A comparative analysis of machine learning models for forecasting JSE Stock Returns

Muir, Cameron James

A comparative analysis of machine learning models for forecasting JSE Stock Returns

Thesis / Dissertation

2025

Publisher

University of Cape Town

Department

Department of Finance and Tax

Faculty

Faculty of Commerce

Abstract

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.

Keywords

JSE

Stock returns

Reference:

Collections

Masters

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