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Auditor conservatism, audit quality, and real consequences for clients


Auditors tend to be more conservative in their audits when they face greater litigation risk. However, it is unclear whether this conservatism is always desirable or whether it can be excessive. To evaluate the usefulness of greater auditor conservatism, we examine whether auditor conservatism improves audit quality and imposes real operating consequences for clients. We examine auditor behavior in a sample of auditors’ client banks, when one of the auditor’s other client banks fails (as deemed by the FDIC). We find that auditors that experience a bank failure within their portfolio become more conservative for surviving clients. However, we find that the larger loan loss provisions at surviving clients are no more timely, are less accurate, and reverse in subsequent periods. This finding suggests that auditors’ initial portfolio-wide response to a specific client failure may be overly conservative. We also document that surviving client banks face real consequences in terms of constraints on their lending because of the auditor’s excessive conservatism. Overall, we conclude that, in some situations, auditors may overreact, leading to excessive conservatism, which in turn has adverse consequences for clients.

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Fig. 1
Fig. 2


  1. 1.

    Kaplan and Williams (2013) argue that auditors have incentives to issue more going concern opinions than is appropriate, which is indicative of excessive auditor conservatism, in order to reduce their liability.

  2. 2.

    In the construction of the auditor’s portfolio of clients, we drop all clients outside of the commercial bank industry. Thus, the auditor’s portfolio is an industry specific portfolio of clients. Further, we require an auditor to have two commercial banks in their portfolio to be retained for our analyses.

  3. 3.

    However, focusing on a single industry offers one distinct advantage in that it allows us to focus on one accrual. Focusing on a single accrual—loan loss provisions—facilitates a sharper separation into its normal and abnormal components than typical accrual measures (Kanagaretnam et al. 2010).

  4. 4.

    The exclusion of banks from empirical analyses eliminates a substantial portion of firms within the economy, as the financial sector accounts for approximately one-fifth of the S&P 500 companies.

  5. 5.

    In October 2012, the Federal Deposit Insurance Corporation (FDIC), as the receiver for the failed Colonial Bank of Montgomery, Alabama, sued PricewaterhouseCoopers, the external auditor.

  6. 6.

    Following prior studies in the audit literature, we capture conservative audit behavior using the client banks’ accruals (DeFond and Subramanyam 1998). Specifically, we use loan loss provisions to measure accruals in banks because this is the most important accrual in the banking industry and because broad accruals-based metrics using models designed for non-financial firms, such as the Jones (1991) model, are inappropriate (Beatty et al. 2002). Larger loan loss provisions decrease earnings and imply more conservative audits, while smaller loan loss provisions result in higher earnings, which are consistent with more lenient audits.

  7. 7.

    For a more in-depth discussion of our client failure variable, please refer to Sect. 3.1.

  8. 8.

    We acknowledge that the loan loss provision is a joint product of the client bank and the auditor. However, we interpret systematic conservatism in loan loss provisions that is related to auditor portfolio events but not supported by client fundamentals as evidence that auditors exerted pressure on clients to report higher loan loss provisions.

  9. 9.

    In our setting the frequency of going concern opinions is low. We therefore acknowledge the limitation of performing this analysis in only a univariate setting (Desai et al. 2016).

  10. 10.

    For example, Kothari and Lester (2012, p. 350) state that “it is clear that the poor implementation of fair value accounting standards has been a factor in causing and prolonging the Great Recession.”.

  11. 11.

  12. 12.

    Two recent studies utilizing audit fees examine whether auditors succeeded in detecting impending bank failures before the onset of the financial crisis and find mixed evidence. On one hand, Desai et al. (2016) find that average audit fees did not increase during the period before the financial crisis and conclude that auditors were unable to predict the impact of the crisis on the banks they audited. On the other hand, Doogar et al. (2015) focus their study specifically on auditors and find that in the year leading up to the financial crisis, audit fees increased for banks with higher levels of audit risk.

  13. 13.

    In our study, we note that we can only identify going concern reports for public client banks. Due to the low number of going concern opinions issued in the year prior to a public bank failure, and to our inability to obtain going concern reporting for the private bank failures, we lack a sufficient sample to separately evaluate the effect of a failure when the auditor anticipated it. As a result, we note that a measure regarding the prior receipt of a going concern opinion is noisy. Nevertheless, we examine whether the prior receipt of a going concern opinion for a public bank failure mitigates the predicted association. In untabulated analyses, we fail to find that the prior receipt of a going concern opinion mitigates the predicted association. However, we acknowledge that the inferences from this additional analysis are limited.

  14. 14.

    To the extent that conservatism results in timely loss recognition (Basu 1997), it could enhance the usefulness of accounting (e.g., Watts 2003; Ball et al. 2008). Yet, in an analytical study, Gigler et al. (2009) show that conservatism decreases the efficiency of debt contracts when it results in unwarranted covenant violations. This finding suggests that conservative accounting may not necessarily be unambiguously desirable.

  15. 15.

  16. 16.

    In untabulated analyses, we examine and find that our results are robust to the exclusion of the financial crisis years. In addition, we further exclude 2010 and 2011 to ensure that we can generalize our results to a period with somewhat normal economic conditions. In untabulated analyses, we continue to find some evidence that the initial response by auditors experiencing a client failure was excessively conservative and did not necessarily improve audit quality at surviving banks. However, it does appear that the extent of excessive conservatism is dampened in a more normal period.

  17. 17.

    Auditor portfolios are formed using all available bank data.

  18. 18.

    We include all banks with available data in our primary sample. However, all of our results are robust to removing banks with less than $500 million in assets, which is the threshold under FDIC regulation 12 CFR part 363 for a bank to have a required external audit.

  19. 19.

    We acknowledge that a lawsuit naming the auditor as a defendant can take years to resolve and therefore this lagged research design is more appropriate in their context.

  20. 20.

    We separate the audit window into four quarters to identify when the first client failure in an auditor’s firm-wide and office-level portfolio occurs. We note that the first firm or office client failure for an auditor within the audit window for the year occurs at a rate of 45.1% in the first 3 months of the window, 38.4% in months 4 through 6, 15.1% in months 7 through 9, and 1.4% in months 10 through 12. This univariate analysis helps alleviate concerns that our time window is unrealistic. Furthermore, we create four indicator variables of Client Failure National (Office) to identify when the first client failure occurs within the firm-wide and office-level portfolio and run our primary regression with all four indicator variables included. We find that Client Failure National (Office) for the first three months and for months four through six is positive and significant. In addition, we note that Client Failure Office is positive and significant in months seven through nine, whereas Client Failure National is insignificant. Finally, we note that the indicator variables for months 10 through 12 are highly insignificant for both client failure variables. This suggests that an auditor’s conservative response to a bank failure within their portfolio is primarily driven by those auditors that experience a failure within the first half of the audit window. Overall, this analysis helps further alleviate the concern that our audit window is unrealistic and indicates that the auditor has adequate time to respond on remaining portfolio clients from the time of their first failure in the audit window.

  21. 21.

    As a robustness check, we use an alternative audit window which aligns with the calendar year, January 1 through December 31. Relating to Fig. 2, our Client Failure National variable would be one for the auditors of bank failure #1 and bank failure #2 in 2010 and for the auditor of bank failure #3 in 2011. In untabulated analyses, we find our results are robust to using this alternative audit window.

  22. 22.

  23. 23.

    The sample of public and private bank-year observations includes only bank-year observations that did not have a failure during the respective year. For example, Imperial Capital Bancorp failed on December 18, 2009. Thus, the 2008 bank-year observation for Imperial bank is included in the analysis, but the 2009 bank-year observation is excluded. Of the 160 bank holding companies that experience a failure, 16 either fail before they can issue their 2008 financial reports or miss the necessary data to estimate our main regression models. Thus, our sample includes 144 banks representing 215 bank-year observations where the bank subsequently failed.

  24. 24.

    While our primary measure of loan loss provisions is scaled by average loan balances, consistent with the prior literature, a potential concern in our study is a mechanical relationship between the dependent variables, Loan Loss Provision and ∆Loans, because of the scalar in Loan Loss Provision. In untabulated analyses, we find no correlation between average loan balances and ∆Loans, which suggests that the dependent variables are not mechanically related. Nevertheless, we examine and find that our main results are robust to alternative scalars for loan loss provisions, such as ending total assets and beginning loan balance.

  25. 25.

    Our main results are also robust to clustering standard errors by bank and calendar year.

  26. 26.

    Since we don’t have a strong prior on when loan loss provisions should reverse, we follow Dechow et al. (2012) and use 1 year.

  27. 27.

    We note that Log(Assets) in Eq. (5) is computed using the lagged values to be consistent with Beatty and Liao (2011).

  28. 28.

    The banking industry is skewed in terms of bank size—a few banks account for a significant proportion of the industry assets. Thus, while 24% of banks are audited by Big 4 auditors, the large banks in our sample are mainly clients of Big 4 auditors (93% of public banks with assets over $10 billion).

  29. 29.

    As in Table 1, we note that certain auditors experience more than one failure in a given year. Therefore, in our robustness analysis, we also include measures for the number of client banks that failed at the national (office) level during the year. In an untabulated analysis, we find that the number of additional bank failures at the national level does not incrementally influence an auditor’s conservative response. However, we find support for an incrementally conservative response at the office level for each additional bank failure.

  30. 30.

    The average loan loss provision is 1% of the total loan value and β1 = 0.002, indicating 20% of the total loan loss provision (0.002/0.010 = 20%). Average total loans for our sample is $8.17 billion; $8.17 billion × 1% × 20% = $16.3 million.

  31. 31.

    Note that the reversals test requires two years of client failure data, which restricts our sample period to 2009–2014 instead of the 2008–2014 period used in our main analysis. This restriction leads to a loss of 781 observations or approximately 14% of our sample (as expected).

  32. 32.

    However, if a powerful client is able to eliminate the excessive conservatism, then we would not expect them to switch auditors. In untabulated analyses, we find that client power does not have a mitigating effect on the conservative response by auditors to a bank failure in their portfolio.

  33. 33.

    If the cost of not detecting a potential bank failure is high enough to the economy as a whole, it may be optimal to allow a few banks to suffer the adverse consequences of an unwarranted going concern opinion. If not, then the auditors’ conservative response is not desirable.

  34. 34.

    Due to the lack of data availability for the various corporate governance measures for non-public banking clients, we are restricted to public banking clients in our sample.

  35. 35.

    Alternatively, we rerun our analyses including auditor fixed effects and find results consistent with our primary analyses.


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Correspondence to J. Scott Judd.

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We thank Stephen Penman (editor) and an anonymous reviewer for the valuable comments and suggestions provided throughout the review process. We are grateful for the helpful comments from Dane Christensen, Keith Czerney, Matt Ege, Bill Felix, Barb Grien, Phil Lamoreaux, Tom Omer, Shyam V. Sunder, workshop participants at Boston University and the University of Oklahoma, and conference participants at the 2014 American Accounting Association (AAA) annual meeting and the 2016 AAA Audit Midyear Meeting.

Appendix A: Variable definitions

Appendix A: Variable definitions

Dependent variables
 Loan Loss Provision Loan loss provision as a percentage of the average of beginning and ending total loans
 Loan Charge Off Loan charge-offs scaled by beginning total assets
 ΔLoans The change in the natural log of the total loan balance from the beginning to the end of the year, scaled by beginning of the year total assets
Independent variables
 Client Failure National Indicator variable equal to one if at least one of the auditor firm’s client banks failed during the year and zero otherwise
 Client Failure Office Indicator variable equal to one if at least one of the auditor office’s client banks failed during the year and zero otherwise
 Big 4 Indicator variable equal to one if the auditor was a Big 4 auditor and zero otherwise
 Log(Auditor Portfolio Size) Logarithm of total assets of all banks within the auditor's portfolio for the year
 Log(Assets) Logarithm of total assets
 Δ Non Performing Loans Change in nonperforming loans as a percentage of the average of beginning and ending total loans
 Loan Loss Reserve Loan loss reserve as a percentage of total loans at the beginning of the year
 LoanR Loans secured by real estate as a percentage of total loans
 LoanC Commercial and industrial loans as a percentage of total loans
 LoanD Loans to depository institutions as a percentage of total loans
LoanA Loans to finance agricultural production as a percentage of total loans
 LoanI Loans to individuals as a percentage of total loans
 LoanF Loans to foreign governments as a percentage of total loans
 Deposits Lagged total deposits scaled by total loans
 Tier 1 Capital Tier 1 capital deflated by total assets
 ΔUnemployment change in the annual unemployment rate
 Public Indicator variable equal to one if the bank is a public company and zero otherwise

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Hall, C., Judd, J.S. & Sunder, J. Auditor conservatism, audit quality, and real consequences for clients. Rev Account Stud (2021).

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  • Auditor conservatism
  • Auditor portfolios
  • Reporting quality
  • Loan loss provisions

JEL Classifications

  • G21
  • M41
  • M42