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.
|Title of host publication
|International Joint Conference on Neural Networks: IJCNN 2002
|Number of pages
|Published - 2002
- 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