LSTM prediction capability on the South African JSE Top 40 of historical and live data
| dc.contributor.advisor | Ndlovu, Godfrey | |
| dc.contributor.author | Elhag, Mohsen | |
| dc.date.accessioned | 2025-07-04T16:59:53Z | |
| dc.date.available | 2025-07-04T16:59:53Z | |
| dc.date.issued | 2025 | |
| dc.date.updated | 2025-07-04T15:38:03Z | |
| dc.description.abstract | This study evaluates the efficacy of Long Short-Term Memory (LSTM) models in stock price forecasting using data from the South African FTSE/JSE Top 40 index, a domain yet to be extensively explored, particularly in real-time data analysis. Addressing the gap in existing research, this study assesses LSTM model predictive capability in the South African stock market on historical data and its adaptability to the dynamic, real-time stock market environment over the period from January 2001 to January 2024. Various LSTM models were trained with different configurations, and the results show that a single-layer LSTM model performs better than its multilayer counterpart in processing historical data, in terms of the mean absolute error (MAE), the root mean square error (RMSE), Mean Absolute Percentage Error (MAPE) and the R-squared. However, when applied to real-time data, the accuracy of the single-layer model diminishes, underscoring the challenges posed by the dynamic and unpredictable nature of live stock market conditions. The findings contribute to the field of financial forecasting by demonstrating the strengths and limitations of the LSTM model in the context of the South African stock market. While showcasing significant potential in historical data analysis, performing on par with previous studies, the study underscores the need for further development of the model for real-time forecasting. Future research directions include extending the testing period, integrating diverse data sets, and exploring a combination of LSTM with other forecasting methodologies. | |
| dc.identifier.apacitation | Elhag, M. (2025). <i>LSTM prediction capability on the South African JSE Top 40 of historical and live data</i>. (). University of Cape Town ,Faculty of Commerce ,School of Economics. Retrieved from http://hdl.handle.net/11427/41537 | en_ZA |
| dc.identifier.chicagocitation | Elhag, Mohsen. <i>"LSTM prediction capability on the South African JSE Top 40 of historical and live data."</i> ., University of Cape Town ,Faculty of Commerce ,School of Economics, 2025. http://hdl.handle.net/11427/41537 | en_ZA |
| dc.identifier.citation | Elhag, M. 2025. LSTM prediction capability on the South African JSE Top 40 of historical and live data. . University of Cape Town ,Faculty of Commerce ,School of Economics. http://hdl.handle.net/11427/41537 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Elhag, Mohsen AB - This study evaluates the efficacy of Long Short-Term Memory (LSTM) models in stock price forecasting using data from the South African FTSE/JSE Top 40 index, a domain yet to be extensively explored, particularly in real-time data analysis. Addressing the gap in existing research, this study assesses LSTM model predictive capability in the South African stock market on historical data and its adaptability to the dynamic, real-time stock market environment over the period from January 2001 to January 2024. Various LSTM models were trained with different configurations, and the results show that a single-layer LSTM model performs better than its multilayer counterpart in processing historical data, in terms of the mean absolute error (MAE), the root mean square error (RMSE), Mean Absolute Percentage Error (MAPE) and the R-squared. However, when applied to real-time data, the accuracy of the single-layer model diminishes, underscoring the challenges posed by the dynamic and unpredictable nature of live stock market conditions. The findings contribute to the field of financial forecasting by demonstrating the strengths and limitations of the LSTM model in the context of the South African stock market. While showcasing significant potential in historical data analysis, performing on par with previous studies, the study underscores the need for further development of the model for real-time forecasting. Future research directions include extending the testing period, integrating diverse data sets, and exploring a combination of LSTM with other forecasting methodologies. DA - 2025 DB - OpenUCT DP - University of Cape Town KW - South African FTSE/JSE LK - https://open.uct.ac.za PB - University of Cape Town PY - 2025 T1 - LSTM prediction capability on the South African JSE Top 40 of historical and live data TI - LSTM prediction capability on the South African JSE Top 40 of historical and live data UR - http://hdl.handle.net/11427/41537 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/41537 | |
| dc.identifier.vancouvercitation | Elhag M. LSTM prediction capability on the South African JSE Top 40 of historical and live data. []. University of Cape Town ,Faculty of Commerce ,School of Economics, 2025 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/41537 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | School of Economics | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | South African FTSE/JSE | |
| dc.title | LSTM prediction capability on the South African JSE Top 40 of historical and live data | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MPhil |