Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy

Philippe du Jardin ()

MPRA Paper from University Library of Munich, Germany

Abstract: We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction.

Keywords: Financial failure; Variable selection; Neural network (search for similar items in EconPapers)
JEL-codes: C45 C51 G33 (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (29)

Published in Neurocomputing 10-12.73(2010): pp. 2047-2060

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:44375

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