Machine Learning in Multi-Asset Investing: A Practical and Principled Guide for Portfolio Managers

Multi-asset investing plays a central role in institutional portfolio design, offering a framework for balancing growth and preservation objectives through diversified exposure across asset classes. Yet the growing dimensionality of financial data, the instability of cross-asset relationships, and the prevalence of regime-dependent market behavior have challenged the adequacy of traditional quantitative frameworks. This article examines the evolving role of machine learning (ML) as an augmentation tool in multi-asset investing. Rather than presenting ML as a replacement for established processes, the authors propose a structured perspective in which ML selectively enhances key components of the investment workflow, signal generation, portfolio construction, risk monitoring, and strategic adaptation, across a range of portfolio types, from balanced funds to global macro and risk parity strategies. They also discuss interpretability, robustness, and governance considerations essential for institutional adoption.