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Corn Pops to a New High

Understanding the ESG Consequences of Factor-Based Investing: Part 2

S&P and Dow Jones Islamic Market Indices Largely Outperformed Conventional Indices in Q2 2019

S&P China 500 Declined 2.8% in Q2 2019; Gains Stood at 20.2% YTD

The Opportunity Cost of Active Management

Corn Pops to a New High

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Jim Wiederhold

Associate Director, Commodities and Real Assets

S&P Dow Jones Indices

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Several catalysts, including U.S. Corn Belt production issues, drove corn futures prices to a new five-year high in June 2019. Investors were net short corn in May 2019, and the short covering contributed to the break higher. Prices broke through the 2017 peak after heavy floods in the Midwest prevented farmers from planting corn in a timely manner. While they have typically finished planting by May, this year U.S. farmers continued to plant until the end of June. As seen in Exhibit 1, it took a full extra month to get the crop planted. With new farming technology, farmers are able to plant 24 hours a day and can get the crop into the ground quickly when given the opportunity. This season, the new U.S. farmer bailout package encouraged farmers to continue planting even under less-than-ideal conditions because government payments are based on acres planted.

There are risks associated with planting corn so late in the season because it can negatively affect the final yield, and this is already apparent with 2019’s crop conditions. As can be seen in Exhibit 2, farmers are experiencing some of the worst crop conditions of the past five years. At this point in the season, we have not seen lower rated conditions since 2012. According to Farm Futures, growers continue rating crop conditions behind those reported in the USDA Crop Progress report. This leads to speculation about how the final crop will turn out, with the potential for a significant cut to supply affecting prices in the second half of 2019.

In past similar scenarios (like in 1995), corn prices moved higher throughout the growing season and the bull run continued into the end of the year after the harvest verified that the poor crop conditions had translated into a lower yield and a smaller crop. This year, much will depend on weather conditions over the remainder of the growing season and, possibly more importantly, the final acreage number. Many market participants expect that the final acreage level will eventually be adjusted lower to reflect the late planting pace.

The S&P GSCI Corn offers market participants the ability to capture these idiosyncratic movements. The index was up 12.655% YTD as of July 11, 2019. Supply shocks can drastically affect the price of the underlying commodities in agricultural markets. Tactical investments in commodities when the conditions are good offer an interesting return stream for investors. This recent move in corn is an example of how an allocation to commodities can do just that.

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

Understanding the ESG Consequences of Factor-Based Investing: Part 2

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Ben Leale-Green

Analyst, Research & Design, ESG Indices

S&P Dow Jones Indices

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In our previous blog, we looked at the S&P Factor Indices’ ESG exposures, showing that factor exposures can have an influence on ESG scores. In this blog, we’ll discuss these scores at the sector level and see how implementing an ESG or carbon reduction strategy on poorer ESG-performing factor indices can help investors gain not only factor exposure but desirable ESG exposures.

What Drives These Low Scores? 

Sector allocations are important for determining carbon metrics. Exhibit 1 shows how the weighted average carbon intensities of the 11 GICS® sectors differ. As with the carbon intensity data distribution, this is heavily skewed. The Utilities sector performs particularly poorly, with average emissions well over double the next-highest-emitting sector. Energy and Materials sectors also showed to be high emitting.

Understanding this, we can infer that factors with large exposures to Utilities, Materials, and Energy sectors are likely to have high carbon footprints.

When it comes to ESG scores, there is a sector bias. This sector bias shows how sectors in the S&P 500 compare to their global peers, as the data is normalized at the industry level based on the S&P Global LargeMidCap and S&P Global 1200 constituents. A skew is apparent, although not as strong as for the carbon intensities data (see Exhibit 2).

These sector skews of carbon intensities and ESG scores can potentially affect the factor indices, alongside other drivers such as stock-specific ESG characteristics.

How Constant Are the Sector Allocations within Factor Indices over Time?

The consistency of sector allocations is factor dependent. Exhibits 3-5 shows weight fluctuations in the Utilities, Materials, and Energy sectors for the various factors. The S&P 500 Equal Weight Index shows little fluctuation over time, whereas the S&P 500 Low Volatility Index and S&P 500 Momentum fluctuate significantly.

Overall, a responsible investor may wish to invest in strategy based on a quality-focused index. Alternatively, this investor may wish to implement an ESG or carbon reduction strategy for poorer ESG-performing factor indices, to gain not only factor exposure, but also desirable ESG exposures.

Furthermore, it could be beneficial to continually revise this type of analysis, since these ESG exposures will fluctuate over time, especially with those factors whose weights fluctuate to a greater degree. Ultimately, understanding the ESG consequences of factor exposure may lead to a more holistic investment approach.

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

S&P and Dow Jones Islamic Market Indices Largely Outperformed Conventional Indices in Q2 2019

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John Welling

Director, Equity Indices

S&P Dow Jones Indices

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Developed Market Indices Continued to Outperform Conventional Indices, Emerging Markets Lagged

Global S&P and Dow Jones Shariah-compliant benchmarks outperformed their conventional counterparts YTD in 2019, as Information Technology—which tends to be overweight in Islamic indices—has been a sector leader, while Financials—which is underrepresented in Islamic indices—continued to underperform the broader market. The S&P Global BMI Shariah and Dow Jones Islamic Market (DJIM) World gained 18.4% and 18.0% YTD, respectively, outperforming the conventional S&P Global BMI by approximately 200 bps.

 The outperformance trend played out across major regions as Shariah-compliant benchmarks measuring U.S., Europe, Asia Pacific, and developed markets each continued to outperform conventional equity benchmarks by meaningful margins. Emerging markets and the Pan Arab region were exceptions, as Shariah-compliant benchmarks in these regions underperformed their conventional counterparts.

U.S. Equities Led the Rest of the World through Q2 2019

Following robust gains in Q1 2019, positive U.S. equity performance continued throughout Q2 2019, leading conventional global equities YTD. A continued dovish stance from the U.S. Federal Reserve and hopes of relief in U.S.-China trade negotiations helped push U.S. equities higher last quarter. European and Asia Pacific equities followed in performance, as each enjoyed healthy gains over the period.

MENA Equities Underperformed – Country Results Varied

MENA equities, as measured by the S&P Pan Arab Composite, lagged marginally behind emerging market equities YTD, with a gain of 12.2%. Following robust Q1 2019 performance, the S&P Bahrain continued to lead the region YTD, with gains of 25.1%, followed by the S&P Egypt BMI, which added 22.0%. The S&P Saudi Arabia, which was promoted to emerging market status in March 2019, gained a favorable 15.9%. The S&P Oman lagged most, falling 2.6% YTD, followed by the S&P Qatar, which rose 1.6% YTD.

Varied Returns of Shariah-Compliant Multi-Asset Indices

The DJIM Target Risk Indices—which combine Shariah-compliant global core equity, sukuk, and cash components—generally underperformed the S&P Global BMI Shariah and DJIM World YTD. Performance of the comparably more risk-averse DJIM Target Risk Conservative Index was constrained by its 20% allocation to global equities in the expanding market environment, and the index ultimately gained 8.7% YTD. Meanwhile, the performance of the DJIM Target Risk Aggressive Index was driven by its 100% allocation to a mix of Shariah-compliant global equities, with the index returning 18.1% YTD, in alignment with the broader S&P Global BMI Shariah and DJIM World.

For more information on how Shariah-compliant benchmarks performed in Q2 2019, read our latest Shariah Scorecard.

A version of this article was first published in Islamic Finance News Volume 16 Issue 27 dated July 10, 2019.

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

S&P China 500 Declined 2.8% in Q2 2019; Gains Stood at 20.2% YTD

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John Welling

Director, Equity Indices

S&P Dow Jones Indices

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The S&P China 500 declined 2.8% in Q2 2019, as the U.S.-China trade tensions and threat of company sanctions weighed on returns, even as expectations for eased tensions improved. The decline followed the 23.6% Q1 2019 surge in the index, ultimately leading to overall healthy gains of 20.2% YTD.

Chinese stocks generally lagged global benchmarks in the quarter, as the majority of global country returns were positive. Chinese offshore stocks slightly outperformed onshore listings, as the S&P China BMI fell a lesser 3.3% compared with the S&P China A BMI’s 5.8% decline. As expected, the S&P China 500 posted average performance relative to most of the major Chinese equity benchmarks QTD and YTD, given its diversified composition across all Chinese share classes and sectors.

Most Sectors Declined in Q2 2019

Of the 11 sectors, 2 overcame the overall lower market performance for the quarter, as Consumer Staples (10.6%) and Financials (3.3%) gained, while Information Technology (-10.0%) fell the most, followed closely by Health Care (-9.7%). Positive YTD returns for each sector were a reflection of the outsized gains achieved by the majority of sectors in Q1 2019. YTD gains were led by the consumer sectors, as Consumer Staples (56.4%) gained the most—driven by the outsized 66.7% return of Kweichow Moutai—followed by Consumer Discretionary (27.6%).

 Financials—representing the largest sector of the index by weight—contributed most to the overall performance in Q2, adding 0.9% to the S&P China 500. The Financials sector gains were easily negated however, as the next three sectors by size—Consumer Discretionary, Communication Services, and Industrials each contributed -0.6%, -0.8%, and -0.8%, respectively, representing over 80% of index performance during the quarter.

Valuation Metrics Were Stable in Q2, in Line with Trailing Averages

The S&P China 500’s trailing price/earnings ratio (P/E) rose slightly in Q2, increasing to 14.4x as of June 28, 2019, from 14.0x in March 2019, nearing the 10-year average P/E of 14.7x, while representing a premium to the one-year moving average P/E of 13.1x. Meanwhile, the index’s forward P/E reflected lower prices over the quarter, falling somewhat to 12.2x as of the quarter’s end.

The S&P China 500’s valuations remained in range of broader emerging market indices, as the S&P Emerging BMI’s trailing and forward P/E ratios stood at 14.3x and 12.7x, respectively. The S&P China 500’s dividend yield, meanwhile, increased from 2.15% to 2.32% for the quarter.

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

The Opportunity Cost of Active Management

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Akash Jain

Associate Director, Global Research & Design

S&P BSE Indices

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Investors typically flock to active funds to pass on the stock-picking decision making to a seasoned fund manager, with the hope that the fund manager’s experience and stock-picking capabilities will enable the investor’s portfolio to grow at a faster pace than that set by the benchmark. By using this approach, investors are able to circumvent one problem, only to get stuck in another problem: which fund manager to choose?

As seen in the SPIVA® India Year-End 2018 Scorecard, a large proportion of active fund managers underperformed the benchmark. Over the 10-year period observed, 64% of the large-cap funds underperformed the benchmark, the S&P BSE 100.

In the Indian Equity Large-Cap and Indian Mid-/Small-Cap categories, there was a wide spread in fund performance across different investment horizons (see Exhibit 1). For example, the spread in returns for an investor in two different large-cap funds over a 10-year horizon ending Dec. 31, 2018, could have been as high as 13.2% CAGR. Therefore, the selection of a fund can play a critical role in portfolio returns. The performance range in the case of the Mid-/Small-Cap category was even higher, at 14.92% over a 10-year horizon. The story remains the same across different time horizons. Furthermore, the average net returns generated by active funds were not far off from the benchmark returns (see Exhibit 1).

 We also studied the distribution of fund returns and calculated their mean, standard deviation, and skew (see Exhibit 2). The study compared the fund returns data distribution with a hypothetical normal curve constructed with the same mean and standard deviation. Again, we considered the large-cap category and mid-/small-cap category for this analysis.

  • Large-Cap Category: We witnessed a positive skew (skewed to the right), which implies that, generally speaking, the mean was higher than median, indicating that few funds generated extraordinary returns, pulling the category average higher whereas the performance of most funds lies to the left of the mean.
  • Mid-/Small-Cap Category: We noticed a negative skew (skewed to the left). This implies that the mean was less than the median, which means that only a few funds with large underperformance were dragging the mean down, but that most funds generated superior funds in this category.

This analysis indicates that, at least in the large-cap category, the majority of the funds failed to beat the category average.

What is more challenging is that the relative peer performance of a mutual fund has not been consistent (see Exhibit 3), which means that funds that have outperformed in one period failed to maintain their superior performance in the following periods. In Exhibit 3, funds were classified into four quartiles based on their performance over the five-year period between Dec. 31, 2008, and Dec. 31, 2013. The columns to the right showcase how many of the funds continued to outperform their peers over the period from Dec. 31, 2013, to Dec. 31, 2018. Some important inferences include the following.

  1. In the case of the large-cap fund category, only 14.8% of the 27 top-quartile funds continued to be in the top quartile the following five years. However, in the mid-/small-cap category, 42.9% of the 14 top-quartile funds continued to be in the top quartile in the following five years.
  2. The worst-performing funds have higher probability to continue their underperformance. For example, in the mid-/small-cap category, 35.7% of the 14 funds in the bottom quartile continued their underperformance and failed to break out from the bottom quartile.
  3. The highest number of fund mergers/liquidations was witnessed in the bottom quartile. For example, in the large-cap category, 35.7% of the 28 funds (i.e., 10 funds) in the bottom quartile failed to survive the period from 2013 to 2018.

The writing on the wall is clear. Fund outperformance is random and predicting an outperforming mutual fund may be as challenging as the stock-selection process. From a purely mathematical point of view, an investor has better odds of flipping a coin than identifying an outperforming active mutual fund. Therefore, investing via a systematic, style consistent, low-cost passive route could be a better bet for an investor.

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