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A comparative analysis of machine learning models for forecasting JSE Stock Returns
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...
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| Format: | Thesis |
| Language: | English English |
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Department of Finance and Tax
2026
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| _version_ | 1867614297868206080 |
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| access_status_str | Open Access |
| author | Muir, Cameron James |
| author2 | Van Rensburg, Paul |
| author_browse | Muir, Cameron James Van Rensburg, Paul |
| author_facet | Van Rensburg, Paul Muir, Cameron James |
| author_sort | Muir, Cameron James |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/42503 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-06-10T12:49:48.830Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Department of Finance and Tax |
| publisherStr | Department of Finance and Tax |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/42503 A comparative analysis of machine learning models for forecasting JSE Stock Returns Muir, Cameron James Van Rensburg, Paul JSE Stock returns 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. 2026-01-09T11:14:19Z 2026-01-09T11:14:19Z 2025 2026-01-06T13:00:08Z Thesis / Dissertation Masters MCom http://hdl.handle.net/11427/42503 en eng application/pdf Department of Finance and Tax Faculty of Commerce University of Cape Town |
| spellingShingle | JSE Stock returns Muir, Cameron James A comparative analysis of machine learning models for forecasting JSE Stock Returns |
| thesis_degree_str | Master's |
| title | A comparative analysis of machine learning models for forecasting JSE Stock Returns |
| title_full | A comparative analysis of machine learning models for forecasting JSE Stock Returns |
| title_fullStr | A comparative analysis of machine learning models for forecasting JSE Stock Returns |
| title_full_unstemmed | A comparative analysis of machine learning models for forecasting JSE Stock Returns |
| title_short | A comparative analysis of machine learning models for forecasting JSE Stock Returns |
| title_sort | comparative analysis of machine learning models for forecasting jse stock returns |
| topic | JSE Stock returns |
| url | http://hdl.handle.net/11427/42503 |
| work_keys_str_mv | AT muircameronjames acomparativeanalysisofmachinelearningmodelsforforecastingjsestockreturns AT muircameronjames comparativeanalysisofmachinelearningmodelsforforecastingjsestockreturns |