On the utility of input selection and pruning for financial distress prediction modelsBecerra, V. M., Galvão, R. K. H. and Abou-Seada, M. (2002) On the utility of input selection and pruning for financial distress prediction models. In: International Joint Conference on Neural Networks: IJCNN 2002, 12-17 May 2002, Honolulu, HI, USA, pp. 1328-1333, https://doi.org/10.1109/IJCNN.2002.1007687. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/IJCNN.2002.1007687 Abstract/SummaryAnalyzes the use of linear and neural network models for financial distress classification, with emphasis on the issues of input variable selection and model pruning. A data-driven method for selecting input variables (financial ratios, in this case) is proposed. A case study involving 60 British firms in the period 1997-2000 is used for illustration. It is shown that the use of the Optimal Brain Damage pruning technique can considerably improve the generalization ability of a neural model. Moreover, the set of financial ratios obtained with the proposed selection procedure is shown to be an appropriate alternative to the ratios usually employed by practitioners.
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