Active Share: Does it Predict Fund Performance?

Active Share: Does it Predict Fund Performance?

June 15, 2017 Factor Investing, Larry Swedroe, Active and Passive Investing
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(Last Updated On: June 20, 2017)

The Holy Grail for mutual fund investors is the ability to identify in advance, which of the active mutual funds (or ETFs nowadays) will outperform in the future. The evidence suggests this task is almost impossible. 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 it has virtually no value whatsoever as a predictor. The only value of past performance being that poor performance tends to persist — with the likely explanation being high expenses. (Mark Carhart’s 1997 paper, “On Persistence in Mutual Funds,” is a great example, and one of the oldest sources, in this line of this research.)

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 the study by Martijn Cremers and Antti Petajisto “How Active Is Your Fund Manager? A New Measure That Predicts Performance.”

The authors concluded the following:

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 deviates from its benchmark index and the funds with the highest active shares have the best performance. Thus, while there’s no doubt that in aggregate active management underperforms, and the majority of active funds underperform every year (and the percentage that underperforms increases with the time horizon studied), if an investor can identify the few future winners by using the measure of Active Share, active management can be the winning strategy. After publication, questions were raised about the findings.

Does Active Share Really Predict Future Winners?

In my blog post on January 7, 2011, “Does the Evidence Behind Active Share Hold Up?” I raised several issues.

Among the issues I discussed:

  • 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 that the only funds that generated reliable out-performance 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 Research took a look at the issue of Active Share as a predictor. Their study covered the 1,461 funds available at the beginning of 2001. The final fund sample comprised 903 funds. Because the study only covered surviving funds, there’s survivorship bias in the data.

The following is a summary of their conclusions:

  • Even with survivorship bias in the data, 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 — 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. He found the following: “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 percent per annum]. 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 percent.

Using the same database that was used 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 their March 2015 paper “Deactivating Active Share” which was published in the March/April 2016 issue of the Financial Analysts Journal. https://www.aqr.com/library/aqr-publications/deactivating-active-share.

The following is a summary of the findings from the AQR paper:

  • The empirical support for the measure is weak and is 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 caps and a majority of low Active Share funds are large caps.
  • 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 the choice of comparing funds using benchmark-adjusted returns rather than total returns. Over this sample period, small-cap benchmarks had large negative four-factor alphas compared to large-cap benchmarks and 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.

Wes and his team have a discussion of the back and forth debate between the academics and AQR here.

And interested readers can read the comprehensive response to the AQR paper from Petajisto here. (1) In the words of Antti Petajisto:

All of the key claims of AQR’s paper were already addressed in the two cited Active Share papers: Petajisto (2013) and Cremers and Petajisto (2009)… So clearly ignoring large and essential parts of the original Active Share papers is simply not the way to conduct impartial scientific inquiry.

They also have a follow on paper, “Do Mutual Fund Investors Get What They Pay For? The Legal Consequences of Closet Index Funds,” which is covered in detail by Alpha Architect here. The authors suggest that active share is such an important aspect of the investment purchase decision that statistics like active share should be required disclosures. Clearly this debate isn’t over and low-active share closet-indexers are on watch.

But the large asset managers with generally low active share are responding. A more recent contribution to the debate on active share comes from Ananth Madhavan, Aleksander Sobczyk and Andrew Ang of BlackRock, Inc. 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. Their database included 1,267 mutual funds with $3.3 trillion in assets under management, and covered the period from September 2010 through June 2015. This period is out-of-sample from the period covered by Martijn Cremers and Antti Petajisto in their 2009 paper “How Active is Your Fund Manager? A New Measure That Predicts Performance.” 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 that 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.

Wait a Minute: Maybe Active Share Does Predict Winners?

There’s one other recent paper we need to review, Martijn Cremers’ August 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 from 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 percent or greater to funds with an active share below 60 percent. He also compared performance against two factor models, a seven-factor model (which uses the market factor, small and mid-cap size factors and small, mid-cap, 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.

The following is a summary of his findings:

  • Using the 7-factor model, the quintile of funds with the highest active share had an abnormal (unexplained) return of 0.71 percent 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 4-factor model, the high active share quintile’s abnormal performance was -0.36% per year, with a t-statistic of -0.49.

With these two findings it seems hard to make 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 5×5 sort on active share and fund holding duration (a measure of the average holding period of the fund), the annualized 7-factor and 4-factor intercepts for the high active share/high duration (low turnover) portfolio are 1.88 percent and 1.69 percent, respectively. The corresponding t-statistics are 2.35 and 1.71. However, as mentioned earlier, a caution is noted in that the chart of the cumulative abnormal 7-factor performance over time indicates that it peaked around 2002 and has declined since then. Cremers did note that the high active share/low turnover funds did outperform from 2007 through 2013, while they underperformed from 2002-2006 and again from 2014 through 2015.

Cremers concluded that while he believes that active share matters, both in large cap and small cap funds, investors should use only funds with low turnover (under 50 percent). 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 percent per year in the lowest quintile, and 1.79 percent in the fifth quintile. Thus, investors should consider active funds that have high active share and low turnover. A more detailed analysis of this concept is provided in Cremers and Pareek (2016), “Patient Capital Outperformance: The Investment Skill of High Active Share Managers Who Trade Infrequently.” (Alpha Architect review is here).

Given that the chart in the paper seemed to indicate that the outperformance had occurred prior to 2002 (see figure below), I contacted Professor Cremers and asked him if he had the performance for the period 2002 through 2015.

He provided me with the table below which shows the results for the 2002-2015 period for the active share quintile portfolios (quintile 1 is the lowest active share) using the 7-factor model:

Quintile 1 2 3 4 5
Alpha -1.05 -1.11 -1.43 -0.68 -0.50
T-Stat (4.4) (4.2) (5.4) (2.0) (1.1)

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 you are going to use an active manager you are better served by choosing one with a high active share. However, it also shows that while perhaps it was once true that active share predicted future outperformance, that time may have gone with the wind.

This evidence is entirely consistent with the thesis of the book I co-authored with Andrew Berkin, The Incredible Shrinking Alpha. In our book we provide the evidence and the 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.

Some Parting Thoughts on Active Share as a Prediction Measure

You can decide for yourself whether you find the evidence on active share compelling enough to use actively managed funds. With that said, 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. With that said, 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.

I want to own a fund that provides me with exposure to factors I care about such as market beta, size, value and momentum. I’m then happy to have minimal alpha so long as I get the beta (loading on a factor I am seeking) which leads to higher returns. In other words, I would rather own a low-cost, passively managed small value fund that provides me with high loadings on those factors, and minimizes or even eliminates the negative exposure to momentum that is typical of value funds, and has no alpha, than an active fund with less exposure to those factors even if it generates a positive alpha — the 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 Dimensional Fund Advisors passively managed DFA U.S. Large Cap Value III Portfolio (DFUVX). I chose these funds because they both have long track records, VEIPX is currently a five-star rated fund by Morningstar, Vanguard’s active funds generally have the lowest fee, and DFA is a leader in passively managed, structured portfolios. The data is from the first full month of performance for DFUVX, March 1995 and ends April 2017 (the latest date factor data was available as of this writing). The annual factor premiums are calculated based upon data from French’s site. The alpha, standard deviation, and Sharpe ratio are annualized from monthly data.

March 1995-April 2017 VEIPX DFUVX Annual Factor Premium
Market Beta  0.79  1.04 6.0
Size -0.21  -0.03 4.3
Value  0.36  0.56 6.1
Momentum -0.03  -0.10 12.3
Annual Alpha  1.35  0.21
R-squared  90  93
Annualized Return 10 10.8
Annualized Standard Deviation 12.8 17.5
Sharpe Ratio 0.63 0.54

First, note that the r-squareds are very high, indicating that the model is doing a good job of explaining returns. Second, as you can see, while VEIPX produced a positive annual alpha of 1.35 percent and DFUVX produced a much lower alpha of 0.21 percent, a difference of 1.14 percent, DFUVX outperformed 10.8 percent versus 10 percent. The reason for the outperformance is clear. In general, DFUVX had much higher (or less negative in the case of the size factor) loadings on factors that delivered premiums. The exception was that DFUVX had a slightly more negative loading on momentum. These differences in loadings allowed DFUVX to overcome the 1.14 percent difference in alpha. The higher loading on market beta provided about 1.5 percent in incremental returns, the higher loading on size provided about 0.8 percent in incremental returns and the higher loading on value added about 1.2 percent. (Note that the incremental differences account for the difference in returns for the average monthly return, but doesn’t necessarily foot to the compounded returns.) It’s also worth noting that DFUVX fund also benefited from a slightly lower expense ratio, currently 0.13 percent versus 0.26 percent for VEIPX.

The bottom line is that 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.


Note: This site provides NO information on our value investing ETFs or our momentum investing ETFs. Please refer to this site.


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Definitions of common statistics used in our analysis are available here (towards the bottom)

References   [ + ]

1. The team at O’shaughnessy Asset Management also have thoughts on the contentious debate.



About the Author

Larry Swedroe

Larry Swedroe

As Director of Research for Buckingham and The BAM ALLIANCE, Larry Swedroe spends his time, talent and energy educating investors on the benefits of evidence-based investing with enthusiasm few can match. Larry was among the first authors to publish a book that explained the science of investing in layman’s terms, “The Only Guide to a Winning Investment Strategy You’ll Ever Need.” He has since authored seven more books: “What Wall Street Doesn’t Want You to Know” (2001), “Rational Investing in Irrational Times” (2002), “The Successful Investor Today” (2003), “Wise Investing Made Simple” (2007), “Wise Investing Made Simpler” (2010), “The Quest for Alpha” (2011) and “Think, Act, and Invest Like Warren Buffett” (2012). He has also co-authored seven books about investing. His latest work, “Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today,” was co-authored with Andrew Berkin and published in October 2016. In his role as director of research and as a member of Buckingham’s Investment Policy Committee and Board of Managers, Larry, who joined the firm in 1996, regularly reviews the findings published in dozens of peer-reviewed financial journals, evaluates the outcomes and uses the result to inform the organization’s formal investment strategy recommendations. He has had his own articles published in the Journal of Accountancy, Journal of Investing, AAII Journal, Personal Financial Planning Monthly and Journal of Indexing. Larry’s dedication to helping others has made him a sought-after national speaker. He has made appearances on national television shows airing on NBC, CNBC, CNN and Bloomberg Personal Finance. Larry is a prolific writer and contributes regularly to multiple outlets, including Advisor Perspectives and ETF.com. Before joining Buckingham and The BAM ALLIANCE, Larry was vice chairman of Prudential Home Mortgage. He has held positions at Citicorp as senior vice president and regional treasurer, responsible for treasury, foreign exchange and investment banking activities, including risk management strategies. Larry holds an MBA in finance and investment from New York University and a bachelor’s degree in finance from Baruch College in New York.


  • Eau de Javelina

    Excellent post illustrating how data can be unintentionally manipulated or misinterpreted.

  • Hijo de la Luna

    interesting post. This research seems to focus on active funds in the US stock market; do you know any good studies on the European or EM markets? (since these markets seem less efficients and thus easier to beat). Also, I was not able to access the Vanguard research you refer to – perhaps there’s a problem with the link?

  • Sam Zaydel

    I think there is merit to this research, at least intuitively some aspects make sense. However, having read some of the research so far it feels very much like data mining more-so than hypothesis validation. I may simply be lacking necessary facilities and understanding, and so am relying more heavily on understanding the stats and data analysis side of this than on I perhaps would have if I better understood many of the complexities which doubtless all the bright folks doing this work understand and accounted for in some form.

    But, it still feels like there is data mining happening and hypothesis seems to evolve, then followed by in some instances different datasets being used to see if new theory sticks. I recognize the unique complexities like not having enough data, varying performance periods, and what seems to be missing from conversation are technological improvements and evolution of tools and systems that may not have even existed during in-sample as opposed to out-of-sample period, or perhaps was gradually gaining momentum, with increasing affect on the data, whereby later time-series data is far more heavily impacted than earliest data.

    But, I will say this… If I am a manager who purposely builds a concentrated portfolio of the most overpriced assets, which also happen to be priced at some peak, after which point those assets begin to mean revert, and for a long period underperform the broader market, in addition to which I am charging fees and have accrued trading costs, etc., I am surely going to have high active share, and may have high conviction, but performance is going to suck.

    Active share offers reasonable intuition for why it might make sense, but I would not use it as the only criteria, but perhaps one of the several criteria when I am choosing a fund. It may be that a better gauge on whether active share is meaningful to consider periods of market downturn. In other words, are concentrated portfolios or those significantly different from benchmarks able to protect invested capital, or are they on part, or worse than their benchmark indexes?

  • Sam Zaydel

    I tend to take issue with the idea that indirectly Active Share, or the concentration, or conviction is a useful predictor of much. Concentration makes sense, and I think the key in my mind anyway is that concentration is about the only way to achieve much greater than market returns. The whole point of concentration is to really ride a small number of winners, to which there is a large percentile allocation in the portfolio. I think of concentration as opposite of diluting potential returns.

    Having said that, it seems to me that having high conviction is not really a meaningful measure of performance, or any way of predicting future returns. Active share to me is a measure of how different, not how much worse or better. How different does not necessarily say very much about whether being positively different one year means in following years difference remains positive and same opportunities continue to exist that made the initially observed positive difference, positive in the first place.

    And of course, if I am a manager who chooses to invest in the most overpriced securities at some peak point, right before market corrects and I choose to hold on to these investments, because I have high conviction in them, I may just be un-lucky enough to ride the mean reversion wave for long periods of time, as my “purchased at the peak high conviction portfolio” with a high Active Share measurement is losing ground, perhaps significantly more than the rest of the market.

    I think any good tool must be able to stand up to some scrutiny and if all it takes to screw with results is badly timed investments, I feel like to tool should not be depended upon, certainly not in isolation.

  • I tend to agree. At a high level, active share is really a necessary, but not a sufficient condition to win over the long-haul. I guess the academic debate of is, well…an academic debate…interesting to ponder but I’m not sure how useful it all might be.

  • Sam Zaydel

    I feel like Active Share is something you could use to condition your probability of outperformance or underperformance upon. I can envision one day there being super-algorithms capable of using hundreds or thousands of factors, each accounting for some small weight when measuring performance historically as a means of projecting into the future.

    I think one other factor that maybe does not receive enough attention is changes in technology, which are only becoming more rapid. It is certain that technology is affecting performance of markets, possibly by making some things more efficient, while at the same time creating systematic inefficiencies through commonly used tools/algorithms, which may all be subject to some specific bias or some myopic choice made by initial designer/adopter. It is quite possible that this heavily factored into this research as well. Technology could very well have been contributing to closing any performance advantages that active managers used to have, perhaps just through various brute force methods of data analysis, which 10 years ago few could have implemented, yet today I can do with some dedicated time in one of the compute environments online, like Azure Machine Learning and the like.

  • Almost certainly there will be an algo which, for argument’s sake, can give an investor a totally unbiased and highly accurate forecast of a firm’s 10 year expected cash flow projections and the appropriate terminal value. However, I’m not sure that will make anyone a better investor…

  • Sam Zaydel

    I think what you are saying is more information does not always lead to better outcomes. I can speak from the computer science perspective that at times we are awash in data, and it still often results in less than desired outcomes. With computers however, unlike with markets, we can reproduce some test enough time to be highly confident about our data and perhaps have a good chance for explaining its meaning.

    Ultimately, more is probably better than less. And, perhaps as a reinforcing argument to your position, algorithms are a procedure or a heuristic, which results in some computation, but I don’t think deriving meaning out of something could be truly algorithmic, at least in the way that we think of algorithms today. Algorithms I think are great when you are handed a very predictable working environment with well defined boundaries and predictable outcomes, like 2+2 always being 4. But from all that I have seen so far, no algorithm can exist that will stand-up to other algorithms which continuously change patterns in the market upon which your algorithm depends. The basic requirement of 2+2 always being 4 cannot be depended upon, and as such I think algorithms at best will only be good enough for some stretches of time, which will increasingly become shorter and shorter, probably as a function of machines getting faster and faster. 🙂

  • Hi Sam,
    Everything you are saying makes a lot of sense. But there is something even deeper here. The real issue with investing is that humans are always involved (even if they don’t control the algos they pick which algo gets their money). Our “God” post makes this remarkably clear — even the perfect algorithm would get fired by most humans. See here: http://blog.alphaarchitect.com/2016/02/02/even-god-would-get-fired-as-an-active-investor/
    Arguably, God could convince his human investors via a good story that he’s got their back. But an algo? Algo aversion is a real thing. Computers don’t tell great stories. Humans love computers when they work, but immediately think they are broken when they catch a bad draw….even though the ex-ante “best bet” is arguably the machine. In some arenas humans have gotten used to algos, but finance/investing is an emotional game with lots of baggage. People still have an innate urge for trust and computers are hard to trust.
    We’ll have to see how it all plays out. I’m a mega fan of technology and tools, but I’m also super long human nature and insanity.

  • Sam Zaydel

    Thank you for thoughtful and thought-provoking responses Wes. I think my take away is that evolution is a far more powerful force than any algorithm could ever hope to be. I think the generation notion of being human is a terminal condition applies here. Neural pathways that I am sure you are referring to are same pathways that were continually reinforced through the eons that allowed us to exist, to persist and to continue to evolve into the irrational creatures that we are today! Is there a better solution than letting machines take over the world and do away with humanity? 🙂

  • So you’re going to believe what AQR publishes? Torture the numbers long enough and they’ll tell you anything. AQR is trying to defend their low market returns while they’re stuck in Quant 1.0, and their investors are redeeming to invest in Quant 2.0 funds.

  • Sam Zaydel

    This is a case of correlation being interpreted as a robust metric that has predictive power. Ultimately, if Active Share is a real thing, pretty soon it won’t be, if you believe that markets over time exploit and eliminate various opportunities. And, if it appears to still persist for many years, chances are it is still correlation without well understood causal relationship. And if it does go away, you could make the argument that it was a real thing and markets eventually cracked that nut.

  • Larry Swedroe

    Hijo
    Just back from a vacation and I tried that link and unfortunately Vanguard has changed the link on that study.
    You might try their research section and search for the date or active share.
    As to other markets, there is really no evidence I’m aware of that shows EM or other developed international markets are less efficient than US markets, at least in terms of ability to deliver alpha (after expenses), as any greater inefficiencies offset at least by higher costs of implementation.
    Great example is the following Morningstar rankings, adjusted for survivorship bias (which I corrected for) for the 15 years ending 2016, using DFA value funds, from recent research I did

    U.S. Large Value III (DFUVX) 1
    U.S. Small Value (DFSVX) 5
    International Value III (DFVIX) 2
    International Small Value (DISVX) 1
    Emerging Markets Value (DFEVX) 2

    Larry

  • Hijo de la Luna

    Thanks Larry for getting back. I didn’t know DFA funds has such good performance – but since you have to purchase them through and adviser I imagine that after the adviser fees the advantage (relative to Vanguard say) might disappear (?) (unless one was going to have an adviser anyway).
    I found an article of yours (Not A Stock Picker’s Market. Again) in which you show the Morningstar rankings and also make the great point of style drift. I think this explains in Europe the recent outperformance of many active funds relative to their large cap benchmark, because they own small and mid cap stocks that have hugely outperformed large caps in Europe since 2008.
    Also interesting from that piece that the Vanguard value funds fare significantly worse than the DFA. They must use different ratios which have an impact on performance.

  • Larry Swedroe

    Hijo
    My pleasure. Couple of things.
    First, re DFA and Vanguard, DFA advantage is mostly, though not all, is that they have higher loadings on the factors that explain performance. But also not being a pure indexer can do algo type patient trading to lower trading costs and use some intelligent design to eliminate the anomalies that plague long only funds (like small cap growth stocks with low profits and high investment, penny stocks, IPOs, etc).
    Second, of course if going to hire an advisor and want to gain exposure than using DFA (or others like Bridgeway and AQR) is superior choice to Vanguard. Whether they cover the advisory fees depends on how much you tilt, the more you tilt to factors the lower the hurdle becomes. But advisors can also add value in many ways that most investors would benefit from, so in that sense it is a “free lunch.”
    Best wishes
    Larry

  • Michael Plitrovsky

    I’m a bit puzzled that the researchers attempt to predict performance using active share. My intuition is that active share indicates (to an extent) independent thought, which is a prerequisite to alpha, but is not sufficient. So if I were a researcher, I’d check whether active share predicts consistency of alpha over time, after subtracting the factor effects. This alpha might be frequently negative, even if the active share is high.

    Finding a subset of funds with a consistent alpha is, of course, extremely useful. It means that past performance becomes, within the subset, indicative of future returns.

  • Jack Vogel, PhD

    The picture above (sourced from the paper) uses a 7-factor model to account for portfolio differences along factor characteristics. Using a 7-factor model should account for factor effects.

  • Michael Plitrovsky

    Yes, but the researchers tried to predict performance, rather than consistency of performance. I humbly suggest that that might be the wrong outcome to predict with active share.