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SPIVA Latin America Year-End 2020 Scorecard: Active Managers Missed an Opportunity

S&P Global Clean Energy Index Expands

Renewable Diesel Feedstock – Green Fuel and Affordable Food Part 2

S&P BSE SENSEX Indices Reach All-Time Highs in Q1 2021

Special Purpose Acquisition Companies (SPACs) – Part III

SPIVA Latin America Year-End 2020 Scorecard: Active Managers Missed an Opportunity

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Maria Sanchez

Associate Director, Global Research & Design

S&P Dow Jones Indices

The recently published SPIVA® Latin America Year-End 2020 Scorecard shows that the volatile environment of 2020, though potentially favorable for high-conviction active managers, did not necessarily translate into success for active managers.

SPIVA scorecards measure the performance of active funds against an appropriate benchmark. For Latin America, S&P Dow Jones Indices began publishing the scorecard in 2014, covering Brazil, Chile, and Mexico.

As of year-end 2020, all categories across all three countries underperformed their benchmarks over the 1-, 3-, 5-, and 10-year periods. These results contrasted those of the SPIVA Latin America Mid-Year 2020 Scorecard, in which Brazilian active managers in the Brazil Equity Funds, Brazil Large-Cap Funds, and Brazil Corporate Bond Funds categories managed to take advantage of the circumstances and outperform over the one-year period (see Exhibit 1).

Median fund managers across all the categories in the report underperformed their benchmarks over 1-, 3-, 5-, and 10-year periods (see Exhibit 2). However, in five out of seven categories, active managers in the first quartile beat their benchmarks over the one- and three-year periods (see Exhibit 3). Top-performing managers in the Brazil Equity Funds category were even able to outperform over the 10-year period.

As evidenced by SPIVA scorecards, the majority of active managers underperform most of the time, especially across the long-term time horizon. Is outperformance luck or skill? Stay tuned for the upcoming Latin America Persistence Scorecard.

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

S&P Global Clean Energy Index Expands

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Ari Rajendra

Senior Director, Strategy & Volatility Indices

S&P Dow Jones Indices

Following a public consultation that concluded in March this year, S&P DJI announced the new composition of the S&P Global Clean Energy Index, which currently comprises 30 leading clean energy-related stocks, on April 2, 2021. The index is set to broaden when changes take effect on April 19, 2021.

Clean energy is an area that has garnered much investor attention over the past year, fueled in part by falling prices of renewables and growing momentum for carbon neutrality. As the global clean energy sector evolves, the S&P DJI Index Committee aims to have a benchmark that reflects the changing opportunity set.

Methodology Changes Lead to the Introduction of 51 New Stocks1

The index will now aim to add all stocks with the purest clean energy exposure (exposure score of 1) without any limit on target number of companies. If there are fewer than 100 eligible stocks, then companies with lower exposure scores are added (up to 100) without breaching a defined dilution threshold.2

Despite an increase in constituent count, there was a slight decrease in the index’s weighted average exposure score (see Exhibit 1). The expectation of a sizeable reduction in volatility could help improve its risk/return profile as the theme continues to take shape in the years to come.3

Higher Liquidity

The consultation proposal outlined the goal to reduce constituent concentration, ease liquidity limitation, and improve index replication. The forthcoming changes seek to address these objectives in several ways, including increasing constituent count and introducing a liquidity weight multiple cap.4 Our analysis5 confirms notable improvements.

Exhibit 3 compares the liquidity profile for current and pro-forma compositions, as determined by the individual stock members. The maximum index liquidity is typically determined by identifying the constraining constituent.6 This may not necessarily be the least liquid stock, as the stock weight plays a role too. As shown, the capacity available to trade 90% of the index increases by six times. A similar magnitude of improvement is observed for trading 100% of the index (USD 333 million versus USD 51 million).

Stock ownership7 is another meaningful measure from an index replication standpoint. Our assessment based on a hypothetical USD 15 billion index portfolio reveals a significant reduction in concentrated ownership (see Exhibit 4), further attesting to the merit of the changes.

Elevated Trading Volumes Could Provide Liquidity for the Rebalance

While the changes are positive, they will result in a degree of turnover. Our estimates indicate that the one-way turnover is likely to be about 55%.

That said, larger rebalance events are typically preceded by an increase in trading activity. To investigate this, we compared the average daily stock liquidity for the six-month period prior to the consultation announcement (six-month ADV) to the volumes since April 2, 2021.5 Exhibit 5 confirms a sharp rise in trading activity for stocks with high days to trade (based on a hypothetical USD 15 billion portfolio and six-month ADV). If volumes remain elevated, the impact of the rebalance may well be attenuated.

What’s Next?

During the March 2021 consultation, the Index Committee also proposed to include emerging markets-listed stocks6 and expanding the clean energy business definition to include other eligible segments (e.g., energy storage companies). As these proposals were not intended for implementation in April 2021, S&P DJI intends to publish an additional consultation upon the completion of the upcoming rebalance. S&P DJI continues to monitor and seek feedback to ensure that the S&P Global Clean Energy Index appropriately meets its objective as this segment continues to evolve and develop.


1 Full details of the methodology changes can be found here.

2 Dilution threshold is defined by the index weighted average exposure score, which is set to 0.85.

3 Brzenk, Phillip. “Why Clean Energy Now.” Indexology® Blog. Feb. 2, 2021.

4 Liquidity weight multiple cap is an additional cap imposed to ensure that a stock’s representation is in line with its liquidity. This cap is set at five times. Liquidity Weight Multiple Cap = Multiple*(Stock Liquidity/Aggregate Liquidity of all Stocks)

5 Based on daily tradable liquidity and ownership concentration.

6 Stock implied index liquidity is calculated for each stock in the index by dividing stock daily turnover by stock weight. The stock that implies the smallest value (constraining stock) determines the maximum index liquidity. For daily turnover, we assume 20% participation.

7 Measured by dividing the U.S. dollar notional held in any stock by its total market capitalization.

8 A hypothetical composition was released on March 12, 2021, as part of the S&P Global Clean Energy Index Consultation (based on October 2020 rebalance reference data).

9 Emerging markets stocks listed on developed exchanges are currently included.

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

Renewable Diesel Feedstock – Green Fuel and Affordable Food Part 2

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Fiona Boal

Head of Commodities and Real Assets

S&P Dow Jones Indices

As market participants increasingly incorporate ESG metrics into all aspects of investing, it is inevitable that commodities investing would collide with ESG. The search for low-carbon fuel feedstocks from renewable sources to meet the demand for green energy is one area where the commodity-ESG conundrum is already apparent. In part 1 of our blog, we introduced renewable diesel feedstock. In this edition, we’ll continue our discussion and how it relates to ESG.

Government mandates, incentives, and standards to lower the carbon intensity of fuel combined with consumer demands for meaningful action on climate change has accelerated the demand for renewable diesel feedstocks. Refiners can produce renewable diesel from animal fats, plant oils, and used cooking oil, but in North America renewable diesel producers will increasingly be relying on soybean and canola oil to run new plants. A renewable diesel boom may have a profound impact on the agricultural sector by swelling demand for oilseeds like soybeans and canola that compete with other crops for finite planting area and affecting food prices.

Rising food prices may be a concern if the predicted demand for crops to generate renewable diesel materializes. Some industry participants have suggested that U.S. renewable diesel production could generate an extra 500 million pounds of demand for soybean oil in 2021, which would represent a 2% year-over-year increase in total consumption.

The food versus fuel dilemma is not new. The corn industry went through a similar structural shift in 2005 with the introduction of the Renewable Fuel Standard (RFS), while sugar has been used as a feedstock for ethanol for decades in countries such as Brazil. While there is little academic evidence to suggest that corn demand from ethanol has structurally increased food prices (on average corn makes up a small percentage of final food expenses), the case with edible oils may be different given that they make up a larger percentage of final food expenses, especially in developing countries.

The FAO Food Price Index (FFPI) measures the monthly change in international prices of a basket of food commodities. Exhibit 1 illustrates the volatility of the basket and its sub-sectors over time. While the FAO Vegetable Oil Price Index reached its highest level since April 2012 in February 2021, this price appreciation should be viewed in the context of higher food prices across the board driven by a combination of disrupted supply chains in the wake of COVID-19, restocking demand, weather-driven supply issues, and higher energy prices.

While renewable diesel from recycled fuels may be a more sustainable and food-price friendly alternative, the transition to lower-carbon-intensity fuels will almost certainly involve increased demand for animal and vegetable oils.

For more information on SPDJI’s commodities indices, visit and be sure to check back as we celebrate the 30th anniversary of the S&P GSCI.

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

S&P BSE SENSEX Indices Reach All-Time Highs in Q1 2021

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Ved Malla

Associate Director, Client Coverage

S&P Dow Jones Indices

The S&P BSE SENSEX Index Series comprises three indices, namely the S&P BSE SENSEX, the S&P BSE SENSEX 50, and the S&P BSE SENSEX Next 50. The S&P BSE SENSEX is the oldest and most tracked index in India and comprises the 30 largest, most liquid, and financially sound companies in the S&P BSE 100. The S&P BSE SENSEX 50 is designed to measure the 50 largest and most liquid companies in the S&P BSE 100. Meanwhile, the S&P BSE SENSEX Next 50 is designed to measure the next 50 largest and most liquid companies in the S&P BSE 100 that are not members of the S&P BSE SENSEX 50.

In this blog, we will compare the returns of the S&P BSE SENSEX, the S&P BSE SENSEX 50, and the S&P BSE SENSEX Next 50 for the first quarter of 2021.

In Exhibit 1, we see that for the period ending March 31, 2021, the absolute returns of the S&P BSE SENSEX, S&P BSE SENSEX 50, and S&P BSE SENSEX Next 50 were 3.85%, 5.21%, and 8.35%, respectively.

In Exhibit 2, we see the total return index levels of the S&P BSE SENSEX, the S&P BSE SENSEX 50, and the S&P BSE SENSEX Next 50. The S&P BSE SENSEX Next 50 consistently outperformed the S&P BSE SENSEX and S&P BSE SENSEX 50 during the first quarter of 2021, despite the high market volatility during this period.

Furthermore, Exhibit 3 shows us that all three indices had their all-time highs in the first quarter of 2021.

To summarize, we can say that the S&P BSE SENSEX Index Series showed promising returns in Q1 2021; despite the increased volatility, all three indices reached all-time highs during this period.

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

Special Purpose Acquisition Companies (SPACs) – Part III

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Jason Ye

Associate Director, Strategy Indices

S&P Dow Jones Indices

In our previous blog (part II), we introduced SPACs’ lifecycles, as well as the potential benefits and risks of investing in SPACs. In this blog, we will focus on SPACs’ liquidity. In general SPACs’ liquidity is poor when seeking the target, surges on the deal announcement date, and remains low relative to the S&P SmallCap 600® after de-SPAC.

As of March 26, 2021, the median market capitalization of all listed SPACs was USD 284 million, much lower than the median market capitalization of USD 1.5 billion of S&P SmallCap 600 constituents. Since most SPACs are small- or micro-cap companies, we compared their liquidity against the S&P SmallCap 600. Based on the lifecycle of a SPAC, we analyzed its liquidity in three stages: post-IPO, deal announcement, and post-deal completion (see Exhibit 1).

We analyzed the 767 SPAC IPOs listed on the NYSE, NASDAQ, and NYSE American since 2008. As discussed in part I of our blog series, the majority of SPAC IPOs occurred in 2020 and 2021. We tracked the history of each SPAC through its lifecycle. Of the 767 SPAC IPOs, 27 were liquidated, while 23 SPACs finished the merger but were further acquired by another company, privatized, or became bankrupt. We excluded these 50 SPACs from our analysis in order to focus on the de-SPAC companies before any further corporate actions. Only common stock is included in our analysis.

Exhibit 2 shows that the average number of months from IPO to deal announcement was 10.8 months, and the average number of months between deal announcement and deal completion was 4.6 months. 57% of SPACs announced a target within 12 months, and 98% of SPACs completed the merger within 12 months.  For our analysis we use 1, 3, and 6 months for post-IPO, and 3, 6, and 12 months for post-completion.


Exhibit 3 shows that the median of the S&P SmallCap 600 constituents’ past three-month median daily value traded (MDVT) was around USD 7 million at each quarter-end during the past five years, and the mean was around USD 11 million.

Exhibit 4 summarizes the SPACs’ MDVT and how that compares to the USD 7 million and USD 11 million benchmark liquidity post-IPO and post-completion, respectively. Exhibit 5 shows how the daily value traded changed 30 days before and 30 days after deal announcement, and Exhibit 6 highlights the distribution of value traded on the announcement day. The data shows the following:

  1. Most of the SPACs’ liquidity was lower than the median liquidity of the S&P SmallCap 600 constituents (see Exhibit 4).
  2. During their lifecycle, SPACs’ liquidity was the worst when searching for a target (see Exhibit 4). On the day of deal announcement, SPACs’ liquidity tended to improve significantly (see Exhibit 5 and Exhibit 6). Once a SPAC completed the deal, it traded with lower liquidity than the median of the S&P SmallCap 600 constituents (see Exhibit 4).
  3. At each stage, SPACs’ liquidity decayed rapidly after the corporate action (see Exhibit 4).
  4. The median liquidity was much lower than the mean liquidity, which shows that the distribution is heavily skewed in each stage (see Exhibit 4 and Appendix).

In our next blog in this series, we will follow the same framework to analyze SPACs’ performance.


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