Practical Portfolio Optimization with Metaheuristics:Pre-assignment Constraint and Margin Trading
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909545492643840 |
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| author | Poon, Hang Kin |
| author_facet | Poon, Hang Kin |
| contents | Portfolio optimization is a critical area in finance, aiming to maximize returns while minimizing risk. Metaheuristic algorithms were shown to solve complex optimization problems efficiently, with Genetic Algorithms and Particle Swarm Optimization being among the most popular methods. This paper introduces an innovative approach to portfolio optimization that incorporates pre-assignment to limit the search space for investor preferences and better results. Additionally, taking margin trading strategies in account and using a rare performance ratio to evaluate portfolio efficiency. Through an illustrative example, this paper demonstrates that the metaheuristic-based methodology yields superior risk-adjusted returns compared to traditional benchmarks. The results highlight the potential of metaheuristics with help of assets filtering in enhancing portfolio performance in terms of risk adjusted return. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15965 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Practical Portfolio Optimization with Metaheuristics:Pre-assignment Constraint and Margin Trading Poon, Hang Kin Portfolio Management Computational Engineering, Finance, and Science Portfolio optimization is a critical area in finance, aiming to maximize returns while minimizing risk. Metaheuristic algorithms were shown to solve complex optimization problems efficiently, with Genetic Algorithms and Particle Swarm Optimization being among the most popular methods. This paper introduces an innovative approach to portfolio optimization that incorporates pre-assignment to limit the search space for investor preferences and better results. Additionally, taking margin trading strategies in account and using a rare performance ratio to evaluate portfolio efficiency. Through an illustrative example, this paper demonstrates that the metaheuristic-based methodology yields superior risk-adjusted returns compared to traditional benchmarks. The results highlight the potential of metaheuristics with help of assets filtering in enhancing portfolio performance in terms of risk adjusted return. |
| title | Practical Portfolio Optimization with Metaheuristics:Pre-assignment Constraint and Margin Trading |
| topic | Portfolio Management Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2503.15965 |