Indexology^® Blog

In our latest S&P 500^® Factor Indices Dashboard, Tim Edwards wrote, “Value’s return to form came after a decade of meagre pickings.  In fact, value recorded in its best annual relative performance since 2000.”

Small wonder then that the S&P 500 Enhanced Value Index and S&P 500 Pure Value were popular topics of financial advisor discussion at the 2017 Inside ETFs Conference in January.

Mike Philbrick, President of ReSolve Asset Management, shared an interesting view: “I like S&P 500 Pure Value because the index isn’t ‘polluted’ with beta.”  I had to follow up on that to see what he meant.  Mike’s point is that since S&P 500 Pure Value’s methodology selects only one-quarter of the benchmark index’s market capitalization (the index has 116 constituents as of the last rebalancing) with the strongest value scores and excludes the rest, it is a better return-enhancing risk premia “mousetrap” than previous generations of value indices.

By taking a look at the factor scoring diagram below of the S&P 500 Value and comparing that to the factor scoring diagram of S&P 500 Pure Value, Mike’s point is made visually.  The S&P 500 Pure Value exhibits a much larger value factor rank than the S&P 500 Value.  In relative terms, the S&P 500 Value does not differ as much from the S&P 500 benchmark (beta) as the S&P 500 Pure Value does.

Speaking of the S&P 500 Pure Value, a question I hear frequently from financial advisors is whether an equal combination of the S&P 500 Pure Value and S&P 500 Pure Growth has outperformed our S&P 500.  Thanks to my friend, Sam Stovall, Chief Strategist at CFRA, one can see that the answer to that question is yes.  Sam calls this “barbell” approach the Mr. Universe Strategy.  Exhibit 3 shows his results after he applied an annual rebalance to the combination of S&P Pure Style Indices.  Based on Sam’s results, Mike Philbrick may have a good point about beta pollution.

At the Inside ETFs event, S&P DJI launched a factor allocator tool designed for us by Optimal Advisors, and we demonstrated that tool to financial advisors who visited our booth.  The tool visually and quantitatively shows how the S&P 500 Pure Value has performed.  Furthermore, the tool allows users to see the effects of combining the value factor with other factors such as quality and momentum.  Powered by more than 15 years of S&P DJI index data, this tool can be found here.  Financial advisors who saw this tool demonstrated at Inside ETFs said it answers some of their questions about how to implement value as a factor and combine value with other factors in a portfolio.  During our webinar for financial advisors on March 22, 2017, we will talk about factor case studies that make use of our factor dashboard and factor allocator.

Futures tied to VSTOXX, the Eurozone’s version of VIX, are signaling risk ahead. The French elections, which take place April 23 (first round) and May 7 (run-off election) and the possibility of Marine Le Pen leading France’s exit from the Eurozone appear to have spooked European options investors. They are now paying higher premiums for put options expiring near the election. As a result, the term structure for VSTOXX futures has assumed an unusual shape.

VSTOXX is usually higher than VIX (on average by 10.6 points over the past five years) but the two benchmarks tend to move in near lockstep.

Should U.S Investors Care?
Here’s a quick quiz for you. What percentage of S&P 500 company sales come from Europe? According to a report issued by S&P Dow Jones Indices last year, 7.8% of S&P 500 sales revenue in 2015 came from Europe.  If France were to leave the Eurozone, this could affect the U.S. in ways beyond those captured in direct sales figures. Still, 7.8% is a good statistic to keep in mind. Leading U.S. companies are less tied to the European economy than many investors would expect.

A Look Back at the Brexit Vote
Traders, policy experts, and journalists are comparing the French elections to the Brexit vote. It’s worth looking back to see how VSTOXX and VIX moved when British voters chose “Leave.”

VSTOXX and VIX are notoriously noisy signals – the “vol of vol” is high – but we can note the following:

• Though most economists predicted “Remain” would win, VSTOXX traded at elevated levels before the election, indicating greater uncertainty in financial markets about the outcome.
• When “Leave” prevailed, both VSTOXX and VIX jumped, but U.S. options investors were more surprised. One day after the Brexit vote, the VSTOXX closed only 8.7% higher than the day before (36.4 to 39.6). In comparison, VIX increased 49.3% between closings.
• Options investors in both markets quickly focused forward and VSTOXX and VIX declined to normal levels in the course of a week.

The French election and Brexit are of course different, but VSTOXX and VIX look like they did leading up to the Brexit vote: VSTOXX is elevated and VIX is low. On April 23 and May 7, we will learn whether U.S. investors have been too complacent.

It was a flattish February for commodities with the S&P GSCI Total Return up 23 basis points for a year-to-date return of -1.2%, and the Dow Jones Commodity Index up 11 basis points bringing its year-to-date return to 78 basis points.  Overall  in the S&P GSCI TR, 3 of 5 sectors were positive and 14 of 24 commodities were positive. The S&P GSCI Precious Metals TR was the best performing sector, gaining 3.7%, and the S&P GSCI Nickel TR was the best performing single commodity up 10.2%.  The worst performing sector was the S&P GSCI Energy TR with a loss of 26 basis points, led by the biggest single commodity drop of 13.1% from the S&P GSCI Natural Gas TR.

Before discussing what a difference one year makes and interest rate impacts on commodities, a few stats from inside the S&P GSCI TR are interesting from this forgettable February:

• Aluminum had its biggest consecutive 2-month gain of 13.5%, in 5 years when it returned 13.9% in Jan.-Feb. 2012.
• Cocoa lost for the 6th consecutive month, its longest losing streak since the 9 months ending in May 1999. Only four times in history since 1984 has cocoa been down this long.  Also it is cocoa’s 2nd biggest drop in history, down 34.2% since Sep. 2016.  Cocoa lost most, -48.2%, in Sep. 98 – May 99. Its other big losses occurred from  Jan. – Jun. 1986 (-25.4%) and Dec. 92 – Jun. 93 (-17.9%.)
• Gold posted its first consecutive 2-month gain, 8.7% since Jun.-Jul. 2016, when it gained 10.9%.
• Kansas Wheat and Wheat posted the first positive consecutive 3-months ending April 2014 for Kansas Wheat and Dec. 2014 for Wheat.  For the 3 months ending Feb., Kansas Wheat gained 10.0% and Wheat gained 7.3%.
• Natural gas lost 27% in Jan.-Feb. 2017. its worst 2-month loss since Jan. – Feb. 2016 when it fell 29.2%.
• Nickel had it best month, up 10.2%, since July 2016 when it gained 12.4%.
• Unleaded gas is down 10.5% in Jan. – Feb. 2016, the most since Jun. – July 2016 when it lost 19.3%.

While energy has struggled to start the year, losing 4.9%, all other sectors are up, reflected in the year-to-date performance of the S&P GSCI Non-Energy Total Return of 5.1%.  As a reminder of how far commodities have come from this time last year, there were 10 commodities in a bear market that caused both the S&P GSCI TR and energy sector to be down more than 20% for the 12 months ending in Feb. 2016, and every single commodity and sector were negative except lead and gold.  Now for the 12 months ending in Feb. 2017, every one of those bears are positive except for Kansas wheat.   Further, there are 11 bulls plus the sectors of energy and industrial metals are each up over 20% and only cocoa is in a bear market.

If commodities performance going forward depends on interest rates, there may be more of the bulls charging.  Historically rising interest rates are positive for commodities.  On average in rising rate periods, the S&P GSCI TR has gained 43.5%, more than the spot index that has gained on average 31.3%, showing that rising rates may drive carrying costs higher and making it less beneficial to hold inventory.

Which commodities benefit most from the rising rates? Energy and metals. Only cocoa and feeder cattle lose in rising interest rate environments.  Additionally, for natural gas and wheat that are both difficult to store, the total return that includes the storage costs declines with rising rates.

Many advisors are unsure whether introducing factor-tilt ‘smart beta’ strategies into portfolios will improve client outcomes. In fact, some factor tilt portfolios appear to provide the equivalent of levered exposures to a diverse set of alternative risk premia. This is because factor tilt portfolios may contain much greater than 100% exposure to several risk factors in aggregate.

Consider the iShares S&P 600 Small-Cap Value Index ETF which is constructed to hold small-cap companies with high book-to-market, earnings-to-price, and sales-to-price ratios. Via simple back-tested regression on the underlying index back to inception in 1997, we find that it offers the following factor exposures, which are all significant at the 0.1% level:

It is useful to think about the factor betas in Table 1 as weights in a synthetic portfolio. Given historical data from Ken French (MKT, SMB, HML and UMD) and AQR (QMJ), and using monthly return data, we calculate the following premia and weighted excess return:

The expected total excess arithmetic return from all factor exposures would be the weighted sum of the factor exposures and the premia, plus the intercept:

1.1*7.6%+0.82*2.7%+.53*4.5%+.26*8.3%+.43*4.2%-4.2% = 12.7%.

Given that the index implies over 300% exposure to risk factors, one might expect the portfolio to exhibit much higher volatility than a market capitalization weighted index. If the factors were all highly correlated, the portfolio would have a volatility approximately equal to the weighted average volatility of all exposures:

1.1*18%+.82*11%+.53*14%+.26*16%+.43*8% = 43.8%.

This would have meaningful implications in terms of compound return, as the implied compound annual return of the portfolio would be the arithmetic mean of 12.7% minus a volatility decay factor equal to half the portfolio variance: 0.127 – 0.5 * 0.438^2 = 3.1%. Compare this to the implied compound return of the market factor alone: 7.6% – 0.5 * 0.18^2 = 6%.

However, the factors are not highly correlated. In fact, the average off-diagonal pairwise correlations of the five factors in Table 1. is just -0.14. As such, we would expect the volatility of the portfolio to be less than the weighted average volatility of the factors. In fact, we can estimate expected volatility of the portfolio (sigma_p) from the following formula:

sigma_p = sqrt(w^T Sigma w),

where w= the factor exposures from Table 1. and Sigma is the covariance matrix of the factors:

Thus, the implied expected portfolio volatility would be about 25%. The compound excess return would be 12.7% – 0.5*.25^2 = 9.6, which now compares quite favorably to the capitalization weighted market factor. Moreover, the Sharpe ratio of the factor-tilted Index is 12.7%/25%=0.51, compared with the market’s Sharpe ratio of .42.

“Stocks Have Froth but No Bubble,” in today’s Wall Street Journal argues that while stocks are sitting at the highest valuations seen in many years, the market is not in a bubble.  Despite similarities to early 2000 by some measures, other distinguishing features of trading bubbles (such as high trading volume and high leverage) are subdued.

Another way to see this is by looking at the dispersion-correlation map. You don’t have to have high dispersion to have a crisis. But, as the chart below shows, times of calamity are often accompanied by higher dispersion as, for example, in 2000 and 2008. S&P 500 dispersion did rise late last year, but after a temporary spike at the end of November 2016, dispersion has fallen back to historically low levels. It is also significantly lower compared to this time last year. Dispersion can change, sometimes quickly, but until it does indications are that there’s no need to worry as of yet.