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The Next Frontier in Footprinting: Carbon Accounting for Sovereign Bonds

Introducing the U.S. S&P Select Industry Dashboard

Assessing the Potential of Value Factors in the Indian Market

The Department of Labor and ESG Guidance: Is the Pendulum Shifting?

How an Industry Reduced Its Carbon Pricing Risk by 922%

The Next Frontier in Footprinting: Carbon Accounting for Sovereign Bonds

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Lauren Smart

Managing Director, Global Head Financial Institutions Business

Trucost, part of S&P Global

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Sovereign bonds remain largely unanalyzed by investors from a carbon risk and reporting perspective. This is despite clear acknowledgement in the Paris Agreement that governments have a critical role in curtailing global warming,[1] and the Financial Stability Board’s warning that climate change could affect all asset classes and the stability of the broader financial system.[2]

A key obstacle has been defining the appropriate metrics. As sovereign bonds provide capital to national governments, it is a government’s carbon emissions that we are focused on, but opinions vary on the appropriate breadth of ownership and responsibility.

  • Are a government’s emissions just those produced directly through their expenditures, or should they also include those of the nation’s people and private companies?
  • What about the emissions embedded within goods produced domestically then shipped overseas or imported goods consumed in the country?
  • Is risk greater if a country consumes more emissions than it produces, if it has more emissions per capita, or if its level of debt is high relative to its emissions?

There is no right or wrong answer, but the choice of metric produces different insights.

Taking a Narrow View

A government’s emissions are those generated from its provision of public services. This limits double counting so it is neat from a carbon accounting perspective, but it is open to criticism for underrepresenting the emissions sovereign debt finances and the role of governments as regulators. The ability of a government to raise capital at competitive rates is based on the strength of the whole economy, not just the public sector. Similarly, the financial impacts of climate change will not be limited to the public sector, and the resilience of the economy will be affected by the influence a government exerts over private markets via regulation and taxation. Carbon taxes and renewable energy subsidies are good examples of the regulatory reach a government can exercise to influence production and consumption patterns of private firms and individuals. A broader approach counts all emissions produced within a country’s territorial boundary so the entire economy becomes the unit of analysis, but this method is exposed to double counting.

Accounting for Carbon in Imports and Exports

Governments generally report emissions produced within their borders, but this means that carbon can essentially be exported. Emissions tend to be exported from high-emitting industries in highly regulated countries to those with less stringent regulation. A carbon “balance of trade” approach helps address the demand side of the equation, and it is notable that net consumers are typically developed economies that typically outsource most production emissions to emerging economies.

To compare countries with economies of different size, we need to create a carbon intensity metric. Normalizing emissions by the level of government debt is one approach, but results are skewed by the different debt levels of countries that may disguise more environmentally relevant production and consumption patterns. An alternative is to express the carbon intensity relative to GDP. This is particularly appropriate if using a production-based approach to quantify a country’s carbon emissions because the territorial boundary mirrors the scope of GDP calculations. To take account of consumption, a per capita denominator provides a different perspective. However, the metrics can produce significantly different insights. As Exhibit 1 highlights, when using GDP-based intensities, OECD countries appear to be among the most carbon efficient in the world, but when using emissions per capita, they are the worst. The opposite is true for most BRIC countries. This illustrates the exportation of emissions from developed to emerging economies, which is not visible in the GDP-based metrics.

So we see that the choice of metric produces different insights and implied actions—the key is understanding when to apply which metric.

To explore in further detail, read Accounting for Carbon: Sovereign Bonds.

[1] https://unfccc.int/process/the-paris-agreement/status-of-ratification

[2] https://www.fsb-tcfd.org/about/

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

Introducing the U.S. S&P Select Industry Dashboard

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Anu Ganti

Senior Director, Index Investment Strategy

S&P Dow Jones Indices

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We recently launched the monthly U.S. S&P Select Industry Dashboard, which provides key metrics, analysis of correlation and dispersion, and historical risk/return data for 20 investable select industry indices.

The dashboard is a natural extension of our U.S. Select Sector Dashboard, which provides analysis for investable sectors across the large-, mid-, and small-cap ranges. For example, in May 2018, technology was the top-performing select sector. This outperformance was driven by strength in the S&P Semiconductors Select Industry Index and S&P Internet Select Industry Index.

One key feature of this new dashboard is that it illustrates each select industry’s distribution across the range of large, mid, and small caps. As seen in Exhibit 1, in May 2018, biotechnology had the most micro-cap exposure, as opposed to homebuilders, which had the highest large-cap exposure.

Another feature of this dashboard is a correlation matrix of the relative returns for all 20 S&P Select Industry indices. Banks and regional banks were highly correlated, as expected, as opposed to biotechnology and oil & gas equipment, which shared a lower correlation (see Exhibit 2).

Finally, as the S&P Select Industry indices are much more granular than their sector counterparts, we report on dispersion across industries (in contrast to our reporting within sectors). As of May 2018, the annualized dispersion among industry returns remained at moderate levels (see Exhibit 3); we shall continue to monitor the results in the coming months.

To sign up to receive the U.S. Select Sector and U.S. S&P Select Industry Dashboards, please refer to this link.

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

Assessing the Potential of Value Factors in the Indian Market

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Akash Jain

Associate Director, Global Research & Design

S&P BSE Indices

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The value factor looks to bucket stocks that have inexpensive valuation and trade at a discount to their fundamental value, with the hypothesis that inexpensive stocks should outperform overvalued stocks. Observations in empirical research suggested that the value factor performed best when the economy was in recovery and growth was accelerating from trough.[1]

We recently investigated the performance of different commonly used valuation parameters (see Exhibit 1) by market participants during the period from September 2005 to March 2018 in the Indian market. The analysis was based on hypothetical equal- and float-market-cap-weighted top quintile portfolios that were semiannually rebalanced in March and September.

Exhibit 1: Valuation Parameters Evaluated in the Analysis
BACK-TESTED UNIVERSE S&P BSE LARGEMIDCAP S&P BSE LARGEMIDCAP

(EX-FINANCIALS)

VALUATION PARAMETERS EVALUATED Sales-to-Price (S/P) EBITDA-to-EV (EBITDA/EV)
Book-to-Price (B/P) Free Cash Flow-to-EV (FCF/EV)
Trailing Earnings-to-Price (E/P)
12-Month Forward Earnings-to-Price (Fwd E/P)
Operating Cash Flow-to-Price (CFO/P)
Free Cash Flow-to-Price (FCF/P)
Dividend Yield (Div. Yield)

Source: S&P Dow Jones Indices LLC. Data from September 2005 to March 2018. All portfolios (except the S&P BSE LargeMidCap) are hypothetical portfolios. Hypothetical portfolios were rebalanced semiannually in March and September. The stock had to be covered by at least three analysts for it to be in the eligible universe for the forward earnings-to-price parameter. Table is provided for illustrative purposes.

Different cyclical characteristics were observed for various valuation parameters in the analysis (see Exhibit 2). For example, the top quintile portfolios for B/P, CFO/P, E/P, Fwd E/P, and S/P significantly outperformed the equal-weighted S&P BSE LargeMidCap portfolio in up markets, but they significantly underperformed in down markets, displaying strong cyclical behavior synonymous with the value factor. In contrast, the top quintile portfolios for Div. Yield, EBITDA/EV, and FCF/EV weren’t penalized in down markets.

Exhibit 2: Average Monthly Excess Return Versus the Benchmark (%, Based on Equal-Weighted Portfolios)
TREND NUMBER OF MONTHS B/P CFO/P DIV. YIELD E/P FWD E/P FCF/P S/P EBITDA/EV FCF/EV
Up 71 1.22* 0.95** 0.40 0.96* 1.31** 0.61* 1.40** 0.49 -0.67
Down 40 -2.13** -1.07** 0.14 -0.93* -1.52** -0.36 -1.89** 0.10 1.58**
Neutral 39 -1.12* -0.23 0.18 -0.25 -0.15 -0.15 -0.51 -0.01 0.90*
All 150 -0.28 0.10 0.27 0.14 0.17 0.16 0.02 0.26 0.34

Source: S&P Dow Jones Indices LLC. Data from September 2005 to March 2018. Excess return versus the equal-weighted S&P BSE LargeMidCap portfolio based on total return in INR. **Represents significance level at 1%. *Represents significance level at 5%. Up market trends refer to periods when the S&P BSE LargeMidCap monthly return was more than 1%. Down market trends refer to periods when the S&P BSE LargeMidCap monthly return was less than -1%. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. The S&P BSE LargeMidCap was launched on April 15, 2015.

Exhibit 3 shows the correlation matrix of the top quintile excess return for all the back-tested value parameters. Due to the absence of finance stocks in the back-tested portfolios for EBITDA/EV and FCF/EV, these two parameters have low-to-negative correlations with other factors that were back-tested with the inclusion of all sectors. E/P and Fwd E/P had similar performance characteristics with a high excess return correlation of 91% (Exhibit 3). As the top quintile Fwd E/P portfolio had similar performance characteristics in comparison with the top quintile E/P portfolio over the long term (Exhibit 2) and the stock coverage of the Fwd E/P data was not as broad as of the E/P data, the trailing E/P seemed to be a more effective value parameter than the Fwd E/P.

Exhibit 3: Correlation Matrix of Equal-Weighted Value Parameters Based on Excess Return Over the Equal-Weighted S&P BSE LargeMidCap (%)
CORRELATION B/P CFO/P DIV. YIELD E/P FCF/P S/P EBTIDA/EV FCF/EV
B/P 78 68 84 87 61 89 -2 -53
CFO/P 78 58 77 79 80 80 16 -33
DIV. YIELD 68 58 74 67 51 65 -1 -41
E/P 84 77 74 91 66 78 3 -46
FWD E/P 87 79 67 91 64 82 0 -51
FCF/P 61 80 51 66 64 65 -9 -25
S/P 89 80 65 78 82 65 0 -54
EBTIDA/EV -2 16 -1 3 0 -9 0 37
FCF/EV -53 -33 -41 -46 -51 -25 -54 37

Source: S&P Dow Jones Indices LLC. Data from September 2005 to March 2018. Correlation calculated based on excess total return over the equal-weighted S&P BSE LargeMidCap concept benchmark index in INR. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. The S&P BSE LargeMidCap was launched on April 15, 2015.

Additionally, the top quintile portfolios of a few valuation parameters had a large sector bias from the broad market universe (the S&P BSE LargeMidCap). Most notably, the basic materials sector was overweighted in the top quintile portfolios across all the valuation parameters, whereas the finance sector was most overweighted in the FCF/P top quintile portfolio (see Exhibit 4).

Exhibit 4: Average Sector Weight Deviation of the Top Quintile Portfolio Versus the S&P BSE LargeMidCap (%, Float Adjusted Market Cap Weighted)
SECTOR B/P CFO/P DIV. YIELD E/P FWD E/P FCF/P S/P EBTIDA/ EV FCF/EV
Energy -2 2 12 1 6 -6 8 9 -1
Utilities 5 1 4 2 0 -3 0 3 0
Information Technology -11 -12 -6 -9 -10 -8 -11 -9 8
Telecom 2 3 -2 -2 -2 1 -2 5 2
Fast Moving Consumer Goods -9 -9 -4 -9 -9 -9 -9 -9 -6
Finance 15 12 -1 12 14 39 1 -25 -25
Basic Materials 13 15 8 15 18 4 15 30 13
Healthcare -5 -5 -5 -5 -5 -4 -6 -5 2
Industrials -4 -1 -4 -1 -4 -8 6 3 0
Consumer Discretionary Goods & Services -3 -6 -2 -6 -7 -6 -3 -3 7

Source: S&P Dow Jones Indices LLC. Data from September 2005 to March 2018. The average sector weight deviation calculated versus the float-adjusted, market-cap weighted S&P BSE LargeMidCap portfolio. For the top quintile float-market-capitalization version of the indices, a 10% stock weight capping was implemented, which aligns with the S&P BSE LargeMidCap, where the largest stock had a weight of 11% in its constituent history. Back-tested portfolios for the EBITDA/EV and FCF/EV did not include finance stocks. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. The S&P BSE LargeMidCap was launched on April 15, 2015.

Representation of Public Sector Undertaking (PSU) companies was also different across top quintile portfolios for various valuation parameters. While PSU companies represented almost 56% of the top quintile Div. Yield portfolio, they only accounted for 15% of the S&P BSE LargeMidCap. This indicates that PSU companies have been paying higher dividend yields than other companies in the Indian market.

Exhibit 5: Average Weight Representation of PSU Companies in Different Portfolios (Float-Weighted Portfolios)
VALUE PARAMETER PSU STOCK WEIGHTS (%)
Div. Yield 55.7
54.7
B/P 54.7
E/P 52.1
S/P 45.9
CFO/P 42.2
FCF/P 33.1
EBITDA/EV 32.9
FCF/EV 21.1
S&P BSE LargeMidCap 14.9
S&P BSE LargeMidCap (ex-Financials) 12.7

Source: S&P Dow Jones Indices LLC. Data from September 2005 to March 2018. All portfolios (except the S&P BSE LargeMidCap) are hypothetical portfolios. Average excess weight calculated over the float-market-cap-weighted S&P BSE LargeMidCap benchmark index in INR. For the float-market-capitalization version of the indices, a 10% stock cap was considered. This was in line with the S&P BSE LargeMidCap, where the largest stock had a weight of 11% in its constituent history. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. The S&P BSE LargeMidCap was launched on April 15, 2015.

[1] Ang, Andrew. “The five Ws of style factors.” BlackRock (Dec. 5, 2017).

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

The Department of Labor and ESG Guidance: Is the Pendulum Shifting?

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Kelly Tang

Director

Global Research & Design

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In late April 2018, the Department of Labor’s (DOL) Office of Regulations and Interpretations issued the Field Assistance Bulletin No. 2018-01, clarifying guidance on how investment managers should interpret the DOL’s prior Interpretive Bulletins (IBs) issued in 2015 and 2016. Issued during the Obama administration, the IBs detail the exercise of shareholder rights, written statements of investment policy, and fiduciary standards when considering economically targeted investments (ETIs).

An ETI is the equivalent to a socially responsible investment to the DOL. The DOL is empowered under the Employee Retirement Income Security Act (ERISA) to regulate fiduciary matters pertaining to employer-sponsored retirement funds, such as 401(k) accounts and pensions. State and local retirement plans, despite being governed by state laws, often refer to the U.S. DOL for guidance.

In the last several years, a number of major public retirement plan managers have become active in submitting numerous shareholder proposals to address their ESG-related concerns. As ESG investing becomes more popular, the DOL is intent on reminding fund managers directly that fiduciaries may not sacrifice returns or assume greater risks in order to promote ESG policy goals when making investment decisions. In short, their position is that returns must come first, regardless of a fund manager’s ethical motivations.

It is helpful to revisit the DOL’s prior IBs issued in 2015 and 2016 (see Exhibit 1). In IB 2015-01, the DOL highlighted the concept of “tie-breakers” in that when competing investments are economically equivalent, then plan fiduciaries can use ESG-related considerations as tie-breakers for an investment choice. IB 2016-01 stated that plan fiduciaries may engage in shareholder proposal activities if they believe it is likely to enhance the value of the plan’s investment in the corporation, after taking into account the costs involved. The 2016 bulletin also noted that “investment policy statements are permitted to include policies concerning the use of ESG factors to evaluate investments, or on integrating ESG-related tools, metrics, or analyses to evaluate an investment’s risk or return.”

Sensing that the pendulum may have shifted too far, the DOL’s current release is a clarification statement. Its clarification points are as follows.

  1. Fiduciaries must always put first the economic interests of the plan in providing retirement benefits
  2. It is not mandatory that investment policy statements contain guidelines on ESG investments or integrating ESG-related tools in order to comply with ERISA
  3. Fiduciaries “may not routinely incur significant plan expenses to pay for the costs of shareholder resolutions … or actively sponsor proxy fights on environmental or social issues”

However, understanding the growing interest in ESG investments, the DOL did indicate that supporting shareholder proposals when the activity is “likely to enhance the economic value of the plan’s investment in that corporation after taking into account the costs involved” would be acceptable.

This current bulletin should not warrant a “doom and gloom” reaction, and instead market participants could view it as a call from the DOL to be more cautionary and judicious in conducting a cost/benefit analysis to ESG investing.

In addition, this bulletin reinforces the importance of materiality and linking ESG-related investing to those issues that are most material for future stock performance. The difficulty lies in the fact that ESG materiality differs for different industries, and identifying these differences requires more research, especially for evidence-based findings.

In terms of implications for passive investing, the current guidance has the potential to sway fiduciaries into index-based ESG solutions that integrate the financial merits of ESG and impact-based goals. The bulletin distinguished between “ESG-themed funds (e.g., Socially Responsible Index Fund, Religious Belief Investment Fund, or Environmental and Sustainable Index Fund),” from funds “in which ESG factors may be incorporated…as one of many factors in ordinary portfolio management and shareholder engagement decisions.” The former seems to be more concerning to the DOL than the latter which opens the possibility of favoring some ESG products and strategies over others.

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

How an Industry Reduced Its Carbon Pricing Risk by 922%

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Rochelle March

Senior Analyst

Trucost, part of S&P Dow Jones Indices

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Companies that act now to invest in low carbon technologies have the chance to maintain their license to grow and avoid carbon pricing costs that would significantly reduce profits. For example, in 2020, the technology sector’s investments in energy efficiency for their U.S. data centers could avoid over USD 6.9 billion in carbon costs and show a 59% reduction in operating margins.

Growing global carbon prices can affect companies through regulatory costs imposed on energy and fuel price increases, or through suppliers passing on these costs to the company. Trucost developed its Corporate Carbon Pricing Tool to help companies understand how this carbon pricing risk exposure can affect their competitiveness in a climate-challenged future.

Carbon pricing risk can affect all business sectors. One sector in particular has been successful in reducing its carbon pricing risk in the future. In just the past five years, data centers have turned what was an exponential increase in energy demand into practically a flat line.

For U.S. data centers alone, there was a 90% increase in electricity use from 2000-2005, as the data center industry saw booming growth.[1] From 2005-2010, this energy use increased only by about 24%. Since 2010, electricity consumption has only increased by about 4%. An overall efficiency trend has helped keep data center energy use steady, despite the technology sector’s continued expansion.

This efficiency trend has helped to drastically reduced the energy usage of data centers, which otherwise would have needed an additional 600 billion kWh by 2020 to meet demand.1 Although many efficiency gains have been made, there remains opportunity to be aggressive in pursuing additional strategies that could decrease electricity consumption by another 33 billion kWh by 2020.

Trucost ran an analysis of three scenarios as depicted in a report1 on U.S. data center usage to help illustrate how data centers have reduced their carbon pricing risk as well as energy intensity. The first scenario depicts the carbon pricing risk for U.S. data centers without any efficiency trend, the second with the current efficiency trend, and the third with adoption of additional efficiency strategies.

 

The analysis shows how data centers have reduced their carbon pricing risk. A number of factors have helped support this efficiency trend.

  • Leading companies have set an example and pushed the industry to innovate quickly in order to save energy costs as well as drive performance. Large internet companies like Google, Facebook, and Amazon have made sizable investments in energy efficiency and renewable energy installations.[2]
  • With as much as 48% of operational costs[3] originally dedicated to data center energy needs, there exists a strong business case to invest in energy efficiency.
  • Data centers continue to experience strong growth,[4] resulting in new builds that are outfitted with updated servers, infrastructure, and networks with increasing energy efficiency.
  • Technological developments, such as server virtualization,[5] movement to cloud services, and more efficient servers has contributed to an overall increase in efficiency.
  • There is an industry movement toward a “hyperscale shift” to large data centers configured for maximum productivity that often need fewer servers to provide the same service as smaller data centers.

As more investors request that companies take responsibility for future climate risks,[6] the case of data centers gives us an example of how it is possible to successfully reduce climate risk exposure while still pursuing continuous market growth.

[1]   https://eta.lbl.gov/sites/all/files/publications/lbnl-1005775_v2.pdf

[2]   http://fortune.com/2016/06/27/data-center-energy-report/

[3]   https://www.energystar.gov/ia/partners/prod_development/downloads/EPA_Report_Exec_Summary_Final.pdf

[4]   https://cloudscene.com/news/2017/12/2018-data-center-predictions/

[5]   https://www.techopedia.com/definition/688/server-virtualization

[6]   https://www.fsb-tcfd.org/

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