Portfolio Optimization

Portfolio optimization aims to allocate assets to maximize returns while managing risk, a problem tackled using various approaches. Current research emphasizes dynamic strategies, employing machine learning models like reinforcement learning (with algorithms such as Soft Actor-Critic and deep Q-learning), graph neural networks for cost-efficient rebalancing, and novel architectures such as bandit networks and Hopfield networks to improve prediction accuracy and computational efficiency. These advancements offer improved risk-adjusted returns and more robust portfolio management, impacting both academic understanding of financial markets and practical investment strategies.