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A hybrid genetic model for the prediction of corporate failure

Anthony Brabazon () and Peter Keenan ()

Computational Management Science, 2004, vol. 1, issue 3, 293-310

Abstract: This study examines the potential of a neural network (NN) model, whose inputs and structure are automatically selected by means of a genetic algorithm (GA), for the prediction of corporate failure using information drawn from financial statements. The results of this model are compared with those of a linear discriminant analysis (LDA) model. Data from a matched sample of 178 publicly quoted, failed and non-failed, US firms, drawn from the period 1991 to 2000 is used to train and test the models. The best evolved neural network correctly classified 86.7 (76.6)% of the firms in the training set, one (three) year(s) prior to failure, and 80.7 (66.0)% in the out-of-sample validation set. The LDA model correctly categorised 81.7 (75.0)% and 76.0 (64.7)% respectively. The results provide support for a hypothesis that corporate failure can be anticipated, and that a hybrid GA/NN model can outperform an LDA model in this domain. Copyright Springer-Verlag Berlin/Heidelberg 2004

Keywords: Corporate failure; Genetic algorithm; Neural network (search for similar items in EconPapers)
Date: 2004
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