Large scale continuous-time mean-variance portfolio allocation via reinforcement learning
Abstract
We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the optimality of a multivariate Gaussian feedback policy, with time-decaying variance, in trading off exploration and exploitation. Based on a provable policy improvement theorem, we devise a scalable and data-efficient RL algorithm and conduct large scale empirical tests using data from the S&P 500 stocks. We found that our method consistently achieves over 10% annualized returns and it outperforms econometric methods and the deep RL method by large margins, for both long and medium terms of investment with monthly and daily trading.
Keywords
Cite
@article{arxiv.1907.11718,
title = {Large scale continuous-time mean-variance portfolio allocation via reinforcement learning},
author = {Haoran Wang},
journal= {arXiv preprint arXiv:1907.11718},
year = {2019}
}
Comments
15 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1904.11392