Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning
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Abstract (arXiv)
This paper studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in citezhou2020mv, the discrete-time model makes more general assumptions about the asset's return distribution. Using entropy to measure the cost of exploration, we derive the optimal investment strategy, whose density function is also Gaussian type. Additionally, we design the corresponding reinforcement learning algorithm. Both simulation experiments and empirical analysis indicate that our discrete-time model exhibits better applicability when analyzing real-world data than the continuous-time model.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 24 Dec 2023.
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11 of 34 references.
References · 11
- 2015Continuous control with deep reinforcement learningcited by 3,290
- 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 126
- 2021Recent Advances in Reinforcement Learning in Financecited by 102
- 2021Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithmscited by 77
- 2022q-Learning in Continuous Timecited by 69
- 2021Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approachcited by 63
- 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
- 2019An intelligent financial portfolio trading strategy using deep Q-learningcited by 14
- 2019Model-Free Reinforcement Learning for Financial Portfolios: A Brief Surveycited by 10
- 2020G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learningcited by 9
- 2019Large scale continuous-time mean-variance portfolio allocation via reinforcement learningcited by 5
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