Portfolio optimization with MOPSO-Shrinkage hybrid model
This paper introduces a novel framework for portfolio optimization that integrates Multi-Objective Particle Swarm Optimization (MOPSO) with shrinkage covariance estimators, referred to as the MOPSO-Shrinkage hybrid model. The main contribution of this study lies in combining the adaptive search capa...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Control and Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720725000396 |
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| author | Minh Tran Nhat M. Nguyen |
| author_facet | Minh Tran Nhat M. Nguyen |
| author_sort | Minh Tran |
| collection | DOAJ |
| description | This paper introduces a novel framework for portfolio optimization that integrates Multi-Objective Particle Swarm Optimization (MOPSO) with shrinkage covariance estimators, referred to as the MOPSO-Shrinkage hybrid model. The main contribution of this study lies in combining the adaptive search capabilities of evolutionary algorithms with robust covariance estimation techniques to enhance portfolio allocation in mature financial markets. Unlike traditional shrinkage covariance models, which struggle in highly dynamic environments, our hybrid model optimally selects stocks and improves risk-adjusted returns. Empirical analysis on US stock market data from 2013 to 2023 demonstrates that MOPSO-Shrinkage models consistently outperform traditional shrinkage models, achieving higher returns, lower volatility, and superior Sharpe ratios. Among the hybrid models, MOPSO-SSIM exhibits the best performance, with an average annual return of 18.86% and a Sharpe ratio of 1.27, while significantly reducing portfolio risk. Rigorous statistical tests confirm the robustness of the model, showing that MOPSO-Shrinkage significantly outperforms traditional methods. These findings suggest that the proposed approach is well-suited for traders seeking higher risk-adjusted returns and portfolio stability in volatile markets. |
| format | Article |
| id | doaj-art-4d8215cd75c542c4b0a258d02d6e6b8b |
| institution | Kabale University |
| issn | 2666-7207 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj-art-4d8215cd75c542c4b0a258d02d6e6b8b2025-08-20T03:30:39ZengElsevierResults in Control and Optimization2666-72072025-06-011910055310.1016/j.rico.2025.100553Portfolio optimization with MOPSO-Shrinkage hybrid modelMinh Tran0Nhat M. Nguyen1Ho Chi Minh University of Banking, 36 Ton That Dam street, Nguyen Thai Binh ward, district 1, Ho Chi Minh, 700000, Viet NamCorresponding author.; Ho Chi Minh University of Banking, 36 Ton That Dam street, Nguyen Thai Binh ward, district 1, Ho Chi Minh, 700000, Viet NamThis paper introduces a novel framework for portfolio optimization that integrates Multi-Objective Particle Swarm Optimization (MOPSO) with shrinkage covariance estimators, referred to as the MOPSO-Shrinkage hybrid model. The main contribution of this study lies in combining the adaptive search capabilities of evolutionary algorithms with robust covariance estimation techniques to enhance portfolio allocation in mature financial markets. Unlike traditional shrinkage covariance models, which struggle in highly dynamic environments, our hybrid model optimally selects stocks and improves risk-adjusted returns. Empirical analysis on US stock market data from 2013 to 2023 demonstrates that MOPSO-Shrinkage models consistently outperform traditional shrinkage models, achieving higher returns, lower volatility, and superior Sharpe ratios. Among the hybrid models, MOPSO-SSIM exhibits the best performance, with an average annual return of 18.86% and a Sharpe ratio of 1.27, while significantly reducing portfolio risk. Rigorous statistical tests confirm the robustness of the model, showing that MOPSO-Shrinkage significantly outperforms traditional methods. These findings suggest that the proposed approach is well-suited for traders seeking higher risk-adjusted returns and portfolio stability in volatile markets.http://www.sciencedirect.com/science/article/pii/S2666720725000396Multi-objective functionParticle swarm optimizationShrinkage estimatorStock selectionPortfolio allocation |
| spellingShingle | Minh Tran Nhat M. Nguyen Portfolio optimization with MOPSO-Shrinkage hybrid model Results in Control and Optimization Multi-objective function Particle swarm optimization Shrinkage estimator Stock selection Portfolio allocation |
| title | Portfolio optimization with MOPSO-Shrinkage hybrid model |
| title_full | Portfolio optimization with MOPSO-Shrinkage hybrid model |
| title_fullStr | Portfolio optimization with MOPSO-Shrinkage hybrid model |
| title_full_unstemmed | Portfolio optimization with MOPSO-Shrinkage hybrid model |
| title_short | Portfolio optimization with MOPSO-Shrinkage hybrid model |
| title_sort | portfolio optimization with mopso shrinkage hybrid model |
| topic | Multi-objective function Particle swarm optimization Shrinkage estimator Stock selection Portfolio allocation |
| url | http://www.sciencedirect.com/science/article/pii/S2666720725000396 |
| work_keys_str_mv | AT minhtran portfoliooptimizationwithmopsoshrinkagehybridmodel AT nhatmnguyen portfoliooptimizationwithmopsoshrinkagehybridmodel |