Bayesian Optimization for CVaR-based portfolio optimization
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913753513066496 |
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| author | Millar, Robert Li, Jinglai |
| author_facet | Millar, Robert Li, Jinglai |
| contents | Optimal portfolio allocation is often formulated as a constrained risk problem, where one aims to minimize a risk measure subject to some performance constraints. This paper presents new Bayesian Optimization algorithms for such constrained minimization problems, seeking to minimize the conditional value-at-risk (a computationally intensive risk measure) under a minimum expected return constraint. The proposed algorithms utilize a new acquisition function, which drives sampling towards the optimal region. Additionally, a new two-stage procedure is developed, which significantly reduces the number of evaluations of the expensive-to-evaluate objective function. The proposed algorithm's competitive performance is demonstrated through practical examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17737 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Optimization for CVaR-based portfolio optimization Millar, Robert Li, Jinglai Portfolio Management Optimization and Control Applications Computation Optimal portfolio allocation is often formulated as a constrained risk problem, where one aims to minimize a risk measure subject to some performance constraints. This paper presents new Bayesian Optimization algorithms for such constrained minimization problems, seeking to minimize the conditional value-at-risk (a computationally intensive risk measure) under a minimum expected return constraint. The proposed algorithms utilize a new acquisition function, which drives sampling towards the optimal region. Additionally, a new two-stage procedure is developed, which significantly reduces the number of evaluations of the expensive-to-evaluate objective function. The proposed algorithm's competitive performance is demonstrated through practical examples. |
| title | Bayesian Optimization for CVaR-based portfolio optimization |
| topic | Portfolio Management Optimization and Control Applications Computation |
| url | https://arxiv.org/abs/2503.17737 |