Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
介绍用神经网络做判别分析，用银行破产数据对比线性分类器、逻辑回归等方法，发现神经网络在预测准确率、适应性和稳健性上表现更好，也讨论了其局限性。
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.