A comparative analysis of machine learning models for forecasting JSE Stock Returns
| dc.contributor.advisor | Van Rensburg, Paul | |
| dc.contributor.author | Muir, Cameron James | |
| dc.date.accessioned | 2026-01-09T11:14:19Z | |
| dc.date.available | 2026-01-09T11:14:19Z | |
| dc.date.issued | 2025 | |
| dc.date.updated | 2026-01-06T13:00:08Z | |
| dc.description.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. | |
| dc.identifier.apacitation | Muir, C. J. (2025). <i>A comparative analysis of machine learning models for forecasting JSE Stock Returns</i>. (). University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax. Retrieved from http://hdl.handle.net/11427/42503 | en_ZA |
| dc.identifier.chicagocitation | Muir, Cameron James. <i>"A comparative analysis of machine learning models for forecasting JSE Stock Returns."</i> ., University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2025. http://hdl.handle.net/11427/42503 | en_ZA |
| dc.identifier.citation | Muir, C.J. 2025. A comparative analysis of machine learning models for forecasting JSE Stock Returns. . University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax. http://hdl.handle.net/11427/42503 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Muir, Cameron James AB - 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. DA - 2025 DB - OpenUCT DP - University of Cape Town KW - JSE KW - Stock returns LK - https://open.uct.ac.za PB - University of Cape Town PY - 2025 T1 - A comparative analysis of machine learning models for forecasting JSE Stock Returns TI - A comparative analysis of machine learning models for forecasting JSE Stock Returns UR - http://hdl.handle.net/11427/42503 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/42503 | |
| dc.identifier.vancouvercitation | Muir CJ. A comparative analysis of machine learning models for forecasting JSE Stock Returns. []. University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2025 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/42503 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Finance and Tax | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | JSE | |
| dc.subject | Stock returns | |
| dc.title | A comparative analysis of machine learning models for forecasting JSE Stock Returns | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MCom |