The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization
Modern Portfolio Theory (MPT), developed by Harry Markowitz, transformed investment practices by wisely balancing risk and return. Nonetheless, its efficacy wanes in fluctuating financial markets due to its dependence on historical data and fixed assumptions. This paper investigates incorporating Ma...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02023.pdf |
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| author | Xu Junhao |
| author_facet | Xu Junhao |
| author_sort | Xu Junhao |
| collection | DOAJ |
| description | Modern Portfolio Theory (MPT), developed by Harry Markowitz, transformed investment practices by wisely balancing risk and return. Nonetheless, its efficacy wanes in fluctuating financial markets due to its dependence on historical data and fixed assumptions. This paper investigates incorporating Machine Learning (ML) techniques into the traditional Markowitz optimization framework to enhance portfolio construction and risk management processes. It highlights the use of supervised learning for forecasting asset returns, unsupervised learning for asset clustering, and reinforcement learning for adjusting portfolios dynamically. An empirical analysis utilizing recent U.S. market data reveals that ML models improve risk assessment, asset selection, and adaptive portfolio allocation. Techniques such as linear regression, clustering algorithms, and principal component analysis (PCA) facilitate superior forecasting and portfolio design in various market environments. The research also shows that ML can enhance Sharpe ratios in specific market conditions compared to conventional MPT. ML increases portfolio flexibility and robustness by aligning predictive modeling with optimization objectives. This evolving methodology lays the foundation for a more responsive, data-informed investment strategy in today’s finance, providing a viable alternative to the limitations of traditional models. |
| format | Article |
| id | doaj-art-3089ca95d8f349bcab3da2c4e847b419 |
| institution | Kabale University |
| issn | 2261-2424 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj-art-3089ca95d8f349bcab3da2c4e847b4192025-08-20T03:31:28ZengEDP SciencesSHS Web of Conferences2261-24242025-01-012180202310.1051/shsconf/202521802023shsconf_icdde2025_02023The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz OptimizationXu Junhao0School of Economics, The University of EdinburghModern Portfolio Theory (MPT), developed by Harry Markowitz, transformed investment practices by wisely balancing risk and return. Nonetheless, its efficacy wanes in fluctuating financial markets due to its dependence on historical data and fixed assumptions. This paper investigates incorporating Machine Learning (ML) techniques into the traditional Markowitz optimization framework to enhance portfolio construction and risk management processes. It highlights the use of supervised learning for forecasting asset returns, unsupervised learning for asset clustering, and reinforcement learning for adjusting portfolios dynamically. An empirical analysis utilizing recent U.S. market data reveals that ML models improve risk assessment, asset selection, and adaptive portfolio allocation. Techniques such as linear regression, clustering algorithms, and principal component analysis (PCA) facilitate superior forecasting and portfolio design in various market environments. The research also shows that ML can enhance Sharpe ratios in specific market conditions compared to conventional MPT. ML increases portfolio flexibility and robustness by aligning predictive modeling with optimization objectives. This evolving methodology lays the foundation for a more responsive, data-informed investment strategy in today’s finance, providing a viable alternative to the limitations of traditional models.https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02023.pdf |
| spellingShingle | Xu Junhao The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization SHS Web of Conferences |
| title | The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization |
| title_full | The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization |
| title_fullStr | The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization |
| title_full_unstemmed | The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization |
| title_short | The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization |
| title_sort | evolution of portfolio theory integrating machine learning with markowitz optimization |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02023.pdf |
| work_keys_str_mv | AT xujunhao theevolutionofportfoliotheoryintegratingmachinelearningwithmarkowitzoptimization AT xujunhao evolutionofportfoliotheoryintegratingmachinelearningwithmarkowitzoptimization |