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Predicting failure in the commercial banking industry

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  • Tatom, John

Abstract

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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 34608.

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Date of creation: 05 Aug 2011
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Handle: RePEc:pra:mprapa:34608

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Keywords: bank failure; banking crises; CAMELS ratings;

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  1. Pierluigi Bologna, 2011. "Is There a Role for Funding in Explaining Recent U.S. Banks' Failures?," IMF Working Papers 11/180, International Monetary Fund.
  2. 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.
  3. Cole, Rebel A., 1998. "The importance of relationships to the availability of credit," Journal of Banking & Finance, Elsevier, vol. 22(6-8), pages 959-977, August.
  4. Olmeda, Ignacio & Fernandez, Eugenio, 1997. "Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction," Computational Economics, Society for Computational Economics, vol. 10(4), pages 317-35, November.
  5. Rebel A. Cole & Jeffery W. Gunther, 1993. "Separating the likelihood and timing of bank failure," Finance and Economics Discussion Series 93-20, Board of Governors of the Federal Reserve System (U.S.).
  6. Canbas, Serpil & Cabuk, Altan & Kilic, Suleyman Bilgin, 2005. "Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case," European Journal of Operational Research, Elsevier, vol. 166(2), pages 528-546, October.
  7. Yuliya Demyanyk & Iftekhar Hasan, 2009. "Financial crises and bank failures: a review of prediction methods," Working Paper 0904, Federal Reserve Bank of Cleveland.
  8. Degryse, H.A. & Laeven, L. & Ongena, S., 2006. "The Impact of Organizational Structure and Lending Technology on Banking Competition," Discussion Paper 2006-67, Tilburg University, Center for Economic Research.
  9. Gerald A. Hanweck, 1977. "Predicting bank failure," Research Papers in Banking and Financial Economics 19, Board of Governors of the Federal Reserve System (U.S.).
  10. Gary Whalen, 1991. "A proportional hazards model of bank failure: an examination of its usefulness as an early warning tool," Economic Review, Federal Reserve Bank of Cleveland, issue Q I, pages 21-31.
  11. Berger, Allen N. & Black, Lamont K., 2011. "Bank size, lending technologies, and small business finance," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 724-735, March.
  12. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
  13. Haslem, John A. & Scheraga, Carl A. & Bedingfield, James P., 1999. "DEA efficiency profiles of U.S. banks operating internationally," International Review of Economics & Finance, Elsevier, vol. 8(2), pages 165-182, June.
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