AN EMPIRICAL ANALYSIS OF AUDITOR INDEPENDENCE IN THE
BANKING INDUSTRY
Kiridaran Kanagaretnam
DeGroote School of Business
McMaster University
1280 Main Street West
Hamilton, Ontario, Canada L8S 4M4
Phone: (905) 525-9140 ext 27857
Fax: (905) 521-8995
E-mail: giri@mcmaster.ca
Gopal V. Krishnan
Department of Accounting
College of Business and Economics
Lehigh University
Bethlehem, PA 18015
Phone : (610)-758-3451
E-mail: gok208@lehigh.edu
Gerald J. Lobo*
C. T. Bauer College of Business
University of Houston
Houston, TX 77204-6021
Tel: (713) 743-4838
Fax: (713) 743-4828
E-mail: gjlobo@uh.edu
October 2008
*Corresponding author
We thank James Bierstaker, Jeff Chen, Chris Jones, Sok-Hyon Kang, Krishna Kumar, Ying Li, Lihong
Liang, Jim Largay III, Erin Moore, Nandu Nayar, Dan Neeley, Mike Peters, H. Sami, Mary Sullivan, Bill
Zhang and seminar participants at George Washington University, Hong Kong Polytechnic University,
Lehigh University, McMaster University, University of Queensland, for their helpful suggestions.
Kanagaretnam and Lobo thank the Social Sciences and Humanities Research Council of Canada (SSHRC)
for its financial support.
AN EMPIRICAL ANALYSIS OF AUDITOR INDEPENDENCE IN THE
BANKING INDUSTRY
Abstract
We examine auditor independence in the banking industry by analyzing the relation
between fees paid to the auditors of banks and the extent of earnings management
through loan loss provisions. We also examine whether this relation differs across large
banks whose managements are required under the Federal Deposit Insurance Corporation
Improvement Act of 1991 to evaluate the internal control over financial reporting and
whose auditors must attest to the report on the effectiveness of internal controls over
financial reporting, and small banks that are not subject to such controls. Our results
indicate a positive association between fees paid to the auditor and income-increasing
earnings management through loan loss provisions. They suggest that, although banks
face high levels of regulatory scrutiny, economic bonding between the auditor and the
bank potentially impairs auditor independence. Our findings also indicate that this
bonding is stronger for smaller banks that are subject to less regulatory oversight than are
larger banks. Our results also suggest that the economic bond between the auditor and the
bank is reflected in delayed recognition of loan write-offs and in a higher incidence of
earnings benchmark beating behavior.
Keywords: Auditor independence, earnings management, auditor fees, bank loan loss
provision, FDICIA.
1
AN EMPIRICAL ANALYSIS OF AUDITOR INDEPENDENCE IN THE
BANKING INDUSTRY
I. INTRODUCTION
Auditor independence is vital to maintaining public confidence in the capital
markets and to the integrity of corporate financial statements. The objective of this study
is to examine auditor independence in the banking industry. Banks represent more than
20% of the total public equity market and are vital to the functioning of the economy as a
whole. Fields et al. (2004, p. 54) state, “Despite the economic importance of the banking
industry, however, accounting researchers have done little to investigate the various
relationships that exist between banks and their auditors.” Specifically, we provide
empirical evidence on the relation between fees paid to auditors of banks and the extent
of earnings management via loan loss provisions (LLP).
The banking industry offers a unique context to study auditor independence for a
number of reasons. First, banks are subject to the scrutiny of the FDIC, the Federal
Reserve Board, and other governmental agencies.
Whether this intense regulatory
oversight enhances auditor independence is clearly an important and relevant question.
Second, external audits are required for all national banks with $500 million or more in
total assets.1 Additionally, 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 reforms on each depository institution with assets exceeding $500 million
and on its auditors (Murphy 2004). Section 112 of the Act requires the management of
these institutions to evaluate the internal control over financial reporting and the auditor
1
The Office of the Comptroller of the Currency strongly encourages smaller national banks to have
external audits performed by independent public accountants. Hawke (2000) reports that the vast majority
of the smaller banks has established some type of external audit program.
2
must attest to the report on the effectiveness of internal controls over financial reporting. 2
Whether auditor independence is greater for larger banks that are subject to greater
regulatory oversight relative to smaller banks is also an interesting and important
question. Currently, there is very little empirical evidence on whether the reforms
initiated by FDICIA contributed to enhanced auditor independence.
Third, bank LLP is well-suited to studying earnings management for the
following reasons. LLP is by far the largest and most important accrual for banks. The
mean (median) ratio of LLP to earnings before LLP is 19.8% (15.1%) for our sample
firms. Further, prior research indicates that banks use LLP to manage earnings (Wahlen
1994; Kanagaretnam et al. 2003; 2004). To the extent that banks can leverage “fee
dependence” to influence their auditors to accept abnormal LLP, examining the relation
between abnormal LLP and auditor fees is likely to reveal such a linkage. We believe
that abnormal LLP is a better proxy for earnings management than the abnormal accrual
measures used in prior research.3 This study mitigates error in measuring managerial
discretion by focusing on a single accrual and a single industry. Focusing on a single
accrual facilitates a sharper separation into its normal (nondiscretionary) and abnormal
(discretionary) components. We use a number of industry-specific variables to better
isolate the abnormal LLP from the normal LLP. Also, focusing on a single, relatively
homogeneous industry provides control over other determinants of cross-sectional
2
However, FDICIA was a controversial legislation. While proponents of FDICIA argued that the reforms
could improve the financial health of the banking industry through better monitoring, opponents argued that
it is “regulatory overkill” (Kaufman 1994).
3
Prior research argues that measures of abnormal accruals commonly used to detect earnings management
are subject to serious measurement error (Guay, Kothari, and Watts 1996; McNichols 2000; 2002; and
Jones, Krishnan, and Melendrez 2008).3 For example, McNichols (2002, page 68) states, “the complexity
associated with modeling the estimation errors in aggregate accruals is daunting, and the construct validity
associated with a proxy based on aggregate accruals seems low.”
3
differences in accruals, thus increasing the reliability of the inferences from our empirical
analysis.
Our sample consists of 1,810 bank-year observations representing years 20002006. We estimate abnormal LLP as residuals from a regression of LLP on beginning
loan loss allowance, total loans outstanding, changes in total loans outstanding, net loan
charge-offs, beginning balance of non-performing loans, change in non-performing loans,
loan mix, and control for years (see Kanagaretnam et al. 2004 and Wahlen 1994). Next,
we estimate a regression of abnormal LLP on fee measures, an indicator variable for
small banks (with total assets of $500 million or less) that are subject to less regulation,
the interaction of the fee and the indicator variable for small banks, auditor type (Big 5
vs. non-Big 5 auditor), and several control variables. We use various measures of fees to
capture an auditor’s economic dependence on the client, including audit fees, nonaudit
fees, total fees, ratio of nonaudit fees to total fees (fee ratio), abnormal (unexpected) audit
fees, abnormal nonaudit fees, and abnormal total fees.
We report several key findings. First, we find that the level of total fee, nonaudit
fee, and audit fee are negatively and significantly associated with negative (incomeincreasing) abnormal LLP for all banks. In other words, bank-year observations with
higher fees exhibit higher income-increasing LLP. Similarly, total and audit fees are
positively associated with positive (income-decreasing) abnormal LLP for all banks.
However, when we use abnormal (unexpected) fee measures, we find a significant
relation between abnormal total fee and abnormal nonaudit fee only with negative
(income-increasing) abnormal LLP. Taken together, these results suggest that the
economic bond (fee dependence) between the auditor and the bank appears to exacerbate
earnings management by banks.
4
Second, in a regression of negative (income-increasing) abnormal LLP, the
coefficient on the interaction of fee and the indicator variable for small banks is negative
and significant for all four fee measures (i.e., total fee, nonaudit fee, audit fee, and fee
ratio). We do not observe such a relation for positive (income-decreasing) abnormal
LLP. These results indicate that small banks engage in more income-increasing earnings
management and there is stronger economic bonding between the small banks and their
auditors. The results also indicate that, while the reforms of the FDICIA are effective at
reducing auditor bonding for large banks, they do not fully eliminate it.
Third, we examine whether current-period auditor fees are associated with nextperiod bank loan charge-offs. We find that after controlling for size, current-period LLP,
and current-period charge-offs, the level of total fee, nonaudit fee, and audit fee are all
positively and significantly associated with next-period charge-offs. We obtain similar
results when we use unexpected fee measures. Given that bank managers have incentives
to postpone writing off bad loans to future periods so that the current period’s financial
position is presented in a favorable manner, our results suggest that the economic bond
between the auditor and the bank is associated with delayed recognition of loan chargeoffs. This finding also holds when we replace actual fees with unexpected fees.
Finally, for the (income-increasing) abnormal LLP sub-sample, we examine the
association between auditor fees and beating earnings benchmarks as do Frankel et al.
(2002) and Ashbaugh et al. (2003). The results indicate a significant, positive relation
between our proxy for benchmark beating behavior and three fee measures – total fee,
nonaudit fee, and audit fee. We obtain similar results when we use unexpected fee
measures.
Our findings indicate that economic bonding between the auditor and the bank is
5
associated with income-increasing earnings management through loan loss provisions.
They also indicate that this bonding is stronger for small banks. Collectively, our results
suggest that auditor fee dependence on the audit client is a threat to auditor independence,
particularly among banks with less than $500 million in assets that are not subject to the
same level of regulatory scrutiny as larger banks.
The rest of this paper is organized as follows. The next section develops the
empirical models used to estimate abnormal LLP and test the relation between abnormal
LLP and fees paid to auditors. Section three describes the sample selection, section four
discusses the results, and section five provides the conclusions of the study.
II. RESEARCH DESIGN
Our conceptual model, described in Figure 1, is based on the predictive validity
model of Kinney and Libby (2002) that was developed by Runkel and McGrath (1972)
and Libby (1981). As in prior research, the economic bond to the audit client and
earnings management by the client (link 1) are the two theoretical constructs we examine.
We identify proxies to measure the economic bond and earnings management because
these two concepts are unobservable. We use multiple fee measures to capture the
economic bond (link 2). Kinney and Libby (2002) state that “more insidious effects on
the economic bond may result from unexpected nonaudit and audit fees that may more
accurately be likened to attempted bribes.” We use unexpected audit, nonaudit, and total
fees as measures of the economic bond and the abnormal component of a bank’s LLP as
the measure of earnings management (link 3). We examine banking firms because they
provide a setting where a stronger link 3 than used in prior research is possible. By using
an industry-specific measure of earnings management, we are better able to separate the
discretionary accruals from the nondiscretionary accruals than has prior research. We
6
control for several bank characteristics that may affect LLP (link 4). We then examine the
relation between the various fee measures and abnormal LLP (link 5) to draw inferences
about link 1. We also conduct two supplementary tests of link 5. First, we examine the
relation between the various fee measures and future loan charge-offs. Second, we study
the extent to which banks use abnormal LLP to meet earnings benchmarks. We describe
these tests in a later section.
[Insert Figure 1 About Here]
We use a two-stage approach to examine link 5. First, we describe the model
used to estimate abnormal LLP (link 4). We first estimate the normal or nondiscretionary
component of LLP by regressing LLP on beginning loan loss allowance, beginning
balance of non-performing loans, change in non-performing loans, net loan charge-offs,
changes in total loans outstanding, total loans outstanding, loan mix, and controls for
period effects using the following model:4
LLPit = γ0 + γ1 BEGLLA + γ2 BEGNPL + γ3 CHNPL + γ4 LCO + γ5 CHLOANS
+ γ6 LOANS + <LOAN CATEGORIES> + <YEAR CONTROLS> + eit
(1)
We define the variables as follows (all variables are deflated by beginning total assets):5
LLP
= Provision for loan losses;
BEGLLA
= Beginning loan loss allowance;
BEGNPL
= Beginning nonperforming loans;
CHNPL
= Change in nonperforming loans;
LCO
= Net loan charge-offs;
4
These variables have also been used in several prior studies (Wahlen 1994; Beaver and Engel 1996; and
Kim and Kross 1998) to estimate the normal component of LLP.
5
We also use beginning total loans as an alternate deflator. Our results are not sensitive to this choice of
deflator.
7
CHLOANS
= Change in total loans outstanding;
LOANS
= Total loans outstanding; and
LOAN CATEGORIES
= amount of commercial loans (COMM), consumer
loans (CON), real estate loans (RESTATE), agriculture
loans (AGRI), loans to foreign banks and governments
(FBG), and loans to other depository institutions
(DEPINS).
The residuals from model (1) are the abnormal component of LLP, referred to as
ALLP. We expect a negative coefficient on BEGLLA (i.e., the accumulated LLP less
write-offs at the beginning of the year) as a higher initial loan loss allowance will require
a lower LLP in the current period. Consistent with prior research, we expect γ2, γ3, γ4 and
γ6 to be positive. Higher levels of nonperforming loans indicate problems with the loan
portfolio will require higher loss provisions. Therefore, the beginning balance of
nonperforming loans (BEGNPL) will be positively related to LLP. Change in
nonperforming loans (CHNPL) in the current period will also have a positive effect on
LLP because an increase in nonperforming loans will require a higher loss provision in
the current period. The amount of net loan charge-offs (LCO) is positively related to LLP.
As noted in Beaver and Engel (1996), “current loan charge-offs can provide information
about future loan charge-offs which, in turn, may influence expectations of the
collectability of current loans” and, hence, current LLP. A higher level of loans (LOANS)
will also require higher provisions. We do not offer a prediction for γ5 because the effect
of change in total loan portfolio on LLP is unpredictable due to the uncertainty in the
quality of incremental loans.
Although nonperforming loans and loan charge-offs serve as measures of risk, we
include six additional variables to control for differences in loan composition which also
8
likely contribute to differences in risk. For example, banks with higher proportions of
commercial and real estate loans are likely to have higher loan loss provisions than banks
with higher proportions of consumer loans. Failure to account for these differences makes
the residuals from the loan loss provision model a function of the bank type. This, in turn,
may affect the inferences of our auditor independence tests because both audit fees and
ALLP may systematically vary across banks based on their loan portfolio mix.
The six loan portfolio composition variables included in the model are
commercial loans (COMM), consumer loans (CON), real estate loans (RESTATE),
agriculture loans (AGRI), loans to foreign banks and governments (FBG), and loans to
other depository institutions (DEPINS).
We also include six year-dummy variables
representing years 2000 through 2005 in model (1) to control for period-specific effects.
Next, we test the association between the signed values of abnormal LLP (ALLP)
and auditor fees separately for negative (income-increasing) ALLP and positive (incomedecreasing) ALLP.6 Negative ALLP are of particular interest because of their positive
impact on reported earnings. We control for the following factors that prior research has
documented are associated with abnormal accruals (Ashbaugh et al. 2003): firm size,
auditor type, market-to-book ratio, level of accruals, and performance. We use log of
market value of equity to measure size. We represent performance by two variables,
existence of loss and earnings before LLP, and growth by market-to-book ratio. We use
past LLP to capture the reversal of accruals over time. To control for any capital
management incentives, we include the beginning of year tier 1 capital ratio and total
6
Hribar and Nichols (2007) argue that the research design choices using unsigned measures of earnings
management heighten the threat of correlated omitted variables and recommend using signed discretionary
accruals to study earnings management.
9
capital ratio.7 Our model is as follows:
ALLP = β0 + β1 FEE + β2 BIG5 + β3 MB + β4 LMVE+ β5 LOSS + β6 PASTLLP + β7 EBP
+ β8 TIER1t-1+ β9 TCAPt-1 + <YEAR CONTROLS> + ε
(2)
We define the variables as follows:
ALLP
=
Abnormal loan loss provision;
FEE
=
Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln
(audit fees + nonaudit fees)) or nonaudit fees (LNAFEE), or fee
ratio (FEERATIO = nonaudit fees/ total fees);
BIG5
=
Indicator variable set equal to 1 if audited by a Big 5 firm, and 0
otherwise;
MB
=
Market-to-book ratio at the end of the year;
LMVE
=
Natural log of market value of common equity;
LOSS
=
Indicator variable set equal to 1 if net income < 0, and 0
otherwise;
PASTLLP
=
prior year’s LLP divided by total assets at the
beginning of the year;
EBP
=
Net income before extraordinary items and loan loss provisions
divided by total assets at the beginning of the year;
TIER1
=
Tier 1 risk-adjusted capital at the beginning of the year; and
TCAP
=
Total risk adjusted capital at the beginning of the year.
The variable of interest in model (2) is FEE. For negative (income-increasing)
ALLPs, a negative coefficient on β1 is consistent with auditor fee dependence on the
client, i.e., higher fees are associated with greater (more negative) income-increasing
7
Banks had incentives to influence regulatory capital through LLP prior to the change in bank capital
adequacy requirements in 1990. This change altered banks’ incentives to manage capital through LLP as
loan loss allowance is no longer considered part of tier I or core capital. Therefore, we do not expect β 8 to
be significantly different from zero. Furthermore, loan loss allowance is included in tier II or
supplementary capital only up to 1.25 percent of risk-adjusted assets. Thus, we predict a negative
coefficient on β9 as higher capital ratios would require a lower LLP, ceteris paribus.
10
ALLP. Ashbaugh et al. (2003) argue that total fee, rather than the fee ratio is the more
appropriate measure of economic bonding.8 In addition to total fee we also use audit fee,
nonaudit fee, and the fee ratio (nonaudit fees/ total fees).
Next, we examine the impact of the FDICIA on auditor independence,
particularly on auditors of smaller banks, i.e., banks with total assets less than $500
million. Recall that FDICIA imposed new auditing, corporate reporting, and governance
reforms on each depository institution with assets exceeding $500 million and on its
auditors. We modify model (2) to include an indicator variable SMALL which equals one,
when beginning of the year total assets is lower than $500 million and also interact the
FEE variable with SMALL. We discuss sample selection in the next section.
III. SAMPLE SELECTION
We identify our sample banks from banks listed in the 2007 Bank Compustat
annual data files and obtain fees paid to the auditors for the years 2000-2006 from Audit
Analytics. The intersection of the Audit Analytics with Bank Compustat data results in an
initial sample of 2,044 bank-year observations. We hand collect non-performing loans
data for the period 1999-2006 from annual reports and obtain data on loan portfolio
composition from the Federal Reserve Bank Holding Company Database (call reports).
Our final sample with available data for all variables comprises 1,810 bank-year
observations for 304 banks.
Panel A of Table 1 reports descriptive statistics for the scaled variables used in the
regressions. More than 52% of the sample observations are audited by Big 5 auditors.
Note that the mean abnormal LLP (ALLP) is zero by construction. Turning to the bank
8
Kinney and Libby (2002) indicate that both the audit fee and the nonaudit fee are capable of increasing
the economic bond. They reason that an auditor might be willing to decrease the audit fee or the nonaudit
fee to maximize the potential total fee. Therefore, it is the total fee derived from a single client, rather than
just the audit fee, that better reflects the economic bond.
11
loan variables, the ratios of average LLP, loan charge-offs and beginning nonperforming
loans to beginning total assets are 0.003, 0.002 and 0.004, respectively. 9
[Insert Table 1 About Here]
Panel B of Table 1 reports correlations for the scaled dependent and independent
variables. As expected, LLP is positively correlated with non-performing loans
(BEGNPL), change in non-performing loans (CHNPL) and loan charge-offs (LCO). Also,
LLP is positively correlated with total loans (LOANS) and change in loans outstanding
(CHLOANS). The correlations between LLP and nonaudit fees and fee ratio are positive
and statistically significant at the 0.01 level while the correlations between LLP and total
fee and audit fee are not significant.
IV. RESULTS
Estimation of Abnormal LLP
We report the estimation results of model (1) in Table 2. The t-statistics reported
in Table 2 and in other tables are based on standard errors adjusted for firm level
clustering. The results in Table 2 show that the coefficients on the determinants of LLP
have the expected signs. The coefficients on BEGLLA, CHNPL, LCO and LOANS are
significant at the 0.01 level and the coefficient on BEGNPL is significant at the 0.10
level. Among the variables that reflect loan type, only commercial and consumer loans
9
These ratios are lower than the values reported in Wahlen (1994) and Kanagaretnam et al. (2004) due to
an increase in overall quality of loans (Edwards and Mishkin 1995). Similarly, our sample data indicate
that mean LLP equals 20 percent of earnings before provisions, considerably lower than the 148 percent
reported for Wahlen’s (1994) sample and lower than the 31 percent reported for Kanagaretnam et al.’s
(2004) sample.
12
are statistically significant. The explanatory power of the model is high (adjusted R2 =
63.15%) indicating that our model describes the variation in LLP quite well.10, 11
[Insert Table 2 About Here]
Association Between Income-increasing (negative) Abnormal LLP and Fee Measures
The results of model (2) relating abnormal LLP to the fee measures (total fee,
nonaudit fee, audit fee, and fee ratio) are reported in Tables 3 and 4. Table 3 reports the
results for negative (income-increasing) ALLP and Table 4 reports the results for positive
(income-decreasing) ALLP. We first discuss the results in Table 3. For each of the four
fee measure, we present the results for two specifications. The first specification does not
distinguish between small and large banks, while the second specification distinguishes
between small and large banks by addding the variable SMALL, which equals 1 for small
banks (with total assets less than $500 million) and 0 for large banks, and its interaction
with the fee variable to model (2). This specification examines whether the association
between ALLP and fees is greater for small banks than for large banks. Recall that the
FDICIA imposed new auditing, corporate reporting, and governance reforms on each
depository institution with assets exceeding $500 million and on its auditors. Therefore,
small banks are subject to less scrutiny than large banks, which may result in more
pronounced fee dependence for small banks relative to large banks.
10
Our model fit compares favorably with the results of prior research that focus on total accruals rather
than on a specific accrual as we do. For example, McNichols (2002) reports that when change in working
capital accruals is regressed on changes in sales and property, plant, and equipment, the adjusted R 2 for the
pooled sample is 7.3%. Ashbaugh et al. (2003) report mean R 2 values of 11% and 18% for their models of
abnormal accruals.
11
We also use an alternate specification where the dependent variable is LLP net of LCO. The adjusted R2
for this specification is 0.23. As before, the following variables continue to be significant at the 0.10 level
or better: BEGLLA, BEGNPL, CHNPL, LOANS, COMM, and CON. When we use ALLP obtained from
this specification in model (2), our results for negative ALLP indicate that the coefficient on the fee
measure for the main effect is not significant for any of the four fee measures. However, the coefficient on
the interaction of SMALL and the fee measure is negative and significant at the 0.001 for total fee, nonaudit
fee, and fee ratio (for a two-tailed test), and at the 0.05 level for audit fee.
13
The results indicate that three of the four fee measures (LTOTFEE, LNAFEE, and
LAFEE) are negatively and significantly (p < 0.01) associated with ALLP, indicating that
our results are not sensitive to the definition of the fee measure. Overall, the results
indicate that abnormal LLP, our measure of earnings management, is more negative (i.e.,
more income-increasing) for banks that pay higher fees to their auditors. In terms of the
control variables, LMVE, PASTLLP, and MB are significant. Interestingly there is no
difference in abnormal LLP between clients of BIG5 and non-BIG5 auditors. As
expected, the capital ratios are not significantly associated with ALLP, confirming the
reduced capital management incentives through ALLP in the post-1990 period.
Turning to the second specification, the results for the main effect (fee variable)
continue to hold for total fee (p < 0.10, one-tailed test), nonaudit fee, and audit fee. More
importantly, the coefficient on the interaction of SMALL and the fee measure is negative
and significant at the 0.05 level for total fee, nonaudit fee, and fee ratio. Furthermore, the
sum of the coefficients on FEE and FEE×SMALL for total fee and nonaudit fee is
significant at the 0.01 level. Overall, these results support the notion that small banks
engage in greater earnings management via income-increasing LLPs relative to large
banks, and suggest that the potential impairment of auditor independence is more serious
for small banks than for large banks.
[Insert Table 3 About Here]
Association Between Income-decreasing (positive) Abnormal LLP and Fee Measures
The results reported in Table 4 indicate that two of the four fee measures (log of
total fees (LTOTFEE) and log of audit fees (LAFEE) are positively and significantly
associated with ALLP at the 0.01 level. Nonaudit fee is marginally significant at the 0.10
14
level for a one-tailed test. The interesting finding in Table 4 is that the coefficient on the
interaction of SMALL and the fee measure is not significant for any of the four fee
measures. Recall that the interaction of SMALL and the fee measure is significant for total
fee, nonaudit fee, and fee ratio for income-increasing ALLP. These findings suggest that
auditor fee dependence is more problematic for small banks relative to large banks.
Further, note that after controlling for the effect of SMALL, LTOTFEE and LAFEE have a
stronger positive association with ALLP, indicating that the associations between
LTOTFEE and LAFEE and income-increasing ALLP are more pronounced for large
banks.
[Insert Table 4 About Here]
We conduct several sensitivity checks of model (1). First, Ahmed et al. (1999)
suggest that the earnings management through LLP reported in prior research is
conditional on the inclusion of beginning non-performing loans (NPLt-1) in the
discretionary LLP model. When we re-estimate model (2) for income-increasing ALLP
after excluding NPLt-1 in model (1), LTOTFEE, LNAFEE and FEERATIO are significant
at the 0.01 level and LAFEE is significant at the 0.10 level. Second, we assess the
sensitivity of our results to extreme values. We re-estimate model (1) after deleting
observations in the top 1% and bottom 1% for each variable (LLP, CHLOAN, LCO,
BEGNPL and CHNPL). This reduces the number of observations from 1,810 to 1709 and
the adjusted R2 is 68.94%. The test results for this reduced sample are consistent with
those reported in Table 3. Third, we scale the variables in model (1) by beginning total
loans instead of beginning total assets. Once again, our results for income-increasing
ALLP are robust to the choice of scaling variable. LTOTFEE and LNAFEE are significant
15
at the 0.01 level, and LAFEE and FEERATIO are significant at the 0.05 level. Fourth, we
estimate model (1) separately for small and large banks. The results based on this
specification indicate that negative ALLP are significantly associated with all four fee
measures for small banks. The results are weaker for large banks.
Unexpected Fee Measures
Auditor fees are also influenced by various economic determinants including size
and complexity of audit task. Therefore, controlling for the economic determinants of
auditor fees and using unexpected (abnormal) auditor fees may be a better measure of
auditor fee dependence than using actual fees. For example, Kinney and Libby (2002)
argue that unexpected fees are a better measure of the auditor-client economic bond
because they reflect the “excess” profit derived from an audit client. Unexpected fees are
estimated in two steps. First, we estimate the expected (normal) fees as the predicted
values from a regression of audit fee or nonaudit fee or total fee on a set of firm
characteristics. We then compute the residuals from this regression which represent the
unexpected fees.
Prior research models auditor fees as a function of a firm’s auditor choice, audit
complexity, and audit risk, in addition to other variables (Firth 1997 and 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
fees, we estimate unexpected fees for three of our fee measures (LAFEE, LTOTFEE and
LNAFEE). Audit fees and other fees are likely to be higher when the auditor is a Big 5
auditor. Auditor size also proxies for client size. We measure firm size as the natural log
of total assets. The normal audit fee is directly related to a bank’s credit risk, operating
risk, liquidity risk and capital risk. We include NPL, LCO, COMM, CON and RESTATE
16
as proxies for a bank’s credit risk and the efficiency ratio (EFFICIENCY) as a proxy for
operating risk. We measure the efficiency ratio as the ratio of total operating expenses to
total revenues. As in Fields et al. (2004), we use SECURITIES as a proxy for liquidity
risk, and intangible assets (INTANG) and total capital ratio (TCAP) to account for capital
risk. We estimate the following model:12
FEE = α0 + α1 BIG5 + α2 LASSETS + α3 SECURITIES + α4 NPL + α5 LOSS
+ α6 INTANG + α7 EFFICIENCY + α8 LCO+ α9 COMM + α10 CON
+ α11 RESTATE + α12 TCAP + <YEAR CONTROLS> + ε
(3)
We define the variables as follows:
FEE
=
Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln
(audit fees + nonaudit fees)) or nonaudit fees (LNAFEE);
BIG5
=
Indicator variable set equal to 1 if audited by a Big 5 firm, and 0
otherwise;
LASSETS
=
Natural log of total assets;
SECURITIES =
[1-(total securities/total assets)];
NPL
=
Nonperforming loans over lagged total loans;
LOSS
=
Indicator variable set equal to 1 if ROA < 0, and 0 otherwise;
INTANG
=
Intangible assets over total assets;
EFFICIENCY =
Total operating expenses over total revenues;
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
12
As in Fields et al. (2004), this model includes three loan categories. We also estimate unexpected fees
after including all the six loan categories used in model (1). The relations between ALLP and UFEE are
qualitatively the same when we use all six loan categories to estimate abnormal fees.
17
TCAP
=
Total risk-adjusted capital ratio.
We also use alternate specifications of model (3) where we include audit fees (nonaudit
fees) as a control variable when we estimate unexpected nonaudit fees (audit fees).
Those results are discussed in a later section.
Panel A of Table 5 reports the results of estimating model (3). As expected, we
find a positive relation between our three fee measures and BIG5, LASSETS,
SECURITIES, NPL, EFFICIENCY, TCAP and COMM. The signs of the coefficients are
generally consistent with Fields et al. (2004). For the audit and total fee models, the
adjusted R2 are, respectively, 82.52% and 74.99%, indicating a very good fit. For the
nonaudit fee model the adjusted R2 is 51.45%. 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).
[Insert Table 5 About Here]
We use the residuals from model (3) as the unexpected (abnormal) fees for our
three fee measures. We then use the following model to examine whether banks’
abnormal accrual choices are associated with abnormal fees paid to the auditors:
ALLP = χ0 + χ1UFEE + χ2 BIG5 + χ3 MB + χ4 LMVE+ χ5 LOSS + χ6 PASTLLP + χ7 EBP
+ χ8 TIER1t-1+ χ9 TCAPt-1 + <YEAR CONTROLS> + ε
(4)
We define the variables as follows:
ALLP
=
Abnormal loan loss provision;
UFEE
=
Unexpected audit fees or total fees or nonaudit fees from model
(3);
18
BIG5
=
Indicator variable set equal to 1 if audited by a Big 5 firm, and 0
otherwise;
MB
=
Market-to-book ratio at the end of the year;
LMVE
=
Natural log of market value of common equity;
LOSS
=
Indicator variable set equal to 1 if net income < 0, and 0
otherwise;
PASTLLP
=
prior year’s LLP divided by total assets at the
beginning of the year;
EBP
=
Net income before extraordinary items and loan loss provisions
divided by total assets at the beginning of the year;
TIER1
=
Tier 1 risk adjusted capital at the beginning of the year; and
TCAP
=
Total risk adjusted capital at the beginning of the year.
Model (4) is identical to model (2) except that we substitute UFEE for FEE where
UFEE is unexpected fees, i.e., the residuals from model (3).
Again, we use three
measures of unexpected fees, total fees, audit fees, and nonaudit fees.
Panel B of Table 5 reports the results of model (4) for income-increasing
(negative) ALLP. The relation between income-increasing ALLP and unexpected total fee
and unexpected nonaudit fee are both positive and significant at the 0.05 level. There is
no significant relation between income-increasing ALLP and unexpected audit fee.
Overall, the results in panel B of Table 5 along with the results in Table 3 provide strong
evidence that income-increasing ALLP is higher for those audit clients that pay higher
fees to their auditors.13
To examine whether the strength of the association between unexpected fees and
income-increasing ALLP is greater for small banks, we add the variables SMALL and
13
When unexpected nonaudit fees are estimated after controlling for audit fees, the relation between
negative ALLP and unexpected nonaudit fees is negative and significant at the 0.01 level.
19
SMALL× UFEE to model (4). We find that the coefficient on the interactive term
SMALL×UFEE is more negative for both unexpected total fee and unexpected nonaudit
fee. Also, the sum of the coefficients on UFEE and SMALL×UFEE is negative and
significant at the 0.01 level for unexpected total fee and unexpected nonaudit fee, a result
which confirms the finding in Table 3 that income-increasing earnings management is
higher for those audit clients of small banks that pay higher fees to their auditors.
We also examine the relation between ALLP and UFEE for income-decreasing
(positive) ALLP. Untabulated results indicate that none of the three abnormal fee
measures is related to income-decreasing ALLP. This is also true when we introduce
controls for small banks.
Analysis of Next Period Loan Charge-Offs
As an additional test we examine the relation between fees paid to auditors in the
current period and the next period’s loan charge-offs. Bank managers do have some
discretion in recognizing loan charge-offs (Wahlen 1994) and might postpone charging
off bad loans to future periods so that the current period’s financial position is presented
in a favorable manner.
We model next-period loan charge-offs as a function of fees paid to auditors,
current period LLP, current-period loan charge-offs (LCO) and the natural log of total
assets, a proxy for bank size. In addition, we include the indicator variable SMALL to
control for differences between small and large banks. Following Altamuro and Beatty
(2006), we use LLP, LCO, SMALL and the natural log of total assets as control variables
in the model explaining future charge-offs which is formulated as follows
LCOt+1 = χ0 + χ1 FEEt + χ2 SMALLt + χ3 LLPt + χ4 LCOt + χ5 SIZEt-1
+ <YEAR CONTROLS> + ε
20
(5)
We define the variables as follows (all variables are deflated by beginning total assets):
LCO
=
Net loan charge-offs;
FEE
=
Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln (audit fees
+ nonaudit fees)) or nonaudit fees (LNAFEE);
SMALL=
1 if beginning total assets are less than $500 million;
LLP
Provision for loan losses; and
=
SIZE =
Natural log of beginning total assets.
Panel A of Table 6 reports the results of model (4) for the full sample. As
expected, all three of our fee variables (LTOTFEE, LAFEE and LNAFEE) have a
significant (p<0.01), positive relation with next-period loan charge-offs (FLCO). The two
control variables, current-period loan loss provisions (LLP) and loan charge-offs (LCO),
have a strong, positive association with FLCO.
[Insert Table 6 About Here]
In Panel B of Table 6, we repeat our analysis using unexpected fees estimated from
model (3). Again, all three of the unexpected fee measures (UTOTFEE, UAFEE and
UNAFEE) have strong, positive relations with next-period loan charge-offs (FLCO).
Overall, the significant association between current-period fees and future loan chargeoffs suggests that bank managers delay recognition of loan charge-offs when the auditorclient economic bond is stronger. These results are consistent with our earlier results on
the association between ALLP and fees in that auditors appear more tolerant of earnings
management via income-increasing LLP or discretionary loan charge-offs when their fees
are higher.
21
Earnings Benchmark Tests
Frankel et al. (2002) and Ashbaugh et al. (2003) examine the association between several
fee measures and earnings benchmark beating behavior of firms.
Following prior
research, we investigate the association between auditor fees and the likelihood of banks
reporting a small earnings increase (INCREASE) for the income-increasing ALLP subsample.14 We estimate the following logit regression for the earnings benchmark tests:
INCREASEt = χ0 + χ1 FEEt + χ2 SMALLt + χ3 ALLPt + χ4 MBt + χ5 LMVEt + χ6 BIG5t
+ χ7 TIER1t-1+ χ8TCAPt-1 + <YEAR CONTROLS> + ε
(6)
We define the variables as follows:
INCREASE = 1 when the change in net income scaled by beginning of year assets falls
in the interval [0.000, 0.002] and 0 otherwise.
FEE=
Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln (audit fees
+ nonaudit fees)) or nonaudit fees (LNAFEE);
SMALL=
1 if beginning total assets are less than $500 million;
ALLP =
Abnormal loan loss provision;
MB=
Market-to-book ratio at the end of the year;
LMVE=
Natural log of market value of common equity;
BIG5=
Indicator variable set equal to 1 if audited by a Big 5 firm, and 0
otherwise;
TIER1=
Tier 1 risk adjusted capital at the beginning of the year; and
TCAP=
Total risk adjusted capital at the beginning of the year.
The variable of interest in model (6) is FEE. A positive association between FEE
14
A parallel test for bench mark beating is examining the relation between fees and meeting or just beating
the analyst forecast. However, for our income-increasing ALLP sub-sample, only 107 of the 724 bank-year
observations fall in the meeting or just beating the analyst forecast group. Given this small number of
observations, we focus our benchmark beating tests on meeting or beating the preceding year’s earnings.
22
and INCREASE will indicate that auditor fee dependence increases the bank’s earnings
management behavior to meet or narrowly beat the previous year’s earnings. Since small
banks have less regulatory monitoring, we expect a positive association between SMALL
and INCREASE. Following Ashbaugh et al. (2003), we include current period abnormal
accruals (ALLP) in the model. More negative ALLP for the income increasing sub-sample
will result in a higher probability of beating the earnings benchmark; therefore, we expect
a negative association between ALLP and INCREASE. Consistent with Frankel et al.
(2002) and Asbaugh et al. (2003), we include controls for growth (MB), size (LMVE) and
Big 5 auditors (BIG5). We also include beginning capital ratios as bank specific controls.
Panel A of Table 7 reports the results of model (6) for the income increasing
ALLP sub-sample. As expected, all three of the fee variables (LTOTFEE, LAFEE and
LNAFEE) are significantly, positively related with bank’s earnings management behavior
to meet or narrowly beat the previous year’s earnings. The indicator variable for small
banks (SMALL) has a positive and significant (p<0.05) relation with INCREASE. As
predicted ALLP is negatively associated with INCREASE and this relation is significant at
the 0.01 level.
[Insert Table 7 About Here]
In Panel B of Table 7, we repeat our analysis using the unexpected fees from
model (3). Again, all three of the unexpected fee measures (UTOTFEE, UAFEE and
UNAFEE) have a strong, positive relation with earnings benchmark beating behavior.
Overall, the significant association between both fees and abnormal fees and earnings
benchmark beating behavior is consistent with the argument that auditors who are paid
higher fees are more tolerant of earnings management via income-increasing LLP.
23
V. SUMMARY AND CONCLUSIONS
We examine auditor independence in the banking industry. Banks are subject to
the scrutiny of the FDIC, the Federal Reserve Board, and other governmental agencies. In
addition, the Federal Deposit Insurance Corporation Improvement Act (FDICIA) which
became effective in 1992, requires the management of depository institutions with assets
exceeding $500 million to evaluate the internal control over financial reporting and the
auditor to attest to the report on the effectiveness of internal controls over financial
reporting. We provide empirical evidence on the relation between fees paid to the auditor
and the extent of earnings management via loan loss provisions in the banking industry.
We also study whether this relation differs across small and large banks.
Our findings indicate a positive association between fees paid to the auditor and
income-increasing earnings management through loan loss provisions. They suggest that
economic bonding between the auditor and the bank potentially impairs auditor
independence. This result is especially interesting given the high level of regulatory
scrutiny faced by banks. Our findings also indicate that this bonding is stronger for small
banks that are subject to less regulatory oversight than large banks. Collectively, our
results suggest that auditor fee dependence on the audit client is a threat to auditor
independence, particularly among banks with less than $500 million in assets that are not
subject to the same level of regulatory scrutiny as larger banks. They suggest that
although the high level of regulatory oversight of banks does not eliminate economic
bonding, it does so to a greater extent for large banks subject to more stringent controls.
Our results have several implications. First, they suggest that the FDIC and other
banking regulators and inspectors should closely review the loan loss provisions of banks
where the fee dependence is high.
Second, that banks can engage in earnings
24
management despite the high level of scrutiny by the FDIC and other regulatory agencies,
suggests that the extent of earnings management in industries that are less closely
regulated could be even greater. Third, the FDICIA can be viewed as a precursor to the
Sarbanes-Oxley Act of 2002 for large banks. There has been considerable discussion
about relaxing the requirements of the Sarbanes-Oxley Act for small firms. If
generalizable to other industries, our results of increased earnings management by small
banks that are less closely regulated suggest that reducing the requirements of the
Sarbanes-Oxley Act for small firms should be approached with caution.
The results of two additional tests corroborate our findings of an economic bond
between the auditor and the bank. First, we find a significant, positive association
between the level of total fee and the next-period charge-offs indicating that the fee level
is positively related to delaying the recognition of loan charge-offs. Second, we observe a
significant, positive relation between various fee measures and benchmark beating
behavior.
We note that one limitation of our study is that the results could be driven by an
alternative hypothesis that the audit fees reflect audit risk that is captured by the
discretionary loan loss provision. Auditors are likely to charge higher fees to firms that
are more difficult to audit and, if firms that are more difficult to audit have higher
incentives to engage in earnings management, this will be manifested in a positive
relationship between audit fees and discretionary loan loss provisions. We attempt to
address this concern by including variables that reflect differences in audit risk across
banks as well examining the relationship using abnormal fees after explicitly taking
account of possible factors driving the normal fees. Nevertheless, we cannot completely
rule out this alternative explanation and recognize it as a limitation of our study.
25
References
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Altamuro, J., A.L. Beatty. 2006. 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.
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.
Edwards, F.R., and F.S. Mishkin. 1995. The decline of traditional banking: Implications
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Firth, M. 1997. Provision of nonaudit services by accounting firms to their clients.
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71-105.
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Hawke, J. 2000. Statement before the SEC. http://www.occ.treas.gov/ftp/release/200057b.txt
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smoothing through loan loss provisions. Review of Quantitative Finance and Accounting
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26
_____, _____, D. Yang. 2004. Joint tests of signaling and income smoothing through
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27
FIGURE 1
Conceptual Model
Independent
Variable
Concepts
Dependent Variable
Economic Bond
to Client
1
2
Operational
Measures
Earnings
Management
3
Multiple
Fee Measures
5
Abnormal Loan
Loss Provision
4
Bank characteristics
affecting independent
and dependent
variables
28
TABLE 1
Panel A: Variables Used in Regressions (N = 1,810)
Variable
Mean
Std. Dev
Minimum
Median
Maximum
LTOTFEE
12.6539
1.1814
10.50
12.50
16.42
LAFEE
12.2121
1.1629
9.81
12.07
15.75
LNAFEE
11.2175
1.5063
7.85
11.07
15.96
FEERATIO
0.3153
0.2044
0.0105
0.2691
0.8413
LLP
0.0028
0.0032
-0.0009
0.0021
0.0131
ALLP
0.0000
0.0017
-0.0033
-0.0001
0.0052
LOANS
BEGLLA
0.7542
0.1960
0.3324
0.7318
1.3489
0.0092
0.0033
0.0031
0.0088
0.0194
CHLOANS
0.1055
0.1350
-0.0968
0.0733
0.6433
BEGNPL
0.0037
0.0038
0.0000
0.0028
0.0190
CHNPL
0.0006
0.0030
-0.0063
0.0000
0.0113
LCO
0.0019
0.0025
-0.0003
0.0013
0.0107
SMALL
0.1923
0.3942
0
0
1
BIG5
0.5232
0.4996
0
1
1
MB
3.1062
2.6696
0.9258
2.3950
13.7300
LMVE
19.7910
1.5136
17
20
25
LOSS
0.0121
0.1096
0
0
0
PASTLLP
0.0026
0.0030
-0.0005
0.0020
0.0124
EBP
0.0148
0.0056
-0.0565
0.0145
0.0292
TIER1
11.8939
3.4777
7
10
20
TCAP
13.5537
3.3327
10
10
20
Variable Definitions:
LAFEE
LTOTFEE
LNAFEE
FEERATIO
LLP
ALLP
LOANS
BEGLLA
CHLOANS
BEGNPL
CHNPL
LCO
SMALL
BIG5
MB
LMVE
LOSS
PASTLLP
EBP
TIER1
TCAP
= Natural log of audit fees
= Natural log of total fees
= Natural log of non-audit fees
= Non-audit fees divided by total fees
= Provision for loan losses deflated by beginning total assets
= Residuals estimated from model (1)
= Total Loans deflated by beginning total assets
= Beginning value of Loan Loss Allowance over beginning total assets
= Change in total loans outstanding deflated by beginning total assets
= Beginning Nonperforming loans deflated by beginning total assets
= Change in Nonperforming loans deflated by beginning total assets
= Loan charge-offs deflated by beginning total assets
= An indicator variable = 1 if beginning assets < 500 million and 0
otherwise
= An indicator variable = 1 if audited by a big 5 firm and 0 otherwise
= Market-to-book ratio at the year end
= Natural log of market value of common equity
= An indicator variable set equal to 1 if net income < 0 and 0 otherwise
= LLP at the beginning of the year deflated by beginning total assets
= Net income before extraordinary items and LLP over beginning total
assets
= Beginning Tier 1 Capital
= Beginning Total Capital
29
Panel B: Correlations between Dependent and Independent Variables (N=1,810)
LLP
ALLP
LOANS
BEGLLA
CHLOANS
BEGNPL
CHNPL
LCO
LTOTFEE
LAFEE
ALLP
LOANS
BEGLLA
CHLOANS
BEGNPL
CHNPL
LCO
0.604
<.001
1.000
0.191
<.001
0.000
1.000
1.000
0.184
<.001
0.000
1.000
0.209
<.001
1.000
0.096
<.001
0.000
1.000
0.805
<.001
-0.026
0.265
1.000
0.255
<.001
0.000
1.000
0.021
0.363
0.331
<.001
-0.095
<.001
1.000
0.209
<.001
0.000
1.000
0.093
<.001
-0.051
0.030
0.067
0.005
-0.167
<.001
0.726
<.001
0.000
1.000
-0.040
0.086
0.428
<.001
-0.146
<.001
0.382
<.001
0.107
<.001
LTOTFEE
LAFEE
LNAFEE
FEERATIO
SMALL
BIG5
MB
LMVE
LOSS
PASTLLP
EBP
TCAP
0.031
0.192
-0.028
0.234
-0.164
<.001
0.023
0.322
-0.075
0.002
-0.001
0.979
-0.046
0.052
0.145
<.001
1.000
-0.015
0.510
-0.025
0.288
-0.174
<.001
-0.002
0.947
-0.073
0.002
0.003
0.912
-0.061
0.010
0.101
<.001
0.944
<.001
1.000
0.075
0.002
-0.041
0.081
-0.114
<.001
0.057
0.016
-0.065
0.006
-0.017
0.469
-0.027
0.254
0.167
<.001
0.806
<.001
0.611
<.001
1.000
0.131
<.001
-0.021
0.369
0.023
0.328
0.080
0.001
-0.013
0.579
-0.017
0.475
0.042
0.075
0.134
<.001
0.170
<.001
-0.155
<.001
0.640
<.001
1.000
0.015
0.536
-0.004
0.853
0.161
<.001
0.001
0.971
0.167
<.001
-0.015
0.515
0.024
0.313
-0.043
0.066
-0.444
<.001
-0.443
<.001
-0.320
<.001
-0.006
0.784
1.000
0.020
0.405
-0.029
0.211
-0.196
<.001
-0.017
0.482
-0.101
<.001
-0.019
0.425
-0.029
0.224
0.094
<.001
0.525
<.001
0.493
<.001
0.414
<.001
0.098
<.001
-0.340
<.001
1.000
0.076
0.001
0.003
0.891
0.207
<.001
0.018
0.434
0.253
<.001
-0.021
0.363
0.003
0.907
-0.013
0.583
-0.041
0.080
-0.096
<.001
0.056
0.017
0.172
<.001
0.124
<.001
0.133
<.001
1.000
-0.001
0.962
-0.039
0.101
-0.139
<.001
-0.024
0.309
-0.027
0.252
-0.065
0.006
-0.060
0.010
0.071
0.002
0.821
<.001
0.769
<.001
0.686
<.001
0.173
<.001
-0.450
<.001
0.530
<.001
0.213
<.001
1.000
0.058
0.013
0.012
0.597
-0.038
0.110
0.221
<.001
-0.055
0.020
0.039
0.101
-0.038
0.106
0.142
<.001
0.013
0.576
0.009
0.694
0.008
0.737
0.013
0.567
0.047
0.048
-0.004
0.872
0.015
0.514
-0.078
0.001
1.000
0.422
<.001
0.096
<.001
-0.006
0.792
0.480
<.001
-0.120
<.001
0.338
<.001
0.012
0.623
0.564
<.001
0.032
0.173
0.004
0.856
0.073
0.002
0.091
0.000
-0.004
0.873
0.044
0.061
0.027
0.249
-0.009
0.705
0.096
<.001
1.000
0.282
<.001
0.124
<.001
0.224
<.001
0.092
<.001
0.208
<.001
0.032
0.174
0.067
0.004
0.185
<.001
0.090
0.000
0.054
0.021
0.120
<.001
0.109
<.001
-0.082
0.001
0.072
0.002
0.151
<.001
0.283
<.001
-0.396
<.001
0.090
0.000
1.000
-0.117
<.001
0.034
0.154
-0.245
<.001
-0.134
<.001
-0.017
0.464
-0.068
0.004
-0.030
0.196
-0.117
<.001
-0.200
<.001
-0.169
<.001
-0.187
<.001
-0.090
0.000
0.179
<.001
-0.145
<.001
-0.159
<.001
-0.203
<.001
-0.016
0.507
-0.114
<.001
0.071
0.002
LNAFEE
FEERATIO
SMALL
BIG5
MB
LMVE
LOSS
PASTLLP
EBP
See panel A for variable definitions. Total number of observations equals 1,810 and the data are for the years 2000 through 2006.
30
TABLE 2
Estimation of Abnormal Loan Loss Provision (ALLP)
Variable
Expected
Sign
Intercept
?
BEGLLA
_
BEGNPL
+
CHNPL
+
LCO
+
CHLOANS
?
LOANS
+
COMM
?
CON
?
RESTATE
?
AGRI
?
FBG
?
DEPINS
?
Coefficient
(t-statistic)
-0.0011
(-3.93)***
-0.2260
(-13.25)***
0.0241
(1.76)*
0.0853
(5.26)***
1.0081
(45.34)***
-0.0006
(-0.98)
0.0046
(10.26)***
0.0005
(2.26)**
-0.0017
(-2.76)***
0.0000
(0.15)
-0.0005
(-0.15)
-0.0105
(-0.45)
0.0085
(0.55)
Yes
1810
63.15%
Year Controls
N
Adjusted R2
Variable Definitions:
LLP
BEGLLA
BEGNPL
CHNPL
LCO
CHLOANS
LOANS
COMM
CON
RESTATE
AGRI
FBG
DEPINS
= Provision for loan losses deflated by beginning total assets
= Beginning value of Loan Loss Allowance deflated by beginning total assets
= Beginning Nonperforming loans deflated by beginning total assets
= Change in Nonperforming loans deflated by beginning total assets
= Loan charge-offs deflated by beginning total assets
= Change in total loans outstanding deflated by beginning total assets
= Total loans outstanding deflated by beginning total assets
= Commercial loans deflated by beginning total assets
= Consumer loans deflated by beginning total assets
= Real estate loans deflated by beginning total assets
= Agriculture loans deflated by beginning total assets
= Loans to foreign banks and governments at deflated by beginning total assets
= Loans to other depository institutions deflated by beginning total assets
***, **, and * indicate respectively, 0.01, 0.05, and 0.10 significance levels for a two-tailed test.
31
TABLE 3
Relation between Income-Increasing (Negative) ALLP and Fee Measures
Variable
Intercept
SMALL
LTOTFEE
SMALL×LTOTFEE
Total Fee
-0.0028***
-0.0025***
(3.15)
(-3.3)
0.0106*
(2.10)
-0.0002***
-0.0001**
(-3.61)
(-2.35)
-0.0009**
(-2.11)
LNAFEE
Nonaudit Fee
-0.0031***
-0.0027***
(-3.33)
(-3.54)
0.0051**
(2.29)
-0.0001***
(-2.66)
SMALL×LNAFEE
Audit Fee
-0.0029***
-0.0025***
(-3.08)
(-3.12)
0.0031
(1.07)
Fee Ratio
-0.0030*** -0.0027***
(-3.3)
(-3.46)
0.0007**
(2.23)
-0.00004
(-1.53)
-0.0005**
(-2.28)
LAFEE
-0.0001***
(-2.60)
SMALL×LAFEE
-0.0001**
(-2.10)
-0.0003
(-1.11)
FEERATIO
-0.0003
(-1.20)
0.0001
(1.47)
-0.0001**
(-2.47)
0.0003***
(3.86)
-0.0040
(-1.02)
-0.0695***
(-2.84)
-0.0297*
(-1.79)
0.00002
(0.52)
-0.00001
(-0.40)
Yes
0.0001
(1.61)
-0.00003
(-1.32)
0.0002***
(3.93)
-0.0036
(-0.99)
-0.0717***
(-2.86)
-0.0270*
(-1.72)
0.00002
(0.55)
-0.00001
(-0.29)
Yes
0.0001
(0.92)
-0.00004*
(-1.75)
0.0002***
(3.08)
-0.0040
(-1.03)
-0.0678***
(-2.80)
-0.0249
(-1.51)
0.00002
(0.56)
-0.00001
(-0.39)
Yes
0.0001
(1.12)
-0.00002
(-0.77)
0.0001***
(3.21)
-0.0036
(-0.99)
-0.0666***
(-2.77)
-0.0221
(-1.44)
0.00002
(0.58)
-0.00001
(-0.24)
Yes
0.0001
(1.18)
-0.00005**
(-2.12)
0.0002***
(4.27)
-0.0040
(-1.02)
-0.0714***
(-2.93)
-0.0253
(-1.62)
0.00003
(0.62)
-0.00001
(-0.48)
Yes
0.0001
(1.05)
-0.00004*
(-1.80)
0.0002***
(4.16)
-0.0039
(-1.10)
-0.0729***
(-2.99)
-0.0246
(-1.60)
0.00002
(0.51)
-0.00001
(-0.22)
Yes
0.0001
(0.79)
-0.00003
(-1.39)
0.0001***
(2.84)
-0.0040
(-1.03)
-0.0694***
(-2.89)
-0.0211
(-1.34)
0.00003
(0.74)
-0.00002
(-0.59)
Yes
0.00004
(0.30)
-0.0025**
(-2.30)
0.0001
(0.71)
-0.00002
(-0.78)
0.0001***
(2.95)
-0.0037
(-1.00)
-0.0648***
(-2.69)
-0.0192
(-1.29)
0.00003
(0.64)
-0.00001
(-0.24)
Yes
1004
13.19%
1004
15.53%
1004
12.76%
1004
15.16%
1004
12.49%
1004
12.90%
1004
12.22%
1004
14.51%
SMALL×FEERATIO
BIG5
MB
LMVE
LOSS
PASTLLP
EBP
TIER1
TCAP
Year Controls
N
R2
***, **, and * indicate respectively, 0.01, 0.05, and 0.10 significance levels for a two-tailed test.
32
TABLE 4
Relation between Income-Decreasing (Positive) ALLP and Fee Measures
Variable
Intercept
SMALL
LTOTFEE
SMALL×LTOTFEE
Total Fee
0.0040***
0.0040***
(4.51)
(3.37)
0.0048
(1.46)
0.0004***
0.0005***
(2.97)
(3.56)
-0.0004
(-1.48)
LNAFEE
Nonaudit Fee
0.0042***
0.0044***
(4.54)
(3.24)
0.0027
(1.37)
0.0001
(0.94)
Audit Fee
0.0038***
(3.23)
0.0026
(0.91)
Fee Ratio
0.0040***
0.0042***
(4.50)
(3.32)
0.0003
(0.99)
0.0001
(1.37)
-0.0003
(-1.4)
SMALL×LNAFEE
LAFEE
0.0003***
(2.69)
SMALL×LAFEE
0.0003***
(2.93)
-0.0002
(-0.95)
FEERATIO
0.0004
(0.65)
-0.0002
(-1.53)
0.00005
(1.33)
-0.0005***
(-4.02)
0.0048
(1.19)
0.0829
(1.59)
0.0686
(2.87)
0.0001
(1.50)
-0.0001
(-1.44)
Yes
-0.0002
(-1.26)
0.0001
(1.63)
-0.0005***
(-3.75)
0.0049
(1.21)
0.0807
(1.54)
0.0705***
(2.92)
0.0001*
(1.70)
-0.0001
(-1.39)
Yes
-0.0001
(-0.69)
0.000004
(0.09)
-0.0003***
(-3.00)
0.0050
(1.20)
0.0900*
(1.73)
0.0627***
(2.59)
0.0001
(1.58)
-0.0001*
(-1.68)
Yes
-0.0001
(-0.47)
0.00001
(0.31)
-0.0003***
(-2.65)
0.0051
(1.23)
0.0876
(1.67)
0.0640***
(2.64)
0.0001*
(1.80)
-0.0001
(-1.63)
Yes
-0.0001
(-1.23)
0.0000
(0.95)
-0.0004***
(-4.06)
0.0051
(1.27)
0.0880
(1.67)
0.0701***
(2.80)
0.0001
(1.45)
-0.0001
(-1.54)
Yes
-0.0001
(-1.06)
0.00004
(1.16)
-0.0004***
(-3.68)
0.0051
(1.27)
0.0872
(1.66)
0.0711***
(2.78)
0.0001
(1.62)
-0.0001
(-1.46)
Yes
-0.0001
(-0.5)
-0.00001
(-0.22)
-0.0002***
(-4.37)
0.0050
(1.21)
0.0925*
(1.75)
0.0612**
(2.36)
0.0001
(1.55)
-0.0001*
(-1.69)
Yes
0.0006
(0.91)
-0.0011
(-1.41)
-0.00005
(-0.4)
-0.00001
(-0.16)
-0.0002***
(-3.07)
0.0051
(1.22)
0.0914*
(1.72)
0.0611**
(2.35)
0.0001*
(1.74)
-0.0001
(-1.61)
Yes
N
806
806
806
806
806
806
806
806
R2
11.44%
11.76%
10.30%
17.65%
10.74%
10.83%
10.16%
17.53%
SMALL×FEERATIO
BIG5
MB
LMVE
LOSS
PASTLLP
EBP
TIER1
TCAP
Year Controls
***, **, and * indicate respectively, 0.01, 0.05, and 0.10 significance levels for a two-tailed test.
33
TABLE 5
Panel A: Estimation of Unexpected (Abnormal) Fee Measures
Variable
Intercept
BIG5
LASSETS
SECURITIES
NPL
LOSS
INTANG
EFFICIENCY
LCO
COMM
CON
RESTATE
TCAP
Year controls
N
Adj. R2
Total Fee
5.2168***
(15.46)
0.3018***
(7.71)
0.6631***
(42.99)
0.9454***
(5.37)
20.6448***
(3.82)
-0.3070
(-1.62)
2.9197***
(2.88)
1.5547***
(6.47)
-0.0459
(-0.45)
0.1919**
(2.58)
-0.2163
(-0.90)
-0.0807*
(-1.73)
0.0114*
(1.91)
Yes
936
82.52%
Nonaudit Fee
2.1344***
(3.15)
0.1892**
(2.32)
0.7361***
(22.71)
1.2029***
(3.27)
38.6089***
(3.44)
-0.7922**
(-2.00)
-4.9025**
(-2.38)
2.8298***
(5.87)
-0.2105
(-0.97)
0.2246
(1.42)
-0.0760
(-0.15)
-0.0938
(-0.95)
-0.0060
(-0.47)
Yes
936
51.45%
Audit Fee
5.4565***
(14.22)
0.2282***
(4.95)
0.6299***
(34.33)
1.1125***
(5.35)
3.4956
(0.55)
0.1063
(0.47)
8.6393***
(7.42)
0.8193***
(3.00)
0.0803
(0.66)
0.2505***
(2.80)
-0.4722
(-1.65)
-0.0541
(-0.97)
0.0198***
(2.76)
Yes
936
74.99%
Variable Definitions:
BIG5
LASSETS
SECURITIES
NPL
LOSS
INTANG
EFFICIENCY
LCO
COMM
CON
RESTATE
TCAP
= Indicator variable set equal to 1 if audited by a Big 5 firm, and 0 otherwise;
= Natural log of total assets;
= [1-(total securities/total assets)];
= Nonperforming loans over lagged total loans;
= Indicator variable set equal to 1 if ROA < 0, and 0 otherwise;
= Intangible assets over total assets;
= Total operating expenses over total revenues;
= Net loan charge-offs over loan loss allowance;
= Total commercial and agriculture loans over total loans;
= Total consumer loans over total loans;
= Total real estate loans over total loans; and
= Total risk-adjusted capital ratio.
***, **, and * indicate respectively, 0.01, 0.05, and 0.10 significance levels for a two-tailed test.
34
Panel B: Relation between Income-Increasing (Negative) ALLP and Unexpected Fee Measures
Total Fee
Intercept
-0.0031***
(-2.89)
SMALL
UTOTFEE
-0.0001**
(-1.99)
SMALL×UTOTFEE
-0.0027***
(-3.02)
-0.0002
(-1.18)
-0.00004
(-0.71)
-0.0007
(-1.41)
UAFEE
Audit Fee
-0.00309***
(-2.95)
-0.0027***
(-3.16)
-0.0001
(-1.12)
0.00001
(-0.20)
0.00003
(0.63)
-0.0002
(-0.53)
SMALL×UAFEE
Nonaudit Fee
-0.00317***
(-2.97)
-0.0029***
(-3.11)
-0.0001
(-0.98)
-0.00009***
(-2.88)
0.00007
(0.79)
-0.00004
(-1.48)
0.0001***
(2.87)
-0.0033
(-1.16)
-0.0625
(-1.46)
-0.0283
(-1.85)
0.00004
(0.76)
-0.00002
(-0.59)
0.0001
(0.66)
-0.00002
(-0.98)
0.0001***
(3.05)
-0.0033
(-1.16)
-0.0673
(-1.67)
-0.0266
(-1.80)
0.00002
(0.53)
-0.000001
(-0.03)
0.00007
(0.75)
-0.00004
(-1.49)
0.00013***
(2.93)
-0.00327
(-1.15)
-0.06136
(-1.41)
-0.02806*
(-1.83)
0.00004
(0.82)
-0.00002
(-0.69)
0.0001
(0.61)
-0.00003
(-1.29)
0.0001***
(3.11)
-0.0033
(-1.14)
-0.0636
(-1.58)
-0.0275*
(-1.82)
0.00003
(0.71)
-0.00001
(-0.42)
0.00005
(0.62)
-0.00004
(-1.50)
0.00013***
(2.94)
-0.00326
(-1.15)
-0.063
(-1.47)
-0.02809*
(-1.84)
0.00004
(0.72)
-0.00002
(-0.56)
-0.0001**
(-1.77)
-0.0003
(-1.61)
0.0001
(-.61)
-0.00002
(-1.05)
0.0001***
(3.16)
-0.0032
(-1.15)
-0.0645
(-1.54)
-0.0258*
(-1.80)
0.00003
(0.57)
-0.000002
(-0.06)
Yes
936
13.53%
Yes
936
14.37%
Yes
936
13.29%
Yes
936
13.52%
Yes
936
13.80%
Yes
936
14.68%
UNAFEE
SMALL×UNAFEE
BIG5
MB
LMVE
LOSS
PASTLLP
EBP
TIER1
TCAP
Year Controls
N
R2
***, **, and * indicate respectively, 0.01, 0.05, and 0.10 significance levels for a two-tailed test.
35
TABLE 6
Relation between Future Loan Charge-Offs on Fee Measures
Panel A: Fee Regressions
Variable
Total Fee
Nonaudit Fee
Audit Fee
Intercept
-0.0031***
(-4.39)
-0.0028***
(-3.57)
-0.0016***
(-3.24)
LTOTFEE
0.0004***
(4.53)
0.0003***
(3.47)
LAFEE
0.0002***
(4.21)
LNAFEE
-0.0003*
(-1.68)
-0.0003*
(-1.86)
-0.0003*
(-1.70)
LLP
0.2884***
(11.03)
0.2893***
(11.13)
0.2933***
(10.93)
LCO
0.2589***
(8.30)
0.2601***
(8.40)
0.2585***
(8.13)
LASSETS
-0.0002**
(-2.10)
-0.0001
(-1.16)
-0.0001
(-1.11)
Yes
Yes
Yes
101.92
100.83
96.20
1666
1651
1599
37.74%
37.70%
37.33%
SMALL
Year controls
F value
N
Adj. R2
Variable definitions (all variables are deflated by beginning total assets):
FLCO=
FEE=
SMALL=
LLP =
LCO=
SIZE =
Next period’s net loan charge-offs,
Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln (audit fees +
nonaudit fees)) or nonaudit fees (LNAFEE);
1 if beginning total assets are less than $500 million;
Provision for loan losses;
Net loan charge-offs; and
Natural log of beginning total assets.
***, **, and * indicate respectively, 0.01, 0.05, and 0.10 significance levels for a two-tailed test.
Panel B: Unexpected (Abnormal) Fee Regressions
Variable
Intercept
UTOTFEE
Total Fee
Nonaudit Fee
Audit Fee
-0.0006
(-1.43)
-0.0007
(-1.61)
-0.0004
(-1.04)
0.0004***
9
(3.55)
9
9
9
0.0003***
(2.68)
-0.0003*
(-1.75)
-0.0003*
(-1.69)
-0.0003*
(-1.77)
LLP
0.2666***
(9.38)
0.2631***
(9.26)
0.2664***
(9.34)
LCO
0.3042***
(8.87)
0.3052***
(8.88)
0.3102***
(9.05)
LASSETS
0.0001***
(2.88)
0.0001***
(3.02)
0.0001***
(2.66)
Yes
Yes
Yes
F value
82.28
81.42
81.33
N
1404
1404
1404
36.68%
36.43%
36.41%
UAFEE
0.0002***
(2.58)
UNAFEE
SMALL
Year controls
Adj. R2
Variable Definitions (all variables are deflated by beginning total assets):
FLCO=
UFEE=
SMALL=
LLP =
LCO=
SIZE =
Next period’s net loan charge-offs,
Unexpected (abnormal) fee (UTOTFEE, UAFEE and UNAFEE);
1 if beginning total assets are less than $500 million;
Provision for loan losses;
Net loan charge-offs; and
Natural log of beginning total assets.
***, **, and * indicate respectively, 0.01, 0.05, and 0.10 significance levels for a two-tailed test.
37
TABLE 7
Relation between Benchmark-beating and Fee Measures for Income Increasing ALLP
Panel A: Earnings Benchmark Tests and Fees
Variable
Total Fee
Nonaudit Fee
Audit Fee
Intercept
-5.9172***
(19.66)
-4.014***
(11.78)
-5.937***
(19.85)
LTOTFEE
0.6647***
(25.63)
0.2703***
(16.31)
LNAFEE
0.5607***
(18.04)
LAFEE
0.4353**
(4.35)
0.4075**
(3.84)
0.4255**
(4.20)
ALLP
-387.10***
(16.81)
-391.80***
(17.40)
-393.70***
(17.45)
MB
0.1611***
(11.54)
0.1139***
(6.91)
0.1344***
(8.73)
LMVE
-0.1869**
(3.68)
0.0225
(0.09)
-0.1012
(1.18)
BIG5
-0.1246
(0.54)
0.0706
(0.19)
-0.1135
(0.44)
TIER1
0.1820***
(7.87)
0.1598**
(6.19)
0.1701***
(7.15)
TCAP
-0.1048
(2.62)
-0.0853
(1.76)
-0.1078*
(2.84)
Year controls
Yes
Yes
Yes
N
1004
1004
1004
SMALL
Wald Chi-Square values in parentheses.
Variable Definitions:
INCREASE =
FEE=
SMALL=
ALLP =
MB=
LMVE=
BIG5=
TIER1=
TCAP=
1 when the change in net income scaled by beginning of year assets falls
in the interval [0.000, 0.002] and 0 otherwise.
Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln (audit fees + nonaudit
fees)) or nonaudit fees (LNAFEE);
1 if beginning total assets are less than $500 million;
Abnormal loan loss provision;
Market-to-book ratio at the end of the year;
Natural log of market value of common equity;
Indicator variable set equal to 1 if audited by a Big 5 firm, and 0 otherwise;
Tier 1 risk adjusted capital at the beginning of the year; and
Total risk adjusted capital at the beginning of the year.
***, **, and * indicate respectively, p < 0.01, 0.01 < p < 0.05, and 0.05 < p < 0.10 significance levels.
Panel B: Earnings Benchmark Tests and Unexpected (Abnormal) Fees
Variable
Total Fee
Nonaudit Fee
Audit Fee
Intercept
-5.2663***
(15.02)
-5.1698***
(14.62)
-5.0865***
(14.31)
UTOTFEE
0.6111***
(17.01)
0.2498***
(12.38)
UNAFEE
0.4496***
(10.00)
UAFEE
0.4181**
(3.79)
0.4001*
(3.48)
0.4018*
(3.53)
-412.00***
(18.40)
-402.10***
(17.62)
-412.20***
(18.49)
0.0640
(2.38)
0.0648***
(2.50)
0.0608**
(2.21)
LMVE
0.2312***
(12.17)
0.2246
(11.62)
0.2239***
(11.64)
BIG5
0.0692
(0.17)
0.0630
(0.14)
0.0652
(0.15)
TIER1
0.1551**
(5.87)
0.1540**
(5.68)
0.1501**
(5.61)
TCAP
-0.0991
(2.32)
-0.0939
(2.05)
-0.0980
(2.31)
Year controls
Yes
Yes
Yes
N
936
936
936
SMALL
ALLP
MB
Wald Chi-Square values in parentheses.
Variable definitions:
INCREASE =
FEE=
SMALL=
ALLP =
MB=
LMVE=
BIG5=
TIER1=
TCAP=
1 when the change in net income scaled by beginning of year assets falls
in the interval [0.000, 0.002] and 0 otherwise.
Unexpected (abnormal) fee (UTOTFEE, UAFEE and UNAFEE);
1 if beginning total assets are less than $500 million;
Abnormal loan loss provision;
Market-to-book ratio at the end of the year;
Natural log of market value of common equity;
Indicator variable set equal to 1 if audited by a Big 5 firm, and 0 otherwise;
Tier 1 risk adjusted capital at the beginning of the year; and
Total risk adjusted capital at the beginning of the year.
***, **, and * indicate respectively, p < 0.01, 0.01 < p < 0.05, and 0.05 < p < 0.10 significance levels.
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