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

V. M. Becerra; R. K. H. Galvão; M. Abou-Seada

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

V. M. Becerra, R. K. H. Galvão, M. Abou-Seada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	International Joint Conference on Neural Networks: IJCNN 2002
Pages	1328-1333
Number of pages	6
Publication status	Published - 2002

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

Cite this

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title = "On the utility of input selection and pruning for financial distress prediction models",

abstract = "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.",

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",

author = "Becerra, {V. M.} and Galv{\~a}o, {R. K. H.} and M. Abou-Seada",

year = "2002",

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pages = "1328--1333",

booktitle = "International Joint Conference on Neural Networks: IJCNN 2002",

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AU - Becerra, V. M.

AU - Galvão, R. K. H.

AU - Abou-Seada, M.

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

KW - 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

M3 - Conference contribution

SP - 1328

EP - 1333

BT - International Joint Conference on Neural Networks: IJCNN 2002

ER -

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

Abstract

Keywords

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