Entropy Regularization under Bayesian Drift Uncertainty
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866913020528033792 |
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| author | Au, Andy |
| author_facet | Au, Andy |
| contents | We study entropy-regularized mean-variance portfolio optimization under Bayesian drift uncertainty. Gaussian policies remain optimal under partial information, the value function is quadratic in wealth, and belief-dependent coefficients admit closed-form solutions. The mean control is identical to deterministic Bayesian Markowitz feedback; entropy regularization affects only the policy variance. Additionally, this variance does not affect information gain, and instead provides belief-dependent robustness. Notably, optimal policy variance increases with posterior conviction $|m_t|$, forcing greater action randomization when mean position is most aggressive. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16862 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Entropy Regularization under Bayesian Drift Uncertainty Au, Andy Optimization and Control Portfolio Management 91G10, 93E20 We study entropy-regularized mean-variance portfolio optimization under Bayesian drift uncertainty. Gaussian policies remain optimal under partial information, the value function is quadratic in wealth, and belief-dependent coefficients admit closed-form solutions. The mean control is identical to deterministic Bayesian Markowitz feedback; entropy regularization affects only the policy variance. Additionally, this variance does not affect information gain, and instead provides belief-dependent robustness. Notably, optimal policy variance increases with posterior conviction $|m_t|$, forcing greater action randomization when mean position is most aggressive. |
| title | Entropy Regularization under Bayesian Drift Uncertainty |
| topic | Optimization and Control Portfolio Management 91G10, 93E20 |
| url | https://arxiv.org/abs/2602.16862 |