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Bank failure prediction: corporate governance and financial indicators

Author

Listed:
  • Noora Alzayed

    (University of Salford
    University of Bahrain)

  • Rasol Eskandari

    (University of Salford)

  • Hassan Yazdifar

    (Derby University)

Abstract

This paper reiterates the importance of corporate governance in banks. Failure prediction studies have mainly relied on using financial ratios as predictors. The most suitable financial predictors for banks are financial ratios following the CAMEL rating system. Also, corporate governance has been proven to be an important aspect of banks, especially after the financial crisis. Given its importance, the novelty of this paper is to test the ability of corporate governance to increase the accuracy and extend the time-horizon of bank failure prediction in the US market. Using discriminant analysis, we predict the failure of banks insured by the Federal Deposit Insurance Corporation from 2010 to 2018. Using financial and non-financial predictors, we find that combining CAMEL ratios with corporate governance variables not only increases the accuracy of prediction but also extends the time horizon to three years before failure. We also show that bank earnings is a more significant predictor than capital structure and asset quality. The results further reveal that CEO compensation, voting rights and institutional ownership are significant predictors. These results are robust when using logit regression and out-of-sample examination. This study shows that corporate governance plays a key role in the success or failure of banks. The regulatory implication of this paper is that more attention needs to be directed to corporate governance and earnings aspects of banks rather than focusing on capital structure.

Suggested Citation

  • Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023. "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 601-631, August.
    • Handle: RePEc:kap:rqfnac:v:61:y:2023:i:2:d:10.1007_s11156-023-01158-z
    DOI: 10.1007/s11156-023-01158-z
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    as
    1. du Jardin, Philippe, 2016. "A two-stage classification technique for bankruptcy prediction," European Journal of Operational Research, Elsevier, vol. 254(1), pages 236-252.
    2. Ioannidis, Christos & Pasiouras, Fotios & Zopounidis, Constantin, 2010. "Assessing bank soundness with classification techniques," Omega, Elsevier, vol. 38(5), pages 345-357, October.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    4. Demirgüç-Kunt, AslI & Detragiache, Enrica & Tressel, Thierry, 2008. "Banking on the principles: Compliance with Basel Core Principles and bank soundness," Journal of Financial Intermediation, Elsevier, vol. 17(4), pages 511-542, October.
    5. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    6. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    7. West, Robert Craig, 1985. "A factor-analytic approach to bank condition," Journal of Banking & Finance, Elsevier, vol. 9(2), pages 253-266, June.
    8. Gasbarro, Dominic & Sadguna, I Gde Made & Zumwalt, J Kenton, 2002. "The Changing Relationship Between CAMEL Ratings and Bank Soundness during the Indonesian Banking Crisis," Review of Quantitative Finance and Accounting, Springer, vol. 19(3), pages 247-260, November.
    9. Pathan, Shams, 2009. "Strong boards, CEO power and bank risk-taking," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1340-1350, July.
    10. Bebchuk, Lucian & Cohen, Alma & Wang, Charles C.Y., 2014. "Golden Parachutes and the Wealth of Shareholders," Journal of Corporate Finance, Elsevier, vol. 25(C), pages 140-154.
    11. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    12. Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
    13. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    14. Mamdouh Abdulaziz Saleh Al-Faryan & Everton Dockery, 2021. "Testing for efficiency in the Saudi stock market: does corporate governance change matter?," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 61-90, July.
    15. Cielen, Anja & Peeters, Ludo & Vanhoof, Koen, 2004. "Bankruptcy prediction using a data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 526-532, April.
    16. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    17. Raymond A.K. Cox & Grace W.-Y. Wang, 2014. "Predicting the US bank failure: A discriminant analysis," Economic Analysis and Policy, Elsevier, vol. 44(2), pages 202-211.
    18. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    19. Kao, Chiang & Liu, Shiang-Tai, 2004. "Predicting bank performance with financial forecasts: A case of Taiwan commercial banks," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2353-2368, October.
    20. Luminita Enache & Khaled Hussainey, 2020. "The substitutive relation between voluntary disclosure and corporate governance in their effects on firm performance," Review of Quantitative Finance and Accounting, Springer, vol. 54(2), pages 413-445, February.
    21. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    22. Joel F. Houston & Liangliang Jiang & Chen Lin & Yue Ma, 2014. "Political Connections and the Cost of Bank Loans," Journal of Accounting Research, Wiley Blackwell, vol. 52(1), pages 193-243, March.
    23. Yadav K. Gopalan, 2010. "Earliest indicator of bank failure is deterioration in earnings," Central Banker, Federal Reserve Bank of St. Louis, issue Spring.
    24. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    25. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    26. Christine Cheng & Stewart Jones & William J. Moser, 2018. "Abnormal trading behavior of specific types of shareholders before US firm bankruptcy and its implications for firm bankruptcy prediction," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 45(9-10), pages 1100-1138, October.
    27. Catherine M. Daily & Dan R. Dalton, 1994. "Corporate governance and the bankrupt firm: An empirical assessment," Strategic Management Journal, Wiley Blackwell, vol. 15(8), pages 643-654, October.
    28. Brogi, Marina & Lagasio, Valentina, 2022. "Better safe than sorry. Bank corporate governance, risk-taking, and performance," Finance Research Letters, Elsevier, vol. 44(C).
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    More about this item

    Keywords

    Corporate governance; CAMEL ratios; Bank failure; Failure prediction;
    All these keywords.

    JEL classification:

    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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