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Robust lessons learned from bank failures during the Great Financial Crisis

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Abstract

Several empirical studies have identified unique characteristics of banks that subsequently failed during the Great Financial Crisis. The notion is that by identifying these risk characteristics we are better able to monitor and regulate the risks to banks during the next crisis. A concern is bank failure is a relatively rare event, therefore inferences based on a single model specification can be sensitive to the choice of variables. We re-examine three studies (DeYoung and Torna in J Financ Intermed 22:397–421, 2013; Jin et al. in J Bank Finance 35:2811–2819, 2011; Ng and Roychowdhury in Rev Acc Stud 19:1234–1279, 2014) of bank failures during the Great Financial Crisis to determine whether these authors’ main findings are robust to accounting for uncertainty in the model’s specification. Our results indicate their results are not robust and that the causes of bank failures during the Great Financial Crisis are similar to those of past periods of crisis and are driven by traditional measures of risk.

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Data Availability

The data used in this study are all from publicly available sources.

Code availability

The R-package BMA (Raftery et al. 2018) is publicly available. The code and dataset necessary to replicate the results will be made available on the author’s website on acceptance.

Notes

  1. Reliance on brokered deposits as a source of funds (liability) can create liquidity issues for a bank during a crisis due to the volatility of their withdrawal, relative to core deposits.

  2. Harvey (2017) notes p-hacking may also involve choice of estimation method (e.g. logit vs survival model) and sample selection (e.g. observation exclusion).

  3. The odds equal = \(\Omega (H_{0} |D) = \frac{{P(H_{0} |D)}}{{1 - P(H_{0} |D)}}\) therefore \(P(H_{0} |D) = \frac{{\Omega (H_{0} |D)}}{{1 + \Omega (H_{0} |D)}}\)

  4. For the logit model the number of observations is equal to the sample size, whereas the Cox proportional hazards model uses the number of events, i.e. failures.

  5. See Enkhtaivan and Lu (2021) for a thorough overview of TARP implementation.

  6. The FDIC uses similar variables in their statistical CAMELS off-site rating (SCOR) model to predict changes in CAMELS ratings (Collier et al. 2003).

  7. Lane et al. (1986) interpret that measures of loan composition reflect management quality. Collier et al. (2003), however, believe that management quality cannot be identified with any financial ratio. An alternative approach to identify differences in management quality is to use textual analysis to reveal differences in banks’ culture, which Luu et al., (2023) observe influences bank stability. Differences in corporate governance measures have also been shown (Alzayed et al. 2023) to influence bank stability. It should be noted that these measures of bank culture and corporate governance are only available for very small samples of the population of US banks.

  8. Wicker (1980), for example, discusses how the failure of the investment bank Caldwell and Company, the largest in the South, contributed directly to the closing of 120 banks affiliated with the firm in a two-week period in November and December of 1930. Wicker (1980) argues other failures in the period originated from the uncertainty caused by Caldwell’s collapse. A result Wicker (1980) notes is due to Caldwell’s heavy borrowing from bank affiliates, which was used to finance the purchase of municipal securities for trading purposes.

  9. Torna (August 29, 2019) indicated in a personal communication that they (DeYoung and Torna 2013) no longer had access to the data or the code needed to replicate exactly their sample and results.

  10. We report the series used to construct the relevant variables in Appendix Table 12 and a comparison of summary statistics in Appendix Table 13, which are available online.

  11. These result appear in Appendix Table 14

  12. The allowances for loan losses counted for risk based purposes deduct the allocated transfer risk reserve and add allowances for credit losses on off-balance sheet credit exposures.

  13. The restrictions are a result of the construction of the control variables, which in some cases use lagged and unlagged values, to avoid dividing by zero.

  14. The series we used to construct Ng and Roychowdhury’s (2014) variables based on their descriptions appear in Appendix Table 15.

  15. See Appendix Table 16.

  16. Roychowdhury (August 26, 2019) indicated in a personal communication they (Ng and Roychowdhury 2014) no longer had access to the data or the code needed to replicate their data and results so it is unclear how their timely measure or dataset more generally was constructed.

  17. Using the difference in unadjusted R2 did not materially affect our results.

  18. The marginal effect reported here and for the effect of equity are based on the authors’ calculations using Ng and Roychowdhury’s (2014) estimates and summary statistics.

  19. These estimates are available in Appendix Table 17. Panel A has the logistic model estimates and panel B the hazard model estimates.

  20. Estimates from the models that consider for inclusion provisions as a share of assets are available in Appendix Table 18. Panel A has the logistic model estimates and panel B the hazard model estimates.

  21. https://www.fdic.gov/regulations/laws/rules/5000-4700.html

  22. The marginal effect is based on our calculation using estimates and summary statistics reported by Jin et al. (2011), where we compare the difference in probabilities evaluated at the variables’ mean values for a bank with and without a Big 4 auditor and first quarter data.

  23. The measures and call report series we used in their construction are included in Appendix Table 19.

  24. Summary statistics for the variables are available in Appendix 20.

  25. The link table is available at https://www.newyorkfed.org/research/banking_research/datasets.html.

  26. Jin (August 3, 2019) in a personal communication provided the series Jin et al. (2011) use to construct LOAN_MIX. In Appendix Table 21 we provide the complete description of each of these series.

  27. The series (RCFDB705, RCFDB706, RCFDB707, RCFDB708, RCFDB709, RCFDB710, RCFDB711) are used to construct the numerator, which is scaled by total assets.

  28. Jin (August 3, 2019) in a personal communication provided the series Jin et al. (2011) use to construct the variable PSLOAN. In Appendix Table 22 we provide the complete description of each of these series.

  29. Series RCON5571 is defined in the Federal Reserve’s Micro Data Reference Manual as “amount currently outstanding of commercial and industrial loans to U.S. addressees (in domestic offices) with original amounts of $100,000 or less”. Source: https://www.federalreserve.gov/apps/mdrm/data-dictionary. The other two series (RCON5573, RCON5575) are also related to commercial and industrial (C & I) loans with loan amounts of more than $100,000 to $250,000 and more than $250,000.

  30. Charge-offs, for example, were almost twice as high in the fourth quarter 2007 than in 2006.

  31. These estimates appear in Appendix Table 23.

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Acknowledgements

This research was supported by a summer research grant from the Nistler College of Business and Public Administration at the University of North Dakota. I would like to thank Margie Tieslau and conference participants of the 56th Missouri Valley Economic Association annual meeting for their comments. In addition, I thank the two anonymous referees and Cheng-Few Lee (editor) for their insightful suggestions, which helped to improve the paper. Gökhan Torna, Justin Jin, and Sugata Roychowdhury were also helpful in responding to my questions in regard to their studies.

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  1. Department of Economics and Finance, University of North Dakota, Nistler Hall Room 330 M, 3125 University Ave Stop 8369, Grand Forks, ND, 58202-8369, USA

    Cullen F. Goenner

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Appendix

Appendix

See Tables

Table 12 Series used to construct the control variables for the non-traditional income model
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Table 13 Summary statistics of the non-traditional income samples (All banks)
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Table 14 BMA estimates of non-traditional banking activities using an alternative set of controls
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Table 15 Call report series used to construct variables for the addbacks to loan loss model
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Table 16 Summary statistics of loan loss reserve sample
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Table 17 Effects of loan losses with alternative controls
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Table 18 The uncertain effects of allowances on bank failure
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Table 19 Call report series used to construct variables for the auditor choice model
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Table 20 Comparison of auditor choice samples
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Table 21 Series used in the construction of LOAN_MIX
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Table 22 Series used in the construction of PSLOAN
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Table 23 Effects of auditor choice with alternative controls
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Goenner, C.F. Robust lessons learned from bank failures during the Great Financial Crisis. Rev Quant Finan Acc 62, 449–498 (2024). https://doi.org/10.1007/s11156-023-01213-9

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