Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. First posted on arXiv 28 Jan 2025.
Builds on
0 of 0 references.
No references here yet.
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2025Multi-period Asset-liability Management with Reinforcement Learning in a Regime-Switching Market
- 2024Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study
- 2026Dynamic mean-variance portfolio selection with no-shorting constraints and unknown investment opportunity sets
- 2026Reinforcement Learning for Dividend Optimization in Partially Observed Regime-Switching Diffusion Model
- 2024Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
- 2024Reinforcement Learning for Jump-Diffusions, with Financial Applications
- 2026Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets
- 2026Portfolio Reinforcement Learning with Scenario-Context Rollout