earnings
to be of higher quality when the auditor is a brand-name auditor. Becker et al. (1996) and
Francis et al. (1999) find that earnings management is lower for clients of Big 5 auditors,
and Krishnan (2003a) reports higher valuations for discretionary accruals of clients of
Big 5 auditors than for non-Big 5 auditors’ clients. In other words, brand-name auditors
appear to enhance the information value of discretionary accruals by constraining
earnings management. Additionally, Basu et al. (2000) document higher levels of
financial reporting conservatism, i.e., more timely reporting of bad news than good news,
for clients of Big 5 auditors.
Francis and Krishnan (1999) document that only brand-name auditors practice
reporting conservatism, i.e., issuing qualified opinions for firms with high accruals.
Since accruals are often used to manage earnings, brand-name auditors attempt to reduce
the risk of audit failure by issuing modified reports. Though empirical evidence on audit
quality in the banking industry is limited, the economic incentives to preserve reputation
capital and mitigate the risk of litigation faced by the Big 5 auditors of banks are no
different from the incentives they face in other industries. Adequacy of loan loss
allowance is a key component of balance sheet quality. In fact, in its May 2006 report on
large firm Public Company Accounting Oversight Board (PCAOB) inspection deficiency
analysis, the American Institute of Certified Public Accountant’s (AICPA 2006) Center
for Public Company Audit Firms indicates that banks’ loan loss allowance ranks number
one among the various deficiencies found by inspectors. To the extent that Big 5 auditors
provide higher audit quality, they should enhance the information component of the
9
discretionary allowance for loan losses. This line of reasoning leads to our first
hypothesis:
Hypothesis 1: The incremental stock market valuation of the discretionary component of
the loan loss allowance over the nondiscretionary component of the loan
loss allowance is greater when the auditor is a Big 5 auditor.
Our next hypothesis relates to the linkage between auditor specialization in the
banking industry and incremental valuation of discretionary loan loss allowance. While
the Big 5/non-Big 5 dichotomy separates auditors in terms of audit quality, industry
specialization is another dimension of audit quality. Although a Big 5 auditor may serve
clients in multiple industries, the auditor may not have a competitive advantage in all
industries the clients represent. One reason is that developing a competitive advantage is
very costly or infeasible because of first-mover advantage enjoyed by other auditors, lack
of economies of scale, limited human capital with industry expertise, and constrained
economic resources. As a result, each Big 5 auditor tends to dominate a select few
industries from among the industries in its client portfolio.
Maletta and Wright (1996) observe that there are fundamental differences in
characteristics of errors and methods of error detection across industries. Specialist
auditors are also likely to develop databases comprising industry-specific best practices
of accounting policies, risk and error characteristics, and peculiar transactions, all of
which can contribute to overall audit effectiveness. Solomon et al. (1999) find that
auditors who are specialists exhibit greater knowledge of non-error frequency relative to
non-specialist auditors. The significance of this finding is that clients tend to propose
non-error explanations for fluctuations in ratios and, therefore, accurate knowledge of
10
non-error frequency is vital in detecting misstatements in financial statements.
A number of researchers have examined the benefits of auditor industry
specialization or expertise on audit effectiveness. For example, Bedard and Biggs (1991)
document that an auditor with experience in the manufacturing industries is better able to
detect errors in a manufacturing client’s data than an auditor without experience in
manufacturing. Similarly, Wright and Wright (1997) find that significant experience in
the retailing industry contributes to increased detection of errors of retail industry clients.
Prior research also documents other benefits of auditor industry specialization. Johnson
et al. (1991) and Carcello and Nagy (2003) find that auditor industry expertise mitigates
financial fraud. Krishnan (2003b) documents that for non-financial firms, discretionary
accruals, a commonly used proxy for earnings management, are lower for clients of
specialist auditors than for clients of non-specialist auditors. Krishnan (2005) finds that
asymmetric timeliness of earnings, a fundamental characteristic of financial reporting is
greater for clients of specialist Big 5 auditors compared to clients of non-specialist Big 5
auditors.
There is also evidence that investors perceive earnings to be more informative
when the auditor is a specialist auditor (Balsam et al. 2004). Audit clients also appear to
value auditor industry expertise as indicated by premium pricing by specialist auditors
(Francis et al. 2005). While the above evidence indicates an association between auditor
industry specialization and the ability to detect material misstatements in financial
statements, there also is evidence that specialist auditors attempt to protect their
reputation capital through increased compliance with generally accepted auditing
standards more than do non-specialist auditors (O’Keefe et al. 1994). In summary, the
11
collective evidence supports the notion that there are benefits to auditor industry
specialization in terms of enhanced audit effectiveness and credibility of financial
statements to investors and audit clients. Thus, our second hypothesis is:5
Hypothesis 2: The incremental stock market valuation of the discretionary component of
the loan loss allowance over the nondiscretionary component of the loan
loss allowance is greater when the auditor is a specialist in the banking
industry.
In the next two hypotheses, we examine the relationship between audit and
nonaudit fees and the incremental valuation of discretionary loan loss allowance. Kinney
and Libby (2002) argue that both audit and nonaudit fees could have an impact on the
independence of auditors and cause economic bonding. However, audit and nonaudit fees
could have different effects on audit quality. Audit fees are more likely to reflect effort
since the audit market is closely regulated and opportunities to earn rents are limited
(Srinidhi and Gul 2007). Therefore, higher audit fees should be associated with reduced
opportunistic managerial discretion in the estimation of loan loss allowance and an
increase in the information component of the discretionary allowance for loan losses.
Accordingly, if audit fees enhance audit quality, we should observe a higher incremental
market valuation of the discretionary loan loss allowance for firms with higher audit fees.
Conversely, if nonaudit services impair auditor independence, then we should
observe a lower or no incremental market valuation of the discretionary loan loss
allowance for firms with high nonaudit fees. The SEC takes the position that nonaudit
services impair auditor independence. The Sarbanes-Oxley Act (SOX) bans most
nonaudit services. This is consistent with the notion that even the provision of an
5
We discuss how we operationalize auditor industry expertise in the banking industry in the next section.
12
immaterial amount of certain nonaudit services could potentially impair auditor
independence. Brandon et al. (2004) examine the perception of the bond market to
auditor-provided nonaudit services and find a negative relation between nonaudit services
and clients’ bond ratings. Krishnan et al. (2005) investigate whether investors perceive
auditor independence as being impaired when auditors supply nonaudit services. They
find a negative relation between nonaudit fees and earnings response coefficients. The
results of these studies are consistent with the notion that investors perceive nonaudit
services as impairing auditor independence.
Analysts also perceive nonaudit services as impairing auditor independence. Of
the 970 financial analysts and other investment professionals who responded to an
Association for Investment Management and Research (currently referred to as the CFA
Institute) survey, the percentages who indicated such services would compromise auditor
independence range from 37% for information technology related services to 65% for
asset valuation and appraisal services (CFA Institute 2000). Further, Goff (2002) reports
that 52% of some 50 asset managers and analysts surveyed believe that investors might
have concerns about companies that purchase consulting services from their auditors, and
a third of them expect that investors will, in fact, discount the share prices of such
companies.
Plitch (2003) provides some evidence that firms that rate companies’
corporate governance practices perceive nonaudit services as potentially compromising
auditor independence.
Finally, there are numerous cases of media coverage of the
negative impact of nonaudit services on auditor independence (see Weil and Tannenbaum
2001 and Bryan-Low 2003). This leads to our final two hypotheses:
Hypothesis 3: The incremental stock market valuation of the discretionary component of
13
the loan loss allowance over the nondiscretionary component of the loan
loss allowance is higher for banks with higher audit fees.
Hypothesis 4: The incremental stock market valuation of the discretionary component of
the loan loss allowance over the nondiscretionary component of the loan
loss allowance is lower for banks with higher nonaudit fees.
III. Empirical Models
Estimation of Discretionary Loan Loss Allowance
Following Beaver and Engel (1996), we use a two-stage approach to test our
hypotheses. We estimate the discretionary component of loan loss allowance in the first
stage and then relate this component to the different measures of audit quality in the
second stage. We adopt the methodology of Beaver and Engel (1996) for estimating
discretionary loan loss allowance and its market valuation. We estimate the discretionary
component of loan loss allowance (DALL) as the residuals from regressing the allowance
for loan losses (ALL) on loan charge-offs, total loans, nonperforming loans, and other
variables using the following model:6
ALLit = γ0 (1/GBV) + γ1COit + γ2 LOANit + γ3NPLit + γ4NIit + γ5BTCAPITALit
+ γ6SMALLit+ Year Controls + eit
[1]
We define the variables as follows:
ALL =
allowance for loan losses deflated by gross book value of common equity
(book value of common equity plus total allowance for loan losses);
GBV
= gross book value of common equity;
CO
= loan charge-offs deflated by gross book value of common equity;
6
Beaver and Engel (1996) also include one-year-ahead change in non-performing loans as a control
variable for the nondiscretionary component of loan loss allowance. Our sample size is reduced to 1,525
observations when we include this variable and the estimated coefficient on one-year-ahead change in nonperforming loans is 0.0000 (t = 0.02). Inclusion of one-year-ahead change in non-performing loans in
model (1) does not alter any of our inferences.
14
LOAN
= total loans deflated by gross book value of common equity;
NPL
= nonperforming loans deflated by gross book value of common equity;
NI
= earnings before loan loss provision scaled by gross book value of common
equity;
BTCAPITAL= total risk-adjusted capital ratio at the beginning of the year; and
SMALL
= an indicator variable that equals to 1 if beginning assets < $500 million.
The residuals from model (1) become the abnormal or discretionary component of
ALL, referred to as DALL, and the fitted values are the nondiscretionary component of
ALL, referred to as NALL. Consistent with prior research, γ1, γ2, and γ3 are predicted to be
positive. The amount of net loan charge-offs (CO) is related to ALL by construction. A
higher level of nonperforming loans indicates problems with the loan portfolio (i.e.,
default risk) and requires a higher loan loss allowance. Therefore, the level of
nonperforming loans (NPL) will be positively related to ALL. Nonperforming loans are
not the sole source of default. The rest of the loan portfolio also has some default risk
exposure; hence total loans will be positively related to ALL. We include earnings before
loan loss provision (NI) to control for earnings management motives and total risk
adjusted capital ratio at the beginning of the year (BTCAPITAL) to control for capital
management motives. We expect a positive relation between earnings before loan loss
provision and loan loss allowance. We do not have predictions on the relation of loan loss
allowance to beginning total capital ratio. We expect a negative sign on SMALL since
small banks have less regulatory scrutiny.7
7
The Federal Deposit Insurance Corporation Improvement Act of 1991 (FDICIA), which was passed in
response to the savings and loan debacle of the 1980’s and became effective in 1992, imposed new
auditing, corporate reporting, and governance requirements on each depository institution with assets
exceeding $500 million. Banks with total assets less than $500 million were not subject to these increased
15
Market Valuation of Nondiscretionary and Discretionary Loan Loss Allowance
Next, as in Beaver and Engel (1996), we test whether the capital market assigns
the same or different valuations to NALL and DALL. We include the level of
nonperforming loans (NPL) to control for any additional discount applied to
nonperforming loans over the discount applied to the nondiscretionary component of
ALL. We also include net income before subtraction of provision for loan losses as an
additional control variable. When net income is omitted, a positive coefficient on DALL
could be interpreted as simply reflecting the valuation relevance of net income (Beaver
and Engel, 1996). Based on prior research (Barth et al. 1995; Beaver and Engel, 1996),
we allow the coefficients for positive and negative net income to differ. Our basic
valuation model is as follows:
MVE = β0 + β1ALL + β2DALL + β3NPL + β4NI_POS + β5NI_NEG
+ Year Controls + ε
[2]
We define the variables as follows:
MVE
= market value of equity deflated by gross book value of common equity;
ALL
= allowance for loan loss deflated by gross book value of common equity;
DALL
NPL
= estimated discretionary portion of the allowance from Equation [1];
= nonperforming loans deflated by gross book value of common equity;
NI_POS
= net income before total provision for loan losses for firms with positive
net income deflated by gross book value of common equity; and
NI_NEG
= net income before total provision for loan losses for firms with negative
net income deflated by gross book value of common equity.
The allowance for loan losses, ALL, in equation [2] equals the sum of the
regulations.
16
nondiscretionary allowance, NALL, and the discretionary allowance, DALL. The
coefficient of DALL is (β1 + β2) and the coefficient of NALL is β1. Therefore, the
incremental market valuation of DALL over NALL equals β2. Consistent with Beaver and
Engel (1996), we predict a negative sign for β1 and a positive sign for β2. As discussed
above, the coefficient on NPL may capture any incremental discount applied to NPL; we
therefore predict a negative sign for β3. We expect β4 (β5) to be positive (negative)
because positive (negative) income will increase (decrease) firm value.
IV. SAMPLE SELECTION
We select our sample from banks included in the 2007 Bank Compustat annual
data files. We obtain data on fees paid to auditors for the years 2000-2006 from Audit
Analytics. The intersection of the Audit Analytics data with the Bank Compustat data
results in an initial sample of 2,044 bank-year observations. Our final sample includes
1,976 bank-year observations for which we have complete data and represents 304
banks.8 We hand-collect the data on non-performing loans for the period 1999-2006 from
annual reports and obtain data on loan portfolio categories from the Federal Reserve
Bank Holding Company Database (call reports).
Panel A of Table 1 reports descriptive statistics for the scaled variables used in the
regressions. The mean value of the allowance for loan losses represents 9.5% of the
sample banks’ gross book value of common equity, while the discretionary component
(the residual from estimating equation [1]) has a mean of 0 by construction. About 52%
of the sample observations are audited by Big 5 auditors. As indicated by deflated net
8
To ensure that our results are not driven by outliers we delete the top 5 and bottom 5 values of our
variables of interest. We also check for influential observations on our regressions using the R-Student
procedure.
17
income before loan loss provisions (NI), firms are, on average, profitable. The average
beginning total capital ratio of 13.58 exceeds the minimum required level.
[Insert Table 1 About Here]
Table 2 presents the correlations among the dependent, independent and control
variables. The correlation between the market value of equity (MVE) and the
discretionary component of the allowance for loan losses (DALL) is positive, consistent
with expectations. Notice that MVE is also positively correlated with ALL, most likely
because of its positive correlation with DALL. MVE is also positively related to whether
the auditor is a Big 5 auditor (Big5) and to whether the auditor is a banking industry
specialist (KPPwC).
[Insert Table 2 About Here]
V. RESULTS
Basic Results
Table 3 presents the results of estimating equations [1] and [2]. Panel A presents
the results for the estimation of discretionary loan loss allowance (DALL). As expected,
the coefficients on loan charge-offs (CO), loans (LOAN), non-performing loans (NPL),
and earnings before loan loss provisions (NI) are positive and significant. Finally, the
negative coefficient on the indicator variable SMALL suggests that smaller banks have
lower allowance for loan losses. One reason for this is that smaller banks have lower
regulatory scrutiny.
[Insert Table 3 About Here]
18
The residual term of equation [1] is the discretionary portion of the allowance for
loan losses (DALL) and is included in equation [2]. The estimation results presented in
Panel B of Table 3 are consistent with those documented by Beaver and Engel (1996).
The coefficient β1, which estimates the market valuation of the nondiscretionary portion
of the allowance for loan losses, is significantly negative indicating that market value
decreases with NALL. On the other hand, the estimated coefficient of DALL is positive
and significant at the 1% level, consistent with the argument that discretionary loan loss
allowance is incrementally positively valued.9 This is consistent with the conjecture that
the discretionary allowance contains an information component that is valued by the
market. The coefficient on non-performing loans (NPL), and the coefficients on positive
(NI_POS) are significant and have the expected signs. In the next section, we test the
importance of audit quality on the incremental market valuation of the discretionary
component of the allowance for loan losses.
Auditor Type and Market Valuation of DALL
To investigate the impact of auditor type on the incremental market valuation of
DALL, we modify equation [2] by adding the indicator variable, BIG5, which equals 1 if
the auditor is a Big 5 auditor and 0 otherwise, and its interactions with the allowance for
loan losses (BIG5*ALL) and the discretionary allowance for loan losses (BIG5*DALL).
9
Although the estimated coefficients on DALL and NALL are high, we note that they do not differ much
from Beaver and Engel’s results for the second period. Beaver and Engel (1996, p. 194) state “The finding
that they are both unusually large but of opposite sign, however, is consistent with the presence of a
common error of opposite sign between the two components. As discussed earlier, without specifying the
full covariance matrix among allowance variables measured without error, the nature of the error, and the
other variables in the system, it is difficult to predict the effect of such error on the estimated coefficients in
the second stage.” Our coefficient estimates on DALL and NALL are also subject to such measurement
error.
19
This results in the following model:
MVE = β0 + β1BIG5 + β2ALL + β3DALL + β4BIG5*ALL + β5BIG5*DALL + β6NPL
+ β7NI_POS + β8NI_NEG + Year Controls + ε
[3]
Equation [3] allows the market valuation coefficients on NALL and DALL to
differ across banks audited by Big 5 and non-Big 5 auditors; therefore, we can use this
model to test for a difference in the incremental market valuation of DALL over NALL
between Big 5 and non-Big 5 audited banks. The coefficient β3 estimates the difference
between the market valuation of DALL and NALL for non-Big 5 audited banks. If DALL
contains an information component, we would expect this coefficient to be positive.
Correspondingly, the coefficient β3 + β5 reflects the incremental difference between the
market valuation of DALL and NALL for banks audited by Big 5 auditors. As with banks
audited by non-Big 5 auditors, if DALL contains an information component, we would
expect β3 + β5 to be positive. Hypothesis 1 posits that if Big 5 auditors enhance the
information component of the discretionary loan loss allowance by providing higher audit
quality, the difference in stock market valuation of DALL over NALL will be greater
when the auditor is a Big 5 auditor. This hypothesis indicates that β5, the incremental
difference between the market valuation of DALL over NALL for Big 5 and non-Big 5
audited banks, is positive. Furthermore, if auditor type enhances the informativeness of
the discretionary component of loan loss allowance we would expect β4 + β5 (the
difference in the coefficient of DALL between the Big 5 and non-Big 5 audited banks,
i.e., (β2 + β3 + β4 + β5) – (β2 + β3)) to be positive. Conversely, if auditor type enhances the
information conveyed by nondiscretionary loan loss allowance, we would expect β4 (the
20
difference in the coefficient of NALL between the Big 5 and non-Big 5 audited banks
(i.e., (β2 + β4) – β2) to be negative.
The results of the regression are presented in Panel A of Table 4. The positive and
significant (p < 0.01) estimate of β5 indicates support for hypothesis 1 that the difference
between the market valuation of DALL and NALL is greater for banks audited by Big 5
auditors than for non-Big 5 audited banks. Furthermore, β3 + β5 is significantly greater
than zero, indicating a positive incremental market valuation of DALL over NALL for Big
5 audited banks. Our results also indicate that β4 is significantly negative, indicating
greater informativeness of nondiscretionary allowance for banks audited by Big 5
auditors than for non-Big 5 audited banks.
[Insert Table 4 About Here]
Controlling for Self-Selection
It is possible that audit clients systematically choose between Big 5 and non-Big 5
auditors. As a result of such self-selection, the observed differences in valuation
coefficients between Big 5 and non-Big 5 auditors’ clients could simply reflect the
fundamental differences between these client groups rather than the difference in audit
quality. We employ the Heckman (1979) two-stage procedure to address this concern.
We first estimate a probit model of auditor choice (Big 5 vs. non-Big 5) to derive the
Inverse Mills Ratio (IMR). We are not aware of a model of auditor choice for banks.
Therefore, we develop the following model that that relates auditor choice to bank
performance, size, and risk:
BIG5 = α0 + α1ROA + α2ROA*LOSS + α3LnLOAN + α4CHLOAN + α5TCAPITAL
21
+ α6LOANRATIO + α7NPLRATIO + ε
[4]
We define the variables as follows:
BIG5
=
an indicator variable that equals 1 for Big 5 clients and 0 for nonBig 5 clients;
ROA
=
net income over lagged total assets;
LOSS
=
an indicator variable that equals 1 the firm has a loss and 0
otherwise;
LnLOAN
=
natural log of total loans outstanding;
CHLOAN
=
change in total loans outstanding;
TCAPITAL
=
risk adjusted total capital ratio (at the year-end);
LOANRATIO =
total loans outstanding divided by total assets; and
NPLRATIO
nonperforming loans divided by total loans.
=
ROA captures performance and is expected to be positive.
By including
ROA*LOSS in the model, we allow the coefficient on ROA to differ across profit and loss
firms. LnLOAN is a proxy for bank size. Since larger banks may prefer Big 5 auditors,
we predict a positive coefficient for LnLOAN. We use several measures of risk, including
CHLOAN, TCAPITAL, NPLRATIO and LOANRATIO. We predict positive coefficients
for CHLOAN, LOANRATIO, and NPLRATIO as banks with greater actual or perceived
risks may opt for a Big 5 auditor to enhance the credibility of their financial reports. We
expect a negative sign for the coefficient of TCAPITAL since higher capital ratios likely
indicate lower risk.
Panel B of Table 4 presents the results of estimating equation [4]. The coefficients
on LnLOAN, TCAPITAL, and NPLRATIO have the predicted signs and are significant at
22
the 5% level. Next, we re-estimate equation [3] after including the IMR obtained from
equation [4] and report the results in Panel C of Table 4. Note that IMR is significant,
indicating that self-selection bias may be driving some of the results reported in Panel A
of Table 4. Nevertheless, the coefficient on BIG5*DALL is 11.2 and significant at the 1%
level, indicating that discretionary loan loss allowance audited by Big 5 auditors is
incrementally positively priced relative to discretionary loan loss allowance audited by
non-Big 5 auditors. The results also indicate that the difference in the coefficient of
DALL between the Big 5 and non-Big 5 audited banks, i.e., β3 + β5 is significantly
positive. Furthermore, β4, the difference in the coefficient of NALL between the Big 5 and
non-Big 5 audited banks is significantly less than zero, suggesting that auditor type
enhances the information conveyed by nondiscretionary loan loss allowance. These
results are similar to the results in Panel A and alleviate the concern that the results in
Panel A are systematically biased because of self-selection.10 In summary, the results in
Table 4 are consistent with hypothesis 1 that the incremental market valuation of
discretionary loan loss allowance over nondiscretionary allowance for loan losses is
conditioned on auditor type.
Auditor Expertise and Market Valuation of DALL
Hypothesis 2 predicts that the incremental market valuation of discretionary loan
loss allowance is greater when the auditor is an expert in the banking industry. GAO
(2003) identifies KPMG and PwC as the top-two market leaders in the banking industry
after 1997. To test hypothesis 2, we add the following two indicator variables to equation
10
Francis and Lennox (2008) discuss the problems with operationalizing the Heckman (1979) two-stage
procedure in an accounting context. Following their suggestion, we exclude several variables from the first
stage model (for example LnLOAN and NPLRATIO) from our second stage valuation model. We also check
for multicollinearity when including IMR in the second stage. The variance inflation factors do not indicate
problems with multicollinearity.
23
[2]: OTHBIG5, which equals 1 for clients of Arthur Andersen, Deloitte and Touche and
Ernst and Young, and 0 otherwise, and KPPwC which equals 1 if the auditor is KPMG or
PwC and 0 otherwise. We also interact each indicator variable with ALL and DALL.
The results are presented in Table 5. In model 1, we include an indicator variable
for KPPwC and its interaction with ALL and DALL. The positive and significant (p <
0.05) coefficient on KPPwC*DALL supports our hypothesis 2. Furthermore, the market
valuation of the discretionary (nondiscretionary) allowance is less (more) negative for
banks audited by the Big 5 than for banks audited by the non-Big 5.
[Insert Table 5 About Here]
Next, we examine whether the difference in the incremental valuation of DALL
over NALL between Big 5 audited and non-Big 5 audited banks is related to auditor type
or auditor expertise or both, using the following model:
MVE = β0 + β1OTHBIG5 + β2KPPwC + β3ALL + β4DALL + β5OTHBIG5*ALL
+ β6 OTHBIG5*DALL + β7KPPwC*ALL + β8KPPwC*DALL + β9NPL
+ β10NI_POS + β11NI_NEG + Year Controls + ε
[5]
This model includes the indicator variables OTHBIG5 (which equals 1 for clients of
Arthur Andersen, Deloitte and Touche and Ernst and Young, and 0 otherwise) and
KPPwC and their interactions with ALL and DALL. The results are presented as model 2
in Table 5. The coefficient β4 + β8 represents the incremental market valuation of
discretionary loan loss allowance over nondiscretionary loan loss allowance for banks
audited by industry leaders KPMG and PwC, while the coefficient β4 + β6 represents the
24
corresponding estimate for banks audited by Arthur Andersen, Deloitte and Touche and
Ernst and Young. Thus, the difference, β8 - β6, is the incremental effect of having an
industry audit specialist over another Big 5 auditor. This difference is significant at the
[fill in] level, implying that the higher incremental market valuation of DALL over NALL
reported in model 1 is primarily driven by auditor expertise.
Audit Fess and NonAudit Fees and Market Valuation of DALL
Our final two hypotheses relate audit and nonaudit fees to the incremental market
valuation of DALL over NALL. Hypothesis 3 predicts a positive association between audit
fees and the incremental market valuation of DALL. Hypothesis 4 predicts that the market
valuation of DALL is negatively related to nonaudit fees. We employ two different
methods to test these hypotheses. First, we divide our sample banks into two groups
based on whether their audit fees (nonaudit fees) are above or below the annual median
fee. Second, we adopt the methodology used by Fields et al. (2004), where we regress the
natural logarithm of audit fees (nonaudit fees) on the determinants of normal fees to
obtain abnormal fees. We then use abnormal fees to assess the incremental market
valuation of DALL conditional on abnormal audit (nonaudit) fees.
Column 3 in Panel A of Table 6 presents the results for audit fees classified above
or below the median audit fee each year. This indicator variable, labeled FEE, is also
interacted with ALL (FEE*ALL) and DALL (FEE*DALL). The results support our third
hypothesis. That is, the coefficient of FEE is positive and significant at the 1% level, the
coefficient of FEE*ALL is negative and significant and, consistent with hypothesis 3, the
coefficient of FEE*DALL is positive and significant at the 1% level.
25
[Insert Table 6 About Here]
Column 4 in Panel A of Table 6 presents the results for nonaudit fees classified
above or below the median nonaudit fee each year. The coefficient of FEE*DALL is not
significantly different from zero. Thus, we are unable to reject the null hypothesis that the
incremental stock market valuation of the discretionary component of the loan loss
allowance over the nondiscretionary allowance for loan losses is lower for banks with
higher nonaudit fees.
Prior research models auditor related fees as a function of a firm’s auditor choice,
audit complexity, and audit risk, in addition to other variables (Firth 1997; Ashbaugh et
al. 2003). Fields et al. (2004) examine the determinants of normal audit fees in the
banking industry. Using the variables identified in Fields et al. (2004) as determinants of
audit (nonaudit) fees, we estimate the unexpected portion of audit (nonaudit) fees. Audit
(nonaudit) fees are likely to be higher when the auditor is a Big 5 auditor. Auditor size
also proxies for client size. We include the natural log of total assets to capture firm size.
The normal audit/nonaudit fee is modeled as a function of a bank’s credit risk and capital
risk. We include NPL, LCO, COMM, CON and RESTATE as proxies for a bank’s credit
risk and total capital ratio (TCAPITAL) to account for capital risk. We estimate the
following model:
FEE = α0 + α1 BIG5 + α2 LnASSETS + α3 NPL + α4 LOSS + α5 LCO+ α6 COMM + α7 CON
+ α8 RESTATE + α9 TCAPITAL + Year Controls + ε
We define the variables as follows:
FEE
=
natural log of audit or nonaudit fees;
26
[6]
BIG5
=
indicator variable set equal to 1 if audited by a Big 5 firm, and 0
otherwise;
LnASSETS
=
natural log of total assets;
NPL
=
nonperforming loans over lagged total loans;
LOSS =
an indicator variable that equals 1 the firm has a loss and 0 otherwise;
LCO
=
net loan charge-offs over loan loss allowance;
COMM
=
total commercial and agriculture loans over total loans;
CON
=
total consumer loans over total loans;
RESTATE
=
total real estate loans over total loans; and
TCAPITAL
=
total risk-adjusted capital ratio (measured at year-end).
Panel B of Table 6 presents the results of the regression of the natural logarithm
of audit (nonaudit) fees on the determinants of normal fees. As expected, we find a
positive relation between our two fee measures and BIG5, LnASSETS, NPL, LCO, LOSS
and COMM. The signs of the coefficients are generally consistent with Fields et al.
(2004). The adjusted R2 of 78.84% for audit fees indicates that the model explains a large
percentage of the variation in audit fees. The adjusted R2 of 55.26% for the nonaudit fee
model is also high. These R2 values are higher than the adjusted R2 values reported in
Ashbaugh et al. (2003) for industrial firms and consistent with the R2 values reported in
Fields et al. (2004).
Column 3 in Panel C of Table 6 presents the results when we use abnormal audit
fees, referred to as AFEE, to assess the incremental market valuation of discretionary
loan loss allowance over nondiscretionary allowance for loan losses. The results are
similar to those reported in Panel A where audit fees are measured as a dichotomous
variable. More precisely, the coefficient for abnormal audit fees is positive, as expected,
27
and significant. Furthermore, consistent with hypothesis 3, the coefficient of
AFEE*DALL is positive and significant at the 1% level.
Column 4 in Panel C of Table 6 contains the results for abnormal nonaudit fees.
The results are very similar to those reported in Column 4 of Panel A; the coefficient of
AFEE*DALL is not significant at conventional levels, indicating that the abnormal
nonaudit fee is unrelated to the incremental stock market valuation of the discretionary
loan loss allowance over the nondiscretionary allowance for loan losses.
Overall, the results in Table 6 indicate that the incremental market valuation of
discretionary loan loss allowance over nondiscretionary allowance for loan losses is
positively related to the size of the audit fee but unrelated to the size of the nonaudit fee.
This result holds regardless of whether we use audit (nonaudit) fee or abnormal audit
(nonaudit) fee.
Additional Analysis
We summarize the results of two additional tests in this section. First, we examine
whether our results differ across smaller and larger banks. Audit quality is likely to play a
more prominent role for smaller banks than for larger banks. Smaller banks may have
weaker internal control systems than larger banks and therefore, auditor type and industry
expertise may be particularly important for smaller banks. For example, the FDIC
Improvement Act of 1992 imposed auditing, corporate reporting, and governance reforms
on banks with assets exceeding $500 million (Altamuro and Beatty, 2008). When we
estimate equation [3] separately for small banks (below $500 million in total assets) and
large banks (above $500 million in total assets), we find that (results not tabulated) the
28
coefficient on the interaction variable, BIG5×DALL, is positive and significant at the 0.01
level for small banks and at the 0.05 level for large banks. Similarly, the coefficient on
KPPwC×DALL is significant at the 0.01 level for small banks and at the 0.10 level for
large banks. These results indicate that the interaction of the market valuation of DALL
and auditor type and expertise varies with the size of the bank and in particular, the
market attaches a higher incremental value to the discretionary loan loss allowance of
small banks served by Big 5 auditors and industry audit experts.
Second, we examine whether the incremental market valuation of DALL is
conditional on the sign of DALL, i.e., whether positive DALL is valued more than
negative DALL. Untabulated results indicate that the incremental effect of auditor type on
the valuation of DALL holds for both positive and negative DALL. However, the
incremental effect of auditor expertise on valuation of DALL holds only for positive
DALL. This finding is consistent with investors viewing positive DALL as more
conservative and of higher quality than negative DALL.
VI. Conclusions
The recent banking crisis has led market participants to focus on the adequacy and
quality of banks’ balance sheet items, especially the allowance for loan losses. Beaver
and Engel (1996) document that the capital market prices the nondiscretionary
component of loan loss allowance negatively and the discretionary component less
negatively. This evidence suggests that the discretionary component conveys favorable
information that is incrementally positively priced relative to the nondiscretionary
component. In this study, we examine whether and how audit quality enhances the
incremental market valuation of the discretionary component of the allowance for loan
29
losses. We use auditor type (i.e., Big 5 versus non-Big 5), auditor industry expertise, and
audit fees as different proxies for audit quality.
Our results indicate that audit quality has a positive impact on the incremental
market valuation of the discretionary component of the allowance for loan losses. More
specifically, we find that the incremental positive pricing of the discretionary allowance
for loan losses is significantly greater for banks audited by Big 5 auditors than for banks
audited by non-Big 5 auditors. This result also holds after controlling for the possibility
that large banks and banks with more complex operations are more likely to hire Big 5
auditors.
The results regarding auditor expertise also support our predictions. That is, when
we examine the effect of auditor expertise, the results indicate that audit specialization
has a positive impact on the incremental market valuation of the discretionary loan loss
allowance. Finally, we examine the effects of audit and nonaudit fees on the incremental
market valuation of the discretionary loan loss allowance. Our findings indicate higher
incremental market valuation of the discretionary component of the allowance for loan
losses for banks paying higher audit fees, but not for banks paying higher nonaudit fees.
The primary contribution of our study is that it is documents how auditor quality,
conditions the market valuation of banks’ loan loss allowance and contributes to our
understanding of how the stock market values bank managers’ discretionary reporting.
Our findings have implications for a variety of capital market participants. For example,
bank managers and members of the audit committee interested in enhancing the
credibility of the reported loan loss allowance could consider hiring a Big 5 auditor,
especially one with expertise in the banking industry. Similarly, bank analysts and
30
investors should assess the adequacy of the loan loss allowance with care particularly
when the auditor is not a Big 5 auditor.
31
References
Ahmed, A.S., C. Takeda, S. Thomas. 1999. Bank loan loss provisions: a reexamination of
capital management, earnings management and signaling effects. Journal of Accounting
& Economics (November): 1-25.
AICPA.
2006.
Large
Firm
PCAOB
Inspection
Deficiency Analysis.
http://www.aicpa.org/cpcaf/download/PCAOB_Inspection_Deficiency_Analysis_January
2006.pdf
Altamuro, J., A.L. Beatty. 2008. Do internal control reforms improve earnings quality?
Working paper, Ohio State University, available on the SSRN.
Ashbaugh, H., R. LaFond, B. Mayhew. 2003. Do nonaudit services compromise auditor
independence? Further evidence. The Accounting Review 78 (July), 611-639.
Balsam, S., J. Krishnan, and J. Yang. 2003. Auditor Industry specialization and earnings
quality. Auditing: A Journal of Practice & Theory (September): 71-97.
Barth, M.E., W.R. Landsman, J.M. Wahlen. 1995. Fair value accounting: effects on
banks’ earnings volatility, regulatory capital, and value of contractual cash flows. Journal
of Banking & Finance 19 (3-4): 577-605.
_____, L. Hwang, C. Jan, C. 2000. Differences in Conservatism Between Big Eight and
Non-big Eight Auditors. Working Paper. City University of New York and California
State University.
Beaver, W.H., and E. Engel. 1996. Discretionary behavior with respect to allowances for
loan losses and the behavior of security prices. Journal of Accounting & Economics 22
(August-December): 177-206.
Becker, C., M. DeFond, J. Jiambalvo, and K. Subramanyam. 1998. The effect of audit
quality on earnings management. Contemporary Accounting Research 15 (Spring): 1-24.
Bedard, J.C., and S.F.Biggs. 1991. The effect of domain-specific experience on
evaluation of management representations in analytical procedures. Auditing: A Journal
of Practice & Theory (Supplement): 77-90.
Beatty, A.L., B. Ke, and K.R. Petroni. 2002. Earnings management to avoid earnings
declines across publicly and privately held banks. The Accounting Review (July): 547570.
Bryan-Low, C. 2003. Accounting firms earn more from consulting – despite SEC
crackdown, buying nonaudit services by companies stays high. Wall Street Journal (April
16): C9.
32
Carcello, J., and A. Nagy. 2004. Client size, auditor specialization and fraudulent
financial reporting. Managerial Auditing Journal 19 (5): 651-668.
CFA Institute. 2000. AIMR calls for public disclosure of auditor-client relationships to
help
analysts,
investors
gauge
auditor
objectivity.
Available
at:
http://www.cfainstitute.org/aboutus/press/release/00releases/00aud_client.html
Craswell, A.T., Francis, J.R., and Taylor, S.L. 1995. Auditor brand name reputations and
industry specializations. Journal of Accounting and Economics 20 (December): 297-322.
DeAngelo, L. 1981. Auditor size and auditor quality.
Economics 3 (December): 183-199.
Journal of Accounting and
Dopuch, N., and D. Simunic. 1982. Competition in auditing: an assessment. Paper
presented at Symposium on Auditing Research IV, University of Illinois at UrbanaChampaign.
Fields, L.P., D.R. Fraser, and M.S. Wilkins. 2004. An investigation of the pricing of audit
services for financial institutions. Journal of Accounting and Public Policy 23
(January/February): 53-77.
Firth, 1997.
Francis, J., E. Maydew, and H. Sparks. 1999. The role of Big 6 auditors in the credible
reporting of accruals. Auditing: A Journal of Practice & Theory 18 (Fall): 17-34.
_____, and J. Krishnan. 1999. Accounting accruals and auditor reporting conservatism.
Contemporary Accounting Research 16 (Spring): 135-165.
_____, K. Reichelt, and D. Wang. 2005. The pricing of national and city-specific
reputations for industry expertise in the U.S. audit market. The Accounting Review 80
(January): 113- 136.
_____, and C. Lennox. 2008. Selection Models in Accounting Research. Working Paper,
Hong Kong University of Science and Technology.
General Accounting Office. 2003. Public accounting Firms. Mandated study on
consolidation and competition. (July).
Goff, J. 2002. Don’t purchase consulting services from auditor, say money managers.
Available at: http://www.cfo.com/printable/article.cfm/3003876?f=options
Heckman, J.J. 1979. Sample Selection Bias as a Specification Error. Econometrica,
47 (1): 153-162.
Hindo, B. 2003. Audit clients get the heave-ho. Business Week (December 1): 7.
33
Johnson, P., K. Jamal, and R. Berryman. 1991. Effects of framing on auditor decisions.
Organization Behavior and Human Decision Processes 50 (October): 75-105.
Jones, K.L., G.V. Krishnan, K. Melendrez. 2008. Do discretionary accruals models detect
actual cases of fraudulent and restated earnings? An empirical evaluation. Contemporary
Accounting Research,(Summer): 499-531.
Kanagaretnam, K., G. Lobo, and Mathieu. 2003. Managerial incentives for income
smoothing through loan loss provisions. Review of Quantitative Finance and Accounting
(20): 63-80.
_____, _____, and D. Yang. 2004. Joint tests of signaling and income smoothing through
bank loan loss provisions. Contemporary Accounting Research (Winter): 843-84.
______, G.V. Krishnan and G.J. Lobo. 2008. Is the Market Valuation of Banks’ Loan
Loss Provision Conditional on Auditor Reputation? forthcoming in Journal of Banking &
Finance.
Krishnan, G.V. 2003a. Audit quality and the pricing of discretionary accruals. Auditing:
A Journal of Practice & Theory (March): 109-126.
_____. 2003b. Does Big 6 auditor industry expertise constrain earnings management?
Accounting Horizons (Supplement): 1-16.
_____. 2005. The association between Big 6 auditor industry expertise and the
asymmetric timeliness of earnings. Journal of Accounting, Auditing & Finance
(Summer): pp. 209-228.
Krishnan, J., H. Sami, and Y. Zhang. 2005. Does the provision of nonaudit services affect
investor perceptions of auditor independence? Auditing: A Journal of Practice & Theory
(November): 111-135.
Maletta, M., and A. Wright. 1996. Audit evidence planning: an examination of industry
error characteristics. Auditing: A Journal of Practice & Theory (Spring): 71-86.
McNichols, M. F. 2000. Research design issues in earnings management studies. Journal
of Accounting and Public Policy 19 (4-5): 313-345.
_____. 2002. Discussion of the quality of accruals and earnings: the role of accrual
estimation errors. The Accounting Review 77 (Supplement): 61-69.
O’Keefe, T., R. King, and K. Gaver. 1994. Audit fees, industry specialization, and
compliance with GAAS reporting standards. Auditing: A Journal of Practice &Theory
(Fall): 41-55.
34
Plitch, P. 2003. Corporate governance gets a rating. Wall Street Journal (February 26):
B6.
Simunic, D. 1984. Auditing, consulting, and auditor independence. Journal of Accounting
Research (Autumn): 679-702.
Solomon, I., M. Shields, and R. Whittington. 1999. What do industry-specialist auditors
know? Journal of Accounting Research (Spring): 191-208.
Srinidhi, B.N., and F.A. Gul. 2007. The Differential Effects of Auditors' Nonaudit and
Audit Fees on Accrual Quality. Contemporary Accounting Research 24(2): 595-629.
Subramanyam, K. 1996. The pricing of discretionary accruals. Journal of Accounting
and Economics 22 (Aug-Dec): 249-281.
Teoh, S., and T. Wong. 1993. Perceived auditor quality and earnings response
coefficient. The Accounting Review 68 (April): 346-367.
Wahlen, J.M. 1994. The nature of information in commercial bank loan loss disclosures.
The Accounting Review (July): 455-478.
Weil, J., and J. Tannenbaum. 2001. Big companies pay audit firms more for other
services. Wall Street Journal (April 10): C1.
Wright, S., and A. Wright. 1997. The effect of industry experience on hypothesis
generation and audit planning decisions. Behavioral Research in Accounting (9): 273294.
35
TABLE 1
Descriptive statistics (N=1,796)
Variable
ALL
DALL
MVE
1/GBV
CO
LOAN
NPL
NI
BTCAPITAL
SMALL
BIG5
KPPwC
LAUDFEE
LNONAUDFEE
ALL
DALL
MVE
1/GBV
CO
LOAN
NPL
NI
BTCAPITAL
SMALL
BIG5
KPPwC
LAUDFEE
LNONAUDFEE
Mean
Std. Dev. 1st quartile
Median
3rd quartile
0.0952
0.0358
0.0742
0.0915
0.1108
0.0000
0.0263
-0.0128
-0.0020
0.0110
2.7805
2.3348
1.6382
2.1678
3.1501
0.0114
0.0162
0.0030
0.0086
0.0159
0.0188
0.0272
0.0051
0.0123
0.0231
6.9250
1.8897
5.7810
6.7650
7.9650
0.0406
0.0441
0.0124
0.0293
0.0535
0.1345
0.0514
0.1091
0.1348
0.1598
13.5847
3.3981
10.0000
10.0000
10.0000
0.1976
0.3983
0.0000
0.0000
0.0000
0.5214
0.4997
0.0000
1.0000
1.0000
0.2938
0.4556
0.0000
0.0000
1.0000
12.2113
1.1637
11.4000
12.1000
12.8000
11.2140
1.5080
10.3000
11.1000
12.0000
= allowance for loan loss deflated by gross book value (net book
value of common equity plus total allowance for loan losses);
= estimated discretionary portion of the allowance from Equation (1);
= market value of equity deflated by gross book value;
= one over gross book value;
= loan charge-offs deflated by gross book value;
= total loans deflated by gross book value;
= nonperforming loans deflated by gross book value;
= earnings before loan loss provision scaled by gross book value;
= capital ratio at the beginning of the year;
= an indicator variable that equals to 1 if beginning assets < $500 million;
=0 for non-Big 5 client banks, 1 for Big 5 client banks;
=1 for KPMG and PwC and 0 for other auditors;
=natural logarithm of audit fees; and
=natural logarithm of nonaudit fees.
36
TABLE 2
Correlation Matrix and Pearson Coefficients (N=1,796)
ALL
DALL
MVE
DALL
MVE
1/GBV
CO
LOAN
NPL
NI
BTCAPITAL
SMALL
BIG5
KPPwC
LAUDFEE
LNONAUDFEE
0.6856
0.1180
0.1562
0.4532
0.5164
0.3300
0.0903
-0.3330
-0.0539
-0.0027
-0.0040
-0.0253
0.0610
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.0001
<.0001
0.0223
0.9098
0.8640
0.2840
0.0097
1.0000
0.0750
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0431
0.0589
0.0191
-0.0925
0.0015
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.0677
0.0125
0.4184
<.0001
1.0000
1/GBV
0.1269
-0.0437
0.1904
-0.0401
0.1785
-0.1489
0.1229
0.1352
0.1222
-0.0949
0.0494
<.0001
0.0641
<.0001
0.0894
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.0364
1.0000
-0.0091
0.2477
0.0120
-0.3194
0.0612
0.5206
-0.3053
-0.1389
-0.4262
-0.2986
0.7012
<.0001
0.6128
<.0001
0.0094
<.0001
<.0001
<.0001
<.0001
<.0001
1.0000
0.0426
0.4232
0.1789
-0.1370
-0.0665
0.0785
-0.0461
0.1073
0.1425
0.0711
<.0001
<.0001
<.0001
0.0048
0.0009
0.0506
<.0001
<.0001
1.0000
0.1235
0.1799
-0.4993
0.0169
-0.1019
-0.0402
-0.1331
-0.0908
<.0001
<.0001
<.0001
0.4739
<.0001
0.0886
<.0001
0.0001
1.0000
0.0897
-0.1358
-0.0110
-0.0198
-0.0332
-0.0298
-0.0266
0.0001
<.0001
0.6401
0.4026
0.1598
0.2068
0.2599
-0.2091
-0.1988
0.1611
0.0138
0.1168
0.1529
<.0001
<.0001
<.0001
0.5578
<.0001
<.0001
1.0000
0.1833
-0.1446
-0.0966
-0.1710
-0.1826
<.0001
<.0001
<.0001
<.0001
<.0001
1.0000
-0.3444
-0.1605
-0.4462
-0.3217
<.0001
<.0001
<.0001
<.0001
CO
LOAN
NPL
NI
BTCAPITAL
SMALL
BIG5
1.0000
KPPwC
LAUDFEE
0.6180
0.4900
0.4112
<.0001
<.0001
<.0001
1.0000
0.2132
0.1598
<.0001
<.0001
1.0000
0.6130
<.0001
All variables are defined in Table 1.
TABLE 3
Estimation and Market Valuation of Discretionary and Nondiscretionary
Allowance for Loan Losses
Panel A: Estimating Discretionary Loan Loss Allowance
ALLit = γ0 (1/GBV) + γ1COit + γ2 LOANit + γ3NPLit + γ4NIit + γ5TCAPITALit
+ γ6SMALLit+ Year Controls + eit
Variable
Pred. Sign
Coefficient
t-statistic
1/GBV
?
0.1515
1.90*
CO
+
0.4761
19.47***
LOAN
+
0.0104
33.46***
NPL
+
0.1037
6.90***
NI
+
0.0261
1.97**
BTCAPITAL
?
0.0001
3.78***
SMALL
-
-0.0007
-3.30***
Year Controls
Yes
N
1796
F Value
3629.58
Adj. R2
93.38%
Notes:
1. Variable Definitions:
ALL
1/GBV
CO
LOAN
NPL
NI
BTCAPITAL
SMALL
= allowance for loan loss deflated by gross book value (net book
value of common equity plus total allowance for loan losses);
= one over gross book value;
= loan charge-offs deflated by gross book value;
= total loans deflated by gross book value;
= nonperforming loans deflated by gross book value;
=earnings before loan loss provision scaled by gross book value;
= capital ratio at the beginning of the year; and
= an indicator variable that equals to 1 if beginning assets < $500 million.
2. *** significant at 0.01 level, ** significant at 0.05 level, * significant at 0.10 level.
Significance levels are based on one-tailed tests when the coefficient sign is predicted and
on two-tailed tests otherwise.
TABLE 3 (cont.)
Panel B: Market Valuation of Components of Loan Loss Allowance
MVE = β0 + β1ALL + β2DALL + β3NPL + β4NI_POS + β5NI_NEG + Year Controls + ε
Variable
Pred. Sign
Coefficient
t-statistic
Intercept
?
0.1175
7.56***
ALL
-
-3.7257
-2.14**
DALL
+
4.8220
2.36***
NPL
-
-2.2558
-2.92***
NI_POS
+
8.1986
12.12***
NI_NEG
+
1.3266
0.44
Year Controls
Yes
N
1796
F Value
57.21
Adj. R2
25.62%
Notes:
1. Variable Definitions:
MVE
ALL
DALL
NPL
NI_POS
NI_NEG
= market value of equity deflated by gross book value;
= allowance for loan loss deflated by gross book value;
= estimated discretionary portion of the allowance from Equation [1];
= nonperforming loans deflated by gross book value;
= net income before total provision for loan losses for firms with
positive net income deflated by gross book value; and
= net income before total provision for loan losses for firms with
negative net income deflated by gross book value.
2. *** significant at 0.01 level, ** significant at 0.05 level, * significant at 0.10 level.
Significance levels are based on one-tailed tests when the coefficient sign is
predicted and on two-tailed tests otherwise.
39
TABLE 4
Effect of Auditor Type on Market Valuation of Discretionary Allowance for Loan
Losses
Panel A: Valuation of Discretionary Allowance for Big5 vs. Non-Big5
MVE = β0 + β1BIG5 + β2ALL + β3DALL + β4BIG5*ALL + β5BIG5*DALL + β6NPL
+ β7NI_POS + β8NI_NEG + Year Controls + ε
Variable
Intercept
BIG5
ALL
DALL
BIG5*ALL
BIG5*DALL
NPL
NI_POS
NI_NEG
Year Controls
Pred. Sign
?
Coefficient
0.0641
t-statistic
2.96***
?
-
0.0918
1.7745
1.3735
-8.7944
10.5159
-2.5879
8.1103
1.3767
Yes
3.56***
0.75
0.50
-3.14***
2.94***
-3.34***
11.86***
0.45
+
?
+
+
+
1796
46.38
26.14%
N
F Value
Adj. R2
Notes:
1. Variable Definitions:
BIG5
ALL
DALL
NPL
NI_POS
NI_NEG
= 0 for non-Big 5 client banks, 1 for Big 5 client banks,
= allowance for loan loss deflated by gross book value;
= estimated discretionary portion of the allowance from Equation [1];
= nonperforming loans deflated by gross book value;
= net income before total provision for loan losses for firms with
positive net income deflated by gross book value; and
= net income before total provision for loan losses for firms with
negative net income deflated by gross book value.
2. *** significant at 0.01 level, ** significant at 0.05 level, * significant at 0.10 level.
Significance levels are based on one-tailed tests when the coefficient sign is
predicted and on two-tailed tests otherwise.
40
TABLE 4 (cont.)
Panel B: BIG 5/non-Big 5 Self-selection Model
BIG5 = α0 + α1ROA + α2ROA*LOSS + α3LnLOAN + α4CHLOAN + α3TCAPITAL
+ α4LOANRATIO + α5NPLRATIO + ε
Variable
Intercept
ROA
ROA*LOSS
LnLOAN
CHLOAN
TCAPITAL
LOANRATIO
NPLRATIO
N
Log likelihood ratio
Predicted
Sign
?
+
?
+
+
+
+
Co-efficient
-1.1263
17.5859
-30.3901
0.6412
-0.0000
-0.0697
-3.7174
0.0001
1784
-934.24
Pr > Chi Sq
0.026
0.049
0.109
0.001
0.9280
0.001
0.001
0.041
Notes:
1. Variable Definitions:
BIG5
ROA
LOSS
LnLOAN
CHLOAN
TCAPITAL
LOANRATIO
NPLRATIO
=
=
=
=
=
=
=
=
0 for non-Big 5 client banks, 1 for Big 5 client banks,
net income over lagged total assets;
an indicator variable that equals 1 the firm has a loss and 0 otherwise ;
natural log of total loans outstanding;
change in total loans outstanding
risk adjusted total capital ratio (measured at year-end);
total loans outstanding divided by total assets; and
nonperforming loans divided by total loans.
TABLE 4 (cont.)
Panel C: Valuation of Discretionary Allowance after Controlling for BIG 5/non-Big 5
Self-selection
MVE = β0 + β1BIG5 + β2ALL + β3DALL + β4BIG5*ALL + β5BIG5*DALL + β6NPL
+ β7NI_POS + β8NI_NEG + β9IMR + Year Controls + ε
Variable
Intercept
BIG5
Pred. Sign
?
?
+
ALL
DALL
BIG5*ALL
BIG5*DALL
NPL
NI_POS
NI_NEG
IMR
Year Controls
+
+
+
?
Coefficient
0.0973
0.0546
t-statistics
4.31***
2.06**
-0.1512
0.4078
-9.3792
11.2037
-2.4153
8.6861
1.3871
0.0358
-0.07
0.15
-3.34***
3.13***
-3.12***
12.53***
0.46
4.80***
Yes
1784
45.04
27.02%
N
F Value
Adj. R2
Notes:
1. Variable Definitions:
MVE
BIG5
ALL
DALL
NPL
NI_POS
NI_NEG
IMR
= market value of equity deflated by gross book value;
= 0 for non-Big 5 client banks, 1 for Big 5 client banks,
= allowance for loan loss deflated by gross book value;
= estimated discretionary portion of the allowance from Equation [1];
= nonperforming loans deflated by gross book value;
= net income before total provision for loan losses for firms with
positive net income deflated by gross book value;
= net income before total provision for loan losses for firms with
negative net income deflated by gross book value; and
= Inverse Mills Ratio.
2. *** significant at 0.01 level, ** significant at 0.05 level, * significant at 0.10 level.
Significance levels are based on one-tailed tests when the coefficient sign is
predicted and on two-tailed tests otherwise.
42
TABLE 5
Effect of Auditor Expertise on Valuation of Discretionary Allowance for Loan
Losses
MVE = β0 + β1OTHBIG5 + β2KPPwC + β3ALL + β4DALL + β5OTHBIG5*ALL
+ β6OTHBIG5*DALL + β7KPPwC*ALL + β8KPPwC*DALL + β9NPL
+ β10NI_POS + β11NI_NEG + Year Controls + ε
Variable
Pred.
Sign
Model 1
Coefficient
0.0942
Model 2
t-statistics
5.45***
Coefficient
0.0786
t-statistics
3.91***
0.1031
3.54***
0.0467
0.1187
1.44
3.85***
0.27
-0.21
-1.30
1.74**
-2.92***
2.92***
-3.20***
11.24***
0.50
Intercept
?
OTHBIG5
KPPwC
?
?
ALL
DALL
OTHBIG5*ALL
OTHBIG5*DALL
KPPwC*ALL
KPPwC*DALL
NPL
+
?
+
?
+
-
-0.9653
1.8936
-0.49
0.84
-8.2238
11.1010
-2.3880
-2.61***
2.50***
-3.09***
0.5873
-0.5311
-4.6913
7.8581
-9.6868
13.4483
-2.4716
NI_POS
NI_NEG
Year Controls
N
F Value
Adj. R2
+
+
7.7116
1.9641
11.29***
0.65
7.7059
1.5428
Yes
1796
47.32
26.54%
Yes
1796
39.21
26.57%
Notes:
1. Variable Definitions:
OTHBIG5
= 1 for Arthur Andersen or D&T or E&Y and 0 for other auditors;
KPPwC
=1 for KPMG or PwC and 0 for other auditors;
ALL
=allowance for loan loss deflated by gross book value;
DALL
= estimated discretionary portion of the allowance from Equation [1];
NPL
= nonperforming loans deflated by gross book value;
NI_POS
= net income before total provision for loan losses for firms with
positive net income deflated by gross book value; and
NI_NEG
= net income before total provision for loan losses for firms with
negative net income deflated by gross book value.
2. *** significant at 0.01 level, ** significant at 0.05 level, * significant at 0.10 level.
Significance levels are based on one-tailed tests when the coefficient sign is
predicted and on two-tailed tests otherwise.
43
TABLE 6
Effect of Audit and Nonaudit Fees on Valuation of Discretionary Allowance for
Loan Losses
Panel A: Valuation of Discretionary Allowance for High/Low Audit/Nonaudit Fees
MVE = β0 + β1FEE + β2ALL + β3DALL + β4ALL*FEE + β5DALL*FEE + β6NPL
+ β7NI_POS + β8NI_NEG + Year Controls + ε
Variable
Intercept
FEE
ALL
DALL
ALL*FEE
DALL*FEE
NPL
NI_POS
NI_NEG
Year Controls
N
F Value
Adj. R2
Pred. Sign
Audit Fee
Nonaudit Fee
?
?
+
?
+/+
Coefficient
0.0597
0.1027
2.7028
-0.3753
-11.2341
9.4840
-2.4482
8.0731
t-statistics
2.84***
4.01***
1.16
-0.14
-4.06***
2.68***
-3.17***
11.87***
Coefficient
0.0893
0.0509
-0.2298
1.4258
-6.4596
6.1432
-2.2559
8.2009
t-statistics
4.28***
1.99**
-0.10
0.48
-2.33**
1.51
-2.92***
12.09***
+
2.6294
0.85
0.6744
0.22
Yes
1796
46.51
26.20%
Yes
1796
45.58
25.80%
Notes:
1. Variable Definitions:
FEE
=1 if audit fee or nonaudit fee is above median for a given year and zero
otherwise
ALL
=allowance for loan loss deflated by gross book value;
DALL
= estimated discretionary portion of the allowance from Equation [1];
NPL
= nonperforming loans deflated by gross book value;
NI_POS
= net income before total provision for loan losses for firms with
positive net income deflated by gross book value; and
NI_NEG
= net income before total provision for loan losses for firms with
negative net income deflated by gross book value.
2. *** significant at 0.01 level, ** significant at 0.05 level, * significant at 0.10 level.
Significance levels are based on one-tailed tests when the coefficient sign is
predicted and on two-tailed tests otherwise.
44
TABLE 6 (cont.)
Panel B: Estimation of Unexpected (Abnormal) Audit/Nonaudit Fees
FEE = α0 + α1 BIG5 + α2 LnASSETS + α3 NPL + α4 LOSS + α5 LCO+ α6 COMM + α7 CON
+ α8 RESTATE + α9 TCAPITAL + Year Controls + ε
Variable
Pred.
Sign
Intercept
BIG5
?
+
LnASSETS
NPL
LCO
LOSS
COMM
CON
RESTATE
TCAPITAL
Year Controls
+
+
+
+
+
+
+
+
Audit Fees
NonAudit Fee
Coefficient
7.74817
0.34462
t-statistics
69.85***
11.19***
Coefficient
5.2834
0.0249
t-statistics
25.28***
0.43
0.59409
54.76***
0.7337
35.90***
11.2952
13.42251
0.20663
0.2258
-0.21973
-0.14387
0.00524
3.44***
3.45***
1.59*
4.52***
-1.72
-3.71***
1.33*
20.6467
10.5107
0.2884
0.2856
-0.5277
-0.1034
-0.0218
3.34***
1.43*
1.18
3.03***
-2.19
-1.41
-2.94***
N
F-value
Adj. R2
Yes
Yes
1779
442.73
78.84%
1779
147.42
55.26%
Notes:
1. t-values are presented in parentheses
2. Variable Definitions:
FEE
=
natural log of audit or nonaudit fees;
BIG5
=
indicator variable set equal to 1 if audited by a Big 5 firm, and 0 otherwise;
LnASSETS
=
natural log of total assets;
NPL
=
nonperforming loans over lagged total loans;
LOSS
=
an indicator variable that equals 1 the firm has a loss and 0 otherwise ;
LCO
=
net loan charge-offs over loan loss allowance;
COMM
=
total commercial and agriculture loans over total loans;
CON
=
total consumer loans over total loans;
RESTATE
=
total real estate loans over total loans; and
TCAPITAL
=
total risk-adjusted capital ratio.
3. *** significant at 0.01 level, ** significant at 0.05 level, * significant at 0.10 level.
Significance levels are based on one-tailed tests when the coefficient sign is
predicted and on two-tailed tests otherwise.
45
TABLE 6 (cont.)
Panel C: Valuation of Discretionary Allowance for High/Low Abnormal
Audit/Nonaudit Fees
MVE = β0 + β1AFEE + β2ALL + β3DALL + β4ALL*AFEE + β5DALL*AFEE + β6NPL
+ β7NI_POS + β8NI_NEG + Year Controls + ε
Variable
Pred.
Sign
?
?
Intercept
AFEE
ALL
DALL
ALL*AFEE
DALL*AFEE
NPL
NI_POS
NI_NEG
Year Controls
+
?
+/+
-
N
F Value
Adj. R2
Audit Fee
NonAudit Fee
Coefficient t-statistics
0.1185
7.70***
0.0517
2.69***
-4.4122
-2.64***
5.9967
3.08***
-4.8183
-2.38**
7.9008
2.97***
-2.9843
-4.04***
8.8099
12.75***
0.8827
0.29
Yes
Coefficient t-statistics
0.1147
7.42***
-0.0112
-0.92
-3.4945
-2.07**
4.9331
2.54***
0.8848
0.67
-0.6608
-0.35
-2.9737
-4.01***
8.3782
12.08***
1.2825
0.42
Yes
1779
46.21
26.25%
1779
45.30
25.86%
Notes:
1. Variable Definitions:
AFEE
ALL
DALL
NPL
NI_POS
NI_NEG
=abnormal audit or nonaudit fees;
=allowance for loan loss deflated by gross book value;
= estimated discretionary portion of the allowance from Equation [1];
= nonperforming loans deflated by gross book value;
= net income before total provision for loan losses for firms with
positive net income deflated by gross book value; and
= net income before total provision for loan losses for firms with
negative net income deflated by gross book value.
2. *** significant at 0.01 level, ** significant at 0.05 level, * significant at 0.10 level.
Significance levels are based on one-tailed tests when the coefficient sign is
predicted and on two-tailed tests otherwise.
46