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Dow Jones Sustainability Indices Continue to Raise the Corporate Sustainability Bar

How Important Are Earnings Revisions Signals for Fundamental Factor Strategies in Asia?

Price-to-Rent as an Overvaluation Metric

Performance Trickery

The S&P Risk Parity Indices: Methodology

Dow Jones Sustainability Indices Continue to Raise the Corporate Sustainability Bar

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Manjit Jus

Managing Director and Global Head of ESG Research & Data

S&P Global

Today, S&P Dow Jones Indices and RobecoSAM announced the results of the annual rebalancing of the Dow Jones Sustainability Indices (DJSI). The DJSI World will be celebrating its 20th anniversary in 2019, making it one of the longest-running sustainability benchmarks in the world. Even after nearly 20 years, the index has lost none of its luster and has seen nearly 1,000 of the world’s largest corporations actively complete RobecoSAM’s Corporate Sustainability Assessment (CSA) in 2018—marking yet another record in participation.

Participation has grown steadily since the indices were first launched in 1999, at a time when the concept of sustainable investing was still very much in its infancy. The annual September rebalancing has since been penciled in as a key date in companies’ corporate sustainability calendars, and it continues to receive the attention of company boards, executive management, and media from around the world. Today, more than ever, recognition for corporate sustainability efforts serves as a key differentiator for companies vis-à-vis their employees and future talents, customers, and—increasingly—investors. The DJSI serve as an important external verification of companies’ efforts on pressing sustainability topics and validation that their approach is business-oriented and more than just greenwashing.

The CSA methodology, which forms the basis for assessing companies and creating the ratings that flow into the index selection, is updated every year to reflect current sustainability challenges and future sustainability trends. In 2018, RobecoSAM added topics to its Corporate Governance criterion—adding topics such as Family & Government Ownership and Dual Class Share Structures.

In 2014, the CSA was one of the first corporate sustainability methodologies to include questions about how companies approached the topic of taxation. Since then, corporate taxation and tax transparency have taken center stage in the sustainability arena, and many companies have a stance on the topic. This year, it was time to test and see whether companies are indeed following the policies they laid out. A new question on companies’ Effective Tax Rate was introduced to determine whether companies are paying significantly less taxes than expected, without any reasonable explanation, and consequently penalizing those that are paying up.

Furthermore, new questions on Biodiversity were also added this year, and the Climate Strategy criterion was revamped to align with major changes made by CDP, with whom RobecoSAM maintains a long-standing collaboration. Most importantly, the changes in the Climate Strategy criterion introduced important concepts recommended by the Task Force for Climate-related Financial Disclosures (TCFD)—a framework that is set to significantly influence company climate disclosures in the coming years and has drawn a lot of attention from investors.

We look forward to engaging with companies on these new topics over the coming months through dialogues, round tables, and a series of webinars explaining the major methodology and scoring changes. The percentile ranks of all assessed companies will be available free of charge to all users of the Bloomberg Professional platform—providing access to investors around the world who are interested in exploring RobecoSAM’s ESG ratings.

For more about the Dow Jones Sustainability Indices and the Corporate Sustainability Assessment, visit



Important legal information: The details given on these pages do not constitute an offer. They are given for information purposes only. No liability is assumed for the correctness and accuracy of the details given. The securities identified and described may or may not be purchased, sold or recommended for advisory clients. It should not be assumed that an investment in these securities was or will be profitable. Copyright© 2018 RobecoSAM – all rights reserved.

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

How Important Are Earnings Revisions Signals for Fundamental Factor Strategies in Asia?

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Utkarsh Agrawal

Associate Director, Global Research & Design

S&P Dow Jones Indices

In our previous blog, “The Hunt for Value With High Earnings Expectations in Asia,” we discussed how a simple sequential earnings revision screen historically delivered positive return alpha over the value screen in the majority of markets. The value screen was constructed based on the average of three underlying factors: book value-to-price ratio, earnings-to-price ratio, and sales-to-price ratio. Since the Asian market is highly fragmented and the distribution of each underlying factor may be different, we examined the earnings revision screen overlay on each underlying factor for both value and quality in our new research paper, “Earnings Revision Overlay on Fundamental Factors in Asia.” For quality, the underlying factors are: accrual ratio, financial leverage ratio, and return on equity ratio.

The earnings revision screen overlay on fundamental factors was done in two steps. First, we selected the top quartile of stocks by each fundamental factor from the base universe. Next, we dropped the bottom quintile of stocks by earnings revision from the stocks selected in the first step. We tested two earnings revision measures: the six-month change in the EPS estimate and the six-month diffusion of the EPS estimate. EPS diffusion was computed as the number of upward revisions minus the number of downward revisions of EPS estimates, divided by the total number of EPS estimates.

Let’s take a closer look at some of the broad market highlights from the paper.

  1. Over the entire back-tested period from March 31, 2006, to March 31, 2018, the earnings revision-screened factor portfolios outperformed their respective comparable underlying factor portfolios across the majority of Pan Asian markets (see Exhibit 1).
  2. The earnings revision screen also lowered the risk of the fundamental factors significantly across the majority of markets over the same period.
  3. During down market periods, the earnings revision-screened factor portfolios outperformed their respective comparable underlying factor portfolios across the majority of markets.
  4. The earnings revision screen did not introduce a strong sector or size bias in the fundamental factor portfolios constructed from the respective broad market universe.
  5. Among various Asian markets, the earnings revision overlay generated the most significant excess return in Australia and Hong Kong for the majority of fundamental factors (see Exhibit 1).

The results of our research suggest that the signals from earnings revisions are important even for fundamental factor strategies, since the earnings revision-screened factor portfolios reduced the risk and enhanced the return of the comparable factor portfolios, historically, across the majority of markets.

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

Price-to-Rent as an Overvaluation Metric

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Frank Nothaft

Executive, Chief Economist, Office of the Chief Economist


Market Conditions Indicator and price-to-rent can locate overheated metros

The S&P CoreLogic Case-Shiller Index has documented that home prices have risen in all metropolitan areas over the last few years.  While price gains vary considerably across urban markets, some places have had especially rapid appreciation that put values above their pre-Great Recession peak, even after controlling for inflation.  Examining the markets included in the 20-City Composite, all 20 markets are up substantially from their trough, 10 markets have surpassed their pre-Great Recession peak, and 5 have surpassed the earlier peak after inflation adjustment.[1]  With prices setting new records, it’s natural to wonder whether the housing market is on the verge of another valuation bubble.

The CoreLogic Market Conditions Indicator provides a gauge to identify urban areas that may be overheating.  The Indicator is based on straightforward intuition: home prices should generally rise in line with income growth of local residents.  If prices grow too fast, then homes are less affordable and price growth should slow while incomes catch up.

The Market Conditions Indicator found that 34 percent of MSAs in the U.S. were potentially ‘overvalued’ by this metric in May.  (Exhibit 1) The last time that one-third of metro areas were overvalued in a rising price environment was Spring 2003.  While many metros were frothy 15 years ago, the valuation bubble was still localized and not national; however, rapid price growth during the following three years led to 68 percent of markets overvalued by 2006.  Thus, while we do not have a national valuation bubble today, continued rapid price growth raises the specter of a new bubble forming within the next few years.  For metros that the Indicator has flagged as ‘overvalued’, it’s important to look at other metrics for confirmation.  A price-to-rent ratio can provide additional perspective on whether prices are out of sync with valuation fundamentals.

To construct a price-to-rent ratio we used the S&P CoreLogic Case-Shiller Index and the CoreLogic Single-family Rent Index, set the ratio equal to one in the first quarter of 2001 when homes were fairly valued in nearly all metros, and observed how the ratio has evolved to today.[2]  That ratio shows that home prices have grown more quickly than rent in most metro areas, which would provide confirmation of overheated values if cap rates had remained roughly the same, but they haven’t.

A cap rate is used by real estate professionals to convert net operating income on an investment property into a market value.[3]  While a cap rate is relatively stable over short time periods within a metro area, cap rates will vary across metros and over a long period will fluctuate based on the level of long-term interest rates, the perceived riskiness of real estate investments, and tax code changes that affect real estate profitability.  Of these three factors, the one that has changed the most between 2001 and today has been the level of long-term interest rates.  Consequently, cap rates for single-family rental homes are down significantly since then.  The cap rate decline implies that the price-to-rent ratio would need to grow by more than 60 percent since 2001 before today’s prices are disconnected with rental income fundamentals.

When we examined select metros that the CoreLogic Market Conditions Indicator found to be ‘normal’ in 2001, we found that price-to-rent ratios were up by more than 60 percent in the Los Angeles, San Francisco, West Palm Beach and New York metros; of these, all but San Francisco were places that the Indicator had flagged as overheated today (Exhibit 2).  Metro areas that the Market Conditions Indicator has tagged as ‘overvalued’ and have a high price-to-rent ratio are at heightened risk of a value correction, especially as long-term interest rates rise.

[1] The Bureau of Labor Statistics’ Consumer Price Index all items less shelter was used to adjust for inflation.  Of the places included in the 20-City Composite, Dallas, Denver, Portland, San Francisco and Seattle have real prices above their pre-recession peak, as measured by the S&P CoreLogic Case-Shiller Index.

[2] Because single-family rental homes have a median value that is less than the median value of all single-family homes, the calculations used the S&P CoreLogic Case-Shiller Low-Tier Index (not seasonally adjusted) for homes with a purchase price within the lowest one-third of the CBSA price distribution.

[3] Market value of a rental home = (Net operating income)/(Capitalization rate)

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

Performance Trickery

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Craig Lazzara

Former Managing Director, Index Investment Strategy

S&P Dow Jones Indices

Suppose you, as a hypothetical financial advisor, encounter a hypothetical marketer who presents the following hypothetical performance data:

Last Year

Trailing 3 Years Trailing 5 Years

Trailing 10 Years



11.9% 16.0%




11.4% 15.8%


Not only did the portfolio beat its benchmark handily in 2017, says our marketer, but it has outperformed consistently over the past decade.  As evidence of this consistency, notice that the portfolio has generated positive value added for the last 3 years, last 5 years, and last 10 years.

Or has it?

Let’s peel the onion a bit.  Here’s the performance of the same hypothetical portfolio for every year in the last 10:

Year Portfolio Benchmark

Value Added


-36.0% -37.0%



26.0% 26.5% -0.5%


15.0% 15.1%


2011 2.0% 2.1%



16.5% 16.0%



32.0% 32.4%



13.5% 13.7%



1.0% 1.4%



11.0% 12.0%



25.0% 21.8%


The portfolio’s value added has been reasonably consistent – it’s been consistently negative, having outperformed in only three years of the past ten.

What’s happening here is that a generally indifferent manager had a really good year in 2017.  The value added in that year compensated for a long history of mediocrity.  Our hypothetical marketer was clever to present his record through a lens that always included 2017.  His numbers were correct, but they were arguably misleading.

There’s a simple lesson in this simple example: If someone shows you trailing performance data, disaggregate.  Look at year-by-year numbers, not cumulative periods ending with the present.  A truly consistent active manager will welcome the scrutiny.

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

The S&P Risk Parity Indices: Methodology

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Berlinda Liu

Former Director, Multi-Asset Indices

S&P Dow Jones Indices

In earlier posts, we analyzed the historical performance, risk contribution versus capital allocation, and return attribution and leverage of the S&P Risk Parity Indices. The results demonstrate that this indices in this series could potentially serve as benchmarks to measure the performance of active risk parity strategies. In this post, we will dig deeper into the methodology and walk through our rationale behind the index rules.

Differences in risk parity strategies arise from the asset classes (and instruments) chosen in the strategy, the risk measurement used, and the handling of the assets’ risk contribution to the portfolio. To build a transparent risk parity benchmark, we use a bottom-up approach that employs long-term realized volatility to allocate across asset classes.

  • Underlying Asset Classes: The underlying asset classes are equity, fixed income, and commodities, as tracked by futures contracts.
  • Liquidity: For all futures contracts used, each has a minimum annual total dollar value traded of USD 5 billion to ensure replicability and tradability.
  • Risk Measurement: We use long-term realized volatility to measure risk. The look-back window has a minimum of a five-year history at the beginning of our back-tested period and is capped at 15 years as we accumulate more data. We use realized volatility rather than forecast volatility to avoid dependency on volatility forecasting models.
  • Weighting Mechanism: We target an equal amount of risk contribution from each asset class to the overall portfolio volatility. In order to do this, we calculate the position weight simply as the pre-defined target volatility divided by the long-term realized volatility for each asset class. Due to correlation among asset classes, the realized volatility of the risk parity portfolio would usually be lower than the target volatility. We then apply a leverage factor to achieve the target volatility. Within each asset class, futures are combined using the same approach to ensure equal risk contribution from futures to the asset class they belong to.
  • Rebalancing Frequency: The indices calculate target weights at month-end and apply them on the second trading day of the next month.

Here is a hypothetical example that illustrates the index construction process of the S&P Risk Parity Index – 10% Target Volatility (TV). In particular, we show how the weight of the S&P 500® futures is determined.

Hypothetical Weighting of the S&P Risk Parity – 10% TV

Suppose the long-term realized volatility of the S&P 500 futures contract is 15%. To reach the target volatility of 10%, we need to allocate 10%/15% = 67% to it and the rest to cash.

Next, we calculate the long-term realized volatility of the equity asset class using weights calculated in step 1. Since there are three futures in the equity asset class, we need to divide their weights by three to construct the equity portfolio.

Suppose the realized volatility of equities is 9%. To reach the target volatility of 10%, we need to apply a multiplier of 10%/9% = 111% to the three constituent futures contract within the asset class. As a result, the weight of the S&P 500 futures in the equity asset class is set to 67% * 1/3 * 111% = 25%.

Finally, we calculate long-term realized volatility of the portfolio using weights calculated in step 2. Since there are three asset classes in the equity asset class, we need to divide their weights by three to construct the multi-asset portfolio.

Let’s say the portfolio’s realized volatility turns out to be 8%. To reach the target volatility of 10%, we need to apply a multiplier of 10% / 8% = 125% to the 26 constituent futures contract. As a result, the weight of the S&P 500 futures is finalized as 25% * 1/3 * 125% = 10%.

The S&P Risk Parity Indices are constructed in a rules-based, transparent manner using tradable, liquid instruments to facilitate implementation. As we have seen in other parts of the blog series, the indices track the composite performance of active risk parity funds much closer than a traditional 60/40 equity/bond portfolio.

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