THE EFFICIENCY OF ENSEMBLE CLASSIFIERS IN PREDICTING THE JOHANNESBURG STOCK EXCHANGE ALL-SHARE INDEX DIRECTION

Thabang Mokoaleli-Mokoteli, Shaun Ramsumar and Hima Vadapalli
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Thabang Mokoaleli-Mokoteli: Wits Business School, University of Witwatersrand, 2 St David’s Place, Parktown, Johannesburg, South Africa
Shaun Ramsumar: Wits Business School, University of Witwatersrand, 2 St David’s Place, Parktown, Johannesburg, South Africa
Hima Vadapalli: School of Computer Science, University of Witwatersrand, 2 St David’s Place, Parktown, Johannesburg, South Africa

Journal of Financial Management, Markets and Institutions (JFMMI), 2019, vol. 07, issue 02, 1-18

Abstract: The success of investors in obtaining huge financial rewards from the stock market depends on their ability to predict the direction of the stock market index. The purpose of this study is to evaluate the efficacy of several ensemble prediction models (Boosted, RUS-Boosted, Subspace Disc, Bagged, and Subspace KNN) in predicting the daily direction of the Johannesburg Stock Exchange (JSE) All-Share index compared to other commonly used machine learning techniques including support vector machines (SVM), logistic regression and k-nearest neighbor (KNN). The findings in this study show that, among all ensemble models, Boosted algorithm is the best performer followed by RUS-Boosted. When compared to the other techniques, ensemble technique (represented by Boosted) outperformed these techniques, followed by KNN, logistic regression and SVM, respectively. These findings suggest that investors should include ensemble models among the index prediction models if they want to make huge profits in the stock markets. However, not all investors can benefit from this as models may suffer from alpha decay as more and more investors use them, implying that the successful algorithms have limited shelf life.

Keywords: Ensemble classifiers; random forest; k-nearest neighbor; logistic regression; stock index direction; support vector machines (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S2282717X19500014

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