Robust Portfolio

Robust portfolio construction aims to create investment strategies that perform well even under uncertainty, addressing limitations of traditional methods like Markowitz mean-variance optimization which are highly sensitive to estimation errors and market fluctuations. Current research focuses on incorporating robust optimization techniques, including distributionally robust approaches and generative meta-learning models, often utilizing deep learning architectures like LSTMs to improve prediction accuracy and portfolio diversification. These advancements enhance portfolio stability, reduce transaction costs, and improve out-of-sample performance, impacting both financial markets and related fields like recommender systems.