Tatom, John (2011): Predicting failure in the commercial banking industry.
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The ability to predict bank failure has become much more important since the mortgage foreclosure crisis began in 2007. The model proposed in this study uses proxies for the regulatory standards embodied in the so-called CAMELS rating system, as well as several local or national economic variables to produce a model that is robust enough to forecast bank failure for the entire commercial bank industry in the United States. This model is able to predict failure (survival) accurately for commercial banks during both the Savings and Loan crisis and the mortgage foreclosure crisis. Other important results include the insignificance of several factors proposed in the literature, including total assets, real price of energy, currency ratio and the interest rate spread.
|Item Type:||MPRA Paper|
|Original Title:||Predicting failure in the commercial banking industry|
|Keywords:||bank failure; banking crises; CAMELS ratings|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G18 - Government Policy and Regulation
G - Financial Economics > G3 - Corporate Finance and Governance > G33 - Bankruptcy; Liquidation
G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks; Depository Institutions; Micro Finance Institutions; Mortgages
G - Financial Economics > G0 - General > G01 - Financial Crises
|Depositing User:||John Tatom|
|Date Deposited:||09. Nov 2011 02:13|
|Last Modified:||15. Feb 2013 12:14|
Barr, R. and T. Siems (1999). "Bank Failure Prediction Using DEA to Measure Management Quality." R. Barr, R. Helgason and J. Kennington, eds., Computer Science and Operations Research: Advances in Metaheuristics, Optimization and Stochastic Modeling Technologies
Barr, R. S., L. M. Seiford, et al. (1993). An envelopment-analysis approach to measuring the managerial efficiency of banks. Third European Workshop on Efficiency and Productivity Measurement, Louvain, Belgium.
Bell, T. B. (1997). "Neural nets or the logit model? A comparison of each model's ability to predict commercial bank failures" International Journal of Intelligent Systems in Accounting, Finance and Management 6: 249-264.
Berger, A. N. and L. K. Black (2011). "Bank size, lending technologies, and small business finance." Journal of Banking & Finance 35: 724–735.
Belogna, Pierluigi (2011). "Is There a Role for Funding in the Recent Bank Failures," IMF Working Paper 11/180, July.
Boyacioglu, M. A., Y. Kara, et al. (2009). "Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey." Expert Systems with Applications 36: 3355-3366.
Canbas, S., S. Canbas, et al. (2005). "Prediction of commercial bank failure via multivariate statistical analysis of financial structure: The Turkish case." European Journal of Operational Research 166: 528-546.
Cole, R. (1998). "The Importance of Relationships to the Availability of Credit." Journal of Banking & Finance 22: 959-977.
Cole, R. and J. Gunther (1995). "Separating the Likelihood and Timing of Bank Failure." Journal of Banking and Finance 17: 1073-1089.
DeGryse, Hans, Luc Laeven and Steve Ongena (2009). "The Impact of Organizational Structure and Leading technologies on banking Competition, " Review of Finance, Oxford University Publishing for the European Finance Association, 13 (2) : 225-259.
Demyanyk, Y. and I. Hasan (2009). "Financial Crises and Bank Failures: A Review of Prediction Methods." Omega 38 (5) 315-324.
Gerken,William and Stephen Dimmock (2011), "Predicting Fraud by Investment Managers," Networks Financial Institute Working Paper 2011-WP-09, May. Forthcoming in the Journal of Financial Economics.
Halling, M. and E. Hayden (2007). Bank Failure Prediction: A 2-Step Survival Time Approach. Proceedings of the International Statistical Institute's 56th Session.
Hanweck, G. A. (1977). "Predicting Bank Failure." Research Papers in Banking and Financial Economics (Financial Studies Section, Division of Research and Statistics, Board of Governors of the Federal Reserve System)(November).
Haslem, J. A., C. A. Scheraga, et al. (1999). "DEA efficiency profiles of U.S. banks operating internationally." International Review of Economics and Finance 8: 165-182.
Kao and Liu (2004). "Predicting bank performance with financial forecasts: A case of Taiwan commercial banks." Journal of Banking and Finance 28: 2353-2368.
Kolari, J., D. Glennon, et al. (2002). "Predicting Large U.S. Commercial Bank Failures." Journal of Economics and Business 54: 361-387.
Lee, K., D. Booth, et al. (2005). "A Comparison of Supervised and Unsupervised Neural Networks in Predicting Bankruptcy of Korean Firms." Expert Systems with Applications 29: 1-6.
Min, J. H. and Y. C. Lee (2005). "Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters." Expert Systems with Applications28: 603-614.
Ohlson, J. A. (1980). "Financial Ratios and the Probabilistic Prediction of Bankruptcy." Journal of Accounting Research 18 (11 Spring).
Olmeda, I. and E. Fernandez (1997). "Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction." Computational Economics 10: 317-335.
Shumway, T. (1999) "Forecasting Bankruptcy More Accurately: A Simple Hazard Model", Journal of Business, 74, 101-124.
Vapnik, V. N. (1995). "The Nature of Statistical Learning Theory." New York: Springer-Verlag.
Whalen, G. (1991). "A Proportional Hazards Model of Bank Failure: An Examination of Its Usefulness as an Early Warning Tool." Economic Review (Q 1): 21-31.
Wheelock, D. C. and P. W. Wilson (2000). "Why Do Banks Disappear? The Determinants of U.S. Bank Failures and Acquisitions." The Review of Economics and Statistics 82(1).
Wu, C.-H., G.-H. Tzeng, et al. (2007). "A real-valued genetic algorithm to optimize the parameters of 3 support vector machine for predicting bankruptcy." Expert Systems with Applications 32(2): 397-408.