The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning

TL;DR

This paper applies a reinforcement learning approach, specifically the soft actor-critic algorithm, to optimize multi-asset portfolios in dynamic markets, demonstrating improved stability and accuracy through a novel parameter division strategy.

Contribution

It introduces a new RL-based method for multi-asset mean-variance portfolio selection with proven convergence and enhanced stability via parameter partitioning.

Findings

The SAC algorithm outperforms traditional methods in simulated markets.

The proposed approach achieves higher stability and learning accuracy.

Numerical results confirm superior performance in real financial markets.

Abstract

In this paper, we study the continuous-time multi-asset mean-variance (MV) portfolio selection using a reinforcement learning (RL) algorithm, specifically the soft actor-critic (SAC) algorithm, in the time-varying financial market. A family of Gaussian portfolio selections is derived, and a policy iteration process is crafted to learn the optimal exploratory portfolio selection. We prove the convergence of the policy iteration process theoretically, based on which the SAC algorithm is developed. To improve the algorithm's stability and the learning accuracy in the multi-asset scenario, we divide the model parameters that influence the optimal portfolio selection into three parts, and learn each part progressively. Numerical studies in the simulated and real financial markets confirm the superior performance of the proposed SAC algorithm under various criteria.