Active skill exists …. but it is challenging to detect reliably and, even when present, decays over time.
The traditional approach to manager selection involves some combination of quantitative and qualitative analysis, but conventional quantitative methods have not been very effective, and qualitative insights without a rigorous study of their predictive effectiveness are inadequate.
The generic quantitative approaches of evaluating managers’ performance suffer from the overwhelming limitation that simplistic measures of past performance are not positive predictors. The reality is the reverse. Managers in the top quartile during one period are more likely to be in the bottom quartile the next than to remain in the top. Selecting managers based on a simple analysis of absolute or relative returns, no matter how well massaged, is a losing game.
Manager search analysts are also at a disadvantage when it comes to qualitative manager assessments in which analysts attempt to discover managers with relative skill by differentiating between the smart and the brilliant (whether people, philosophies, or stories). But there are more equity managers than equities, and managers have the advantage of telling a single story to dozens of consultants while, in contrast, search consultants, with limited time, must critique many dozens of different stories, all while laboring under the huge information gap into portfolio details. Investment managers, collectively, hold all power in qualitative interviews.
But what if you could isolate the part of performance that does indicate skill and is predictive?
And what if you could quantify all current risk exposures which reliably predict future return due to market effects?
Wouldn’t manager discussions, armed with that information, lead to meaningful qualitative assessments and improved performance?
New Equity Factor Risk Models, built specifically for oversight using a limited number of passively investable factors, accurately isolate performance due to market timing and security selection from the performance due to passive bets relative to the benchmark. This approach effectively eliminates the noise that has always overwhelmed any active skill signal. These models are highly predictive and easily validated by comparing ex-ante predictions with ex-post results.
Managers in the top security-selection skill decile in one period are twice as likely to outperform in the subsequent three years. Managers in the bottom skill decile are more than twice as likely to underperform.
Skill by itself, however, is insufficient. Managers must also take enough security-selection risk to overcome fees. One-third of active mutual funds, and half of fund assets, take too little active risk to overcome active fees, even with top-tier skill. We require managers with security-selection skill who also take sufficient security-selection risk.
Our manager search process begins by screening out those managers who take too little active risk relative to fees, as well as those that fail to show evidence of security selection skill, allowing us to focus deep qualitative assessments on a small subset (6-8 percent) of all managers.
Manager interviews incorporating equity factor risk data allow search analysts to focus discussions on the true drivers of performance and understand investment processes at levels never before imagined.
New equity factor risk models built for oversight are a game-changer in the search for active skill and put the power back in the hands of asset owners, where it belongs.
If you’d like to see an analysis of a particular manager or portfolio, please email firstname.lastname@example.org.