October 19, 2015

by Bob Veres

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So far in my series on selecting superior active fund managers, I’ve broken the most promising research into two very different areas of focus: 1) identifying which segments of the market, or which types of funds, are most promising; and 2) what characteristics to look at in the funds themselves. The most interesting research in category 1 was the “active share” analyses by Antti Petajisto, which concluded that closet index funds (which happen to hold a near-majority of assets in the fund industry) tend to be consistent losers on an after-fee basis, while high-active-share funds with a stock-picking mentality tended to beat their benchmarks by 126 basis points a year.

But of course not all the stock-pickers were winners, and the winning funds tended to be scattered all over the various sectors of the market. Is there a way to analyze the different segments of the global opportunity set, and determine the best places to look for those outperforming managers?

[drizzle]As it happens, this is exactly the research that is being conducted by Dan Kern, president of Advisor Partners in Walnut Creek, CA. Kern has an unusual background; he spent eight years as a portfolio manager on the U.S. Growth Equity team at Montgomery Asset Management in San Francisco, and then moved over to researching funds as the managing director of Charles Schwab Investment Management.

“Having worn both hats,” he says, “I’ve learned that investment success is frequently a temporary state of affairs. Today’s high flier is tomorrow’s loser.”

Today, before he looks for potential outperforming funds, Kern subjects different sectors of the market to three basic tests, which tell him whether he’ll even bother to look at the funds that operate in that space.

Payoff: the performance spread

Test one is something he calls “payoff.” Is the potential excess return (payoff) worth the risk you would be taking if you decided to invest with an active manager in that sector? Another way to describe this factor is the “performance spread”: Where is it tightest, and least tight?

To answer that question, Kern identified the percentage return that would qualify a fund for the upper quadrant (25th percentile) in different asset sectors, and compared it to the return that a fund would have to achieve to fall into the upper 75th percentile. He also looked at the index return. This allowed him to calculate three derivative figures: the 25th percentile return minus the 75th percentile return, the 25th percentile return minus the index return, and the percentage difference between the 25th quartile fund return and the index.

Figure 1 shows the results for certain foreign stock and bond sectors, plus one U.S. bond sector, for the five years ending December 31, 2014. As you can see, the highest 25th-minus-75th spreads can be found in the emerging markets equity funds sector (3.02% a year), followed by foreign small/midcap equity funds (2.81%). The spread is tightest in the intermediate-term bond category. In reverse order, those two asset classes also have the highest spread between the 25th percentile returns minus the index, and the emerging-markets equity funds have by far the highest percentage difference between the highest 25% of the funds and the index.

Figure 1 – Active-Passive Payoff Analysis

Active Managers

At the other end of the spectrum, the average emerging-markets bond fund loses to the index. The other lowest payoff categories are foreign large-blend equity funds and intermediate-term bonds.

Persistence: luck versus skill

Test two, the second dimension of the analysis, measures what Kern calls “persistence.” The relevant question here is: How often has outperformance been repeatable in this sector? This analysis offers clues as to how much of the success of outperforming funds in the past could be attributable to luck or timing versus skill. If a high percentage of the outperformers are beating their peers on a consistent basis, then your bets on active managers would have a higher chance of succeeding over the next market cycle.

Looking at the data set for five-year periods ending December 31, 2009 and December 31, 2014 (see Figure 2), Kern calculated two figures. First: how many of the top quartile funds in the first time period were also ranked in the top quartile in the second? And: how many first-quartile funds in the first five years had dropped to the bottom quartile during the second period?

Figure 2 – Active-Passive Analysis – Persistence

Active Managers

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