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 [Formula: see text]-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.
Highlights
The prediction of stock market index direction is a topic of signicant interest in theeld of modernnance and investments
The purpose of this study is to evaluate the e±cacy of several ensemble prediction models (Boosted, random undersampling (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 results are presented in terms of percent correctly classied (PCC) and receiver operating characteristic (ROC) and presented for each of the six possible data input method combinations i.e. continuous and trend deterministic inputs each based on 0%, 0.5% and 1.0% thresholds of the response variable
Summary
The prediction of stock market index direction is a topic of signicant interest in theeld of modernnance and investments. Vadapalli price index is important for developing trade strategies (Leung et al 2000) and for hedging against potential market risks which enable speculators and arbitrageurs to prot by trading in stock index (Manish & Thenmozhi 2005; Kumar et al 2006). The task of predicting price movements can be challenging because of multiple, nonpredictable factors that impact on the stock market such as natural disasters, political instabilities, varying economic climates, etc. The prediction of the stock market index is made even more di±cult by the fact that stock markets are characterized by high noise, nonlinearity, dynamic and deterministically chaotic data (e.g. Lu (2013), Hsu (2013))
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