Portfolio Management
Portfolio management, the optimization of investment portfolios to maximize returns while minimizing risk, is a core area of finance undergoing rapid transformation through the application of machine learning. Current research heavily emphasizes deep reinforcement learning (DRL) algorithms, such as A2C, PPO, and DQN, often combined with techniques like knowledge distillation and self-supervised learning, to develop sophisticated trading strategies that adapt to dynamic market conditions and incorporate factors like ESG scores. These advancements offer the potential for improved risk-adjusted returns and more efficient portfolio construction, impacting both academic understanding of financial markets and practical investment strategies.
Papers
Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis
Qishuo Cheng, Le Yang, Jiajian Zheng, Miao Tian, Duan Xin
An Empirical Study of Challenges in Machine Learning Asset Management
Zhimin Zhao, Yihao Chen, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan