Neural networks and genetic algorithms for bankruptcy predictions

Abstract We are focusing on three alternative techniques-linear discriminant analysis, logit analysis and genetic algorithms-that can be used to empirically select predictors for neural networks in failure prediction. The selected techniques all have different assumptions about the relationships between the independent variables. Linear discriminant analysis is based on linear combination of independent variables, logit analysis uses the logistical cumulative function and genetic algorithms is a global search procedure based on the mechanics of natural selection and natural genetics. In an empirical test all three selection methods chose different bankruptcy prediction variables. The best prediction results were achieved when using genetic algorithms.

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