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Predicting Credit Default in an Agricultural Bank: Methods and Issues

Author

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  • Odeh, Oluwarotimi O.
  • Featherstone, Allen M.
  • Sanjoy, Das

Abstract

This study examines the performance of logistic regression, artificial neural networks and adaptive neuro-fuzzy inference system in predicting credit default using data from Farm Credit System. Empirical findings show that credit default predictions vary with empirical model used.

Suggested Citation

  • Odeh, Oluwarotimi O. & Featherstone, Allen M. & Sanjoy, Das, 2006. "Predicting Credit Default in an Agricultural Bank: Methods and Issues," 2006 Annual Meeting, February 5-8, 2006, Orlando, Florida 35359, Southern Agricultural Economics Association.
    • Handle: RePEc:ags:saeaso:35359
    DOI: 10.22004/ag.econ.35359
    as

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    References listed on IDEAS

    as
    1. Malhotra, Rashmi & Malhotra, D. K., 2002. "Differentiating between good credits and bad credits using neuro-fuzzy systems," European Journal of Operational Research, Elsevier, vol. 136(1), pages 190-211, January.
    2. Jacobson, Tor & Roszbach, Kasper, 2003. "Bank lending policy, credit scoring and value-at-risk," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 615-633, April.
    3. Terry L. Kastens & Allen M. Featherstone, 1996. "Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 400-415.
    4. Lopez, Jose A, 2001. "Evaluating the Predictive Accuracy of Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 87-109, March.
    5. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
    6. Ani L. Katchova & Peter J. Barry, 2005. "Credit Risk Models and Agricultural Lending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 194-205.
    7. Jose A. Lopez, 1999. "Methods for evaluating value-at-risk estimates," Economic Review, Federal Reserve Bank of San Francisco, pages 3-17.
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