Indexology^® Blog

The S&P GSCI posted its best quarterly return in decades, as inflation continued to post the highest readings in decades. Commodities rose another 9.63% in March after an 8.8% rise in February. Geopolitical conflict and inflation were the two main reasons for the broad-based uptick in commodities prices (see Exhibit 1).

The S&P GSCI Energy continued to lead the way last month, up 12.47% in March. The uncertain supply situation from Russia, the world’s largest natural gas exporter and third largest oil exporter, led the U.S. to release a record amount of emergency oil from the Strategic Petroleum Reserve. Germany, highly dependent on Russian energy, initiated an emergency plan that could lead to energy rationing.

The S&P GSCI Agriculture rose 6.15%, as cotton and sugar rose by double digits, while coffee and soybeans dipped negative. The S&P GSCI Corn rose 8.44%, while the S&P GSCI Wheat rose 7.75% in March. Both grains were top exports from Russia and Ukraine but now shipments have been disrupted as the conflict continues and Black Sea ports have been sidelined. Egypt is highly dependent on wheat imports, with 80% coming from that region. Delayed shipments led the country to look to alternative countries such as the U.S. Wheat supplies in the U.S. are at their lowest levels in 14 years.

Within the S&P GSCI Industrial Metals, nickel recorded one of its biggest spikes in the history of the contract. After a wild ride with trading suspended after a massive short squeeze harangued the world’s largest nickel and stainless steel company, the S&P GSCI Nickel finished the month up 31.25%. The S&P GSCI Zinc rose 14.21%, as smelter production stalled and exchange stocks continued to move lower toward 15-year lows.

While all this volatility occurred in other sectors, the S&P GSCI Precious Metals quietly rose 2.70% due to its safe-haven status and a somewhat more cautious U.S. Fed tightening expectations caused tailwinds.

Multi-asset strategies have been getting more attention the last few years from market participants seeking pre-packaged solutions to diversification. As more strategies evolve to be increasingly complex, including black-box allocation algorithms, multiple signals, and 10 or more components, we felt it was time to highlight a simpler, transparent index-based approach.

The S&P Target Risk Indices follow a target allocation strategy with preset allocations to equities and fixed income. The suite consists of four multi-asset indices, each corresponding to a particular risk level, designed to represent a risk spectrum from conservative to aggressive. Each index has varying levels of allocation to equities and fixed income aligned with their respective risk bucket—the more conservative indices have higher exposure to fixed income, while the more aggressive indices have higher exposure to equities (see Exhibit 1).

Designed to offer diversification by asset class and by region, the indices comprise seven liquid components across the U.S., developed and emerging markets representing equities and fixed income (see Exhibit 2).

While the stock-bond allocations are set, the methodology follows a set of rules to determine weights among the components within each asset class. Within fixed income, weights are distributed with 85% to the USD broad market and 15% to international aggregate.

Within equities, weights are based on the relative proportions of the float-adjusted market capitalization (FMC) of the reference indices for each component. For example, weights are first distributed between developed and emerging markets using the FMC of the S&P Developed BMI and the S&P Emerging BMI. We further distribute the developed market weight between the U.S. and ex-U.S. following the same procedure, and so on.

The addition of fixed income into an index can limit downside and may provide stability during bear markets. During the bear markets in 2002 and 2008, we see this come into play as the more conservative indices (conservative and moderate) produced better returns than the more aggressive ones (growth and aggressive), while the S&P Composite 1500® was the worst performer (see Exhibit 3). Stability in returns balances the relatively lower total returns during periods of strong equity performance.

The risk-reduction benefits of larger allocations to less volatile fixed income assets are more prominent when measured over the long term (see Exhibit 4). The risk-adjusted returns of the portfolios, defined as annualized return over annualized volatility, was highest in the conservative index (1.09) and decreased as the portfolios became more aggressive. While the aggressive index was not as risk efficient (0.54), it had the highest annualized return in absolute terms; market participants with higher risk tolerances typically have longer investment horizons and tend to place a higher focus on long-term capital growth.

In this blog, we highlighted the key features of the S&P Target Risk Indices, including the convenience of a simple and transparent pre-packaged multi-asset solution, the flexibility in choosing different risk profiles and the potential risk reduction provided by the addition of fixed income.

After peaks in S&P 500^® concentration, the S&P 500 Equal Weight Index has tended to outperform, suggesting that there is a relationship between changes in concentration and the relative performance of equal weighting. But, does this relationship also occur at the sector level?

Using the historical adjusted HHI (Herfindahl-Hirschman Index), we’ve previously established that concentration tends to mean-revert in most sectors. Changes in concentration affect the relative performance of the equal-weighted versions of each sector. Exhibit 1 compares the relative performance of equal-weighted sector strategies to their adjusted HHI. Equal-weighted sectors tend to outperform after peaks in their sector concentration. This is particularly noticeable for Information Technology.

The negative correlations that we consistently observe in Exhibit 1 between monthly changes in adjusted HHI and relative equal-weighted performance illustrate an important point: changes in equal-weighted relative performance and changes in concentration are not two separate things, but two aspects of the same thing. If larger stocks outperform smaller ones, concentration will increase, and equal weight will underperform. Similarly, if smaller stocks outperform, concentration will decrease, and equal weight will outperform.

Recent levels of concentration vary across sectors, as we see in Exhibit 2, which plots the historical range of the adjusted HHI for the S&P 500 and its sectors, along with a bar chart showing current levels. The range of concentration also varies widely across sectors. For example, Industrials had the widest adjusted HHI range, while Utilities had the narrowest range among sectors. We can also infer that the adjusted HHIs for Energy, Industrials and Materials have been at historically low levels, while those for Information Technology and Consumer Discretionary have been relatively high.

Sector concentration has important implications for index weighting decisions. Since Information Technology and Consumer Discretionary’s adjusted HHIs are at historically high levels, equal weighting within these sectors might be worth considering rather than cap weighting, since concentration tends to mean-revert over time. In contrast, the adjusted HHIs for Energy, Industrials and Materials are at historically low levels. This means that cap-weighted sector strategies could be beneficial if these currently low concentration levels move upward.

Recently announced results for a consultation on the S&P PACT™ Indices (S&P Paris-Aligned & Climate Transition Indices) reveal that they will now target country and sector neutrality. This has the potential benefit of comparing companies, as much as possible, to close peers (those in the same sector and country), while reducing active risk.

The EU’s minimum requirements for Climate Transition and Paris-aligned Benchmarks (CTB and PAB, respectively) represent a new paradigm within climate investing—an absolute decarbonization pathway, meaning that strategies may have to achieve significantly higher levels of decarbonization relative to a benchmark in the future than they do today, if the benchmark does not significantly decarbonize (see Exhibit 1).

Given we do not know whether the world will decarbonize, we stress tested the methodology to understand, even when getting to a 90% decarbonization relative to the underlying universe, whether the S&P PACT Indices can meet the index objective. We see the index methodology finds a solution, becoming more active as the decarbonization grows, as expected, but remaining relatively benchmark like (see Exhibit 2).²

As we don’t know how much of a relative decarbonization we will need in the future, we can’t know how active the index will need to be. So, what’s the best way to address sector and country active risk that can continue to work throughout time?

A constraint on sector allocation may yield suboptimal outcomes. If we were to implement a tight sector constraint that worked well at inception (30% decarbonization), this may result in extreme stock-specific risk to maintain sector neutrality at greater levels of decarbonization, which may potentially be required in the future—e.g., sector constraints could be met by allocating all of the weight of one sector to one or two stocks with the lowest carbon intensity, causing concentration risk. Alternatively, we could take a more relaxed approach and set a sector constraint that would work well at a closer to 80% decarbonization, which would likely take on significantly more active sector risk than required to meet the ESG and climate objectives.

A more flexible solution might be to incorporate active sector weight penalization within the objective function, to produce an optimal balance among stock-specific active risk, country active risk and sector active risk—the change we will see in the upcoming rebalance, based on the recent consultation. This new objective function allows for a broad reduction in active sector weight across indices (see Exhibit 3) and potential levels of decarbonization required.

How do these reduced sector and country active risks, alongside other methodology changes, translate into predicted tracking error? We see large reductions within the largest regional indices, for both the climate transition and Paris-aligned variants (see Exhibit 4).

Impact analysis reveals changes to the S&P PACT Indices via this consultation would have caused companies to be compared more with their direct country and sector peers and reduced tracking error, while still meeting all the climate objectives, within a glass-box optimization framework. This allows for a sophisticated, multifaceted ESG and climate index and has historically provided benchmark-like characteristics.

¹ For frame of reference, between 1950 and 2019, the global CO₂ emissions grew by 2.66% (Our World in Data, 2022).

² Note that this stress test is based on a hypothetical index based on the S&P Developed ex-U.S. universe. This stress test assumes that only the decarbonization rate required will change over time.

In part one of the Beyond Bitcoin series, we introduced the importance of applying a sector classification system to the emerging digital assets asset class. We examined what it looks like for an investor to expand their view of the digital assets markets beyond the oft-cited mega-cap assets (such as Bitcoin and Ethereum) and discussed how a sector classification could potentially benefit portfolio managers making active investment decisions.

This part of the series explores how this would potentially increase access for market participants through the transparency, clarity, and stability that comes from applying a sector classification.

Benefits of Adopting a Standardized Sector Classification

The broad adoption of a digital assets sector classification system has the capability to provide benefits to market participants, supplement the growth of digital assets, and enable new investors to participate in the market. Notable benefits include:

Adding Efficiency to the Portfolio Management Process

Asset sector categorization allows investors to overlay traditional portfolio management methods within the portfolio construction process. For example, investors could compare risk and return metrics across different sectors as well as examine historical returns to see how specific assets have performed compared to their sector. Going further, investors could study the correlation of returns across sectors with the goal of reducing idiosyncratic risk within a portfolio.

These types of analyses, which are common in mature markets, support the overall portfolio construction process. However, the adoption of similar practices within the digital assets space has yet to come to fruition in the same way it is used in public equities markets, largely due to barriers in classifying digital assets in a standard and consistent way.

HOW IT COULD WORK

A hypothetical investor may compare the 12-month historical performance and standard deviation of returns for assets within the Smart Contract Platforms and Scaling sectors. They see that the Smart Contract Platforms sector outperformed the Scaling sector in the last 12 months, while maintaining a comparable level of risk. This information, plus a manager-driven macro-level view of the fundamentals within the digital assets market, could inform an investment thesis for overweighting or underweighting an allocation to the Scaling sector.

Creation of New Investment Products

As the digital assets asset class grows and matures, the demand for new products and tailored exposures from market participants continues to increase. Using a sector classification system to build investment products that help formulate tailored exposures in the digital assets market could benefit issuers, investors, and the overall market structure. As regulatory clarity unfolds, investors may see the introduction of products that enable quick exposure to specific corners of the digital assets market.

HOW IT COULD WORK

Similar to how investors are able to gain exposure to certain sectors via sector-specific ETFs in the stock market today, they may demand similarly tailored exposure to digital assets. Investors may choose from an array of products that provide exposure to a specific sector, such as Metaverse ecosystems, Play to Earn tokens, or DeFi platforms, allowing investors to create more diverse and efficient portfolios.

Enable Index Construction to Assist in Benchmarking

Adoption of a standardized sector classification system could lead to new digital asset-specific indices, which can offer greater transparency to market participants within the digital assets space as well as provide a standardized point of comparison for benchmarking. Experienced market participants, including sage index providers, recognize this is a critical requirement to advance market maturity.

A great example of this is the S&P Dow Jones Indices suite of digital asset indices designed to track exposure to different areas of the market. Indices help investors benchmark asset or portfolio performance, compare professional investment managers and set trackable risk and return goals.

HOW IT COULD WORK

Self-directed investors could opt to invest in digital assets and gauge their performance against an index that tracks the broader market, such as the S&P Cryptocurrency Broad Digital Market Index. From another side of the market, a Fund of Funds Manager may look to construct a portfolio of active digital asset hedge funds primarily based on previous performance against the S&P Cryptocurrency Broad Digital Market Index.

Investor Portfolio Governance and Guideline Supervision

The application of a standardized classification system allows for tailored and rigorous controls in portfolio mandate documentation and subsequent portfolio monitoring. In addition, classification empowers professional money managers and clients to specify more granular controls for investment managers within traditional agreements, such as an investment policy statement or financial plan.

HOW IT COULD WORK

The client and money manager could agree that no more than a certain percentage of the portfolio value can be allocated to DeFi strategies, and only another percent can be allocated to the digital assets asset class. These rules could be crucial for new investors who are hesitant to enter the market due to risk or volatility concerns. The correct implementation of such guidelines could enable a risk-focused approach to entering the market and promote broader adoption.

The underlying theme in all of the above benefits is that they make digital assets more accessible for investors. A classification system removes significant barriers to entry for new investors and makes the asset class more digestible at all levels of market participation. Bypassing cryptocurrency jargon and viewing the market via a standardized digital assets sector classification system can help investors understand their investment and increase transparency, stability and participation.

About Lukka

Lukka is a firm that helps solve some of the greatest financial challenges in crypto and has the intellectual resources, along with the data and processing capabilities, to test hypothetical scenarios like the one here. For more information on how Lukka puts data to work across multiple finance sectors, traditional and decentralized, supporting industries from insurance to Formula E, go to our website at Lukka.tech

For all press-related inquiries, please visit Lukka.tech/press/

Lukka, Inc. is the provider of S&P Dow Jones Indices’ cryptocurrency pricing, custody and reference data. S&P Global, Inc., the parent of S&P Dow Jones Indices, is an investor in L ukka.  For information on S&P Global’s investment in Lukka, please see here. In addition, representatives of Lukka may provide consultative services to the S&P Digital Assets Index Committee from time to time.

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