Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case

Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case

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Article ID: iaor20062599
Country: Netherlands
Volume: 167
Issue: 2
Start Page Number: 518
End Page Number: 542
Publication Date: Dec 2005
Journal: European Journal of Operational Research
Authors: , ,
Keywords: forecasting: applications, fuzzy sets
Abstract:

A comparative study of the performance of a number of classificatory devices, both parametric (LDA and Logit) and non-parametric (perceptron neural nets and fuzzy-rule-based classifiers) is conducted, and a Monte Carlo simulation-based approach is used in order to measure the average effects of sample size variations on the predictive performance of each classifier. The paper uses as a benchmark the problem of forecasting the level of profitability of Spanish commercial and industrial companies upon the basis of a set of financial ratios. This case illustrates well a distinctive feature of many financial prediction problems, namely that of being characterized by high dimension feature space as well as a low degree of separability. Response surfaces are estimated in order to summarize the results. A higher performance of model-free classifiers is generally observed, even for fairly moderate sample sizes.

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