CBGA-SAC: A CVaR-Constrained Deep Reinforcement Learning Framework for Portfolio Optimization
This paper proposes CBGA-SAC, a unified deep reinforcement learning framework that integrates CNN-BiLSTM-Attention for temporal risk extraction and Graph Attention Networks for dynamic asset correlations to optimize portfolio rebalancing under Conditional Value-at-Risk constraints, demonstrating superior risk-adjusted returns and robustness during market regime shifts compared to traditional and baseline deep learning models.