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Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction

Listed author(s):
  • Olmeda, Ignacio
  • Fernandez, Eugenio
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    This paper compares the accuracy of parametric and nonparametric classifiers on the problem of predicting Bankruptcy. Among the single classifiers an artificial neural network is found to provide the best results. Two ways of combining classifiers are considered and an additive aggregation method is proposed. We show that both ways of combining produce classifiers whose forecasts are more accurate than the ones obtained with any single model. We suggest that an optimal system for risk rating should combine two or more different techniques. Citation Copyright 1997 by Kluwer Academic Publishers.

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    Article provided by Springer & Society for Computational Economics in its journal Computational Economics.

    Volume (Year): 10 (1997)
    Issue (Month): 4 (November)
    Pages: 317-335

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    Handle: RePEc:kap:compec:v:10:y:1997:i:4:p:317-35
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