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ESG Meets Behavioral Finance: Part 1

Mexican Sovereign Debt Structure

Evolving From Single-Factor to Multifactor Investing

Commodities Are Like a Box of Chocolates, if You Only Factor in Interest Rates and the U.S. Dollar

The Rise of Sectors: Active and Passive Applications

ESG Meets Behavioral Finance: Part 1

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Lauren Smart

Managing Director, Global Head Financial Institutions Business

Trucost, part of S&P Global

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Behavioral economics has had a transformational effect on the fortunes of millions of people saving for retirement through the introduction of auto enrollment, default plans, and “save more tomorrow” schemes. In a series of blogs, I will explore how insights from behavioral economics could be used to revolutionize ESG investing, providing critical levels of capital flows to finance the transition to a more sustainable economy.

‘Nudging’ Sustainable Finance Into the Mainstream: How Behavioral Finance Could Transform Capital Flows to ESG

Richard Thaler won his Nobel Prize for incorporating psychological realism into economic theory, helping transform the pension industry through supposedly irrelevant behavioral “nudges” and making millions of people better off in their old age. He highlighted how human inertia means many people do not join pension schemes, even when employers contribute, essentially turning down “free” money. The rational human of classic economic theory would not behave in this way, but people are not always rational; they are often predictably irrational. Humans prefer the status quo, procrastinate despite best intentions, and underestimate future risks, such as inadequate retirement savings. When these are addressed, for example through auto enrollment, pension participation shoots up. In a recent paper addressing inertia in the Swedish Pension Plan, only 0.9% of people were actively selecting their pension choice, while 99% were on the default plan.[1]

Can We Harness the Power of Inertia to Provide Additional Outcomes for Retirees?

In surveys, millennial market participants are clear that they want more than just a good annuity in retirement. Members enrolling today may not retire for 50 years, during which time the impact of climate change, pollution, and resource scarcity could affect their investments, health, and living environment. The EU taskforce on sustainable finance recommends that pension funds consult beneficiaries on their sustainability preferences and reflect those in their investments;[2] however there is an intent-action gap between what new members say they want and how they actually invest. This can be explained, in part, by inertia, because sustainability funds are usually opt-in. A solution would be for pension funds to default to a sustainability option as NEST, the UK government-backed DC scheme, does. It allocates to UBS’s “Climate Aware World Equity Fund,” which delivers index returns but tilts companies to address climate risks and opportunities. Their rationale is not moral, but rather improved outcomes for members in the face of a green economic transition, because they are “shareholders in that future.”[3] In 2017, HSBC’s UK Pension Scheme transitioned the GBP 1.85 billion equity component of its DC default strategy to LGIM’s “Future World Fund,” a factor-weighted, passive global equity strategy incorporating climate change tilts and exclusions. They echo concern for long-term outcomes for members, “the climate factor tilts [are] especially important as 60% of our members are under 40 years old.”[4] If all schemes defaulted to climate-aware strategies, it could have a profound impact on capital flows to mitigate some of the most damaging financial impacts of climate change and holistically improve outcomes for retirees.

The green elephant in the room is what stands in the way of all pension funds adopting similar approaches. It is a common assumption that sustainability compromises returns; however, this can be traced to cognitive biases such as the “no free lunch” heuristic, confirmation bias, and irrational exuberance about future risks. This is compounded by confusion between “ethical” investing based on values and “sustainable” investing grounded in long-term value generation. Lack of knowledge is another obstacle, both in terms of the pervasive impacts of climate change on asset valuations and an outdated understanding of how sustainability can be incorporated into portfolio construction to deliver myriad risk/return objectives. These topics will be tackled in this blog series and in another recent blog, “Can “Being Green” Deliver Enhanced Returns?

[1]   Cronqvist, Thaler, and Yu (2018), “When Nudges are Forever: Inertia in the Swedish Premium Pension Plan.”

[2]   EU Commission (2018), “Final Report of the High Level Expert Group on Sustainable Finance.”

[3]   National Employment Savings Trust (2017), “NEST responds to climate change.”

[4]   HSBC (2017), “The Best of Both Worlds?

If you enjoyed this content, join us for our Seminar Discover the ESG Advantage in
London on May 17, 2018.

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

Mexican Sovereign Debt Structure

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Jaime Merino

Director, Asset Owners Channel

S&P Dow Jones Indices

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A couple of months ago, we took a look at the Chilean sovereign bond market and indices. This time, we will analyze the case of Mexico, starting with the local bond market, followed by its structure, and ending with its index performance.

Mexican domestic sovereign debt is issued by the Ministry of Finance (Secretaría de Hacienda y Crédito Público—SHCP) through the Central Bank (Banco de México—Banxico). It is issued through weekly auctions based on the annual finance plan, and on a quarterly basis, the Auction Program of Sovereign Securities is published.

The auctioned securities are:

  1. CETES: Mexican Federal Treasury Certificates are the oldest tradable debt instruments issued by the federal government, issued for the first time in 1978. They are zero-coupon securities that are traded at a discount rate, with a face value of MXN 10 and maturity terms of 28, 91, 182, and 364 days.
  2. MBONOS: Mexican Federal Government Development Bonds with a fixed interest rate are securities issued for terms longer than one year. They pay a coupon every six months, have a nominal value of MXN 100, and have maturity terms of 3, 5, 10, 20, and 30 years.
  3. UDIBONOS: Federal Government Development Bonds, denominated in Investment Units (UDIs), which are inflation linked, were developed in 1996. They are investment instruments that protect the holder from unexpected changes in the inflation rate. UDIBONOS pays a coupon every six months based on a fixed rate plus a gain or loss that is indexed to the performance of the UDI. They have a face value of 100 UDI’s and maturity terms of 3, 10, and 30 years.
  4. BONDES D: Federal Government Development Bonds are instruments that pay floating coupons every 28 days based on the weighted average interbank funding rate, with a maturity term of five years.

Using outstanding amount data, we can see the structure for these four types of bonds with a total of USD 270,000 million (see Exhibit 1). Exhibit 2 shows the maturity profile, including the total per bucket, and we can see that one-third of the total maturities occur between 2019 and 2021. In 2018, without taking into account CETES, USD 32,000 million in bonds are expected to mature between Bondes D and MBonos.

The S&P/BMV Fixed Income Indices have more than 25 different indices, which are mainly divided into maturity buckets, that track the performance of such bonds. Four of them cover the complete curves, tracking more than 170 bonds.  Their performance and annual returns are shown in Exhibits 3 and 4.

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

Evolving From Single-Factor to Multifactor Investing

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Akash Jain

Associate Director, Global Research & Design

S&P BSE Indices

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This article is the third in a series of blogs. The previous two were titled “Factor Investing 101” and “How Do Single Factors Perform in Different Market Regimes in India?” This blog discusses sectoral tilts of different single factors and  varying correlations between factors in different market cycles.

In our report, sector bias typically existed in factor portfolios, and differentials on sector exposure across factor portfolios were strongly associated with the unique cyclical nature of factor performance. Exhibit 1 highlights the two most overweight and most underweight sectors, on average, for each factor over the period from March 2006 to March 2017. Value and dividend were overweight in basic materials, whereas momentum, quality, and size were overweight in consumer discretionary goods & services. The finance sector was most underrepresented in the momentum, quality, low volatility, and size portfolios, and the information technology sector was underweight in value, dividend, and size portfolios.

Exhibit 1: Sector Bias Versus the S&P BSE LargeMidCap
FACTOR Most Overweight Sectors and Weight Differential (%) Versus Benchmark Most Underweight Sectors and Weight Differential (%) Versus Benchmark
Value Basic Materials, 13.7 Information Technology, -10.9
Energy, 7.7 Fast Moving Consumer Goods, -8.1
Momentum Healthcare, 6.2 Finance, -6.0
Consumer Discretionary
Goods & Services, 5.4
Energy, -5.8
Quality Fast Moving Consumer Goods, 9.6 Finance, -22.9
Consumer Discretionary
Goods & Services, 9.0
Utilities, -4.0
Low Volatility Healthcare, 11.1 Finance, -16.9
Fast Moving Consumer Goods, 6.8 Industrials, -5.2
Dividend Basic Materials, 10.8 Information Technology, -7.0
Energy, 4.2 Healthcare, -4.7
Size Utilities, 4.4 Information Technology, -6.1
Consumer Discretionary
Goods & Services, 4.2
Finance, -5.2

Source: S&P Dow Jones Indices LLC. Data from March 2006 to March 2017. The S&P BSE Dividend Portfolio and S&P BSE Equal-Weighted Portfolio are hypothetical portfolios. Figures in the table are average figures for the semiannually rebalanced portfolios. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance.  Please see the Performance Disclosure in the report for more information regarding the inherent limitations associated with back-tested performance.

Despite some single-factor portfolios outperforming the market over the long term, they experienced periods of underperformance in different macroeconomic conditions depending on their cyclical characteristics, as noted in the previous blog. Therefore, blending factors to form multifactor portfolios may potentially help deliver smoother excess return across business and market cycles. Correlation among factors is a common consideration in the construction of multifactor portfolios. However, we observed that factor correlations did not remain constant across various market regimes, and it is important to be mindful of the changes when blending different factors in a portfolio. For example, correlation between size and momentum was negative (-43%) during bull and recovery markets, but switched to positive (32%) in bearish markets. Large shifts in correlation were also observed in the low volatility-momentum and quality-value pairs across different market cycle phases (see Exhibits 2 and 3).

Exhibit 2: Correlation Among Single Factors – Recovery and Bull Market Cycles
FACTOR VALUE MOMENTUM QUALITY LOW VOLATILITY DIVIDEND SIZE
VALUE -42% -38% -50% 86% 83%
MOMENTUM -42% 42% 49% -39% -43%
QUALITY -38% 42% 73% -18% -22%
LOW VOLATILITY -50% 49% 73% -31% -39%
DIVIDEND 86% -39% -18% -31% 82%
SIZE 83% -43% -22% -39% 82%

Source: S&P Dow Jones Indices LLC. Data from October 2005 to June 2017. The S&P BSE Dividend Portfolio and S&P BSE Equal-Weighted Portfolio are hypothetical portfolios. Correlation calculated using excess price returns over S&P BSE LargeMidCap. Index performance based on price return in INR. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. Please see the Performance Disclosure in the report for more information regarding the inherent limitations associated with back-tested performance.

Exhibit 3: Correlation Among Single Factors – Bear Market Cycles
FACTOR VALUE MOMENTUM QUALITY LOW VOLATILITY DIVIDEND SIZE
VALUE 11% 13% -20% 78% 59%
MOMENTUM 11% -13% -50% 9% 32%
QUALITY 13% -13% 73% 37% 2%
LOW VOLATILITY -20% -50% 73% 9% -4%
DIVIDEND 78% 9% 37% 9% 53%
SIZE 59% 32% 2% -4% 53%

Source: S&P Dow Jones Indices LLC.  The S&P BSE Dividend Portfolio and S&P BSE Equal-Weighted Portfolio are hypothetical portfolios. Data from October 2005 to June 2017. Correlation calculated using excess price returns over S&P BSE LargeMidCap. Index performance based on price return in INR. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. Please see the Performance Disclosure in the report for more information regarding the inherent limitations associated with back-tested performance.

Please refer to Factor Performance Across Different Macroeconomic Regimes in India for more information on this research paper.

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

Commodities Are Like a Box of Chocolates, if You Only Factor in Interest Rates and the U.S. Dollar

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Marya Alsati

Product Manager, Commodities, Home Prices, and Real Assets

S&P Dow Jones Indices

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The Dow Jones Commodity Index (DJCI) was down 1.9% for the month and up 0.7% YTD, and the S&P GSCI was down 3.3% and flat YTD. Energy was the worst-performing sector, while agriculture was the best. Of the 24 commodities tracked by the indices, 14 were negative in February. Cocoa was the best-performing commodity for the month, while natural gas was the worst.

The purpose of this blog is to assess how commodities perform during different interest rate environments, as well as with a background of a strengthening and weakening U.S. dollar.

Higher interest rates are usually indicative of healthy economic conditions, and hence equity markets tend to do better when interest rates are higher. Meanwhile, bond markets have an inverse relationship with interest rates. Commodity prices and interest rates are sometimes seen as having an inverse relationship due to the cost of financing storage and stockpiling, since central banks are able to set the levels for short-term borrowing. However, the cost of storage or convenience yield is not equal for all commodities, as we discussed in “Commodities: A Deeper Dive Into the Five Potential Sources of Return.” For example, natural gas is costly to store because it has to be kept in liquid or gaseous form at low temperatures, and some agricultural products may deteriorate over time, while base metals are relatively easy to transport and store. Exhibit 2 depicts the analysis of the correlation levels between the S&P 500®, the S&P U.S. Aggregate Bond Index, and the 24 commodities included in the S&P GSCI against the U.S. Federal Funds Rate, from Dec. 31, 1999, to Jan. 31, 2018.

The analysis showed positive correlation between interest rates and stocks, and an inverse relationship for bonds, while commodities resulted in diverse results. All the grain commodities had an inverse relationship with interest rates, while most of the softs, except for cocoa, had a positive relationship. Energy, industrial metals, and the livestock commodities were mixed. Silver had a positive correlation, while gold, which is considered a safe-haven asset, had an inverse relationship with interest rates, since market participants tend to move away from gold in search of better yield.

In general, commodities tend to exhibit a negative relationship with the U.S. dollar because commodity prices are weighed down by a stronger U.S. dollar, since a rising U.S. dollar can make buying commodities that are pegged to the U.S. dollar more expensive for market participants using relatively weaker currencies. However, the sensitivity to the greenback is not equal among all commodities (see Exhibit 3).

The correlation between the 24 commodities included in the S&P GSCI and the U.S. dollar were all negative for the period studied, with the petroleum commodities having the highest inverse relationship. However, while there was a negative relationship in the long term, the mechanics of supply and demand, as well as shortages and surpluses seem to mostly drive commodity prices in the short term. This could add a diversification benefit to a portfolio constructed of stocks and bonds in the long term, across varying interest rates, and in currency fluctuation environments.

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

The Rise of Sectors: Active and Passive Applications

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Tim Edwards

Managing Director, Index Investment Strategy

S&P Dow Jones Indices

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Investment strategies that switch between sectors or industries – and the related activity of using sectoral performances to discern broader macroeconomic trends – have long been an part of professional investing.  However, it is surprisingly difficult to find even basic research explaining how sectors are classified, why they are important and how they might be applied to diversification or performance goals.  Our newest paper offers an introduction to these topics.

The increasing adoption of index-linked products over the past several years is a major defining trend within the investment industry.  The consequences, naturally, remain a topic of debate.  Sometimes, the argument frames “active” investors as an opposing tribe to “passive” investors; either blindly following the whole market or endlessly (fruitlessly) picking single stocks in the hope of outperformance.  Such simplifications are unhelpful at best – investing doesn’t work like that.

Instead, both passive- and actively-inclined investors must make decisions around a host of shared criteria: risk tolerances, income or growth preferences, asset allocations, inflation sensitivities, time horizon, moral and ethical considerations, regulatory conditions, and so on.  They also ought to consider the appropriate benchmarks for their performance carefully.  Facing the same problems, it is not surprising that they might find similar solutions.

One example of how the ETF market has offered solutions for both “tribes” is provided by the greater availability of liquid products providing access to broad benchmarks such as the S&P 500®.  Active investors can use such products to express tactical views efficiently on the broad U.S. market.  Passive investors might use them to make long-term, diversified allocations to U.S. equities.  Sector-based products similarly offer tools for diversification, or timing.

Sectors are particularly important for relating broader events to their market effects.  The grouping of companies into peer groups facing similar circumstances can diminish the impact of idiosyncratic (single-company) risks and provide a clearer link to macro trends.  Thus, the stocks of a particular sector might be highly correlated to each other, even while the different sectors maintain only moderate correlations to each other.  Such considerations may help to explain the dramatic rise in the volumes and assets under management of sector-linked ETFs, as evidenced by the growth in products linked to S&P Dow Jones Indices’ U.S. sector and industry indices.

To read more on the hows, whats and whys of sector and industry indices, as well as an examination of the relative value of insight of sector selection in comparison to stock- or asset class-based alternatives, please download our paper – available here.

 

 

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