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Monitoring Progress Toward an Income Goal

Low Volatility Rate Response – Interest Rate Changes and Relative Performance

Women May Nurse This Old Stock Market Bull

The Next Frontier in Footprinting: Carbon Accounting for Sovereign Bonds

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

Monitoring Progress Toward an Income Goal

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Philip Murphy

Managing Director, Global Head of Index Governance

S&P Dow Jones Indices

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In a previous blog post, I described some benefits of having an income goal and a method of estimating one’s retirement income liability. I reviewed a hypothetical example to illustrate steps 1-2 from the list of benefits below, and I calculated a net estimated retirement liability (the portion of retirement income one must fund with personal savings). This post will focus on the remaining steps, 3-5.

  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 step 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 desired 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.

The reason step 3 is true is that a future income level is a future stream of cash flows, and we can find the present value of future cash flows with certainty by using the current, observable yield curve. Related to its S&P STRIDE Indices, each month, S&P DJI publishes the present value of specific inflation-adjusted cash flows (called “Income Cost”), commencing at particular points in the future. The cash flows begin at target years spaced over five-year increments from 2005 to 2060, and each one lasts 25 years from its respective target year. For example, the 2035 cash flow starts in January 2035 and continues until 2060. These hypothetical cash flow streams are designed to represent inflation-adjusted retirement income.

Using the Income Cost for 2035 (assuming that is when I want to retire), and continuing the example from my last post, if I have to fund $58,000 in annual spending, then I can measure the current value of income-hedging assets that would be required to lock in that cash flow in real terms, beginning in January 2035:

$58,000 X $19.29 (cost of 2035 income as of April 2018) = $1,118,670

If I invested $1.12 million in a portfolio of income-hedging assets, I would essentially lock in my inflation-adjusted retirement cash flow over a period of 25 years – which is the reason step 4 is true. The more of my assets that I dedicate to income-hedging assets, the more certain I can be of my future cash flows. The opportunity cost of doing so is that those assets cannot be invested for growth. However, the benefit is lowering risk to future income.

Finally, step 5 flows from step 4. Once I have an amount invested in income-hedging assets that, given the current income cost, secures future cash flows sufficient to meet my net estimated retirement liability, then any remaining assets can be invested for growth without risk to income. In other words, I can sleep soundly without worrying about market risk. Where does one find income-hedging assets? S&P STRIDE Indices incorporate income-hedging into their framework by allocating index weight to S&P U.S. TIPS sub-indices designed to hedge income risk.

Putting steps 1-5 into practice allows one to periodically check progress toward an income goal. In other words, you can measure your funded status, the currently funded portion of your net estimated retirement liability. A funded ratio, expressed as a percentage, assuming you have $500,000 of income-hedging assets, would be calculated as:

$500,000 ÷ $1,118,670 = 44.7%

Now that you can measure your funded status, it can be utilized to adjust current spending, saving, and portfolio allocations in order to manage future income risk. If you periodically add income-hedging assets, your funded ratio will grow over time. When it gets close to 100%, you have effectively hedged income risk over a period of 25 years, commencing at retirement.

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

Low Volatility Rate Response – Interest Rate Changes and Relative Performance

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Phillip Brzenk

Senior Director, Strategy Indices

S&P Dow Jones Indices

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In a prior post, we saw that during sharp rising interest rate periods, the S&P 500® Low Volatility Rate Response fared better than the S&P 500 Low Volatility Index, even though both indices generally underperformed the S&P 500. In this post, we examine if there is a relationship between the magnitude of interest rate changes (positive and negative) and the relative performance between the two indices. We first plot the historical monthly excess returns of the rate response index over the low volatility index against monthly changes in interest rates (see Exhibit 1).

Exhibit 1 shows a clear trend in performance differential for 1) the direction and 2) the magnitude of interest rate changes. The linear regression trend line (the dotted line going across the chart) has a coefficient of 1.03 to interest rate changes. With a coefficient t-stat of 6.01, the results are statistically significant at the 99% confidence interval.

The coefficient approximates that for every 1% change in interest rates, the excess returns over the low volatility index is to change by roughly 1.03%. Given the positive slope coefficient, the rate response index generally outperformed the low volatility index when interest rates rose and underperformed when rates declined. Moreover, as shown by the upward-sloping regression line, the larger the increase in interest rates, the higher the excess return for the rate response index was compared with the low volatility index.

We also checked to see if the results held true for interest rate changes that were longer than one month. Exhibit 2 charts the rolling 12-month excess returns of the rate response index versus the low volatility index on the primary axis and the 12-month change in interest rates on the secondary axis.

Exhibit 2 shows that the relationship between relative excess returns and changes in interest rates persisted for longer time horizons. As interest rates increased, the rate response index outperformed, and when rates declined, the low volatility index outperformed. Since the comparison goes back to 1991, the relationship can be observed throughout multiple market cycles. Exhibit 3 shows the hit rate and average excess returns for the rolling 12-month periods.

For the 12-month time horizons, when rates increased, the rate response index outperformed the low volatility index during nearly 86% of the periods, with an average outperformance of about 2.3%. When rates decreased, the low volatility index outperformed the majority of the time, with an average outperformance of 0.8%.

While the rate response index generally underperformed the S&P 500 in rising interest rate periods, it fared better than the low volatility index in the short-term (one month) and long-term (12 months). The results in this blog further confirm initial findings from the first blog that the rate response strategy reduces interest rate risk of a low volatility portfolio. In the final post of this series, we will investigate the performance of the two indices in down markets.

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

Women May Nurse This Old Stock Market Bull

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Jodie Gunzberg

Managing Director, Head of U.S. Equities

S&P Dow Jones Indices

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“Not enough progress has been made in closing the gender gap, and in fact, in some countries, you have seen gender inequality increasing…” – Ángel Gurría, secretary-general of the Organization for Economic Cooperation and Development (OECD).

This was the opening of a recent speech at #WomenRule by Politico on a paper newly published by S&P Global.  Unfortunately, one of those countries is the United States as evidenced by  the decline of women’s participation to just 57%, from roughly 60% in 2000, according to the Bureau of Labor Statistics.  Also, as stated by the OECD, as recently as 1990, labor force participation rate (LFPR) among prime-age American women was near the top of advanced economies in the OECD. In 1990, LFPR for women of prime working age in the U.S. reached 74%. Since then, that rate has stayed roughly stable while increasing steadily elsewhere, pushing the U.S. down to 20th place among 22 advanced OECD economies by 2016.

In a prior paper by S&P Global, there was an economic scenario that showed the potential to add 5%-10% to nominal GDP.  If women entered, and stayed, in the workforce at a pace in line with, say, Norway, the U.S. economy would be $1.6 trillion larger than it is today.  However, the U.S. women’s LFPR has fallen from a key reason that the U.S. is the only country in the OECD that doesn’t provide income support during maternity or parental leave by law.  As one possible solution, S&P Global suggests a Congressional Budget Office (CBO)-like “score” could assess the impact legislation would have on the economic feasibility and accessibility to the workforce for women.

If the U.S. were to increase women’s LFPR to that of other advanced countries, S&P Global Ratings estimates an addition $455 billion output could be added to its baseline forecast for growth, or alternatively could add 0.2 percentage point per year on average for the next ten years.  That additional U.S. GDP growth could propel not just the U.S. stock market but the global markets as well, potentially adding an extra $2.87 trillion to the S&P 500 market value and $5.87 trillion to the S&P Global Broad Market Index over the next decade.

For every 1% of GDP growth, historically the S&P 500 has gained 3.4% on average, so based on that sensitivity, all else equal, the extra 0.2 percentage point may add another 0.7% of total return on average annually.  The information technology sector is the most sensitive with an average total return of 5.6% for every 1% of GDP growth historically.  If there were a higher female labor force participation rate, there could be an extra 1.1% total return annualized on average based on the historical sensitivity. 

U.S. GDP growth impacts not just the U.S. stock market, but markets globally since the U.S. economy is largely driven by consumer spending.  Generally, the more a country exports as a percentage of its output to the U.S., the greater the stock market sensitivity is to the U.S. growth. The mechanism for this is fairly simple: Because the U.S.’s “trade elasticity” is such that the country’s imports tend to increase at a faster pace than GDP growth during expansionary periods (while shrinking at closer to a 1-to-1 rate during times of economic contraction), manufacturers and other exporters to the U.S. enjoy outsized benefits when the American economy expands.

Since China exports almost four times as much to the U.S. as it imports in American goods and services in dollar terms, with exports to the U.S. accounting for 4.2% of the Chinese economy, its sensitivity to 1% of U.S. growth is 6.2% historically. For Japan, exports to the U.S. are roughly only twice its American imports, and exports to the U.S. account for just 2.8% of Japanese economic output, lowering its sensitivity to 1% of U.S. growth to just 2% total return on average.

Further evidence that this dynamic is at least partly responsible for the disparity includes the fact that South Korea—the U.S.’s sixth-biggest trade partner and No. 11 on the list of national economies—sends just about a third more to the U.S. than it receives, but those goods and services make up a substantial 4.6% of Korea’s GDP.  This has driven its stock market total return up 9.3% on average per 1% of U.S. GDP growth historically.

Most notably from its sensitivity to U.S. growth, Korea, which currently has the smallest market value of the major countries in the S&P Global Broad Market Index, could benefit so extraordinarily from U.S. GDP enhanced by increased female LFPR that it would jump to No. 3, just behind Japan and the U.S. in the 10 years examined, surpassing China.  

As this bull market, the second oldest one on record, continues to age, perhaps women are the key to unlocking the growth to give it a longer life.

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

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.