Robustifying Conditional Portfolio Decisions via Optimal Transport
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866913306134970368 |
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| author | Nguyen, Viet Anh Zhang, Fan Wang, Shanshan Blanchet, Jose Delage, Erick Ye, Yinyu |
| author_facet | Nguyen, Viet Anh Zhang, Fan Wang, Shanshan Blanchet, Jose Delage, Erick Ye, Yinyu |
| contents | We propose a data-driven portfolio selection model that integrates side information, conditional estimation and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return trade-off, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem. If portfolio decisions are made based on either the mean-variance or the mean-Conditional Value-at-Risk criterion, the resulting reformulation can be further simplified to second-order or semi-definite cone programs. Empirical studies in the US equity market demonstrate the advantage of our integrative framework against other benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_16451 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Robustifying Conditional Portfolio Decisions via Optimal Transport Nguyen, Viet Anh Zhang, Fan Wang, Shanshan Blanchet, Jose Delage, Erick Ye, Yinyu Portfolio Management Optimization and Control Machine Learning We propose a data-driven portfolio selection model that integrates side information, conditional estimation and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return trade-off, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem. If portfolio decisions are made based on either the mean-variance or the mean-Conditional Value-at-Risk criterion, the resulting reformulation can be further simplified to second-order or semi-definite cone programs. Empirical studies in the US equity market demonstrate the advantage of our integrative framework against other benchmarks. |
| title | Robustifying Conditional Portfolio Decisions via Optimal Transport |
| topic | Portfolio Management Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2103.16451 |