Swedroe: Do ETFs Harvest Factors & Shrink Premiums?

March 31, 2017

Financial research has uncovered many relationships between investment factors and stock returns. For investors, an important question is whether the publication of this research can impact the future size of factor premiums. Asking this question is crucial on two fronts.

First, if anomalies are the result of behavioral errors, or even investor preferences, and the publication of research into them draws the attention of sophisticated investors, it’s possible that post-publication arbitrage would cause the premiums to disappear.

Those seeking to capture these identified premiums could quickly move prices in a manner that reduces the return spread between assets with high and low factor exposure. However, limits to arbitrage, such as aversion to shorting, and its high cost, can prevent arbitrageurs from correcting pricing mistakes. And the research shows that this tends to be the case when mispricing exists in less liquid stocks, where trading costs are high.

Factor Return Decay

Second, even if the premium is fully explained by economic risks, as more cash flows into the funds acting to capture the premium, the size of the premium will be affected. At first, publication will trigger inflows of capital, which drive prices higher and thus generate higher returns. However, these higher returns are temporary because subsequent future returns will in turn be lower.

R. David McLean and Jeffrey Pontiff, authors of the January 2015 study, “Does Academic Research Destroy Stock Return Predictability?” re-examined 97 factors that had been published in tier-one academic journals and were only able to replicate the reported results for 85 of them. That the remaining 12 factors were no longer significant could have been due to a variety of reasons, such as incomplete details in the original paper or changes in databases.

They also found that, following publication, the average factor’s return decayed by about 32%.

Institutions Can Correct Anomaly Pricing

Paul Calluzzo, Fabio Moneta and Selim Topaloglu contributed to our understanding of how markets work and become more efficient over time (the adaptive markets hypothesis) with their December 2015 study, “Anomalies Are Publicized Broadly, Institutions Trade Accordingly, and Returns Decay Correspondingly.” They hypothesized: “Institutions can act as arbitrageurs and correct anomaly mispricing, but they need to know about the anomaly and have the incentives to act on the information to fulfill this role.”

To test their hypothesis, they studied the trading behavior of institutional investors in 14 well-documented anomalies. I identify and explain those anomalies in my October 2016 blog, “Published Results Impact Future Results.” Note that many of them are either specific examples of well-known investment factors or are explained by those factors.


What Their Study Found

To determine if investors exploit these anomalies and help bring stock prices closer to efficient levels, Calluzzo, Moneta and Topaloglu constructed portfolios from them that were long stocks with positive expected returns and short stocks with negative expected returns. Their study covered the period January 1982 through June 2014. Following is a summary of their findings:

  • For both the annual and quarterly versions of the anomalies, trading with them was profitable during the original in-sample period. The alpha of the portfolio equally weighted across each of the anomalies was 1.54% per quarter.
  • Raw returns in the period after publication decay to an average of 1.05%, a 32% relative reduction (matching the findings of McLean and Pontiff). Using the Fama-French three-factor model, there is a reduction in nine of the 14 anomalies.
  • During the in-sample, prior-to-publication period, institutional investors did not take advantage of stock return anomalies.
  • In the post-publication period, institutions trade to exploit the anomalies. The net change in aggregate holdings (the change in the long leg minus the change in the short leg) is positive.
  • Partitioning institutional investors into hedge funds, mutual funds and others, the results are strongest among hedge funds and then among actively managed mutual funds with high turnover.
  • There is a significant negative relationship between institutional trading and future anomaly returns in the ex-post portfolio. Institutional trading after anomaly publication is related to the post-publication decay in anomaly returns. That is, as institutional trading increases, subsequent anomaly returns decrease.
  • There is a significant increase in trading by hedge funds in the period just before publication, suggesting that hedge funds have knowledge about the anomalies prior to journal publication (likely through presentations at conferences or from postings on the Social Science Research Network).

The authors concluded: “Institutional trading and anomaly publication are integral to the arbitrage process, which helps bring prices to a more efficient level.” Their findings demonstrate the important role that both academic research and hedge funds (in their role of arbitrageurs) play in making markets more efficient.

Are ETFs Harvesting Factors?

David Blitz contributes to the literature with his February 2017 paper, “Are Exchange-Traded Funds Harvesting Factor Premiums?” He notes that while some exchange-traded funds are specifically designed for harvesting factor premiums, such as the size, value, momentum and low-volatility premiums, other ETFs implicitly go against these factors.

Blitz explains: “Most ETFs, however, are not factor-based but based on a different philosophy. Many ETFs, for example, target a specific sector. Implicitly this also tends to bring along factor exposures, although not necessarily in the right direction from a factor-investing perspective.”

For example, he found that eight of the 10 ETFs with the most negative HML (the return on high book-to-market stocks minus the return on low book-to-market stocks, or value factor) exposures are health care and biotechnology sector funds, reflecting large differences in exposures to factors across sectors.


Similarly, ETFs focusing on the utilities and consumer staples have high exposure to the low volatility factor, as do high-dividend ETFs. On the other hand, biotech, information technology and energy all had large negative exposures to this factor.

Blitz’s sample consists of all U.S.-listed ETFs that invest in U.S. equities and that have at least 36 months of return history as of December 2015. This amounts to 415 distinct funds with combined assets under management of more than $1.2 trillion, about 5% of the entire U.S. equity market.

ETFs With Large Positive & Negative Exposure

He analyzed the factor exposures of these ETFs and found that “for each factor there are not only funds which offer a large positive exposure, but also funds which offer a large negative exposure towards that factor.”

He also found: “On aggregate, all factor exposures turn out to be close to zero [ranging from -0.03 to 0.03], and plain market exposure is all that remains. This finding argues against the notion that factor premiums are rapidly being arbitraged away by ETF investors and also against the related concern that factor strategies are becoming ‘overcrowded trades.”

Blitz found that the so-called smart-beta ETFs have positive exposures toward the size, value, momentum and low-volatility factors, with the largest and most significant exposure being toward the size factor. That must mean that the conventional ETFs have significant negative exposures toward the size and value factors. And that is just what he found.

‘Betting Against Each Other’

He concluded: “These results imply that, from a factor investing perspective, smart-beta ETFs tend to provide the right factor exposures, while conventional ETFs tend to be on the other side of the trade with the wrong factor exposures. In other words, these two groups of investors are essentially betting against each other.”

Blitz also concluded that while investors looking only at the tens of billions of dollars in smart-beta ETFs may be concerned about the sustainability of the low-volatility premium going forward due to overcrowding, the funds in question only represent a small fraction of the total ETF market. Looking at the other end of the spectrum, it turns out there are a similar number of funds that provide the exact opposite factor exposure. Looking at the entire ETF market, the net exposure toward the smart-beta factors is indistinguishable from zero.

These results imply that ETF investors, on aggregate, are not arbitraging the premiums away or making the trades overcrowded.

Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.


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