Risk Models Built with Passive ETFs as Factors
Asset
Owners
Detect statistically significant evidence of security-selection skill that is likely to persist.
Avoid funds taking too little active risk to ever overcome fees… even with skill.
Investment Consultants
Offset risk from unintended market exposures with ETFs or better fund combinations.
Avoid closet-indexing the aggregate portfolio.
Asset
Managers
Identify and offset unintentional systematic risk exposures that can overwhelm skill.
Identify key contributors to idiosyncratic risk.
Benefits to Asset Owners and Consultants
Conventional Barra-type risk models use factors that are not directly investable. This precludes allocators from acting on model insights, which begs the question, “what use is the information?”
Our models, equally robust, use only passive ETFs as factors.
For allocators, this changes everything. For the first time, allocators can:
Separate return due to security selection from return due to unintentional passive risk exposures…. and detect statistically significant evidence of security selection skill.
Offset the unintended passive exposures that can overwhelm skill.
Identify and avoid the one-third of funds taking too little active risk to ever compensate for fees — even assuming skill.
Ensure the aggregate portfolio is taking sufficient security-selection risk to justify fees and has no hidden risk exposures.
Comparisons with Conventional Risk Models
Risk and Skill Analytics
Type
Robust
No
Intuitive
Yes
Validation
Easily Disproven
Investable Factors
N/A
Accurately Measure Current Risk
No
Quantify all Current Drivers of Future Performance
No
Distinguish Active from Passive Risk
Attempted
Offset Unintended Risk Exposures
No
Minimize Unintentional Passive Risk while Maintaining or Increasing Active Risk
No
Detect Statistically Significant Evidence of Security Selection Skill
No
Cost
Conventional Barra-Type Factor Models
Type
Robust
Yes
Intuitive
No
Validation
Can be Proven
Investable Factors
No
Accurately Measure Current Risk
Yes
Quantify all Current Drivers of Future Performance
Yes (but less meaningful)
Distinguish Active from Passive Risk
No
Offset Unintended Risk Exposures
No
Minimize Unintentional Passive Risk while Maintaining or Increasing Active Risk
No
Detect Statistically Significant Evidence of Security Selection Skill
No
Cost
Peer Analytics Passive-Factor Risk Models
Type
Robust
Yes
Intuitive
Yes
Validation
Easily Proven
Investable Factors
Yes
Accurately Measure Current Risk
Yes
Quantify all Current Drivers of Future Performance
Yes
Distinguish Active from Passive Risk
Yes
Offset Unintended Risk Exposures
Yes
Minimize Unintentional Passive Risk while Maintaining or Increasing Active Risk
Yes
Detect Statistically Significant Evidence of Security Selection Skill
Yes
Cost
The Problem with Performance Evaluation
Performance evaluation is useful only if it can lead to actionable insights.
How ESG Overlays Lead to Unintended Market Bets
Factor Risk Models Designed for Allocators
Manager Selection: The Quixotic Search for Skill
We’ve been fortunate in some wonderful clients, including:
Risk Models for Insurance Companies
DFA/ALM Models
Cloud-based, user-friendly, transparent, flexible yet robust, stochastic asset-liability models designed to be easily vetted.