Indexology^® Blog

News that the Dow broke through 25,000 yesterday was not universally celebrated.  A market index achieved a specific numerical barrier — so what?  The importance of such events is at best anecdotal, and their celebration in the media is increasingly regarded as humbug.  Is there any truth – one might ask – to the theory that such events are “psychologically important”?  That is to say, do they change the behavior of market participants?

Certainly, some numerical values are of demonstrable psychological importance to humans undertaking economic transactions.  Otherwise, supermarket items priced at 4.99 instead of 5.00 monetary units would not sell disproportionately better.

Beyond the average supermarket shopper, or even the most cynical of market participants, nearly everyone has celebrated an anniversary or set a round number goal as an ambition.  To an independent observer, changes in behavior based on the achievement of arbitrary numerical values would seem a most human trait.   So it is logical to expect buyers or sellers whose motivating impulse was the price reaching a particular number to have some impact on capital flows, and therefore the markets.

However, even though investment flows are (for the time being) largely dominated by the decisions of humans, investing has become increasingly professionalized – and, increasingly, non-human.   Do so-called “psychologically important barriers” still exist in any real sense for market indices?  And even if they do, is there anything important about the Dow passing 25,000?

I suspect the impact of such occasions may be less material today than when markets were less professionalized.  Or, perhaps the particular barriers that are considered important will change.  The S&P 500 index today passed above the level of 2730 for the first time, which doesn’t sound very interesting to the average human, but in binary that means it has passed 101010101010.  Perhaps some computers are celebrating this fact as I write, perhaps – now that you know – you will, too.

Yet here I am, writing about the DJIA crossing through yet another 1,000 point milestone – 25,000, no less – a mere 23 trading days after the last one and right on the tail of the 5 successive 1000 point marks achieved last year.

With the 152.45 points (0.61%) gained during today’s session, the Dow closes at 25,075.13 and is up:

• 1.44% year-to-date (over all of 3 trading days)
• 27.08% since Trump’s inauguration (I’ll leave to others on where to ascribe credit)
• 283.00% since the Financial Crisis low of 6,547.05 hit on March 9, 2009
• 77.03% since the pre-Financial Crisis high of 14,164.53 hit on October 9, 2007

As for elapsed time, this 23 day sprint from 24k to 25k is – by a single day – the fastest the DJIA has ever covered that ground; the moves from 10-11k and 20-21k each took 24 days.  The returns for each climb, however, are markedly different:  10-11k represented a 10.1% return, for example, while the most recent mark is just a 3.31% increase.

And what are the drivers?  Following are top 5 point contributors to each of the most recent milestones as well as the march from 15k to 25k (which transpired from May 2013 to the present).  Boeing (BA) is the clear standout recently having contributed 17.7% of the run from 20-25k; as for the climb from 15-25k, Boeing was again the top stock followed by UnitedHealth Group (UNH) and 3M (MMM).

Again, when discussing this topic the standard caveats apply:  a) though the aggregate DJIA advance is certainly notable, each 1,000 point mark is, while an interesting emotional milestone, an otherwise arbitrary threshold, and b) as noted above, each new mark represents a smaller percentage gain than its antecedents.

By the way, if you’re looking for a “Dow 25,000” hat to add to your collection, don’t come to me.  At this rate, I don’t have the personal budget to keep printing them.

* – Don’t be confused as to how a 1,000 point milestone can be covered with 800 or 900 points.  Remember that for this exercise, we’re striking from the closing DJIA value on the day each milestone was crossed and these never land right on the even thousand point mark.  In the most recent case (24-25k), for example, the move was from 24,272.35 to 25,075.13 (or 802.77 points).

Not all indices are created equally, especially those representing US small-cap equities.  Many investors are surprised to learn that the S&P SmallCap 600 has outperformed the Russell 2000 by 2% annualized since inception (Dec. 31, 1994.)  This outperformance is persistent through different time periods, bull and bear market cycles, and with less risk.  The performance results of the S&P SmallCap 600 should redefine and raise the bar for passive small-cap beta. 

This is the 2nd part of our blog series containing 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.

THE S&P SMALLCAP 600 OUTPERFORMS THE RUSSELL 2000
Let’s begin with a simple example to demonstrate the long-term outperformance of the S&P SmallCap 600 over the Russell 2000 by showing the cumulative growth of USD 100 from June 1, 1995 to September 29, 2017. The S&P SmallCap 600 would end with a hypothetical value of USD 1,114.38, while the Russell 2000 ends with a hypothetical value of just USD 740.93. Another way to view this is that a market participant would hypothetically return 373% more with the S&P SmallCap 600. Over the period studied, the Russell 1000 slightly outperformed the Russell 2000 and the S&P 500. Since the return of the Russell 1000 was similar to that of the S&P 500, the S&P 500 will be used as the large-cap market benchmark in the analysis. It is also worth noting  that the Russell 1000 includes both large- and mid-cap stocks, whereas the S&P 500 is purely a large-cap index.

Another way to measure S&P SmallCap 600 outperformance is by annualized return and risk, as measured by the standard deviation of returns. Since inception, the S&P SmallCap 600 had an annualized return of 11.6% and annualized risk of 18.3%, generating a Sharpe ratio of 0.50.
Over the same time period, the Russell 2000 had an annualized return of 9.6%, annualized risk of 19.2%, and a Sharpe ratio of 0.37. The S&P SmallCap 600’s returns were higher than the Russell 2000 over every time period analyzed, while its risk was lower in every period.

Since the S&P SmallCap 600 was launched in 1994, there are five bear and bull market cycles (as defined by peak to trough and trough to peak periods of the S&P 500) to analyze, and the S&P SmallCap 600 outperformed the Russell 2000 in four of those cycles. From the beginning of the data set on June 1, 1995, through Sept. 1, 2000, small caps underperformed large caps, but the S&P SmallCap 600 returned 129%, which was 15% more than the Russell 2000. In the following bear market, the S&P SmallCap 600 outperformed the Russell 2000 by 15% again.  Although the next two cycles returned similarly, the S&P SmallCap 600 has been significantly outperforming in the current bull run since the global financial crisis ended in March 2009, beating the Russell 2000 by 66% and the S&P 500 by 108%. These are compelling returns to argue for a strategic allocation to not just small-cap stocks, but to the S&P SmallCap 600 rather than the Russell 2000.

Lastly, the S&P SmallCap 600 had relatively strong monthly and annual returns compared to the Russell 2000. On a monthly average, the Russell 2000 gained 0.89%, slightly more than the 0.83% gain for the S&P 500, but noticeably less than the average monthly return of 1.03% for the S&P SmallCap 600. Perhaps more important is that the S&P SmallCap 600 also provided downside protection. The down market capture ratio for the S&P SmallCap 600 was just 104.8 versus 119.2 for the Russell 2000. This means for every 1% drop in the S&P 500, the Russell 2000 fell an extra 0.2%. Also, the Russell 2000 fell in 45% of months that the S&P 500 fell, which is slightly worse than the 43% rate for the S&P SmallCap 600.

Referring to Exhibit 5, in any given calendar year for the past 23 years, the S&P SmallCap 600 outperformed the Russell 2000 for at least four months. Only in five years did the S&P SmallCap 600 outperform in less than 6 of the 12 months. On average, the S&P SmallCap 600  outperformed by 1.9% per year. The greatest underperformance of 8.9% occurred in 1999 and
the greatest outperformance of 14.8% occurred in 2000.

The performance shown here should at least beg the question of why the S&P SmallCap 600 is not more commonly used as a benchmark. The performance measurement of managers may be held to a higher standard compared with the S&P SmallCap 600. It may bring more credibility to the managers who beat the benchmark, or it could bring to light the managers who might be charging for active management but generating negative alpha.

In the next post of this series, we will show the small cap index performance versus the active small-cap peer group.  The results will show the S&P SmallCap 600 could be considered not just as a benchmark replacement, but rather it could more widely serve as the underlying index for investable passive funds.

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.