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Not All Strategies Are Created Equal: A Look at the S&P MARC 5% (ER) Index versus Other Multi-Asset Strategies

What’s Inside the S&P China A-Share Factor Indices? Sector Allocation versus Stock-Selection Effect

August 2019 Commodities Performance Highlights – Tweets, Trade, and Tumult

Mapping the S&P 500 Trading Ecosystem

What Mega Insurers’ Turn to Passive Could Mean for Other Large Institutions

Not All Strategies Are Created Equal: A Look at the S&P MARC 5% (ER) Index versus Other Multi-Asset Strategies

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Joe Kairen

Senior Director, Strategy & Volatility Indices

S&P Dow Jones Indices

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In this blog, we compare the S&P MARC 5% Excess Return (ER) Index with a peer group of 16 multi-asset 5% volatility-controlled excess return strategy indices currently in the market.[1] Overall, we observed that the diversification and weighting strategy of the S&P MARC 5% (ER) Index provided potential for upside while avoiding some of the broader market drawdowns and selloffs.

As part of the analysis, we made several different comparisons. First, we looked at the annualized returns over different periods across two different categories. Second, we compared relative performance across three different categories. After that, we looked at annualized returns over different periods by grouping the strategies into three performance groups over the respective periods. Finally, we examined these groups on a calendar year basis.

Comparison 1: Looking at the annualized returns over different periods, we used two different metrics.[2]

  • The simple average return of all the strategies; and,
  • The median return of all the strategies.

The S&P MARC 5% (ER) Index consistently outperformed the average and median of all the strategies over all timeframes with the exception of the seven-year period (see Exhibit 1). In the latter, the index was on par with the median and only slightly below the average performance.

Comparison 2: During the one-year period, we could see that much of the outperformance of the S&P MARC 5% (ER) Index was because it did not suffer from the market selloffs that other strategies experienced in October and December of 2018 (see Exhibit 2). The strategy ended the year positive, despite the challenging fourth quarter. From January 2019 to July 2019, the index experienced strong, sustained outperformance due to the asset diversification within the strategy. Each of the asset classes within the index saw 4%-18% growth between Dec. 31, 2018, and July 31, 2019.

Comparison 3: We looked at the relative performance of the S&P MARC 5% (ER) Index compared with other strategies, including the average returns of the top five, bottom five, and middle six strategies over a given period (see Exhibit 3). Similar to what we saw in Comparison 1, with the exception of the seven-year period, the S&P MARC 5% (ER) Index outperformed the middle six strategies across all periods. In addition, the index outperformed the bottom five strategies across all periods.

Comparison 4: We looked at how the performances stacked up on a calendar year basis. While it was not always the top-performing strategy, the S&P MARC 5% (ER) Index provided relatively consistent outperformance over the bottom five strategies in any given year, and typically performed in line with the average and median of each universe (see Exhibit 4).

[1]   The data is aggregated and anonymized to avoid focusing on any specific index strategy. We have also standardized the holiday convention to match the S&P MARC 5% (ER) Index.

[2]   The number of strategies in a given period may vary depending on the availability of history; these groupings do not include the S&P MARC 5% (ER) Index.

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

What’s Inside the S&P China A-Share Factor Indices? Sector Allocation versus Stock-Selection Effect

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Liyu Zeng

Director, Global Research & Design

S&P Dow Jones Indices

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After examining the risk factor exposure of the S&P China A-Share Factor Indices in our previous blog, we further explore the sector bias and associated impact on index performance.

Apart from the S&P China A-Share Enhanced Value Index, all the S&P China A-Share Factor Indices tended to underweight the Financials sector,[1] though other unique sector biases were observed in various factor indices. While the S&P China A-Share Enhanced Value Index was historically overweight in the Financials and Materials sectors, the S&P China A-Share Short-Term Momentum Index was tilted more toward the Information Technology and Health Care sectors. The S&P China A-Share Low Volatility Index, which weights constituents by the inverse of their volatility, allocated more to the Utilities and Industrials sectors, while the S&P China A-Share Quality Index showed bias toward the Consumer Staples and Health Care sectors. The S&P China A-Share Dividend Opportunities Index had an average sector bias toward Consumer Discretionary and Industrials (see Exhibit 1).

Despite significant sector biases observed among the S&P China A-Share Factor Indices, the performance attribution analysis over the period from July 31, 2006, to April 30, 2019, indicated that, except for the S&P China A-Share Quality Index, a larger part of the active returns were attributed to the stock-selection effect (see Exhibit 2).

Apart from the S&P China A-Share Short-Term Momentum Index, the stock-selection effect contributed positive active returns across the majority of sectors for all of the factor indices, implying the effectiveness of these factor strategies across different sectors. In comparison, active returns attributed to the sector allocation effect were less consistently positive across sectors, except for the S&P China A-Share Short-Term Momentum Index (see Exhibit 3). The underweight in Financials, one of the best-performing sectors over the studied period, resulted in negative sector excess return contributions in the small cap portfolio, S&P China A-Share Dividend Opportunities Index, and S&P China A-Share Quality Index.

[1] Compared to the eligible universe, which includes constituents of the S&P China A BMI and S&P China A Venture Enterprises Index with a float-adjusted market capitalization of no less than RMB 1 billion and a three-month average daily value traded not below RMB 20 million.

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

August 2019 Commodities Performance Highlights – Tweets, Trade, and Tumult

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Fiona Boal

Head of Commodities and Real Assets

S&P Dow Jones Indices

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Commodities markets struggled under the weight of an acceleration in trade tensions between the U.S. and China and growing evidence of a slowdown in global economic activity in August. The S&P GSCI was down 5.6% for the month but remained up 6.7% YTD. The Dow Jones Commodity Index (DJCI) was down 2.7% in August and up 3.2% YTD, reflecting its lower energy weighting. Ongoing rallies in nickel and gold markets were insufficient to override notable declines across the energy, agriculture, and livestock markets, leaving the broad commodities indices notably lower for the month.

The S&P GSCI Petroleum ended the month down 7.0%. Market participants put greater weight on a weakening demand trajectory, further deterioration of U.S.-China trade relations, and an ongoing need for OPEC constraint as the month progressed. Beijing’s announcement that it would levy a 5% tariff on U.S. crude oil marked the first time the fuel had been targeted since the world’s two largest economies started their trade war more than a year ago.

Industrial metals displayed divergent monthly performance, as most metals were lower, while nickel extended its impressive performance, up 24.3% for the month. The S&P GSCI Nickel’s 69.3% YTD gain made it the best-performing commodity YTD, passing iron ore to take the top spot. On the last trading day of the month, the Indonesian government confirmed expectations that it would ban exports of nickel ore starting on Jan. 1, 2020, two years earlier than initially indicated, pushing the market into a deficit. With less liquidity due to the events on the last trading day of August and no daily trading limits set by the London Metals Exchange, the price spiked over 8.8% that day—the biggest daily price move in nickel in 10 years.

Gold continued its strong YTD performance by starting the month breaking through the USD 1,500/oz. level on the back of a new front in the trade war, as China allowed its currency to break the psychological level of CNY 7.00 versus the USD. With geopolitical and trade war issues at the forefront of investors’ minds amid global central bank easing, gold continues to be one of the more popular assets in 2019. In August, gold ETF holdings were the highest since 2013. As more government bonds across the globe display negative yields, gold seems positioned well to be the safe-haven alternative for investors.

It was a difficult month for agricultural commodities, with the S&P GSCI Agriculture falling 6.8%. The S&P GSCI Corn led the decline, down 9.5% over the month, following a USDA crop report in the middle of the month that stunned the market with a lofty forecast for the size of the U.S. corn crop, despite uncertainties surrounding this year’s late-planted crop. Meanwhile, the S&P GSCI Sugar fell 8.6% in August. A slumping Brazilian real, which has encouraged more exports from the world’s largest producer, and India’s move to provide export incentives to help clear its domestic stockpile both weighed on the supply side of the market in August.

A fire at one of the largest beef packer plants in the U.S. in the middle of August sent livestock markets markedly lower, as the S&P GSCI Livestock fell 8.7% for the month. The plant is estimated to represent 6% of U.S. beef packing capacity and could be out of action until 2020, creating a notable void in processing capacity that will likely affect the cattle market for many months.

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

Mapping the S&P 500 Trading Ecosystem

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Tim Edwards

Managing Director, Index Investment Strategy

S&P Dow Jones Indices

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A new paper published today provides a new perspective on the active usage of products linked to S&P DJI indices, and illustrates the network of liquidity that has developed around the S&P 500® and other popular benchmarks.

“Active” and “passive” are colloquial terms, and it can be hard to distinguish one from the other at times.  A portfolio replicating a broad, capitalization-weighted index is the archetypal passive strategy, yet timing the market by buying and selling such a portfolio on a daily basis would qualify under most definitions of active investing.

Seeking for a precise definitional distinction between active and passive investments may be a distraction: some investors will trade more frequently than others, nearly all will adjust their positions over time.  What is needed is a sense of not whether an investment strategy is active, but how much activity occurs. 

Exchange-traded funds (ETFs) illustrate the point.  An ETF tracking the S&P 500 is likely to be passively managed by the fund’s sponsor, but may have active owners who trade in and out of their positions frequently.  Futures and options seem easier to classify: with their predetermined expiry dates, they are built to serve shorter-term needs.  Yet such products can be used to replicate passive portfolio performance, potentially for years or decades if positions are rolled.

A Window on Index Liquidity

Our new research provides a snapshot of trading volumes associated with the range of tradeable products linked to S&P DJI indices – including futures, ETFs, options and other listed products.  These statistics begin to fill in some of the gaps in our understanding of the active use of ‘passive’ products, enabling us to infer average holding periods, or map out where liquidity may be found.

The data range over 1,300 individual products linked to 500 different indices, traded in more than 30 countries.  With annual volumes in the trillions of U.S. dollars for more popular indices, one conclusion of the research is that active investors play a major role in products linked to S&P DJI’s indices: average holding periods of a few months or less are typical.

The S&P 500 Ecosystem

Since the launch of index options and futures in the 1980s, followed by ETFs in the 1990s, the S&P 500 index has provided the basis for investors to access a growing range of exposures.  And – while several of our indices are associated to significant trading – the S&P 500 stands apart.

Over time, a S&P 500 ‘trading ecosystem’ has developed, with links extending across different product lines such as futures and options, and different—but related—indices such sectors, factors (“smart beta”) and other derivatives of the parent index.  The paper illustrates this network, and the value of associated trading in billions of U.S. dollars in the 12 months ending June 30, 2019.

The S&P 500 Ecosystem – Index Equivalent Trading Volume in Billions of U.S. Dollars

The proportion of assets managed ‘passively’ has become a much-debated statistic, particularly for large-cap U.S. equities.  But some of the universe putatively owned by passive investors may be mislabelledClick here to read the full report.

 

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

What Mega Insurers’ Turn to Passive Could Mean for Other Large Institutions

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Kelsey Stokes

Associate Director, Marketing

S&P Dow Jones Indices

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Of the more than USD 3.4 trillion invested in ETFs in the U.S.,[1] retail investors comprise the majority of the market. While pensions and endowments have been slow to use ETFs in their investment portfolios, one segment of the institutional market—insurance—has been steadily increasing their usage of ETFs. Earlier this year, S&P DJI analyzed the use of ETFs in the U.S. insurance industry, using regulatory data. These trends may offer insight for other institutional investors.

Despite a market correction in Q4 2018, insurance companies continued to increase their use of ETFs last year, holding assets in-line with long-term growth trends with USD 26.2 billion invested in ETFs. The insurance industry, however, exhibited a divergence in its investment patterns; with varying levels of investment depending on factors like size. Companies that had previously been slow to adopt ETFs increased their usage, while others that were more heavily invested in ETFs cut back.

Insurers have increasingly used ETFs in their portfolios for a range of strategic and tactical functions. Mega insurers, or those companies with more than USD 50 billion in assets, in particular, have historically employed ETFs for cash equitization, as a “liquidity sleeve” (an overlay for liquidity management), or as part of a risk barbell strategy, for example. Based on 2018 data, Mega insurers are investing more assets in ETFs than ever before, which could be a case study for other large institutions who have not yet begun investing in ETFs.

Mega insurance companies owned most of the admitted assets belonging to insurance companies in 2018, and they held approximately one-third of the insurance ETF holdings (see Exhibit 2).

What’s notable, however, is that these Mega companies increased their AUM by 39% over 2017 (see Exhibit 3).

While Large companies comprised the majority of insurance ETF assets in 2018, Mega companies were quickly reaching parity, demonstrating the greatest compound annual growth rates, across 1-, 3-, 5-, and 10-year time horizons. By contrast, Large companies’ ETF investments saw a 25% decrease in 2018.

Unlike prior years, equity ETFs—not fixed income ETFs—drove the growth in AUM from Mega insurers, exhibiting a 43% growth rate versus 2017. In 2018, equity ETFs comprised 63% of Mega insurers ETF assets invested.

As other large institutions, such as pension funds, endowments, and foundations, seek efficient and low-cost investment vehicles for their portfolios, the growth of ETF usage among mega insurers may serve as inspiration for their investments.

To learn more about the 2018 trends in ETF usage among insurers, read our latest analysis of “ETFs in Insurance General Accounts.”

[1] Source: Investment Company Institute

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