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Managerial Applications of Neural Networks: The Case of Bank Failure Predictions

  • Kar Yan Tam

    (Department of Business Information Systems, School of Business and Management, The Hong Kong University of Science and Technology, Hong Kong)

  • Melody Y. Kiang

    (Department of Decision and Information Systems, Arizona State University, Tempe, Arizona 85287-4206)

Registered author(s):

    This paper introduces a neural-net approach to perform discriminant analysis in business research. A neural net represents a nonlinear discriminant function as a pattern of connections between its processing units. Using bank default data, the neural-net approach is compared with linear classifier, logistic regression, kNN, and ID3. Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness. Limitations of using neural nets as a general modeling tool are also discussed.

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    File URL: http://dx.doi.org/10.1287/mnsc.38.7.926
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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 38 (1992)
    Issue (Month): 7 (July)
    Pages: 926-947

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    Handle: RePEc:inm:ormnsc:v:38:y:1992:i:7:p:926-947
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