Deep reinforcement learning-driven intelligent portfolio management with green computing: Sustainable portfolio optimization and management
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Y Xu
Sustainable Computing: Informatics and Systems, 2025
Elsevier
Portfolio management remains a key area in quantitative trading. To address limitations in existing deep reinforcement learning (DRL)-based models, which fail to adapt trading strategies and properly utilize supervisory information, we propose a Dynamic Predictor Selection-based Deep Reinforcement Learning (DPDRL) model. The DPDRL model integrates multiple predictors to forecast stock movements and dynamically selects the most accurate predictions, optimizing investment allocation via a market environment evaluation …
