Portfolio Optimization Model

Portfolio optimization models aim to construct investment portfolios that balance risk and return, a challenge addressed through various approaches. Current research focuses on integrating advanced machine learning techniques, such as deep reinforcement learning (with architectures like actor-critic networks and transformers), robust optimization methods handling data uncertainty, and hybrid models combining AI-driven stock selection with established quantitative strategies. These advancements aim to improve portfolio performance, enhance interpretability of investment decisions, and address limitations of traditional mean-variance models, particularly in volatile markets, impacting both academic understanding and practical investment strategies.