Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning

Autor:	Cui, Xiangyu, Li, Xun, Shi, Yun, Zhao, Si
Rok vydání:	2023
Předmět:	Quantitative Finance - Mathematical Finance Computer Science - Machine Learning Quantitative Finance - Portfolio Management
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:1904.11392 by other authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.15385 Zobrazit plný text záznamu View this record from Arxiv