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 |
Language: | English |
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Nanyang Technological University
2023
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Online Access: | https://hdl.handle.net/10356/166475 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | 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. |
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