Indexology^® Blog

The first half of 2026 was notable for U.S. equity markets¹ and was characterized by sharp sector performance reversals, particularly in S&P 500^® Information Technology and S&P 500 Energy. Both sectors were up 20% YTD through Q2,² but they took very different paths to get there.

During the first quarter, Energy stocks had an outsized effect on the broader market as oil supply shortages, fueled by the war in Iran, rippled through supply chains. This pushed the Energy Select Sector up 38%, while the Technology Select Sector declined 8%. The tide turned in Q2 when enthusiasm surrounding AI companies and their related dependencies sparked a major turnaround in the Technology sector, sending the Technology Select Sector up 43%, while the Energy Select Sector fell 13% due to retreating oil prices and easing geopolitical concerns.

Looking across the pond, similar trends were observed in Europe. The S&P Europe 350 – Energy was up 40% in USD terms in Q1 while the S&P Europe 350 – Information Technology rose 42% in Q2.

Despite their similar sector co-movements in the short term, the historical outperformance of S&P 500 compared to the S&P Europe 350³ highlights the relevance of U.S. equities for European market participants (see Exhibit 2).

Adopting a sectoral perspective can help explain the long-term outperformance of the S&P 500 versus the S&P Europe 350. Exhibit 3 shows that the S&P 500 is concentrated in Information Technology, while the S&P Europe 350 has a greater weight in Financials and Industrials.

Another explanation for the S&P 500’s historical outperformance is its comparatively stronger within-sector performance. In fact, since 2020, 9 of the 11 GICS^® sectors in the S&P 500 outperformed their European peers.

Notably, Exhibit 5 shows that the overweight to Information Technology and the outperformance of stocks within the sector accounted for a large portion of the S&P 500’s outperformance versus the S&P Europe 350.

Recently, heightened geopolitical concerns and fluctuating oil prices have put the Energy sector back in the spotlight, while recent AI-related jitters have caused a pullback in Information Technology in both the U.S. and Europe. While we don’t know if another reversal⁴ is in order, evaluating the U.S. market through a sectoral perspective offers important context for European market participants.

¹ For more information, see our U.S. Dashboard.

² For more information, see our U.S. Sector Dashboard.

³ Tim Edwards et al., “Why Does The S&P 500 Matter to the U.K.?” S&P Dow Jones Indices LLC., Jan 17, 2023.

⁴ Anu Ganti, “Regimes, Reversals and Risk”, S&P Dow Jones Indices LLC., July 9, 2026.

For many market participants, the priority is not pursuing every inch of a bull market run but rather participating while managing downside risk. This desire for a more predictable investment experience has contributed to the growth of buffered (or defined outcome) strategies, which aim to balance participation in market gains with a defined level of protection against losses.

What Is a Buffered Strategy?

A buffered strategy combines equity investment (such as one that tracks the S&P 500^®) with a series of options designed to shape the potential outcomes. Typically, this involves positions in three options with the same maturity (e.g., one year): buying a protective put option, and selling a further out-of-the-money put option and an out-of-the-money call option to finance the purchase. The resulting payoff profile has three key features.

1. Buffer: Protection against moderate declines. For example, the S&P 500 10% Buffered Index Series targets a fixed downside protection of up to 10% of any decline in the underlying index at the option expiration.
2. Cap: A limit on upside participation, reflecting the cost of downside protection.
3. Market Exposure beyond Thresholds: Declines beyond the buffer are not mitigated, with no participation in gains above the cap.

The result is a narrower payoff profile, allowing market participants to trade off upside potential for a more defined range of outcomes.¹

Why Use Buffered Strategies?

While the primary motivation is risk management, a secondary reason can be building for the long term: by moderating the impact of downturns, buffered strategies may help maintain long-term participation in market gains.

Exhibit 1 compares the performance of the S&P 500 10% Buffered Index Series with the S&P 500 between rebalance dates. The index is designed to track the S&P 500 in years of modest gains while mitigating losses in years of modest declines. The downside protection feature has provided outperformance during market downturns, although some of those periods still produced negative results. Conversely, capped upside participation has led to underperformance during strong bull markets.

How Often Do Such Strong Bull Markets Occur?

To illustrate the trade-off between capped upside and downside protection, Exhibit 2 shows the historical distribution of one‑year rolling performance for the S&P 500 from March 1957 to June 2026.

• The average one-year gain was 8.9%, with a median of 10.6%.
• By comparison, the historical average call strike for the S&P 500 10% Buffered Index Series was 15.2%.²

This suggests that the S&P 500 10% Buffered Index Series may have matched or exceeded the S&P 500 in more than half of these one‑year windows on an absolute return basis—assuming the rebalancing horizon aligned with the one‑year period. Consistent with this observation, the S&P 500 10% Buffered Index Series matched or outperformed the S&P 500 in 52% of the one‑year intervals shown in Exhibit 1 between June 2011 and June 2026.

Risk-Adjusted Performance

Buffered strategies may also be evaluated through a risk-adjusted lens. From September 2018 to June 2026:

• The S&P 500 posted an annualized performance of 14.8% with 17.0% volatility.
• The S&P 500 10% Buffered Index Series averaged 11.1% performance with 10.6% volatility.

While historical performance was lower in absolute terms, the reduction in volatility resulted in higher performance-to-risk ratios compared with the broad equity market.

Hypothetical Portfolio Implications

Buffered strategies can also play a role in portfolio construction. Their distinct risk/return profiles and correlation dynamics may complement traditional equity and fixed income exposures. In environments where equity and bond correlations remain elevated, narrowing the range of potential outcomes for equity allocations may be particularly relevant.

As an illustration, if a classic 60/40 equity/bond index mix was hypothetically augmented to include a buffered allocation, it could have raised the effective equity exposure while maintaining similar volatility levels, resulting in higher hypothetical risk-adjusted performance (see Exhibit 4). This reflects the potential for buffered strategies to act as a bridge between growth-oriented and defensive allocations.

In short, buffered indices provide a transparent, rules-based framework for benchmarking and analyzing the potential outcomes from buffered strategies. By combining equity with option overlays, they reflect a structured approach to balancing participation and protection. For a deeper dive into buffered indices, please see “Defining Paths with Options-Based Index Strategies.”

¹ See “Introducing the S&P 500 Defined Outcome Index Series” for the examples of S&P DJI’s buffered indices

² The average call option strike price is calculated based on the back-tested data of the S&P 500 10% Buffered Index March, June, September and December Series between June 2011 and December 2025.

Moving beyond Narrative: A Fundamental Lens on Digital Assets

Digital asset markets have evolved rapidly, but the tools used to evaluate them have not kept pace. Many crypto strategies have historically been shaped by market narratives, momentum or broad exposure to the largest tokens.¹ Designed with the crypto and blockchain investment firm Pantera Capital and powered by Artemis Analytics data, the S&P Pantera Digital Asset Index takes a different approach: it measures the market performance of a subset of digital assets that demonstrate observable and recurring economic activity through protocol-level revenue generation.

In traditional equity markets, investors often look for evidence of fundamental strength through measures such as revenues, earnings, margins or cash flows. Digital assets do not map as neatly onto that framework, but some protocols do generate measurable economic activity through mechanisms such as transaction fees, token burns or other protocol-level revenue streams. The S&P Pantera Digital Asset Index uses this activity as an indicator of financial viability, mirroring the higher entry threshold of the S&P 500^® compared to other U.S. equity indices² alongside standard index construction toolkits related to investability criteria and diversification.

Constructing the S&P Pantera Digital Asset Index

The starting universe, the S&P Cryptocurrency Broad Digital Asset (BDA) Index, applies a base level of filters to remove smaller assets. One unique feature of digital assets is that the barrier to creating a tradable token is extremely low. Unlike public equities, where exchange listings come with baseline disclosure requirements and business standards, crypto offers no equivalent universal filter. The S&P Pantera Digital Asset Index has a seasoning rule of three months and constituents must be listed on a vetted exchange, recognizing that some newly launched crypto projects may achieve high market capitalization and liquidity in the very short run post-initial coin offering (ICO). Please see the S&P Pantera Digital Asset Index Methodology for more details.

Composition and Constituents of the S&P Pantera Digital Asset Index

Here we look at the impact of the index filters on the final composition. The index doesn’t apply a fixed count, allowing constituents that meet the thresholds to be added over time. The current constituent count as of the June 2026 rebalance was 18, with a minimum constituent count of 5. While the early history had few protocols, over the last two years, the constituent count has been consistently above 10, perhaps indicating the increased maturity of the type of protocols available to market participants. While the number of protocols in our initial revenue pool was 48, around 15 were dropped based on the underlying benchmark and adjusted market cap, 2 failed to meet the liquidity threshold and 13 were excluded as their revenue falls within the bottom 1% of eligible constituents.

The revenue filter on the index means that, in practice, the type of protocols we typically see in the final selection are often smart contract platforms like Ethereum and Solana and decentralized finance applications like Hyperliquid. The largest five constituents are Ethereum, Binance Coin, Solana, TRON and Hyperliquid. The largest non-constituents when compared to the S&P Cryptocurrency BDA Index are Bitcoin and XRP, which are significant weights in standard market-cap-weighted indices.

Performance Characteristics of the S&P Pantera Digital Asset Index

Despite not having Bitcoin in the index, based on back-tested analysis, the S&P Pantera Digital Asset Index has outperformed indices like the S&P Cryptocurrency BDA Index where Bitcoin accounts for approximately 70% of the index weight. Notably, over the three-year back-tested period ending June 30, 2026, the S&P Pantera Digital Asset Index had an annualized return of 25% versus 14% for the S&P Cryptocurrency BDA Index, an excess return of more than 10 percentage points, with a similar picture across the five-year period and since its first value date of June 18, 2021 (see Exhibit 4a and Exhibit 4b).

Conclusion

The S&P Pantera Digital Asset Index may serve a dual purpose: as a benchmark for actively managed crypto strategies and as a foundation for index-linked products. For different types of market participants, the S&P Pantera Digital Asset Index reflects the continued maturation of crypto benchmarking. By anchoring selection to observable protocol-level revenue, it offers a disciplined framework for evaluating digital assets through fundamentals. In a market often driven by narrative trading, the index’s emphasis on measurable activity offers a more data-driven way to understand where economic value may be forming in the digital asset ecosystem.

Learn more about the S&P Pantera Digital Asset Index in our brochure.

¹ Watkins, Ryan, “The Productive Cryptoeconomy: A Thesis for Adoption,” Syncracy, Feb. 25, 2025.

² S&P Dow Jones Indices and Pantera Capital Launch New Index for Digital Assets – Index Launches | S&P Dow Jones Indices

³ Preston, Hamish, “Seasoning to Taste,” S&P Dow Jones Indices, March 3, 2026.

⁴ Weseley, Alex, “Crypto Revenue,” Artemis, Sept. 12, 2025.

As U.S. equity markets continue to reach new highs, market leadership has become increasingly concentrated among a handful of large-cap stocks. The S&P 500 Capped 35/20 Indices use weight caps to maintain diversification within sector-specific benchmarks. Each index in the range comprises all constituents from its respective GICS^® sector of the S&P 500^® while mitigating single-stock dominance. Capped indices such as these have emerged to meet the need for benchmarks that may facilitate diversification rules, rather than as an endorsement of any single approach to managing concentration.

The 35/20 Capping Mechanism

The S&P 500 Capped 35/20 Indices implement a multi-tier capping framework¹ to control concentration risk. The capping thresholds are designed to facilitate the ability of regulated investment companies to meet certain diversification requirements under Directive 2009/65/EC (the “UCITS Directive”)² of the European Parliament. The UCITS Directive imposes obligations on such investment companies, not on the index or its providers, and replication of the index does not guarantee compliance with the UCITS Directive at any given time.

At quarterly rebalancing (effective after the close of the third Friday of March, June, September and December), constituents are weighted according to float-adjusted market capitalization (FMC), with the largest and second-largest companies capped at 31.5% and 18% of the total index weight, respectively. Reference prices for the rebalance are taken from the Wednesday prior to the second Friday of the quarterly rebalance month.

In addition, two further capping checks occur in all months of the year:

• Mid-Month: Checks are performed on the Wednesday prior to the third Friday using reference weights from that date. If the 35%/20% caps are breached, reweighting is effective after the close of the third Friday.
• End-of-Month: Checks are performed on the third-to-last business day of the month. If the 35%/20% caps are breached, reweighting is effective after the close of the last business day.

In all capping scenarios, excess weight is proportionally redistributed to uncapped companies through an iterative process.

Constituent weights may fluctuate between reference and rebalance dates due to market movements and may exceed the capping threshold during such periods.

The Impact of Capping across Sectors

Exhibit 1 shows the impact of different capping mechanisms on the weights of the largest companies within the S&P 500 Capped 35/20 Indices against their uncapped S&P 500 Sector counterparts and the Select Sector^® range (which apply a different, 25/5/50 capping rule).³

For broadly diversified sectors such as Industrials and Financials, the weights of the largest companies have naturally fallen below capping thresholds over the last 10 years. Consequently, the 35/20 indices mirror their capped and uncapped counterparts in constituent weighting and historical performance over the period.

On the other hand, sectors dominated by mega-cap names show the biggest variations in weight under the different capping methodologies. For example, Alphabet’s substantial 60.3% uncapped weight in Communication Services falls to 32.7% under the 35/20 methodology, and 23.5% under the Select Sectors’ 25/5/50 rules. Similarly, in Consumer Discretionary, Amazon’s 38.9% uncapped weight is reduced to 30.5% and 22.2% in the S&P Capped 35/20 and Select Sector indices, respectively.

In sectors where capping has been triggered, the differences in constituent weights result in varying risk and performance profiles across the different methodologies. As the capping mechanisms act to redistribute weight away from the largest names, absolute performance can fluctuate depending on the relative performance of those companies versus their smaller peers over a given period. While this can lead to noticeable differences in absolute performance, risk-adjusted performance tends to align more closely across methodologies. Over the three-year period ending in June 2026, performance on this basis was largely similar, with the S&P 500 Capped 35/20 indices reflecting a marginally better performance per unit of risk in all three sectors compared to their uncapped and Select Sector counterparts.

Conclusion

The S&P 500 Capped 35/20 Indices measure S&P 500 sector constituents while utilizing a 35/20 capping framework that has historically provided diversification among companies within each index. By moderating single-stock dominance while maintaining weight in market leaders, the 35/20 methodology offers a more balanced approach to reflecting evolving market dynamics.

Disclaimer

S&P Dow Jones Indices is an index provider and does not provide investment advice. The index capping rules are designed to facilitate the ability of regulated investment companies to meet certain diversification requirements but do not guarantee compliance with the UCITS Directive or any other regulatory framework. Fund managers and other market participants remain solely responsible for ensuring that their products comply with all applicable regulatory requirements.

¹ For the full index methodology of these indices, please see the S&P U.S. Indices Methodology.

2 For more information on capping thresholds, please refer to the Regulatory Capping Requirements section of S&P Dow Jones Indices’ Equity Indices Policies & Practices Methodology.

³ For further details of the Select Sector Indices, see Preston, Hamish “Explaining Changes to Select Sector Indices,” S&P Dow Jones Indices, Sept. 10, 2024.

S&P GSCI Outpaced BCOM, Stocks and Bonds

We recently published The S&P GSCI: Built for the Cycle, a 10‑year analysis of how broad commodity indices behaved across differing inflation regimes, and why the S&P GSCI’s production‑weighted design historically demonstrated a higher inflation beta than alternatives such as the Bloomberg Commodity Index (BCOM). This blog is a follow‑up, turning the focus to 2026 YTD performance. In the first five months of 2026, inflation re‑accelerated due to global conflicts constraining energy supplies. YTD performance aligned with findings explored in the longer study.

The S&P GSCI rose 37.7% through May 29, 2026, outperforming the Bloomberg Commodity Index (BCOM), which gained 25.0%. Both commodity benchmarks meaningfully outperformed equities (the S&P 500^® was up 11.3%) and bonds (the S&P U.S. Aggregate Bond Index was up 0.6%) during the period. We’ll see how an inflation shock highlighted the inflation-hedging characteristics of commodities, the implications of production weighting in the S&P GSCI and the substantial contribution of the Energy sector to commodity performance.

Commodities Outperformed as Inflation Accelerated

After ending 2025 at about 2.7% year-over-year, U.S. consumer inflation (CPI-U) climbed to about 4.3% year-over-year by May 2026, an increase of 1.6 percentage points since the start of the year. A key driver was rising energy prices, due to a sharp oil price shock that rippled through broader prices.

Historically, inflation shocks have typically benefited commodities, and 2026 was a textbook example. Commodity performance took off in early 2026 as inflation rose. By late March, the S&P GSCI TR had climbed to double-digit gains, peaking in mid-May with a YTD increase of over 50% before settling at 37.7% at the end of May. In contrast, the S&P 500 TR ended May up a solid but considerably lower 11.3%, while the S&P U.S. Aggregate Bond Index was essentially flat (up 0.6%) over the same period. In short, commodities (especially those that are Energy driven) significantly outperformed traditional assets during this inflationary spike.

GSCI versus BCOM: Production Weighting Shows Its Value

Within commodities, the S&P GSCI outpaced BCOM YTD in 2026, continuing a familiar pattern from the past decade. Through the end of May, the S&P GSCI’s gain of 37.7% topped the BCOM’s 25.0% by over 12 percentage points. This outperformance under inflationary conditions is consistent with the findings of the 10-year analysis: the S&P GSCI’s inflation beta was about 1.7× higher than the BCOM’s (9.1 versus 5.5, respectively), meaning that the S&P GSCI has historically moved about 65% more for each 1% move in inflation. The 2026 YTD data were consistent with this relationship, with the S&P GSCI reacting more strongly to the inflation surprise than BCOM.

To explain the S&P GSCI’s outperformance, we can point to the production-weighted structure embedded in the index weighting scheme, which may have contributed to relative performance when inflation-hedging characteristics were particularly relevant. This result is consistent with findings from the past decade—the S&P GSCI has exhibited stronger performance in higher-inflation regimes.

Energy Takes the Lead in 2026

The 2026 commodity rally was led by Energy. Within the S&P GSCI, the Energy sector gained 76.3% through the end of May, significantly exceeding the gains in all other sectors. For context, Industrial Metals was up 16.0%, Precious Metals rose 5.3%, and Agriculture and Livestock each climbed roughly 2% YTD.

These figures are consistent with the central findings of The S&P GSCI: Built for the Cycle, which highlighted how production weighting aligns a commodity index with the real-economy cost structure. When Energy prices have risen, an index weighted by the scale of global production tended to reflect that move in proportion to Energy’s economic footprint. The heavy Energy weight in the S&P GSCI is an important characteristic of inflation-responsive commodity weighting. Of course, greater sensitivity means accepting higher volatility, but as YTD 2026 has shown, the advantages could be significant during inflationary surges.

This content may be AI-assisted and is composed, reviewed, edited, and approved by S&P Global.