Discrete-time mean-variance strategy based on reinforcement learning

doi:10.1080/01605682.2026.2627279

Discrete-time mean-variance strategy based on reinforcement learning

2026-03-13

0

PRE

AI

S

Si Zhao

X

Xun Li *

X

Xiangyu Cui *

DOI：10.1080/01605682.2026.2627279

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Abstract

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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.

Keywords:

Mean-variance

reinforcement learning

model exploration

Discrete-time mean-variance strategy based on reinforcement learning

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