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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2502.07868 |
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| _version_ | 1866910822405505024 |
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| author | Pemy, Moustapha Zhang, Na |
| author_facet | Pemy, Moustapha Zhang, Na |
| contents | This paper studies the ubiquitous problem of liquidating large quantities of highly correlated stocks, a task frequently encountered by institutional investors and proprietary trading firms. Traditional methods in this setting suffer from the curse of dimensionality, making them impractical for high-dimensional problems. In this work, we propose a novel method based on stochastic optimal control to optimally tackle this complex multidimensional problem. The proposed method minimizes the overall execution shortfall of highly correlated stocks using a reinforcement learning approach. We rigorously establish the convergence of our optimal trading strategy and present an implementation of our algorithm using intra-day market data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Minimal Shortfall Strategies for Liquidation of a Basket of Stocks using Reinforcement Learning Pemy, Moustapha Zhang, Na Trading and Market Microstructure Optimization and Control Computational Finance This paper studies the ubiquitous problem of liquidating large quantities of highly correlated stocks, a task frequently encountered by institutional investors and proprietary trading firms. Traditional methods in this setting suffer from the curse of dimensionality, making them impractical for high-dimensional problems. In this work, we propose a novel method based on stochastic optimal control to optimally tackle this complex multidimensional problem. The proposed method minimizes the overall execution shortfall of highly correlated stocks using a reinforcement learning approach. We rigorously establish the convergence of our optimal trading strategy and present an implementation of our algorithm using intra-day market data. |
| title | Minimal Shortfall Strategies for Liquidation of a Basket of Stocks using Reinforcement Learning |
| topic | Trading and Market Microstructure Optimization and Control Computational Finance |
| url | https://arxiv.org/abs/2502.07868 |