Predicting stock returns by classifier ensembles

The problem of predicting stock returns has been an important issue for many years. Advancement in computer technology has allowed many recent studies to utilize machine learning techniques such as neural networks and decision trees to predict stock returns. In the area of machine learning, classifier ensembles (i.e. combining multiple classifiers) have proven to be a method superior to single classifiers. In order to build a better model for predicting stock returns effectively and efficiently, this study aims at investigating the prediction performance that utilizes the classifier ensembles method to analyze stock returns. In particular, the hybrid methods of majority voting and bagging are considered. Moreover, performance using two types of classifier ensembles is compared with those using single baseline classifiers (i.e. neural networks, decision trees, and logistic regression). These two types of ensembles are ‘homogeneous’ classifier ensembles (e.g. an ensemble of neural networks) and ‘heterogeneous’ classifier ensembles (e.g. an ensemble of neural networks, decision trees and logistic regression). Average prediction accuracy, Type I and II errors, and return on investment of these models are also examined. Our results indicate that multiple classifiers outperform single classifiers in terms of prediction accuracy and returns on investment. In addition, heterogeneous classifier ensembles offer slightly better performance than the homogeneous ones. However, there is no significant difference between majority voting and bagging in prediction accuracy, but the former has better stock returns prediction accuracy than the latter. Finally, the homogeneous multiple classifiers using neural networks by majority voting perform best when predicting stock returns.