Evaluating multiple classifiers for stock price direction prediction. Issue 20 (15th November 2015)

• Record Type: Journal Article
• Authors: Ballings, Michel; Van den Poel, Dirk; Hespeels, Nathalie; Gryp, Ruben
• Is Part Of: Expert systems with applications. Volume 42:Issue 20(2015)
• Page Start: 7046
• Page End: 7056
• Publication Date: 2015-11-15
• Subjects: Ensemble methods -- Single classifiers -- Benchmark -- Stock price direction prediction; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.05.013
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We predict long term stock price direction. We benchmark three ensemble methods against four single classifiers. We use five times twofold cross-validation and AUC as a performance measure. Random Forest is the top algorithm. This study is the first to make such an extensive benchmark in this domain. Abstract: Stock price direction prediction is an important issue in the financial world. Even small improvements in predictive performance can be very profitable. The purpose of this paper is to benchmark ensemble methods (Random Forest, AdaBoost and Kernel Factory) against single classifier models (Neural Networks, Logistic Regression, Support Vector Machines and K-Nearest Neighbor). We gathered data from 5767 publicly listed European companies and used the area under the receiver operating characteristic curve (AUC) as a performance measure. Our predictions are one year ahead. The results indicate that Random Forest is the top algorithm followed by Support Vector Machines, Kernel Factory, AdaBoost, Neural Networks, K-Nearest Neighbors and Logistic Regression. This study contributes to literature in that it is, to the best of our knowledge, the first to make such an extensive benchmark. The results clearly suggest that novel studies in the domain of stock price direction prediction should include ensembles in their sets of algorithms. Our extensive literature review evidently indicates that this is currently not the case.