Machine Learning for Capital Market Research and Portfolio Optimization
Selecting stocks from a large number of active stocks is a critical investment decision. In this study, traditional and machine learning techniques are employed to identify promising stocks. The proposed strategies incorporate historical price paths into momentum techniques and remove stocks with ex...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/95690 |
| _version_ | 1848766044256075776 |
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| author | Aslam, Bilal |
| author_facet | Aslam, Bilal |
| author_sort | Aslam, Bilal |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Selecting stocks from a large number of active stocks is a critical investment decision. In this study, traditional and machine learning techniques are employed to identify promising stocks. The proposed strategies incorporate historical price paths into momentum techniques and remove stocks with extreme returns. It enhances the fundamental investment decision of stock selection to construct optimized portfolios. These methodologies outperform the standard momentum technique, reduces transaction costs and hedges investors during financial crises. |
| first_indexed | 2025-11-14T11:44:53Z |
| format | Thesis |
| id | curtin-20.500.11937-95690 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:44:53Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-956902024-08-12T03:29:29Z Machine Learning for Capital Market Research and Portfolio Optimization Aslam, Bilal Selecting stocks from a large number of active stocks is a critical investment decision. In this study, traditional and machine learning techniques are employed to identify promising stocks. The proposed strategies incorporate historical price paths into momentum techniques and remove stocks with extreme returns. It enhances the fundamental investment decision of stock selection to construct optimized portfolios. These methodologies outperform the standard momentum technique, reduces transaction costs and hedges investors during financial crises. 2024 Thesis http://hdl.handle.net/20.500.11937/95690 Curtin University restricted |
| spellingShingle | Aslam, Bilal Machine Learning for Capital Market Research and Portfolio Optimization |
| title | Machine Learning for Capital Market Research and Portfolio Optimization |
| title_full | Machine Learning for Capital Market Research and Portfolio Optimization |
| title_fullStr | Machine Learning for Capital Market Research and Portfolio Optimization |
| title_full_unstemmed | Machine Learning for Capital Market Research and Portfolio Optimization |
| title_short | Machine Learning for Capital Market Research and Portfolio Optimization |
| title_sort | machine learning for capital market research and portfolio optimization |
| url | http://hdl.handle.net/20.500.11937/95690 |