Indexology^® Blog

Though passive investing is galloping its way into some allocations, it’s still not a significant percentage. Passive assets are concentrated in the developed markets, with the U.S. claiming a majority share, followed by Europe and Japan.

The use of core and satellite strategies based on passive vehicles can be a further catalyst to the growth in using indexing to attain investment objectives. Indexing—whose benefits can include low cost, diversification, flexibility, and transparency—offer a simple solution to gain exposure to a broad spectrum of investment strategies. The core satellite strategy is useful not only for active versus passive allocation but can be used tactically to allocate among indexing areas, diversifying within asset classes, geographies, or themes.

Indian investors tend to have had a firm home bias, which has not changed over the last few decades. The pandemic shifted those dynamics, as the benefit of international diversification became evident. The assets in international schemes grew from INR 9,062 crores to INR 24,129 crores, a growth of 166%.¹ While U.S. markets led the interest, allocations to emerging markets were also made. In 2021, the Securities and Exchange Board of India revised its limits to permit USD 1 billion per mutual fund, with a total cap of USD 7 billion for the industry. However, the industry has reached those limits and is now seeking a revision for them, as interest and assets are flowing into this segment.

The growth in assets, products, and investor interest compels passive product providers to offer a wider selection that covers diverse needs. In addition to country exposures, the demand has moved to broadening the scope beyond beta exposures. In addition to its broad offerings, S&P DJI has some more index series, such as the following.

• The S&P MAESTRO 5 Index (Multi-Asset Equal Risk Factor Contribution) is designed to measure the performance of a multi-asset risk parity strategy with a 5% target volatility. It allocates risk equally among seven equity, fixed income, and commodities indices, and further mitigates equity market volatility by dynamically allocating to S&P VIX^® futures indices.
• Newer ideas like Growth at a Reasonable Price (GARP), a known and practiced fundamental-driven investment strategy, are also making their way into indexing. The S&P 500 GARP Index is a multi-factor framework with an objective of exposure to growth stocks with good quality and attractive valuation. It seeks a balance between pure growth and pure value.
• Ideas focused on speculative returns have uncertain levels of risk over an uncertain period. This type of outcome-based investing encourages targeting a specific defined payoff profile, with an allowance for a specific defined level of risk, at a specific point in time in the future. The CBOE S&P 500 Target Outcome Indexes work differently, by seeking to incorporate defined exposures into the S&P 500, where the downside protection levels, upside growth potential, and outcome period are all defined prior to investing.
• Social media has evolved to encompass commentary about stocks and financial markets. That, coupled with technological developments, has enabled these views to be analyzed, which results in the interpretation of online community dialogue on a specific company by aggregating an analysis of these messages. The S&P 500 Twitter Sentiment Index Series measures market sentiment using Twitter data, specifically Tweets containing $cashtags, which indicate that the Tweet is referring to a particular stock. Using artificial intelligence technology to analyze the sentiment around these stocks, a sentiment score is generated for the companies within the S&P 500.

The above indices reflect the change in interests and aligning to the new market demand for strategies.

¹ Source: AMFI. Data from Dec. 31, 2020, to Dec. 31, 2021.

After the exceptional performance of large-cap stocks in recent years, concentration concerns naturally come to mind.

There are many ways to measure concentration. A simple method is to add up the weight of the top names, but the drawback with this approach is it doesn’t incorporate all the constituents in an index. The Herfindahl-Hirschman Index (HHI), defined as the sum of the squared index constituents’ percentage weights, is more favorable from this aspect and is widely used.

But the HHI faces an issue, which is that even for completely unconcentrated equal weight portfolios, the HHI value is inversely related to the number of names. If we want to use the HHI to examine the history of concentration within an index or to make cross-sector comparisons, we need to adjust for the number of names. In our paper Concentration within Sectors and Its Implications for Equal Weighting, we define the adjusted HHI as the index’s HHI divided by the HHI of an equally weighted portfolio with the same number of stocks.

A higher adjusted HHI means that an index is becoming more concentrated, independent of the number of stocks it contains. Exhibit 1 shows that the adjusted HHI for the Energy sector decreased from 2014 to 2019, despite an increase in its raw HHI. This is because the number of constituents in the sector decreased from 43 in 2014 to 28 in 2019.

Concentration tends to mean-revert in most sectors. This is particularly noticeable in Energy, Industrials, Information Technology, and Materials, as we see in Exhibit 2. These data imply that when concentration is relatively high, as we see for Information Technology presently, it subsequently tends to decline. Meanwhile, when concentration is relatively low, as we see for Industrials, Energy, and Materials, it subsequently tends to increase.

Rising sector concentration implies that larger-cap names are outperforming smaller caps, and that a cap-weighted index should outperform its equal-weighted counterpart. Falling concentration implies the opposite. Since concentration tends to mean revert, using relative concentration to alternate between cap-weighted and equal-weighted sector exposures is a potential source of value added.

The S&P Indices Versus Active (SPIVA^®) Scorecard¹ provides a rich tapestry of data and insights on the active versus passive debate by comparing the performance of actively managed funds to their corresponding passive benchmarks. The SPIVA Latin America Scorecard is one of several regionally focused reports and covers the performance of active funds in Brazil, Mexico, and Chile. Below are some takeaways from our latest year-end 2021 report.

1. It Is Difficult to Outperform the Benchmark over the Long Run.

In the short run, most Brazilian Government Bond Funds outperformed the benchmark, but this swiftly turned into underperformance for holding periods of three years or more. Brazilian Large-Cap Equity Funds fared better, as slightly more than half of actively managed funds outperformed the S&P Brazil LargeCap for up to five years. In the short run, it is not unusual to see the average active manager flipping between underperformance and outperformance. After a decade, however, at least 70% of active funds underperformed in each category; underperformance among Chile’s active equity funds was the highest, with 98% of funds lagging their benchmark over the past 10 years.

2. Funds Struggle to Survive across Categories.

The price of being a poor-performing active fund is hefty, with the worst-performing funds liquidating or merging with other funds. Exhibit 2 shows that Brazil Corporate Funds had the lowest survival rate, with only 32% of funds still alive after 10 years. Interestingly, despite low rates of outperformance among Mexican equity funds, 78% managed to keep their doors open after a decade.

3. Even Among Survivors, Outperformance Rates Are Poor.

Using Exhibits 1 and 2, we can divide the SPIVA database into three groups: funds that did not survive, funds that survived but underperformed, and funds that both survived and outperformed. We can see that even among the funds that survived, more than two-thirds underperformed in nearly every fund category.

4. The Picture Doesn’t Improve Much on an Asset-Weighted Basis

Exhibit 1 shows the outperformance rate of funds on an equal-weighted basis. Some may make the argument that funds with larger assets have more assets because they have historically outperformed, and that they maintain relatively large assets because they produce “alpha.” Report 4 of our recent SPIVA Latin America Scorecard shows that in only one out of seven active fund categories were funds able to provide an average asset-weighted return above its appropriate benchmark over a 10-year period. Brazil Large-Cap Funds provided the one “success” story, but only narrowly.

5. And Finally, There Is a Wide Range of Performance among Active Funds.

While “alpha” may exist, identifying outperforming managers is difficult and the performance distribution of actively managed funds is skewed. The interquartile range, which is the spread between the first and third quartile breakpoints of active funds in their respective category, is quite wide, showing there is a large difference between top and bottom fund performances.

Get the latest numbers and more from the SPIVA Latin America Scorecard here.

¹ SPIVA Scorecards: An Overview. https://www.spglobal.com/spdji/en/education/article/spiva-scorecards-an-overview

U.S. equities have had a tough start to the year amid rising inflation concerns, anticipated rate hikes by the Federal Reserve, earnings misses from several mega-cap companies, and ongoing geopolitical tensions emanating from the Russia-Ukraine conflict. Exhibit 1 shows that the S&P 500^®, S&P MidCap 400^®, and S&P SmallCap 600^® all declined in the first two months of 2022, as did the majority of their respective sector, style, and pure style indices. Energy was a notable outlier, boosted by surging commodity prices.

While recent news flow had a similar directional impact on U.S. equities, the magnitude of impacts varied across sector and style segments. For example, Exhibit 2 shows that a whopping 41% separated the best-performing S&P 500 sector (Energy, up 28%) and the worst-performing sector (Real Estate, down 13%) in the first two months of 2022, while the S&P 500 Value (-3%) beat the S&P 500 Growth (-12%) by 9%. Such differences suggest that sector and style perspectives could have been helpful to navigate the recent market environment; avoiding the worst-performing segments or identifying the outperformers could have offered sizeable performance improvement.

Comparing dispersion figures for S&P 500 stocks, sectors, and styles offers a more formal way to assess the potential opportunity for outperformance (or embarrassment) from picking S&P 500 segments. Exhibit 3 shows that, on average, annualized monthly stock level dispersion (24%) was typically higher than sector dispersion (11%), which in turn was greater than style dispersion (3%).

In other words, about half the potential value of stock picking came from picking sectors, with about 14% of the potential value of selecting stocks coming from style selection. This is unsurprising, given that the S&P 500 comprises 505 stocks and 11 GICS sectors, but only two styles. Notably, though, style and sector dispersion figures have been similar in the first two months of 2022, averaging 8% and 13%, respectively: the potential value of style insights was similar to sector insights in recent months.

The potential value of style perspectives is even more pronounced after adjusting for capacity, since both the potential opportunity for outperformance (measured by dispersion) and the potential size of active positions (measured by the average constituent size) are relevant when comparing the value of insights into different segments. This is especially the case given the proclivity for smaller market segments to exhibit higher dispersion.

Exhibit 4 shows the average capacity-adjusted dispersion figures for S&P 500 style and S&P 500 sectors relative to S&P 500 stocks. Over the entire period, driven by the relative size of their respective components, insights into style were eight times more valuable than those for stocks, with sector insights being six times more valuable than for stocks. More recently, elevated style dispersion at the start of 2022 meant style insights could have been more than twice as valuable as sector insights at the start of the year, and over 10 times as valuable as stock insights.  

Hence, while it can sometimes be useful to do nothing, the recent divergence in style performance means some may find it useful to express views through a style perspective.

When it comes to ESG indices, different objectives require different solutions. Indices can range from simple to sophisticated, concentrated to benchmark-like, broad environmental, social and governance to climate-focused, and more. Our growing suite of ESG indices aims to serve a wide range of ESG needs and support the alignment of investments with ESG principles.

When assessing ESG indices, users may have two questions.

1. What is the investment objective in terms of:
   1. ESG benefits; and
   2. Active risk?
2. Given the objective, which approach is more suitable:
   1. Simple; or
   2. Sophisticated?

Here, we focus on these questions by highlighting the outcomes achieved by S&P 500^® ESG Indices.

ESG Benefits and Active Risk

ESG benefits and active risk are two important factors to be considered within any ESG index strategy. There is generally a trade-off between the ESG benefits achieved by the index and its active risk relative to the benchmark; hence, the two aspects should be looked at collectively. When viewing ESG enhancements through an active risk lens, we can assess how much improvement in S&P DJI ESG Scores the index achieves per unit of tracking error.¹ Exhibit 1 shows S&P DJI ESG Score improvement against three-year tracking error, with the bubble size representing carbon intensity reduction.²

If the objective is to achieve high ESG score improvement per unit of tracking error, the S&P 500 ESG Tilted Indices (with various tilting levels) were efficient in meeting this goal. Within the various ESG series, the high-conviction S&P 500 ESG Elite Index led the way for S&P DJI ESG Score improvement, realizing similar carbon intensity reduction to its S&P 500 ESG Leaders Index counterpart, albeit with a higher tracking error. The flagship S&P 500 ESG Index stood firmly as the sustainable, benchmark-like option, with relatively low tracking error. Likewise, if carbon intensity reduction is required, the S&P PACT™ Indices (Paris-Aligned and Climate Transition Indices) achieved that most efficiently, while aligning with the Paris Agreement goals.

Performance

How have all the S&P 500 ESG Indices performed? The S&P PACT Indices are among the notable outperformers (see Exhibit 2).

Level of Sophistication

We saw how different ESG indices have reached specific ESG outcomes. But which approaches could be used to achieve those objectives? Exhibit 3 shows the spectrum of sophistication of S&P ESG Indices.

Simpler exclusions-based indices, such as the S&P Sustainability Screened and S&P Fossil Fuel Free Indices, remove companies involved in specific business activities. Going one step further, some strategies combine exclusions with a single ESG or climate objective, such as increasing exposure to best ESG performers (S&P ESG and S&P ESG Tilted Indices) or to low-carbon emitters (S&P Carbon Efficient Indices). When multiple ESG and climate considerations are required, a sophisticated approach like the S&P PACT Indices might be more appropriate.

Our growing suite of S&P ESG Indices could help provide clients with the necessary tools to navigate the diverse and evolving nature of ESG needs. When assessing ESG indices, it’s essential to consider the required investment and ESG objectives. Again, there’s an ESG solution for every need.

¹ Tracking error is calculated as the standard deviation of monthly excess returns of the ESG strategy relative to the benchmark.

² Carbon intensity is calculated using operational and first-tier supply chain GHG emissions, please see here for more information.