A comparison of machine-learning classifiers for selecting money managers

Abstract

Machine-learning algorithms have performed well on noisy datasets that are typical of financial data. This paper compares the performance of three types of machine-learning classifier for selecting money managers. Naïve Bayes, neural network and decision tree learners were applied to a dataset of US equity managers. Although other studies have suggested that the performance of classifiers appears to be highly dependent on the nature of the problem and the dataset, the learning algorithms each had similar predictive accuracy and all outperformed by a substantial margin simple manager selection rules that are typical of the ways in which money managers and mutual funds are selected by investors. The results indicate that machine learners can be used as a decision-support aid to improve the selection of money managers. Copyright © 2005 John Wiley & Sons, Ltd.