Understanding Ensemble Active Management: Innovation in Action

Abstract

Last September, this article’s three authors were Contributing Editors to a new White Paper published by the EAM Research Consortium (“Ensemble Active Management – The Next Evolution in Investment Management”1) which introduced the public to the breakthrough potential of Ensemble Active Management (“EAM”). The Paper explained how EAM Portfolios are the result of applying time‐tested “Ensemble Methods” (a core component of Artificial Intelligence (“AI”) and Machine Learning for decades) to the high conviction stock selections of actively managed portfolios, and that the superior performance for EAM Portfolios are both repeatable and persistent.

The EAM White Paper focused on the AI and mathematical principles enabling EAM, The White Paper did not, however, include a comprehensive discussion of the time‐tested investment principles that explained how, and why, EAM Portfolios performed as well as the White Paper demonstrated. This article is designed to address that omission.

Keywords: Portfolio Active Management, Tail Risks, Ensemble Active Management

JEL Classification: G11, G10