Browsing by Author "Boakes, Jamie"

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    An analysis of the performance and interpretability of machine learning classification algorithms to predict long-term share returns on the JSE
    (2024) Boakes, Jamie; Moodley, Deshendran
    The prediction of long-term share returns is an essential yet complex task in financial analysis and formulating investment strategy. Machine learning is a promising approach for improving the accuracy of these predictions. However, the outputs of machine learning models are not transparent or interpretable, which limits their usability for real-world decision making. There is a lack of research on the use of machine learning algorithms to predict long-term share returns on the Johannesburg Stock Exchange (JSE), with no studies that specifically examine the interpretability of machine learning algorithms. This study investigates the use of machine learning algorithms to predict long-term share returns on the JSE based on fundamental data and analyses the interpretability of the top performing algorithms. Based on a review of the literature, eight machine learning classification algorithms were selected and compared to predict tercile class 12-month share returns using fundamental data, spanning a period of two decades. The XGBoost, Random Forest, and GradBoost algorithms were found to outperform the Support Vector Classifier, Logistic Regression, Decision Tree, Artificial Neural Network, and AdaBoost algorithms. XGBoost and Random Forest were further investigated using SHAP (SHapley Additive exPlanations) global summary plots to identify the most influential input features and to analyse the interpretability of these algorithms. The study found that ensemble-based classification algorithms, i.e. XGBoost, Random Forest and GradBoost, outperformed the other algorithms. Further analysis of the results varied, with some sectors outperforming the overall market. An analysis of the input features identified the most important valuation and profitability ratios that contributed to prediction performance, and thus improves the transparency and interpretability of the models. This research is the first to comprehensively compare and analyse the interpretability of machine learning algorithms to predict long-term share returns on the JSE.