Indexology^® Blog

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.

U.S. equities have been whipsawed by tariff fears in the past couple of months, and particularly over the past week, with sharp double-digit swings for the S&P 500^®. The index’s slide into correction territory has coincided with a rise in implied volatility, with VIX^® rising briefly above the 50-point handle. In environments characterized by both market declines and high volatility, we typically hear that active management can have an advantage over index funds.

While the current macro environment is unusual, to say the least, we can look to history for a better understanding of active manager performance trends. We start by analyzing the 24-year history of our SPIVA^® U.S. Scorecard, where we measure the performance of active managers versus their appropriate benchmarks. Exhibit 1 shows that 5 out of these 24 years were characterized by market declines. Notably, all five years coincided with majority underperformance for large-cap funds, ranging from 51% in 2022 to 68% in 2002.

But how has the performance of active managers during market declines compared relative to their performance during market gains? A simple way to analyze the conditions for stock selection is to measure the percentage of constituents that beat the benchmark. Exhibit 2 illustrates that, on average, 56% of member stocks beat The 500™ during the five years when the index declined, outpacing the 46% during the 19 years when the market posted gains. This makes sense, as down markets can provide an easier hurdle for stocks to beat.

Despite this advantage, 61% of large-cap funds underperformed The 500 during down markets, only slightly better than the 65% underperformance during up markets and the 64% average across all 24 years.

As well as market downturns, the other potentially advantageous element for active managers to examine is the rise in volatility and specifically dispersion, which measures how differently stocks are performing relative to each other. The value of stock-selection skill rises when dispersion is high, which could mean greater opportunities for stock pickers to outperform.

In Exhibit 3, we divided the years in our SPIVA database into low and high dispersion periods, defined as when the benchmark S&P 500’s dispersion was below the historical 25^th percentile and above the 75^th percentile, respectively. As expected, large-cap managers generally fared better in high dispersion periods, with 56% underperforming the S&P 500, lower than the 67% during low dispersion periods. Still, high dispersion regimes were characterized by majority underperformance.

In addition to analyzing historical dispersion environments, we can also look to implied dispersion to understand the potential for future opportunities for stock selection. We observe in Exhibit 4 that the Cboe S&P 500 Dispersion Index (DSPX), which uses listed options to measures the expectations for dispersion over the next 30 calendar days, reached an all-time live high of 46.5 on April 10, 2025, and is currently just below the 40-point handle. This level means that the market expects that the spread of annualized S&P 500 stock returns will have a standard deviation of close to 40% next month, which could signify plentiful future potential prospects for active stock selection.

History tells us that although active managers tended to fare better during market declines and high dispersion periods, majority underperformance prevailed regardless of market conditions. The future dispersion environment may be fortuitous, but the question of the hour is whether stock pickers will be able to shine during these unique circumstances. Thanks to our SPIVA Scorecards, we will be watching.