Browsing by Author "Muir, Cameron James"

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    A comparative analysis of machine learning models for forecasting JSE Stock Returns
    (2025) Muir, Cameron James; Van Rensburg, Paul
    This study examines the application of machine learning models to predict the cross-section of Johannesburg Stock Exchange (JSE)- listed share returns. Four models are developed and compared using monthly data from 2005 to 2021: neural networks, random forest, long short- term memory (LSTM) networks, and conventional linear regression. The explanatory variables comprise nine firm-specific financial metrics, motivated by prior research. The sample is divided into a training period (2005–2016) and a testing period (2016–2021), further split into 1-year, 3-year, and 5-year testing intervals. The results show that the LSTM model performsbest across most evaluation metrics and investment scenarios, with the random forest model close behind, offering slightly better risk-adjusted returns.