Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning
Mathematical Finance
2023-12-27 v1 Machine Learning
Portfolio Management
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.
Keywords
Cite
@article{arxiv.2312.15385,
title = {Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning},
author = {Xiangyu Cui and Xun Li and Yun Shi and Si Zhao},
journal= {arXiv preprint arXiv:2312.15385},
year = {2023}
}
Comments
arXiv admin note: text overlap with arXiv:1904.11392 by other authors