The First Predictive Risk and Skill Factor Models

Passively-available beta differences with a benchmark are a byproduct, typically unintentional, of any stock-selection process.  Since consistent passive differences, once properly identified, can be freely obtained or offset, they are not part of active contribution.

 Isolating active performance from the impact of consistent passive differences offers tremendous oversight advantages. 

Unfortunately, current methodologies (Brinson attributions and Active Share) fail to properly define passive exposures. As a result, current analytics fail to predict future performance, and analytics are only valid to the extent they are predictive.

If risk analytics are valid, they’re predictive.  Analytics that fail to predict future performance are invalid.

We offer highly predictive statistical factor risk models built to isolate active contributions from passively-available exposures — revealing statistical evidence of security-selection skill with a strong tendency to persist, true active risk, opportunities to reduce relative risk (tracking error) without sacrificing security-selection risk, to avoid closet indexing, and to detect any hidden unintentional bets that may endanger performance.