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On the utility of input selection and pruning for financial distress prediction models

Becerra, 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.

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To link to this article DOI: 10.1109/IJCNN.2002.1007687

Abstract/Summary

Analyzes 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.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:19199
Uncontrolled Keywords:British firms, Optimal Brain Damage, data-driven method, financial distress classification, financial distress prediction models, financial ratios, generalization ability, input selection, linear models, model pruning, neural network models
Publisher Statement:2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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