Large scale continuous-time mean-variance portfolio allocation via reinforcement learning

Haoran Wang

Papers from arXiv.org

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.

Date: 2019-07, Revised 2019-08
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (7)

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