Optimal Portfolio

Optimal portfolio construction aims to maximize returns while minimizing risk, a challenge addressed through diverse approaches. Current research focuses on improving portfolio selection using advanced machine learning techniques, such as deep learning models (e.g., iTransformers, LSTMs) and novel optimization algorithms (e.g., proximal alternating linearized minimization), often incorporating risk measures beyond traditional variance (e.g., CVaR). These advancements enhance portfolio efficiency across various asset classes (e.g., hedge funds, stocks, motion pictures), impacting both theoretical understanding of risk and return and practical applications in finance and beyond.