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Performance Characteristics of the S&P/B3 Low Volatility High Dividend Index

S&P High Yield Dividend Aristocrats Part I: Strategy Characteristics

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

Performance Characteristics of the S&P/B3 Low Volatility High Dividend Index

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Smita Chirputkar

Director, Global Research & Design

S&P Dow Jones Indices

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After exploring the rationale behind the implementation of a low volatility high dividend strategy in Brazil in our previous blog, we will now examine the recently launched S&P/B3 Low Volatility High Dividend Index.

The index is designed to measure the performance of the least volatile stocks among a specified group of high-dividend-yielding constituents from its benchmark, the S&P Brazil BMI, and is subject to diversification and tradability requirements. The constituents are weighted by their trailing 12-month dividend yield. To accommodate trading capacity, the maximum weight is capped at the lower of 15% and five times its liquidity weight.[1]

Historically, the S&P/B3 Low Volatility High Dividend Index exhibited better risk/return characteristics than the benchmark, especially over the mid- and long-term periods. In investment horizons longer than five years, the index outperformed the benchmark on an absolute and risk-adjusted basis. In the 12-year back-tested period ending in August 2019, the strategy exhibited a significantly lower maximum drawdown (-26.9%) compared with the S&P Brazil BMI (-49.5%; see Exhibit 1).

 The S&P/B3 Low Volatility High Dividend Index provided downside protection in periods of market turbulence. During all the months in which the benchmark was down between June 2007 and August 2019, the S&P/B3 Low Volatility High Dividend Index outperformed 82.3% of the time and generated a monthly average excess return of 2% over the benchmark.[2] This defensive characteristic is typical of a low volatility strategy (see Exhibit 2).

The S&P/B3 Low Volatility High Dividend Index was also able to generate higher yield than the S&P Brazil BMI. Over the studied period, the S&P/B3 Low Volatility High Dividend Index had an average historical yield of 5.5%, compared with 3.1% for the benchmark.

We will explore more about dividend strategies in Brazil in our next blog.

[1] Liquidity weight is measured as a security’s six-month median daily value traded.

[2] Up months are defined as periods when the S&P Brazil BMI had a positive monthly return. Down months are defined as periods when the S&P Brazil BMI had a negative monthly return.

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

S&P High Yield Dividend Aristocrats Part I: Strategy Characteristics

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Bill Hao

Director, Global Research & Design

S&P Dow Jones Indices

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With the 10-Year Treasury yield around just 1.5% and the potential for more interest rate cuts on the horizon, yield-seeking investors may become more interested in equity dividend yield strategies. Dividend strategies can satisfy investors’ needs in several regards, namely higher dividend income, favorable risk-adjusted returns, lower volatility, and more downside protection in bearish market environments.

Here and in subsequent blogs, we introduce the S&P High Yield Dividend Aristocrats®. We will cover its methodology and yield characteristics, risk/return profile, and performance attribution.

The S&P High Yield Dividend Aristocrats was launched in November 2005. The index is designed to track a basket of stocks from the S&P Composite 1500® that have consistently increased their total dividends per share every year for at least 20 consecutive years.[1] The index universe covers large-cap, mid-cap, and small-cap stocks in the U.S. equity market.

As shown in Exhibit 1, the S&P High Yield Dividend Aristocrats has consistently had higher yields than its benchmark. The average yield of the index was 3.5%, ranging from 2.5% to 5.8%. In contrast, the average yield of the S&P Composite 1500 was 1.8%, with a range from 1.5% to 2.8%. On average, the dividend yield gap between these two indices was 1.7%.

For comparison purposes, Exhibit 2 lists recent yields of selected securities and indices. As of July 31, 2019, the S&P High Yield Dividend Aristocrats had a dividend yield of 2.8%, which was higher than the yields of the other securities and indices shown, with the exception of the S&P 500 Bond Index.

In the next blog, we will take a deeper dive into the risk/return profile of the S&P High Yield Dividend Aristocrats.

[1] For further information about the index, please see the S&P High Yield Dividend Aristocrats Methodology.

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

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.