Indexology^® Blog

Sector-based approaches have historically shown they can be valuable for diversification, among other investor goals, but, until now, sustainability-inclined investors have been presented with limited options. With the recent creation of the S&P ESG Enhanced Sector Indices and the subsequent licensing of the Information Technology, Energy, Financials, and Health Care indices for a select range of ETFs in Europe, the potential for sector-based approaches to sustainable investing has increased significantly.

In assessing such potential, two questions are natural: first, what difference is there in sustainability profile for the ESG enhanced sectors? Second, what difference is there in performance—particularly in the context of a multi-sector allocation? Intuition tells us what to expect: while performance differentials at the individual sector level might be expected to be diminished by diversification effects, we should expect sustainability improvements to be preserved.

Focusing attention on the individual sector indices, Exhibits 1 and 2 summarize their sustainability and performance characteristics as compared to their benchmark sectors, using the same analytical engine driving S&P DJI’s Climate & ESG Index Dashboard.

The four S&P ESG Enhanced Sector Indices achieved significant ESG score and weighted average carbon intensity (WACI) improvements against their benchmarks, while maintaining tracking error ranges from 1.7% to 3.6%, for the Financials and Energy sector variants, respectively, and similar annualized returns across the full suite (see Exhibit 3). In addition to improvements in ESG score and WACI, these indices also achieve benchmark-relative enhancements in other climate metrics, such as carbon earnings at risk as a % of EBITDA as of March 31, 2023.

Exhibit 3 shows that even the limited tracking error reported in Exhibit 1 largely cancelled out over a longer (back-tested) performance horizon, for the whole range of S&P ESG Enhanced Sector Indices. Over the 10 years ending March 2023, the differentials in annualized total returns ranged from -0.1% to 1.8% across all 11 ESG enhanced sectors, with an annualized average absolute difference of just 0.5%.

But what about a hypothetical multi-sector allocation? To offer an illustration, a common approach is to harness the different risk and performance characteristics of sectors to navigate the market’s cycles. Sectors considered “defensive” are typically preferred in falling markets, while “procyclical” sectors are often preferred in rising markets. To evaluate the compatibility of ESG enhanced sectors with such approaches, we constructed four hypothetical portfolios, with and without the substitution of selected ESG variants (see Exhibit 4).

Results for the four blends over a decade suggest that the substitution of ESG enhanced sectors did not substantially alter the performance profile of the defensive and procyclical blends, as shown by similar outcomes for combinations with and without the S&P ESG Enhanced Sector Indices (see Exhibit 5, based on 10-year returns as of March 2023).

Bucketing periodic excess returns by months when the S&P Developed Ex-Korea LargeMidCap was rising (up markets) or falling (down markets) further illustrates the compatibility of ESG enhanced sectors with traditional defensive and procyclical approaches (see Exhibit 6). In a hypothetical blended portfolio, these behaved collectively even more like traditional sector indices, with a tracking error of only 2.1% for the Procyclical Blend with ESG and 2.2% for the Defensive Blend with ESG. These figures are lower compared to tracking error in ranges from 1.7% to 3.6% when ESG enhanced sectors are considered in isolation.

Overall, these exhibits showcase that building sustainability elements into sector indices may not necessarily come at the cost of desired benchmark-like characteristics. These indices offer the best of both worlds: an enhanced sustainability profile with performance characteristics closely aligned with traditional sector-based index strategies, making them a potentially valuable tool in the investor kit. Key performance and sustainability metrics for these indices can be monitored in S&P DJI’s Quarterly Climate & ESG Index Dashboard.

The author would like to thank Joseph Nelesen for his contributions.

GLOSSARY

WACI: Weighted Average Carbon Intensity. The index weighted average of individual company intensities (Scope 1, Scope 2, Scope 3 upstream and downstream emissions, normalized with enterprise value including cash).

FFR: Fossil Fuel Reserves. The carbon footprint that could be generated if the proven and probable fossil fuel reserves owned by index constituents were burned per USD 1 million invested.

Financial Impact of Physical Climate Risk: Financial losses (e.g., CapEx, OpEx, Business Interruption) reflected as a percentage of asset value due to exposure to climate-related physical hazards across scenarios and time periods. High Risk Scenario and 2050 time period are used for this Exhibit. For more information, please visit here.

Carbon Earnings at Risk as a % of EBITDA: This dataset helps to understand and assess the potential impact to a company’s earnings today if the company has to pay a future price for their greenhouse gas emissions. For more information, please visit here.

WAECI: Weighted Average Environmental Cost Intensity. This dataset helps to assess the index weighted average of environmental costs across key dimensions such as carbon emissions, land, water, air pollutants, and waste disposal, natural resource and water use. For more information, please visit here.

Board Female Representation (%): The index weighted average of the number of women on a company’s board of directors/supervisory board divided by the total number of board directors. For more information, please visit here.

¹ Backward Data Assumption Date: https://www.spglobal.com/spdji/en/education/article/faq-esg-back-testing-backward-data-assumption-overview/

How has equal-weight compared with cap-weight historically? S&P DJI’s Hamish Preston and Invesco’s Nick Kalivas explore how differences in index construction have influenced index performance, sector and factor composition, and results in the latest SPIVA Scorecards.

The S&P GSCI fell 0.8% in April as the Fed’s preferred measure of inflation, Personal Consumption Expenditures (PCE), fell slightly on a year-over-year basis to 4.6%. Continued elevated readings of inflation, albeit cooling, led to market expectations that the Fed would hike rates again in May to bring inflation back to its 2% target. Commodities are generally good inflation hedges because they tend to move with changes in inflation to the upside and, most recently, to the downside.

Within the broad S&P GSCI benchmark, several commodity sectors declined, with the S&P GSCI Industrial Metals falling the most, down 3.0%. The S&P GSCI Copper and the S&P GSCI Zinc dragged down performance, as China’s Politburo stated their economy is still in a recovery phase, which will require continued fiscal and monetary support due to insufficient domestic demand. Expectations for China’s industrial sector to pick up on the demand side have not come to fruition yet, and recent issues with trade relations between Australia and China have not helped the industrial metals space.

The S&P GSCI Agriculture was the second-worst-performing sector, down 2.9% in April, with the S&P GSCI Grains the lowest performer, down 6.9%. Corn, soy and wheat were all dragged lower owing to strong crop progress and waning demand, as the U.S. Department of Agriculture said private exporters recently canceled several hundred thousand tons of corn. Brazil has a massive corn crop this year, and their less expensive crop has weighed on U.S. grain prices. The S&P GSCI Softs rose 12.3%, powered by the S&P GSCI Sugar, which rose 22.1% to a new 10-year high.

Competing forces were at play for the S&P GSCI Energy, leading to flat performance for the month. This sector constitutes more than half of the weight in the headline S&P GSCI and tends to have the highest inflation beta (or sensitivity to changes in inflation) of all commodities. Our recently launched S&P GSCI Climate Aware is the first-to-market commodities benchmark incorporating environmental considerations, and currently has a third of its weight toward energy commodities.

The S&P GSCI Gold flirted with a new all-time high as the U.S. dollar moved lower and uncertainty across markets led to a safe-haven bid.

The outperformance of equal weight indices is well documented, especially for the S&P 500^® Equal Weight Index’s 20 years of live history. Equal Weight’s relative returns reflect the impact of several important index characteristics. For example, smaller-size exposure and (anti-) momentum effects together account for around 75% of the historical variation of its relative returns.

Elevated sector dispersion has made sector allocations more important in explaining Equal Weight’s recent relative returns. Indeed, the S&P 500 Equal Weight Index’s greater exposure to Energy and lower exposure to Communication Services, Information Technology and Consumer Discretionary accounted for around two-thirds of its 7% outperformance in 2022. These exposures also helped to explain the index’s underperformance in Q1 2023.

At the same time, it’s important not to overlook the impact of equal weighting within each sector. To see why, we construct hypothetical “intermediate” portfolios that represent steps along distinct paths between the S&P 500 and the S&P 500 Equal Weight Index. Exhibit 1 summarizes these two hypothetical paths.

The hypothetical “Sectors Matching EQW” portfolio adjusts S&P 500 sector weights so that they match those of the Equal Weight Index while ensuring that companies within the same sector remain float market-cap weighted. Alternatively, the hypothetical “EQW Within Sectors” portfolio retains the S&P 500’s sector allocations but weights every name equally within each sector.

Exhibit 2 shows that equally weighting within each sector mattered more when explaining the S&P 500 Equal Weight Index’s historical returns. Indeed, the cumulative total return for the hypothetical “EQW Within Sectors” portfolio is graphically indistinguishable from that of the S&P 500 Equal Weight Index. While the equal weight index may have sporadically benefited from its distinct sector allocations, having more exposure to the smaller names within each sector was the most important driver of the S&P 500 Equal Weight Index’s returns.

The relative degree of concentration within sectors suggests a potential tactical application. Exhibit 3 shows the distribution of adjusted Herfindahl-Hirschman Index (HHI) figures for the S&P 500 and its 11 GICS sectors; a higher adjusted HHI figure means that concentration is higher, independent of the number of stocks in each index. The current concentration is elevated in several sectors, particularly in Information Technology.

By definition, equally weighted sector indices have less exposure to larger names than do their float market-cap counterparts. All else equal, falling concentration indicates underperformance from the largest names, and vice-versa. Hence, one would expect equally weighted sectors to outperform when concentration falls.

Exhibit 4 confirms this expectation: the S&P 500 Equal Weight Information Technology Index typically outperformed its float market-cap counterpart when the sector’s adjusted HHI declined. Hence, to the extent that current concentration levels decline, market participants may wish to consider the potential applications of an equally weighted sector strategy.

For more information on the interaction between concentration and equal weight performance, and to celebrate the S&P 500 Equal Weight Index’s 20^th birthday, check out the replay of our Index Investment Strategy call from April 20, 2023.

Many investors have a so-called “home bias,” allocating to their domestic market in greater proportion than would be expected based on its representation in global equity markets. Asia-based investors are no exception. Here we present our U.S. equity icons as one potential way to provide diversification for Asian investors.

The breadth and depth of the U.S. equity market means that investors risk overlooking a significant chunk of the global equity opportunity set by under-allocating to U.S. equities, which may result in a large active share compared to a global benchmark. For example, Exhibit 1 shows that the U.S. was nearly three times larger than the entire investable Asian equity market, with smaller U.S. equity segments as large as entire local stock markets. The S&P 500^® makes up nearly half of the pie, with the S&P MidCap 400^®  and SmallCap 600^® being larger than the Australian and Hong Kong stock markets, respectively.

Beyond U.S. equities representing a significant portion of the global opportunity set, their distinct sector weights may help investors to overcome domestic sector biases. Exhibit 2 shows GICS^® sector weights of the S&P Pan Asia BMI and the relative weight compared to the S&P Global BMI and the S&P 500. The S&P Pan Asia BMI’s largest weights are in Financials (17%) and Information Technology (16%), with its smallest weight in the Energy sector, at 3%. Some key differences between the S&P Pan Asia BMI and S&P Global BMI and S&P 500 are that the global and U.S. benchmarks have a larger weight in Health Care and Information Technology and lower weights in Consumer Discretionary, Materials and Industrials.

The performance of U.S. equities may also motivate some to consider incorporating U.S. equities alongside domestic equities. Exhibit 3 shows the cumulative performance, in USD terms, of the S&P Pan Asia BMI versus U.S. equity indices since Dec. 30, 1994. The right-hand bar chart shows the annualized total returns of various single stock market indices against the S&P 500, S&P MidCap 400, S&P SmallCap 600 and DJIA^®. Quite clearly, the U.S. equity indices outperformed, historically.

Exhibit 4 shows that the outperformance of U.S. equities was not driven by currency effects. Indeed, the S&P 500 outperformed single-market indices (as represented by the S&P Global BMI sub-indices) in local currency terms as well.

Exhibit 5a also shows the potential diversification benefit of incorporating U.S. equities: there was a non-perfect correlation with Asian equities over the last 28 years. Exhibit 5b also highlights that several single-market indices rank significantly lower in terms of correlation, with China having a 0.4 correlation to the U.S. since Dec. 30, 1994.

Unsurprisingly, perhaps, incorporating allocations to the S&P 500 could have improved risk-adjusted returns. For example, Exhibit 6 shows the annualized returns and volatility for various hypothetical combinations of the S&P 500 and the S&P Pan Asia BMI. These hypothetical combinations rebalance back to the target weights at each year end.

Portfolios that included some proportion of the S&P 500 posted higher returns than a 100% allocation to the S&P Pan Asia BMI.  The high returns were also achieved at a lower annualized risk.

Check out more research and insights on the S&P 500 and DJIA at https://www.spglobal.com/spdji/en/education/article/comparing-iconic-indices-the-sp-500-and-djia/ and https://www.spglobal.com/spdji/en/education/article/regional-relevancy-of-sp-500-and-dow-jones-industrial-average-futures-in-asia.