Applying Machine Learning to Model South Africa’s Equity Market Index Price Performance
| dc.contributor.author | Nokeri, Tshepo Chris | |
| dc.contributor.co-supervisor | Mulaudzi, Rudzani | |
| dc.contributor.supervisor | Ajoodha, Ritesh | |
| dc.date.accessioned | 2024-10-20T19:15:10Z | |
| dc.date.available | 2024-10-20T19:15:10Z | |
| dc.date.issued | 2023-07 | |
| dc.description | A thesis submitted in fulfillment of the requirements for the degree of Master of Science (M.Sc. Computer Science) by Dissertation, to the Faculty of Science, in the School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | Policymakers typically use statistical multivariate forecasting models to forecast the reaction of stock market returns to changing economic activities. However, these models frequently result in subpar performance due to inflexibility and incompetence in modeling non-linear relationships. Emerging research suggests that machine learning models can better handle data from non-linear dynamic systems and yield outstanding model performance. This research compared the performance of machine learning models to the performance of the benchmark model (the vector autoregressive model) when forecasting the reaction of stock market returns to changing economic activities in South Africa. The vector autoregressive model was used to forecast the reaction of stock market returns. It achieved a mean absolute percentage error (MAPE) value of 0.0084. Machine learning models were used to forecast the reaction of stock market returns. The lowest MAPE value was 0.0051. The machine learning model trained on low economic data dimensions performed 65% better than the benchmark model. Machine learning models also identified key economic activities when forecasting the reaction of stock market returns. Most research focused on whole features, few models for comparison, and barely focused on how different feature subsets and reduced dimensionality change model performance, a limitation this research addresses when considering the number of experiments. This research considered various experiments, i.e., different feature subsets and data dimensions, to determine whether machine learning models perform better than the benchmark model when forecasting the reaction of stock market returns to changing economic activities in South Africa. | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier | 0000-0003-0718-5805 | |
| dc.identifier.citation | Nokeri, Tshepo Chris. (2023). Applying Machine Learning to Model South Africa’s Equity Market Index Price Performance. [Master's dissertation, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/41753 | |
| dc.identifier.uri | https://hdl.handle.net/10539/41753 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Computer Science and Applied Mathematics | |
| dc.subject | Statistical Models | |
| dc.subject | Machine Learning Models | |
| dc.subject | Stock Market Index Returns Forecasting | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-9: Industry, innovation and infrastructure | |
| dc.title | Applying Machine Learning to Model South Africa’s Equity Market Index Price Performance | |
| dc.type | Dissertation |