Fortunately, there is a reasonably easy way to test the veracity of this claim: run the same regression we did on GHS, but on multi-factor ETFs using a variety of explanatory factor indices.

Here is a quick outline of the Factors we will utilize:

FactorSourceDescription
Market – RFRFama/FrenchTotal U.S. stock market return, minus t-bills
HML DevilAQRValue premium
SMBFama/FrenchSmall-cap premium
UMDAQRMomentum premium
QMJAQRQuality premium
BABAQRAnti-beta premium
LV-HBNewfoundLow-volatility premium

Note: Academics and practitioners have yet to settle on whether there is an anti-beta premium (where stocks with low betas outperform those with high betas) or a low-volatility premium (where stocks with low volatilities outperform those with high volatilities).   While similar, these are different factors.  However, as far as we are aware, there are no reported long-short low-volatility factors that are publicly available.  We did our best to construct one using a portfolio that is long one share of SPLV and short one share of SPHB, rebalanced monthly.

We will test a number of mixed-approach ETFs and a number of integrated-approach ETFs as well.

Of those in the mixed group, we will use Global X’s Scientific Beta U.S. ETF (SCIU), Goldman Sachs’ ActiveBeta US Equity ETF (GSLC), as well as the inverse-volatility weighted factor portfolio (“NFFRPI”) employed here at Newfound as the basis of our U.S. Factor Defensive Equity portfolio.

In the integrated group, we will use John Hancock’s Multifactor Large Cap ETF (JHML), JPMorgan’s Diversified Return US Equity ETF (JPUS), iShares’ Edge MSCI Multifactor USA ETF (LRGF), and FlexShares’ Morningstar U.S. Market Factor Tilt ETF (TILT).

We’ll also show the factor loadings for the SPDR S&P 500 ETF (SPY).

If the argument from FTSE Russell holds true, we would expect to see that the factor loadings for the mixed approach portfolios should be significantly lower than the integrated approach portfolios.  Since SCIU and GSLC both target to have four unique factors under the hood, and NFFPI has five, we would expect their loadings to be 1/5th to 1/4th of those found on the integrated approaches.

The results:

Source: AQR, Kenneth French Data Library, and Yahoo! Finance.  Calculations by Newfound Research.

Before we dig into these, it is worth pointing out two things:

  • Factor loadings should be thought of both on an absolute, as well as a relative basis. For example, while GSLC has almost no loading on the size premium (SMB), the S&P 500 has a negative loading on that factor.  So compared to the large-cap benchmark, GSLC has a significantly higher
  • Not all of these loadings are statistically significant at a 95% level.

So do integrated approaches actually create more internal leverage?  Let’s look at the total notional factor exposure for each ETF:

total-notional-multi-factor

Source: AQR, Kenneth French Data Library, and Yahoo! Finance. Calculations by Newfound Research.

It does, indeed, look like the integrated approaches have more absolute notional factor exposure.  Only SCIU appears to keep up – and it was the mixed ETF that had the most statistically non-significant loadings!

But, digging deeper, we see that not all factor exposure is good factor exposure.  For example, JPUS has significantly negative loadings on UMD and QMJ, which we would expect to be a performance drag.

Looking at the sum of factor exposures, we get a different picture.

total-factor-exposure

Source: AQR, Kenneth French Data Library, and Yahoo! Finance.  Calculations by Newfound Research.

Suddenly the picture is not so clear.  Only TILT seems to be the runaway winner, and that may be because it holds a simpler multi-factor mandate of only small-cap and value tilts.

Conclusion

The theory behind the FTSE Russell argument behind preferring an integrated multi-factor approach makes sense: by trying to target multiple factors with the same stock, we can theoretically create implicit leverage with our money.

Unfortunately, this theory did not hold out in the numbers.

Why?  We believe there are two potential reasons.

  • First, selecting for a factor in a mixed approach does not mean avoiding other factors. For example, while unintentional, a sleeve selecting for value could contain a small-cap bias or a quality bias.
  • In an integrated approach, preferring securities with high loadings on multiple factors simultaneously may avoid securities with extremely high factor loadings on a single factor. This may create a dilutive effect that offsets the benefit of capital efficiency.

In addition, we have concerns as to whether the integrated approach may degrade some of the very significant diversification benefits that can be harvested by combining factors.

Ultimately, while an interesting theoretical argument, we do not believe that capital efficiency is a justified reason for preferring the opaque complexity of an integrated approach over the simplicity of a mixed one.

Client Talking Points

  • At the cutting edge of investment research, there is often disagreement on the best way to build portfolios.
  • While a strongly grounded theoretical argument is necessary, it does not suffice: results must also be evident in empirical data.
  • To date, the argument that an integrated approach of building a multi-factor portfolio is more capital efficient than the simpler mixed approach does not prove out in the data.