3 papers
cs.LG2026
Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman
Daniil Mikriukov, Ruoyu Sun, Angelos Stefanidis +2
Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data. However, these…
cs.LG2025
Factor-MCLS: Multi-agent learning system with reward factor matrix and multi-critic framework for dynamic portfolio optimization
Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang +1
Typical deep reinforcement learning (DRL) agents for dynamic portfolio optimization learn the factors influencing portfolio return and risk by analyzing the output values of the re…
cs.LG2025
A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
Ruoyu Sun, Yue Xi, Angelos Stefanidis +2
Deep Reinforcement Learning (DRL) has been extensively used to address portfolio optimization problems. The DRL agents acquire knowledge and make decisions through unsupervised int…