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Neural Networks: A New Tool for Predicting Thrift Failures*

Linda M. Salchenberger · E. Mine Çinar · Nicholas A. Lash

533 citations0/0 files storedDecision Sciences

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Financial Distress and Bankruptcy Prediction Banking stability, regulation, efficiency Forecasting Techniques and Applications

Abstract

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.

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1983Limited-dependent and qualitative variables in econometrics

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