Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning

• URL: http://arxiv.org/abs/2312.15385v1
• Date: Sun, 24 Dec 2023 02:08:49 GMT
• Title: Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning
• Authors: Xiangyu Cui, Xun Li, Yun Shi and Si Zhao
• Abstract summary: We use entropy to measure the cost of exploration to derive the optimal investment strategy. We design the corresponding reinforcement learning algorithm. Our model exhibits better applicability when analyzing real-world data than the continuous-time model.
• Score: 5.8184275610981615
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in \cite{zhou2020mv}, 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.

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