Optimal mean-variance portfolio selection with mean-field reinforcement learning
We study the mean-variance portfolio selection problem which is important in the finance field. The objective of the mean-variance portfolio selection problem is to find an optimal allocation strategy that achieves a great balance between expected return and risk. Because of the non-separable varian...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166475 |
| _version_ | 1811676895748554752 |
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| author | Cheng, Zhengxing |
| author2 | Patrick Pun Chi Seng |
| author_facet | Patrick Pun Chi Seng Cheng, Zhengxing |
| author_sort | Cheng, Zhengxing |
| collection | NTU |
| description | We study the mean-variance portfolio selection problem which is important in the finance field. The objective of the mean-variance portfolio selection problem is to find an optimal allocation strategy that achieves a great balance between expected return and risk. Because of the non-separable variance term, it is challenging to directly utilize dynamic programming or standard reinforcement learning to solve the problem.
In this work, we construct a novel mean-field reinforcement learning framework to find the optimal strategy of the multi-period mean-variance portfolio problem in the discrete time-space. We first build a mean-field formulation of the mean-variance portfolio selection problem for mean-field reinforcement learning. After that, we propose and implement the multiple-period mean-field Q-learning with function approximation algorithm to obtain the optimal strategies. We design the linear quadratic Q-functions that fit the objective function and discrete time-space of the problem. we also per- form evaluations in various parameter settings to demonstrate the effectiveness of our proposed mean-field reinforcement learning framework. |
| first_indexed | 2024-10-01T02:28:45Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166475 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:28:45Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1664752023-05-08T15:38:19Z Optimal mean-variance portfolio selection with mean-field reinforcement learning Cheng, Zhengxing Patrick Pun Chi Seng School of Physical and Mathematical Sciences cspun@ntu.edu.sg Science::Mathematics We study the mean-variance portfolio selection problem which is important in the finance field. The objective of the mean-variance portfolio selection problem is to find an optimal allocation strategy that achieves a great balance between expected return and risk. Because of the non-separable variance term, it is challenging to directly utilize dynamic programming or standard reinforcement learning to solve the problem. In this work, we construct a novel mean-field reinforcement learning framework to find the optimal strategy of the multi-period mean-variance portfolio problem in the discrete time-space. We first build a mean-field formulation of the mean-variance portfolio selection problem for mean-field reinforcement learning. After that, we propose and implement the multiple-period mean-field Q-learning with function approximation algorithm to obtain the optimal strategies. We design the linear quadratic Q-functions that fit the objective function and discrete time-space of the problem. we also per- form evaluations in various parameter settings to demonstrate the effectiveness of our proposed mean-field reinforcement learning framework. Bachelor of Science in Mathematical Sciences 2023-05-02T05:59:57Z 2023-05-02T05:59:57Z 2023 Final Year Project (FYP) Cheng, Z. (2023). Optimal mean-variance portfolio selection with mean-field reinforcement learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166475 https://hdl.handle.net/10356/166475 en application/pdf Nanyang Technological University |
| spellingShingle | Science::Mathematics Cheng, Zhengxing Optimal mean-variance portfolio selection with mean-field reinforcement learning |
| title | Optimal mean-variance portfolio selection with mean-field reinforcement learning |
| title_full | Optimal mean-variance portfolio selection with mean-field reinforcement learning |
| title_fullStr | Optimal mean-variance portfolio selection with mean-field reinforcement learning |
| title_full_unstemmed | Optimal mean-variance portfolio selection with mean-field reinforcement learning |
| title_short | Optimal mean-variance portfolio selection with mean-field reinforcement learning |
| title_sort | optimal mean variance portfolio selection with mean field reinforcement learning |
| topic | Science::Mathematics |
| url | https://hdl.handle.net/10356/166475 |
| work_keys_str_mv | AT chengzhengxing optimalmeanvarianceportfolioselectionwithmeanfieldreinforcementlearning |