Snapshots of Machine Learning Meets Markowitz: Portfolio Optimization in the Age of Big Data

Quickly apply original, key PMR-published papers with Snapshots—a short article companion that distills PMR research into compressed, digestible takeaways, so you can put the paper’s core ideas to work in your investment process—fast. This Snapshot article is based on research arguing that machine learning (ML) return forecasts generated with elastic net models, when integrated into a Markowitz framework, deliver stronger portfolio performance than historical-mean inputs and passive benchmarks across the study’s markets, firm sizes, covariance estimates, and position limits.