Ensemble Committees for Stock Return Classification and Prediction

This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a nonlinear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. This selection of algorithms is appealing for two reasons: first, there is strikingly little research in economic time-series forecasting that employs learners beyond neural networks and clustering algorithms, and this construction offers a viable alternative; second, this selection incorporates an array of techniques that have both theoretically optimal classification properties and high empirical success rates in areas outside of finance, in addition to offering a mixture of parametric and nonparametric models. The ensemble committee is augmented by a boosting meta-algorithm and feature selection is performed by a supervised Relief algorithm. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.