[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119310-en":3,"doc-seo-119310-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119310,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Applying Machine Learning to Model South Africa’s Equity Market Index Price Performance","Policymakers often rely on statistical multivariate forecasting models to predict how stock market returns react to changing economic activities, yet these models can underperform when non-linear relationships are not modeled flexibly. This research compares machine learning models against a benchmark vector autoregressive model for forecasting South Africa’s equity market index price performance. Results report a benchmark MAPE of 0.0084 and the lowest machine learning MAPE of 0.0051, with low-dimensional models improving performance by 65%, alongside identification of key economic activities.","UNIVERSITY OF THE WITWATERSRAND  \nM. SC . COMPUTER SCIENCE BY DISSERTATION  \nApplying Machine Learning to Model South Africa’s Equity Market Index Price Performance  \nAuthor:  \nTshepo Chris NOKERI  \nSupervisors:  \nDr. Ritesh AJOODHA and Mr. Rudzani MULAUDZI  \nA thesis submitted in fulfillment of the requirements for the degree of M.Sc. Computer Science by Dissertation  \nin the  \nSchool of Computer Science and Applied Mathematics  \nJuly 14, 2023  \nii  \nDeclaration of Authorship  \nI, Tshepo Chris NOKERI, declare that this thesis titled, “Applying Machine Learning to Model South Africa’s Equity Market Index Price Performance” and the work presented init, are my own. I confirm that:  \n• This work was done wholly or mainly done while in candidature for the M.Sc. Computer Science by Dissertation degree in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand.  \n• Where any part of this thesis has previously been submitted for a degree or any other qualification at the University of the Witwatersrand or any other institution, this has been clearly stated.  \n• Where I have consulted the published work of others, this is always clearly attributed.  \n• Where I have quoted from the work of others, the source is always given. Except for such quotations, this thesis is entirely my own work.  \n• I have acknowledged all main sources of help.  \n• Where thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.  \nSignature:  \nDate: July 14, 2023  \niii  \nUNIVERSITY OF THE WITWATERSRAND  \nAbstract  \nFaculty of Science  \nSchool of Computer Science and Applied Mathematics  \nM.Sc. Computer Science by Dissertation  \nApplying Machine Learning to Model South Africa’s Equity Market Index Price  \nPerformance  \nby Tshepo Chris NOKERI  \nPolicymakers 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.  \niv  \nContents  \nDeclaration of Authorship ii  \nAbstract iii  \n1 Introduction to the Research 1  \n1.1 Introduction to the Research .......................... 1  \n1.2 Problem Statement ............................... 2  \n1.3 Purpose Statement ............................... 2  \n1.4 Research Questions ............................... 3  \n1.5 Research Contributions ............................","cbCaidZUAolLo81a","https://ap.wps.com/l/cbCaidZUAolLo81a","pdf",1135829,1,91,"English","en",105,"# Contents\n## Declaration of Authorship\n## Abstract\n## 1 Introduction to the Research\n## 1.1 Introduction to the Research\n## 1.2 Problem Statement\n## 1.3 Purpose Statement\n## 1.4 Research Questions\n## 1.5 Research Contributions\n## 1.6 Research Motivation\n## 1.7 Research Structure\n## 2 Literature Review\n## 2.1 The Stock Market in South Africa\n## 2.2 The use of Asset Pricing Models in Forecasting the Reaction of Stock Market Returns to Changing Economic Activities\n## 2.3 The use of Conventional Statistical Models in Forecasting the Reaction of Stock Market Returns to Changing Economic Activities\n## 2.4 State of Literature\n## 2.5 Research Gaps\n## 2.6 The use of Machine Learning Models","[{\"question\":\"What is the main goal of the research?\",\"answer\":\"To compare machine learning models with a vector autoregressive benchmark for forecasting the reaction of stock market returns to changing economic activities in South Africa.\"},{\"question\":\"How do the benchmark and machine learning models perform?\",\"answer\":\"The vector autoregressive model achieves a MAPE of 0.0084, while the lowest machine learning MAPE is 0.0051.\"},{\"question\":\"What experimental factor does the study emphasize to address research gaps?\",\"answer\":\"It varies feature subsets and data dimensions (reduced dimensionality) through multiple experiments to test how these choices change forecasting performance.\"}]","Applying Machine Learning to Model South Africa’s Equity Market Index Price Performance | 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