Forecasting Business Efficiency by Using Classification Techniques: A Comparative Analysis Based on a Spanish Case

Abstract

A comparative, simulation-based 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. The paper uses as a benchmark the problem of forecasting the level of efficiency of Spanish commercial and industrial companies upon the basis of a set of financial ratios. This case illustrates well some distinctive features/complexities of many financial prediction problems, namely that of being characterized by a high dimension feature space and low degree of separability. Results indicate a higher performance of model-free classifiers, even for moderate sample sizes. The average effects of sample size variations on the predictive of each classifier are also measured by using Monte Carlo simulations, and response surfaces are estimated in order to summarize these effects.