Discrete-time Mean-variance Strategy Based on Reinforcement Learning

This article studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in the literature, 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 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 analysing real-world data than the continuous-time model.