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S&P Composite 1500®: Providing Efficient, Non-Overlapping Coverage of the U.S. Equity Market

Fixed Income Indices Take the Stage as Appetite for Credit-Focused Passive Strategies Grows

How S&P DJI’s Fixed Income Indices Harness Data and Technology to Power the Markets of the Future

Volatility and Performance of Options-Related Indexes in the 2010s

Latin America – 2019 in Review

S&P Composite 1500®: Providing Efficient, Non-Overlapping Coverage of the U.S. Equity Market

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Aye Soe

Managing Director, Global Head of Product Management

S&P Dow Jones Indices

When it comes to measuring the performance of U.S. equity market, some broad market indices have more constituents than others. But having a larger number of constituents does not necessarily lead to significantly wider market capitalization coverage. That’s because broad equity benchmarks are usually market-capitalization weighted, with the majority of the index weight concentrated in the top 100 securities. Therefore, as one goes down the market cap spectrum, smaller companies understandably command less weight in the index.

For example, the S&P Composite 1500 and the Russell 3000 both seek to measure the performance of the U.S. equity market, with the latter including twice the number of companies. However, the S&P Composite 1500 covered 90% of the U.S. market cap at the end of 2019 (see Exhibit 1). That means the largest 1,500 stocks not included in the index, many of them members of the Russell 3000, only represented around 10% of the U.S. equity market capitalization.

Over the long-term investment horizon, the two indices had returns correlation of 99.8% and roughly identical risk/return profiles.[1] Therefore, there is no noticeable return premium for having a greater or lower number of securities in the universe. Nevertheless, market participants looking to passively replicate the returns of the U.S. equity market may wish to consider additional factors such as trading costs, liquidity, and turnover. Data show that, on average, the S&P Composite 1500 had lower annual turnover (4%) than the Russell 3000 (9.31%).[2] With half the names to trade, the S&P Composite 1500 appears to be more capital efficient and cost effective.

From a total portfolio perspective, another important consideration for market participants is ensuring non-overlapping market capitalization exposure. The S&P Composite 1500 is made up of distinct market cap ranges—the S&P 500®, the S&P MidCap 400®, and the S&P SmallCap 600®. In other words, there are clearly defined market capitalization cut-offs between each market cap segment with no overlapping securities.

Non-overlapping size segmentation means market participants can avoid taking on unintended exposure to size, style, or risk factors that they do not get compensated for. For example, consider an investor that is passively replicating a large-cap and a mid-cap index. If there is any overlap between the indices, the investor would be doubling down on their size exposures. Given the historical differences in risk/return characteristics between large- and mid-cap stocks, such exposure may have a sizeable impact on a portfolio’s characteristics

In sum, the S&P Composite 1500 represents an efficient, cost-effective way to measure the performance of 90% of the domestic equity market. And because of distinct market cap segmentation, the index avoids exposing market participants to unintended risk exposures that they do not wish to take.

[1] Based on monthly data from Dec. 30, 1994, through Dec. 31, 2019. The S&P Composite 1500 has annualized return and volatility of 10.36% and 14.65%. Russell 3000 has annualized return and volatility of 10.21% and 14.84%. Total returns in USD.

[2] Source: Factset. Turnover figures based on annual averages between 2006 and 2019. iShares Russell 3000 ETF used as a proxy for the Russell 3000 index.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Fixed Income Indices Take the Stage as Appetite for Credit-Focused Passive Strategies Grows

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Koel Ghosh

Head of South Asia

S&P Dow Jones Indices

There is healthy demand among global investors for reliable and transparent indices and benchmarks to gauge sector-level and overall market performance. This demand coincides with investors seeking out cost-efficient investment strategies and shifting their asset allocations to lower-cost passive or index-based products, such as exchange-traded funds (ETFs), from actively managed funds.

In India alone, passive investment strategies have been steadily increasing, with ETF assets crossing the USD 20 billion mark as of September 2019.[1] The Indian government has played a critical role in the growth of these passive strategies. For example, the Employee’s Provident Fund Organization’s (EPFO) equity ETF allocations and the Department of Investment and Public Asset Management’s (DIPAM) ETF usage in their disinvestment program have helped lead the way for growth in the Indian market.

Globally, the growing interest in passive strategies spans different asset classes, including equities, fixed income, and commodities. In addition to the cost effectiveness, investors tend to be attracted to passive strategies because of the performance records of vehicles such as ETFs, which by design closely mirror the risk/return profiles of their respective indices and benchmarks.

For nearly two decades, S&P Dow Jones Indices has been tracking the performance of actively managed funds against their benchmarks in key markets such as India. Although the results vary, certain trends emerge over time, such as actively managed funds underperforming their benchmarks over short-and long-term periods.

In India and globally, equity-focused strategies have historically been more popular among investors. In fact, global assets in equity ETFs account for USD 4 trillion of the total passive market segment, which was just above USD 5 trillion as of September 2019.[2]

Today, we are seeing passive strategies extend beyond equities to offer investors the tools to measure different segments of the global fixed income market. The launch of India’s first bond ETF, for instance, reflects the demand for more diversified exposures to equity and fixed income markets.

Indeed, fixed income passive strategies were one of the high points of 2019, with roughly USD 1 trillion in assets invested in credit-focused passive vehicles,[3] and many asset owners expect this number to grow in the years to come. The underlying principle that anchors fixed income passive strategies is similar to that of the more established equity-focused offerings, although instead of a basket of stocks, investors buy into a basket of fixed income securities.

As the potential risks and opportunities of fixed income investing generate more attention, so does the need for independent and reliable global indices and benchmarks that enable investors to effectively monitor the performance of different segments of the debt market in order to make informed decisions. Each credit segment, whether it be investment grade, high yield, money market, or sovereign and public finance debt, has its own unique risk/return characteristics.

Lastly, independence, transparency, and liquidity are crucial factors for fixed income passive strategies to gain momentum and build an investor base in India and globally. While we make no predictions surrounding asset growth, we believe that an ongoing commitment to investor education and awareness will be key to the increased adoption of passive fixed income strategies.


[1], September 2019.

[2]  Ibid.

[3]   Ibid.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

How S&P DJI’s Fixed Income Indices Harness Data and Technology to Power the Markets of the Future

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Brian Luke

Global Head of Fixed Income Indices

S&P Dow Jones Indices

Thanks to the clever ad campaign by Policygenius, riders of the NYC subway have been reminded that we were promised glass-domed houses, flying cars, and vacation homes in outer space by 2020. Although robot maids may have come true via Amazon’s Alexa, they definitely fall short of the robot maid Rosie from the cartoon The Jetsons. When it comes to technological advances in fixed income, we may be somewhere in between those high expectations and reality. While over-the-counter trading still exists, a large and growing portion of trades are conducted through electronic trading instead of the traditional telephone shouting matches. In the world of fixed income indexing, advances in data processing, index calculations, and analytics have arrived to better meet the needs of investors.

A Big Data Solution for the Bond Market

Every day, S&P Dow Jones Indices calculates millions of data points in the fixed income market that cover hundreds of thousands of bonds. In January 2020, our largest bond index, the S&P Municipal Bond Index, grew to include over 200,000 bonds and has a market value that now exceeds USD 2.5 trillion. With thousands of additional indices and up to 30 years of daily inputs, we require a significant amount of calculation prowess to be able to power the markets of the future.

We built index capabilities with new technology that is better suited to iteratively scan through millions of records and return a set of bonds that qualify for a particular index. Our technology team leveraged MongoDB for our platform, creating more scale and flexibility than previous technology offered. Through its single document storage mechanism, we have achieved greater performance and efficiency to respond to increasing demand.

Inside the S&P Dow Jones Fixed Income Calculation Engine

In addition to index returns, S&P DJI’s fixed income indices provide various risk measures for a given security or index. Going beyond yield, sophisticated measures such as duration, option-adjusted spread, spread duration, and convexity are critical to assess a bond’s risk. For the purposes of providing calculations related to the evaluation of interest rate risk, a proprietary interest rate model was built to calculate individual bond and index level characteristics. These models rely on a market-based approach to the term structure of interest rates. Security prices used for index return calculation, as well as the terms and conditions used for security selection, are fed into the model. This approach is a useful tool when evaluating interest rate risk for bonds with embedded options. This can be illustrated by observing the volatility surface on Aug. 1, 2019, when the 10-year yield had its largest one-day drop in 2019 and CBOE 10-Year U.S. Treasury Futures volatility spiked 17%.

Our proprietary calculation engine leverages the Hull-White Model for evaluating interest rate risk. This market-based approach maintains a no-arbitrage principal to align with widely held market standards for interest rate modeling. The model holds certain advantages, namely a mean-reversion parameter and the ability to evaluate negative interest rates. Like other models, a series of assumptions are made. The key assumptions used are constant volatility and a normal distribution of interest rates. Each interest rate node is calculated using a trinomial lattice, with assumptions applied to the term structure of rates, volatility assumptions, and mean-reversion parameters. Calculations are based on the currency of the bond (e.g., USD-denominated bonds will be priced to a USD model). Future plans include expansion into multi-currency bonds and models that incorporate tax implications for individual investors.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Volatility and Performance of Options-Related Indexes in the 2010s

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Matt Moran

Head of Global Benchmark Indexes Advancement

Cboe Global Markets

As we enter the 2020s with interest in worldwide geopolitical volatility, here are some key points about volatility and the performance of options-related indexes the 2010s.

How Did Equity Volatility in the 2010s Compare to Volatility in Previous Decades?

The average of the daily closing values of the VIX® Index in the 2010s was 16.9 (24% below the 22.1 average for the previous decade). In recent years some observers have questioned whether there was too much complacency in the markets, and if the VIX Index was unusually low in light of trade wars and geopolitical uncertainties. In answer to these questions, one could note that:

(1) The average of the daily closing values for the S&P 500® Index 30-day historical volatility was only 13.6 in the 2010s, but 16.2 over the past 90 years, (and the VIX Index arguably was not “low” when compared with historical volatility in the 2010s), and

(2) In the 2010s the average of the daily closing values of the Cboe® SKEW Index was 126.2 (about 8% higher than the previous two decades). The SKEW Index is a measure of the relative demand for tail risk protection. With the relatively high levels of the SKEW Index in recent years, it could be argued that there has not been too much market complacency regarding demand for tail risk protection.

SKEW, VVIX and VIX Indexes in the 2010s

In 2017 the VIX Index had its lowest average daily closing value for a year (11.1), but the SKEW Index had its highest average daily closing value for a year (134.8), with growth in the relative demand for tail risk protection.

Benchmark Indexes Over Ten Years

In the 2010s the VPDSM Index rose 271%, and the S&P 500 Index rose 257%.

Six indexes that use S&P 500 (SPX) options had the lowest standard deviations in the Standard Deviations chart below.

While the 2010s generally saw higher-than-average equity growth and lower-than-average equity volatility, a number of analysts have suggested that in the 2020s there may be less growth and more overall volatility.  Some indexes had less volatility in the 2010s, and this information may be useful to investors who are exploring ways to dampen their own portfolio volatility.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Latin America – 2019 in Review

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Silvia Kitchener

Director, Global Equity Indices, Latin America

S&P Dow Jones Indices

From a political and economic standpoint, 2019 was a challenging and interesting year for the Latin American region, but the region still finished strong. The S&P Latin America 40, Latin America’s blue-chip index, ended Q4 with a return of 9.0%, and it was up 13.9% for the year. Small-cap stocks in the region fared well, helping the broad S&P Latin America BMI return 11.1% for the quarter and nearly 22% for the year. Despite the strong performance, Latin American indices still lagged some of the global equity indices. The S&P 500® returned 9.1% for the quarter and an outstanding 31.5% for the year. The S&P Global 1200 yielded 8.9% for the quarter and 28% for the year. The S&P Emerging BMI also had a great year (up 19.9%), but still underperformed Latin America (as measured by the S&P Latin America BMI).

In 2019, all 11 GICS® sectors for Latin America posted positive returns, as measured by the S&P Latin America BMI Sector Indices. Some sectors yielded considerable returns; Health Care gained 22.0% for the quarter and was up 72.2% for the year. Information Technology and Real Estate were next, yielding 43.5% and 42.3% for the year, respectively. Materials had a strong quarter, returning 16%, which helped to bring the sector into positive territory for the year, ending with a mere 4.4%—the lowest annual return of the sectors.

Besides sectors, it is clear that Brazil’s positive performance was a big contributor to the overall performance of the region. Brazil not only had a stellar quarter with the local benchmarks, as the IBrX 100 and the S&P Brazil BMI both yielded around 11%, but the country also had a banner year, providing returns in the mid-30% range in local currency terms.

With the growing trend of factor indices in Brazil, we see that most did well for the year, and in most cases returns were above 40% in local currency and USD. However, the risk data was also high, particularly for the USD-denominated indices. Local investors took on less risk when removing the currency exchange rate from the performance calculation.

Chile was the worst performer amid the political unrest the country underwent during the last quarter. No headline index was unscathed, and they all ended the quarter and the year in the red. Hardest hit were mid-cap stocks, as measured by the S&P/CLX IGPA MidCap, which lost 14% for the quarter. The banking sector was hit particularly hard, with losses of 21%, as measured by the S&P/CLX Banks Index. The S&P/CLX Utilities & Telecom Index surprisingly gained 7.5% for the quarter and nearly 16% for the year.

Mexico, the second-largest market in the region after Brazil, had a good year. Mexico’s flagship index, the S&P/BMV IPC, returned 1.2% for the quarter and 4.6% for the year. The S&P/BMV FIBRAS Index, which seeks to track the performance of the local real estate income trust stocks, and the recently launched S&P/BMV Ingenius Index, which is designed to measure 12 of the most innovative companies in the world trading in Mexico, each had returned over 40% for the year. For the quarter, the S&P/BMV China SX20 Index had the best return, up 10.8%.

Among factor indices in local currency, the S&P/BMV IPC CompMx Short-Term Momentum Index and the S&P/BMV IPC CompMx Quality Index (each with 15 stocks representing the top companies within each factor) had returns of 16.5% and 18.4% for the year, both with relatively low risk.

Argentina was the most volatile market in the region in 2019, mostly triggered by the extreme depreciation of the Argentinian peso, the continuous increases in inflation rates, and the uncertainty following the recent change in government. For the year, the S&P MERVAL Index returned 37.6% in ARS. The Argentinian market generated the highest volatility data of the region, as the three- and five-year risk rates based on standard deviation for the S&P/BYMA Argentina General Index were 42% and 38%, respectively.

Finally, markets in Colombia and Peru had good years. The flagship indices, the S&P Colombia Select Index and the S&P/BVL Peru General Index returned 30.0% and 6.1% in local currency terms, respectively.

As a recap for the year, Exhibit 1 shows the performance of the three Latin American regional indices compared with other global equity markets and regions, based on monthly returns. Overall, we found that the markets were steady; the ups and downs were not as drastic as the previous year. The last month was particularly intense for Latin America, as the region experienced a strong recovery.

With the new year, new challenges and new opportunities arise. It will be interesting to see the development of these markets in the coming months.

The posts on this blog are opinions, not advice. Please read our Disclaimers.