A study of using artificial neural networks to develop an early warning predictor for credit union financial distress with comparison to the probit model

Abstract

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

Citation

Tan, C.N.W. and Dihardjo, H. (2001), "A study of using artificial neural networks to develop an early warning predictor for credit union financial distress with comparison to the probit model", Managerial Finance, Vol. 27 No. 4, pp. 56-77. https://doi.org/10.1108/03074350110767141