Get Indexology® Blog updates via email.

In This List

Divergence in Sector Returns

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

Divergence in Sector Returns

Contributor Image
Hamish Preston

Head of U.S. Equities

S&P Dow Jones Indices

After a pullback in the market over the last week, it remains to be seen if U.S. equities will finish the month in the black.  However, as of yesterday’s close, the S&P Composite 1500, which represents over 90% of U.S. equity capitalization, was up 1.21% since the turn of the year as large cap gains offset declines in mid and small caps.  Indeed, the S&P 500 stood 1.42% higher than at the end of 2019, while the S&P MidCap 400 (-0.73%) and the S&P SmallCap 600 (-1.58%) fell.

While the market has had its fair share of news to digest this month – including U.S.-Iran relations, corporate earnings, and the signing of a so-called “Phase One” trade agreement between the U.S. and China – news of a virus spreading in China has taken a lot of the headlines.  Indeed, fears of contagion weighed on global equity markets this week, and the S&P 500 recorded a daily price decline of 1.57% on January 27th – its first daily price decline of more than 1% since October 2019 and ending its 10th longest streak (74 trading days) in the last 50 years without such a decline.

At a sector level, Utilities has been the best-performing large and mid cap sector, perhaps reflecting the bouts of unease observed at various points this month.  And in signs of some good news impacting the market, the Information Technology and Communication Services sectors benefited from several strong earnings reports.  At the other end of the spectrum, Energy was by far the worst performing sector this month as a decline in oil prices provided headwinds: the S&P GSCI Crude Oil Index is set for its worst monthly total return since May 2019, down 12.47% as of yesterday’s close.

More broadly, and in large part because of Energy’s woes, there was a sizeable difference between the best- and worst-performing U.S. equity sectors this month.  For example, the 15.14% separating the best-performing S&P 500 sector (Utilities) and the worst-performing (Energy) is the largest monthly difference in large cap sectoral performance since October 2017. Similarly, the performance spread in S&P 400 sectors (22.84%) and S&P 600 sectors (24.88%) are the highest readings since September 2017 and September 2015, respectively.

As a result, although it remains to be seen which trends will continue to dominate headlines and shape sentiment – and caution should be taken when using January’s performance to try and predict subsequent returns – the performance of U.S. equities in January highlights the potential for sizeable divergence in returns based on idiosyncratic reactions to news.  Incorporating sectoral views may therefore be useful when navigating market movements.

For monthly updates on our indices, sign-up for our monthly dashboards here.


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

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

Contributor Image
Aye Soe

Former Managing Director, Global Head of Core and Multi-Asset 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

Contributor Image
Koel Ghosh

Former 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

Contributor Image
Brian Luke

Senior Director, Head of Commodities, Real & Digital Assets

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

Contributor Image
Matt Moran

Head of Index Insights

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.