12 Worthy Nondividend Smart Beta ETFs

April 06, 2015

This article is part of a regular series of thought leadership pieces from some of the more influential ETF strategists in the money management industry. Today's article is by John Eckstein, chief investment officer and director of research at Astor Investment Management.

 

As I threatened to do in my last column published on ETF.com, I return with a second round of smart beta analysis. Last time I focused on dividend strategies; this time I’ll look at nondividend strategies. I found the best-performing fund surprising, and I have a story that might explain why it has done the best.

 

Recalling The Lessons On Backtests

First, a quick refresher on what I did last time. I chose to use the funds’ backtests from before their launches. A backtest is produced with the advantage of history shaping the index rules. With that in mind, if a “smart beta” fund were to launch today, you can bet it would have a simulated history. 

 

The history is a careful application of the indexes' rules to the past, but in looking at index returns before and after they go live, I found a statistically significant relationship in the direction of increased beta in real time and a statistically marginal relationship between alpha before and after launch. 

 

In other words, the typical fund in my analysis has performance closer to the market with lower idiosyncratic returns than in the simulation.

 

But even considering that, I’ll still look at the longer history that the backtests offer because they have more than double the amount of data we can study. Also, using backtests allows us to look at performance around the financial crisis.

 

As I did last time, I’m going to define “smart beta” by taking both words seriously. 

 

Defining Smart Beta

I take "beta" to mean that the strategy has to be a modification of some major index. The "smart" in “smart beta” means abandoning the cap-weighted standard and finding some other sort of way to weigh constituents.

 

The new weighting techniques I’m considering here are mainly based on one or more factors.

These factors have been identified in the academic literature as being some well-defined characteristic of stocks that can be used to sort stocks from highest to lowest.

 

An example is value, which may be defined as price-to-book ratio. You could rank every stock from the one with the highest price to book ratio to the lowest and call, say, the 30 percent of stocks with the lowest price-to-book ratio value stocks. 

 

The smart-beta strategies I’m considering here use one or more of these factors to reweight an index or select a subset of stocks. 

 

Strategies, Not Labels

The universe of ETFs I am looking at today are the “smart beta” ETFs that—unlike in my previous article—do not weight by dividends. I choose the biggest ETFs, with at least $200 million in assets and—crucial to my purposes here—have indexes calculated back to at least the year 2000. This gives me 12 funds and their associated indexes to examine.

 

When I downloaded the data from ETF.com’s ETF Screener & Database, these funds have total assets of about $26 billion. That’s about a third of the assets in the dividend-focused ETFs that I examined in my previous article.

 

It’ll take some effort to discuss the different philosophies expressed by the various funds presented here. 

 

What I mean to say is that no one should be looking to invest in a “smart beta” per se. That’s because the term encompasses a variety of philosophies, or strategies, really. And in some cases, these various strategies can in some sense cancel each other out.

 

For example, if you put an equal investment in the 12 funds I examine here, you would end up with S&P 500-like exposure (beta around 0.9) with a slight value exposure of about 15 percent. 

 

  Compound Annual Growth Rate 2001-2014 Annualized Risk-Adjusted Returns Worst Draw-down Compound Annual Growth Rate 2010-2014 Annualized Risk- adjusted returns IndexLive Weighing Scheme
FEX 9% 0.54 -54% 16% 1.04 2007-04 Composite (growth & value)
TILT 7% 0.42 -53% 15% 0.96 2011-09 Composite (size & value)
RSP 8% 0.46 -55% 16% 1.05 2003-01 Equal Weight
IUSG 4% 0.24 -50% 15% 1.06 2000-03 Growth
MTUM 7% 0.44 -52% 17% 1.23 2013-02 Momentum
QUAL 6% 0.42 -41% 15% 1.15 2012-12 High Quality
SPHQ 7% 0.49 -54% 18% 1.4 2010-05 High Quality
IUSV 7% 0.41 -56% 15% 1.01 2000-03 Value
VLUE 6% 0.35 -56% 15% 1.02 1997-12 Value
SPLV 9% 0.82 -36% 16% 1.61 2011-04 Volatility
USMV 7% 0.57 -41% 16% 1.62 2008-06 Volatility
SIZE 8% 0.58 -49% 17% 1.37 2011-06 Size
 
SPY 5% 0.33 -51% 15% 1.08 1954-01  

 

Nondividend smart-beta ETFs, performance of index tracked by funds, adjusted for fees 2001-2014. Source: Bloomberg, ETF.com, Astor calculations.

 

 

Making Sense Out Of Variety

What does make sense is to find a fund that fits a goal you have for your portfolio, whether it’s more small-cap or some value exposure. I hope to give readers a lay of the land for some of the more important products. 

 

  • Momentum – invests more in stocks that have been rising in price over the past six and 12 months.
  • Value – invests in the cheaper companies in the index as determined by familiar metrics such as book value or cash flow levels. This is probably the most familiar factor to investors.
  • Growth – while this looks at forecasting increasing growth, it also looks at companies that have high price-to-book values. As such, it can usefully be thought of as a complement to value-tilted funds. It has the same beta but the opposite exposure to value stocks. Both growth and value funds have had very low alpha (returns independent of the broad stock market) compared with the other funds.
  • Quality – While intuitive to market participants, this factor has only recently become more academically acceptable as it has been embraced by the grandfathers of factor investing, Eugene Fama and Kenneth French. As an example, quality indexes select stocks with low debt-to-equity ratios or high return on equity. The stocks selected by a quality screen tend to be skewed toward large-caps.

 

The Interesting Case Of Minimum-Volatility Funds

The volatility-minimizing funds have done the best over both the long and short periods we studied.

 

They have had returns as good or better than the S&P 500, with significantly higher risk-adjusted returns and lower drawdowns during the financial crisis of 2008-2009. 

 

This is actually a bit of a problem for finance types, as what should happen is that higher-volatility stocks are expected to deliver a higher return, at least over a long period of time, to compensate holders for the extra risk.

 

We see the opposite in reality and, in fact, if you look at longer periods in stocks or if you add other securities like bonds into the mix, you would find the same puzzling pattern of low-volatility stocks beating the market.

 

Risk Parity Portfolios

This empirical observation—the outperformance of low-volatility stocks—is also what’s behind another recent market trend; namely, risk parity portfolios. For an accessible description of risk parity portfolios, see the article by Cliff Asness, Andrea Frazzini and Lasse Pedersen, “Leverage Aversion and Risk Parity.”

 

In addition to documenting the phenomenon, Asness and his colleagues suggest a reason it may exist: leverage aversion. Because most investors cannot or will not employ leverage, they often need to make the poor substitute of high-beta stocks. 

 

The leverage-aversion story makes sense to me, but in his very worthwhile book, “Asset Management,” Columbia professor Andrew Ang suggests that leverage aversion may account for why high beta (and hence more volatile) stocks are overpriced, and not why low-volatility stocks are underpriced. Ang also points out that indexing itself can lead to managers being penalized by being overweight low-beta stocks in terms of increased tracking error.

 

Whatever the source of this pattern of returns, it is longstanding and, to my mind, the most interesting type of the “smart beta” funds. 

 

 

Composite Funds

Composite funds use more than one factor in adjusting their weights. 

 

For example, the First Trust Large Cap Core AlphaDex Fund (FEX | B-81) includes both value and growth factors, while the FlexShares Morningstar U.S. Market Factor Tilt ETF (TILT | A-87) tilts toward both small-cap and value stocks.

 

Equal-weight funds might be usefully considered as composite funds as well. 

 

By far the largest fund in this group—with 40 percent of the total assets—is the Guggenheim S&P 500 Equal Weight ETF (RSP | A-84). With RSP, investors are getting a hybrid tilt to their portfolios here as well as extra exposure to the smaller companies in the S&P 500.

 

More surprisingly, RSP investors are getting a substantial value tilt as well, perhaps as a consequence of rebalancing away from companies whose prices have risen. I suspect that the extra exposure to value is a function of the underlying index, because when I run the same analysis on the First Trust Nasdaq-100 Equal Weighted ETF (QQEW | B-63), it shows the opposite tilt; that is, away from value stocks.

 

Another fund that has composite effects that are not immediately apparent is the iShares MSCI USA Size Factor ETF (SIZE | B-82). While this fund overweights the smaller stocks in its universe, it also uses volatility to set position sizes, favoring the lower-volatility stocks. The end result is that SIZE only has a modest exposure to the traditional small-cap outperformance factor, that Kenneth French of Dartmouth was instrumental in articulating.

 

Conclusion & Next Steps

If there’s one thing the industry can agree on, it’s the regret that the “smart beta” label was ever coined and, worse yet, has proven to be so resilient. We can't change that now. What we can do is decide if some of these “smart beta” strategies make sense in our portfolios.

 

For an index-based portfolio, I think several of the strategies presented here have the chance to improve a client’s outcome. But I suggest starting from the point of view of what’s missing from a portfolio and looking to find a factor exposure that can fill that gap. 

 

An additional caveat is that all of the strategies only pick out the stocks that are the best by their metric, disregarding the aggregate. The value strategies, for example, will select the relatively most undervalued stocks, but it could be the case that the market as a whole is overvalued. 

 

More specifically, I think careful consideration needs to be given to substituting the minimum-volatility portfolios for some amount of core equity holdings that are currently in an index. The outperformance is longstanding and widespread—though, critically, that doesn’t necessarily mean it will continue into the future.

 

We have one more painful mile to go in our journey, as next month I will return with an analysis of what ETF.com classifies as alpha-seeking strategies.

 


At the this article was written, the author’s firm owned FEX and RSP.

Astor Investment Management is a money manager with an active and economically grounded approach to asset allocation. We believe investment opportunities arise based on the ability to identify fundamental trends and changes in the economy. We build portfolios of ETFs appropriate for our analysis of the business and monetary policy cycles. For more information, see www.astorim.com; for our blog, see www.astorinsights.com.

 

 

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