Indexology^® Blog

Following an index announcement stating Nektar (NKTR) among other stocks would be moving to the S&P 500 prior to the open on March 19, 2018, an inquiry came in about the index impact of NKTR on the S&P SmallCap 600 Health Care Index performance.   Given the stock rose over 70% in 2018, it seemed reasonable to ask about how one company can influence a market cap-weighted index.

In order to answer this question, the weights and returns of each constituent in the index were measured for a contribution to return.  For this particular analysis, data from Jan. 2, 2018 through Mar. 16, 2018 was used.  Through this time, the weight of NKTR increased from 8.6% to 13.4% with an average weight of 11.4%.  The compounded daily index contribution from NKTR was 7.5% to the 16.4% of the S&P 600 Health Care Index.  The graph shows the daily index contribution of NKTR (in red), making it clear that the contribution does not rise directly just with the weights or returns but depends on the combination, that in turn depends on the performance of other stocks in the index.

If NKTR, the one stock that rose 70% didn’t drive the whole small-cap health care sector, what else mattered?  The index had 78 stocks contributing to the return, of which 64 stocks or 82% (more than 4 of 5) were positive.  The worst stocks in the best sectors generally benefit.  However, then why have the small caps outperformed the large caps by so much in health care if they are in the same sector?

Looking deeper than the sector level is required to answer this question, and more granular groups by GICS classifications can say a lot about the performance of a sector.  When analyzing sector performance using (in order from broadest to narrowest) industry groups, industries and sub-industries, insight can be gained into what is driving a sector and whether a stock is going with its group or if individual fundamentals are at play.

In the case of the health care sector, there are two industry groups, illustrated below with weights in each of large caps and small caps through time. Note that large cap healthcare equipment & services increased through time from 33% to 41% and pharmaceuticals, biotechnology and life sciences decreased in large caps from 67% to 59%.  The opposite happened in small caps where the pharmaceuticals, biotechnology and life sciences increased from 31% to 40% while the healthcare equipment & services decreased from 69% to 60%.

Next breaking down the sector weights by industry group, industry and sub-industry shows large cap Health Care Equipment and Managed Health Care are biggest and have grown most from 13.8% to 19.2% and 9.4% to 13.4%, respectively, while the Biotechnology and Pharmaceuticals fell most from 24.3% to 20.6% and 38.4% to 32.2%, respectively.  In small caps, the Biotechnology and Pharmaceuticals grew most from 10.1% to 14.3% and 16.9% to 23.2%, respectively.  On the other hand in small caps, Health Care Equipment dropped from 25.0% to 10.0% and Health Care Technology fell from 10.1% to 3.9%. Also, in small caps, the Health Care Distributors are almost gone with only Aceto Chemicals left at 0.19%.

Knowing the weight distribution helps focus where to look for performance contribution.  The 3-year annualized return spread of S&P 600 out-performance over the S&P 500 for the health care sector, industry group and industries shows an interesting split.  While (very slightly) more of the out-performance came from Pharmaceuticals, Biotechnology & Life Sciences (Industry Group) at 14.1%, all 3 industries from Health Care Equipment & Services (Industry Group) showed a small cap premium.  Only Pharmaceuticals did from its broader industry group, but it outperformed by 21.8% annualized over the three years.

This is the information needed to measure the contribution to returns.   Overall the returns of the health care sector were 27.2% and 81.0% for the S&P 500 and S&P 600, respectively.  Healthcare Equipment & Services contributed 16.1% and 48.7% to large caps and small caps, respectively – that was more than the Pharmaceuticals, Biotechnology & Life Sciences contribution of 11.0% and 32.2%, respectively, to large and small caps. Healthcare equipment & supplies contributed most of that at 8.5% and 27.4% , respectively for large and small cap. Pharmaceuticals added 21.1% to small caps that was the 2^nd most significant industry behind healthcare equipment & supplies.

The result of this sort of deeper sector analysis can lead to understanding the drivers of performance.  In this case of small caps outperforming large caps in health care, the credit can be given to deal making, increased expectations for acquisition of smaller companies, stronger innovation from smaller companies and that smaller companies may be more immune to concerns about regulatory pressures in healthcare.  These reasons are all mentioned in this article that leads to conclusions about specific stock and ETF choices as ways to benefit from these intra-sector moves.

The S&P U.S. High Yield Low Volatility Corporate Bond Index (the HYLV index) was launched on Dec. 20, 2016, with the aim of capturing high yield bonds with less credit risk and lower return volatility than the broad investment universe of U.S. high yield bonds. One year after the index launch date, we present a performance analysis of the HYLV index versus the benchmark S&P U.S. High Yield Corporate Bond Index for 2017.

Exhibit 1 shows the performance decomposition for both indices. In 2017, carry was the biggest driver of performance, returning 5.9% for the benchmark and 4.8% for the HYLV index. Spread tightening contributed 2.8% return for the benchmark and 2.0% for the HYLV index, with the option-adjusted spread being tightened by 62 bps and 36 bps for the benchmark and the HYLV index, respectively.

Exhibit 1 also demonstrates the sources of performance difference between the HYLV index and the benchmark. Overall, the HYLV index underperformed the benchmark by 2.3%, including a carry of 1.1% and a spread of 0.8%. Underperformance of the HYLV index from carry and spreads in a spread-tightening environment is not surprising, given its lower yield profile and more defensive positioning of credit risk than the benchmark. More importantly, does the HYLV index mitigate portfolio risk and provide downside protection as it intends to?

Exhibit 2 shows the monthly returns for the benchmark and the HYLV index. The HYLV index did show lower volatility, at 1.67% compared to 2.0% for the benchmark. Exhibit 3 includes the scatterplot of the relative return of the HYLV index over the benchmark from spread changes versus benchmark spread returns. The negative correlation of -0.66 between these two variables indicates that when the broad high yield universe benefited from spread tightening, the HYLV index underperformed the benchmark from spread changes, and that spread widening would have less downward impact on the HYLV index than the benchmark.

In a year with strong equity returns in India, the S&P BSE 100 ended in the black with a 33.3% annual return. However, the S&P Indices Versus Active (SPIVA) India Year-End 2017 Scorecard shows that a majority of active funds in the Indian Equity Large-Cap and Mid-/Small-Cap categories lagged their respective benchmarks over the one-year period ending in December 2017.

The biannual SPIVA India Scorecard attempts to capture the performance of active funds (both equity and bond funds) domiciled in India against the S&P BSE benchmarks over different time horizons. The study revealed that over the one-year period ending in December 2017, 59.4% of Indian Equity Large-Cap funds, 27.9% of Indian ELSS funds, and 72.1% of Indian Equity Mid-/Small-Cap funds underperformed their respective benchmark indices. Additionally, the majority of the Indian Composite Bond funds underperformed the S&P BSE India Bond Index over the 3-, 5-, and 10-year periods, and the majority of Indian Government Bond funds underperformed the S&P BSE India Government Bond Index over the 1-, 3-, 5-, and 10-year periods (see Exhibit 1). 

In the three equity fund categories—Indian Equity Large-Cap, Indian ELSS, and Indian Equity Mid-/Small-Cap—the asset-weighted average fund returns were higher than their respective equal-weighted average fund returns over the 10-year horizon. This difference was most pronounced in the Indian Equity Large-Cap category (87 basis points) among the three equity categories, indicating that the larger funds by AUM in this category performed better over the 10-year period (see Exhibit 2). However, an opposing trend was observed for the one-, three-, and five-year periods, when the equal-weighted average fund returns exceeded their respective asset-weighted average returns, in the Indian Equity Mid-/Small-Cap category, which implies that the alpha generation from mid- and small-cap selection was more constrained for the large funds, due to the low investment capacity of mid- and small-cap stocks in recent years.

Source: S&P Dow Jones Indices LLC, Morningstar, and Association of Mutual Funds in India. Data from December 2007 to December 2017, based on the SPIVA India Year-End 2017 Scorecard. Past performance is no guarantee of future results. Chart is provided for illustrative purposes.

In the year-end 2017 report, the number of Indian Equity Large-Cap active funds at the start of the three-year period shrunk to 100 from 155, a drop of 55 funds in the universe compared with the figures reported in the SPIVA India Mid-Year 2017 Scorecard. This drop was mainly caused by the introduction of a new fund category, Flexicap, by Morningstar in the second half of 2014. The Flexicap category includes funds that invest at least 65% of their total assets in Indian equities but invest no more than 65% of total assets in either large-cap stocks or small-/mid-cap stocks. Of these 55 large-cap funds, 46 were reclassified to Flexicap while the seven other funds were merged during this six-month period.

Additionally, the number of Indian Equity Large-Cap active funds at the start of the one-year period in the SPIVA India Year-End 2017 Scorecard decreased to 64 from 87, a drop of 23 funds in the universe compared with the figures reported in the SPIVA India Mid-Year 2017 Scorecard. This drop was due to a change in the definition of large-cap funds in the second of 2016 by Morningstar. The minimum requirement for percentage of assets in large-cap stocks for large-cap funds was changed from 65% to 75%. As a result, 20 large-cap funds were reclassified to Flexicap funds.

To discover more about the performance of Indian active funds versus their benchmarks, check out the SPIVA India Year-End 2017 Scorecard.

S&P Dow Jones Indices recently launched the S&P Japan 500 Equal Weight Index, an equal-weight version of the S&P Japan 500.  Over the 15-year period ending in February 2018, encompassing the latter part of Japan’s so-called “lost decades” of stagnant equity returns, the equal-weight index would have outperformed the cap-weighted Japanese equity benchmark by a stonking 7.41%, annualized.  This compares quite favorably with the 2.2% and 2.1% outperformance of equal-weight indices in Europe and the U.S., respectively (see our recent paper).  So what explains incremental returns of this magnitude?

Exposure to smaller stocks is important for equal-weight.  To examine the impact of size we construct a hypothetical “Size Match” portfolio using a combination of the S&P/TOPIX 150 and the S&P Japan SmallCap 250 Index that matches the equal-weight index’s average exposure to larger and smaller companies.  (The exhibits in this post are constructed following the process outlined in the paper linked above.)  Exhibit 1 shows how the smaller size bias could explain 76% of the variation in the relative returns of the S&P Japan 500 Equal Weight Index.

Exhibit 1: Smaller size explained a majority of the variation in equal-weight excess returns.

Anti-Momentum also helped, particularly in Japan.  Equal-weight indices have an intuitive relationship with (anti-) momentum effects.  Specifically, equal-weight indices rebalance by selling those stocks that have outperformed the average, and by purchasing those that have underperformed the average.  This is opposite of what a trend-following strategy seeks to do.  Momentum, generally speaking, is considered to be a rewarded strategy in equities.  However, quite famously, it isn’t so-well rewarded in Japan.  So what role did momentum play in the relative performance shown in Exhibit 1?

Exhibit 2 shows the negative relationship between the returns to equal-weight not explained by smaller size exposure and the relative performance of the S&P Momentum Japan LargeMidCap Index to the S&P Japan LargeMidCap Index.  According to the relationship evidenced in Exhibit 2, 53% of the excess returns not accounted for by size effects can be attributed to anti-momentum effects.  (Again, we present only the results of a standardized attribution process described more fully in the paper).

Exhibit 2: Anti-momentum effects in Japan

Through the perspectives of its size and momentum exposures, around 89% of the outperformance of equal-weight in Japan over the past 15 years might accordingly be “explained”, or at least understood through the familiar concepts of size and rebalancing effects.

And what of the future?  Of course, there is no way of telling if the S&P Japan 500 Equal Weight Index will outperform its cap-weighted parent over the next 15 years.  However, at least one additional perspective may be useful:  if smaller stocks outperform larger stocks, the overall concentration of market benchmarks logically decreases.  Exhibit 3 evidences this relationship as applied to the S&P Japan 500.  Notably, the Herfindahl-Hirschman Index concentration of the S&P Japan 500 has declined considerably over the period we are examining – from a peak of 160 down to a level of 70 at the end of December 2017.  The hypothetical minimum concentration that a 500-stock portfolio can display is 20 – achieved when every stock has the same weight – which suggests that a similar “boost” from declines in concentration levels may not be repeated.

Whatever the future brings, market participants may be well-served to consider multiple perspectives on equal-weight indices when trying to explain their characteristics and potential applications.

Exhibit 3: S&P Japan 500 concentration and the relative performance of Equal Weight

Although market volatility has retreated somewhat since the spike in early February, it has remained elevated. In the last 30 trading sessions, the S&P 500 moved by more than 1% (in either direction) 14 times.

VOLATILITY FOR S&P 500 (21-Day Rolling)

Volatility manifests itself in both dispersion (a measure of the magnitude of differences among an index’s constituent returns) and correlation (the tendency of assets to move in the same direction at the same time).  This means that observing combinations of dispersion and correlation can give us some insight into the dynamics of market volatility.

In February, we put the markets’ record decline—and spike in volatility—in the context of the dispersion-correlation map. The chart below provides an update of where we currently stand in this framework. Immediately following the S&P 500’s 4.1% drop on February 5^th, both dispersion and correlation jumped significantly. In the days following, both measures climbed further before they started to retrace. Currently, correlation remains elevated but dispersion is much closer to the levels we saw in January.

ROLLING 21-DAY DISPERSION-CORRELATION YEAR TO DATE

In context of broader history, dispersion is below the median measured from 1991 through 2017 (annual levels are averages of monthly figures), though correlation is still well above median. Things are no longer tranquil like they were in 2017, but we’re also far removed from tumultuous years like 2000 and 2008 when dispersion levels were much higher.

DISPERSION-CORRELATION MAP