A study offers lessons for investors striving to add value through fund selection.
This article first appeared in the April/May 2011 issue of Morningstar Advisor magazine. Get your free subscription today!
We think that one of our biggest responsibilities as an investment manager is to properly set expectations. We want the advisors with whom we work to have confidence in our abilities yet also to understand the attributes of our investment philosophy that can have an impact on our short-term performance. This helps ensure that advisors share our long- term orientation, which helps us to execute the strategies on their clients' behalf.
In the course of having these discussions with advisors, the subject sometimes turns to what returns might be possible. Specifically, advisors seek to estimate a portfolio's "excess returns" over a market cycle, which they define as the portfolio's return less the benchmark return, often irrespective of risk.
In this commentary, we present a framework to estimate the potential excess returns of various asset classes, as well as a hypothetical 65% equity/35% bond portfolio. We also draw on the findings of our study, as well as our perspective managing portfolios, to offer recommendations on how to improve the odds of selecting outperforming funds.
There are a number of ways to estimate the excess returns of a diversified portfolio. In this study, we opt for an approach that we think is familiar to investors--percentile rankings.
Percentile rankings are easy to understand and uniformly apply. However, it's not always clear how a given level of outperformance, expressed as a percentile ranking, might translate to excess returns. Thus, we set out to devise a framework for estimating the potential excess returns associated with various mutual fund percentile rankings.
To that end, we compiled monthly returns for all U.S. open-end mutual funds in the following asset classes: domestic large cap, domestic mid-cap, domestic small cap, developed foreign stock, emerging-markets stock, investment- grade bond, high-yield bond, foreign bond, real estate, and absolute return (excluding leveraged funds). To eliminate survivorship bias, we added the returns of merged and obsolete funds back to the dataset. The study covered the 20-year period from January 1990 to December 2010, encompassing more than 6,000 unique funds in all. Using the monthly data, we calculated rolling one- and three-year annualized returns for every fund in each asset class. We then calculated the 10th, 25th, and 50th percentile return breakpoints for every rolling period of each asset class. For example, within the domestic large-cap group, the 25th percentile return for the rolling one-year period ended July 2007 was 18.2%. For each class with a complete dataset, we calculated percentile returns for all 241 rolling one-year periods and 217 rolling three-year periods that make up the 20-year span.
From there, we compared the 10th, 25th, and 50th percentile returns of each rolling period with the relevant index return. Returning to the previous example, we compared the 25th percentile domestic large-cap return for the rolling one-year period ended July 2007 with the Russell 1000 Index's one-year return during the same period and calculated the difference. We repeated that exercise for every rolling period of every asset class, the only exceptions being rolling periods in which there were too few funds to calculate a meaningful percentile return.