Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
AbstractThis 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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Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 38 (1992)
Issue (Month): 7 (July)
neural networks; artificial intelligence; discriminant analysis; bank failure predictions;
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