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: | Andrs Javier de, Landajo Manuel, Lorca Pedro |
Keywords: | forecasting: applications, fuzzy sets |
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.