Machine Learning’s Role in the Hunt for Alpha

April 30, 2024

As soon as the vast “neural network” of algorithms hummed to life, the portfolio managers (PMs) began to extract from it knowledge and investment recommendations. When they were skeptical of the wisdom of a suggestion to buy or sell a stock, the PMs nudged their proprietary machine to better appreciate the economic rationales for investing. And it would learn. A virtuous cycle, undertaken to boost long-term investment results for Vanguard shareholders, was born.

Our actively managed equity funds that employ machine learning (ML)—and their shareholders—are starting to benefit, according to Cesar Orosco, CFA, and Scott Rodemer, CFA, of Vanguard’s Quantitative Equity Group (QEG). The group develops quantitative models that attempt to replicate what a good fundamental investor would do, but systematically and at scale.

“There are a few ways machine learning has tended to boost alpha,” or marginal returns above those of a portfolio’s benchmark, said Rodemer, head of factor-based strategies. “For example, alpha exposures have become more dynamic; growth signals more price sensitive; and sources of excess returns more intertwined.”

As intended, neither portfolio turnover nor aggregate measures of portfolio risk, such as tracking error relative to a benchmark, have changed appreciably.