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Erschienen in:
1995 | ReviewPaper | Buchkapitel
Bankruptcy prediction with Artificial Neural Networks
verfasst von : Eugenio Fernández, Ignacio Olmeda
Erschienen in: From Natural to Artificial Neural Computation
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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In this paper we compare the forecasting accuracy of feedforward neural networks against various competing models (C4.5, MARS, Discriminant Analysis and Logit) on the problem of predicting bankruptcy. The neural network model is found to provide generally better results, though the computational effort is several orders of magnitude higher. We also consider mixtures of the methods and show that many of these are always more accurate than any single method. We suggest that an optimal system for risk rating should include two or more of the models considered.