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Integrating Carbon Risk With the Quality Factor

Maintaining Risk Reduction While Reducing Interest Rate Risk

Capital Market Performance During the Four Years of Narendra Modi’s Government

Livestock: A Drag on Commodities

The Benefits of Having a Retirement Income Goal and How to Estimate It

Integrating Carbon Risk With the Quality Factor

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Wenli Bill Hao

Director, Factors and Dividends Indices, Product Management and Development

S&P Dow Jones Indices

In a prior blog, we demonstrated that a sector-relative, carbon-efficient portfolio was superior to a sector-unconstrained one when forming low-carbon portfolios. In this blog, we explore the integration of carbon risk in quality factor portfolios. High-quality companies seek to generate higher profitability and enjoy more stable growth than “average” companies. Equally important, high-quality companies seek to adopt a conservative, yet effective, capital structure that allows them to grow. Finally, high-quality companies tend to exercise prudence in the administration of company affairs.[1]

We capture the quality investment style by equally weighting three factors: financial leverage ratio, return on equity (ROE), and balance sheet accruals ratio.

Correlation of Carbon Intensity and Quality Factors

We first analyzed the firm-level correlation between carbon intensity and each of the three quality factors in our test universe for each rebalance period (every three months), then we took the average of the cross-sectional correlations and calculated the t-statistics (see Exhibit 1).[2]

We can see that companies that are more carbon-efficient (or have lower carbon intensity) tended to have lower financial leverage ratios while displaying higher ROE, both of which were statistically significant at a 95% confidence level. In sum, carbon-efficient firms tended to be high-quality companies. Such findings are not surprising, as high-quality companies have more prudent capital structure, higher profitability, and higher earnings quality than their competitors. As a result, high-quality companies may have the financial strength to meet the market obligations that come with moving toward a low-carbon economy.

Integrating Carbon Risk With Quality Portfolios

One way to incorporate carbon risk with the quality factor is to construct an integrated quality-carbon composite score. The quality style score is defined as the equal-weighted combination of the three quality factors, while the quality-carbon composite score is defined as the equal-weighted combination of the quality style score and the carbon-efficiency score. Quintile portfolios were constructed based on the integrated quality-carbon composite score.

We compared the hypothetical quality-carbon-integrated portfolios (quality + carbon efficiency and sector-relative (SR) quality + carbon efficiency) to the unconstrained carbon-efficient portfolio, the quality portfolio, and the underlying benchmark (see Exhibit 2).

The quality + carbon efficiency portfolio had slightly lower risk-adjusted returns (0.78) than the quality portfolio (0.80). However, the carbon intensity of the quality + carbon efficiency portfolio was reduced to 19% of the underlying universe. The sector-relative quality + carbon efficiency portfolio also outperformed the benchmark on a risk-adjusted basis, albeit with a lower Sharpe ratio than its quality and quality + carbon efficiency counterparts.

Integrated Quality-Carbon Portfolios Maintained Target Factor Exposure

In this section, we examine the quality style exposure of integrated quality-carbon portfolios. We compared the weighted average style z-score of the integrated portfolios to the pure factor portfolio, as well as the broad benchmark (see Exhibit 3).

We can see that combining carbon efficiency with quality portfolios had little impact on quality style exposure, as measured by the weighted average z-score and t-statistics (the critical value of 95% confidence level is 1.99) from two sample t-tests.

The results from Exhibits 1, 2, and 3 showed that carbon-efficient firms tend to be high-quality companies. Moreover, integrated quality-carbon-efficient portfolios tend to have improved risk-adjusted returns and tend to be more carbon efficient over the underlying benchmark, while maintaining similar factor exposure level in comparison with pure quality factor portfolios. In the next blog, we will explore sector composition, risk exposure, and risk composition of quality-carbon-efficient portfolios.

[1]   D. Ung, P. Luk, and X. Kang. “Quality: A Distinct Equity Factor?” 2014. S&P Dow Jones Indices LLC.

[2]   B. Hao, A. Soe, and K. Tang. “Carbon Risk Integration in Factor Portfolios.” 2018. S&P Dow Jones Indices LLC.

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

Maintaining Risk Reduction While Reducing Interest Rate Risk

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Phillip Brzenk

Managing Director, Global Head of Multi-Asset Indices

S&P Dow Jones Indices

Previously, we highlighted that the S&P 500® Low Volatility Rate Response Index fared better than the S&P 500 Low Volatility Index when interest rates increased. The objective of low volatility portfolios is to deliver lower portfolio volatility than the broad market benchmark, leading to higher risk-adjusted returns over a long-term investment horizon.

In this blog, we demonstrate that minimizing the interest rate exposure does not have to come at the expense of portfolio volatility reduction. We first look at a multi-horizon risk/return chart for the two indices compared with the S&P 500, going back to 1991 (see Exhibit 1).

Over the longer time horizons, the low volatility and rate response indices outperformed the S&P 500, with lower volatility. In fact, the rate response index performed better than both the low volatility index and the S&P 500 for all measured periods. The rate response index was slightly more volatile than the low volatility index—nevertheless, it had a cumulative risk reduction of 19.3% relative to the S&P 500 (the low volatility index had a risk reduction of 23%). Exhibit 2 shows the annualized risk reduction of the two strategies compared with the S&P 500 for the different periods.

Exhibit 2 shows that both the rate response and low volatility indices had lower volatility than the S&P 500 across different lookback periods. In recent years, stocks have been in one of the longest-running bull markets with low volatility, leading to somewhat moderate volatility reduction for the two indices. However, for the time horizons that cover at least one full market cycle (bull and bear markets), the risk reduction of the two indices versus the S&P 500 was more evident.

Together with the analysis provided in the first blog, we have seen that the rate response index has been able to perform better than the low volatility index in periods of rising interest rates, while also retaining the volatility reduction characteristics of a low volatility strategy. In a future post, we will further examine the relative exposure of interest rate changes between the rate response and low volatility indices.

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

Capital Market Performance During the Four Years of Narendra Modi’s Government

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Ved Malla

Associate Director, Client Coverage

S&P Dow Jones Indices

On May 16, 2014, Lok Sabha election results were announced, and Narendra Modi’s Bharatiya Janata Party got a clear mandate to form the government. Narendra Modi was sworn in as the 14th Prime Minister of India on May 26, 2014, and his government recently passed the four-year mark of being in power. This government has one more year before India has its next general election, so both the government and the opposition parties are now in election mode.

Over the past four years, the government has made several landmark policy decisions and initiatives that have had a major impact on the Indian economy. Some of the major policy and regulatory changes that have been initiated/implemented by the government are as follows.

  1. Goods and Services Tax (GST)
  2. Demonetization
  3. Arbitration and Conciliation (Amendment) Act
  4. Real Estate (Regulation and Development) Act
  5. Insolvency and Bankruptcy Code

Even after disruptive reforms like the demonetization and GST, economic growth is back on track. The government also eased foreign investment norms in important sectors like construction, retail, and aviation, which is expected to have a positive impact on these key sectors. Inflation during these four years has been moderate due to easing commodity prices, a good agricultural harvest, and the Reserve Bank of India’s monetary policies targeting inflation. However, the rupee has weakened since this government came into power.

When this government came into power, crude oil prices were over USD 100 per barrel; however, these prices have fallen substantially, which worked in the government’s favor and gave Narendra Modi room to roll out various reforms. However, oil prices have climbed over the past few months, and this will pose a challenge for the government, especially as elections are less than one year away.

Capital markets in India have been on a bull run since this government came into power. The S&P BSE SENSEX total return value moved from 32,735.68 on May 31, 2014, to 50,572.53 on May 31, 2018; that is a four-year absolute return of 54.49%. The S&P BSE AllCap, a broad benchmark index with over 900 constituents, had a four-year absolute return of 69.79%. Among the size indices, the four-year absolute return of the S&P BSE MidCap was the highest, at 98.31%, followed by the S&P BSE SmallCap, at 98.25%, while the S&P BSE LargeCap was at 56.85%. Exhibit 1 depicts the total returns of the S&P BSE SENSEX, S&P BSE AllCap, S&P BSE LargeCap, S&P BSE MidCap, and S&P BSE SmallCap for the four-year period ending on May 31, 2018.

Exhibit 1: Total Return of the S&P BSE SENSEX and S&P BSE Size Indices 

Exhibit 2 provides the four-year absolute returns of the S&P BSE AllCap series. We can see that among the sub-sector indices in the S&P BSE AllCap, the S&P BSE Consumer Discretionary Goods & Services and the S&P BSE Finance posted the best four-year absolute returns of 113.95% and 93.60%, respectively, while the S&P BSE Telecom had the worst return of -6.06%.

Exhibit 2: Four-Year Absolute Returns of the S&P BSE AllCap Series
S&P BSE AllCap 2,964.50 5,033.34 69.79
S&P BSE LargeCap 3,169.73 4,971.62 56.85
S&P BSE MidCap 9,486.29 18,811.84 98.31
S&P BSE SmallCap 10,151.26 20,125.14 98.25
S&P BSE Consumer Discretionary Goods & Services 2,221.81 4,753.64 113.95
S&P BSE Finance 3,685.57 7,135.23 93.60
S&P BSE Fast Moving Consumer Goods 8,059.50 14,144.68 75.50
S&P BSE Information Technology 9,615.65 16,593.38 72.57
S&P BSE Basic Materials 2,244.72 3,835.42 70.86
S&P BSE Energy 3,247.41 5,062.84 55.90
S&P BSE Industrials 2,898.42 4,003.18 38.12
S&P BSE Healthcare 11,138.54 14,339.33 28.74
S&P BSE Utilities 1,971.70 2,522.48 27.93
S&P BSE Telecom 1,364.29 1,281.66 -6.06

Source: S&P Dow Jones Indices LLC. Data from May 31, 2014, to May 31, 2018. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. The S&P BSE AllCap, S&P BSE LargeCap, S&P BSE Consumer Discretionary Goods & Services, S&P BSE Finance, S&P BSE Basic Materials, S&P BSE Energy, S&P BSE Industrials, S&P BSE Utilities, and S&P BSE Telecom were launched on April 15, 2015.

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

Livestock: A Drag on Commodities

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Marya Alsati

Former Product Manager, Commodities, Home Prices, and Real Assets

S&P Dow Jones Indices

The S&P GSCI was up 1.5% for the month and up 8.9% YTD. Precious metals was the worst-performing commodity, while livestock was the best.

Of the 24 commodities tracked by the index, 18 were positive in May. Nickel was the best-performing commodity for the month, while cocoa was the worst.

The S&P GSCI Agriculture was up 1.3%. Chicago wheat and Kansas wheat were the best-performing grain commodities, up 3.3% and 1.1%, respectively. Soybeans was the worst-performing grain commodity, down 2.7% for the month, affected by the tariffs imposed by the U.S. on aluminum and steel and the impact this could have on trade with China, which currently imports around 60% of U.S. soybeans, as well as record plantings of the grain in Brazil and the U.S. Cotton was the best-performing softs commodity, up 11.3% for the month and up 18.3% YTD, benefiting from harvest and quality issues, as well as dry weather conditions in the southern plains that could further hinder output.

The S&P GSCI Livestock was up 2.2% for the month. Cattle commodities were positive in May, with feeder cattle up 1.0% and live cattle up 0.3%. Feeder cattle prices were supported by a USDA report that showed a decline in the number of cattle placed on feed in April 2018. Lean hogs was also positive, up 5.6% for the month, due to seasonal demand.

The S&P GSCI Energy was up 1.5%. All the energy commodities, except for WTI crude oil, were positive for the month. Gasoil was the best-performing petroleum commodity, up 5.2% and up 15.0% YTD. WTI crude oil was the worst-performing commodity, down 2.0% over concerns that the Organization of the Petroleum Exporting Countries (OPEC) would raise production levels for the first time since 2016, which further weighed down prices affected by high levels of production. Natural gas was the best-performing commodity in the energy sector, up 6.2%, after the U.S. Energy Information Administration (EIA) reported below average inventory levels combined with high demand during the summer months.

The S&P GSCI Industrial Metals was up 2.1%. All the base metals, except for zinc, were positive in May. Nickel was the best-performing commodity in the sector, up 11.5% due to demand outpacing supply, as nickel is utilized in electronic vehicle batteries, which have seen solid global demand. Zinc was the worst-performing commodity in the sector for the second consecutive month, down 0.8%, bringing its YTD performance to -6.1% due to high supply levels. The S&P GSCI Precious Metals was down 1.2%. Gold fell 1.4%, weighed down by expectations of a moderate increase in inflation levels and rising interest rates, as well as an increase in consumer spending. Silver was up 0.5%, with the benefits from its industrial use outweighing its precious metal safe-haven status.

Exhibit 2 depicts the annualized risk/return characteristics of the S&P GSCI sector and single commodity indices. In terms of annualized returns, the S&P GSCI Livestock was the worst-performing sector in the index, down 13.3%, while the S&P GSCI Energy was the best-performing sector, up 40.6%. Energy also presented the highest level of volatility this past year, with an annualized risk level of 20.9%. Live cattle was the worst-performing commodity, down 17.4% year-over-year, while nickel, the best performer in May, was also the best performer year-over-year, up 73.4%. Natural gas, which declined 12.5% over the past year, presented the highest volatility, with an annualized one-year risk level of 31.4%. Natural gas tended to exhibit the highest level of volatility, as can be seen in its 1%-10% annualized risk levels, due to the impact of weather conditions that affect the fundamentals of supply and demand. Furthermore, natural gas has changed significantly since 2009, with the evolution of new technologies focused on extracting natural gas, which has lowered prices.

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

The Benefits of Having a Retirement Income Goal and How to Estimate It

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Philip Murphy

Former Managing Director, Global Head of Index Governance

S&P Dow Jones Indices

Individuals preparing for life after full-time employment may find that planning for a particular range of inflation-adjusted retirement income is more effective than planning for a particular range of wealth. Wealth levels are unintuitive because they do not provide practical spending guidelines. They are also heavily influenced by random variables (market returns). Using one’s wealth as a primary gauge may lead to overspending in early retirement by those unprepared to manage a prudent withdrawal program, or underspending over the course of retirement by those overly concerned with outliving their savings.

In contrast, income level planning has several key advantages.

  1. Income levels are intuitive because they provide built-in budgeting guidelines.
  2. Estimating required future income can be tailored to individual circumstances and does not require specialized financial knowledge.
  3. Estimating a future income level (that would be attained upon conversion of one’s savings into income risk hedging assets) does not require guesstimating future market returns the way that estimating one’s future wealth level would.
  4. As a result of #3, there can be more certainty about one’s future income than about one’s future wealth, as long as some of one’s assets are managed to hedge income risk and more savings are devoted to those assets over time.
  5. Once a required future income level has been attained through the allocation to income risk hedging assets (essentially locking in future income), if other capital is available, it can remain invested for long-term growth without great risk of income impairment during market downturns.

In this post, I’ll cover steps #1 and #2 and show a simple way of estimating required future income.

Even if you do not do a great job of tracking current expenses, you can back into personal expenditures as long as you know how much you save and how much you pay in taxes. Every dollar earned must be either spent or saved, so if you know your savings and taxes paid, you can derive personal expenditures by subtracting savings and taxes from gross income.

Once you estimate current personal expenses, directly by logging receipts or indirectly through the method outlined above, you can adjust for expected changes in spending during retirement. For example, suppose the following.

  • I currently spend USD 100,000 a year on personal consumption
  • I estimate that in retirement my personal expenses may decrease by about USD 17,000
  • -USD 10,000 because I will not need to save in retirement accounts
  • -USD 15,000 because I expect to have my mortgage paid off
  • -USD 2,000 because I expect lower clothing and commuting costs
  • +USD 5,000 due to higher expected medical bills
  • +USD 5,000 due to higher travel costs

Adjusting my current level of spending (USD 100,000) for changes in spending patterns after retirement equates to about USD 83,000 (in today’s U.S. dollars) to maintain my current lifestyle in retirement. USD 83,000 per year is therefore my estimated retirement income liability.

Next, we should take account of guaranteed retirement income, such as Social Security benefits. You can get an estimate of expected benefits from the U.S. Social Security Administration website calculator. Suppose the calculator tells me I should receive USD 25,000 per year under current assumptions. Then my net estimated retirement liability, the portion I will have to fund with personal savings in current U.S. dollars, is:

Having completed these calculations, I have a reasonable estimate of the income level that I need to fund in order to maintain my lifestyle in retirement. In my next post, I’ll show how to measure progress toward such a goal well ahead of one’s retirement date.

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