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

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 a 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.