Audit Firm Tenure, Bank Complexity and Financial Reporting Quality
Brian Bratten
Von Allmen School of Accountancy
University of Kentucky
Lexington, KY 40506 USA
[email protected]
Monika Causholli
Von Allmen School of Accountancy
University of Kentucky
Lexington, KY 40506 USA
[email protected]
Thomas C. Omer
School of Accountancy
University of Nebraska-Lincoln
Lincoln, NE USA 68588
[email protected]
December 2016
We are grateful to Herman Van Brenk, Mathijs Van Peteghem, Marjorie Shelley, participants at the
2015 International Symposium of Audit Research (ISAR), 2015 Audit Mid-year Meeting in Miami,
2015 Boston Accounting Research Colloquium at Boston College, Nyenrode Business University,
and University of South Florida for providing helpful comments. We thank Philip Chung, Meng
Huang, and Chong Wang for research assistance. Brian Bratten and Monika Causholli gratefully
acknowledge financial support from the Von Allmen School of Accountancy at the University of
Kentucky. Thomas Omer gratefully acknowledges the financial support of the Delmar Lienemann Sr.
Chair of Accounting at the University of Nebraska Lincoln.
Audit Firm Tenure, Bank Complexity and Financial Reporting Quality
Abstract
Theory from organizations and economics research posits that in an inter-organizational exchange
relationship, both parties invest in relationship-specific knowledge which in turn facilitates the
effectiveness of the relationship while strengthening the attachment between the exchange parties. In
complex settings where there are more opportunities for knowledge creation, the investments will be
larger and the attachment stronger. Because banks are complex institutions that present unique
challenges to auditors, we suggest that effective audits critically depend on the accumulation of
significant investments in client-specific expertise through a long association with the client. We find
that in banks audit firm tenure is positively associated with financial reporting quality, and this
association is particularly strong in more complex banks. Also, contrary to recent research we find
that benefits of audit firm tenure for complex banks accrue even for long tenure and are not limited to
intermediate tenure. Our findings largely support the notion that a long relationship with the client
reflects the underlying demand for expertise, which is critical for high quality audits of complex
organizations. Imposing short-term limits on audit firms would adversely impact the investments in
client-specific expertise especially in the cases where this expertise is needed the most. Our findings
do not support calls for mandatory audit firm rotation for large complex institutions.
Audit Firm Tenure, Bank Complexity and Financial Reporting Quality
1. Introduction
On October 18, 2013, Ernst & Young was ordered to pay $99 million to investors for its role
in the fall of Lehman Brothers marking the first settled lawsuit against an audit firm related to the
financial crisis (Brown 2013). The financial crisis led many to question the role of auditing in
improving financial information, particularly in banks (Alexander 2012; Carson, Fargher, Geiger,
Lennox, Raghunandan, and Willekens 2013). Critics in the United States and the European Union,
including regulators, called for mandatory audit firm rotation for banks and other institutions of
economic significance (Bowsher 2012; Berger 2012). For example, Charles Bowsher, former
Comptroller General of the United States (US) stated “it would be wise to limit the adoption of audit
firm rotation at the beginning to somewhere between 25 and 40 very large companies … [including]
all the major financial institutions.” Nathalie Berger of the European Commission stated mandatory
rotation should be restricted “to statutory audits of such entities which include listed undertakings,
banks, insurance and other financial sector undertakings.” In the European Union, the debate
culminated with the passage of new regulation in April of 2014 requiring public interest entities to
rotate auditors after ten years. In contrast to this perspective, in July of 2013, the US House of
Representatives voted against mandatory audit firm rotation in the United States.
Auditing firms vigorously opposed auditor rotation for banks by arguing that “for larger,
more complex companies, and in particular the banking industry, the adverse impact (of mandatory
rotation) is likely to be even more severe. Banking is a heavily-regulated complex industry with
unique features… the institutional knowledge, skills and experience required to perform a highquality audit of a large complex bank are not easily or quickly replicated… A deep understanding of
the business being audited is critical to audit quality – particularly to audit the many complex
judgments reflected in a banks’ financial statements” (PwC 2013).
1
Regardless of the potential merits of a mandatory rotation, we have no evidence to date on
how audit firm tenure affects financial reporting quality in banks because the majority of prior
research excludes financial institutions. The effect of audit firm tenure on the financial reporting
quality of banks is an important question to examine because banks are vital to the development of
the economy and society. We thus examine the association between audit firm tenure and financial
reporting quality in bank holding companies. Policy makers and regulators have traditionally asserted
that a long relationship with the client can detrimentally affect auditor independence and audit
quality (PCAOB 2011; European Commission 2011), and regulators seem particularly concerned
with auditor independence in the banking industry. In many countries, the policies about auditor
rotation are more restrictive for banks than other companies. Currently, the global mandatory rotation
rules for banks range from a minimum of every two to three years (e.g., in Canada, pre-1991;
Albania) to a maximum of every seven years (e.g., in Croatia and Ukraine). Lennox (2012) reports
that in many countries regulators have imposed very strict rules and, in many cases, requiring audit
firm changes only for financial institutions. 1
While regulators’ preference for imposing short-term limits on bank auditors is
understandable given the role of banks in any economy, the majority of prior studies outside the
banking industry report a positive relationship between audit firm tenure and financial reporting
quality (Geiger and Raghunandan 2002; Johnson, Khurana, and Reynolds 2002; Myers, Myers, and
Omer 2003; Mansi, Maxwell, and Miller 2004; Ghosh and Moon 2005; Chen, Lin, and Lin 2008;
Bell, Causholli, and Knechel 2015). However, a few recent studies find that tenure effects exhibit an
inverted U-shape, that is, increased tenure improves quality up to a point beyond which additional
1
Even in countries with economy-wide mandatory rotation in place, the rules are stricter for banks. For example, in
Morocco and Serbia while other firms are required to change auditors every twelve and ten years respectively, the
requirement is every six, and every five years, respectively for banks. There are exceptions to these rules, however.
Brazil, for example, requires mandatory rotation for all firms except banks (see Lennox 2012). In the United States,
the audit partner, but not the firm, is required to rotate every five years from all public client audits including banks.
2
increases reduce quality (Davis, Soo, and Trompeter 2009; Lim and Tan 2010; Brooks, Cheng, and
Reichelt 2012). However, because of heavy regulation of the banking industry, its complex
transactions and complex financial reporting rules, the ability of an audit firm to provide high quality
audits depends on understanding the issues confronting banking clients and the implications for each
bank (PwC 2013). The more complex the issues, the more time necessary to accumulate knowledge
to provide a high quality audit. For banks, the need for client-specific knowledge is paramount for
audit quality; however, as discussed above, in this setting independence concerns are also
heightened. Our first research question thus examines the effect of audit firm tenure on bank’s
financial reporting quality.
Theoretical arguments against short-term limits follow from organizations and economics
research positing that the strength of the attachment between exchange partners in interorganizational business exchanges reflects the amount of relationship-specific expertise required to
maintain an effective exchange (Williamson 1981; Seabright, Levinthal, and Fichman 1992). That is,
the greater the expertise needed to ensure an effective and efficient business relationship, the greater
the investments in relationship-specific knowledge and expertise. The main attributes of these
investments are that (1) they can only arise over time, and (2) they are relationship-specific, that is,
they cannot be transferred to another exchange and lost when the business relationship is dissolved
(Levinthal and Fichman 1988). These arguments suggest that the strength of the attachment between
business partners is beneficial to the exchange, particularly in cases where opportunities for learning
and the levels of investments are substantial (Levinthal and Fichman 1988).
In an audit engagement, theory suggests that if clients have unique characteristics demanding
more expertise and knowledge, audit firms must invest more resources to understand these client
characteristics. Levinthal and Fichman (1988) argue that the nature of the underlying task can
influence the benefits from a long association. The more complex and intricate the task the greater
the investments required for an effective relationship and therefore the longer it will take for the
3
investments to accumulate to produce benefits for the exchange. 2 This perspective suggests that a
long tenure between the client and audit firm is more important and provides greater benefits in audit
settings that are more complex. Thus, our second research question addresses whether the expected
benefits of audit firm tenure for financial reporting quality vary with banks’ level of complexity.
To address our research questions, we compile a sample of bank holding companies from the
SNL database from the period 2000 through 2012. Our primary measure of financial reporting
quality is the association between the discretionary portion of the loan loss provision (LLP) and premanaged earnings or smoothing. We focus on this measure for a variety of reasons: First, because
loans are the largest assets in any bank’s balance sheet, the loan loss reserve is material and auditing
the reserve requires significant judgment and expertise. 3 Liu and Ryan (2006) assert that earnings
smoothing using the LLP attracts auditor scrutiny. Second, the PCAOB is focusing its attention on the
loan loss allowance during the inspection process because it is an area where a significant number of
audit deficiencies occur. The board has made it very clear that “the allowance for loan losses is one
of the most significant estimates made by many issuers in the financial services industry. …If
auditors do not properly test issuers’ estimates of the ALL (allowance for loan losses), auditors might
fail to detect material misstatements in issuers’ financial statements relating to loan portfolio values,
and investors might be misled (PCAOB 2010a: p. 14).” Third, the loan loss reserve has attracted the
attention of bank regulators and the SEC, but with differing objectives. While bank regulators are
concerned with underprovisioning, the SEC has primarily focused on restricting overprovisioning
(Beck and Narayanamoorthy 2013). The interplay between different regulators and auditors makes
this an interesting setting to examine the effects of auditor tenure.
2
Note that while we emphasize the investments made by auditors, clients also invest in their relationship with the
auditor in the form of spending time to familiarize the auditor with the business. This effectively ensures that both
parties have an economic interest in maintaining continuity to their relationship. DeAngelo (1981) referred to this
natural bond as bilateral monopoly.
3
The loan loss reserve (also referred to as the loan loss allowance) is a balance sheet reserve estimating
uncollectible loans. The loan loss provision is the change in this reserve, most often the bank’s largest accrual.
4
We use three measures of audit firm tenure: (1) the number of years the audit firm has
audited the bank, (2) an indicator variable for long tenure (i.e., greater than or equal to six years), and
(3) dual indicator variables with one for short tenure (i.e., less than or equal to three years) and one
for long tenure (i.e., greater than or equal to nine years). The third specification allows us to examine
the effect of long tenures versus medium tenures. Also, we follow prior research and utilize several
measures that proxy for different aspects of banks’ complexity, which affect the necessary level of
audit effort and investments in learning, and through effort, audit quality, and banks’ financial
reporting quality. Our complexity measures are the percent of commercial loans, the percent of
heterogeneous loans, the percent of non-performing loans, the standard deviation of return on assets,
and foreign operations (Fields, Fraser, and Wilkins 2004; Liu and Ryan 2006; Ettredge, Xu, and Yi
2014). To remove noise and redundancy we perform a principal component analysis which extracts
common properties among the complexity variables.
We find that long audit firm tenure mitigates banks’ ability to engage in earnings smoothing.
This effect is stronger for income-increasing smoothing than income-decreasing smoothing,
consistent with audit firms being more concerned about income-increasing earnings management
because of the increased litigation that arises from earnings overstatements (Lys and Watts 1994;
Barron, Pratt, and Stice 2001). We also find that the negative association between audit firm tenure
and income-increasing earnings management is stronger for complex banks. Moreover, we find some
evidence that long tenure (nine years or more) is associated with additional improvements in
financial reporting quality (i.e., further limiting earnings management) over and above the initial
improvements obtained in medium tenured engagements (with tenures between four to eight years).
In additional analyses, we examine other measures of reporting quality including the
likelihood of loss avoidance, just meeting or beating earnings targets, restatements, and the
likelihood of bank failure. In each of these analyses, our results support a positive effect on financial
reporting quality for either intermediate, long tenure, or both. We also partition our income
5
smoothing analysis into the financial crisis and non-financial crisis time periods and find a negative
association between tenure and smoothing in both time periods.
Finally, we perform a path analysis to address the endogeneity between bank complexity and
audit tenure when examining income smoothing. Results of this analysis suggest that, controlling for
the indirect effect of complexity on audit firm tenure, longer tenure leads to a reduction in banks’ use
of the loan loss provision to smooth income.
Our study makes four contributions to the audit literature and the worldwide discussion of
mandatory audit firm rotation. First, our findings suggest audit firm tenure is especially beneficial in
settings where client-specific knowledge is of greater importance and, in this setting, the benefits of
tenure on knowledge acquisition appear to more than offset the effects of social or economic bonding
with the client. Our results are in contrast to many policy initiatives specifying limits on audit firm
client relationships around the world and the European Union rules which mandate rotation of audit
firms for banks and other institutions (European Commission 2013). Instead, consistent with
organization theory and the position taken by the audit firms (e.g., PwC 2013), our results suggest
that benefits of audit firm tenure on audit quality of banks continue to accrue when tenure is long.
We suggest this is because bank audits are a challenging task, and tenure allows audit firms to
accumulate the necessary expertise to perform high quality audits. While regulators prefer short-term
audit firm relationships with clients, anecdotal evidence suggests that relationships between audit
firms and client banks have been long (Christodoulou 2011; McKenna 2012). Our findings suggest
this long association does not necessarily result in impaired independence, but it may indicate the
need for and acquisition of client-specific knowledge that improves overall audit quality.
Second, our paper advances the idea that knowledge acquisition during tenure is even more
important in complex organizations. As banks have become more complex worldwide, our evidence
suggests the recent regulatory changes in Europe may have unintended consequences.
6
Third, we provide initial evidence on the role of audit firm tenure in a regulated industry.
Because of the unique interplay that characterizes the banking sector such as the presence of multiple
monitoring mechanisms (e.g., bank regulators), some market participants have cast doubt on the role
of audit firms in this industry in contributing to higher quality financial statements (e.g., Alexander
2012). Thus, the role of the audit firm, much less audit firm tenure, in financial reporting quality is
unclear. Our results suggest audit firm tenure is important in generating high quality financial
information in banks. Moreover, we document a strong negative association between audit firm
tenure and income-increasing earnings management but find a weaker positive association between
tenure and income-decreasing earnings management. These results provide direct evidence on the
interplay between bank regulators and auditors. Because bank regulators primarily focus on overprovisioning, (i.e., income-decreasing earnings management), auditors have turned their attention to
under-provisioning (i.e., income-increasing earnings management). With the benefit of hindsight and
the lessons from the financial crisis, it has become clearer that under-provisioning is risky and could
lead to significant negative consequences for the markets and economy.
Finally, our results are relevant for policy makers worldwide because they indicate that a
one-size fits all approach to the mandatory audit firm rotation may not be appropriate. Our results
suggest the possible benefits of adopting more principled policies that consider differences in
industry or firm characteristics because of the heightened positive effect of tenure on financial
reporting quality in complex industries and firm environments.
2. Auditor tenure, audit quality and the quality of financial reporting: Theory and evidence
2.1 Audit quality and the quality of financial reporting
Most definitions of audit quality include both inputs (i.e., knowledge and process) and
outcomes (i.e., financial reporting quality) (Gaynor, Kelton, Mercer, and Yohn 2016). Auditor
competence and auditor independence are the key elements for a high quality audit (DeAngelo 1981).
7
Competence is the ability of the auditor to discover material misstatements when they exist, and
independence is the willingness of the auditor to disclose these misstatements. The ability to discover
misstatements depends on the auditor’s general, industry, and client-specific knowledge.
In this paper, we focus on client-specific knowledge, obtained from regularly interacting with
the client in a sustained relationship (i.e., tenure) over time. The benefits of client-specific knowledge
on audit quality notwithstanding, tenure also has the potential to diminish auditor independence if the
auditor experiences social or economic bonding with the client that reduces his willingness to
disclose material misstatements. The constant tension between competence from client specific
knowledge and the potential for independence impairment suggests that it is important to understand
the potential benefits and costs of mandatory audit firm rotation on financial reporting.
2.2 Banking and regulation
Three features of the banking industry make financial reporting and the role of the auditor
unique. First, banks are heavily regulated and supervised by various agencies including the Federal
Reserve, the Federal Deposit Insurance Corporation (FDIC), and the Office of the Comptroller of the
Currency (OCC) as well as state regulatory agencies. The federal and state banking agencies are
responsible for establishing regulations that govern banks’ operations, supervising banks’ operations,
and evaluating banks’ financial condition, safety, quality of management, adequacy of capital, and
asset quality (AICPA 2012). For bank auditors to provide high quality audits, they must be familiar
with how regulations affect each of their bank clients. Public banks are also subject to Securities and
Exchange Commission (SEC) oversight. However, bank regulators and the SEC often have
conflicting objectives. Balla and Rose (2011) suggest that bank regulators “love the loan loss
reserve” for ensuring bank soundness and safety. The SEC is more concerned with financial
reporting accuracy and has frequently required banks to decrease the amounts of reserves in boom
periods (Liu and Ryan 2006; Beck and Narayanamoorthy 2013). For example, the SEC argued that
SunTrust had over-reserved and in 1998 forced the company to reduce its reserve allowance by $100
8
million. The SEC followed by issuing SAB 102, which provided additional guidance on estimating
loan loss reserves and preventing future instances of over-reserving (Liu and Ryan 2006). 4 PCAOB
inspection reports also point to issues with loan loss reserves. Given that regulators operate with
differing objectives; it is not immediately apparent how auditors respond.
Second, banks are required by the Federal Financial Institutions Examination Council
(FFIEC) to file financial information with Call Reports every quarter, which are inspected by the
FDIC for errors. The interactions and communications between auditors and bank examiners are
considerable. FDI Act Section 36(h) requires banks to provide the auditor with the most recent Call
Reports, and auditors consider these as audit evidence. Bank auditors are also encouraged to
consider their bank clients’ examination results as audit evidence, attend meetings between
examiners and the bank’s representatives, or even meet with bank examiners directly. The Federal
Deposit Insurance Corporation Improvement Act of 1991 (FDICIA) requires auditors of larger banks
to make the audit workpapers available to regulators upon request (Myers 2001). 5 Bank examiners
can also review the auditor’s report and attend meetings between the auditor and the client. This
interaction between auditors and bank regulators is unique to the banking industry. The potential for
dual monitoring from bank regulators and the PCAOB combined with these regulators’ differing
objectives can affect auditor incentives about how to approach the audit and ultimately affects audit
quality.
4
The SEC staff also noted that “Explanations offered by some registrants have indicated a lack of reasoned analysis
or discipline in the establishment of the loss allowance” (SEC SAB 102). During the SEC’s intervention, some in
the banking community expressed concern with the position of the SEC. For example, during the Congressional
testimony, a member of the Board of Governors of the Federal Reserve stated that “…the federal banking agencies
were concerned about these actions from a safety and soundness standpoint” (Beck and Narayanamoorthy 2013).
5
The FDIC, the OCC, the Board of Governors of the Federal Reserve System, and the Office of Thrift Supervision
(the agencies) have jointly issued rules that establish when agencies can remove, suspend, or bar an audit firm from
performing audit and attestation services for insured depository institutions subject to the annual audit and reporting
requirements of Section 36 of the FDIC Act (FDIC 2003). Bank examiners may question auditors if workpapers
indicate low audit quality (Myers 2001), and even go as far as suggesting a change in the audit firm to the bank.
9
Third, the banking sector is also characterized by complex transactions, requiring additional
audit knowledge. The loan loss reserve is the largest estimate in most banks’ financial statements,
and auditors spend a considerable amount of time examining portfolio and loan-specific details to
determine the appropriateness of this reserve given client-specific circumstances. Many banks also
engage in risk management transactions (i.e., hedging), carry off-balance sheet positions, generate
fees from highly specialized transactions, and mark to market certain assets carried at fair value.
Each of these scenarios further complicates banks’ financial reporting responsibilities and provides
significant challenges to auditors. A long-tenured auditor with client-specific knowledge will be in a
better position to understand the implications for their clients and adjust the audit plan accordingly.
Because both the federal and state governments regulate the banking industry, there is
constant change in the business environment for banks providing additional challenges and
opportunities. Banks’ business models have grown more complex as banks seek new ways to remain
profitable, thwart competition, and adopt new approaches and new technologies to process
transactions efficiently. Because of the inherent complexity, regulatory changes, and the changes in
the business models of banks, it takes time for the auditor to accumulate knowledge required to
achieve an effective audit. Recognizing the dynamic environment in banking the AICPA’s guide on
Depository and Lending Institutions asserts that “Obtaining an understanding of the entity and its
environment, including the entity’s internal control is a continuous, dynamic process of gathering,
updating, and analyzing information throughout the audit (AICPA 2012, section 5.24).”
Auditing standards emphasize a need for knowledge for audit effectiveness. Auditors are
required to gain an understanding of the company under audit. This understanding includes the
company’s selection and application of accounting principles and disclosures, its objectives and
strategies, measurement and analysis of its financial statements, and evaluation of changes in the
client’s internal control (PCAOB 2010b). The AICPA guide on Depository and Lending Institutions
emphasizes a need for knowledge specific to each bank including the business model, risk
10
management strategies, organizational and capital structures, product markets, accounting
information systems, internal controls, client personnel, sensitivity of earnings to changes in interest
rates, loan composition, and other aspects relevant to the audit task (AICPA 2012). The SEC (2001)
suggests that the loan loss allowance methodology is a function of the entity-specific factors such as
the loan portfolio characteristics, the entity’s size, organizational structure, and management
information systems among others. To achieve a high quality audit of the loan portfolio, auditors
must have knowledge not only of the extensive financial reporting and auditing rules for the banking
industry but also bank-specific objectives and strategies. These strategies include how the bank
approaches the lending processes, growth, changes in loan composition, credit risk, training and
experience of bank’s loan officers, market specialty and other factors unique to each bank.
Application of regulations and the audit process will vary according to the unique individual
circumstances of each bank. Beck and Narayanamoorthy (2013) suggest that banks’ loan portfolios
change over time. A long-tenured auditor is in a better position to understand the implications of
these changes to the bank’s reported performance and provide an effective audit. They can analyze
the effect of changes on a specific bank more efficiently and incorporate them into the audit plan.
Notwithstanding these arguments, the importance of the auditor for banks in general, and the
role of tenure specifically are not without tension. Given extensive regulation, oversight by multiple
agencies, regulator-auditor interaction, and the internal control requirements related to complex
transactions, many have questioned the role of auditors in improving banks’ financial reporting
quality (Alexander 2012). Also, regulators have expressed concern that long auditor tenures in banks
might lead to increased bonding between clients and auditors resulting in lower auditor independence
and financial reporting quality.
2.3 Tenure and knowledge
Seabright et al. (1992) focus on recurring transactions in a business exchange and posit that
the effectiveness of the exchange depends on the commitment of the exchange parties to invest in
11
relationship-specific assets. A key attribute of relationship-specific investments is non-transferability
across relationships to other organizations. If the investments transfer easily, there is little to bind the
exchange parties. Without relationship-specific investments, a buyer can switch to a new supplier
without incurring much cost, and a seller can sell to an alternative buyer. If, however, investments
are sufficiently specific to the relationship, the relationship will be more stable, and the buyer and
seller are tied together in a bilateral exchange (Williamson 1981). Another important feature at work
in this setting is that relationship-specific investments can only form with the passage of time and,
with time, these investments strengthen the relationship between the business exchange partners.
In audit engagements, audit firms commit to making client-specific investments that will
facilitate the audit process and enhance the quality of the audit. These can be physical investments
such as opening a new office to serve the client, or non-physical investments such as developing
human capital and skills, enhancing expertise by institutionalizing more formal audit procedures,
knowledge databases or organizational routines (Danos and Eichenseher 1982; Levinthal and
Fichman 1988). Levinthal and Fichman (1988) suggest that investments take a longer time to develop
in settings with greater learning opportunities and those that require larger investments to maintain
the effectiveness of the relationship. Thus, where demand for relationship-specific knowledge is
greater a longer association will generate positive outcomes and bring benefits to the exchange.
Because of the unique regulatory oversight and reporting rules present in the banking sector, we
suggest that the need for relationship-specific investments is high, suggesting incremental positive
effects from a longer association with the client.
Although prior research has not examined the length of audit firm tenure in the banking
industry, anecdotal evidence suggests that audit firm tenure in banks is very long. For example,
McKenna (2012) reports that PwC has been auditing JPMorgan Chase and Bank of America since
1965 and 1958 respectively, while KPMG has audited Wells Fargo since 1931. Proponents of
mandatory audit firm rotation point to these and similar examples and call for audit firm rotation in
12
banks and other institutions where audit firm tenure appears to be very long (Bowsher 2012). The
long audit firm tenure observed in the banking industry, however, does not necessarily suggest
impaired independence. On the contrary, theory suggests that the longer the association, the greater
the demand for client-specific expertise to ensure an effective audit. Because effective audits of
banks require considerable expertise, we posit that longer audit firm tenure is important to the quality
of bank audits. Thus, to the extent long tenure reflects the underlying need for client-specific
knowledge, we should observe a positive relationship between tenure and audit quality in banks.
2.4 Tenure and auditor independence
Regulators have been concerned with audit firm tenure for decades and have proposed that
auditors should maintain a reasonable distance from the client. At the heart of the debate is the issue
of whether a long association with the client leads to social bonding and economic bonding, both of
which can impair auditor judgment and independence (GAO 2003; PCAOB 2011; European
Commission 2010, 2011, 2013). Independence concerns are particularly salient in the banking sector
because of the long relationships between auditors and banks (McKenna 2012) and the ripple effect
of an adverse opinion on banks for the entire economy. Recognizing these issues, many regulators
worldwide have put in place more restrictive limits on auditor tenure. Therefore, if tenure leads to
reduced independence, then the bank setting is where we are likely to find problems.
2.5 Prior literature and hypotheses
The majority of prior research examining the effect of audit firm tenure in non-banking
organizations report that long audit firm tenure is associated with less earnings management (Johnson
et al. 2002; Myers et al. 2003; Chung and Kallapur 2003; Gul et al. 2009; Chu, Church, and Zhang
2012), fewer restatements (Stanley and DeZoort 2007), fewer accounting and auditing enforcement
actions (Carcello and Nagy 2004) and higher audit quality ratings (Bell et al. 2015). Prior research
also reports that auditor tenure is associated with more credible financial statements as evidenced by
13
a negative association between auditor tenure and cost of debt (Mansi et al. 2004). 6 In Italy where
mandatory audit firm rotation has been in place since the mid-1980s, and auditors can remain with
the client for up to three renewable three-year terms, Cameran, Prencipe, and Trombetta (2015) find
auditors are more conservative in the last three-year term (immediately prior to rotation), compared
to the first two. Their results suggest benefits to longer tenure in a mandatory rotation environment. 7
However, the evidence is not conclusive worldwide. Some findings suggest short tenure leads to
better audit quality consistent with a perspective that new auditors bring “fresh eyes.” Using data
from Belgium with renewable three-year audit rotation rules, Vanstraelen (2000) reports that the
propensity to issue a going concern is higher in the first two years of tenure compared to the third. In
contrast, Knechel and Vanstraelen (2007) find no evidence that long audit firm tenure affects the
likelihood of going concern opinions.
Another important stream of literature suggests that the effect of audit firm tenure on audit
quality is non-linear. As tenure increases, audit quality improves initially, but this effect only lasts for
a few years and then reverses as tenure exceeds a certain point (Davis et al. 2009; Brooks, Cheng,
Johnson, and Reichelt 2011; Brooks et al. 2012). These results support regulators’ views that very
long audit firm tenure can adversely impact auditor independence leading to lower audit quality.
Overall, prior evidence suggests that audit firm tenure effects depend on specific conditions or client
characteristics as well as the interplay between required knowledge acquisition and social or
economic bonding. For example, the effects of long audit firm tenure may be positive in settings
where the value of client-specific knowledge outweighs the possibility of lower independence from a
longer association.
6
One the other hand, Boone, Khurana, and Raman (2008) find that longer tenure is associated with a higher equity
risk premium suggesting investors consider long auditor tenure as a company specific nondiversifiable risk factor.
7
Cameran et al. (2015) argue that auditor incentives change over time. As auditors are initially interested in
renewing the contract, they are more likely to appease the client during the first two terms compared to the last term.
14
Given a longer association could both increase client-specific knowledge and decrease
independence, coupled with uncertainty about the effect of multiple regulators in the banking
industry, the net effect of audit firm tenure on financial reporting is not immediately clear. Therefore,
we state our first hypothesis in the null.
H1: Audit firm tenure is not associated with financial reporting quality in banks.
The amount of knowledge and expertise needed to perform an effective audit is likely greater
in banks that have complex transactions and accounts. However, while independence concerns are
present in complex banks, there is no reason ex-ante to expect that these concerns are greater for
more complex banks than other banks. We thus posit that knowledge effects from tenure dominate as
complexity increases. Consistent with this premise, Srinidhi, Leung, and Gul (2010) suggest that long
audit firm tenure is especially beneficial for audits of clients that are different from industry peers,
demanding unique client-specific knowledge. Among complex banks, the demand for knowledge can
be satisfied with repeated interactions with clients. Our second hypothesis thus establishes the
relationship between audit firm tenure, complexity, and financial reporting quality:
H2: The association between audit firm tenure and financial reporting quality is greater for
complex banks.
3. Research design and sample
3.1 Discretionary portion of the loan loss provision, a measure of financial reporting quality
One key audit task for bank auditors is evaluating the adequacy of the loan loss reserve.
Managers have considerable discretion over this account, and prior research suggests they use this
discretion to smooth earnings (Ahmed, Takeda, and Thomas 1999; Beatty, Ke, and Petroni 2002; Liu
and Ryan 2006). 8 Thus, we follow prior research and estimate the discretionary component of the
8
Bank managers prefer smooth earnings for many reasons, e.g., lower risk premium (Graham, Harvey, and Rajgopal
2005; Erickson, Hewitt, and Maines 2010), compensation (Cheng, Warfield, and Ye 2011; Ramanna and Watts 2012),
and access to external financing (Beatty, Chamberlain, and Magliolo 1996; Kanagaretnam, Lobo, and Mathieu 2004).
15
loan loss provision (Beatty et al. 1995; Beatty et al. 2002; Kanagaretnam, Lim, and Lobo 2010;
Bratten, Causholli, and Myers 2016). To do this, we estimate a first stage model based on the
economic determinants of the loan loss provision using equation (1) below:
PLLt = α0t + α1BEGLLAt-1 + α2BEGNPLt-1 + α3ΔNPLt-1 + α4LCOt-1
+ α5ΔLOANSt-1 + α7LOANSt-1 + α8LOANCONt + α9LOANCt + α10LOANRt
+ α11LOANAt + εt
(1)
In this model, PLL is the gross provision for loan losses scaled by the beginning total assets;
BEGLLAt is the beginning balance for the loan loss allowance scaled by beginning total assets;
BEGNPLt is the beginning balance for non-performing loans scaled by beginning total assets; ΔNPL
is the change in non-performing loans from the beginning to the end of the year, scaled by the
beginning total assets; LCO is net loan charge-offs scaled by beginning total assets; ΔLOANS is the
change in total loans outstanding scaled by beginning total assets; LOANS is the total loans
outstanding scaled by beginning total assets; LOANCON is the ratio of consumer loans to beginning
assets; LOANC is the ratio of commercial loans to beginning assets; LOANR is the ratio of real estate
loans to beginning assets; and LOANA is the ratio of agricultural loans to beginning assets. We use
the association between the residual, which we refer to as the discretionary portion of the loan loss
provision (DLLP), and pre-managed earnings as our primary measure of financial reporting quality.
3.2 Bank complexity
We utilize five measures of bank complexity that prior research has shown to affect audit
effort and fees (Fields et al. 2004; Liu and Ryan 2006; and Ettredge et al. 2014). 9 Our first proxy is
the ratio of commercial and industrial loans over total loans (COMM%). Commercial loans are
complex and difficult to audit for several reasons. First, they often require collateralization. Second,
they are often syndicated (Fields et al. 2004) which lacks transparency. Both characteristics require
9
Fields et al. (2004) argue that audit fees depend on client size, risk and complexity. However, in the banking sector
it is difficult to separate the risk component from the complexity component therefore many of the measures we
discuss in this paper contain elements of both. Fields et al. (2004) provide an example of a bank that has complex
contracts with risky borrowers and is thus both complex and risky.
16
additional audit effort and attention. Finally, because they are large, auditors select and review these
loans individually and in detail when assessing loan loss reserve adequacy (AICPA 2012, 9.95).
A related measure is the percent of heterogeneous loans (HET%) in the loan portfolio.
Following Liu and Ryan (2006), we measure HET% as the ratio of commercial and industrial loans,
direct lease financing, other real estate loans, agriculture loans and foreign loans over total loans. Liu
and Ryan (2006) suggest the loss provision for heterogeneous loans requires more judgment from
bank managers. There is more discretion for heterogeneous than homogenous loans because the latter
is determined using statistical methods at the portfolio level which requires less judgment. Given the
differences in evaluating the provision for heterogeneous vs. homogenous loan portfolios, we suggest
it takes more time for an audit firm to understand each loan in the portfolio of heterogeneous loans.
Thus, the knowledge acquired from auditing the portfolio over time can benefit the current year audit
because the audit firm can utilize the accumulated knowledge to restrict bank managers’ discretion.
Our third measure of bank complexity is the percent of nonperforming loans (NPL%),
measured as the ratio of non-performing loans to total loans. Banks are required to re-classify the
book value of loans that are more than 90 days past due as nonperforming loans (Liu and Ryan
2006). Non-performing loans require additional attention and effort from the audit firm.
Our fourth measure of bank complexity is the standard deviation of the bank’s return on
assets (STDROA) or net income over assets during a 5-year period ending in year t. This measure
represents banks’ underlying risk and operational complexity. Higher risk and operational
complexity likely require auditors to exert higher effort to acquire client-specific knowledge.
Our final measure of bank complexity represents the complexity of banks’ international
operations. We define FDEP% as the ratio of the bank’s international deposits to total deposits. 10
10
One concern about foreign operations is that if another auditor audits the bank subsidiaries (component auditor),
our measure of tenure will refer only to the principal auditor. We follow a procedure similar to Czerney, Schmidt,
and Thompson (2014) and download audit opinions from Audit Analytics for all banks in our sample with any level
of foreign operations (352 observations from 2000 to 2012). We carefully read each opinion to see whether the
17
We perform an initial principal component analysis (PCA) to capture the common underlying
features among the complexity measures. This estimation results in two factors with eigenvalues > 1.
The first (second) factor has the highest loadings for heterogeneous loans, nonperforming loans, and
the standard deviation of ROA (commercial loans and foreign deposits). To reduce noise, we perform
PCA again, using only the three variables with high loadings on factor 1, and separately using only
the two variables with high loadings on factor 2. We suggest the first factor with loadings of
heterogeneous loans, nonperforming loans, and standard deviation of ROA of 0.39, 0.64, and 0.62,
respectively, represents bank’s operational complexity (OC) because nonperforming loans are
difficult to manage and the standard deviation of ROA reflects underlying volatility. We suggest the
second factor with loadings on commercial loans and foreign deposits of 0.71 and 0.71 respectively
represents transactional complexity (TC) because the origination and servicing of commercial loans
require transactions that are loan-specific and thus inherently complex, and a higher level of foreign
deposits suggests complexities related to transactions occurring in other countries. We include both
OC and TC in all analyses testing H2. 11
3.3 Empirical models
We use equation (2a) and (2b) below to examine the effect of audit firm tenure on financial
reporting quality of banks. We estimate these equations using a regression technique that excludes
observations with a Cook’s distance greater than 4/N estimated for each model (see, e.g., Li 1985;
Fox 1997). We also include year fixed effects and cluster standard errors at the bank level. 12
DLLPt = β0t + β1(TENUREt) + β2(LPMEt) + β3(TENUREt×LPMEt) + β4(BIGNt) +
β5(CAPt)+ β6(CHGNPAt)+ εt
(2a)
principal auditor refers to another auditor in the opinion indicating a division of responsibility with a component
auditor, but found that none of the opinions indicated such a division of responsibility.
11
We also perform separate analyses by including each complexity variables one at a time. We describe these results
in the additional analyses section.
12
We cluster only at the bank level rather than by both bank and year because our sample includes only 13 years of
data and cluster-adjusted tests assume a large number of clusters and a sufficient number of observations in the
cluster (Cameron and Miller 2015). Our results are similar if we include fixed effects for the state of the bank’s
headquarters or bank fixed effects.
18
DLLPt = β0t + β1(TENUREt) + β2(HPMEt) + β3(TENUREt×HPMEt) + β4(BIGNt) +
β5(CAPt)+ β6(CHGNPAt)+ εt
(2b)
TENURE measures the length of the relationship between the audit firm and their clients. We
use several measures of TENURE: TENURE = TENYRS, TEN6, or TENSHRT/TENLNG where
TENYRS equals the number of years the audit firm has audited the bank (described previously);
TEN6 is an indicator variable set to 1 if the audit firm’s tenure is greater than or equal to 6 years, the
median tenure in our sample, 0 otherwise; TENSHRT is an indicator variable set to 1 if the audit firm
tenure is less than or equal to three years, 0 otherwise; TENLNG is an indicator variable set to 1 if the
tenure is equal to or greater than nine years, 0 otherwise.
We rely on prior research that suggests that the extent to which banks engage in earnings
management depends on the level of pre-managed earnings (Collins, Shackelford, and Wahlen 1995;
Ahmed et al. 1999; Beatty et al. 2002; Liu and Ryan 2006; Beck and Narayanamoorthy 2013; Bratten
et al. 2014). For example, Beck and Narayanamoorthy, (2013, p. 47) state “…profitable banks have
incentives to accelerate loss provisioning while less profitable banks will have incentives to delay
provisioning”. We thus utilize pre-managed earnings to identify banks’ incentives to smooth
earnings. HPME is an indicator variable set to 1 when pre-managed earnings are in the top quintile of
sample observations, and 0 otherwise. LPME is an indicator variable set to 1 when pre-managed
earnings are in the bottom quintile of sample observations, and 0 otherwise. We measure premanaged earnings as net income scaled by assets plus the discretionary loan loss provision (DLLP
from model (1) above). A bank with low pre-managed earnings has a strong incentive to engage in
income-increasing earnings management by lowering the loan loss provision expense and should
result in a negative and significant coefficient on LPME (β2 < 0 in equation 2a). A bank with high
pre-managed earnings has incentives to increase the loan loss provision expense. In this case, a
positive and significant coefficient on HPME indicates downward smoothing. Thus, we expect (β2 >
19
0 in equation 2b). 13 BIGN is an indicator variable set to 1 if one of the large international audit firms
audits the bank, 0 otherwise; 14 CAP is a bank’s tier 1 risk-based capital ratio; CHGNPA is the change
in non-performing assets divided by beginning of year total loans.
Our variables of interest to test H1 are the interactions LPME×TENURE and
HPME×TENURE. To the extent audit firm tenure reduces bank managers’ ability to engage in
smoothing, the coefficient on LPME×TENURE will be positive in equation (2a) (i.e., longer tenure
reduces upwards earnings management) and the coefficient on HPME×TENURE will be negative in
(2b) (i.e., longer tenure reduces downwards earnings management). If on the other hand, audit firm
tenure is associated with impaired auditor independence, we would expect to observe insignificant
coefficients on LPME×TENURE and HPME×TENURE. We do not have an expectation for the sign
of the coefficient on TENURE.
We use equations (3a) and (3b) to test whether the tenure effect differs with bank complexity.
DLLPt = β0t + β1(TENUREt) + β2(LPMEt) + β3(COMPLEXITYt)
+ β4(TENUREt×LPMEt) + β5(TENUREt×COMPLEXITYt)
+ β6(COMPLEXITYt×LPMEt) + β7(TENUREt×COMPLEXITYt×LPMEt)
+ β8(BIGNt) + β9(CAPt)+ β10(CHGNPAt)+ εt
(3a)
DLLPt = β0t + β1(TENUREt) + β2(HPMEt) + β3(COMPLEXITYt)
+ β4(TENUREt×HPMEt) + β5(TENUREt×COMPLEXITYt)
+ β6(COMPLEXITYt×HPMEt) + β7(TENUREt×COMPLEXITYt×HPMEt)
+ β8(BIGNt) + β9(CAPt)+ β10(CHGNPAt)+ εt
(3b)
COMPLEXITY is the two factors we described earlier, operational complexity (OC), and
transactional complexity (TC). We expect the effect of audit firm tenure to be greater for complex
banks. Thus, we expect the coefficient on β7 to be positive and negative for equations (3a) and (3b),
respectively, suggesting that audit firm tenure mitigates smoothing of complex banks to a greater
13
We estimate equations (2a) and (2b) separately because coefficient estimates can be interpreted as differences
between LPME and HPME, respectively, and all other banks. If we combine LPME and HPME and include both
sets of interactions in one model our inferences remain unchanged.
14
The market share for each of the Big N audit firms is 19.0% audited by KPMG, 4.8% by PwC, 7.8% by EY, 6.7%
by Deloitte, and 1.2% for Arthur Andersen.
20
degree. As with the previous model, we use a regression that excludes observations with Cook’s
distance greater than 4/N estimated for each model to reduce the influence of outliers, include year
fixed effects, and cluster standard errors at the bank level.
3.4. Data
Table 1 provides the sample selection process (Panel A) and frequency (Panel B). Our
sample is composed of 6,721 US bank-years for the period 2000-2012 obtained from the intersection
of SNL Financial containing bank financial information and Audit Analytics. The sample is further
reduced to 6,011 bank-years for our DLLP tests because of missing loan data variables needed to
estimate equation (1).15 We use the Auditor Engagement Module from Audit Analytics and
determine the length of auditor tenure based on the difference between the current fiscal year and the
“Auditor_Since” variable provided in this module which identifies the first year of the engagement.
This variable is available for the majority of our sample. In cases where the “Auditor_Since” variable
is not available; we compute the length of auditor tenure as the difference between the current fiscal
year and the first year for which Audit Analytics lists the auditor as the bank’s auditor. 16
Table 2 provides descriptive statistics for our sample. Mean (median) assets are 5.8 (1.0)
billion. The median of audit firm tenure is 6 years, with a minimum and maximum of 1 and 82 years
respectively (untabulated). The mean (median) level of pre-managed earnings (PME) to assets is
0.007 (0.009). The discretionary loan loss provision (DLLP) has a mean (median) of 0.000 (-0.0001).
The average level of our measures of complexity, COMM%, HET%, NPL%, STD_ROA, and FDEP%
are 17, 43, 2, 0.4, and 0.2 percent, respectively. Table 2 also indicates that just over 14 percent of
banks avoid losses in the current period (LOSS_AV), and almost 45 percent meet or beat previous
15
The sample includes only US bank holding companies and excludes insurance and other financial intermediaries.
Audit Analytics begins in 2000, so, for cases in which the “Auditor_Since” year is not available, we hand collect
auditor data beginning in 1996 from banks’ 10-Ks forms by searching the SEC’s EDGAR website, and set 1996 firm
tenure equal to one in these cases. This effectively constrains some bank’s audit tenure to a maximum of 5 (17)
years in 2000 (2012) though in some instances a firm’s actual tenure may be longer.
16
21
period earnings by only a small amount (JMBE).
4. Results
4.1 Tests of H1: Audit firm tenure and financial reporting quality
Table 3 presents the results of equations (2a) and (2b) testing H1, examining the association
between audit firm tenure and banks engaging in upwards earnings management (LPME×TENURE
in Panel A) or downwards earnings management (HPME×TENURE in Panel B). In Panel A,
consistent with prior research, the coefficient on LPME is negative and significant suggesting that
banks with low pre-managed earnings engage in income-increasing earnings management by
decreasing the amount of the loan loss provision. The coefficient on LPME×TENYRS is positive and
significant (p<0.01) suggesting that longer audit firm tenure mitigates the ability of bank managers to
manage earnings upward. 17 The coefficient on LPME×TEN6 is also positive and significant (p<0.01)
suggesting that banks with longer tenured audit firms compared to those of shorter audit firm tenure
engage in less upwards earnings smoothing. The final two columns in Panel A present the results
using the indicators TENSHRT and TENLNG. This model specification examines whether the
benefits of increased tenure continue for longer audit firm tenures (those equal to or greater than 9
years) compared to medium tenures (those between 4-8 years). The coefficient on LPME×TENLNG
represents the benefits from longer audit firm tenure compared to medium audit firm tenures. The
coefficient on LPME×TENSHRT represents the difference between short and medium audit firm
tenures. The results from Panel A indicate a positive and significant (p<0.10) coefficient on
LPME×TENLNG and a negative and significant (p<0.05) coefficient on LPME×TENSHRT. These
results suggest an association between short tenure and more upwards earnings management than
17
H1 is stated in the null, so we report two-tailed p-values for these tests. We compute the Variance Inflation Factors
(VIFs) for all models presented in Table 3. All VIFs are less than 10, suggesting multicollinearity is not a concern.
22
medium tenure and that longer auditor tenure mitigates managing earnings upward. This effect
continues even when tenure is long and not restricted to medium tenures.
Table 3, Panel B, presents the results for income decreasing earnings management.
Consistent with prior research, the coefficient on HPME is positive and significant suggesting that
banks with high pre-managed earnings engage in income-decreasing earnings management by
increasing the amount of the loan loss provision. The coefficient for HPME×TENYRS is negative and
significant (p<0.01) while the coefficient for HPME×TEN6 is not significant providing some
evidence of an association between the length of tenure and less downward earnings management. In
the last two columns, the results indicate a negative and significant coefficient for the interaction
HPME×TENLNG and an insignificant coefficient for HPME×TENSHRT. These results suggest that
the benefits of tenure accrue when tenure is long in the case of downward earnings management.
Overall, the results in Panels A and B suggest that tenure effects accrue at different points in time
depending on the direction of earnings management. For upwards earnings management, the benefits
of tenure begin earlier and continue into long tenure. For downward earnings management, the
benefits of tenure do not accrue until later when tenure is very long. The different patterns indicate it
is important to examine tenure effects separately for different types of earnings management.
4.2 Tests of H2: Audit firm tenure, bank complexity and financial reporting quality
In this section, we examine whether tenure effects differ depending on the complexity of
banks. Table 4 presents the results of estimating equations (3a) in panel A, and (3b) in panel B,
testing H2 that predicts an association between audit firm tenure, bank complexity, and financial
reporting quality. The first two columns show the results for TENYRS, the next two columns for
TEN6, and the last two columns TENSHRT/TENLNG. In Panel A, we are interested in the sign and
significance of the three-way interactions LPME×OC/TC×TENURE. The coefficients on
LPME×OC×TENYRS, LPME×OC×TEN6, and LPME× TC×TEN6 are positive and significant
(p<0.01), suggesting that the effect of long audit firm tenure on financial reporting quality is
23
incrementally beneficial for complex banks. The last two columns provide us with additional insights
as to when the benefits of audit firm tenure accrue for complex banks. The coefficient on
LPME×OC×TENSHRT is negative and significant while the coefficients on LPME×TC×TENLNG
and LPME× TC×TENLNG are positive and significant (p<0.01). We conclude that the positive effect
of tenure on complex banks is positive for complex banks even when tenure is already long. This
result suggests that increased complexity presents additional challenges to the audit firm and the
knowledge benefits from audit firm tenure continue to accumulate much later during an audit firm’s
tenure for complex banks, consistent with H2 and theory.
In Panel B, only the coefficient on HPME×TC×TEN6 is negative and significant (p<0.05).
This result provides limited evidence of an incremental effect of audit firm tenure as bank complexity
increases for income-decreasing smoothing.
Overall our results are consistent with the theoretical argument that longer tenure is necessary
when the required client-specific investments in knowledge are substantial as is the case of complex
banks. Our results show that audit firms have the ability to accumulate client-specific knowledge
from repeated audits and use this information to mitigate bank managers’ ability to manipulate
earnings. Our results also indicate that auditor tenure effects are more pronounced for income
increasing earnings management than income-decreasing earnings management.
5. Additional Analyses
5.1 Industry specialization
Although our focus is on client-specific knowledge, we also consider the effect of industry
specialization on financial reporting and audit quality in banks (Kanagaretnam et al. 2010; Bratten et
al. 2014; Lim and Tan 2010). To examine whether our results for H1 are affected by industry
specialization, we include in equation (2a) an indicator variable SPEC, equal to 1 if the audit firm is a
24
specialist, defined as the auditor that audits the largest number of clients in the industry. 18 We also
interact this variable with LPME to measure the effect of industry specialization in mitigating upward
earnings management. Our results presented in Table 5 indicate an association between an industry
specialist auditor and a reduction in smoothing. However, the effect of tenure is robust to controlling
for industry specialist auditors. The coefficient on LPME×SPEC (HPME×SPEC) is positive
(negative) and significant (p<0.01) in all columns of Panel A (Panel B), but the coefficients on
LPME×TENYRS and LPME×TEN6 continue to be positive and significant (p<0.05). These results
suggest that auditor’s industry and client-specific knowledge in tandem contribute to improved
financial reporting quality for banks.
5.2 Meet or beat earnings thresholds and loss avoidance
In an additional analysis, we examine the association between audit firm tenure and banks’
ability to meet earnings thresholds. We utilize two thresholds used in prior research: loss avoidance
and just meeting or beating prior-period earnings. For this analysis, we remove banks with high premanaged earnings (HPME = 1) because these banks have the least incentive to meet thresholds,
suggesting a smaller benefit to longer auditor tenure. 19 We follow Kanagaretnam et al. (2010) and
model the likelihood of banks meeting these benchmarks using equation (4) below and logistic
regression:
LOSS_AV/JMBE = β0t + β1(TENUREt) + β2(ROAt) + β3Log(ASSETSt) + β4(LOANSt)
+ β5(BIGNt) + εt
(4)
LOSS_AV is an indicator variable equal to 1 if the bank’s ROA (income before taxes divided
by total assets) is in the interval between 0 and 0.002, 0 otherwise. JMBE is an indicator variable
equal to 1 if the change in ROA between t-1 and t is in the interval between 0 and 0.0005. We control
18
Consistent with prior research, KPMG is the industry specialist in our sample (Kanagaretnam, Krishnan, and Lobo
2009; Bratten et al. 2014). We performed an additional analysis where we dropped each of the Big N one at a time.
Our inferences remain unchanged.
19
We discuss the sensitivity of our results to this design choice in section 5.7.
25
for determinants of banks’ ability to meet these benchmarks, including banks’ level of ROA, size
measured as the natural log of its ASSETS, relative proportion of loans to assets (LOANS/ASSETS),
and whether the auditor is a Big N audit firm. In this model, the dependent variable
(LOSS_AV/JMBE) identifies banks with incentives to engage in upwards earnings management, and
we directly examine the effect of tenure on the likelihood that banks meet these thresholds.
Table 6 provides the results of estimating equation (4) using LOSS_AV as the dependent
variable. The coefficients on TENYRS and TEN6 are negative and significant (p<0.10 and p < 0.01
respectively), and the coefficient on TENSHRT is positive and significant (p<0.05), suggesting banks
with long audit firm tenure are less likely to avoid losses.
Table 7 presents the results when the dependent variable is JMBE. The coefficient for TEN6
is negative and significant (p<0.05). This result suggests longer audit firm tenure limits bank
managers’ ability to meet or beat earnings benchmarks. In the last two columns, the coefficient on
TENSHRT is positive and significant (p<0.01) suggesting banks with a short audit firm tenure are
more likely to meet or beat. When banks change audit firms, the new auditor may lack the requisite
client-specific knowledge to mitigate banks’ ability to meet or exceed earnings benchmarks by a
small amount.
5.3 Restatements
We also examine restatements as an alternative measure of financial reporting quality. Our
dependent variable RESTATE equals 1 if the bank restates year t earnings, 0 otherwise. 20 We regress
RESTATE on each of our three measures of tenure and control for BigN, asset growth (GROWTH),
the natural log of total assets (Log(ASSETS)), and capital ratio (CAP). Table 8 provides the results of
estimating the logit regression. The coefficients on TENYRS and TEN6 are negative and significant
(p<0.01, and p<0.05, respectively), suggesting a lower likelihood of restatement as tenure increases.
20
Once again, we restrict our analysis to the sample of banks without high pre-managed earnings.
26
We also find a negative association between TENLONG and restatements that is close to significance
(p<0.11, two-tailed). TENSHRT is positive and significant (p<0.10). Overall, the results in this table
are consistent with our conclusion about the benefits of long auditor tenure for the financial reporting
quality of banks.
5.4 Bank failures
We next consider the association between audit firm tenure and the likelihood of bank
failures. We limit our sample to banks without high pre-managed earnings and follow Cantrell,
McInnis, and Yust (2014) who model the likelihood of bank failure as a function of the Tier 1 capital
leverage ratio (Leverage), loans for which the bank no longer accrues interest as a percent of total
assets (NonAccr), loans over 90 days past due as a percent of total assets (PastDue), other real estate
owned as a percent of total assets (OREO), return on assets (ROA), the absolute difference between
assets and liabilities that will mature or be repriced within one year as a percent of assets
(AbsMaturityGAP), and the natural log of total assets (Size). We add audit firm tenure (TENYRS).
Following Cantrell et al. (2014), we define three measures, FAIL1, FAIL2 and FAIL3 each equaling 1
if the banks fail within one, two, or three years after the availability of annual accounting data, 0
otherwise. Table 9 provides the results of this estimation. The coefficient on TENYRS is negative and
significant for all three measures of failure. This result suggests an association between audit firm
tenure and a lower chance of a bank failure.21
5.5 The financial crisis
21
Prior auditing research has also utilized the auditor’s issuance of a going concern opinion as a measure of audit
quality. However, going concern opinions are rare for banks and some have suggested that auditors are less willing
to issue going concern to banks because of the grave ramifications associated with banks failures (Carson et al.
2013). In our sample, only 86 (1.3%) bank-year observations receive a going concern opinion, of which 61 were
between 2008 and 2010. Given the sparse literature on going concern opinions for banks, we model the likelihood of
issuance of going concern opinion using the same controls used in the model predicting bank failure. Our results
(untabulated) show a positive and significant coefficient on TENYRS (coeff. = 0.03674, p = 0.072), suggesting a
higher likelihood of longer tenured auditors to issue a going concern opinion. This finding is contrary to suggestions
that long tenure is associated with reduced independence.
27
We also perform a test to determine whether the effect of tenure differed during the recent
financial crisis. We define Crisis as an indicator variable which takes the value of 1 for the years
2007 and 2008, and 0 otherwise. Using the full sample, we include Crisis, its two-way interactions
LPME/HPME×Crisis & Crisis×TENYRS and three-way interaction LPME/HPME×Crisis×TENYRS.
We also estimate models (2a) and (2b) separately for crisis and non-crisis years. Table 10, Panel A
provides results for upward earnings smoothing. We find a positive and significant coefficient for
the interaction (LPME×TENYRS) in both the non-crisis years and the crisis years (p<0.01 and p<0.05
for non-crisis and crisis respectively). We also find no significant difference in the interaction
coefficients between the two periods (LPME×Crisis×TENYRS, p>0.10). Table 10, Panel B provides
results for downward earnings smoothing. We find a negative and significant coefficient on the
interaction (LPME×TENYRS) during the non-crisis years (p<0.05), but not during the crisis years.
5.6 Reduced sample
As discussed in footnote 16, for some observations we do not have the precise start date for
the auditor and truncate our tenure measure consistent with prior research. As a robustness test, we
restrict our sample to banks for which the first year of the auditor is known, or we have at least ten
years of auditor data. Our inferences using this reduced sample (untabulated) are similar to those
reported in Table 3 using the full sample, except that the coefficient on LPME×TENSHRT
(LPME×TENLNG) is insignificant (significant). These results are consistent with the results
discussed earlier.
5.7 Alternative models of financial reporting quality
Our primary measure of financial reporting quality, the association between DLLP and premanaged earnings, provided in Tables 3 and 4, necessarily require interactions between the level of
pre-managed earnings and Tenure. We also utilize an alternative model of earnings smoothing that
does not use interactions. Specifically, we follow Kanagaretnam et al. (2010) and estimate a model
28
separately for positive (income decreasing) DLLP and negative (income increasing) DLLP. We
include auditor tenure as the key independent variable of interest. Our results (untabulated) using this
alternative model indicate an association between longer tenure and less income-increasing earnings
smoothing. We find no association between tenure and income-decreasing earnings smoothing using
this alternative model.
While our other measures of financial reporting quality do not rely on banks’ smoothing
incentives, for our results in Tables 6-9, we remove banks with high pre-managed earnings because
we believe that the remaining banks can benefit the most from longer audit firm tenure. Consistent
with this intuition, in an untabulated analysis, we examine high pre-managed earnings banks
separately and do not find evidence of a positive benefit of tenure, except for bank failures (TENYRS
is negatively associated with FAIL1 and FAIL2 even among banks with high pre-managed earnings).
Including all banks (including high pre-managed earnings banks) in our analyses does not affect our
inferences when LOSS_AV, JMBE, or FAIL1/2/3 are the dependent variable, but we obtain
insignificant coefficients for our tenure measures using two-tailed tests when RESTATE is the
dependent variable.
5.8 Path Analysis and the potential endogeneity between complexity and tenure
Our original results suggest that longer auditor tenure moderates banks’ attempts to manage
earnings upward by reducing the loan loss provision and that this effect is stronger with bank
complexity. However, there is a potential endogeneity issue between auditor tenure and bank
complexity that can affect our results. The endogeneity relates to the difference in expectations
regarding the effect of tenure on financial reporting quality. The accounting literature suggests that
tenure positively affects the acquisition of client-specific knowledge but has potentially negative
effects on auditor independence. In the organizational literature, complexity likely results in greater
investments in relationship building which could result in both better financial reporting outcomes
29
for banks and longer auditor tenure. To address this, we use path analysis to control for the
endogeneity between bank complexity and auditor tenure and re-estimate our model without the
three-way interaction of auditor tenure, low pre-managed earnings, and complexity but include a path
from complexity to both auditor tenure and low pre-managed earnings (See Figure 1). 22 By
including these paths, we address both the relation between the propensity to manage earnings
upward that may be more likely because of bank complexity and the relation between bank
complexity and auditor tenure. We find a positive coefficient for the path between bank complexity
and low pre-managed earnings and an insignificant path between bank complexity and auditor tenure
for all of our continuous measures of complexity. The standardized root mean squared residual from
the path models ranges from 0.06 to 0.067 suggesting good model fit. We find no direct effect of
auditor tenure and the discretionary loan loss provision. However, we continue to find a positive and
significant coefficient for the interaction of auditor tenure and low pre-managed earnings. This result
confirms our earlier results suggesting longer audit tenure moderates banks’ incentive to manage
earnings upward by lowering the loan loss provision. 23
5.9 Economic bonding and audit fees
Next, we performed an analysis to more explicitly consider the extent to which auditor
independence, measured by the magnitude of audit fees, affects financial reporting quality of banks
(Lim and Tan 2010). To examine the impact of fee dependence we first replicate the audit fee
model in Ettredge et al. (2014) and Fields et al. (2004). As a robustness test (untabulated), we then
add to our main model, the residual from the fee model and its interaction with LPME. Adding these
22
Path analysis is a regression technique that can be used to tease out the causal effects among a set of variables
(Acock 2013)
23
We also performed a simpler test of whether complexity impacts the relationship between tenure and DLLP by
partitioning on complexity. For each continuous measure of complexity, we separately estimated equation (3) in a
subsample of above-median complexity and a subsample of below-median complexity. In the above-median
complexity subsamples, we find the coefficient on our variable of interest, LPME×TENYRS is positive and
significant across all four continuous complexity measures. In the below-median complexity subsamples,
LPME×TENYRS is positive and significant only when HET% is used to form the complexity subsamples.
30
additional variables does not change our inferences, the coefficient on TENURE×LPME continues
to be positive and significant. The coefficient on the interaction between residual fees and LPME is
also positive suggesting that economic bonding is likely not an issue.
5.10 Individual complexity variables
Finally, we conduct all analyses related to H2 by separately including each of the
five complexity variables one at a time. Our results (not tabulated) are consistent with
our using the two complexity factors. Specifically, when complexity is proxied by the
percent of commercial loans or heterogeneous loans, the coefficient on the three-way
interaction LPME×COMPLEXITY×TENLNG is positive and significant suggesting
additional improvement in audit quality when tenure is long, compared to medium and
short tenure. When complexity is proxied by the standard deviation of ROA or foreign
operations, the coefficient for LPME×COMPLEXITY×TENSHRT is negative and
significant suggesting that the association between complexity and financial reporting
quality for medium audit firm tenure is higher when compared to a short audit firm
tenure. 24
6. Discussion and conclusions
This study examines the role of audit firm tenure in a regulated industry where regulatory
safeguards potentially limit the role of the audit firm. We base our investigation on a theory that
suggests the knowledge investment required in a business exchange plays a key role both in
improving the activities of the business exchange while limiting the value of that investment outside
the exchange. We suggest that the banking sector is a complex and challenging environment for
24
As an alternative to FDEP%, we hand-collected both the number of direct domestic and foreign subsidiaries for
each bank from their 10-K in the last year the bank existed in our sample, and back-filled the values based on the
assumption that the number of subsidiaries has minimum variation over time. We then included both the number of
domestic and foreign subsidiaries as alternative complexity variables. Our results (untabulated) provide weak
evidence of a positive association between the number of foreign subsidiaries, tenure, and low pre-managed
earnings.
31
audit firms, and a higher quality audit requires significant client-specific knowledge. Thus, we test
the association between audit firm tenure and banks’ financial reporting quality. We also suggest that
the need for client-specific knowledge is even greater when the bank is more complex, and thus long
audit firm tenure is of even greater importance for the acquisition of complex knowledge.
Our results indicate a positive association between long audit firm tenure and improved
financial reporting quality for banks. We also find that the effect of long audit firm tenure is
particularly important for complex banks. Also, contrary to recent research suggesting very long
audit firm tenure is detrimental to audit quality, our findings indicate that additional benefits accrue
with very long audit firm tenure above and beyond the benefits to audit quality from medium audit
firm tenure. Our findings contrast global regulatory policies, which impose short-term limits on bank
audit firms, and suggest that unintended consequences may result from shorter audit firm tenure
especially among complex banks. During the PCAOB deliberations in 2011, many argued for
mandatory audit firm rotation for large firms and banks because these are important to the economy.
However, our results suggest the opposite: that the economy may benefit from the longer audit firm
tenure observed for many of these firms.
We make four contributions. First, we provide initial evidence on the benefits of long audit
firm tenure for the quality of the audit in the banking sector. Our results highlight the importance of
client-specific knowledge in the quality of financial reporting and question the merit of recent
policies in the European Union, which impose mandatory audit firm rotation in companies significant
to the economy. Instead, our results are consistent with organization theory and the view of the
profession. Second, our paper suggests that long tenure can play an even more important role in
complex organizations for which knowledge is of far greater importance in ensuring audit quality,
and recent policies on mandatory rotation could generate negative consequences for complex banks.
Third, to the best of our knowledge, our study is one of the few examining the role of audit firm
tenure in a regulated industry. Because multiple regulatory agencies heavily monitor banks, many
32
critics have questioned the role of auditor. Our results suggest the importance of the audit firm in
generating high quality financial information in banks despite the presence of alternative monitoring
mechanisms. In contrast to prior research, we find that audit firms with long tenure significantly limit
the extent to which banks under-provide for loan losses and upwardly manage earnings in an industry
in which overstatement of assets is of primary importance, at least as evidenced by the financial
crisis. Finally, our results are relevant for policy makers worldwide and indicate the potential need
for an alternative approach to regulation related to audit firm rotation that considers how industryspecific factors can influence the regulation’s effectiveness.
33
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38
Figure 1
Path Analysis
Auditor
Tenure
a
d
f
c
Complexity
b
Low premanaged
earnings
Discretionary
Loan Loss
Provision
e
g
Controls
The path “a” is the effect of complexity on auditor tenure
The path “b” is the effect of complexity on low pre managed earnings
The path “c” is the direct effect of complexity on the discretionary loan loss provision
The path “d” is the direct effect of auditor tenure on the discretionary loan loss provision
The path “e” is the direct effect of low pre-managed earnings on the discretionary loan loss
provision
The path “f” is the moderating effect of auditor tenure on management’s incentive to use the loan
loss provision to manage earnings.
The path “g” is the effect of the remaining control variables on the discretionary loan loss
provision
39
Table 1
Sample
Panel A: Sample Selection
U S Bank Holding Companies with assets, loan and earnings data on SNL 2000-2012
Less: Observations without auditor data on Audit Analytics
Final Sample (Used in Table 1 Panel B and 2)
Less: Observations without sufficient data to calculate DLLP or controls
DLLP Sample (Used in Tables 3; Table 4 further reduced by 941 observations)
Panel B: Number of Observations by Year
Year
Freq.
Percent
2000
403
6.00
2001
524
7.80
2002
578
8.60
2003
617
9.18
2004
566
8.42
2005
568
8.45
2006
555
8.26
2007
519
7.72
2008
481
7.16
2009
479
7.13
2010
486
7.23
2011
477
7.10
2012
468
6.96
Total
6,721 100.00
40
7,139
(418)
6,721
(710)
6,011
Table 2
Descriptive Statistics
Variable
TENYRS
Mean
Std_Dev
1st Pctl
25th Pctl
Median
75th Pctl
99th Pctl
8.1665
8.5378
1
3
6
10
41
5,756
19,456
103
496
986
2565
15,2015
LOANS/ASSETS
0.7408
0.1720
0.3230
0.6327
0.7303
0.8389
1.2861
DLLP
0.0000
0.0023
-0.0058
-0.0009
-0.0001
0.0007
0.0082
PME
0.0066
0.0116
-0.0421
0.0044
0.0087
0.0123
0.0233
BIGN
0.3935
0.4886
0
0
0
1
1
CAP
0.1489
0.0510
0.0817
0.1190
0.1356
0.1609
0.3860
CHNPA
0.0039
0.0143
-0.0367
-0.0014
0.0010
0.0063
0.0701
COMM%
0.1697
0.1311
0.0000
0.0736
0.1392
0.2378
0.6133
HET%
0.4333
0.1811
0.0326
0.2987
0.4329
0.5649
0.8595
NPL%
0.0182
0.0261
0.0000
0.0032
0.0080
0.0218
0.1456
STD_ROA
0.0041
0.0051
0.0003
0.0012
0.0021
0.0042
0.0246
FDEP%
0.0022
0.0120
0
0
0
0
0.0835
LOSS_AV
JMBE
0.1449
0.3520
0
0
0
0
1
0.4492
0.4974
0
0
0
1
1
ASSETS ($mil)
The main sample includes 6,721 observations of public banks between 2000 and 2012 with available data from SNL
Financial, Audit Analytics, and supplemental hand collection to compute audit tenure. ASSETS is total assets in
millions. TENYRS is the current auditor’s tenure in years determined based on the difference between the current
fiscal year and the Auditor_Since year listed an Audit Analytics. DLLP & PME (6,362 obs). DLLP is the residual
from equation (1), PME is pre-managed earnings defined as net income plus DLLP, BIGN is a dummy variable set
to one indicating whether the bank is audited by one of the large international audit firms, zero otherwise, CAP is
tier 1 risk-based capital ratio (6,342 obs.), CHNPA is the change in non-performing assets divided by beginning of
year total loans (6,678 obs.), COMM% is the percent of commercial and industrial loans over total loans (6,488
obs.), HET% is the percent of commercial and industrial loans, direct lease financing, all other real estate loans,
agriculture loans and foreign loans over total loans (5,733 obs.), NPL% is the percent of nonperforming loans over
total loans (6,684 obs)., STDROA equals the standard deviation of net income over assets during a 5 year period
ending in year t, FDEP% equals international deposits divided by total deposits (6,307 obs.), LOSS_AV is an
indicator variable equal to one if the bank has a small increase in ROA (income before taxes divided by total assets)
in the interval between 0 and 0.002, zero otherwise, JMBE is an indicator variable equal to one if the change in ROA
between t-1 and t falls in the interval between 0 and 0.0005, zero otherwise. With the exception of TENURE and
DLLP (the dependent variable) all continuous variables are winsorized at the 1st and 99th percentile.
41
Table 3
Earnings Management and Audit Firm Tenure
Panel A: Upward Earnings Management and Audit Firm Tenure (LPME)
Pred.
DLLP p-value
DLLP p-value
LPME
-0.00089 <0.001 -0.00079 <0.001
TENYRS
-0.00001
0.047
LPME×TENYRS (H1)
?
0.00003 <0.001
TEN6
-0.00007
0.129
LPME×TEN6 (H1)
?
0.00039
0.002
TENSHRT
TENLNG
LPME×TENSHRT (H1)
?
LPME×TENLNG (H1)
?
BIGN
-0.00003
0.493 -0.00006
0.214
CAP
-0.00031
0.422 -0.00026
0.503
CHNP
-0.00874 <0.001 -0.00899 <0.001
Intercept
0.00008
0.380 0.00007
0.437
Year Effects
Adjusted R2
Included
0.046
Included
0.045
Panel B: Downward Earnings Management and Audit Firm Tenure (HPME)
Pred.
DLLP p-value
DLLP p-value
HPME
+
0.00056 <0.001 0.00047 <0.001
TENYRS
0.00000
0.465
HPME×TENYRS (H1)
?
-0.00001
0.009
TEN6
0.00001
0.766
HPME×TEN6 (H1)
?
-0.00007
0.459
TENSHRT
TENLNG
HPME×TENSHRT (H1)
?
HPME×TENLNG (H1)
?
BIGN
-0.00003
0.597 -0.00005
0.258
CAP
-0.00010
0.797 -0.00008
0.835
CHNP
-0.01100 <0.001 -0.01135 <0.001
Intercept
-0.00016
0.086 -0.00013
0.153
Year Effects
Adjusted R2
Included
0.036
Included
0.035
DLLP p-value
-0.00061 <0.001
-0.00003
-0.00012
-0.00027
0.00025
-0.00004
-0.00017
-0.00884
0.00007
0.505
0.029
0.035
0.092
0.376
0.660
<0.001
0.475
Included
0.046
DLLP p-value
0.00052 <0.001
-0.00009
-0.00002
-0.00004
-0.00024
-0.00004
0.00006
-0.01106
-0.00012
0.070
0.690
0.726
0.024
0.390
0.889
<0.001
0.209
Included
0.037
The sample used for this analysis includes 6,011 bank-year observations from 2000 to 2012 with available data to
compute DLLP, audit tenure, and control variables. LPME is an indicator variable set to one when pre-managed
earnings are in the bottom quintile of sample observations, and zero otherwise; HPME is an indicator variable set to
one when pre-managed earnings are in the top quintile of sample observations, and zero otherwise; TENYRS is the
current auditor’s tenure in years determined based on the difference between the current fiscal year and the
Auditor_Since year listed an Audit Analytics. TEN6 is a dummy variable, which equals one if audit firm tenure is
greater than 6 years, zero otherwise. TENSHRT is a dummy variable equal to one if the audit firm tenure is less than
or equal to 3 years, zero otherwise; TENLNG is a dummy variable is audit firm tenure is equal to or greater than 9
years, zero otherwise. All other variables are defined in Table 2. All regressions are estimated and presented after
eliminating the effect of influential observations where Cook’s D is greater than 4/N. One-tailed p-values are
presented for predicted coefficients based on standard errors clustered at the bank level.
42
Table 4
Earnings Management, Audit Firm Tenure, and Bank Complexity
Panel A: Upward Earnings Management and Audit Firm Tenure (LPME)
LPME
TENYRS
LPME×TENYRS
OC
TC
LPME×OC
LPME×TC
OC×TENYRS
TC×TENYRS
LPME×OC×TENYRS (H2)
LPME×TC×TENYRS (H2)
TEN6
LPME×TEN6
OC×TEN6
TC×TEN6
LPME× OC×TEN6 (H2)
LPME×TC×TEN6 (H2)
TENSHRT
TENLNG
LPME×TENSHRT
LPME×TENLNG
OC×TENSHRT
TC×TENSHRT
OC×TENLNG
TC×TENLNG
LPME× OC×TENSHRT (H2)
LPME×TC×TENSHRT (H2)
LPME×OC×TENLNG (H2)
LPME×TC×TENLNG (H2)
Controls
Year Effects
Adjusted R2
Pred.
DLLP p-value
DLLP p-value
DLLP p-value
-0.00091 <0.001 -0.00099 <0.001 -0.00083 <0.001
-0.00001
0.010
0.00001
0.486
0.00035 <0.001 0.00036 <0.001 0.00030 <0.001
-0.00007
0.010 -0.00005
0.110 -0.00005
0.209
-0.00021
0.003 -0.00015
0.025 0.00004
0.612
0.00024
0.057 -0.00005
0.653 0.00005
0.756
-0.00001
0.003
0.00000
0.045
+
0.00002 <0.001
+
0.00001
0.211
-0.00013
0.028
0.00020
0.186
-0.00019
0.006
-0.00000
0.928
+
0.00035 <0.001
+
0.00062 <0.001
0.00003
0.703
-0.00011
0.122
-0.00024
0.145
0.00003
0.860
0.00010
0.189
-0.00002
0.724
-0.00011
0.162
0.00001
0.889
-0.00032
0.001
-0.00009
0.328
+
0.00020
0.051
+
0.00055
0.003
Included
Included
Included
Included
Included
Included
0.080
0.085
0.089
43
Table 4 (continued)
Earnings Management, Audit Firm Tenure, and Bank Complexity
Panel B: Downward Earnings Management and Audit Firm Tenure (HPME)
Pred.
DLLP p-value
DLLP p-value
HPME
+
0.00047 <0.001 0.00045 <0.001
TENYRS
-0.00000
0.574
HPME ×TENYRS
0.00000
0.631
OC
0.00012
0.005 0.00013
0.001
TC
-0.00003
0.352 -0.00004
0.263
HPME ×OC
0.00010
0.389 0.00012
0.286
HPME ×TC
-0.00009
0.071 -0.00000
0.952
OC×TENYRS
0.00000
0.459
TC×TENYRS
0.00000
0.040
HPME×OC×TENYRS (H2)
0.00000
0.639
HPME×TC×TENYRS (H2)
-0.00000
0.432
TEN6
-0.00004
0.479
HPME×TEN6
0.00009
0.472
OC×TEN6
0.00001
0.785
TC×TEN6
0.00008
0.085
HPME×OC×TEN6 (H2)
0.00000
0.513
HPME×TC×TEN6 (H2)
-0.00013
0.031
TENSHRT
TENLNG
HPME×TENSHRT
HPME×TENLNG
OC×TENSHRT
TC×TENSHRT
OC×TENLNG
TC×TENLNG
HPME×OC×TENSHRT (H2)
+
HPME×TC×TENSHRT (H2)
+
HPME×OC×TENLNG (H2)
HPME×TC×TENLNG (H2)
Controls
Included
Included
Year Effects
Included
Included
Adjusted R2
0.050
0.043
DLLP p-value
0.00052 <0.001
0.00021
-0.00002
0.00008
-0.00008
<0.001
0.704
0.524
0.190
-0.00012
0.050
-0.00006
0.334
-0.00007
0.673
-0.00009
0.572
-0.00014
0.022
-0.00003
0.664
-0.00006
0.360
0.00004
0.505
0.00006
0.356
0.00009
0.190
-0.00001
0.475
-0.00003
0.360
Included
Included
0.047
The sample used for this analysis includes 5,070 bank-year observations from 2000 to 2012 with available data to
compute DLLP, audit tenure, OC, TC, and control variables. Operational complexity (OC) is the first factor
extracted from a principal component analysis of Het%, NPLL and STD_ROA. Transactional complexity (TC) is the
first factor extracted from a principal component analysis of COMM% and FDEP%. All other variables are defined
in Table 2 and 3. Controls (as in Table 3) are included in each estimation, but not tabulated. All regressions are
estimated and presented after eliminating the effect of influential observations where Cook’s D is greater than 4/N.
One-tailed p-values are presented for predicted coefficients based on standard errors clustered at the bank level.
44
Table 5
Auditor Industry Specialization
Panel A: Upward Earnings Management and Audit Firm Tenure (LPME)
DLLP
p-value DLLP
p-value DLLP
p-value
LPME
-0.00095 <0.001
-0.00083 <0.001
-0.00070 <0.001
TENYRS
0.00000
0.082
LPME×TENYRS
0.00003 <0.001
TEN6
-0.00006
0.215
LPME×TEN6
0.00029
0.023
TENSHRT
-0.00004
0.420
TENLNG
-0.00013
0.020
LPME×TENSHRT
-0.00021
0.101
LPME×TENLNG
0.00022
0.142
BIGN
-0.00007
0.258
-0.00009
0.111
-0.00009
0.149
CAP
-0.00035
0.365
-0.00029
0.461
-0.00033
0.394
CHNP
-0.00781
0.001
-0.00821 <0.001
-0.00913 <0.001
SPEC
0.00000
0.998
0.00001
0.889
0.00001
0.863
LPME×SPEC
0.00068 <0.001
0.00070 <0.001
0.00067
0.001
Intercept
0.00010
0.291
0.00009
0.307
0.00012
0.186
Year Effects
N
Adjusted R2
Included
6,011
0.048
Included
6,011
0.047
Included
6,011
0.050
Panel B: Downward Earnings Management and Audit Firm Tenure (HPME)
DLLP
p-value DLLP
p-value DLLP
p-value
HPME
0.00061 <0.001
0.00052
<0.001
0.00058
<0.001
TENYRS
0.00000
0.958
HPME×TENYRS
-0.00001
0.094
TEN6
-0.00001
0.798
HPME×TEN6
0.00003
0.739
TENSHRT
-0.00007
0.166
TENLNG
-0.00006
0.333
HPME×TENSHRT
-0.00006
0.564
HPME×TENLNG
-0.00013
0.223
BIGN
-0.00010
0.104 -0.00011
0.066
-0.00010
0.103
CAP
-0.00023
0.563 -0.00022
0.581
-0.00024
0.544
CHNP
-0.01077 <0.001 -0.01022
<0.001
-0.01072
<0.001
SPEC
0.00020
0.009
0.00021
0.007
0.00020
0.010
HPME×SPEC
-0.00036
0.001 -0.00040
<0.001
-0.00033
0.002
Intercept
-0.00015
0.113 -0.00014
0.147
-0.00009
0.324
Year Effects
N
Adjusted R2
Included
6,011
0.036
Included
6,011
0.033
Included
6,011
0.034
This table reports the results of the OLS model testing the association between audit firm tenure and financial
reporting quality after controlling for auditor industry specialization. The sample includes 6,011 bank-year
observations from 2000 to 2012 with available data to compute DLLP, audit tenure, and control variables. SPEC is
the industry specialist auditor defined as the auditor with the highest number of bank clients in a given year (KPMG
in all cases). All other variables are defined in Table 2 and 3. All regressions are estimated and presented after
eliminating the effect of influential observations where Cook’s D is greater than 4/N. Two-tailed p-values are
presented in parenthesis below each coefficient based on standard errors clustered at the bank level.
45
Table 6
Loss Avoidance and Audit Firm Tenure
TENYRS
TEN6
TENSHRT
TENLONG
ROA
Log(ASSETS)
LOANS/ASSETS
BIGN
Year Effects
N
χ2
p-value
Area under ROC
curve
LOSS_AV p-value
LOSS_AV p-value
LOSS_AV
p-value
-0.01551
0.081
-0.29065
0.004
0.23142
0.017
-0.14550
0.221
5.81238
0.043
5.74590
0.044
5.93233
0.038
-0.25625
<0.001
-0.26719
<0.001
-0.26698
<0.001
-1.11405
<0.001
-1.11714
<0.001
-1.11867
<0.001
-0.06183
0.616
-0.04900
0.688
-0.04372
0.722
Included
5,105
183.75
<0.001
Included
5,105
187.84
<0.001
Included
5,105
351.30
<0.001
0.6775
0.6794
0.6797
The sample for this analysis includes all observations except those with high pre-managed earnings (HPME = 0).
ROA is net income divided by average total assets at the end of the year. All other variables are defined in Table 2
and 3. All regressions are estimated using a logistic regression. Two-tailed p-values are presented for all coefficients
based on standard errors clustered at the bank level.
46
Table 7
Propensity to Just Meet and Audit Firm Tenure
TENYRS
TEN6
TENSHRT
TENLONG
ROA
Log(ASSETS)
LOANS/ASSETS
BIGN
Year Effects
N
χ2
p-value
Area under ROC
curve
JMBE
p-value
JMBE
p-value
JMBE
p-value
-0.00348
0.454
-0.15893
0.017
0.20952
0.005
0.01269
0.866
84.83478
<0.001
85.35081
<0.001
85.70265
<0.001
-0.07980
0.016
-0.07801
0.018
-0.08221
0.012
0.44511
0.021
0.43122
0.026
0.43020
0.026
0.13473
0.084
0.14997
0.053
0.14991
0.054
Included
5,105
539.83
<0.001
Included
5,105
541.64
<0.001
Included
5,105
538.85
<0.001
0.7319
0.7327
0.7335
The sample for this analysis includes all observations except those with high pre-managed earnings (HPME = 0).
ROA is net income divided by average total assets at the end of the year. All other variables are defined in Table 2
and 3. All regressions are estimated using a logistic regression. Two-tailed p-values are presented for all coefficients
based on standard errors clustered at the bank level.
47
Table 8
Restatements
TENYRS
TEN6
TENSHRT
TENLONG
BIGN
Log(ASSETS)
GROWTH
CAP
Year Effects
N
χ2
p-value
Area under ROC curve
RESTATE p-value
RESTATE p-value
RESTATE p-value
-0.03874
0.007
-0.45631
0.034
0.33887
0.085
-0.36912
0.104
0.84246
0.002
0.87477
0.001
0.90378
0.001
0.20248
0.012
0.13561
0.055
0.14334
0.041
-0.38950
0.493
-0.36107
0.526
-0.40074
0.473
-1.96714
0.444
-2.41024
0.358
-2.49778
0.337
Included
4,782
78.03
<0.001
0.6923
Included
4,782
82.20
<0.001
0.6839
Included
4,782
79.34
<0.001
0.6901
The sample for this analysis includes all observations except those with high pre-managed earnings (HPME = 0)
with sufficient data to compute the control variables. RESTATE equals one if the bank restates earnings in year t,
zero otherwise. GROWTH is defined as the one-year percentage change in assets . All other variables are defined in
Table 2 and 3. All regressions are estimated using a logistic regression. Two-tailed p-values are presented for all
coefficients based on standard errors clustered at the bank level.
48
Table 9
Bank Failures and Audit Firm Tenure
TENYRS
Leverage
NonAccr
PastDue
OREO
AbsMaturityGAP
ROA
Size
Intercept
N
χ2
p-value
Area under ROC
curve
FAIL1
p-value
FAIL2
p-value
FAIL3
p-value
-0.02569
0.066
-0.03281
0.024
-0.04473
0.004
-0.04293
0.090
-0.02308
0.332
0.00197
0.941
-15.92545
0.159
-11.88905
0.359
-19.04056
0.133
0.95732
<0.001
-0.16420
0.604
-0.28125
0.560
120.78488
<0.001
78.83217
<0.001
41.21179
0.032
0.00283
0.711
0.00133
0.821
-0.00059
0.918
-18.19626
0.126
-12.33109
0.287
0.26934
0.979
0.62943
<0.001
0.64044
<0.001
0.59046
<0.001
-12.86010
<0.001
-12.65597
<0.001
-11.92492
<0.001
3,595
147.35
<0.001
3,595
122.97
<0.001
3,595
85.02
<0.001
0.8717
0.8037
0.7695
This table reports the results of logit regression testing for the association between the likelihood of bank failure and
audit firm tenure. The sample for this analysis includes all observations except those with high pre-managed
earnings (HighPME = 0) with sufficient data to compute the control variables. TENYRS is the current auditor’s
tenure in years determined based on the difference between the current fiscal year and the Auditor_Since year listed
an Audit Analytics. We follow Cantrell et al. (2014) and define all other variables as follows. The dependent
variables FAIL1, FAIL2, and FAIL3 are indicator variables equal to one if a bank fails within one, two, and three
years, respectively, after the availability of annual accounting data in year t. Leverage is Tier 1 capital leverage ratio.
NonAccr is loans for which the bank is no longer accruing interest divided by total assets as of the end of the year.
PastDue is loans over 90 days past due divided by total assets at the end of the year. OREO is other real estate
owned divided by total assets at the end of the year. AbsMaturityGAP is the absolute difference between assets and
liabilities that are due to mature or be repriced within one year divided by total assets at the end of the year. ROA is
net income divided by average total assets at the end of the year. Size is the natural log of total assets at the end of
the year. Two-tailed p-values are presented for all coefficients based on standard errors clustered at the bank level.
49
Table 10
Crisis vs. Non-Crisis
Panel A: Upward Earnings Management and Audit Firm Tenure (LPME)
Full DLLP Sample
Non-crisis years
Crisis Years
DLLP
p-value DLLP
p-value DLLP
p-value
LPME
-0.00108 <0.001
-0.00109 <0.001
-0.00018
0.308
TENYRS
-0.00001
0.008
-0.00001
0.022
0.00000
0.747
LPME×TENYRS
0.00003 <0.001
0.00003 <0.001
0.00004
0.039
BIGN
-0.00004
0.367
-0.00010
0.041
0.00039 <0.001
CAP
-0.00042
0.279
-0.00053
0.191
0.00024
0.788
CHNP
-0.00973 <0.001
-0.01206
0.000
-0.00027
0.954
Crisis
-0.00042
0.001
LPME×Crisis
0.00089 <0.001
Crisis×TENYRS
0.00001
0.036
LPME×Crisis×TENYRS
0.00002
0.392
Intercept
0.00012
0.203
0.00017
0.075
-0.00029
0.071
LPME×TENYRS+
LPME×Crisis×TENYRS
Year Effects
Adjusted R2
F = 6.92
0.009
Included
0.053
Included
0.059
Included
0.026
Panel B: Downward Earnings Management and Audit Firm Tenure (HPME)
Full DLLP Sample
Non-crisis years
Crisis Years
DLLP
p-value DLLP
p-value DLLP
p-value
HPME
0.00057
0.000
0.00058
0.000
-0.00019
0.294
TENYRS
0.00000
0.670
0.00000
0.970
-0.00001
0.474
HPME×TENYRS
-0.00001
0.028 -0.00001
0.022
0.00002
0.166
BIGN
-0.00001
0.778 -0.00009
0.077
0.00054
0.000
CAP
-0.00010
0.804 -0.00026
0.542
0.00087
0.335
CHNP
-0.01051
0.000 -0.01395
0.000
0.00138
0.770
Crisis
-0.00015
0.244
HPME×Crisis
-0.00072
0.000
Crisis×TENYRS
0.00001
0.377
HPME×Crisis×TENYRS
0.00003
0.055
Intercept
-0.00016
0.082 -0.00010
0.316
-0.00044
0.009
HPME×TENYRS+
HPME×Crisis×TENYRS
Year Effects
Adjusted R2
F = 1.81
0.179
Included
0.036
Included
0.038
Included
0.029
The sample used for this analysis includes 6,011 bank-year observations from 2000 to 2012 with available data to
compute DLLP and control variables. Crisis is an indicator variable equal to one for years 2007 and 2008 (853
observations), and zero otherwise (5,158 observations). All other variables are defined in Table 2 and 3. All
regressions are estimated and presented after eliminating the effect of influential observations where Cook’s D is
greater than 4/N using the estimation from the full DLLP sample. Two-tailed p-values are presented for all
coefficients based on standard errors clustered at the bank level.
50