Luck, Skill and Their Role in Passive Fund Returns
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Investors may choose a passive fund because they don’t believe they can distinguish between luck and skill among active fund managers. But new research shows that the issue of luck and skill plays an important role in passive fund returns too.
One of the great challenges for investors is distinguishing between luck (which is random) and skill (which persists) when evaluating performance. While it is easy to identify past alpha generators (outperformance on a risk-adjusted basis), the challenge is finding future alpha generators. One problem is that, given the large number of active funds, it is hard to distinguish skill from luck – even if no fund manager is good or bad, many funds will do well and many will do poorly purely by chance. In other words, when you examine fund-by-fund performance, you need to determine whether the range of outcomes is wider than expected by chance. If that is the case, you can infer that there are bad fund managers overpopulating the left tail of outcomes and good managers overpopulating the right tail.
The authors of studies such as, “Luck, Skill, and Investment Performance,” “Luck versus Skill in the Cross-Section of Mutual Fund Returns,” “Conviction in Equity Investing” and “Luck vs. Skill Across Different Fund Categories” have found that it is very difficult to distinguish between luck and skill. And the study, “Scale and Skill in Active Management,” found that this is true even though the authors found evidence of increasing skill among fund managers. That is why the SEC requires the disclaimer that past performance is not a guarantee of future performance, and The American Law Institute, in its Third Restatement of Trusts, concluded: “Evidence shows that there is little correlation between a fund manager’s past successes and their ability to produce above-market returns in subsequent periods.”
Most investors will be surprised to learn that, because of the significant role serendipity can play, being able to separate luck from skill can be difficult even when evaluating two index funds (and other systematic strategies) in the same asset class. This makes it difficult to determine if a passive fund is creating “smart beta” via its fund construction rules or is just lucky. For example, how often an index reconstitutes can impact returns. Most indices (such as the Russell and RAFI Fundamental Indices) reconstitute annually. The lack of frequent reconstitution can create significant style drift as stocks migrate from one asset class to another over the course of 12 months. That migration, from small stocks to large, or from value to growth, causes a small and/or value fund’s exposure to the size and value factors to become lower over the course of the year, lowering expected returns. To avoid this problem, many systematic funds, such as those of Dimensional, reconstitute their asset class on a much more frequent basis, including daily. This difference in methodology can be very important if over short periods examined there is seasonal variation in premiums (that is not evident over the long term).
Consider the RAFI indices, which reconstitute in March. Let’s assume that over some five-year period there is a positive value premium from April through October and a negative value premium from November through March. Because it reconstitutes only annually, a fund based on the RAFI Index will have its greatest value exposure in the April through October period, with migration causing its value exposure to become lower in November through March. If we compare the performance of the RAFI Index-based fund with another fund that reconstitutes more frequently and thus has more consistent exposure to the value premium, we would expect to see the RAFI Index-based fund outperform because it has less exposure to the value factor when it had a negative premium. However, that would be a serendipitous event, as there is no evidence (or logical explanation) that over the long term there is a seasonal value premium that would indicate the best time to rebalance is March. In a simpler example, because funds that reconstitute less frequently lose exposure to a factor relative to funds that reconstitute more frequently, if a factor has positive returns, all else equal, the fund that reconstitutes less frequently will have lower returns.
Corey Hoffstein, Nathan Faber and Steven Braun demonstrate how large a role serendipity can play in returns with their September 2020 study, “Rebalance Timing Luck: The (Dumb) Luck of Smart Beta.” They began by noting that prior research, such as the study, “Portfolio Construction Matters,” had demonstrated that differences in the approaches to constructing systematic strategies can lead to significant dispersion in results even for strategies targeting the same investment style. They added: “While substantial effort is spent researching new factor signals, refining previously discovered signals, and developing portfolio construction techniques, the seemingly innocuous activity of choosing when to rebalance these strategies is largely absent from the existing literature.” That led them to research the impact of the timing of rebalancing on systematic strategies. Given different rebalance schedules, positive and negative impacts can make a given manager appear more or less skilled.
To illustrate the role rebalancing timing can play, they constructed long-only U.S. equity strategies designed to capture value, momentum, quality and low volatility tilts, where the universe of eligible securities is obtained from the S&P 500 universe. For each style, they varied the target number of holdings (which determines the amount of exposure to a factor – the fewer the number, the greater the exposure) as well as the rebalance frequency to target specific sensitivities to these decisions. The time period for these results was July 2000 to September 2019. Following is a summary of their findings:
- The impact of rebalancing timing luck (RTL) is directly influenced by the number of holdings (the fewer the holdings, the greater the impact), the portfolio turnover realized by the strategy (the greater the impact), and the rebalance frequency.
- Higher turnover styles, such as momentum, exhibit higher realized RTL as opposed to lower turnover styles, such as low volatility.
- The choice when to rebalance is material and affected annualized returns by as much as 200 basis points for higher turnover strategies, with one-year performance discrepancies as high as 41.7 percentage points.
- The overall significance of any persistent outperformance is low, indicating that no rebalance schedule shows significant outperformance over other versions of the strategy. Out of the 15 permutations of the momentum style, no combinations were found to be statistically significant, and similar results were found in the remaining styles.
- Elevated bouts of broad market volatility tend to increase the amounts of absolute dispersion (e.g., 14.6 percentage points in 2002 and 14.1 percentage points in 2009).
As one example demonstrating the impact of the choice of rebalancing schedule, the rolling 252-day performance difference between two different rebalancing schedules for a semiannually rebalanced 100-stock momentum strategy found that the seemingly trivial decision to rebalance the portfolio in May and November resulted in a 20 percentage point return difference when measured against the same strategy with its rebalance shifted by only one month (April and October).
Their findings led Hoffstein, Faber and Braun to conclude: “Our results suggest significant potential problems for return-based strategy comparisons and analysis. For example, failing to inoculate a benchmark against the effects of RTL can cause a strategy to appear skilled or un-skilled by relative comparison when the performance dispersion is actually an artifact of luck.” The conclusion you should draw is that when selecting a systematic fund, your focus should be on portfolio construction rules and the amount of exposure to the factors you want to allocate assets to, not on the dates a fund chooses to rebalance.
Larry Swedroe is the chief research officer for Buckingham Wealth Partners.
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