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Most S&P and Dow Jones Islamic Indices Outperformed Conventional Benchmarks in Q1 Driven by Strength in the Technology Sector

Revenue Exposure of the S&P/ASX 200

The Impact of Style Classification on Active Management Performance in 2017: Part 2

Low Volatility and Market Regime Shifts: Lessons From the First Quarter

Carbon-Efficient Portfolio Construction Part 2: Sector-Relative Improves Efficiency

Most S&P and Dow Jones Islamic Indices Outperformed Conventional Benchmarks in Q1 Driven by Strength in the Technology Sector

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Michael Orzano

Senior Director, Global Equity Indices

S&P Dow Jones Indices

Most S&P and Dow Jones Shariah-compliant benchmarks outperformed their conventional counterparts in Q1 2018, as the information technology sector—which tends to be overweight in Islamic Indices—led the market by a wide margin, and financials—which is underrepresented in Islamic indices—matched the returns of the broad market.

The Dow Jones Islamic Market World Index and S&P Global BMI Shariah closed the quarter slightly in the red but outperformed the conventional S&P Global BMI by 0.6% and 0.9%, respectively. Shariah-compliant benchmarks tracking Asian and European equities beat their conventional counterparts by the widest margins, while the S&P 500® Shariah eked out a small gain over the conventional S&P 500.

Global Equities Finished the Quarter in the Red in a Volatile Start to the Year

After a strong January in which the S&P Global BMI Shariah gained more than 5%, volatility set in as various risks rose to the forefront, including the potential for rising U.S. interest rates and increased global trade tensions. European and U.S. equities fared the worst among major regions, while developed markets in the Asia Pacific region and emerging markets finished the quarter in positive territory. The Dow Jones Islamic Market World Emerging Markets Index rose 1% in Q1 2018 on the heels of a gain of over 40% in 2017.

MENA Equity Markets Rebounded Following 2017 Weakness

MENA equities had a strong quarter, as the S&P Pan Arab Composite Shariah gained 6.8%, driven by strength in Saudi Arabia and Egypt. Egypt was the region’s top performer in Q1 2018. The S&P Egypt BMI jumped 17.3% in U.S. dollar terms, adding to its 21% gain in 2017, as the nation’s macroeconomic environment continued to stabilize following the IMF-supported reforms initially enacted in November 2016. The S&P Saudi Arabia BMI finished the quarter up 10.5%, as the country’s own economic and equity market reforms have, likewise, led to improved investor sentiment.

*This article was first published in Islamic Finance News, Volume 15 Issue 15, on April 11, 2018.

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

Revenue Exposure of the S&P/ASX 200

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Utkarsh Agrawal

Associate Director, Global Research & Design

S&P Dow Jones Indices

The S&P/ASX 200 is widely regarded as the institutional investable benchmark in Australia. It is composed of the largest 200 companies listed on the Australian Securities Exchange by float-adjusted market capitalization. Although the majority of the companies are domiciled in Australia, a lot of them derive a significant portion of their revenue from foreign markets. As of year-end 2017, only 60 companies in the S&P/ASX 200 derived their revenue solely from the domestic market, while the rest of the companies had exposure to foreign markets (see Exhibit 1). Consequently, potential risk from political and economic shocks in foreign markets cannot be ignored. Hence, it is worthwhile to review the global revenue exposure of the index.

Some of the key highlights from the total revenue exposure[1] breakdown of the S&P/ASX 200 as of year-end 2017 are as follows (see Exhibit 2).

  1. Only 62% of the index’s total revenue came from Australia.
  2. The index had the highest international revenue exposure to the U.S. (7.9%), followed by China (7.6%) and New Zealand (5.9%).
  3. At the sector level, total revenue exposure was most dominated by financials (28%), followed by materials (21.6%) and consumer staples (17%).

Further observation of international revenue exposure revealed the following (see Exhibit 2).

  1. Out of the 37.9% attributed to international revenue, 17.7% came from the materials sector and 7.0% came from financials.
  2. The materials sector’s revenue exposure to China (6.6%) exceeded its domestic revenue exposure (3.9%).

Since almost 38% of the S&P/ASX 200 revenue came from foreign countries, the economic and political conditions in foreign markets could have a significant impact on the index’s performance. Hence, understanding global revenue exposure is essential to comprehend the index’s inherent potential risk.

[1]   We used the FactSet Geographic Revenue Exposure (GeoRevTM) dataset to calculate revenue exposure. It provides a geographic breakdown of revenues at the country level for all companies with available data. Due to the lack of standardization in the reporting of geographic revenue segments, the dataset uses a normalization/estimation process to assign revenues to specific countries. For more information please visit

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

The Impact of Style Classification on Active Management Performance in 2017: Part 2

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Ryan Poirier

Former Senior Analyst, Global Research & Design

S&P Dow Jones Indices

In our previous blog, we highlighted the contribution to domestic equity market returns by mega-cap stocks in 2017 and the implications for active management. In this blog, we focus our discussion on investment style classification. Specifically, we analyze the impact of the style classification scheme on managers’ performance analysis, such as in the SPIVA® U.S. Year-End 2017 Scorecard.

A managers’ investment style is the philosophy and process in which they have stated they invest by (a characteristic guideline of acceptable investments). Moreover, investment style sets the evaluation framework through which managers’ performance and risk exposures can be measured.

Traditional “style box” investing divides investment styles along a size and fundamental valuation metrics spectrum. Exhibit 1 shows the returns of the nine S&P U.S. Style Indices over various trailing one-year periods, ending each June and December since 2015. The returns are color coded so that the darkest color indicates the best-performing style, and the lightest denotes the opposite.

As of Dec. 31, 2017, large-cap growth performed the best, while small-cap value performed the worst. The remaining styles fell in between those two categories such that shifting across the market cap range or style (as shown by the row or column, respectively) allowed for potential additional return pickup. For example, small-cap value managers would have performed better had they owned mid-cap value securities or moved closer to small-cap core.

The direction (of the green hue) and magnitude make a big difference in whether the managers of a certain style box had a better (or worse) opportunity to outperform their stated benchmark by moving styles. With respect to the direction of the green hue, one might conclude that in 2016, large-cap managers could drift down in capitalization to harvest the size premium (Exhibit 1, center table).

While style drift can potentially offer return opportunities for managers who can time correctly, there are limitations to such a decision. One potential restriction stems from the classification rules set forth by the fund ranking providers for each style. For example, according to Lipper style classifications, large- (or mid-) cap managers are defined as funds that invest at least 75% of their assets in securities that are larger (or smaller) than 300% of the 750th largest security in the S&P Composite 1500®. Similarly, small-cap managers are those that invest at least 75% of the assets in securities that are smaller than 250% of the 1,000th largest security in the S&P Composite 1500.[1]

The result is that large-, mid-, and small-cap managers have an opportunity set that is roughly represented by the 400 largest, 400th largest and below, and 600th largest and below stocks, respectively (see Exhibit 2). In other words, mid- and small-cap managers have more autonomy to express their view on the size factor without officially “drifting” outside of their defined style classification.

Therefore, depending on the market cap cutoffs used by the benchmark providers, there may be a mismatch between the funds and the benchmarks they are compared against. This blog serves as a foundation to our next discussion in which we will attempt to quantify this mismatch in style using mid- and small-cap managers as an example. Furthermore, we will discuss how to address the comparison bias through index construction.

Market participants should use this blog not solely to identify potential market environments in which style classification may be most influential, but also to prompt further investigation into whether their managers’ returns are style-consistent so as to set proper risk/return expectations. We show that there may be significant cross over between style boxes, and thus a given manager’s style should not be taken at face value.

[1]   Funds are classified into different styles by Lipper. More information can be found here:

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

Low Volatility and Market Regime Shifts: Lessons From the First Quarter

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Anu Ganti

U.S. Head of Index Investment Strategy

S&P Dow Jones Indices

Since antiquity, people have measured time in months. Unsurprisingly, investors tend to evaluate performance in monthly increments. This can be troublesome, as we will see in the case of low volatility, particularly during market regime changes.

Low volatility strategies are designed to provide investors with protection in falling markets and participation in rising markets. Disappointments can occur in two ways:

  1. The strategy underperforms falling markets
  2. The strategy falls during rising markets

Success rates for the S&P 500® Low Volatility Index approximate 85% on both of these dimensions.

The first quarter of 2018 was unusual, as the market initially underwent an expansive phase, during which cyclical sectors outperformed, up until the market peak on Jan. 26, 2018. Subsequent to this peak, the market began to decline when defensive sectors eventually outperformed.

How was low volatility affected by this regime shift? Let us start with January, when the U.S. market was off to a booming start, with the S&P 500 up 5.73%, while the S&P 500 Low Volatility Index lagged, only up 2.65%. This outcome is expected: Underperformance in a rising market is not a failure, but one of the inherent features of low volatility strategies.

In February, low volatility underperformed the market, with the S&P 500 Low Volatility Index down 4.24%, while the S&P 500 was down 3.69%. This outcome is unexpected: Underperformance in a falling market is a failure of protection, one of the key benefits of low volatility strategies.

But before we can declare failure, we need to understand better what happened in February.

An underweight to information technology, which was the best-performing sector in February, was the main driver of low volatility’s February underperformance, along with an overweight to utilities and real estate. These three sector tilts together cost the strategy 1.05% (in a month when it underperformed by only 0.55%).

But low volatility did indeed subsequently deliver protection. March’s performance was a complete reversal from February’s—low volatility now handily beat the market, with the S&P 500 Low Volatility Index up 0.85%, compared to the S&P 500’s bleak performance, down 2.54%. Overweights to utilities, real estate, and financials, along with an underweight to information technology, contributed 2.85% to the strategy’s outperformance. These are the very same sector tilts that detracted from February’s performance.

The outperformance in March required enduring some underperformance in February. This illustrates a broader, more important principle: Evaluating any factor strategy over a period as short as one quarter requires paying careful attention to the nature of the investment environment.

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

Carbon-Efficient Portfolio Construction Part 2: Sector-Relative Improves Efficiency

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Wenli Bill Hao

Director, Factors and Dividends Indices, Product Management and Development

S&P Dow Jones Indices

In a prior blog, we demonstrated that unconstrained carbon-efficient portfolios have significant unintended (and unfavorable) sector and risk factor tilts that can drag down performance. In this follow-up blog, we explore potential ways sector-relative, carbon-efficient portfolios can address the drawbacks of sector-unconstrained, carbon-efficient portfolios. To form sector-relative, carbon-efficient quintile portfolios, we ranked and grouped securities within each sector based on individual companies’ carbon intensity.[1]

Sector-relative, carbon-efficient portfolios were evaluated from several metrics: weighted average carbon intensity, risk/return profile, sector allocation, and active risk exposures. Carbon intensity data were provided by Trucost, part of S&P Dow Jones Indices, and are defined as greenhouse gas emissions measured in tons of carbon dioxide equivalent per USD 1 million of revenue (CO2e/USD 1 million).

The most sector-relative, carbon-efficient portfolio, Quintile 1, substantially reduced the carbon intensity by nearly 80% to 66 CO2e/USD 1 million compared with the benchmark, the S&P United States LargeMidCap (see Exhibit 1). Moreover, on an absolute return basis, Quintile 1 outperformed most of its peers and the underlying benchmark, except for Quintile 2. On the other hand, Quintile 1 had marginally higher volatility among all the portfolios, thereby resulting in comparable risk-adjusted return to Quintiles 2, 3, and 5, but higher risk-adjusted returns than Quintile 4 and the benchmark. The sector-relative, carbon-efficient portfolio improved its efficiency in terms of carbon efficiency and risk-adjusted returns over its underlying benchmark.

We further analyzed sector composition and sector attribution of the sector-relative, carbon-efficient portfolio to explore potential sector biases relative to the underlying benchmark and their impacts on portfolio efficiency.

The sector-relative, carbon-efficient portfolio displayed minor sector deviations from the underlying benchmark. During the period studied, average active sector weights maximized at about 5% (see Exhibit 2). Moreover, with the sector-relative, carbon-efficient portfolio, the sector allocation effect was positive (with an annualized return of 0.32% on a monthly average basis; see Exhibit 3) and contributed positively to its active return over the benchmark. In contrast, the unconstrained carbon-efficient portfolio had a sector allocation effect of an annualized underperformance of -1.51% on a monthly average basis (see Exhibit 3) due to its large sector bias in financials, as shown in the previous blog.

 Analysis of active risk exposures showed that the sector-relative, carbon-efficient portfolio had much less positive active exposures in beta, value, liquidity, and price volatility. On the other hand, it had large negative exposure to yield, size, earnings variability, and high leverage (see Exhibit 4).[2] Therefore, during the period studied, the sector-relative, carbon-efficient portfolio tended to have higher exposure to quality and limited exposure to value than the benchmark. In addition, the portfolio had some positive exposure to EPS growth rate and relative strength (momentum).

The results from Exhibits 2, 3, and 4 showed that the sector-relative, carbon-efficient portfolio had limited sector biases and more favorable risk factor tilts. Such unrewarded systematic risk reduction made the portfolio more efficient, while simultaneously meeting the carbon reduction objective.

Based on the findings of the two blogs, we advocate forming sector-relative, carbon-efficient portfolios over unconstrained ones when portfolio decarbonization is the main goal. In the next blog, we will explore how to integrate carbon risk with factor portfolios.

[1]   B. Hao, A. Soe, and K. Tang. “Carbon Risk Integration in Factor Portfolios.” 2018. S&P Dow Jones Indices LLC.

[2]   We used the Northfield U.S. Fundamental Risk Model to estimate the risk exposure.

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