The Conventional Approach to Evaluating past performance does not tell us anything useful about the future.
Portfolio performance evaluation is useful only if it can lead to actionable insights.
The uncomfortable truth is that conventional performance measurement has little to no value, and it is almost never actionable. We all understand that past returns are not positive predictors of future returns. In fact, managers in the top quartile in one period are more likely to be in the bottom quartile the next than to remain in the top.
The reason past performance is not a positive predictor is that the impact of market randomness overwhelms any return due to security selection skill. Every active portfolio has systematic bets relative to the benchmark, typically unintentional, and common techniques cannot effectively distinguish return due to security selection from the return due to these passive differences.
The trick is to separate performance due to security selection from performance due to passive systematic exposures (e.g., market, sector, and size betas) that differ from the benchmark.
Brinson attribution and Active Share – common techniques of risk and performance analytics software – attempt to distinguish active return (Brinson) and risk (Active Share) from their passive counterparts. Unfortunately, both approaches implicitly assume that individual securities all have identical systematic exposures, including identical market and sector betas.
In fact, betas vary significantly. For example, as of 12/31/2019, U.S. market betas ranged from -1.4 to 4.6 and U.S. sector betas ranged from -4.1 to 5.7.
The wide variation in market and sector betas is one of the fatal flaws with Brinson attribution and Active Share. Failure to consider individual security betas prevents these methods from being effective. If they were effective, they would prove predictive.
Consider your own performance reports. Is there any data metric that, if it were different, would trigger an action?
New factor risk models, which model individual securities using a handful of (cheaply and passively investable) risk factors, accurately distinguish between return due to security selection and return due to passive factor betas. The result is a persistent metric of security selection performance. Skill exists, can be identified, and is persistent. But it does not last forever. This decay of skill makes it even more imperative to identify skilled managers quickly and reliably.
We hire managers with statistical evidence of skill who take sufficient security-selection risk to overcome fees and combine them to mitigate unintentional market exposures. We then monitor them continually, looking for reasons to make changes.
- Does performance indicate that our confidence level in a manager’s skill has fallen below 90%?
- Has active risk declined below the active fee threshold?
- Have passive exposures started changing too much to be effectively offset?
- Is the manager becoming overcapitalized?
- Has the manager’s contribution to aggregate portfolio risk changed?
None of the above reasons to take action can be detected without robust factor risk models designed specifically for manager and portfolio oversight.
If you’d like to see an analysis of any particular manager or portfolio, please email a couple of fund names to firstname.lastname@example.org