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Sorting Luck From Skill

In many walks of life, trying to discern the lucky from the skilled can be a difficult task. For example, it seems like every time a professional sports draft occurs, debate again flares up over whether the evaluation of college (or even high school) athletes is an exercise in skill or in luck.

Were the Portland Trail Blazers unskilled in the 1984 NBA draft when they selected Sam Bowie with the second overall pick, just ahead of Michael Jordan? Or were they just unlucky? (Bowie went on to an uneventful and injury-plagued career, while Jordan led the Chicago Bulls to six championships and is considered by many to be the greatest basketball player ever.)

The point is that sometimes it’s not easy to distinguish luck from skill. This difficulty extends to the evaluation of active mutual fund managers. With that in mind, we’ll review the academic literature on the subject of luck versus skill in mutual fund performance. We will examine the results of eight studies, including one that is surprising and provides important insights. The first study is by Bradford Cornell.

A Study Of Skill And Serendipity
Cornell, who contributed to the literature with his study “Luck, Skill, and Investment Performance,” which was published in the Winter 2009 issue of The Journal of Portfolio Management, noted: “Successful investing, like most activities in life, is based on a combination of skill and serendipity. Distinguishing between the two is critical for forward-looking decision-making because skill is relatively permanent while serendipity, or luck, by definition is not. An investment manager who is skillful this year presumably will be skillful next year. An investment manager who was lucky this year is no more likely to be lucky next year than any other manager.” The problem is that skill and luck are not independently observable.

Because this is the case, we are left with observing performance. However, we can apply standard statistical analysis to help differentiate the two, which is precisely what Cornell did. He used Morningstar’s 2004 database of mutual fund performance to analyze a homogenous sample of 1,034 funds that invest in large-cap value stocks.

Cornell’s findings were consistent with previous research that showed the great majority (about 92%) of the cross-sectional variation in fund performance is due to random noise. According to Cornell, this result demonstrates that: “most of the annual variation in performance is due to luck, not skill.” He concluded: “The analysis also provides further support for the view that annual rankings of fund performance provide almost no information regarding management skill.”

Our second study is by Eugene Fama and Kenneth French, who contributed to the literature with their study “Luck versus Skill in the Cross-Section of Mutual Fund Returns,” which was published in the October 2010 issue of The Journal of Finance. They found fewer active managers (about 2%) were able to outperform their three-factor (beta, size and value) model benchmark than would be expected by chance.


Stated differently, the very-best-performing traditional active managers have delivered returns in excess of the Fama-French three-factor model. However, their returns have not been high enough to be confident in concluding they have enough skill to cover their costs or their past performance will persist.

Fama and French concluded: “For (active) fund investors the simulation results are disheartening.” They did concede their results appear better when looking at gross returns (the returns without the expense ratio). But gross returns are irrelevant to investors unless they can find an active manager willing to work for free.

Declining Alpha
Our third study is “Conviction in Equity Investing” by Mike Sebastian and Sudhakar Attaluri, which appears in the Summer 2014 issue of The Journal of Portfolio Management. Their study is of interest because it shows a declining ability to generate alpha. The authors found:

  • Since 1989, the percentage of managers who evidenced enough skill to basically match their costs (showed no net alpha) has ranged from about 70% to as high as about 90%, and by 2011 was about 82%.
  • The percentage of unskilled managers has ranged from about 10% to about 20%, and by 2011 was about 16%.
  • The percentage of skilled managers—those showing net alphas (demonstrating enough skill to more than cover their costs)—began the period at about 10%, rose to as high as about 20% in 1993, and by 2011, had fallen to just 1.6%, virtually matching the results of the paper by Fama and French.

Our fourth study is “Scale and Skill in Active Management,” which appeared in the April 2015 issue of the Journal of Financial Economics. The authors, Lubos Pastor, Robert Stambaugh and Lucian Taylor, provided further insight into why the hurdles to generating alpha have been growing. Their study covered the period 1979 to 2011 and more than 3,000 mutual funds. They concluded that fund managers have become more skillful over time.

They wrote: “We find that the average fund’s skill has increased substantially over time, from -5 basis points (bp) per month in 1979 to +13 bp per month in 2011.” However, they also found that the higher skill level has not translated into better performance.

More Skill Needed

They reconcile the upward trend in skill with no trend in performance by noting: “Growing industry size makes it harder for fund managers to outperform despite their improving skill. The active management industry today is bigger and more competitive than it was 30 years ago, so it takes more skill just to keep up with the rest of the pack.”

Pastor, Stambaugh and Taylor came to another interesting conclusion: The rising skill level they observed was not due to increasing skill within firms. Instead, they found that “the new funds entering the industry are more skilled on average than the existing funds. Consistent with this interpretation, we find that younger funds outperform older funds in a typical month.”

For example, the authors found that “funds aged up to three years outperform those aged more than 10 years by a statistically significant 0.9% per year.”

The authors hypothesized this is the result of newer funds having managers who are better educated or better acquainted with new technology, though they provide no evidence to support that thesis. They also found all fund performance deteriorates with age as industry growth creates decreasing returns to scale, and newer, and more skilled, funds create more competition.



Active Managers Can’t Cover Costs
Our fifth study is “Mutual Fund Performance through a Five-Factor Lens,” an August 2016 research paper by Philipp Meyer-Brauns of Dimensional Fund Advisors. His sample included 3,870 active funds over the 32-year period from 1984 to 2015.

Benchmarking their returns against the newer Fama-French five-factor model—which adds profitability (RMW, robust minus weak) and investment (CMA, conservative minus aggressive)—to the Fama-French three-factor model (market beta, size and value), he found an average negative monthly alpha of -0.06% (with a t-stat of 2.3). He also found that about 2.4% of the funds had alpha t-stats of 2 or greater, which is slightly fewer than what we would expect by chance (2.9%).

Meyer-Brauns also found that the distribution of actual alpha t-stats had shifted to the left of what would be expected from chance if all managers were able to produce excess returns over the five-factor model sufficient to cover their costs.

No Outperformance Expected

He concluded: “There is strong evidence that the vast majority of active managers are unable to produce excess returns that cover their costs.” He added that “funds do about as well as would be expected from extremely lucky funds in a zero-alpha world. This means that ex-ante, investors could not have expected any outperformance from these top performers.”

Meyer-Brauns extended his work in March 2017 in his paper “Luck vs. Skill Across Different Fund Categories.” He examined four separate categories of U.S. equity mutual funds (large-cap value, large-cap growth, small-cap value and small-cap growth) over the period January 2000 through June 2016.

His goal was to determine whether the ability of active managers to outperform the Fama-French five-factor model varies across fund categories. Following is a summary of his findings:

  • The best-performing funds perform no better than would be expected by chance alone in a zero-alpha world. For example, the by-chance distributions indicate that if all funds could cover their costs, slightly more than 2% should be expected to have alpha t-stats larger than 2. Looking at the actual distributions across fund categories, he found that in two of the four categories, large-cap value and large-cap growth, not a single fund had an alpha t-stat above 2. For the two other categories, small cap value (1.8%) and small cap growth (1.1%), the percentage was lower than would be expected by chance.
  • The reverse is true when looking at the number of funds with reliably negative five-factor intercept t-stats. Substantially more than 2% of funds reliably underperformed the five-factor benchmark: 18.8% of large-cap value funds, 8.2% of large-cap growth funds, 10.3% of small-cap value funds, and 11.4% of small-cap growth funds all had alpha t-stats below -2.

Meyer-Brauns concluded: “Taken together, this evidence across different fund categories suggests that the vast majority of active managers have been unable to produce excess returns, with respect to the Fama/French five-factor model, that cover their costs.”

Later this week, we’ll continue this discussion by reviewing two more academic articles related to luck versus skill in mutual fund performance.

Larry Swedroe is the director of research for The BAM Alliance.

This commentary originally appeared May 15 on ETF.com

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