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Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks

During the last three decades various models have been proposed by the literature to predict the risk of bankruptcy and of firm insolvency. In this work there is a survey on the methodologies used by the author for the analysis of default risk, taking into account several approaches suggested by the literature. The focus is to analyse the Artificial Neural Networks as a tool for the study of this problem and to verify the ability of classification of these models. Finally, an analysis of variables introduced in the Artificial Neural Network models and some considerations about these.

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File URL: http://www.ceris.cnr.it/ceris/workingpaper/2006/WP_10_06_FALAVIGNA.pdf
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Paper provided by Institute for Economic Research on Firms and Growth - Moncalieri (TO) in its series CERIS Working Paper with number 200610.

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Length: 35 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:csc:cerisp:200610
Contact details of provider: Postal: Via Real Collegio, 30 10024 - Moncalieri TO
Phone: +39-11.6824.911
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Web page: http://www.ceris.cnr.it/
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  1. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
  2. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  3. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
  4. Longstaff, Francis A & Schwartz, Eduardo S, 1995. " A Simple Approach to Valuing Risky Fixed and Floating Rate Debt," Journal of Finance, American Finance Association, vol. 50(3), pages 789-819, July.
  5. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
  6. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
  7. Platt, Harlan D. & Platt, Marjorie B., 1991. "A note on the use of industry-relative ratios in bankruptcy prediction," Journal of Banking & Finance, Elsevier, vol. 15(6), pages 1183-1194, December.
  8. Treacy, William F. & Carey, Mark, 2000. "Credit risk rating systems at large US banks," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 167-201, January.
  9. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
  10. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
  11. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
  12. Jarrow, Robert A & Lando, David & Turnbull, Stuart M, 1997. "A Markov Model for the Term Structure of Credit Risk Spreads," Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 481-523.
  13. Haluk Unal & Dilip Madan & Levent Guntay, 2001. "A Simple Approach to Estimate Recovery Rates with APR Violation from Debt Spreads," Center for Financial Institutions Working Papers 01-07, Wharton School Center for Financial Institutions, University of Pennsylvania.
  14. Jarrow, Robert A & Turnbull, Stuart M, 1995. " Pricing Derivatives on Financial Securities Subject to Credit Risk," Journal of Finance, American Finance Association, vol. 50(1), pages 53-85, March.
  15. Wruck, Karen Hopper, 1990. "Financial distress, reorganization, and organizational efficiency," Journal of Financial Economics, Elsevier, vol. 27(2), pages 419-444, October.
  16. West, Robert Craig, 1985. "A factor-analytic approach to bank condition," Journal of Banking & Finance, Elsevier, vol. 9(2), pages 253-266, June.
  17. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-54, May-June.
  18. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
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