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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%

Low Inflation isn’t Unusual

Momentum's Minsky Moment?

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.

Low Inflation isn’t Unusual

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David Blitzer

Managing Director and Chairman of the Index Committee

S&P Dow Jones Indices

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Fed watchers and bond holders worry that inflation could spike, tempting the Fed to boost rates much farther than the current half percentage point the market expects in the rest of 2018.  With the Federal Open Market Committee, the central bank’s policy makers, meeting today and tomorrow these concerns are front and center.  This morning’s report from the Bureau of Labor Statistics showed the CPI up 0.2% in May and 2.8% over the last 12 months. Excluding the volatile factors of food and energy, the 12 month figure was 2.2%.  These figures are a bit higher than recent numbers: since the start of 2016, the average inflation rate was 1.8%.

Over the last 70 years inflation was as low as -3.0% during the 1948-49 recession and as high as 14.6% in 1980 during the second oil crisis.  High inflation is caused by oil price surges or wars. Inflation usually falls when the economy slows, but it takes a deep recession like the last one in 2007-9 to send it into negative numbers.  The chart shows the history of inflation since 1949, the shaded sections are recessions. There were four times when inflation topped 7.5%.  The first in 1948-9 was a spending surge at the end of World War II followed shortly by the Korean War. The two peaks in the 1970s were the 1973 and 1979 oil crises. Most notable though is the general trend – over seven decades the inflation rate remains essentially between zero and five percent. The average over the entire period, including spikes, is 3.5%.

Recently some analysts suggested that the internet and the rising share of on-line retail sales compared to traditional shopping is keeping inflation down. Research cited in the New York Times shows that price increases for goods sold on-line are generally lower than price increases for the same goods sold off-line.[i]  The impact of on-line sales are likely to increase. The share of retail sales on-line is now about 10%; at this rate it will be double that in 2024.

Today’s inflation rate is lower than the long term average.  Barring a war or another oil embargo and crisis, the figure should stay close to current levels.

[i] Austan Goolsbee and Peter Klenow, “Internet Rising, Prices Falling: Measuring Inflation in a World of E-Commerce,” working paper 2018-35, University of Chicago

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

Momentum's Minsky Moment?

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Tim Edwards

Managing Director, Index Investment Strategy

S&P Dow Jones Indices

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U.S. equity funds following momentum (or relative strength) strategies have generally performed well recently, and their performance has been rewarded with inflows.  This is important because momentum, uniquely among investment styles, is self-reinforcing – until it isn’t.

Typically, as factors become more popular, their excess returns are likely to diminish.  For example: the more value investors dominate the market, the harder it becomes to find cheap stocks.  But momentum is different: momentum-based strategies, by their nature, focus on the market’s recent winners.  Flows into those strategies may further inflate those winning stocks, which can attract (or convert) still more trend followers, and so on in an inflationary cycle.

However, a “Minsky Moment” may await: eventually, bubbles pop.  The tipping points are hard to predict; it could be that valuations become so extended that they deter other investors, or it may even be a relatively minor event – such as a disappointing earnings report from one of the market’s current darlings.  Momentum-driven bubbles are inherently unstable.  At the first hint of a change in the trend, the most sensitive momentum investors will sell, amplifying the downtown and thereby reinforcing the strength of the “sell” signal to other momentum investors.  The more trend followers there are, the more dramatic the reversal.  The popularity of momentum accelerates both its performance and its sensitivity to a change in regime.  Hence, therefore, identifying when momentum appears to be both gaining in popularity – and performing particularly well – can determine whether a cautious or even contrarian approach is more prudent.

But what of the present?  2017 was a banner year for momentum – as represented by the S&P 500 Momentum Index.  The first six months of 2018 have seen this outperformance accelerate.  Exhibit 1 provides historical context by plotting the historical relative outperformance of momentum, controlled for the S&P 500’s concurrent stock-level dispersion.

Exhibit 1: Momentum’s Dispersion-Adjusted Relative Performance Reaches a 12-Year High.

The historical relative performance of momentum is clearly cyclical: when the series becomes elevated, it subsequently falls.  These declines represent periods of underperformance for investors tracking the momentum index.  The relatively high current reading suggests, at a minimum, that caution may be in order.  Nevertheless, it remains a hard task to predict exactly when the trend might reverse.  Indeed, as was the case in the late 1990s, we may yet see further relative outperformance.  But the longer this outperformance continues, the more unstable it may become.  And as Vanguard’s John Bogle put in his 10 rules for investing “reversion to the mean is a virtual certainty.”

 

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