Indexology^® Blog

A calendar that offers a forecast for every day of the coming year is not uncommon in Chinese households.  I don’t often hear references to the calendar on most days. But every now and again I would hear my mom marvel, “Oh that calendar was right on for today!” In investing, there is also particular penchant for prediction at the beginning of the year. The start of a new year means we have put the previous one behind us, and knowledge of what the coming year will bring will be helpful.

But rarely do people look back to see whether those forecasts actually panned out…except, of course, on the rare occasions when some did materialize. As an example, “Why 2017 could be the year stock pickers regain their edge,” was published in early 2017. Among the many predictions used to justify a “stock pickers’ market” were: inflation should rise, interest rates should rise, value stocks will do well, correlations will be low and dispersion will be high.

Many of these just did not happen in 2017; inflation remains tame, the 10-Year Treasury rate barely budged year over year, value did not make a comeback and dispersion was lower on average in 2017 compared to 2016.

One prediction, however, did happen. Correlations were lower on average in 2017 compared to 2016, and this is a commonly used justification for a better stock pickers’ market.  Unfortunately,  it is dispersion that defines the opportunities available for outperformance—and SPIVA data show that the lowest dispersion environments correspond with the worst manager performance).

More Managers Underperformed in Low-Dispersion Environments

In contrast, active managers are no more likely to outperform when correlation is low than when it is high.

Correlation Had No Significant Influence on the Outcome of Manager Performance

Undoubtedly, more predictions will be forthcoming for 2018 and likely, many will be the same ones that were made for 2017. But at least if we come across the one about low correlations we can check it off the list because it is irrelevant to the outcome of active performance.

The Dow Jones Commodity Index (DJCI) was up 3.0% for the month and up 4.4% YTD, and the S&P GSCI was up 4.4% with a YTD return of 5.8%. In December, livestock was the worst-performing sector in the indices, while industrial metals was the best. Of the 24 commodities tracked by the indices, 16 posted positive YTD returns. Aluminum was the best-performing commodity for the year, while natural gas was the worst.

Of the S&P GSCI commodities, five industrial metals made up the top five YTD performers in 2017. Aluminum was up 30.7% for the year, which marks the third-largest annual gain for the commodity in the history of the S&P GSCI, after 1994 (up 72.9%) and 2009 (up 33.7%). Aluminum posted seven monthly gains in 2017. The gains were partly due to plans from China, the world’s largest producer and consumer of the base metal, to reduce excess aluminum production to address pollution, along with a Chinese fiscal stimulus targeting infrastructure, which bolstered demand for all base metal commodities.

Copper had its best year-end performance since 2010, as the Chinese government banned imports of scrap metal in a period of high demand. Lead, nickel, and zinc benefited from low inventories; both lead and zinc had their best years since 2009.

Coffee was down, recording its third consecutive negative year, due to a global production surplus. The wheat commodities reported their fifth negative annual decline due to increased global supply, as Russia increased production and competition with Australia, Canada, and the U.S. over the Middle Eastern import market due to its geographical proximity. Sugar was weighed down by increased output levels in 2017 plus reduced production in both Brazil and India that had failed to meet demand in 2016.

Natural gas was down 36.5%, reporting its worst year-end decline since 2014, due to two consecutive mild weather winter conditions in 2016 and 2017, as well as strong production. A report from the U.S. (Energy Information Administration (EIA) showed an increase in inventories to 36 billion cubic feet in December 2017.

Sometimes first isn’t better, and sometimes bigger isn’t better.  In this case, first and bigger are mostly worse.  Here’s some background behind the launch timing of the Russell 2000 versus the S&P SmallCap 600.  The timing and business around the Russell 2000 launch really helped the popularity of the index, but with an extra decade’s worth of research, the S&P SmallCap 600 has a higher quality design, resulting in higher performance and less risk.

This blog series will contain excerpts from our new paper where we discuss the outperformance of the S&P SmallCap 600 versus the Russell 2000, the performance of the indices compared with active managers, and the case supporting the performance.

A recent paper by FTSE Russell rightly pointed out the well-timed launch of the Russell 2000® in 1984, an index meant to measure the small-cap segment of the U.S. equity market. The launch was on the back of breakthrough research by Rolf Banz finding that “smaller firms have had
higher risk-adjusted returns, on average, than larger firms.”  At the time, the launch of this benchmark enabled Russell Investment’s consulting clients to gauge the success of small-cap managers.

However, it was not until the early 1990s when the “small-cap premium” concept was really solidified. Nobel Prize winner Eugene F. Fama and co-author Kenneth R. French introduced the three-factor model of market risk, value, and small-cap factors that now serves as the foundation for much of the current research on the topic.  Following this research, the S&P SmallCap 600 was launched in 1994.

Although the S&P SmallCap 600 took an extra decade’s worth of research into account in its construction as a benchmark, the Russell 2000 is far more widely used. According to eVestment Alliance, as of June 30, 2017, 93% of small-cap funds and 81% of assets in the space are benchmarked to the Russell 2000, compared with 3% of funds and 5% of assets benchmarked to the S&P SmallCap 600.

Perhaps the longevity and prevalence of the Russell 2000 as a small-cap benchmark is why it has been widely used in research doubting whether the small-cap premium exists.  Even in FTSE Russell’s own research, they show the Russell 2000 had a lower Sharpe ratio than the large and mid-cap Russell 1000 (0.34 versus 0.41, respectively) from June 1996 through August 2015. According to eVestment Alliance, from June 1996 through September 2017 the Sharpe ratio of the Russell 2000 was 0.31 while that of the Russell 1000 was 0.42.

However, the S&P SmallCap 600 had a Sharpe ratio of 0.43 over the same period. Not only has the S&P SmallCap 600 had a higher Sharpe ratio than the Russell 2000 historically, but the S&P SmallCap 600 has outperformed the Russell 2000 over various time periods and market cycles. Furthermore, according to the S&P Dow Jones Indices SPIVA® U.S. Mid-Year 2017 Scorecard, the S&P SmallCap 600 outperformed 93.8% of all small-cap funds over a five-year period.

In the next post of this series, we will show the actual outperformance of the S&P SmallCap 600 versus the Russell 2000 over the long term, the higher returns and lower risk over different time periods, and through different bull and bear market cycles.

In the last year, plain old beta performed remarkably well in comparison to the so-called “smart” alternatives tracking large-cap U.S. equities.  Of the 17 different strategies reported in our year-end factor dashboard, less than a third outperformed the S&P 500’s total return of 21.83% over the last 12 months.

When they are criticized as the basis of investment strategies (or at any rate when alternatives are promoted) cap-weighted equity benchmarks tend to be ascribed with certain “flaws” – in particular: an undue concentration in the largest stocks or sectors; a disregard for valuations; and a propensity to overweight recent outperformers.  Ironically, it was the supposed flaws of capitalization-weighted benchmarks that drove their relatively strong performance last year.

In 2017, size mattered.  The smallest quintile of S&P 500 constituents (based on prior year-end market capitalizations) averaged a total return of only 8.4%, while the largest stocks gained 22.3%.  In between, returns increased monotonically with size quintile.

Moreover, the U.S. market’s largest sector (Information Technology) had a banner year, soaring by 39% over the year and driving strong correlations between an index’s allocation to the sector, and its total return over the year. 

These size and sector perspectives are useful because nearly all smart beta strategies will – unless explicitly controlled for – underweight the largest stocks or sectors.   (Strategies based on momentum, growth or quality can provide occasional exceptions.)

As we’ve argued previously, the performance of an equal weight index is a useful tool to gauge whether capitalization weighting alone helped or hindered returns.  Last year, the S&P 500 Equal Weight underperformed its parent by around 3% – a material hurdle for smart beta to overcome through stock selection and additional weighting tweaks.  Those that did outperform largely did so through exposures to the factors said to weaken market benchmarks – owning larger stocks, or underweighting value, or simply following the trend.

Yesterday, the first markets day of the New Year, the Dow Jones Industrial Average^® continued its advance, finishing up 104.79 points for a 0.42% gain. How does that stack up against the post-holiday return to trading in prior years? Meh…it was nothing special.

Not to look a gift horse in the mouth, mind you. A gain is a gain is a gain. But it’s far from the best first day ever. In fact, it only ranks as 51^st of 121 observations since the DJIA’s inception. The best—Jan. 4, 1988—came as world banks reported buying dollars to curb the U.S. currency’s decline.

But wait. How does the first day presage annual performance? Is it at all predictive of the year, does it set the tone for the next 12 months? Well, when the Dow rises on Day 1, the whole year experiences a positive return 68% of the time. Sounds good, right? Not really. Similarly, when the Dow falls on Day 1, the whole year is up 65% of the time.

Fact is, the DJIA has a positive annual return 66% of the time. Period. In other words, the DJIA closes the year with a gain two-thirds of the time, regardless of what happens on Day 1. So, no. No predictive power whatsoever. But, of course, you already knew that. There is simply too much time, too many things that influence stocks over the course of a year. The first day no more sets the tone than does Bangladeshi butter production.

Turns out this post is a tale, told by this idiot, full of sound and fury and signifying nothing. Go back to your business.