Large scale continuous-time mean-variance portfolio allocation via reinforcement learning
This addresses portfolio optimization for investors by providing a scalable and data-efficient method, though it is incremental as it builds on existing RL frameworks.
The paper tackles the large-scale Markowitz mean-variance portfolio allocation problem by using reinforcement learning, achieving over 10% annualized returns and outperforming econometric and deep RL methods in empirical tests on S&P 500 stocks.
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.