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Summary­

  • New research from Axioma suggests that tilting less – through lower target tracking error – can actually create more academically pure factor implementation in long-only portfolios.
  • This research highlights an important question: how should long-only investors think about factor exposure in their portfolios?Is measuring against an academically-constructed long/short portfolio really appropriate?
  • We return to the question of style versus specification, plotting year-to-date excess returns for long-only factor ETFs.While the general style serves as an anchor, we find significant specification-driven performance dispersion.
  • We believe that the “right answer” to this dispersion problem largely depends upon the investor.

When quants speak about factor and style returns, we often do so with some sweeping generalizations.  Typically, we’re talking about some long/short specification, but precisely how that portfolio is formed can vary.

For example, one firm might look at deciles while another looks at quartiles.  One shop might equal-weight the holdings while another value-weights them.  Some might include mid- and small-caps, while others may work on a more realistic liquidity-screened universe.

More often than not, the precision does not matter a great deal (with the exception of liquidity-screening) because the general conclusion is the same.

But for investors who are actually realizing these returns, the precision matters quite a bit.  This is particularly true for long-only investors, who have adopted smart-beta ETFs to tap into the factor research.

As we have discussed in the past, any active portfolio can be decomposed into its benchmark plus a dollar-neutral long/short portfolio that encapsulates the active bets.   The active bets, then, can actually approach the true long/short implementation.

To a point, at least.  The “shorts” will ultimately be constrained by the amount the portfolio can under-weight a given security.

For long-only portfolios, increasing active share often means having to lean more heavily into the highest quintile or decile holdings.  This is not a problem in an idealized world where factor scores have a monotonically increasing relationship with excess returns.  In this perfect world, increasing our allocation to high-ranking stocks creates just as much excess return as shorting low-ranking stocks does.

Unfortunately, we do not live in a perfect world and for some factors the premium found in long/short portfolios is mostly found on the short side.1  For example, consider the Profitability Factor.  The annualized spread between the top- and bottom-quintile portfolios is 410 basis points.  The difference between the top quintile portfolio and the market, though, is just 154 basis points.  Nothing to scoff at, but when appropriately discounted for data-mining risk, transaction costs, and management costs, there is not necessarily a whole lot left over.

Which leads to some interesting results for portfolio construction, at least according to a recent study by Axioma.2  For factors where the majority of the premium arises from the short side, tilting less might mean achieving more.

For example, Axioma found that a portfolio optimized maximize exposure to the profitability factor while targeting a tracking error to the market of just 10 basis points had a meaningfully higher correlation than the excess returns of a long-only portfolio that simply bought the top quintile.  In fact, the excess returns of the top quintile portfolio had zero correlation to the long/short factor returns.  Let’s repeat that: the active returns of the top quintile portfolio had zero correlation to the returns of the profitability factor.  Makes us sort of wonder what we’re actually buying…

Source: Kenneth French Data Library; Calculations by Newfound Research.

 

Cumulative Active Returns of Long-Only Portfolios

So, what does it actually mean for long-only investors when we plot long/short equity factor returns?  When we see that the Betting-Against-Beta (“BAB”) factor is up 3% on the year, what does that imply for our low-volatility factor ETF?  Momentum (“UMD”) was down nearly 10% earlier this year; were long-only momentum ETFs really under-performing by that much?

And what does this all mean for the results in those fancy factor decomposition reports the nice consultants from the big asset management firms have been running for me over the last couple of years?

Source: AQR. Calculations by Newfound Research.

We find ourselves back to a theme we’ve circled many times over the last few years: style versus specification.  Choices such as how characteristics are measured, portfolio concentration, the existence or absence of position- and industry/sector-level constraints, weighting methodology, and rebalance frequency (and even date!) can have a profound impact on realized results.  The little details compound to matter quite a bit.

To highlight this disparity, below we have plotted the excess return of an equally-weighted portfolio of long-only style ETFs versus the S&P 500 as well as a standard deviation cone for individual style ETF performance.

While most of the ETFs are ultimately anchored to their style, we can see that short-term performance can meaningfully deviate.

Source: CSI Analytics.  Calculations by Newfound Research.  Results are hypothetical.  Results assume the reinvestment of all distributions.   Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes, with the exception of underlying ETF expense ratios.  Past performance is not an indicator of future results.   Year-to-Date returns are computed by assuming an equal-weight allocation to representative long-only ETFs for each style.  Returns are net of underlying ETF expense ratios.   Returns are calculated in excess of the SPDR&P 500 ETF (“SPY”).  The ETFs used for each style are (in alphabetical order): Value: FVAL, IWD, JVAL, OVLU, QVAL, RPV, VLU, VLUE; Size: IJR, IWM, OSIZ; Momentum: FDMO, JMOM, MMTM, MTUM, OMOM, QMOM, SPMO; Low Volatility: FDLO, JMIN, LGLV, OVOL, SPLV, SPMV, USLB, USMV; Quality; FQAL, JQUA, OQAL, QUAL, SPHQ; Yield: DVY, FDVV, JDIV, OYLD, SYLD, VYM; Growth: CACG, IWF, QGRO, RPG, SCHG, SPGP, SPYG; Trend: BEMO, FVC, LFEQ, PTLC.  Newfound may hold positions in any of the above securities.

 

Conclusion

In our opinion, the research and data outlined in this commentary suggests a few potential courses of action for investors.

  • For certain styles, we might consider embracing smaller tilts for purer factor exposure.
  • To avoid specification risk, we might embrace the potential benefits of multi-manager diversification.
  • Or, if there is a particular approach we prefer, simply acknowledge that it may not behave anything like the academic long/short definition – or even other long-only implementations – in the short-term.

Academically, we might be able to argue for one approach over another.  Practically, the appropriate solution is whatever is most suitable for the investor and the approach that they will be able to stick with.

If a client measures their active returns with respect to academic factors, then understanding how portfolio construction choices deviate from the factor definitions will be critical.

An advisor trying to access a style but not wanting to risk choosing the wrong ETF might consider asking themselves, “why choose?”  Buying a basket of a few ETFs will do wonders to reduce specification risk.

On the other hand, if an investor is simply trying to maximize their compound annualized return and nothing else, then a concentrated approach may very well be warranted.

Whatever the approach taken, it is important to remember that results between two strategies that claim to implement the same style can and will deviate significantly, especially in the short run.

 

 

  1. Some may even argue that the factor premia themselves really just specters in the data, existing only due to limits of arbitrage (e.g. shorting costs) that prohibit investors from actually pursuing the returns.
  2. What is a Factor?  Part 2: The Impact of the Long-Only Constraint (https://go.axioma.com/rs/240-ASI-005/images/WhatIsAFactorPt2final1.pdf)

Corey is co-founder and Chief Investment Officer of Newfound Research. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University. You can connect with Corey on LinkedIn or Twitter.