Indexology^® Blog

After exploring the characteristics and lifecycle of special purpose acquisition companies (SPACs) in our previous SPAC blog series, we will now examine the recently launched S&P U.S. SPAC Index.

The S&P U.S. SPAC Index is designed to measure the performance of a minimum of 30 common stocks of SPACs listed on U.S. exchanges. The constituents are weighted by their one-month median daily value traded (MDVT), subject to a single constituent weight cap of 15%.

As of Aug. 31, 2021, the median market capitalization of all listed SPACs was USD 270 million, much lower than the median market capitalization of USD 1.6 billion of S&P SmallCap 600^® constituents. Since most SPACs are small- or micro-cap companies, we compared the performance of the S&P U.S. SPAC Index against the S&P SmallCap 600, which is used as its benchmark. Historically, the S&P U.S. SPAC Index exhibited better risk/return characteristics than the benchmark, especially over the short- and mid-term periods. In the 45-month back-tested period ending in August 2021, the strategy exhibited a lower maximum drawdown (-26.8%) compared with the S&P SmallCap 600 (-36.1%; see Exhibit 2).

Over the period studied, the S&P U.S. SPAC Index provided downside protection in times of market turbulence. During all the months in which the benchmark was down between December 2017 and August 2021, the S&P U.S. SPAC Index outperformed 84.6% of the time and generated a monthly average excess return of 6.8% over the benchmark¹ (see Exhibit 3).

The ability to provide downside protection exists due to the structure of SPACs. IPO proceeds are held in a trust account and are invested in U.S. Treasuries while the management team is looking for the target. Thus, they have a fixed income component to provide downside protection.

Stay tuned for our upcoming research paper on SPACs!

¹ Up months: When the S&P SmallCap 600 has a positive monthly return.
Down months: When the S&P SmallCap 600 has a negative monthly return.

Active managers’ performance was disappointing in 2020, despite the market’s heightened volatility. As the market continues to march upward in 2021, it’s natural to wonder if current conditions are favorable for stock pickers.  We expect active managers’ difficulties to persist.

We can think of volatility in terms of its components: dispersion and correlation. Active managers should prefer above-average dispersion, because stock selection skill is worth more when dispersion is high. At the same time, the price of an active strategy—in terms of incremental volatility—will be relatively small when correlations are high. Both correlation and dispersion are currently below average, as we see in Exhibit 1, indicating relatively inauspicious conditions for active managers.

As a result, the required incremental return for large-cap active managers has risen as correlations have declined, which means they are giving up a greater diversification benefit. Meanwhile, lower dispersion makes it harder to add value. We see similar results for smaller-cap active managers, signaling a relatively more challenging environment for active management.

Large-cap active managers face an additional style bias hurdle. Most active portfolios are closer to equal than cap weighted, which means that they have an advantage when smaller stocks outperform. Unfortunately, Exhibit 2 shows that the largest names have recently been performance leaders.

The dominance of mega caps also hinders stock selection, as illustrated in Exhibit 3. In the first quarter of 2021, 59% of S&P 500 members beat the index, when smaller-caps were outperforming. Since then, the tide has turned, and only 34% of stocks outperformed the index, as mega caps outperformed during this period.

The current bleak environment does not bode well for active managers. We recall that 2020 was characterized by relatively favorable conditions for stock selection, and most active managers still underperformed, proving that genuine stock selection skill is rare. If these trends continue, when SPIVA results for 2021 become available, it would not be surprising if we saw lackluster active management performance once again.

On Aug. 10, 2021, the US Senate approved the much-debated Infrastructure Bill, a cornerstone of President Joe Biden’s agenda. The bill overall is worth over $1 trillion. Yet the impact on the digital asset community, while small from a dollar perspective, may be equally outsized. It is estimated that the bill will increase tax revenue collected from digital asset investors by $28 billion, not by raising tax rates or creating new taxes but rather by applying new information reporting requirements on participants in the digital asset industry.

Generally, under Section 6045 rules, those falling under the definition of a “broker” are required to issue a Form 1099 to report gross proceeds on a transaction-by-transaction basis together with their customers’ names, social security numbers, and other relevant information. The bill expands the definition of a broker to include “any person… effectuating transfers of digital assets, including any decentralized exchange (DEX) or peer-to-peer marketplace.”

This development could have a significant impact on many participants in the digital asset ecosystem, particularly within decentralized finance (DeFi), and would likely put a strain on the entire sector.

Complying with Section 6045 information reporting requirements may not prove possible for many DeFi participants as they have no visibility to the parties sending or receiving digital assets. The approved bill’s language has an extensive net, potentially covering miners, services that stake digital assets, node operators, validators, smart contracts, open source developers, hardware and software wallet manufacturers, DAO token holders, and others. This also touches individual investors who are either:

1. • Buying or selling crypto from other individuals
   • Trading crypto for crypto through a crypto exchange (or equivalent) as crypto/crypto trades are simply two transfers occurring at the same time that a broker or dealer facilitates, most often through the use of technology, or through a DEX, where there may not be an institution or any entity at all and only technology facilitating an exchange between individuals

This would effectively require affected individuals to report at the level of institutions. Transactional reporting is easily done by traditional financial institutions, where it is clear who the broker is (for example, an entity like fund manager) and who the customers are (for example, investors in fund manager’s funds).

The issue was recognized by a number of Senators, including Senators Wyden, Toomey, and Lummis, who advocated revising the language to clearly exclude miners, hardware and software wallets providers, and limit the reporting obligations to the parties who can actually report (for example, crypto exchanges).

However, the narrowed scope was determined to reduce the projected revenue by approximately $5.17 billion. Despite Senator Portman tweeting that an agreement was reached on “an amendment to clarify IRS reporting rules for crypto transactions without curbing innovation or imposing information reporting requirements on stakers, miners, or other non-brokers,” the language remained in the originally proposed form.

If the bill becomes law without narrowing down the definition of a broker it may result in overly broad application of the information reporting rules by the IRS, in turn resulting in the outflow of talent and innovation overseas as venues owned and administered by foreign persons are out of reach of US reporting rules. This could materially limit the economic benefits of the digital asset industry in the United States compared to the global economy. Since most cryptocurrencies themselves are borderless, this may not impact cryptocurrency indices overall, but rather it could affect which projects grow in the US versus ex-US.

Because the Treasury and the IRS are yet to issue any formal guidance on the taxation of digital assets, it is not unreasonable to expect at least 18-24 months of preparation time before the new rules come into effect. Currently, the law is expected to be implemented in 2023.

Tracking the performance of a selection of cryptocurrencies, including Bitcoin and Ethereum, the S&P Cryptocurrency Indices are designed to bring transparency to this evolving, unique asset class. You can learn more about S&P DJI’s digital asset solutions here.

Disclaimer:

The views and opinions of any third-party author are his/her own and may not necessarily represent the views or opinions of S&P Dow Jones Indices or any of its affiliates.

In a previous blog, we highlighted the objective of the S&P Multi-Asset Dynamic Inflation Strategy Index and summarized its methodology. We justified our strategy choice for each inflation regime (high, medium, low) and illustrated how the index dynamically switches between these strategies based on a monthly inflation forecast. In this blog, we will take a closer look at the performance of the index in comparison to its underlying strategies and demonstrate some possible advantages of the dynamic switching approach.

During a recent three-year period ending July 2021 (see Exhibit 1), we see that the S&P Multi-Asset Dynamic Inflation Strategy Index (SPMADIST) outperformed its component strategies as well as some of the popular “inflation hedge” asset classes, such as broad commodities (GSCI), real estate (REIT), and inflation-protected bonds (TIPS). To understand how the index achieved this, let us examine the inflation regimes in detail.

Based on our definitions, inflation stayed in the medium regime until February 2020, during which time the volatility-weighted (VolWt) strategy took effect. This led to gains from exposure to fixed income (Bonds, TIPS), which had relatively lower volatility. During the low inflation regime that followed, the index switched into the 60/40 strategy, outperforming REIT and GSCI, which were particularly hard hit during the early months of the COVID-19 pandemic. Starting March 2021, higher CPI (YoY) readings led the index to switch into the pro-inflation beta (ProIB) strategy, which yielded strong returns due to being overweight GSCI and Gold.

Cumulatively, over the course of the three-year period, the observed outperformance of the index is the result of these dynamic adjustments. This case study demonstrates how the switching mechanism could enhance the performance of the index as the inflation regime changes over time.

Performance Attribution

To assess how the dynamic index times its exposure to the underlying asset classes, we ran a performance attribution analysis over the same three-year period, treating each strategy as a separate benchmark. The results (see Exhibit 2) indicate that in comparison to 60/40, the index performance was adversely affected by its GSCI exposure, but this was more than compensated for by gains from its exposure to Bonds, Gold, and TIPS, leading to an outperformance of 1.46%. While this may not seem significant, it is important to note that this outperformance is achieved with lower volatility, leading to a higher risk-adjusted return.

The index exhibited stronger outperformance in comparison to VolWt primarily due to its higher equity exposure, as well as better timing of its exposure to Bonds and TIPS. The ProIB strategy had a higher exposure to GSCI and lower exposure to TIPS, which explains the bulk of its underperformance relative to the index over the entire three-year period.

Correlation to Unexpected Inflation

During periods of rising inflation, we are likely to obtain inflation readings higher than expected for several months in a row. For our index to effectively hedge inflation during such periods, its performance must be positively correlated to unexpected inflation. If the forecast of CPI (YoY) is considered the “expected” inflation rate for a given month, then the unexpected portion would be the excess (i.e., above the “realized” inflation) for that month.

Exhibit 3 illustrates that the dynamic index had a relatively better correlation to unexpected inflation compared to some of its component strategies. While ProIB had a higher correlation overall, its performance was affected by significantly higher volatility and more severe drawdowns.

Inflation is one of the most significant risks to investment returns over the long term. Core equities and conventional bonds tend to deliver below-average returns in rising inflation environments, which can encourage investors to seek out inflation-sensitive assets, such as commodities, inflation-linked bonds, REITs, natural resource stocks, and gold, to protect their portfolios from inflation shocks.

Currently, the risk of inflation centers on whether the post-pandemic recovery will be merely reflationary or truly inflationary. It may be difficult for market participants to assign a probability to a sustained period of inflation and to adapt portfolio construction, should the probability be sufficiently high.

For an investor seeking protection against rising or high inflation, there is no “one-size-fits-all” solution—the portfolio must dynamically adjust to changes in the market environment, and this is exactly what the S&P Multi-Asset Dynamic Inflation Strategy Index is designed to do. The S&P Multi-Asset Dynamic Inflation Strategy Index seeks to measure the performance of a dynamic rotational strategy across a series of subindices designed to reflect the most appropriate response to the prevailing inflation regime in the U.S., as measured by the U.S. CPI. The index switches between three multi-asset strategies that have historically performed well in either a low, medium, or high inflation regime. The component indices within each multi-asset strategy are weighted based on dynamic weighting allocations (see Exhibit 1).

Component indices included in the S&P Multi-Asset Dynamic Inflation Strategy Index include those representing U.S. equities, commodity futures, U.S. sovereign bonds, U.S. inflation-linked bonds, and U.S. REITS.

The inflation regime is identified based on cutoffs for specific levels of inflation, as illustrated in Exhibit 2. For each inflation regime, a suitable strategy is identified based on inflation sensitivity, historical performance, and economic justification. Each month, the inflation regime is determined using the realized U.S. CPI (YoY), and the appropriate allocation is chosen based on the strategy identified for that regime.

The index demonstrated strong inflation beta in the high inflation regime and is relatively easy to implement and manage. The back-tests (though limited in terms of history) show that its performance and turnover were both reasonable. In a low inflation environment, equities tend to perform well, and a traditional 60/40 allocation that is overweight in equities can be expected to provide reasonable returns. During periods of medium inflation, fixed income assets tend to yield better risk-adjusted returns, so the volatility-weighted strategy that overweights them is a reasonable choice. In a high inflation regime, it makes sense to switch to the pro inflation beta strategy, which overweights commodities and real assets, since those asset classes have better inflation-hedging properties.

Exhibit 3 demonstrates that the dynamic index generally outperformed the component strategies on a risk-adjusted basis, and its drawdowns were not as severe. It is also worth noting that it had consistently higher inflation sensitivity than the 60/40 or volatility weighted strategies.

For more information, please visit the index webpage and read the thought leadership research paper.