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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.13126 |
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| _version_ | 1866917574028034048 |
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| author | Upadhya, Nakul Granzer-Guay, Alexandre |
| author_facet | Upadhya, Nakul Granzer-Guay, Alexandre |
| contents | This work discusses the benefits of constrained portfolio turnover strategies for small to medium-sized portfolios. We propose a dynamic multi-period model that aims to minimize transaction costs and maximize terminal wealth levels whilst adhering to strict portfolio turnover constraints. Our results demonstrate that using our framework in combination with a reasonable forecast, can lead to higher portfolio values and lower transaction costs on average when compared to a naive, single-period model. Such results were maintained given different problem cases, such as, trading horizon, assets under management, wealth levels, etc. In addition, the proposed model lends itself to a reformulation that makes use of the column generation algorithm which can be strategically leveraged to reduce complexity and solving times. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13126 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Transition Strategies for Small to Medium Sized Portfolios Upadhya, Nakul Granzer-Guay, Alexandre Computational Finance This work discusses the benefits of constrained portfolio turnover strategies for small to medium-sized portfolios. We propose a dynamic multi-period model that aims to minimize transaction costs and maximize terminal wealth levels whilst adhering to strict portfolio turnover constraints. Our results demonstrate that using our framework in combination with a reasonable forecast, can lead to higher portfolio values and lower transaction costs on average when compared to a naive, single-period model. Such results were maintained given different problem cases, such as, trading horizon, assets under management, wealth levels, etc. In addition, the proposed model lends itself to a reformulation that makes use of the column generation algorithm which can be strategically leveraged to reduce complexity and solving times. |
| title | Optimizing Transition Strategies for Small to Medium Sized Portfolios |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2401.13126 |