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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

VIX® Dropped Below S&P 500® Realized Volatility

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.

VIX® Dropped Below S&P 500® Realized Volatility

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Berlinda Liu

Director, Global Research & Design

S&P Dow Jones Indices

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While everyone has been concerned about the inverted yield curve, the CBOE Volatility Index® (VIX) has been under the 21-trading-day realized volatility of the S&P 500 since Aug. 16, 2019. Since volatility traders care not only about what is expected but also what actually transpired, the spread between implied volatility and realized volatility is one of the most important gauges for them to keep an eye on.

Historically, implied volatility tends to stay above realized volatility due to the skewed distribution of stock returns. When implied falls below realized, it usually suggests option premiums are relatively cheap, therefore favoring option buyers.

Implied volatility represents the current market price for volatility, or the fair value of volatility based on the market’s expectation for movement over a defined future period of time. VIX is arguably the most-followed gauge of the U.S. equities market implied volatility in the next 30 calendar days.

Realized volatility, on the other hand, is the actual movement that occurred in a given underlying over a defined past period. For VIX, that underlying is the S&P 500. Since the S&P 500 trades only when the market is open, the convention is to compare VIX with the realized volatility from the previous 21 trading days, approximately one calendar month.

Calendar-year averages since 2000 show that it is “normal” when VIX is above the realized volatility of the broad equity market (see Exhibit 1). The spread, calculated as VIX minus the 21-trading-day realized volatility of the S&P 500, is usually around 3-4 points. It tended to narrow during periods of market turbulence (e.g., in 2000 and 2008); 2008 was the only year that average VIX readings, which were higher than usual, fell short of the realized volatility. On the contrary, while the 2017 VIX level was lower than usual, the relationship between implied and realized volatility remained fairly “normal.” 

On a day-to-day basis, if we marked all the days as 1 when VIX was higher than the realized volatility and as -1 when VIX was lower than the realized volatility, we can visualize the frequency of positive and negative implied/realized volatility spreads over the past 20 years (see Exhibit 2). Since July 1, 1999, negative spreads occurred on 796 days out of 5,070 (15.7% of the time). Prolonged periods of negative spreads tended to occur in turbulent markets such as those in 2000 and 2008. The most recent stretch of negative spreads occurred from Q4 2018 to early 2019 during the market sell-off.

Why is implied volatility normally higher than realized? From a behavioral finance perspective, this is an indication of risk aversion—investors are willing to pay a premium to buy protection against risk. From an option pricing perspective, it is because stocks and stock indices do not follow the log-normal distribution assumption of Black-Scholes. The empirical distribution of stock returns has a negative skew and hence reflects larger losses than a normal or log-normal model using the ex-post mean and standard deviation would predict. Implied volatility takes into account large but rare events, while realized volatility will only include such events if they have occurred in the look-back calculation period. Since low-probability events are rare by definition, realized volatility tends to understate the potential for large losses most of the time. However, in a distressed market, realized volatility tends to overstate the risk of large losses when these sudden moves indeed occurred in the calculation window.

Nevertheless, in the relationship between implied and realized volatility, realized volatility serves as the baseline, while implied volatility defines the relative values of option premiums. The negative spread between VIX and realized volatility reflects overall complacency in the market.

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