Friday, September 08, 2006

Mutual Fund Selection - Fiduciary Fund Ranker

Selecting mutual funds that outperform ex ante is notoriously difficult. Evidence suggests that the median alpha for actively managed mutual funds is negative, yet they continue to predominate the investment lineups of many 401K plans (as well as small pension plans & individual portfolios). Plan sponsor selection efforts have added some value however. Elton & Gruber found that although 401(k) plan sponsors on average selected funds with negative alpha, they selected funds that had smaller negative alphas than a random selection would produce. Plan sponsors and investors alike might improve the value of their mutual fund selection process by:

1) Using a fund ranking process instead of a linear fund screening process that eliminates funds

When searching for a mutual fund it is impractical to examine every available alternative. Many investors (professionals included) use a linear fund elimination process to reduce a large population of fund alternatives down to a manageable few. The full population of The funds for consideration are reduced by those which do not meet certain qualifying tests (ie. outperform market benchmark for 5 years or top half in peer universe over 5 years, etc.). This kind of screening certainly expedites the selection process but it also eliminates good investment options from consideration.

2) Using multiple relative performance statistics instead of focusing predominantly on historical nominal returns,
The universal caveat, "Past performance may not be indicative of future results" acknowledges the pervasive uncertainty facing investors. It does not recognize the substantive value differential between a cursory review of nominal returns and a comprehensive historical quantitative/qualitative fund review. Even John Bogle, the champion of indexing has said that “while you should disregard a single aggregate number showing a fund's past long-term return you can indeed learn a great deal by studying the nature of its past returns”.

Investors should use a multi statistic and time horizon weighted fund ranking methodology where the full universe of funds can be inspected across a variety of customized time horizons and quantitative statistics. This method overcomes the naïve assumption in a fund elimination process that good investments must simultaneously meet all of a defined set of standards and that all investors value each of the screening criteria equally. This process also a better understanding of the nature, consistency and risks associated with each fund. These fund characteristics, unlike nominal returns tend to be persistent through time.

We provide a partial sample of a Fiduciary Fund Ranker Domestic Large Cap Value Mutual Fund search below. In this model we weight each of the fundamental and performance statistics as well as the variety of time horizons in order to customize the ranking process to accommodate the characteristics of the asset class/styles as well as the objectives and preferences of each client investor.

For each mutual fund class we collect relative performance statistics (correlation to the asset class/style benchmark, risk adjusted returns and downside risk measures, alpha and information ratios against generic and beta adjusted benchmarks, tracking error and fund batting averages as well as a few pieces of fundamental data. We then rank each fund by statistic and weighted time horizon. The total fund rank can also be adjusted to accommodate different factor weightings. For instance if a client were looking for a well diversified fund that well represented a particular style box and would not present significant monitoring issues we would begin the search by overweighting correlation, tracking error while looking for high risk adjusted returns and equivalent style and benchmark alphas.

The Fiduciary Fund Ranker has limitations as does any purely quantitative process. While no selection process can overcome the uncertainty inherent in investing, this process can close the gap between investment expectations and outcomes, and improve the investment decision making process by assuring good investments aren’t arbitrarily lost in a screen and that a broad set of investment characteristics are used in the selection process. Importantly, it promotes interaction to provide a means to clarify investment objectives. Additionally it profiles and positions each fund in a peer universe in way that provides a very robust and technically supportable methodology for prudent fund selection under ERISA.


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