Machine Learning for Portfolio Optimization

The field of portfolio optimization is being rapidly transformed by the application of Machine Learning (ML) techniques, moving beyond the constraints of traditional mean-variance optimization. This course provides a practical yet rigorous exploration of how various ML algorithms, including reinforcement learning, deep learning, and advanced time series models, can be leveraged to improve asset allocation, risk modeling, and trade execution. Specifically tailored for investment professionals in the reserve management and sovereign wealth space, the program focuses on models that enhance robustness, handle high dimensionality, and capture non-linear relationships often missed by conventional methods. Participants will gain the conceptual understanding and practical intuition necessary to critically evaluate, implement, and govern ML-driven investment strategies, ensuring they align with the safety and liquidity mandates unique to official sector institutions. Emphasis will be placed on model interpretability, robustness to market regimes, and managing the associated model risk.