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

1992-07-01articleManagement Science1,121 citationsclosed

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Financial Distress and Bankruptcy Prediction Neural Networks and Applications Forecasting Techniques and Applications

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.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY

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