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EXPLORATORY MEAN-VARIANCE PORTFOLIO OPTIMIZATION WITH REGIME-SWITCHING MARKET DYNAMICS

Yuling Max Chen et al.

International Journal of Theoretical and Applied Finance2025https://doi.org/10.1142/s0219024925500141article
AJG 2ABDC B
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0.50

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.

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https://doi.org/https://doi.org/10.1142/s0219024925500141

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@article{yuling2025,
  title        = {{EXPLORATORY MEAN-VARIANCE PORTFOLIO OPTIMIZATION WITH REGIME-SWITCHING MARKET DYNAMICS}},
  author       = {Yuling Max Chen et al.},
  journal      = {International Journal of Theoretical and Applied Finance},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219024925500141},
}

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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R · text relevance †0.50 × 0.4 = 0.20

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