Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms

by Barbro Back of Turku School of Economics and Business Administration,
Teija Laitinen of the University of Vaasa,
Kaisa Sere of the University of Kuopio, and
Michiel van Wezel of Utrecht University

September 1996

Abstract: We are focusing on three alternative techniques that can be used to empirically select predictors for failure prediction purposes.  The selected techniques have all different assumptions about the relationships between the independent variables.  Linear discriminant analysis is based on linear combination of independent variables, logit analysis uses the logistic cumulative probability function and genetic algorithms is a global search procedure based on the mechanics of natural selection and natural genetics.  Our aim is to study if these essential differences between the methods (1) affect the empirical selection of independent variables to the model and (2) lead to significant differences in failure prediction accuracy.

Keywords: Bankruptcies, genetic algorithms, neural networks.

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