Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics

TL;DR

This paper introduces a novel reinforcement learning approach for mean-variance portfolio optimization in regime-switching markets, demonstrating improved convergence and performance over traditional methods in simulated and real data.

Contribution

It develops the EMVRS framework with a new OC learning algorithm, addressing limitations of TD learning and enhancing portfolio optimization under market regime changes.

Findings

OC learning converges to true market parameters

EMVRS with OC outperforms in simulated markets

Higher mean and lower volatility in real market data

Abstract

Considering the continuous-time Mean-Variance (MV) portfolio optimization problem, we study a regime-switching market setting and apply reinforcement learning (RL) techniques to assist informed exploration within the control space. We introduce and solve the Exploratory Mean Variance with Regime Switching (EMVRS) problem. We also present a Policy Improvement Theorem. Further, we recognize that the widely applied Temporal Difference (TD) learning is not adequate for the EMVRS context, hence we consider Orthogonality Condition (OC) learning, leveraging the martingale property of the induced optimal value function from the analytical solution to EMVRS. We design a RL algorithm that has more meaningful parameterization using the market parameters and propose an updating scheme for each parameter. Our empirical results demonstrate the superiority of OC learning over TD learning with a clear convergence of the market parameters towards their corresponding ``grounding true" values in a simulated market scenario. In a real market data study, EMVRS with OC learning outperforms its counterparts with the highest mean and reasonably low volatility of the annualized portfolio returns.