Will ETFs Destroy Factor Investing? Nope.

/Will ETFs Destroy Factor Investing? Nope.

Will ETFs Destroy Factor Investing? Nope.

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(Last Updated On: August 18, 2017)

One of the popular investing truisms is the following (inspired by Bill Sharpe):

For somebody to beat the market (win) someone else has to lag the market (lose).

This becomes an even more daunting (efficient market) statement when changed to the following:

For someone to consistently beat the market (win) someone else has to be consistently willing to lag the market (lose).

This correctly implies that it is very hard to consistently beat the market and that in aggregate, it’s impossible.

Recent research (including this article by Wes) shows that the statement above doesn’t always hold…although its still true the majority of the time.

However, what interests me isn’t the minutia about how “passive” passive investors really are, but important questions like the following:

How can value investing work if everyone already knows about it and there are now a plethora of ETFs and mutual funds that follow that investing style?

I am not alone in asking this question, as none other than investing legend Cliff Asness asks and answers the same question here.(1)

Cliff provides some good insights, but doesn’t get down into the weeds by looking at who, if anyone, is volunteering to be on the losing team by being consistently short value, momentum, low volatility or size.  Perhaps we may never know…that is until I stumbled across this interesting paper by David Blitz that directly looks at the flows into the various factors.

Will ETF Factor Investing Destroying the Efficacy of Factors?

What David finds is that across the large ETF universe–400+ funds with at least 3 year track records–don’t have any meaningful factor exposures other than a beta of nearly 1.0 to the Mkt-Rf risk factor.

Looking further into the weeds, David divides the 400+ ETF universe into “smart beta” funds that explicitly look to target a risk factor (value or HML, momentum or WML, small or SMB and low volatility or LV-HV) and everyone else.  He includes fundamental weighted ETFs, equal weighted ETFs and dividend ETFs in the “smart beta” group.

He finds that when the ETF universe is divided in this way, there are roughly 100 “smart beta” funds and 300+ ETFs that are not “smart beta”.  The “smart beta” ETFs, in aggregate, have large and positive exposures to HML, SMB and LV-HV, but not WML (there aren’t many ETFs that target momentum and the ETFs that did have a positive factor loading on WML were sector funds which were likely caused by performance over the measurement period and not a targeted exposure to WML).  This is a good finding for the ETF owners as they are (in aggregate) getting exposure to the “smart beta” risk factors.

Here is a picture of the fund universe “HML” value factor loadings:

hml beta

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Note that in aggregate there really isn’t a specific movement strongly in favor or against value.

Here is a picture of the fund universe “WML” momentum factor loadings:

umd beta

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Momentum is really not exploited by ETFs, either directly, or in aggregate. This is pretty interesting, given momentum is arguably the strongest market anomaly.

The non “smart beta” ETFs have negative exposures to HML, SMB and LV-HV, but don’t show a meaningful negative exposure to WML.  This shows that there are (large) groups of investors who knowingly or unknowingly maintain negative exposure to “smart beta” factors.

There are several other interesting findings from this paper:

  • David found that many of the ETFs classified as “value” didn’t have strong loadings to the HML factor.  This highlights how important index and ETF portfolio construction are when trying to target a specific factor.
  • David found that the best HML factor loadings were from Energy and Financials sector ETFs.  This may be surprising at first, but HML is constructed using Price/Book Value and Energy and Financials typically have the lowest Price/Book Value of the sectors.
  • David found that (generally) ETFs that stated that they targeted the SMB or LV-HV factors generally had some of the largest factor loadings to those factors.

Note: If you’re new to factor investing, here is a good overview piece.

Are Exchange-Traded Funds Harvesting Factor Premiums?

  • David Blitz
  • A version of the paper can be found here.


Some exchange-traded funds (ETFs) are specifically designed for harvesting factor premiums, such as the size, value, momentum and low-volatility premiums. Other ETFs, however, may implicitly go against these factors. This paper analyzes the factor exposures of US equity ETFs and finds that, indeed, 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. On aggregate, all factor exposures turn out to be close to zero, 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’.

  • The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).
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References   [ + ]

1. Here is another take from Wes

About the Author:

Andrew Miller, CFA, CFP: Andrew is Chief Investment Officer at Miller Financial Management, LLC where he is primarily responsible for investment and financial planning research, asset allocation, and integrating client’s financial plans with their investment portfolio. Andrew is a Chartered Financial Analysis and is a Certified Financial Planner® practitioner. Andrew graduated from the Indiana University Kelley School of Business in 2004 with a Bachelors degree in Business Administration with a concentration in Finance. Andrew’s research interest is in using academic investment research to create investment portfolios that improve the withdrawal rates and financial outcomes for clients.
  • Jerry

    I think telling us what HML, WML, SMB and LV-HV stand for would be a meritorious addition to your article.

  • Hi Jerry, there is a note in the post highlighting what the symbols mean, but it is not easy to find.
    value = HML, momentum = WML, small = SMB and low volatility = LV-HV
    These are long/short factor portfolios built using the classic Fama/French construction — see page 128 on this document for details: http://home.uchicago.edu/~taelee/fama_french.pdf

  • Stu

    Interesting article. I’m curious where QVAL would be on those charts

  • Hey Stu,
    We can’t comment on ETF related issues on this part of our website. Sorry about that.

  • Govind

    The author looks at ETFs over the 5 year period of Jan 2011 through Dec 2015. The average look back is around the year 2013. There has been an enormous inflow of capital into factor-oriented ETFs since then. In 2015 a dozen specific multi-factor ETFs came to market. The enormous inflow of capital into factor-based ETFs that has been happening the last 2 years and is likely to keep happening over the next few years according to BlackRock makes the author’s conclusions about the lack of ETF influence based on earlier data suspect in my opinion.

  • appreciate the comments, but if you can provide data and robust analysis on your hypothesis it would be more useful. Perhaps you update their study through the last couple of years and see what the data actually say?

  • Govind

    Have you seen the paper on capacity constraints by Novy-Marx and Velikov? http://rnm.simon.rochester.edu/research/ToAatTC.pdf

  • Yes, an interesting paper. I try and read every paper related to frictional costs and we manage some “anomaly” strategies in real-time so we can get live data associated with frictional costs. I don’t think this research directly addresses the paper at hand, however, which highlights that supply/demand are often matched. This obviously has dramatic effects on frictional costs, and if the anomaly trader is a liquidity maker and the anti-anomaly trader is a liquidity taker — which I’d argue is frequently the case — ‘capacity’ questions can get very muddy.
    As I mentioned, love to see analysis over the last 2 years suggesting that the balance of factor tilts has dramatically shifted.