SPHQ for Quality

The Case for Investing in Quality

To someone like me who has a long-held belief in the efficacy of value investing, the idea of investing in “quality” seems counterintuitive.  After all, what makes value investing provide excess returns if not the “yuck factor” that causes investors to underprice value stocks?  On the surface, quality investing seems to be the opposite of value investing.

However, many famous investors include some notion of quality in their investing criteria, including some value investors.  Warren Buffett has cited good returns on equity, consistent earnings power, and low debt as elements that he considers, and is famous for saying that “it is far better to buy a wonderful business at a fair price than to buy a fair business at a wonderful price.”  

Over the past several years, academic researchers have been finding that quality matters, both as a stand-alone factor and in conjunction with other factors, particularly value.  For example, in an influential paper entitled “Quality Minus Junk,” AQR’s Asness, Frazzini and Pedersen (2014) found that “a quality minus junk (QMJ) factor that goes long high-quality stocks and shorts low-quality stocks earns significant risk-adjusted returns in the U.S. and across 24 countries.”  Their definition of quality involves quite a number of attributes, including profitability, growth, safety, and payout.   In a widely-cited 2012 paper, Novy-Marx found that a relatively simple measure of quality, gross profits to assets, provided “roughly the same power as book-to-market predicting the cross-section of average returns.”  (Book-to-market is perhaps the most widely recognized value factor.)  Kozlov and Petajisto (2013) describe high earnings quality as “one of the most robust long-term patterns documented in the literature (e.g., Sloan, 1996, and Fama and French, 2008).”   Studying the period 1988 to 2012, they found that quality had a higher Sharpe ratio (0.69) than either value (0.56) or the market (0.25).  Using a composite quality factor combining profitability, accruals, and leverage, they found that after controlling for market, size, and value (the Fama-French three-factor risk model), a long-short alpha of 7.8% per year was achieved.   Impressive results.

Theories to explain why high-quality stocks offer investors excess risk-adjusted returns vary.  Novy-Marx describes quality investing as “the other side of value” in that both value investors and quality investors seek to acquire assets undervalued by other investors.   Value investors count on the fact that the poor profitability of value firms tends to mean-revert to some extent over time.  Quality investors count on the superior profitability of quality firms to persist, and profit from the fact that investors tend to underappreciate, and underprice, high quality firms.      

SPHQ:  Our Favorite Quality ETF

The growing popularity of quality as a factor is reflected in the success of several ETFs that use various measures of quality as the focus of their portfolio construction.  Of those focused on the U.S. stock market, the largest and most liquid include:

• Invesco S&P 500 Quality ETF (SPHQ)
• iShares Edge MSCI USA Quality Factor ETF (QUAL)
• VanEck Vectors Morningstar Wide Moat ETF (MOAT)

This paper will focus on SPHQ because, at least at present, it is our preferred quality factor play.  While QUAL is the largest and most liquid of the three, it uses a sector-neutral index.   Although in a sense that makes it a “purer” play on quality, in our opinion, by neutralizing the sector tilts that would otherwise result, the quality effect is somewhat diluted.  MOAT takes its strategy from the Warren Buffet philosophy of buying companies with a “wide moat” that protects the corporation’s franchise value.  This factor is a variation on quality, certainly, but compared to SPHQ and QUAL, which both have an expense ratio of only .15%, with an expense ratio of .49% MOAT is noticeably more expensive.  In fact, we own all three, but because of its tactical attractiveness, SPHQ is our largest holding among the three.  

These three ETFs each have a different way of defining and measuring quality.  SPHQ recently (March 18, 2016) changed its underlying index from the S&P 500 High Quality Rankings Index (Bloomberg: SPXQRUT) to the S&P 500 Quality Index (Bloomberg: SPXQUT).  The new quality score is based on three fundamental measures:  return on equity, financial leverage ratio, and accruals ratio.   The three fundamental ratios are defined as follows:

• Return on Equity (ROE). This is calculated as a company’s trailing 12-month earnings per share divided by its latest book value per share.

• Financial Leverage Ratio. This is calculated as a company’s latest total debt divided by its book value.

• Accruals Ratio. This is computed using the change of a company’s net operating assets over the last year divided by its average net operating assets over the last two years. 

By the way, two of these three attributes, ROE and leverage, are the same attributes that QUAL uses in its definition of quality.  (QUAL’s third and final quality attribute is earnings variability.)  By using accruals as its third factor, SPHQ is tied more closely to the work of Sloan (1996) among others, showing that investors systematically over-emphasize the accrual components of GAAP earnings and under-emphasize the cash components, which are much more sustainable.  

In addition to its razor-thin expense ratio of .15%, SPHQ has a very high level of liquidity and an average bid-ask spread of only .03%.  The costs of owning and trading it are very low.  

SPHQ:  Disentangling Residual Return from Risk-Related Return

At Sapient Investments, we are very careful to separate returns, both historical returns and expected future returns, into their two major components:   1) risk-related returns and 2) residual returns (or “alpha”).  

We measure the sensitivity of a fund’s historical returns to four risk factors that capture much of the risk common to most ETFs:

• Stock market risk (MKT), as measured by the S&P 500 Index
• Bond market risk (LTB), as measured by the 10 Year Treasury Benchmark Index
• Currency risk (DLR), as measured by the U.S. Dollar Index
• Commodity risk (OIL), as measured by the West Texas Intermediate Crude Oil Index

In fact, because ETFs are passively managed based upon an index, sometimes we are able to use the historical returns of that index to get a much longer return history.  By subtracting the expense ratio from the historical return of the index, we can create a set of pro-forma ETF returns that are an excellent representation of how the ETF would have performed back in time.  This provides much more data with which to analyze the risks and evaluate the risk-adjusted returns of an ETF.

This methodology is also particularly handy when an ETF changes its benchmark index, as SPHQ did on March 18, 2016.  Even though the inception date of the ETF is December 6, 2005, any live fund returns before March 18, 2016 are of little value in describing how the fund is likely to perform in the future.  Consequently, we ignore live returns before that date and instead use pro-forma returns based upon the index minus the expense ratio.  

The graph below shows the historical risk factor sensitivities of SPHQ using this methodology.  SPHQ’s index history goes back to December 31, 1994, but we need some history in order to estimate its risk factor sensitivities (often called betas) using 36-month exponentially-weighted multiple regressions. Consequently, the graph starts on December 31, 1997.   Of the four risk factors, equity market beta (labeled MKT in blue) is its only consistently significant risk factor sensitivity.  Its historical equity market sensitivity has generally been between 70% and 100% (or a beta of .7 to 1.0).  The other three risk factors are not consistently significant, but interest rate sensitivity (labeled LTB in gold) does pop up occasionally.

The next graph (below) disaggregates SPHQ’s historical return into return due to each of the four risk factor sensitivities and residual return or “alpha.”   Most of the return is explained by its equity market sensitivity (blue), as expected for a fund with a MKT sensitivity of 70% to 100%.   (To calculate the return from MKT sensitivity, we simply multiply the fund’s previous month-end MKT sensitivity times the monthly price return of the S&P 500.  We use the same methodology for the other three risk factors.)  The residual return (black) is the total return minus the return from the four risk factor sensitivities.  Residual return, or alpha, is what we want to maximize in our portfolios.  

SPHQ generated an average alpha of about 2.55% per year since 1997.   That’s a very impressive number.  Most ETFs, and their benchmark indexes, have no discernible alpha—their return is entirely explained by risk factor sensitivities.  

SPHQ’s cumulative alpha peaked 2013-2014 and since then has trailed off.  However, our models lead us to expect that SPHQ will resume its history of positive residual returns in the near future.  Our models include factors that measure momentum in residual return itself, and clearly SPHQ does not have much of that.  However, our models always have a broad array of factors related to value, momentum, and quality.  In particular, the quality factors have had very high returns recently.  Not surprisingly, quality ETFs such as SPHQ, QUAL, and MOAT are heavily loaded on these factors.  We will take a closer look at one of those factors below.

Forecasting Residual Return

As is true of all funds, the residual return of SPHQ has had some definite variability.  There have been periods when it has been positive (when the black residual return line above has been upward sloping) and some when it has been negative (downward sloping).   

We spend a lot of time building models that help us to forecast residual return at Sapient Investments.  We try to identify sensible, fundamentally-based factors that help give us an edge.  The factors that we use are various ways of quantifying our three investment themes: value, momentum, and quality.  We get the data from FactSet, which provides a bottom-up aggregation of factor exposures for ETFs based upon the fact that the constituents of ETFs are public information that is available daily.  Thus, we are able to analyze ETFs in much the same way we would analyze individual stocks.     

We put factor-based ETFs like SPHQ into a research universe of other fairly similar ETFs.  The “alternative equity ETFs” universe includes ETFs focused on factors such as size, value, growth, volatility, momentum, quality, buybacks, insider activity, dividend yield, IPOs, and hedge fund holdings replication.  Most limit their holdings to U.S. stocks, but some include global or international holdings.  Currently, the alternative equity ETF universe has 92 constituents.

The construction of the universe matters because our models use cross-sectional regression analysis to measure the factor payoffs within each universe.   That is, in a given month, our analysis will tell us how much residual return was associated with a unit of factor exposure on average within the universe.  So, we are assuming that the residual returns of the ETFs within the universe respond uniformly to a given factor exposure.  This is a simplifying assumption, of course.  Mathematically, it is true on average.  The more homogeneous the ETF universe, the more likely that assumption will apply to each and every ETF in the universe.  

Return on Invested Capital (ROIC) Trend  

One of our quality factors that has had particularly high payoffs within the alternative equity ETFs universe is return on invested capital (ROIC) trend, which is a measure of the trend in profitability.  More specifically, the way that we define this particular factor is to compare the trailing 12-month ROIC with its median over the past 36 months.  

The graph below illustrates one of our research tests for this factor.  We begin our test at the end of 2006 when the number of ETFs with data for this factor reached a critical mass of 27.  (Currently, of the 92 ETFs in the alternative equity ETFs universe, 76 have data for this factor.)  At the end of each month, we form a “Top 5” portfolio of the 5 ETFs with the highest ROIC Trend.  Each has a 20% weight.  We calculate the residual (risk-adjusted) return of this portfolio during the month and then re-select and rebalance the “Top 5” portfolio at the end of the next month, and so on.  Similarly, at the end of each month we also form a “Bottom 5” portfolio of the five ETFs with the smallest, or most negative, ROIC Trend, re-selecting and rebalancing monthly. 

In the graph above, the blue line is the cumulative log of residual return of an equal-weighted portfolio of the 5 ETFs with the highest ROIC Trend, rebalanced monthly.  Since 2006, the average log of residual return (net of risk effects such as equity market and interest rate betas) has been 1.7% per year on average.  The orange line is the same thing but investing in the 5 with the lowest (or most negative) ROIC Trend.  That return has been -3.8%. The green line is a long-short implementation of the strategy.  Its return has been 5.5% per year.

These may not seem like very large numbers, but in fact, they are quite impressive.  Remember, the average ETF has no residual return at all.  Any factor that has had a strong and persistent payoff can provide an extremely valuable edge in forecasting residual return.     

As of July 31, 2019, SPHQ had the highest ROIC Trend of any ETF in our alternative equity ETFs universe.  Based upon its factor loading and our model of the expected residual return associated with the factor, we were forecasting that SPHQ would have a .30% residual return during July.  (Our models only forecast one month ahead.)  If sustained, this would result in an annualized alpha of 3.60% (.30% X 12 = 6.48%).  

Residual Return from Other Factors

The graph above depicts the test for only one factor.  At Sapient Investments we use several other factors to forecast the residual returns of ETFs.  ROIC Trend is a quality factor.  (Thus, it is not surprising that SPHQ and other quality-related ETFs such as QUAL and MOAT would have a high loading on this factor.)  We also use various value-related and momentum-related factors to forecast residual return.  While SPHQ is not highly loaded on all of these, the incremental expected residual returns from these other factors add incrementally to its overall residual return forecast.  

Conclusion

• Quality is one of the strongest and most persistent factor anomalies
• It is well-supported in the academic literature
• SPHQ is a very low-cost way to exploit the quality effect
• Very positive average ROIC trend in SPHQ’s holdings indicate that it is likely to achieve above-average alpha in the near future