The holy grail for mutual fund investors is the ability to identify in advance which of the very few active mutual funds will go on to outperform in the future.
To date, the overwhelming body of academic research has demonstrated that past performance not only doesn’t guarantee future performance (as the required SEC disclaimer states), but has virtually no value whatsoever as a predictor. The only value of past performance seems to be that poor performance tends to persist—with the likely explanation being high expenses.
Believers in active management were offered hope that the holy grail had been found with the publication, in the September 2009 issue of The Review of Financial Studies, of a study by Martijn Cremers and Antti Petajisto, “How Active Is Your Fund Manager? A New Measure That Predicts Performance.”
The authors concluded: “Active Share predicts fund performance: funds with the highest Active Share significantly outperform their benchmarks, both before and after expenses, and they exhibit strong performance persistence.”
Active share is a measure of how much a fund’s holdings deviate from its benchmark index, and funds with the highest active shares tend to have the best performance. Thus, while there’s no doubt that, in aggregate, active management underperforms, and that the majority of active funds underperform every year (and the percentage that underperform increases with the time horizon studied), if an investor is able to identify the few future winners by using active share as a measure, active management can be the winning strategy.
Today I’ll update my survey of the literature on active share as a performance predictor, published earlier this year, to include some additional research on the topic.
Revisiting Older Studies
We’ll begin by returning to our discussion of the 2009 Cremers and Petajisto study. After publication, questions were raised about its findings. I myself raised several issues. Among them were:
- The results could be due to a skewed distribution. A few highly concentrated funds may have enormous returns, increasing the average for the stock pickers. It would have been helpful to report the median.
- When funds are sorted by both fund type and fund size, only the very smallest quintile of stock-picking mutual funds showed a statistically reliable abnormal return. This tells us the only funds that generated reliable outperformance were the very smallest of the stock pickers. This reinforces the idea that skewness could be driving the results. In addition, their success would attract assets, which raises the hurdles to delivering alpha.
- The smallest funds typically are young funds. Thus, the well-documented incubation bias could be driving the results. (Incubation bias results when a mutual fund family wishing to launch a new fund nurtures several at a time. Funds that beat their benchmarks go public, while poorly performing ones never see the light of day.) If this bias exists, the reported returns for small funds don’t mean much.
In May 2012, Vanguard’s research team looked at the issue of active share as a predictor. Their study covered the 1,461 funds available at the beginning of 2001. Their final fund sample comprised 903 funds. Because the study only covered surviving funds, there’s survivorship bias in the data.
Following is a summary of their conclusions:
- Even with survivorship bias, higher levels of active share didn’t predict outperformance.
- The higher the active-share level, the larger the dispersion of excess returns.
- The higher the active-share level, the higher the fund costs.
The bottom line is that, while active share didn’t predict performance, it did increase risks as the dispersions of returns increased. In other words, investors paid more for the privilege of experiencing greater risk without any compensation in the form of greater returns.
Petajisto updated his study in 2013, adding six more years of data.
Again he found: “Over my sample period until the end of 2009, the most active stock pickers have outperformed their benchmark indices even after fees and transaction costs [by 1.26% per year]. In contrast, closet indexers or funds focusing on factor bets have lost to their benchmarks after fees.” The specific recommendation was to avoid funds with active shares below 60%.
Using the same database employed in the Petajisto studies, Andrea Frazzini, Jacques Friedman and Lukasz Pomorski of AQR Capital Management examined the evidence and the theoretical arguments for active share as a predictor of performance and presented their findings and conclusions in the January 2015 2016, “Deactivating Active Share,” which was published in the March/April 2016 issue of the Financial Analysts Journal.
Following is a summary of the findings from the AQR paper:
- The empirical support for the measure is weak and entirely driven by the strong correlation between active share and the benchmark type—high active share funds and low active share funds systematically have different benchmarks. A majority of high active share funds are small-cap, and a majority of low active share funds are large-cap.
- While active share correlates with benchmark returns, it doesn’t predict actual fund returns. Within individual benchmarks, active share is just as likely to correlate positively with performance as it is to correlate negatively.
- Active share results are very sensitive to comparisons using benchmark-adjusted returns rather than total returns. Over this sample period, small-cap benchmarks had large, negative four-factor alphas compared with large-cap benchmarks; this was crucial to the statistical significance of the results.
- Controlling for benchmarks, active share has no predictive power for fund returns, predicting higher fund performance within half of the benchmark indexes and lower fund performance within the other half.
Fresh Looks At Active Share
A recent contribution to the debate regarding active share comes from Ananth Madhavan, Aleksander Sobczyk and Andrew Ang of BlackRock, with their October 2016 paper, “Estimating Time-Varying Factor Exposures with Cross-Sectional Characteristics with Application to Active Mutual Fund Returns.”
Their study used cross-sectional risk characteristics (such as valuation ratios and market capitalization) to determine if active share predicted returns. The database included 1,267 mutual funds with $3.3 trillion in assets under management, and it covered the period September 2010 through June 2015. This period is out-of-sample from the period covered by Cremers and Petajisto in their 2009 paper.
They found that the measure of active share proposed by Cremers and Petajisto actually was negatively correlated (-0.75) to fund returns after controlling for factor loadings and other fund characteristics.
Thus, they concluded that “it is not the case that high conviction managers outperform.” While they noted there clearly were active managers with skill, active share isn’t the way to identify them ahead of time. And they didn’t suggest another method.
3 Pillars Of Active Management
Another recent paper we need to review is Cremers’ December 2016 study, “Active Share and the Three Pillars of Active Management: Skill, Conviction and Opportunity.” Cremers introduced a new measure of active share that emphasizes that a fund’s active share is reduced by its overlapping holdings. His study covered the period 1990 through 2015, and is free of survivorship bias.
Using quintile sorts, comparing high and low active share funds generally meant comparing funds with an active share of 95% or greater to funds with an active share below 60%. He also compared performance against two factor models, a seven-factor model (which uses the market factor, small- and midcap size factors, and small-, midcap and large value factors as well as momentum) and the standard Fama-French-Carhart four-factor model (beta, size, value and momentum). Cremers also examined the impact of turnover on performance.
Following is a summary of his findings:
- Using the seven-factor model, the quintile of funds with the highest active share had an abnormal (unexplained) return of 0.71% per year. While economically significant, the abnormal return was not statistically significant, as the t-statistic was just 1.37. Importantly, a chart in the appendix appears to show that all of the cumulative outperformance over time occurred in the brief period from 1999 through 2001 (during which the tech bubble burst, indicating that the high-active-share funds were able to sidestep the bubble). The low-active-share funds exhibited underperformance throughout the period.
- Using the four-factor model, the high-active-share quintile’s abnormal performance was -0.36% per year, with a t-statistic of -0.49.
These two findings seem to make it difficult to build a compelling case for active share alone being a predictor of future performance. However, Cremers also examined the impact of turnover on performance. Funds in the highest turnover quintile had average holdings of about eight months, while those in the lowest turnover quintile had average holdings of at least two years.
Using an independent 5x5 sort on active share and fund holding duration (a measure of the average holding period of the fund), the annualized seven-factor and four-factor intercepts for the high-active-share/high-duration (low turnover) portfolio were 1.88% and 1.69%, respectively. The corresponding t-statistics were 2.35 and 1.71.
As mentioned earlier, a caution is warranted insofar as the chart of cumulative abnormal seven-factor performance over time indicates it peaked around 2002 and has declined since then. Cremers did observe that the high-active-share/low-turnover funds did outperform from 2007 through 2013, while they underperformed from 2002 through 2006, and again from 2014 through 2015.
Low Turnover Matters
Cremers concluded that while he believes active share matters, both in large-cap and small-cap funds, investors should use only funds with low turnover (under 50%). He noted that the evidence that high-active-share funds outperformed low-active-share funds was considerably stronger for funds with low expense ratios.
Ranking funds by their expense ratio, Cremers found that the average expense ratio was 0.71% per year in the lowest quintile, and 1.79% in the fifth quintile. Thus, investors should consider active funds that have high active share and low turnover.
Given that the chart in the paper seemed to indicate the outperformance had occurred prior to 2002, I contacted professor Cremers and asked him if he had performance for the period from 2002 through 2015.
He provided me with the table below, which shows the results over that time frame for the active share quintile portfolios (the first quintile is the lowest active share) using the seven-factor model:
The active managers in each of the quintiles produced negative alphas, with only the highest-active-share quintile not showing statistical significance. The evidence does suggest that, if investors are going to use an active manager, they are better served by choosing one that has a high active share. However, it also shows that while perhaps it was once true that active share predicted future outperformance, its time may have gone with the wind.
This evidence is entirely consistent with the thesis of a book I co-authored with Andrew Berkin, “The Incredible Shrinking Alpha.” In it, we provide the evidence and explanations for why, over time, it has become persistently more difficult to generate alpha as the markets have become more efficient and the competition for alpha has gotten tougher.
We have one last research paper to review, one that provides an out-of-sample test. Out-of-sample tests are important because they can minimize the risk of data mining—if active share works, it should work outside the U.S. as well. A January 2017 study by Morningstar provides us with just such a test.
Using data from January 2001 through December 2015, Morningstar examined historical active share levels among actively managed Canadian equity funds relative to the bellwether S&P/TSX Composite Index. The researchers tested active share’s predictive power in two 10-year periods, 2001-2010 and 2006-2015.
They also divided each into two five-year subperiods. The first half provided an in-sample observation period in which they calculated active share as well as other measures of activeness, and the second half an out-of-sample evaluation period where they measured the results.
Using gross-of-fee returns, they found after adjusting for the market, size, value and momentum factors that there was no predictive value for the active share measure. The only thing that active share predicted, which should be expected, was a wider dispersion of performance outcomes.
A counterintuitive finding was that, despite the wider dispersion of outcomes, there was no relationship between active share and standard deviation or maximum drawdown.
It’s important to note that the preceding results were based on gross returns; investors only get to spend net returns. Morningstar chose to use gross returns because it would be a better measure of manager skill. Unfortunately, there wasn’t any such evidence.
Making matters worse is that, just as in the U.S., Canadian funds with higher active shares charge higher fees. Because fund costs and future performance are negatively correlated, funds with high active share may be more likely to underperform after fees.
This out-of-sample test strikes a further blow against the usefulness of active share as a predictor of future performance. With that said, you can decide for yourself whether you find the evidence on active share compelling enough to use actively managed funds.
As you consider the issue, remember that Cremers makes a compelling case that if you are going to use active funds, you should avoid all funds with low active share, high turnover and high expense ratios.
I would certainly agree. I would add that, when it comes to picking mutual funds, investors should care less about alpha (by whatever measure) and more about actual returns.
It's Not Only About Alpha
Investors may want to own a fund that provides exposure to factors they care about, such as market beta, size, value and momentum. They should then be happy to have minimal alpha as long as they get the beta (loading on a factor) they are seeking, which leads to higher returns.
In other words, such investors should rather own a low-cost, passively managed small value fund that provides high loadings on those factors and minimizes or even eliminates negative exposure to momentum typical of value funds and has no alpha than an active fund with less exposure to those factors even if it generates positive alpha.
Its positive alpha would have to be great enough to overcome the loss of returns due to the lower loading on the factors. To illustrate this point, consider the following example.
We’ll compare the returns, loadings on factors and alphas for two funds from the same asset class (U.S. large value): the actively managed Vanguard Equity Income Fund (VEIPX) and the passively managed DFA U.S. Large Cap Value III Portfolio (DFUVX). (Full disclosure: My firm, Buckingham Strategic Wealth, recommends DFA funds in constructing client portfolios.)
The data is for the 15-year period March 2002 through February 2017. The factor loadings come from Portfolio Visualizer and use the Fama-French benchmark factors and the three-factor model.
First, observe that the r-squared figures are very high, indicating the model is doing a good job of explaining returns. Second, as you can see, while VEIPX produced a positive annual alpha of 1.25% and DFUVX produced a negative alpha of -0.21%, a difference of 1.46 percentage points, DFUVX outperformed 8.8% versus 8.1%.
The reason for the outperformance is clear: DFUVX had much higher loadings on factors that delivered premiums. This allowed DFUVX to overcome the 1.46 percentage point difference in alpha. The higher loading on market beta provided about 0.74% in incremental returns, the higher loading on size provided about 0.78% in incremental returns and the higher loading on value added about 0.24%.
While alpha is nice, you only get to spend returns. Thus, it’s important to consider all of these issues, including turnover, expense ratios and loading on factors.
Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.