Title: Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
Abstract: 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.
Publication Year: 1992
Publication Date: 1992-07-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1112
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