Indexology^® Blog

As uncertainties around tariffs, inflation and interest rates continue to make headlines, how are yield seekers viewing munis? S&P DJI’s Jennifer Schnabl and Vanguard’s David Sharp discuss key performance drivers of munis in challenging markets.

The S&P 500^® Top 20 Select Index series launched in August 2024 and is designed to measure the capped market-capitalization-weighted performance of the largest 20 companies, by float market cap, in the S&P 500. Previous blogs introduced the S&P 500 Top 20 Select Indices, covering their construction, objectives and historical performance. This installment examines the index’s volatility, correlation and dispersion to provide insight into its historical behavior relative to The 500™.

Slightly Higher Volatility

Exhibit 1 provides a calendar year breakdown of the S&P 500 Top 20 Select Index’s volatility compared to The 500. In several years, the Top 20 recorded slightly lower volatility than The 500—particularly during certain notable market events (e.g., the global financial crisis in 2008 and 2009). Conversely, during periods of elevated volatility for the S&P 500 Top 20 Select Index (e.g., 2020 and 2022), volatility closely tracked broader market moves, suggesting that these differences may be driven more by external market forces than by the index’s composition.

More broadly, over the past 10 years (2015-2024), the average annual volatility for the S&P 500 Top 20 Select Index was 18.5%, slightly above the broader S&P 500’s 16.2%. Over the recent five-year period, volatility remained modestly higher (22.5% versus 19.5%).

How Closely Do Mega-Cap Stocks Move in Sync?

The S&P 500 Top 20 Select Index comprises a smaller number of constituents than The 500. Historically, the performance of these mega-cap constituents has tended to move more closely together over time. On average, the index exhibited higher correlation among its constituents compared to the broader S&P 500 (see Exhibit 2).

How Varied Are Returns within the Index?

Dispersion measures how differently individual components perform compared to the average. Lower dispersion indicates more uniform performance across constituents, while higher dispersion suggests greater variability. Exhibit 3 illustrates that the S&P 500 Top 20 Select Index consistently showed lower dispersion compared to the broader S&P 500 over both 10-year and 5-year periods. This lower dispersion suggests that the individual performance of the index constituents typically differs less from the index average, indicating less variability in how these mega-cap constituents perform relative to the index average.

Conclusion

A closer look at volatility, correlation and dispersion reveals that the S&P 500 Top 20 Select Index has exhibited modest differences compared to the broader S&P 500. These include slightly higher average volatility and correlation, along with somewhat lower average dispersion over the past 5- and 10-year periods. Taken together, these metrics provide a historical view of how the index has behaved across different market environments.

The world is undergoing significant changes as it navigates the energy transition, marking a shift toward innovative and more efficient energy sources. In this evolving context, indices can play a crucial role by offering diverse measurements that represent various facets of the energy transition.¹

Buildings, which account for a substantial portion of global energy consumption attributed to the real estate sector,² are at the forefront of this transition. The Dow Jones Global Select ESG Tilted Real Estate Securities Index (RESI) exemplifies how indices can provide a framework for evaluating Real Estate that is responsive to the demands of the energy transition.

Launched in 2021, the Dow Jones Global Select ESG Tilted RESI measures the performance of publicly traded Real Estate securities from its benchmark, the Dow Jones Global Select RESI, that meet sustainability criteria. The index targets to improve the GRESB Total ESG score relative to the underlying index by overweighting those companies with higher GRESB scores and underweighting those with lower or no scores.³

As illustrated in Exhibit 1, since its launch, the index performed similarly to its benchmark, the Dow Jones Global Select RESI, and achieved an excess total return of 0.12% for the one-year period ending March 31, 2025.

Digging deeper into the GICS Real Estate sub-industries within this index reveals important insights related to their contribution to the Dow Jones Global Select ESG Tilted RESI’s relative performance for the one-year period ending March 31, 2025, as well as their respective carbon intensity, as illustrated in Exhibit 2. The size of the bubbles corresponds to the level of carbon intensity, with larger bubbles representing a greater carbon footprint. Additionally, the color gradient illustrates the different levels of carbon intensity, ranging from dark blue for the highest intensity to light blue for the lowest.

A notable example is Data Center Real Estate Investment Trusts (REITs), which have become increasingly relevant due to the surge in demand for artificial intelligence, which is powered by data centers. Data centers are energy-intensive, requiring substantial energy supplies to function. Despite their significance in a technology-driven economy, they detracted from the Dow Jones Global Select ESG Tilted RESI’s relative performance by 0.11% and stood out as the most carbon intensive Real Estate sub-industry.

Conversely, the Health Care sub-industry had the largest contribution to the index’s relative performance, with 0.34%, but it also had the second-highest carbon footprint.

In conclusion, the Dow Jones Global Select ESG Tilted RESI can serve as a vital tool for understanding how the energy transition is influencing the Real Estate sector. As the world continues to evolve toward more efficient and newly emerging energy practices, such indices can be instrumental in measuring the unique role the Real Estate sector plays in the world’s energy future.

¹ For an overview of indices in the energy transition context, see: Beyhan, Maya and William Kennedy. “The Role of Indices in the Energy Transition.” S&P Global. Look Forward Journal. March 4, 2025.

² For a thorough overview of the role of buildings in energy systems, see: International Energy Agency: https://www.iea.org/energy-system/buildings

³ See the Dow Jones ESG Real Estate Indices Methodology.

⁴ For more information, see: Index Carbon Metrics Explained.

How can an index help market participants benchmark the developed world? Take a deep dive into the S&P World Index as we explore its coverage and usage, its performance history and why it continues to be a tough index for actively managed funds to beat with S&P DJI’s John Welling and Anu Ganti.

Amid macroeconomic uncertainty and elevated volatility, the S&P 500^® has experienced a notable decline. In the challenging market conditions from Dec. 31, 2024, to April 4, 2025, the S&P 500 Quality Index not only outperformed the S&P 500 but also showcased its defensive characteristics, which have been evident throughout its history. In this blog, we will explore the performance, attribution and key characteristics of the S&P 500 Quality Index.

Performance

During this turbulent period, the S&P 500 Quality Index outperformed the S&P 500 by 3.58% YTD, while exhibiting lower volatility (see Exhibit 1). Such performance highlights the defensive and resilient characteristics of this index.

Moreover, this recent outperformance aligns with its historical track record, which indicates that tracking companies with strong fundamentals has been an effective approach for achieving consistent and long-term performance. As shown in Exhibit 2, the S&P 500 Quality Index has outperformed the S&P 500 in terms of total return and risk-adjusted return across almost all periods studied while maintaining consistently lower volatility.

The historical capture ratios show that the S&P 500 Quality Index has typically participated one-for-one in up markets,¹ while delivering significant outperformance during down markets. This makes sense, as quality indices tend to track companies with durable business models and sustainable competitive advantages.

Methodology Overview

High quality is typically associated with a company’s strong profitability, high earnings quality and robust financial strength. To effectively reflect these quality characteristics, the S&P Quality Indices utilize three key metrics: return on equity (ROE), balance sheet accruals ratio (BSA) and financial leverage ratio (FLR) (see Exhibit 3).

The selection process for the S&P Quality Indices involves identifying the top 20% of eligible stocks within their respective universes, ranked by overall quality scores. Index constituents are then weighted based on the product of their market capitalization and quality scores, while adhering to specific constraints.³

Performance Attribution

In Exhibit 4, the S&P 500 Quality Index’s YTD performance is broken down into three components: ROE, BSA and FLR attributions.⁴ ROE contributed the most to the outperformance, followed by BSA.

Performance in Different Macroeconomic Conditions⁵

Exhibit 5 shows the performance of the S&P 500 Quality Index in different macroeconomic conditions. Historically, the index had comparable performance to the S&P 500 in rising growth regimes while delivering outperformance in slowing growth regimes.

Conclusion

The S&P 500 Quality Index has demonstrated historical resilience and defensive characteristics during times of market volatility and economic uncertainty. By selecting high quality companies with strong profitability, superior earnings quality and robust financial strength, the index not only outperformed the broader S&P 500 but also provided lower volatility in both the short and long term.

¹   The market is defined as the monthly performance of the underlying benchmark from Dec. 31, 1994, to March 31, 2025.

²   For more information, see Richardson, Scott Anthony, Sloan, Richard G., Soliman, Mark T. and Tuna, Ayse Irem, “Accrual Reliability, Earnings Persistence and Stock Prices,” Journal of Accounting & Economics, Vol. 39, No. 3, September 2005.

³   For further information about the factor definition, factor score calculation and index design, please see the S&P Quality Indices Methodology.

⁴   S&P 500 Quality BSA Attribution: Securities in the eligible universe are selected for index inclusion based on their accruals ratio z-score determined during the semiannual rebalancing of the S&P 500 Quality Index. The values for all securities are ranked in ascending order.

S&P 500 Quality Leverage Attribution: Securities in the eligible universe are selected for index inclusion based on their financial leverage ratio z-score determined during the semiannual rebalancing of the S&P 500 Quality Index. The values for all securities are ranked in ascending order.

S&P 500 Quality ROE Attribution: Securities in the eligible universe are selected for index inclusion based on their return-on-equity z-score determined during the semiannual rebalancing of the S&P 500 Quality Index. The values for all securities are ranked in ascending order.

⁵   Hao, Wenli Bill and Watts, Rupert, “A Historical Perspective on Factor Index Performance across Macroeconomic Cycles,” S&P Dow Jones Indices LLC, Nov. 14, 2024.