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2018 Retirement Funding Update for DC Account Holders

Latin America Scorecard: Q4 2018

S&P MARC 5% ER 2018 Performance: Diversification and Allocation Provide Stability

What Is the Impact of a Company’s Environmental Data on its Weight in an Index?

Combining the Quality Factor With Dividend Yield: A Study of S&P DJI Dividend Strategies

2018 Retirement Funding Update for DC Account Holders

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Philip Murphy

Managing Director, Global Head of Index Governance

S&P Dow Jones Indices

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2018 produced negative absolute returns across a number of asset classes, particularly international stocks. A broad benchmark of stocks traded outside of the U.S., the S&P Global ex-U.S. BMI (US Dollar) Gross Total Return Index, lost 14.18% of its value. Nevertheless, many investments kept pace with the change in cost of securing future retirement income, because an upward shift of real rates decreased the present value of future inflation-adjusted cash flows. For instance, the constant maturity 10-Year Treasury Inflation-Indexed Security Rate,[1] published by the St. Louis Fed, almost doubled from 0.54% in January 2017 to 1.02% on Dec. 1, 2018.

Exhibit 1 shows the change in present value, for 2017 and 2018, of 25-year inflation-adjusted cash flows that begin paying in the respective future years on the chart’s horizontal axis. For example, savers planning to retire around 2030 saw the cost of providing themselves 25 years of inflation-adjusted income decrease by 9.96% in 2018 after an increase of 7.51% in 2017.

Exhibit 2 shows the total returns of U.S. stocks and bonds, as measured by the S&P 500® and the S&P U.S. Aggregate Bond Index, as well as a hypothetical 60/40 mix of the two, for 2017 and 2018.

Exhibit 3 displays excess total returns of the benchmarks from Exhibit 2 over the decrease in the cost of income for each respective year (from 2020 to 2060). Despite negative and near-zero absolute returns for U.S. stocks and bonds, respectively, both outpaced the change in cost of future income for all of the target years, as did the 60/40 stocks/bonds mix.

Finally, Exhibit 4 shows excess returns of specific S&P STRIDE Indices over the decrease in cost of future income for the same target years.

The S&P STRIDE Indices also outpaced the decrease in cost of future income for all target years from 2020 to 2060. The magnitude of their excess returns over the decrease of income cost was generally not as strong as it was for U.S. stocks and bonds. However, these indices are designed to adjust to changes of income cost more reliably than stocks or bonds. One of the characteristics of the S&P STRIDE Index Series methodology[2] is that the index weight of near-dated S&P STRIDE Indices is heavily allocated to a mix of U.S. Treasury Inflation-Protected Securities matching the duration of retirement income for the respective target year.

If income risk is not managed, the question of whether investment returns will keep pace with changes in income cost is unpredictable, because the relationship between the value of assets and the value of a retirement income liability is essentially random. 2018 provided an example of negative absolute returns outpacing a decrease in the cost of income, so investors saving for retirement may not be as bad off as they feel when they open their year-end 401(k) statements. It may not be immediately apparent, but in income terms, the 2018 scenario is better than a situation wherein positive absolute returns fail to keep pace with increases of income cost. In such a scenario, investors may feel wealthier without considering that their wealth buys less income. Of course, the most detrimental scenario is when negative absolute returns combine with rising income cost, in which case one loses ground in terms of both wealth and income, which may be the strongest argument for managing income risk as retirement draws closer.

[1]   See https://fred.stlouisfed.org/series/FII10.

[2]   https://spindices.com/documents/methodologies/methodology-sp-stride-index-series.pdf

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

Latin America Scorecard: Q4 2018

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Silvia Kitchener

Director, Global Equity Indices, Latin America

S&P Dow Jones Indices

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It is that time of year when we look back at the memorable moments that made 2018 so special. In our case, we will look at the Latin American markets to see their development and performance. This past year was particularly exhilarating and terrifying at the same time, similar to the feeling one gets when riding a rollercoaster. The ups and downs make you sick, you scream for help, you want to get off—but once you’re on it, there is nothing you can do but ride the monster. After it is all over and despite the aggravation and fear, you cannot wait to get back on it. Welcome to the Latin American rollercoaster.

The last quarter of 2018 was particularly horrifying for most global markets, with the S&P 500® giving back nearly 14%. The S&P Europe 350® and the S&P/TOPIX 150, which seeks to measure Japanese blue chip companies, lost 13% and 15%, respectively. This resulted in global returns of nearly -13% for the quarter, as reflected by the S&P Global 1200, which is designed to measure the 1,200 most important stocks in the world.

Despite the gloom and doom of the fourth quarter, Latin America actually did well compared with other regions. The S&P Latin America BMI, which measures companies in Brazil, Chile, Colombia, Mexico, and Peru, had a positive return of 1.2% in U.S. dollars, while the widely used S&P Latin America 40 was flat at -0.1%. Over the entire year, the S&P Latin America 40, which returned -6%, did not fare as well as the S&P 500, which yielded -4%. However, it did well compared with the S&P Europe 350 and S&P/TOPIX 150, which had returns of -14% and -13% for the year, respectively.

What fueled the Latin American rollercoaster of 2018? The region underwent some of the most important changes it had seen in many years. In 2018, there were major presidential elections in Brazil, Colombia, and Mexico, and new presidents took office in Chile (following the 2017 elections) and Peru (following the resignation of the recently elected president). Chile and Colombia elected moderate candidates, while Brazil and Mexico chose different administrations from the ones that had governed those countries previously. The potential changes brought great uncertainty to the region and, with it, great volatility to the markets.

While most countries ended in the red for the quarter, Brazil was the shining star of the region, with the S&P Brazil BMI returning nearly 15% in U.S. dollars—no other country in Latin America came anywhere near this return. In U.S. dollar terms, Mexico and Colombia had the worst returns. The S&P/BMV IPC was down nearly 20%, and the S&P Colombia Select Index was down almost 18%. While Chile and Peru also had negative returns for the quarter, their returns did not drop as much as the two other countries. The S&P Chile LargeMidCap, a proxy for the S&P/CLX IPSA, returned -3.4% in U.S. dollars, and the S&P/BVL Peru Select Index posted -2%. In their respective local currencies, the indices had better performance.

Looking at longer periods, local investors from Argentina fared the best. Peruvian and Brazilian investors were somewhat behind but still held on to significant returns. Argentina generated returns of over 51% and 48% for the three- and five-year periods, respectively. However, the large devaluation of the Argentine peso against the U.S. dollar and the high inflation rates diminish the value of the returns. Following Brazil’s election of a “market-friendly” presidential candidate, the country entered into a rally that sustained the gains of the three- and five-year periods. The S&P Brazil BMI showed U.S. dollar returns of 27% and 11%, respectively. Peruvian investors also managed to hold on to their gains for these periods, with the S&P/BVL Peru Select Index reporting 27% and 8% in local currency, respectively. Chile did well for both periods, while Colombia had mixed results, with a positive return for the three-year period but a nearly flat local currency return for the five-year period.

In 2018, Mexico struggled to hang on to past gains. The uncertainties of the new government’s policies, the renegotiation of the North American Free Trade Agreement (NAFTA), and the fall of the Mexican peso brought uncertainty and volatility. The market is still struggling to find its footing, although during the first two weeks of 2019 (Jan. 1-14, 2019), the S&P/BMV IPC displayed a positive upturn of nearly 5%.

This new year has so much to look forward to. Latin American is poised for great changes with all these new leaders. It will be interesting to see the direction they take their countries and the outcomes and opportunities this will bring to local and international investors.

To see more details about performance in Latin America, please see: S&P Latin America Equity Indices Quantitative Analysis Q4 2018.

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

S&P MARC 5% ER 2018 Performance: Diversification and Allocation Provide Stability

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Joe Kairen

Senior Director, Strategy & Volatility Indices

S&P Dow Jones Indices

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Despite a rocky start and end to 2018 that negatively affected the performance of all the asset classes within the S&P MARC 5% Excess Return (ER) Index,[1] the index maintained a relatively stable performance throughout the year, although it ended the year in red. When you look at the asset classes in the S&P MARC 5% ER, on an excess return basis, equities, as measured by the S&P 500®, and commodities, as measured by the S&P GSCI Gold, had the largest losses with -6.1% and -4.7%, respectively (see Exhibit 1). Taking these returns and multiplying them by the average weight of the asset classes in the index provides a good indication of which asset classes affected the index performance the most.

What is surprising is that 2018 started on different footing from where it ended. Looking at early 2018, with the exception of February to April, there was a clear dispersion of performance between the different underlying asset classes, and that became more pronounced after April when equities began to rally and gold sold off through the end of Q3 2018. Looking at Q4 2018, the bifurcation of performance continued with equities selling off sharply, while the other asset classes and the S&P MARC 5% ER moved higher toward the end of the year.

When comparing the component assets to the S&P MARC 5% ER to see what periods the components outperformed or underperformed the index, the story is similar. The diversification built into the index design shows that no single asset class consistently performed in line with the S&P MARC 5% ER throughout 2018, but that instead there was a diversification of relative performance between the components and the index itself (see Exhibit 3).

On the surface, it looks like the index was allocated to cash after the February drawdown because of the almost sideways performance; however, when looking at the index weights, what becomes apparent is the index allocation throughout the year. Unlike many other strategy indices, which moved to cash or short-dated Treasuries for extended portions of 2018, the S&P MARC 5% ER held sizeable positions across equities, Treasuries, and gold with an average of 23%, 84%, and 28%, respectively. In addition to the distribution across asset classes, the S&P MARC 5% ER had an average index allocation of 135.7% in 2018.

For more information, please see the S&P MARC 5% Index Methodology.

[1]   Throughout the rest of this blog, the S&P MARC 5% Excess Return Index will be referred to as the S&P MARC 5% ER.

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

What Is the Impact of a Company’s Environmental Data on its Weight in an Index?

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Ryan Christianson

Senior Analyst, ESG and Strategy Indices

S&P Dow Jones Indices

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One of the key objectives for the recently launched S&P/JPX Carbon Efficient Index is to motivate companies within the baseline index, TOPIX, to increase their level of environmental disclosure. By incorporating a mechanism to adjust constituent weights by their respective environmental data, the S&P/JPX Carbon Efficient Index has been designed to incentivize all companies, regardless of their GICS® industry group, to decrease their carbon emissions relative to their peers and to disclose their environmental impact information.

The S&P/JPX Carbon Efficient Index takes into consideration three different criteria of the S&P Carbon Global Standard[1] when adjusting the constituent weights of the index. The S&P Carbon Global Standard is updated annually at each rebalance reference date using the global universe, the S&P Global LargeMidCap. In Exhibit 1, we present the three different adjustment criteria, and how the carbon weight adjustment may have affected the constituent weights within the S&P/JPX Carbon Efficient Index.

Decile Classification

The decile classification, determined by the S&P Carbon Global Standard, is created by finding the decile thresholds for each of the GICS industry groups based on carbon revenue footprints. Using these thresholds, the eligible constituent universe for the S&P/JPX Carbon Efficient Index is then bucketed into its respective decile classification.

The breakdown of the companies within the S&P/JPX Carbon Efficient Index is relatively evenly distributed among the deciles (see Exhibit 2).

The average percent change of constituent weights shows that there is a significant difference (22% versus -34%) in how the weights are adjusted between companies that have lower carbon-to-revenue footprints compared with those in the bottom three deciles.

Disclosure Status

One of the key goals of the S&P/JPX Carbon Efficient Index is to increase carbon disclosure among the constituents. To achieve this objective, individual companies have been categorized as either “disclosed” or “non-disclosed,” with increased weight allocated to the former. As indicated in Exhibit 1, the difference in weight between disclosed and non-disclosed companies is 10% for each respective decile.

Of the Japanese companies measured by TOPIX, 22.91% have been identified as disclosed companies (see Exhibit 3). When compared to the universe of companies within the S&P Global Ex-Japan LargeMidCap Carbon Efficient Index, which has a 63.55% disclosure rate, it is clear that Japanese companies still have a long way to go for corporate disclosures.

While the disclosure status of a company may not have as big of an impact on the weight adjustment compared to its respective decile, it is clear that it is beneficial for companies to disclose carbon emissions. While the increase in weight may not appear to be significant, the 8.62% decrease in weight as a result of being classified as a non-disclosed company can be quite significant.

Industry Group Impact Factor

When there is a low range of carbon emissions within an industry group, the overall impact on the portfolio of a single company improving their carbon-to-revenue footprint will be low. On the other hand, if a company in an industry group that has a tendency to have a high amount of carbon emissions (such as those from the Energy or Materials sectors) were to improve its carbon-to-revenue footprint, it would potentially make a big impact on the overall portfolio’s carbon-to-revenue footprint.

As a component of the S&P Global Carbon Standard, each GICS industry group is classified as high, mid, or low impact, depending on the range of the carbon-to-revenue footprint, and a larger weight adjustment is made for companies in high impact industry groups (see Exhibit 4).

A high impact classification does not necessarily imply that the industry group is bad for the environment. Because this classification is determined by the range of the carbon-to-revenue footprint within an industry group, it is likely that a high impact industry group is not universally efficient, and thus may have a high opportunity for companies to review their environmental data, which could make it a leader within their respective industry. As the range of carbon-to-revenue footprints within an industry group decreases, it is likely that the companies within the industry group are operating their businesses in the most efficient manner relative to its peers.

The statistics provided in this report are based on constituent weights as of Oct. 31, 2018, and the carbon disclosure information as of the index rebalancing reference date (February 2018).

For the purposes of this report, only constituents that had reported Trucost data at the time of the index rebalancing reference date are included. Since the rebalancing reference date in 2018, Trucost has expanded the amount of coverage companies globally to over 14,000 and has over 99% coverage of TOPIX, as a percentage of total market capitalization.

The TOPIX Index Value and the TOPIX Marks are subject to the proprietary rights owned by the Tokyo Stock Exchange, Inc. and the Tokyo Stock Exchange, Inc. owns all rights and know-how relating to the TOPIX such as calculation, publication and use of the TOPIX Index Value and relating to the TOPIX Marks.

[1] For more details on the S&P Carbon Global Standard, please visit https://spindices.com/topic/carbon-efficient.

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

Combining the Quality Factor With Dividend Yield: A Study of S&P DJI Dividend Strategies

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Qing Li

Director, Global Research & Design

S&P Dow Jones Indices

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As of Dec. 31, 2018, the passive implementation of dividend strategies measured approximately USD 141 billion based on assets under management (AUM) of dividend-focused ETFs listed in the U.S. This is a staggering amount considering that only 10 years ago the AUM amounted to just over USD 6 billion.[1] The growth in assets, as well as the number of passive dividend-oriented investment products, is a testament to the popularity of dividend investing.

Dividend strategies come with various investment objectives and target different characteristics. For example, some aim to achieve absolute dividend yields, while others may target steady dividend growth rates or combine other fundamentals such as quality or low volatility with yield. The selection and allocation to different dividend strategies can also play impactful roles. No matter what the yield seeker’s focus is, common challenges they face include dividend cuts and elimination. In addition, companies exhibiting high dividend yield may fall in a “dividend trap,” since high dividend yield can be caused by decreasing stock prices rather than the increasing dividends payments. To minimize these risks, many market participants incorporate quality[2] metrics or other filters such as volatility into dividend-focused strategies. These measures aim to ensure that a given dividend strategy is only selecting companies that are capable of maintaining stable dividend distributions and dividend yield, even during periods of market stress.

In this blog, we explore what the impact would be if a quality factor were added to a dividend strategy. For the analysis, we used the S&P 500® Quality High Dividend Index, which is designed to measure the S&P 500 companies that rank among the top 200 in terms of their quality scores and dividend yield.[3] We then compared this strategy against the pure dividend yield, dividend growth, quality, and dividend and low volatility strategies, which are represented by the S&P 500 High Dividend Index, S&P 500 Dividend Aristocrats, S&P 500 Quality Index and S&P 500 Low Volatility High Dividend Index.

Over the long-term investment horizon, all of the dividend-oriented strategies outperformed the S&P 500 on both an absolute return and relative return basis, with the quality and dividend strategy leading the outperformance (see Exhibit 1). Specifically, the quality and dividend strategy outperformed the S&P 500 by 5.42% per year, with an annualized total return of 11.04% versus 5.62% during the past 20 years. The quality and dividend strategy held up relatively well in all market environments, with an average monthly excess return of 0.28%, which was the highest among all the strategies.

Adding quality or volatility filters to a dividend strategy allowed for quicker recovery from the bear market. During the 2008-2009 financial crisis, when all of the yield strategies experienced losses and drawdowns, the data showed that the quality and dividend combination had the smallest drawdown and quickest rebound (see Exhibit 2). The strategy recovered from the financial turmoil within 29 months, versus 53 months for the overall stock market. Meanwhile, the pure dividend yield strategy, as measured by the S&P 500 High Dividend Index, took the longest time to recover.

As displayed in Exhibit 3, the quality and dividend strategy sustained an average dividend yield of 2.92% historically, compared with 1.76% for the S&P 500. While the S&P 500 High Dividend Index historically had the highest dividend yield of 4.47%, it also exhibited the lowest quality traits among all the dividend strategies. As expected, the strategies that incorporated the quality factor demonstrated much stronger quality characteristics.

Dividends are one of the most important drivers in generating investment returns. As we analyzed in this blog, dividend-focused strategies have historically exhibited better long-term outperformance over the market. In addition, when combined with factors such as quality or low volatility, dividend strategies can potentially achieve higher returns and shorten recovery time from bear markets. Specifically, integrating quality and dividend helped generate the highest overall excess return, shortest rebound time, and the highest quality characteristics without sacrificing the yield over the period studied.

[1]   Source: ETF.com; FactSet.

[2]   Quality definition by S&P Dow Jones Indices:

We define quality as the combination of profitability, earnings and financial robustness, and use return on equity, accruals ratio, and financial leverage ratio to represent these factors.

  • Return on equity (ROE): Indicator of a company’s profitability. ROE is computed as [trailing 12-month earnings per share/book value]
  • Accruals ratio: Indicator of a company’s operating performance. It is computed as [change of net operating asset over past 1-year/average of net operating assets over past two-year period]
  • Financial leverage ratio: Indicator of a company’s capability in meeting its financing obligations. It is computed as [total debt/book value].

[3]   An S&P 500 member company is selected as a constituent if it ranks within the top 200 of the index universe by quality score and ranks within the top 200 of the index universe by indicated annual dividend yield. To reduce sector concentration risk and overall volatility, the stocks are weighted equally and sector weight is constrained at 25%.

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