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Optimizing Environmental, Social and Governance Factors in
Portfolio Construction: Analysis of three ESG-tilted strategies
Zoltán Nagy ([email protected])
Doug Cogan ([email protected])
Dan Sinnreich ([email protected])
February 2013
Abstract Institutional investors wanting to integrate Environmental, Social and Governance (ESG) factors
in their investment strategies need the right tools to measure portfolio risk characteristics and
performance. MSCI‟s BarraOne and Barra Portfolio Manager can provide this utility with
Intangible Value Assessment (IVA) ratings from MSCI ESG Research.1 In this study, we
examine the use of IVA ratings with the Barra Global Equity Model (GEM3) to build optimized
portfolios with improved ESG ratings, while keeping risk, performance, country, industry and
style characteristics similar to conventional benchmarks, such as the MSCI World Index. The
currently available dataset of IVA scores allowed us to compare three strategies during the
period between February 2008 and December 2012, using current IVA ratings methodology. Of
the three strategies, we found the best active returns during this period were achieved by
overweighting firms whose IVA ratings improved over the recent time period, showing ESG
momentum. Underweighting assets with low ESG ratings also raised portfolio performance
during this period. The highest ESG rated assets had more uneven performance; they did better
in periods of limited risk appetite during this volatile market cycle.
1 MSCI ESG Research is produced by Institutional Shareholder Services Inc. or its subsidiaries (“ISS”). ISS is a wholly owned subsidiary of MSCI Inc.
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Introduction
For the last two decades, institutional investors have debated whether considering
Environmental, Social and Governance (ESG) factors can lead to better financial returns.
Skeptics maintain that so-called “Socially Responsible Investing” (SRI) does not support
fundamental analysis, restricts the investable universe, and runs counter to tenets of Modern
Portfolio Theory. Proponents argue that markets do not efficiently price ESG factors because
they address long-term risks that have not been absorbed by the economy, and that alpha
generation is possible as markets begin to recognize these undervalued influences.
Academic studies and industry analyses have supported both sides of this argument, although on
balance they find that investors employing ESG factors do not impose a significant performance
penalty, that investors can achieve comparable risk-adjusted returns to non-ESG tilted strategies,
and that investors may be able to enhance their returns through the use of certain ESG strategies.
For example:
Two reports by Mercer found that 20 of 36 peer-reviewed studies showed evidence of a
positive relationship between ESG factors and financial performance, while five showed
mixed results and eight showed a neutral relationship. Three others found negative
results. 2
A recent Deutsche Bank study of 56 peer-reviewed papers found that 89 percent linked
consideration of ESG factors with market-based outperformance, and that application of
exclusionary SRI screens was responsible for any negative results.3
These findings support an evolving view of ESG investing strategies. What started as a “values-
versus-profits” proposition, focused on screening out objectionable stocks from an SRI
perspective, has moved toward “best-in-class” investing that attempts to seize on mispriced
market risks and opportunities.4 Another commonly cited view is that ESG factors form a
fundamental measure of risk that correlates with the cost of capital – equity or debt – and that
companies with high ratings tend to outperform on operational and accounting-based metrics.5
Still, there are lingering questions about the alpha-generating power of ESG-tilted strategies.
One line of argument is that the market has evolved to the point where ESG factors – led by
governance and followed by environmental factors – are now efficiently priced so it is harder to
gain a stock-picking advantage as opportunities have become narrower and are moving away
from values-driven investors.6 At the same time, many large asset owners may have
2 See, for example, Mercer Investment Consulting, “Shedding Light on Responsible Investment: Approaches, Returns, Impacts,” November 2009; and Mark Fulton,
Bruce Kahn and Camilla Sharples, “Sustainable Investing: Establishing Long-term Value and Performance,” Deutsche Bank Climate Change Advisors, June 2012.
3 Mark Fulton, Bruce Kahn and Camilla Sharples, “Sustainable Investing: Establishing Long-term Value and Performance,” Deutsche Bank Climate Change Advisors,
June 2012.
4 Jeroen Durwall, Kees Koedijk and Jenke Ter Horst, “A tale of values-driven versus profit-seeking social investors,” Journal of Banking and Finance, January 2011.
5 See, for example, Sadok El Ghoul, Omrane Guedhami, Chuck C. Y. Kwok, and Dev R. Mishra “Does corporate social responsibility affect the cost of capital?”
Journal of Banking & Finance, September 2011; Subdeer Chava, “Environmental Externalities and the Cost of Capital,” Georgia Institute of Technology, 2011; and
Rob Bauer and Daniel Hann, “Corporate Environmental Management and Credit Risk,” Maastricht University, European Centre for Corporate Engagement, June 30,
2010.
6 Lloyd Kurtz and Dan DiBartolomeo, “The Long-term Performance of a Social Investment Universe,” The Journal of Investing, Fall 2011.
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fundamental considerations related to asset allocation and risk tolerance that may limit their
ability to pursue alpha-oriented ESG strategies.
However, if the size and diversification of institutional investor portfolios is such that they
effectively own a cross-section of the global economy, possible externalities associated with
portfolio holdings effectively become internal.7 And if not owning those companies exposes
them to a possible performance penalty, then they have the option of building a strategy that tilts
the portfolio to capture positive ESG exposures while minimizing negative ones.8
Applying MSCI ESG Research Ratings in Portfolios
Two recent studies have investigated the results of using such a “best-in-class” strategy from the
application of ESG ratings available through MSCI ESG Research. Intangible Value
Assessment (IVA) scores (originally developed by Innovest Strategic Value Advisors, a firm
MSCI acquired in 2010) evaluate sector-specific ESG material risks and opportunities. IVA
scores are distributed on a seven-point letter scale („AAA‟-„CCC‟) to reflect the relative strength
of companies' management of these risks:
RCM, an investment management company owned by Allianz Global Investors, found
that a strategy of investing in the top two of five tiers of IVA-ranked companies added
1.6 percent a year in excess returns for the period December 2005 through September
2010. The study found that returns from portfolios of European companies represented
the largest and most consistent spread between best-in-class and worst-in-class
companies, reflecting greater integration of ESG factors in Europe. The volatility of this
ESG-tilted portfolio did not increase relative to the MSCI World Equal Weighted Index
(encompassing 24 developed markets) during this period.9
Credit Suisse used IVA ratings over a slightly more recent time series and produced
similar results. From May 2006 through May 2012, this study found that companies with
an IVA rating of „A‟ or above outperformed the MSCI World Equal Weighted Index.
However, it also found that companies with an IVA rating of „CCC‟ outperformed the
index. The paper went on to explain possible factors for this countervailing result. One
of the possible factors is that the IVA ratings had a greater proportion of companies in
Europe with high ratings that were disproportionately exposed to the European debt crisis
in 2011 (which emerged after the RCM study was concluded). Second, companies with
smaller market capitalizations and lower price-earnings ratios also had lower average
ratings in the IVA data set. This study concluded that higher-rated companies performed
better during this period when markets were in a defensive, risk-off condition, and
companies with improving ESG ratings showed the most momentum during this period in
raising their market values.10
7 This is sometimes referred to in the ESG literature as a “universal owner” concept. For a seminal example, see James P. Hawley and Andrew T. Williams, “The
Emergence of Fiduciary Capitalism,” Corporate Governance, Vol. 5, No. 4 (1997). Available at SSRN: http://ssrn.com/abstract=11430
8 For more background, see Remy Briand, Chin-Ping Chia, Roger Urwin, MSCI Inc., “Integrating ESG in the Investment Process: From Aspiration to Effective
Implementation,” MSCI Research Insight, August 2011.
9 RCM Sustainability White Paper, “Sustainability: Opportunity or Opportunity Cost?” Allianz Global Investors, July 2011.
10 Credit Suisse Strategy Paper, “ESG Investing Themes: Are „good‟ stocks good investments?” Credit Suisse, Oct. 11, 2012.
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These findings set the stage for this MSCI Research Insight paper where we analyze the effect of
ESG ratings on portfolio performance for the period of February 2007 through December 2012,
and the effect of ratings changes starting in February 2008.11
For the ratings, we apply
Intangible Value Assessment (IVA) scores that evaluate sector-specific ESG material risks and
opportunities on a 10-point scale, which are converted into final letter grades of „AAA‟ to
„CCC.‟ We adopt the MSCI World Index as our performance benchmark and as the asset
universe for the optimized portfolios. The Barra Global Equity Model (GEM3) is used to build
and analyze three families of optimized ESG-tilting strategies.
Unlike the majority of studies that seek to test the alpha generation of companies with high or
low ESG ratings, we use the model to create portfolios that have region, sector and investment
style risk profiles similar to the benchmark‟s exposures. The risk model also allows us to
separate systematic sources of active return – that is, common factor contributions – from asset
specific return sources associated with IVA scores. While our study was designed primarily as
an enhanced indexing exercise, focused on achieving benchmark returns comparable to the
MSCI World Index, we also found three possible strategies during the observed period that can
raise ESG ratings and improve active returns with minimal effects on benchmark tracking error.
We acknowledge that this research is based on a short sample of data, spanning nearly six years.
As additional data becomes available to extend the time period, investors may wish to conduct
further research on these strategies to compare the results presented here.
Three Strategies that Lead to an ESG Tilt
We explore three optimized strategies that implement an ESG tilt of the MSCI World Index,
based on the IVA scores of underlying portfolio holdings.
The first strategy is called an “ESG worst-in-class exclusion” approach (hereafter
referred to as “ESG exclusion”). It is based on excluding the companies with the lowest
current ratings („CCC‟), which results in a narrower investment universe. We first
analyze the performance of this restricted market cap weighted portfolio. As a second
step, we further enhance this pure exclusion strategy by overweighting stocks with high
current ESG ratings and underweighting those with low current ratings inside the smaller
universe, while maintaining other exposures of the portfolio very close to the
benchmark‟s exposures.
The second strategy is called a “simple ESG tilt” approach. Here we do not exclude any
stocks based on their ESG ratings. Rather, we overweight stocks with high current ESG
ratings and underweight those with low current ratings, while maintaining other
exposures of the portfolio very close to the benchmark‟s exposures.
The third strategy is called an “ESG momentum” approach. Here again, we do not
exclude any stocks based on their ESG ratings. Instead, we overweight stocks that have
improved their ESG ratings during the preceding 12 months over the time series, and
underweight stocks that have decreased their ESG ratings over the same period. We
11 This period was selected to allow back-testing of a time series using current IVA ratings methodology.
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expect the resulting portfolio to reflect companies whose ESG trajectory is positive, even
though it will be less tilted towards companies with high current ESG ratings than the
simple ESG tilt portfolio referenced above.
The implementations of these strategies share some common characteristics, especially regarding
the investment constraints that were applied in the construction of the portfolios. The goal of
these constraints is to assure that the optimized portfolios have region, sector and style
characteristics close to the benchmark‟s characteristics and close to each other, and thus
minimize the effect of these common factors on portfolio performance.
In this section, we provide a high-level comparison of these strategies. In subsequent sections,
we give more details on each strategy, and on the sources of their performance. General
optimization methodology and settings are described in greater technical detail in the Appendix.
Table 1: Comparison of ESG Strategies, February 2008 – December 2012.12
In Table 1, we compare risk and performance characteristics of our three approaches relative to
the MSCI World Index. Due to the one-year lag in applying ratings changes in the ESG
momentum strategy, we show statistics for the common time period ranging from February 2008
through December 2012. All three strategies achieved positive active returns, with important
asset specific contributions (except for the ESG tilt strategy), meaning that ESG scores provided
these active returns once other systematic contributions were factored out.
At the same time, the resulting portfolios were tilted towards companies with higher ESG scores.
ESG exclusion and ESG tilt strategies led to a higher average ESG portfolio rating than ESG
momentum strategies, but their risk-adjusted performance trailed by a distinct margin. We
conclude that during this period it would have been possible to raise the ESG tilt of the MSCI
World Index by one ratings notch, from „BBB‟ to „A,‟ without harming portfolio performance in
terms of active returns, while maintaining a small tracking error.
12 To be consistent in comparing results, the figures presented in this table are for February 2008 – December 2012 only, corresponding to the time period available for
the ESG momentum strategy. Portfolio ESG score is calculated over the indicated time period as the market capitalization weighted average score of portfolio
constituents, where missing values are replaced by zero. The benchmark portfolio is the MSCI World Index and had an average IVA score of 5.4 during the period
February 2007 – December 2012, as well as during the period February 2008 – December 2012.
ESG strategyESG
ExclusionESG Tilt
ESG
Momentum
Active return (annual, %) 0.10 0.05 0.35
Common factor contribution (annual, %) 0.06 0.03 0.08
Asset specific contribution (annual, %) 0.05 0.01 0.27
Tracking error (ex-post, annual %) 0.45 0.46 0.36
Information ratio 0.23 0.10 0.97
Average improvement in ESG score 1.27 1.21 0.46
Average relative improvement in ESG score (%) 23 22 8
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Details of ESG Worst-in-Class Exclusion Approach
The ESG worst-in-class exclusion approach comes closest to traditional forms of Socially
Responsible Investing. However, instead of excluding entire sectors based on objectionable
business practices, for this exercise we only excluded „CCC‟-rated companies from the
investment universe, representing approximately 7 percent of MSCI World stocks on a market
capitalization basis. This approach takes advantage of IVA ratings that reflect ESG risks of
companies relative to their industry peers; therefore, the excluded companies are less likely to
concentrate in specific industries. No industries are excluded entirely.
As shown in Table 2, the average active weight of any sector was less than 1 percent above or
below the benchmark sector weight. The largest deviation was for Health Care, with an active
sector weight 2.05 percent below the benchmark value.
Table 2: Active Sector Weights of the MSCI World ex „CCC‟ portfolio, February 2007 – December 2012.
We consider this first strategy as a reference point for the ESG exclusion approach (referred to as
Strategy 1). Strategies 2-4 take the process one step further, and apply an optimized tilt towards
higher rated stocks and away from lower rated stocks inside the reduced investment universe.
We also aim to maintain country, industry and style exposures of the portfolio close to the
benchmark‟s exposures, and vary the investor‟s aversion to tracking error only (see the Appendix
for execution details). Summary statistics for the full period are gathered in Table 3.
Sector
Average
Active Weight
(%)
Minimum
Active
Weight (%)
Maximum
Active Weight
(%)
Energy -0.64 -1.10 -0.08
Materials 0.03 -0.30 0.32
Industrials 0.25 0.04 0.49
Consumer
Discretionary0.66 -0.05 1.29
Consumer Staples 0.17 -0.96 0.61
Health Care -0.86 -2.05 -0.37
Financials -0.99 -1.91 -0.35
Information
Technology-0.15 -0.62 0.35
Telecommunication
Services0.94 0.73 1.16
Utilities 0.58 0.05 1.05
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Table 3: Summary Statistics of ESG Exclusion Strategies, February 2007 – December 2012.
Excluding „CCC‟-rated companies (Strategy 1) without any further adjustment led to a low
tracking error of 0.66 percent and a small negative active return of -0.16 percent. This was due
mainly to country biases that contributed -0.39 percent to the annual active return (see Figure 1
below). However, the exclusion itself contributed positively, as evidenced by the 0.13 percent
asset specific contribution, meaning that eliminating „CCC‟-rated companies raised portfolio
performance once the other residual factors were factored out. The average improvement in the
portfolio ESG score was modest, however, rising only 0.6 points, from 5.4 to 6.0 (or 12 percent),
on a scale of 0 to 10.
Strategies 2-4 yielded better improvements in average ESG portfolio scores, ranging from 6.6 to
7.6. This put the realized ESG tilt well into the „A‟-rated category. At the same time,
benchmark tracking errors could be reduced. However, Strategy 4, which provided the highest
ESG portfolio tilt, increased the tracking error and significantly reduced active returns, asset
specific contributions and information ratios.
These results suggest that the lowest-rated ESG stocks tended to underperform during the study
period relative to the benchmark, since eliminating them from the portfolio raised active returns.
This supports our hypothesis that worst-in-class ESG-rated stocks could potentially be
eliminated without significantly changing risk and performance characteristics relative to the
MSCI World Index. However, this does not resolve whether employing other ESG tilt strategies
can provide additional performance benefits. We address this question next.
ESG
Exclusion
1
ESG
Exclusion
2
ESG
Exclusion
3
ESG
Exclusion
4
Active return (annual, %) -0.16 0.02 -0.01 -0.20
Common factor contribution (annual, %) -0.30 0.04 0.04 0.01
Asset specific contribution (annual, %) 0.13 -0.02 -0.05 -0.21
Tracking error (ex-post, annual %) 0.66 0.42 0.52 1.02
Information ratio -0.24 0.05 -0.02 -0.19
Average improvement in ESG score 0.64 1.19 1.42 2.20
Average relative improvement in ESG score (%) 12 22 26 41
Turnover (annual, %) 4 20 20 20
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Figure 1: Return Decomposition of ESG Exclusion Strategy 1, February 2007 – December 2012.
-3
-2.5
-2
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-0.5
0
0.5
1
1.5C
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ive
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ntr
ibu
tio
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Styles Industries Countries Asset Specific Total Active
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Details of ESG Tilt Approach
We now analyze the effect of an optimized tilt towards higher-rated stocks in the entire MSCI
World universe without excluding low-rated stocks (see the Appendix for optimization details).
Summary statistics of these strategies, with varying tracking error levels, are presented in Table 4
and compared to the ESG Exclusion Strategy 2, showing the best results.
Table 4: Comparison of Statistics of the ESG Tilt and ESG Exclusion Strategies, February 2007 – December 2012.
We found that allowing for larger tracking error in an effort to maximize ESG portfolio scores
did not lead to superior returns over the time period (see especially Strategy 3). Also, strategies
with much more limited active risk produced a minimal negative active return, while improving
the 5.4 ESG score of the MSCI World Index by at least 1.1 points. Even so, these low-risk ESG
tilt strategies did not match the performance of the lowest risk exclusion strategy, as defined in
the previous section as ESG Exclusion 2 in Table 4 above.
ESG Tilt
1
ESG Tilt
2
ESG Tilt
3
ESG
Exclusion
2
Active return (annual, %) -0.01 -0.03 -0.19 0.02
Common factor contribution (annual, %) 0.02 0.03 0.01 0.04
Asset specific contribution (annual, %) -0.03 -0.06 -0.20 -0.02
Tracking error (ex-post, annual %) 0.44 0.54 1.01 0.42
Information ratio -0.03 -0.06 -0.19 0.05
Average improvement in ESG score 1.10 1.37 2.15 1.19
Average relative improvement in ESG score (%) 20 25 40 22
Turnover (annual, %) 20 20 20 20
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Figure 2: Return Decomposition of ESG Tilt Strategy 1, February 2007 – December 2012.13
Taking ESG tilt Strategy 1 as an example and looking at its performance over the entire time
period, we observe a cyclical behavior in our sample (see Figure 2). Most notably, there was a
two-year period of outperformance of the strategy between May 2008 and April 2010, relative to
the MSCI World Index. This corresponded with a “flight to quality” in stock selection at the
beginning of the period that favored companies with strong long-term reputations and high ESG
ratings.14
The outperformance of high ESG-rated assets continued as global markets recovered
and assumed more of a “risk-on” mentality. The outperformance ended as market signals turned
neutral, completing a cycle of risk-off, risk-on and neutral phases.15
The European debt crisis and U.S. credit downgrade in the summer of 2011 once again reset
market expectations. A new cycle began with high ESG-rated assets in Europe particularly
subject to downward pricing pressure in the first risk-off phase. As the market bottomed and
entered a new risk-on phase, high ESG-rated assets outperformed in the first half of 2012,
13 Vertical lines denote May 2008 and April 2010.
14 For historical comparisons, see Robert Eccles, Ioannis Ioannu and George Serafeim, “The Impact of Corporate Culture of Sustainability on Corporate Behavior and
Performance,” Harvard Business School Working Paper 12-035, Nov. 4, 2011; and Sandra A. Waddock and Samuel B. Graves. "Finding the Link Between
Stakeholder Relations and Quality of Management," Journal of Investing,Winter 1997.
15 Risk on, risk off has recently become a common way to describe market behaviour, but the delimitation of the different periods is not well-defined. See Costabile,
Marossy “Risk On, Risk Off in a Multifactor World” for a more quantitative approach to the problem. They describe and estimate a global regime switching model
with three market states: risk-on, risk-off, and neutral. In this paper, we use the risk-on/risk-off/neutral periods defined therein.
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6C
um
ula
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turn
Co
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ibu
tio
n (
%)
Styles Industries Countries Asset Specific Total Active
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followed by choppier performance in the second half of the year. In other periods of this time
series, the asset specific contribution of high ESG-rated assets was mostly negative; hence, we
realized a slight negative contribution from an ESG tilt over the entire time series.
Analysis of the asset specific component
The asset specific component – reflecting the effect of ESG tilt net of any other systematic risk
source – can be further decomposed into contributions from both overweighted and
underweighted stocks. For the purpose of this paper, we selected the decomposition of Strategy
1 shown in Figure 3 below, but the results are qualitatively similar for the other strategies.
Figure 3: Decomposition of the Asset Specific Component of ESG Tilt Strategy 1, February 2007 – December 2012.
Figure 3 provides further insight to this cyclical behavior of the asset specific contribution into
contributions, and allows us to disentangle the role of higher- and lower-ESG rated stocks. The
intended result of employing this ESG tilt strategy is a “symmetric” positive contribution to
asset specific returns, whereby lower-ESG rated assets tend to underperform (and thus have a
positive contribution due their underweighting), while high-ESG rated assets tend to outperform
and are overweighted to boost returns. However, our results over the observed time period show
an “asymmetry,” where underweighted assets tended to underperform as intended with this
strategy, while overweighted assets did not outperform consistently and showed a mostly
negative and more uneven, cyclical contribution. In line with our previous observation, the
positive contribution was concentrated mainly in the “risk-off” period, but not exclusively.
-1.5
-1
-0.5
0
0.5
1
1.5
Feb-07 Aug-07 Feb-08 Aug-08 Feb-09 Aug-09 Feb-10 Aug-10 Feb-11 Aug-11 Feb-12 Aug-12
Cu
mu
lati
ve A
ctiv
e R
etu
rn C
on
trib
uti
on
(%
)
Overweighted Assets Underweighted Assets Total Asset Specific
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In general, this finding suggests that the market‟s overall reaction to companies with above
average and below average ESG practices and ratings was not treated equally during this period.
The market placed a more consistent pricing penalty on companies with lower ESG scores than it
rewarded companies with higher ESG scores.
We offer some possible explanations for this asymmetric result. Due to investors‟ aversion to
loss, downside ESG risks may be incorporated more quickly into stock prices than long-term
upside ESG opportunities. Lower ESG ratings take into account event-driven “bad” news like
accidents, product recalls, supply chain interruptions, governance controversies and regulatory
decisions that pose immediate threats to financial performance.
Conversely, higher ESG ratings reflect and anticipate “good” news that is more secular and long-
term in nature and, hence, more subject to market discounting. Examples include:
implementation of sustainable resource practices, progress toward low-carbon energy use,
increases in employee training and motivation, consumer-led product innovations and
shareholder-led governance reforms. It follows that company efforts to enhance their ESG
performance may take longer to appreciate in market value than their ESG shortcomings, which
are more instantly recognized and priced into the market.
To further investigate this phenomenon we look at the sector and region breakdown of the asset
specific component. The results are presented in Table 5 and Table 6.
Table 5: Decomposition of the Cumulative Asset Specific Contribution by Sectors, ESG Tilt Strategy 1, February 2007 –
December 2012.
In the Telecommunication Services and Utilities sectors, the stock specific contributions of
overweighted and underweighted stocks were symmetric and all positive (highlighted in brown
in the table above) during the study period. This was the intended result of implementing an
ESG strategy in portfolio construction.
SectorOver-
weighted
Under-
weighted
Total Asset
Specific
Energy -0.02 0.05 0.03
Materials -0.42 0.00 -0.42
Industrials -0.22 -0.18 -0.40
Consumer Discretionary 0.48 -0.40 0.08
Consumer Staples -0.06 0.13 0.07
Health Care -0.23 0.47 0.23
Financials -0.62 0.78 0.16
Information Technology -0.30 -0.11 -0.41
Telecommunication Services 0.05 0.04 0.09
Utilities 0.14 0.21 0.35
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In the Industrials and Information Technology sectors, the asset specific contributions of over-
and underweighted stocks were symmetric but negative – opposite of the intended result
(highlighted in orange in Table 5).
In the remaining sectors, the asset specific contributions from over- and underweighted stocks
were asymmetrical during the observed time period, from February 2007 through December
2012, producing mixed results for asset specific returns. In the Energy, Materials, Consumer
Staples, Health Care and Financials sectors, the underweighting of low-rated companies led to a
positive contribution to asset specific returns, but overweighting higher rated ones also led to a
negative contribution. The opposite phenomenon was observed only in the Consumer
Discretionary sector, also producing mixed results. On balance, these sector results provide
further evidence that low ESG-rated companies have seen a greater market penalty with respect
to risk-adjusted performance over this time period than high ESG-rated companies have seen
rewards for their positive behavior and practices.
Further analysis may be conducted to explain such symmetric or asymmetric results, but it is
beyond the scope of this paper to do so. As a general observation, ESG ratings may be seen as a
proxy for forces external to the market that are subject to near-term regulatory control. Over the
February 2007 – December 2012 time period, we observe that high-rated ESG assets tended to
outperform at times of heightened anticipation of regulatory risks and then underperformed as
these regulatory risks subsided.
As a further exploration of this hypothesis, two industry sectors are briefly examined here. For
Utilities, the symmetric positive result came at a time of persistent regulatory expectations to
constrain carbon emissions from power production. During the study period, there was an initial
rise in market expectations with the introduction of U.S. climate change legislation and progress
toward an international control agreement, and then a similar fall in these expectations as these
policy initiatives stalled.
Conversely, the Financial sector, which had the greatest asymmetric mixed result over the study
period, faced a considerable degree of government intervention during the recent financial crisis.
During this period, low ESG-rated financial services firms benefited portfolio performance
because they were underweighted, while high ESG-rated firms harmed portfolio performance
because they were overweighted. Government efforts taken during this time rescued the most
highly distressed firms, and legislative reforms were enacted to rein in high-risk banking
practices. However, these reforms did not fundamentally alter the structure and business lines of
financial services firms. Accordingly, those firms with more traditional and conservative lending
practices – which generally had higher ESG ratings over the period – did not realize a distinct
competitive advantage as a result of these subsequent government interventions.
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Table 6: Decomposition of the Cumulative Asset Specific Contribution by Regions, ESG Tilt Strategy 1, February 2007 –
December 2012.
In terms of regions, we again found diverse results, as shown in Table 6. In the EMEA region,
contributions were symmetrical and positive over the study period, as intended. But they were
asymmetrical for the Americas and Pacific regions, producing mixed results. A more granular
analysis reveals that asset specific contributions were symmetrical and positive – among others –
in Germany, France, Italy, Denmark, Austria, Portugal and New Zealand. Asymmetrical results
were found in the United States, United Kingdom and Switzerland.
We observe that the positive symmetric results came mainly in the EMEA region, which
arguably has the most extensive regulation of ESG factors and, hence, the most internal market
pricing signals. Conversely, the Pacific region arguably has the least extensive regulation of
ESG factors. In this region, high ESG-rated companies that were overweighted contributed to
portfolio outperformance, while low ESG-rated companies that were underweighted contributed
to underperformance. The Americas region was in-between these two poles of comparison.
This region had the worst results in terms of total asset selection, with the overweighting of high
ESG-rated companies contributing significantly to portfolio underperformance.
Details of ESG Momentum Approach
The high latency that may characterize the incorporation of positive ESG ratings into stock
prices, as explored earlier, is a motivation for a third and final ESG investing strategy addressed
in this paper. In the ESG momentum strategy, we replace the ESG ratings with the change in
ratings over the past 12 months as our input variable for portfolio optimization. By doing this,
we aim to overweight, relative to the MSCI World Index, companies that increased their ESG
ratings during the recent past and underweight those with decreased ESG ratings (see the
Appendix for technical details). Due to the 12-month cycle over which ratings changes are
calculated, our analysis for this strategy starts in February 2008.
RegionOver-
weighted
Under-
weighted
Total Asset
Selection
Americas -1.81 0.95 -0.86
EMEA 0.09 0.54 0.63
Pacific 0.48 -0.50 -0.02
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Table 7: Comparison of ESG Momentum Strategies with ESG Tilt Strategy 1, February 2008 – December 2012.16
Results corresponding to a range of tracking error levels are summarized in Table 7. We find that
during the observed period low active-risk ESG momentum strategies performed better on a risk
adjusted basis than the lowest-risk ESG tilt strategies. Another observation is that the
contribution of the asset specific component was also higher than in the simple ESG tilt
strategies (see Figure 4 for the time series return attribution). This may reflect that markets are
more likely to react to news of companies showing improvement in their ESG scores than to
those who had already attained top ratings in their sectors.
16 To be consistent in comparing results, the figures presented in this table are for February 2008 – December 2012 only, corresponding to the time period available for
the ESG momentum strategy. They correspond with figures shown in Table 1.
ESG
Momentum
1
ESG
Momentum
2
ESG
Momentum
3
ESG Tilt
1
Active return (annual, %) 0.35 0.40 0.29 0.05
Common factor contribution (annual, %) 0.08 0.10 0.09 0.03
Asset specific contribution (annual, %) 0.27 0.30 0.21 0.01
Tracking error (ex-post, annual %) 0.36 0.43 0.84 0.46
Information ratio 0.97 0.92 0.35 0.10
Average improvement in ESG score 0.46 0.52 0.65 1.21
Average relative improvement in ESG score (%) 8 10 12 22
Turnover (annual, %) 20 20 20 20
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Figure 4: Return Decomposition of ESG Momentum Strategy 1, February 2008 – December 2012.
On the other hand, this relatively better performance came at the price of a smaller improvement
in the portfolio‟s overall ESG rating. For Strategy 1, the gain with respect to the MSCI World
Index was only 0.46 points for the ESG momentum strategy, compared to 1.18 points for the
simple ESG tilt strategy.
Comparing the asset specific contributions of the ESG tilt and ESG momentum strategies on
Figure 3 and Error! Reference source not found., we find some similarities and differences. On the
one hand, we again observe an asymmetry between contributions from underweighted assets that
received ratings downgrades over the time period and contributions from overweighted assets
that were upgraded. As Error! Reference source not found. shows, downgraded assets that were
underweighted tended to produce a consistent positive contribution to portfolio performance,
whereas upgraded stocks that were overweighted had a small, more cyclical and overall negative
contribution over the time period.
On the other hand, the asymmetry between over- and underweighted assets‟ contributions was
much smaller in the ESG momentum strategy than in the simple ESG tilt strategy, and more in
line with the intended results to improve portfolio performance. Underweighted assets in the
ESG momentum strategy contributed 1.6 percent in positive cumulative active returns over the
study period, compared to 0.6 percent for the ESG tilt strategy shown in Figure 3. While
overweighted assets in the ESG momentum strategy still contributed to portfolio
underperformance, the effect was smaller than in the ESG tilt strategy. As shown in Figure 5,
the negative cumulative active return was 0.4 percent over the study period, compared to a
negative return of 1.2 percent for the ESG tilt strategy.
-1
-0.5
0
0.5
1
1.5
2
Feb-08 Aug-08 Feb-09 Aug-09 Feb-10 Aug-10 Feb-11 Aug-11 Feb-12 Aug-12
Cu
mu
lati
ve A
ctiv
e R
etu
rn C
on
trib
uti
on
(%
)
Styles Industries Countries Asset Specific Total Active
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This decomposition of ESG momentum results reinforces our earlier finding that companies
associated with negative ESG factors – as reflected in low ratings or ratings downgrades –
consistently underperformed the market benchmark over the study period, while those with high
ratings or ratings upgrades had more uneven market performance. This lends further support to
the hypothesis that downside ESG risks, as reflected in low ratings or ratings downgrades,
receive a more instantaneous market reaction than long-term upside ESG opportunities, as
reflected in high ratings or ratings upgrades. Moreover, over the study period the market
appeared to be more sensitive to ratings changes than to the effect of absolute ESG scores.
Ratings downgrades appeared to compound market perception of potential adverse material
impacts among the affected firms, while ratings improvements drew added investor recognition
to positive ESG factors, with less market discounting among companies showing such ESG
momentum.
Figure 5: Decomposition of the Asset Specific Component of ESG Momentum Strategy 1, February 2008 – December 2012.
Finally, while the ESG momentum strategy limited the overall ESG tilt of the portfolio, the
trade-off came in exchange for a higher asset-specific contribution and information ratio over the
study period. This strategy rewarded companies demonstrating progress in managing ESG risks
and realizing ESG opportunities, even if they had not yet achieved top-level scores.
Accordingly, for this study period, institutional investors tilting toward companies making
progress in addressing ESG material risks would have seen a lower increase in ESG rating for
their portfolio overall, relative to the simple tilt strategies. On the other hand, those investors
would have achieved higher active returns and lower tracking error.
-1
-0.5
0
0.5
1
1.5
2
Feb-08 Aug-08 Feb-09 Aug-09 Feb-10 Aug-10 Feb-11 Aug-11 Feb-12 Aug-12
Cu
mu
lati
ve A
ctiv
e R
etu
rn C
on
trib
uti
on
(%
)
Overweighted Assets Underweighted Assets Total Asset Specific
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Conclusion
We examined possible implementations of ESG-tilt strategies based on MSCI‟s ESG Research
Intangible Value Assessment (IVA) scores. Our observations are limited to the historical period
that we studied (February 2007 through December 2012), and do not necessarily reflect the
results or performance of other periods or future periods. As additional data becomes available
to extend the time series, investors may wish to conduct further research on these strategies to
compare the results presented here.
Using the MSCI World Index as a benchmark, and the Barra Global Equity Model (GEM3) as a
risk model, we ran a series of backtests for the period February 2007 – December 2012 with the
same constraints on systematic portfolio exposures, but with increasing tracking error levels. We
found that during the observed period that it was possible to build portfolios with equivalent risk,
performance, industry, country or style characteristics as conventional benchmarks, while also
raising ESG portfolio scores. We introduced three families of ESG strategies, all based on the
IVA dataset.
The first ESG strategy – based on the exclusion of low-rated („CCC‟) companies before the
application of the ESG tilt – led to a small improvement in ESG scores, and positive
performance results once common factor attributions were stripped out. Applying a further ESG
ratings tilt in addition to the „CCC‟ exclusion reduced the residual contributions and produced a
more positive active return for this lowest risk strategy, raising the ESG portfolio rating from
„BBB‟ to „A.‟
When we applied optimized ESG tilt strategies to the entire MSCI World universe, we found that
the active return was generally small, and slightly negative. At the low-risk end of the ESG-
tilted spectrum, a 44 basis point tracking error strategy attained a one-notch increase in overall
ESG portfolio rating to „A‟ with returns that matched the market. Decomposing active
performance into systematic and stock-specific components, we observed that the cumulative
performance in the ESG tilt strategy was mainly due to the cyclical nature of the stock-specific
component.
Between May 2008 and April 2010, and more recently in the first half of 2012, the active
performance of higher-rated ESG companies turned significantly positive. We observed that
during this period high-rated ESG assets started to outperform when the market was in a
defensive, “flight to quality” mode and extended their gains when more of a “risk-on” market
mentality took hold. The outperformance ended when market signals turned neutral, completing
a regime switching cycle. We hypothesize that downside ESG risks may be incorporated into
stock prices more quickly than long-term ESG opportunities. This is because downside risks
tend to be more event-driven and instantly recognized than long-term ESG opportunities that are
evolving, discounted, and correlated with changing market and geo-political conditions.
Based on this observation, we implemented a third series of strategies based on the change in
company ESG scores. Our analysis revealed that during the observed period the ESG
momentum strategy tended to deliver the best risk-adjusted performance of the strategies we
analyzed. We found that when we decomposed the ESG momentum strategy, ESG ratings
downgrades had a consistent negative effect on asset specific performance over the time series,
while ESG ratings upgrades had a more positive effect during “risk off – risk on” periods in the
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market regime switching cycle. While the ESG momentum strategy came with a lower ESG tilt,
it still supported positive movement on ESG scores at the portfolio level.
Our study‟s main conclusion is that asset managers would have been able to employ ESG factors
to attain higher ESG portfolio scores with low active risk, and still achieve moderate benchmark
outperformance over the time period from February 2008 through December 2012 in which the
three strategies are compared.
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Appendix: Backtest Parameters
The benchmark portfolio was the MSCI World Index. The company universe was also the MSCI
World Index, except for the ESG exclusion strategies, where the universe was the MSCI World
portfolio without the „CCC‟-rated stocks.
The long-horizon Barra Global Equity Model (GEM3L) was used as a risk model.
Stock-level alpha was calculated as follows. The IVA scores (for ESG tilt and exclusion
strategies), or the changes in IVA scores (for ESG momentum strategies) were transformed into
alpha following the heuristic method described by Richard C. Grinold, in “Alpha is Volatility
times IC times Score.”17
Namely, when defining the alpha, the stock‟s standardized score was
adjusted by the specific volatility forecast of the stock.
We applied the following constraints on country, industry and style characteristics:
Maximum +/-1% region weight deviation relative to the benchmark. The three regions are North
America, EMEA and Asia Pacific
Maximum +/-1% GICS® sector weight deviation relative to the benchmark
Maximum +/-0.25 GEM3 style factor active exposure relative to the benchmark
We set a lower and upper bound on the weight of benchmark stocks in the optimized portfolio at
0.001 percent and 5 percent (except for the ESG exclusion strategies).
The portfolios were rebalanced quarterly starting in February 2007, with a 5 percent turnover
limit.
The risk aversion parameter ran through the values of 1, 4 and 6. The ratio of common factor to
asset specific risk aversion was set to 1.
17 Richard C. Grinold, “Alpha is Volatility times IC times Score,” The Journal of Portfolio Management, Summer 1994
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Biographies
Zoltán Nagy is a member of MSCI‟s Applied Research team based in the Budapest office; he
focuses on portfolio management and risk-related research for asset owners and investment
managers. Dr Nagy joined MSCI in 2008, at first working on the development of new index
methodologies and on other index-related research.
Prior to entering finance, Dr. Nagy was a post-doctoral researcher at the University of Algarve,
Faro, Portugal. His area of research was Quantum Integrable Sytems. He has several
publications in this field.
Dr. Nagy holds a PhD in Theoretical Physics from the University of Cergy-Pontoise, France, and
an engineering degree from the Ecole Polytechnique, France. He is also a CFA® charterholder.
Douglas Cogan is a Vice President of MSCI ESG Research and a member of MSCI‟s Asset
Owner Consultant team, serving clients throughout North America. He started his career at the
Investor Responsibility Research Center in 1982 and served as Deputy Director of IRRC‟s Social
Issues Service from 1996 through 2005. He then assumed ESG leadership positions at
Institutional Shareholder Services and RiskMetrics Group, which MSCI acquired in 2009.
Mr. Cogan is the author of several books and many reports on energy and environmental topics.
His 1992 book, The Greenhouse Gambit, was one of the first to address the business and
investment implications of climate change. He has also written reports on divestment actions
and fiduciary responsibility, and connections between corporate governance and climate change
for the Investor Network on Climate Risk.
Mr. Cogan holds a Bachelor of Arts degree in Political Economics from Williams College. He is
associated with MSCI‟s Boston office and is based in Plainfield, New Hampshire.
Dan Sinnreich is an Executive Director at MSCI in the Risk Management Analytics product
management team. He joined Barra in 2003, initially managing Barra‟s fixed income risk
analytics products. He later served as the lead technical product manager for several multi-asset
class product releases. Mr. Sinnreich is currently focused on expanding the Risk Management
Analytics business in the Americas.
Prior to his current position at MSCI, Mr. Sinnreich was a product manager for an electronic
mortgage software startup in Silicon Valley, and designed enterprise-wide management reporting
applications at Oracle Corporation. Mr. Sinnreich started his career with KPMG‟s management
consulting practice and received his Bachelor of Arts degree in Management Information
Systems from Auburn University. He is based in Berkeley, California.
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