Neural Networks: A New Tool for Predicting Thrift Failures*
研究开发了一个基于财务比率的神经网络模型，用于预测储蓄机构的财务健康状况，并与传统统计模型比较，发现神经网络假设更少、预测更准、更稳健。
ABSTRACT A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions. The network's ability to discriminate between healthy and failed institutions is compared to a traditional statistical model. The differences and similarities in the two modelling approaches are discussed. The neural network, which uses the same financial data, requires fewer assumptions, achieves a higher degree of prediction accuracy, and is more robust.