Measuring Active’s Underperformance Against Investable Benchmarks
The underperformance of actively managed mutual funds compared to passive investments is well-documented in the literature—as is evidence on the lack of persistence of outperformance beyond just the randomly expected.
For example, in their study “Luck versus Skill in the Cross-Section of Mutual Fund Returns,” published in the October 2010 issue of the Journal of Finance, Eugene Fama and Kenneth French found that only managers in the 98th and 99th percentiles showed evidence of statistically significant skill.
Little Evidence Of Positive Returns
A related study by Laurent Barras, Olivier Scaillet and Russ Wermers, “False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas,” which was published in the January 2010 issue of the Journal of Finance, found only 0.6% of mutual funds have a true positive risk-adjusted net return, while 24.0% have a true negative risk-adjusted net return. And even these very low figures would be lower for taxable investors, as they are all based on pretax returns.
Further evidence is provided by the study “Conviction in Equity Investing” by Mike Sebastian and Sudhakar Attaluri, which appears in the Summer 2014 issue of The Journal of Portfolio Management. The authors found that it’s actually getting harder to outperform. Specifically:
- 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 at about 82%.
- The percentage of unskilled managers has ranged from about 10% to about 20%, and by 2011, was at about 16%.
- The percentage of skilled managers began the period at about 10%, rose to as high as about 20% in 1993 and by 2011, had fallen to just 1.6%.
Alpha’s Increasing Difficulty
Lubos Pastor, Robert Stambaugh and Lucian Taylor, authors of the April 2015 paper “Scale and Skill in Active Management,” provided insight into why the hurdles to generating alpha are getting higher. The authors, whose study covered the period 1979 to 2011 and more than 3,000 mutual funds, concluded that fund managers have become more skillful over time.
They write: “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 been translated into better performance. 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.” These findings are consistent with the other research we have discussed so far.
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: “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: “Funds aged up to three years outperform those aged more than 10 years by a statistically significant 0.9% per year.”
Pastor, Stambaugh and Taylor hypothesized this was a result of newer funds having managers who were better educated or better acquainted with new technology—though they provide no evidence to support that thesis.
The authors also found that all fund performance deteriorates with age, as industry growth creates decreasing returns to scale, and newer, and more skilled, funds create more competition.
Benchmarking Against ETFs
A recent contribution to the literature on the likelihood of actively managed funds to outperform comes through an October 2016 study by Timothy Riley, “Investible Benchmarks for Actively Managed Mutual Funds.”
While most of the academic literature compares the returns of active funds to risk-adjusted benchmarks using indices and/or factor regressions, Riley instead used ETFs to build investable benchmarks for actively managed mutual funds. He did so because, while the returns of benchmark indices don’t include implementation costs, the returns of ETFs do. Thus, we have a more real-world comparison.
Riley’s benchmarking process did not make assumptions based on a mutual fund’s stated style or benchmark. Rather, he selected the appropriate benchmark based on a fund’s exposure to common factors (beta, size, value, momentum, investment and profitability) and return history.
He noted: “The benchmarks can be identified in advance, do not require shorting or leverage, and require only annual rebalancing.”
Riley also observed that a similar approach has shown that a portfolio of ETFs can be used to replicate the performance of hedge funds. Given that ETFs are relatively new, and that he needed a sufficient number of ETFs to complete the comparison, his study covered the period 2003 through 2014.
Following is a summary of his findings:
- The ETFs used in the study had an average expense ratio of 0.31% compared with 1.21% for the actively managed mutual funds. The results using index funds would have been very similar given their average expense ratio was just slightly higher at 0.36%.
- On average, just four ETFs were needed to create a benchmark, with the majority of the benchmarks consisting of a single ETF. For example, among benchmarks that held six ETFs, the highest-ranking ETF had an average weight of 51.6%. The next two highest-ranking ETFs had a combined weight of 34.2%, and the last three ETFs had a combined weight of only 14.1%.
- Benchmarks were effective at replicating the general return pattern and factor exposures of the actively managed mutual funds, with the average correlation between fund daily returns and benchmark daily returns being 0.97, while the average absolute difference in pricing factor exposures (e.g., beta, SMB, HML) ranged between 0.06 and 0.12.
- While there was some variation in replication quality, the benchmarks closely tracked their designated fund even in the tails of the distribution. The 10th percentile of correlation was 0.94, and the 90th percentile of tracking error was 0.43%.
- How well a benchmark replicated a fund was highly predictable.
- The average actively managed fund underperformed its benchmark (produced a negative alpha versus the benchmark) by 1.03% per year (with a t-stat of 2.81, which indicates statistical significance at the 5% level) after accounting for expenses. The difference in expense ratios explains 90% of the underperformance.
- The level of underperformance varies depending on investment style. For example, the average large-cap fund had an alpha of -1.54%, while the average midcap fund had an alpha of -0.42%.
- Only 38.7% of fund-years had a positive alpha.
- Actively managed mutual funds in the lowest expense ratio decile underperformed by only 0.35% per year, while those with the highest expense ratios underperformed by 1.61% per year.
- There was a nearly 1:1 ratio between expense ratio and performance. Thus, if investors must choose actively managed funds (which unfortunately is the case in many 401(k) plans), they should choose the ones with the lowest costs within the desired risk profile.
- There was no evidence that past performance provides valuable information. The best and worst performers in the previous year had performance that differed by only 0.23% in the next year.
Riley concluded that because the actively managed funds in his sample managed about $2.5 trillion at the end of 2014, underperformance of 1% per year represented an opportunity cost of $25 billion per year for investors selecting active management.
That’s the cost of the triumph of hype, hope and marketing over wisdom, experience and the evidence. And even that cost understates the true cost for taxable investors, as it’s often the case that, for such investors, the greatest cost of active management is taxes.
Because strategies don’t have costs, whenever I perform an analysis on active funds, I use passively managed investable alternatives. For a summary of the results from my series evaluating the performance of actively managed fund families, see this recent article.
Given the overwhelming evidence that choosing actively managed funds is playing the loser’s game—a game that, while possible to win, has odds of success so low that it’s not prudent to even try—we are presented with an anomaly: It’s estimated that today only about 15% of individual investor assets are invested in index funds (though perhaps as much as 40% of institutional assets are).
This anomaly is especially puzzling because, with relative ease, average retail investors can purchase a passive portfolio that will likely outperform (because of lower costs) an actively managed mutual fund while capturing the same systematic risk exposures.
This commentary originally appeared June 23 on ETF.com
By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.
The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.
© 2017, The BAM ALLIANCE