Cross‐Market Portfolio Optimization via Structure‐Aware Deep Reinforcement Learning

Financial markets exhibit high levels of non‐stationarity and structural heterogeneity, which pose significant challenges to reinforcement learning (RL)‐based portfolio optimization methods. To address these challenges, this paper proposes a Structure‐Aware Deep Reinforcement Learning (SADRL) framework for cross‐market portfolio optimization. The proposed framework explicitly models market structural dynamics through a structure encoder that identifies latent market regimes, while a policy learner adapts investment strategies accordingly. This dual‐level learning mechanism enables the model to generalize across heterogeneous markets and remain stable under regime shifts. Extensive experiments on multiple cross‐market datasets demonstrate that SADRL achieves superior risk‐adjusted returns and improved robustness compared with conventional RL‐based baselines. These findings highlight the potential of structure‐aware learning for developing intelligent and adaptive decision‐making systems in financial markets.