Indexology^® Blog

The Federal Open Market Committee voted to raise the Federal Funds rate by 25 bps on March 16, 2022. This move was well telegraphed and not at all surprising—but that doesn’t mean that we won’t hear concerns about how rising rates will impact equity returns. Finance theory teaches us that, other things equal, rising interest rates are not good for the performance of stocks, as rising borrowing costs and higher discount rates tends to translate to lower future performance. For the much of history, empirical evidence has aligned with the theory. But in more recent data, we have noticed that “other things” may not have always been equal.

At a cursory glance, rising rates have not necessarily boded ill for equity performance, at least in the period from 1991 through 2021. There were eight episodes when the 10-Year U.S. Treasury yield rose. The S&P 500^® declined in none of these; in two cases equities were flat, and the S&P 500 rose in six, in some instances quite substantially.

Breaking down the period in Exhibit 1, there were 156 months when the 10-Year U.S. Treasury yield rose (and 216 months when it declined). Of the months when the 10-Year U.S. Treasury yield rose, the S&P 500 gained in 115 (74%) and declined in 41; the S&P 500 rose nearly three times as often as it fell when interest rates rose. On average, the S&P 500 gained 1.57% each month that rates rose, versus just 0.55% in months when rates declined.

We can also look at these months graphically in the scatter plot in Exhibit 3. Here we plot the change in the 10-Year U.S. Treasury yield against the performance of the S&P 500 for the same period from 1990 through 2021. Each point represents a monthly observation, and we see no discernible relationship. The blob speaks for itself—or rather, it doesn’t. History does not provide evidence of a clear link between changes in interest rates and changes in the equity market.

The Russia-Ukraine conflict is now in its third week and markets remain volatile. The major U.S. equity benchmarks dropped about 10% from their peaks, with the exception of the Energy sector. The CBOE Volatility Index (VIX^®), the so-called “fear gauge,” has been hovering above 30, which is the 90^th percentile of its historical value. Its level on March 10, 2022, was more than two standard deviations above its one-year average. Although it remains unclear how long these geopolitical tensions will last and how much it will affect the global economy, the U.S. equity market has managed to avoid the VIX levels seen two years ago, which were triggered by pandemic-driven sell-offs.

More importantly, historical data show that the equity markets tend to bounce back quickly after elevated volatility. We look at all the trading days on which VIX hit above 30 and calculate the S&P 500® performance in the subsequent 6 months and 12 months. The scatter charts in Exhibit 2 show that the vast majority of these 557 days were followed by positive performance in the next 6 months (82%) and 12 months (88%).

We further compare the 6- and 12-month performance after these highly volatile days with rolling returns on all historical days. On average, the 12-month performance after VIX hit above 30 was two times higher than the 12-month rolling returns on any business day since Dec. 31, 1999. Similar results are reflected in the small-cap space (see Exhibit 3).

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