Variable selection for financial distress classification using a genetic algorithm

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model a...

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Bibliographic Details
Main Authors: Galveo, R.K.H., Becerra, V.M., Abou-Seada, M.
Format: Conference Proceeding
Language:English
Subjects:
Companies
Context modeling
Cybernetics
Failure analysis
Finance
Genetic algorithms
Government
Input variables
Predictive models
Statistics
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Summary:This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.
DOI:10.1109/CEC.2002.1004550