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

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.

• 저널Management Science · 38(7) · 926–947
• 토픽Financial Distress and Bankruptcy Prediction · Accounting
• DOI10.1287/mnsc.38.7.926
• 저자Kar Yan Tam, Melody Y. Kiang