THE ESG
RISK RATINGS
POTENTIAL APPLICATIONS
FOR INVESTORS
White Paper – Volume 3 June 2019
Doug Morrow, MBA 
Director, 
Thematic Research
Sophia Burress, CFA 
Manager, 
ESG Rating Products

The ESG Risk Ratings: Potential Applications for Investors, White Paper – Volume 3                                 June 2019
 
 
  
 
 
 
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About Sustainalytics 
 
Sustainalytics is a leading independent ESG and corporate governance research, 
ratings and analytics firm that supports investors around the world with the 
development and implementation of responsible investment strategies. For over 
25 years, the firm has been at the forefront of developing high-quality, innovative 
solutions to meet the evolving needs of global investors. Today, Sustainalytics 
works with hundreds of the world’s leading asset managers and pension funds 
that incorporate ESG and corporate governance information and assessments 
into their investment processes. With 17 offices globally, Sustainalytics has 
more than 500 staff members, including over 200 analysts with varied 
multidisciplinary expertise across more than 40 industry groups. Over the last 
three consecutive years, investors named Sustainalytics among the top three 
firms for both ESG and corporate governance research in the Independent 
Research in Responsible Investment Survey. For more information, visit 
www.sustainalytics.com  
 
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Table of Contents 
Foreword .................................................................................................................. 4 
Executive summary ................................................................................................. 5 
Introduction ............................................................................................................. 7 
Industry tilts ............................................................................................................. 9 
WACC adjustments ............................................................................................... 13 
Smart beta ESG ..................................................................................................... 18 
Best-in-class strategies ........................................................................................ 25 
Screening techniques ........................................................................................... 30 
Thematic investing ................................................................................................ 36 
Engagement and voting ........................................................................................ 41 
Conclusion ............................................................................................................. 44 
Endnotes ................................................................................................................ 46

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Foreword 
 
Completing the white paper series 
 
 
Dr. Hendrik Garz 
Executive Director, ESG Rating 
Products & Thematic Research 
hendrik.garz@sustainalytics.com 
 
 
With the release of Sustainalytics’ ESG Risk Ratings, we have moved into a new 
phase in the evolution of corporate ESG ratings. The rating is built on key features 
including financial materiality, granularity and comparability, and we foresee 
numerous applications for our clients in the context of investment decision-
making. Through the publication of our three-part series of reports on 
Sustainalytics’ ESG Risk Ratings, we deliver on our promise to provide as much 
transparency as possible for our clients and other stakeholders.  
This study completes the three-volume ESG Risk Ratings white paper series. The 
series began in October 2018 with the publication of Moving up the Innovation 
Curve: White Paper – Volume 1. The report offered a detailed description of the 
methodology behind the ESG Risk Ratings, analysis of rating outcomes, and 
introduced the results of our empirical testing. The second volume, entitled 
Exploring the Internet Software and Services Subindustry, White Paper – Volume 
2, was published in November 2018. The second volume moved the reader step 
by step through a complete subindustry-specific discussion of the rating and 
concluded with a case study of Facebook. 
Shifting gears to application 
In this study, we shifted the discussion from a description of the ESG Risk 
Ratings to one focused on application, with a view to demonstrating how 
investors might be able to make use of Sustainalytics’ new flagship ratings 
product across seven use cases: industry tilts, WACC adjustments, smart beta 
ESG, best-in-class strategies, screening techniques, thematic investing and 
engagement. 
Creating value for our clients 
We hope the examples and analysis introduced throughout this report create 
value for our clients by generating ideas for potential applications of the ESG 
Risk Ratings. We also trust that they are a valuable contribution to the ongoing 
industry discussion about ESG integration. The empirical tests we report on in 
this study support the idea that ESG factors can add value in investment 
decision-making by increasing equity portfolio returns and/or reducing risk. 
However, the evidence we have provided so far can only be the starting point. 
Much more needs to be done to explore the financial value of ESG integration 
and we look forward to contributing to the ongoing debate by providing 
additional and broader empirical tests of Sustainalytics’ flagship ratings product 
in future publications. I hope you will enjoy reading this report. 
 
Sincerely,

The ESG Risk Ratings: Potential Applications for Investors, White Paper – Volume 3                                 June 2019
 
 
  
 
 
 
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Executive summary 
 
Applying the ESG Risk Ratings 
Authors:1 
 
Doug Morrow, MBA 
Director, Thematic Research 
doug.morrow@sustainalytics.com 
 
Sophia Burress, CFA 
Manager, ESG Rating Products 
sophia.burress@sustainalytics.com 
 
 
 
 
In this report, the third and final installment in Sustainalytics’ ESG Risk Ratings 
white paper series, we discuss how investors could potentially apply the ESG 
Risk Ratings to their investment process. We focus on seven specific use cases: 
industry tilts, weighted average cost of capital (WACC) adjustments, smart beta 
ESG, best-in-class (BIC) strategies, screening techniques, thematic investing and 
corporate engagement.  
Our objective is to aid Sustainalytics’ clients in generating ideas about how the 
ESG Risk Ratings could be operationalized across a range of investment 
processes. More broadly, we seek to contribute to ongoing industry dialogue 
about how ESG information can be integrated into investment decision-making. 
Key takeaways 
Industry tilts 
▪ 
The ESG Risk Ratings proved useful in creating industry tilts that investors 
could potentially use to address the differences in industry-level ESG risk. 
▪ 
Sample portfolios built to reflect three tilt scenarios (conservative, 
moderate and aggressive) led to reductions in ESG risk up to 3.6% 
compared to the FTSE All-World index.  
▪ 
The sample portfolios outperformed the FTSE All-World index during a 
2014-2018 holding period from a total return and risk-adjusted perspective, 
with Sharpe ratios up to 0.50 compared to 0.45 for the benchmark. 
WACC adjustments 
▪ 
We investigate the relationship between estimated WACC values and ESG 
Risk Rating scores for 3,542 companies. 
▪ 
We find a positive, albeit weak, relationship (correlation coefficient of 0.03) 
between these variables, which could suggest that the market has yet to 
learn how to price material ESG risk into the cost of capital. 
▪ 
Using a simplified discounted cash flow (DCF) model, we examine the 
effects of using an ESG risk-adjusted WACC on the net present value (NPV) 
of an estimated constant 2020 free cash flow (FCF). The effects for a 
selection of FTSE All-World constituents range from +0.5% to -0.4%. 
Smart beta ESG 
▪ 
We construct a parsimonious smart beta ESG strategy using dividend yield, 
return on assets and ESG Risk Rating scores. 
▪ 
Sustainalytics’ Smart Beta ESG FTSE 100 Sample Portfolio achieved a 
positive active return (3.4%) and an improved Sharpe ratio (0.36 vs 0.32) 
compared to the FTSE 100 index during a 2014-2018 holding period. 
▪ 
The results of our analysis showcase the potential of combining the ESG 
Risk Ratings with fundamental inputs and factor strategies in a rules-based 
portfolio construction process.

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Best-in-class (BIC) strategies 
▪ 
We introduce a basic, three-tiered framework for thinking about the ESG 
Risk Ratings in the context of BIC strategy development. 
▪ 
Nearly half (44%) of the 48 BIC sample portfolios analyzed as part of our 
research deliver statistically significant alpha, with annualized values 
ranging from 11.8 to 2.3.  
▪ 
Our analysis indicates that momentum-based BIC strategies, which select 
companies based on their year-over-year improvement in unmanaged ESG 
risk, may be more likely to deliver risk-adjusted abnormal returns than BIC 
strategies based on spot ESG performance. 
Screening techniques 
▪ 
We develop a novel screening strategy using unmanageable risk that 
eliminates companies in 18 high-risk subindustries, including oil & gas and 
precious metals mining. 
▪ 
Sustainalytics’ Unmanageable Risk Screened Sample Portfolio achieved a 
3.1% reduction in ESG risk compared to the FTSE All-World index.  
▪ 
The sample portfolio outperformed the benchmark during a 2014-2018 
holding period from both a total return and risk-adjusted perspective, with a 
Sharpe ratio of 0.54 compared to 0.45 for the FTSE All-World index. 
Thematic investing 
▪ 
We create an innovative thematic investing strategy that blends the ESG 
Risk Ratings with Sustainalytics’ Sustainable Products Research.  
▪ 
The approach demonstrates how investors could potentially gain upside 
exposure to positive impact themes, such as energy efficiency, green 
buildings and renewable energy, while minimizing ESG risk. 
▪ 
The Sustainable Products Low ESG Risk Sample Portfolio, a concentrated 
portfolio of only 27 names, posted a Sharpe ratio of 0.71 and achieved an 
active return of 26.6% against the FTSE All-World index during a 2014-2018 
holding period. 
Corporate engagement 
▪ 
We illustrate how the ESG Risk Ratings could potentially be used by 
investors to enhance their engagement processes.  
▪ 
We map MEIs in the ESG Risk Ratings framework to four common 
engagement themes, including risk management, climate change and 
human rights. 
▪ 
Using the Carbon – Own Operations MEI as a case study, we demonstrate 
how the ratings could potentially help investors identify specific industries 
and companies to engage.

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Introduction 
 
Exploring possible use cases 
The first two white papers focused on 
the rating’s methodology 
 
 
Moving up the Innovation Curve: White Paper - Volume 1, published in October 
2018, introduced the methodology behind the ESG Risk Ratings and summarized 
the results of our empirical testing.2 Exploring the Internet Software and Services 
Subindustry, White Paper - Volume 2, released in November 2018, analyzed 
subindustry-specific rating outcomes, supported by a case study of Facebook.3 
This study considers potential 
applications of the ESG Risk Ratings 
In this report, the third and final installment in our white paper series, we move 
the conversation from a description of the ratings to one focused on potential 
applications. As shown in Figure 1, we concentrate on seven possible use cases: 
industry tilts, WACC adjustments, smart beta ESG, BIC strategies, screening 
techniques, thematic investing and corporate engagement. Our objective with 
this report is idea generation: we seek to assist Sustainalytics’ clients by 
reviewing how the ESG Risk Ratings could potentially be applied across a range 
of investment processes. 
 
Figure 1: Possible use cases of the ESG Risk Ratings 
 
Source: Sustainalytics 
 
 
ESG Risk 
Ratings
Industry   
tilts
WACC 
adjustments
Smart beta 
ESG
Best-in-class 
strategies
Screening 
techniques
Thematic 
investing
Corporate 
engagement

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The ESG Risk Ratings: a refresher 
The ESG Risk Ratings measure the 
unmanaged risk of a company on 
financially material ESG issues 
As the ESG Risk Ratings underpin the bulk of the analysis contained in this report, 
it is worth revisiting the basic methodology of the ratings and what they seek to 
measure. The ESG Risk Ratings measure the unmanaged risk of a company vis-
à-vis a set of ESG issues that are considered financially material for that 
company, or what we refer to as material ESG issues (MEIs).4 A company’s 
overall ESG Risk Rating score is the sum total of its unmanaged risk on all 
applicable MEIs. 
Unmanaged risk is what is left over 
after taking exposure, management 
and other factors into account 
As shown in Figure 2, a multistep process is required to calculate a company’s 
unmanaged risk. It begins with exposure, which reflects the extent to which the 
company is exposed to the MEI in question. Next, we consider how much of the 
exposure is unmanageable.5 We then assess the extent to which the company is 
addressing that portion of its exposure that is manageable (i.e. manageable risk) 
through relevant policies, programmes or other initiatives. The end result is 
unmanaged risk: that portion of a company’s exposure to an MEI that can be 
managed but, for whatever reason, is not (currently) being managed.6 
 
Figure 2: Unmanaged risk – the final output of the ESG Risk Ratings 
 
Source: Sustainalytics 
 
Financial analysis 
The results require careful 
interpretation 
In several of the upcoming chapters, Sustainalytics presents the results of 
portfolio analyses that investigate the risk/return effects of using the ESG Risk 
Ratings in investment decision-making. While the results are encouraging and 
point to the potential of the ESG Risk Ratings to add investment value, the results 
of any financial backtest are subject to inherent limitations. While Sustainalytics 
has transparently described the portfolio analysis conducted in this report, we 
stress that the results require careful interpretation. With that caveat in mind, 
Sustainalytics believes the findings from this report demonstrate that the ESG 
Risk Ratings are worthy of further exploration by investors, and we look forward 
to testing ever-new applications of the ratings in future publications.  
Exposure
Manageable Risk
Unmanageable 
Risks
Managed Risk
Management 
Gap
Unmanaged Risk

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Industry tilts 
 
Capturing ESG risk differentials 
Industry tilts address differences in 
industry-level ESG risk 
The first case study that we discuss is industry tilts. We define industry tilts as 
changes to the active weights of industries based on their ESG risk profile. 
Industry tilts are one of several tools that investors can potentially use in their 
investment process to address differences in industry-level ESG risk. These 
differences are significant: average ESG Risk Rating scores by subindustry range 
by over 40 points, from 52.7 (Oil & Gas Exploration and Production) to 11.3 
(Technology Distribution).7 Overweighting low ESG risk industries and 
underweighting high-risk ones can help investors reduce portfolio-level ESG risk 
and, given the growing financial materiality of ESG factors, potentially improve 
portfolio returns as well.8 
 
Scenario analysis 
We create three tilt scenarios: 
conservative, moderate and 
aggressive 
In the example below we show, for illustrative purposes, how such a tilting 
strategy could be implemented in practice. We use the FTSE All-World index as 
the benchmark for this exercise.9 With this benchmark in hand, we create three 
tilting scenarios: conservative, moderate and aggressive. The industry weights 
in the benchmark and three tilt scenarios are shown in Figure 3.10  
 
Figure 3: Industry weights across tilt scenarios, 2018 
 
Source: Sustainalytics, Bloomberg Financial, L.P., Vanguard 
 
Determining industry tilts 
Some industries are fundamentally 
riskier than others from an ESG point 
of view 
Industry weights are determined through a five-step process. First, an ESG score 
is assigned to each ICB industry represented in the FTSE All-World index based 
on the average score of companies in the industry.11 As shown in Figure 3, 
industries’ ESG Risk Rating scores range from a low of 19.6 (Consumer Services) 
to a high of 40.1 (Oil & Gas). As mentioned earlier, these scores reflect 
fundamental differences in the ESG risk profile of these industries. Companies 
in some industries, such as Oil & Gas, display, on average, much higher levels of 
unmanaged risk than those operating in other industries. 
Conservative
Moderate
Aggressive
Financials
26.5
21.2%
21.7%
21.9%
22.4%
Technology
22.6
19.7%
21.0%
21.6%
22.8%
Consumer Goods
24.1
14.0%
14.7%
15.0%
15.7%
Industrials
28.1
10.4%
10.5%
10.5%
10.6%
Health Care
27.7
9.0%
9.1%
9.2%
9.3%
Consumer Services
19.6
8.0%
8.7%
9.1%
9.8%
Oil & Gas
40.1
6.5%
5.2%
4.7%
3.7%
Basic Materials
39.0
5.1%
3.9%
3.3%
2.1%
Telecommunications
21.5
3.1%
3.4%
3.5%
3.7%
Utilities
37.8
2.8%
1.9%
1.2%
0.0%
Tilt Scenario
ICB Industry
ESG Risk 
Rating Score
Benchmark 
weight

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Active ESG risk captures the 
dispersion in industry-level ESG scores 
Second, we compute each industry’s active ESG risk by calculating the distance 
between the average ESG Risk Rating score and each industry’s ESG score and 
dividing by 100. For instance, the average of all subindustry ESG scores 
displayed in Figure 3 is 28.7 and the Financials industry’s score is 26.5. The 
Financials industry’s active ESG risk is therefore 0.02 (28.7 – 26.5 = 2.2/100 = 
0.02). The Financials industry’s active ESG risk is positive because it has a below 
average ESG score (in the ESG Risk Ratings model, the lower a company’s score, 
the lower the level of unmanaged risk). 
Tilts are determined by multiplying 
active ESG risk by the benchmark 
weight 
Third, we calculate the actual tilts by multiplying each industry’s active ESG risk 
by its benchmark weight. Carrying on with the Financials industry as an example, 
the industry’s active ESG risk is 0.02 and it constitutes approximately 21.2% of 
the FTSE All-World index.12 Thus its tilt is 0.02 x 21.2% = 0.5%.13  
Fourth, we create each industry’s revised weight by adding the tilt to each 
industry’s benchmark weight. This step is the final calculation for the 
conservative scenario and the penultimate calculation for the moderate and 
aggressive scenarios. To illustrate, as the Financial industry has an approximate 
benchmark weight of 21.2%14 and a (positive) tilt of 0.5%, its weight in the 
conservative scenario becomes 21.7% (21.2% + 0.5% = 21.7%).15 
 
Amplifying the signal 
The moderate and aggressive 
scenarios use an amplifier 
The fifth step is a modification that applies to the moderate and aggressive tilt 
scenarios. These scenarios follow the same process outlined above except that 
the tilts are amplified by 50% in the moderate scenario and 150% in the 
aggressive scenario.16 For example, in the aggressive scenario, the tilt for the 
Financials industry, 0.5%, is multiplied by 1 + 150%. The tilt thus becomes 1.2% 
and the final industry weight 22.4% (21.2% + 1.2% = 22.4%) instead of 21.7% as 
in the conservative scenario. 
 
Can industry tilts add portfolio value? 
Constituents were taken from the 
benchmark on December 31st of each 
year 
To test the effects that industry tilts could potentially have on both portfolio ESG 
risk and portfolio returns, Sustainalytics created three sample portfolios based 
on the conservative, moderate and aggressive tilt scenarios described above. 
We ran the sample portfolios from 1 January 2014 to 31 December 2018 for a 
total of five years or 60 months. The basic process was that constituents were 
taken from the FTSE All-World index on 31 December of each year, subjected to 
the revised industry weights shown in Figure 3 and reweighted within each 
industry on a market cap basis.17

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Weights were increased/decreased on 
a market cap-weighted basis 
To illustrate, the Oil & Gas industry constituted approximately 6.5% of the 
benchmark based on closing prices on 31 December 2017, the final rebalancing 
date of the sample portfolios. The desired weight of the Oil & Gas industry in our 
2018 conservative scenario is 5.2%. We thus decreased the weight of the 164 Oil 
& Gas constituents from the benchmark on a market cap-weighted basis such 
that their total weight summed to 5.2%. We repeated this process for all 10 ICB 
industries and for all three scenarios, ensuring that the industry weights summed 
to 100% in each case. 
 
Results 
 
ESG risk reduction 
The benchmark has an ESG Risk 
Rating score of 23.6 
The results of our analysis are shown in Figure 4. First, we consider the ESG risk 
reduction effects of each scenario compared to the benchmark. As of 31 
December 2017, we calculate the ESG Risk Rating score of the FTSE All-World 
index to be 23.6.18 This value represents the sum of the weighted ESG Risk 
Rating scores for all companies in the benchmark on the final rebalancing date.19 
All of the sample portfolios have a 
lower ESG Risk Rating score than the 
benchmark 
As they were purposely designed to overweight low ESG risk industries and 
underweight high-risk ones, the sample portfolios all achieve a lower ESG Risk 
Rating score than the benchmark. The sample conservative portfolio has a score 
of 23.3, a 1.5% reduction from the benchmark. The sample moderate portfolio 
has a score of 23.1 (a 2.2% reduction) and the sample aggressive portfolio has 
a score of 22.8 (a 3.6% reduction).20  
The ESG risk reductions, while 
significant, are not particularly 
dramatic 
While significant, these reductions are not particularly dramatic. The clustering 
of ESG Risk Rating scores is largely a result of the tilting strategy itself, in the 
sense that a) all industries represented in the starting index remain in the sample 
portfolios, and b) the tilts must sum to zero. Increasing the amplifier used in the 
moderate and aggressive scenarios would boost the ESG risk reduction, but 
ultimately this approach leads to a situation where some industries are weighted 
to zero, which we consider to be a screening strategy. 
 
Figure 4: Comparing ESG scores and financial returns across tilt scenarios 
 
Source: Sustainalytics, Bloomberg Financial, L.P., Vanguard21 
 
Financial returns 
All of the sample portfolios 
outperformed the benchmark on a 
total return basis 
The sample conservative portfolio had an active return of 2.0% during the 
investment period, based on a total return of 29.2% compared to 27.2% for the 
benchmark. The sample portfolios built with the amplifier had higher active 
returns, with 2.3% under the moderate scenario and 3.2% under the aggressive 
scenario. 
Conservative
Moderate
Aggressive
ESG Risk Rating Score
23.6
23.3
23.1
22.8
ESG risk reduction 
-
-1.5%
-2.2%
-3.6%
Total return
27.2%
29.2%
29.5%
30.4%
Active return
-
2.0%
2.3%
3.2%
Standard deviation
15.4
15.9
16.1
16.3
Sharpe ratio
0.45
0.48
0.49
0.50
Measure
FTSE All-World 
index
Tilt Scenario

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Increased volatility 
However, the positive active returns of the sample portfolios came with an 
increase in volatility. The sample baseline portfolio had a standard deviation of 
return of 15.9, while the values for the amplified tilt portfolios were higher still 
(16.1 for the sample moderate portfolio and 16.3 for the sample aggressive 
portfolio). Compared to a standard variation of 15.4 for the FTSE All-World index, 
all three tilting scenarios produced a more volatile returns stream. 
The sample portfolios had a slightly 
higher Sharpe ratio 
The Sharpe ratio of all three sample portfolios was higher than that of the 
benchmark, indicating an improvement in risk-adjusted returns.22 Over the full 
holding period the Sharpe ratio of the sample conservative portfolio (0.48), 
sample moderate portfolio (0.49), and sample aggressive portfolio (0.50) were 
all above that of the benchmark (0.45). 
 
Variations 
Building upon the case study  
The tilting strategy described above is one of several ways that industry tilts 
could be implemented. For instance, investors could calculate active ESG risk 
using a more granular industry classification. Investors could also explore the 
effects of using unmanaged risk scores for particular MEIs instead of overall 
unmanaged risk scores. Another option could be to combine a tactical tilting 
strategy with a sector rotation strategy, whereby industry tilts adjust in response 
to shifts in the business cycle.  
Investors might also consider an alternative approach that applies rules within 
subindustries to actively remove constituents with ESG Risk Rating scores above 
a certain threshold. The approach that we presented above was passive, in the 
sense that all benchmark constituents were included in the sample portfolios 
but were simply reweighted according to the aggregate ESG performance of their 
respective industry. 
 
Conclusion – tilting industry weights based on ESG 
Industry tilts seek to capture 
differences in industries’ unmanaged 
ESG risk  
Industry tilts are one of several approaches that investors could potentially take 
to integrate the ESG Risk Ratings into their investment process. Industry tilts 
seek to capture the vast differences that exist in the ESG risk profile of different 
industries. The three sample portfolios that Sustainalytics built using the 
conservative, moderate and aggressive tilt scenarios all achieved a reduction in 
portfolio-level ESG risk compared to the benchmark. They also outperformed the 
benchmark from 2014 – 2018 from a total return and Sharpe ratio perspective, 
albeit with increased portfolio volatility. While these findings are not conclusive, 
they provide preliminary evidence that industry tilts can not only reduce portfolio-
level ESG risk but potentially contribute to outperformance against market cap-
weighted benchmarks.

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WACC adjustments 
 
Creating an ESG risk-adjusted WACC 
WACC can be thought of as the 
opportunity cost of investing in a firm 
A second example of how investors could integrate the ESG Risk Ratings into 
their investment process is through the weighted average cost of capital 
(WACC), which usually serves as the discount rate in discounted cash flow (DCF) 
models. Simply put, WACC can be defined as an all-in measure of a firm’s 
average financing costs.23  
ESG factors can be captured in a DCF 
model in two main ways 
As shown in Figure 5, ESG factors can be captured in DCF models in two main 
ways. The first is through future FCF estimates. This idea involves making 
assumptions about how a company’s ESG risk profile could contribute, either 
positively or negatively, to the company’s ability to generate FCF. For instance, a 
low ESG risk firm might reasonably be expected to face fewer regulatory 
penalties, which would push up FCF and ultimately increase the NPV of the firm.  
The second way is by integrating ESG factors into WACC values, which is the 
option we expand upon in the discussion below. 
 
Figure 5: Capturing ESG factors in a DCF model 
 
 
Source: Sustainalytics 
 
Rationale for adjusting WACC 
Adjusting a company’s WACC may 
provide a more complete picture 
Our intellectual starting point in this exercise is that adjusting a company’s 
WACC to reflect unmanaged ESG risk might provide investors with a more 
complete picture of the company’s ability to generate the FCFs that the market 
is forecasting. From Sustainalytics’ perspective, this is not an altogether 
unreasonable assumption – while markets are efficient, there is good reason to 
believe that financially material ESG factors remain mispriced.24 
 
 
Present 
value of 
TV
Present 
value of 
FCF
Free Cash Flows (FCF)
WACC
Value
time
Terminal 
Value (TV)
WACC
ESG factors

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14 | P a g e  
 
Relationship between ESG risk and WACC 
Analysis based on estimated WACC 
values for 3,542 companies 
Before moving to the mechanics of how WACC values could potentially be 
adjusted, it is worth reflecting on the general relationship between ESG Risk 
Rating scores and WACC values. Our analysis is based on estimated WACC 
values collected for 3,542 companies.25 
Correlation coefficient of 0.03 
The main takeaway from our analysis is that we find no significant relationship 
between ESG risk and WACC values. The datasets have a positive correlation but 
it is extremely weak (correlation coefficient of 0.03). The lack of a strong positive 
or negative correlation can be seen in the scatterplot in Figure 6.  
 
Figure 6: Scatterplot of ESG Risk Rating scores and WACC values 
 
Source: Sustainalytics, Bloomberg Financial, L.P.26 
One interpretation is that the market 
has yet to learn how to price ESG risk 
This relationship (or lack of a relationship) can be interpreted in several ways, 
but in Sustainalytics’ view the data may indicate that the market has yet to learn 
how to price material ESG risk into cost of capital decision-making. More 
longitudinal research would be needed to develop this hypothesis (i.e. testing to 
see changes in the correlation coefficient over time). 
 
Sector analysis 
Deeper investigation 
To assess whether the overall relationship holds up across industries, we 
segmented the 3,542 companies in our sample into their respective sectors27 
and calculated the correlation coefficient between ESG Risk Rating scores and 
WACC values. We also took a look at each sector’s average WACC and ESG 
score, as well as the standard deviation of each. The results of our analysis are 
presented in Figure 7.  
 
 
0
10
20
30
40
50
60
70
0
5
10
15
20
25
30
ESG RIsk Rating scores
WACC

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15 | P a g e  
The correlation coefficient within 
sectors tends to be weak 
We see three key storylines in the data. First, most sectors follow the overall 
trend, which is to say the correlation coefficient between ESG Risk Rating scores 
and WACC values is weak and tends to hover around 0.  
Correlation appears to be strongest in 
two sectors 
The correlation appears to be strongest in the Healthcare and Chemicals and 
Technology, Media and Telecom sectors, where the coefficients are 0.16 and 
0.15 respectively. While these values are still indicative of a weak relationship, 
they are much stronger than the coefficients in other sectors. This finding could 
suggest that ESG risk and WACC values are, for whatever reason, more likely to 
move in the same direction in these sectors than in others. 
Figure 7: Relationship between WACC and ESG Risk Rating scores within sectors 
 
Source: Sustainalytics, Bloomberg Financial, L.P.28 
Absolute levels of ESG risk do not 
appear to make any significant 
difference 
Second, the fact that some sectors are much risker than others from an ESG 
point of view does not appear to make any significant difference in the 
correlation between ESG risk and WACC. For instance, the Extractives and 
Utilities sector and Financials sector have an identical correlation coefficient 
(0.02) even though they are separated by over 13 points of ESG risk (average 
ESG Risk rating scores of 36.6 and 23.1, respectively). Thus, it does not appear 
that the market is particularly effective or ineffective at pricing unmanaged risk 
into the cost of capital in higher risk sectors. 
WACC values tend to be clustered 
irrespective of the sector’s ESG risk 
profile 
Finally, the standard deviation data in Figure 7 reconfirms the overall picture. 
WACC values do not appear to be any more or less variable in sectors with high 
or low ESG risk. WACC values tend to be clustered irrespective of the sector’s 
ESG risk profile. The standard deviation of WACC values ranges from 2.4 
(Healthcare and Chemicals) to 3.3 (Extractives and Utilities and Technology, 
Media and Telecom).  
 
Measuring an ESG risk premium  
Adding an ESG risk premium  
The lack of a correlation between ESG risk scores and WACC values suggests 
that investors may find value in adding an ESG risk premium in DCF models. In 
the section below we outline a simplified approach that investors could 
potentially take to create ESG risk-adjusted WACC values. 
 
 
Average
Standard 
deviation
Average
Standard 
deviation
Consumer Goods
526
8.4
2.7
23.4
8.5
-0.03
Extractives and Utilities
505
8.4
3.3
36.6
10.5
0.02
Financials
816
7.1
3.1
23.1
5.9
0.02
Healthcare and Chemicals
438
9.2
2.4
28.9
8.0
0.16
Industrials & Infrastructure
721
8.6
2.8
27.6
9.0
-0.01
Technology, Media and Telecom
536
9.6
3.3
20.0
5.5
0.15
Overall
3,542
8.4
3.1
26.2
9.4
0.03
WACC
ESG Risk Rating scores
Sector
Number of 
companies
Correlation 
coefficient

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16 | P a g e  
Investors may benefit from adding an 
ESG risk premium 
For the purposes of this brief overview, Sustainalytics took the straightforward 
approach of applying static WACC adjustment factors based on a company’s 
ESG Risk Rating category (we refer to these factors as the ESG risk premium). 
The mapping is as follows:  
▪ 
negligible = -50 bps 
▪ 
low = -25 bps 
▪ 
medium = unchanged 
▪ 
high = +25 bps, and  
▪ 
severe = +50 bps.  
While they seemed to us like a reasonable starting point, these factors are 
ultimately arbitrary and could be increased (decreased) to amplify (dampen) the 
contribution of the ESG risk premium on a company’s final WACC value. 
To test the effects of incorporating an ESG risk premium in a company’s WACC 
we built a simplified DCF model that discounts companies’ consensus 
forecasted FCF in 2020 by both their conventional and ESG risk-adjusted 
WACC.29  
 
Results 
The NPV of Thomson Reuters Corp’s 
estimated FCF in 2020 increases by 
0.5% 
Figure 8 shows the results of this process for five representative companies 
from the FTSE All-World index (one from each ESG Risk Rating category). The 
percentage change in the NPV of companies’ estimated FCF in 2020 ranges from 
+0.5% to -0.4%.  
At the top end, Thomson Reuters Corp, the Canadian publishing firm, has an ESG 
Risk Rating score of 8.7 and falls into the negligible risk category. The company’s 
estimated WACC of 6.9 is therefore lowered by 50 bps to 6.4. This in turn 
increases the NPV of the company’s constant consensus forecasted FCF in 
2020 by 0.5%, from USD 907mn to 911mn. 
The NPV of Rosneft’s estimated FCF in 
2020 decreases by 0.4% 
At the low end, Rosneft, the Russian oil and gas major, has an ESG Risk Rating 
score of 41.0 and falls into the severe risk category. The company’s estimated 
WACC of 14.0 is therefore increased by 50 bps to 14.5. This in turn decreases 
the NPV of the company’s constant consensus forecasted FCF in 2020 by 0.4%, 
from USD 8,866mn to 8,827mn. 
Figure 8: The effects of using ESG risk-adjusted WACC values 
 
Source: Sustainalytics, Bloomberg Financial, L.P.30 
 
 
Company
ESG Risk 
Rating 
category
WACC
WACC 
adjustment
Adjusted 
WACC
FCF 2020E 
(USD mn)
NPV of FCF with 
WACC 
(USD mn)
NPV of FCF with 
adjusted WACC 
(USD mn)
Percentage 
change in 
NPV
Thomson Reuters Corp
Negligible
6.9
- 50 bps
6.4
970
907
911
0.5%
L'Oreal SA
Low
8.9
- 25 bps
8.6
4,910
4,509
4,520
0.2%
Roche Holding AG
Medium
9.3
0 bps
9.3
14,982
13,710
13,710
0.0%
Walmart Inc.
High
6.9
+ 25 bps
7.2
14,769
13,815
13,783
-0.2%
Rosneft
Severe
14.0
+ 50 bps
14.5
10,111
8,866
8,827
-0.4%

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17 | P a g e  
A theoretical framework for investors 
to contemplate ESG risk premiums  
This model is largely conceptual in the sense that it looks at only one year’s worth 
of FCF estimates and leaves aside terminal value considerations. However, it 
provides a theoretical framework for investors to use as they contemplate ESG 
risk premiums and how they could be integrated into a DCF model. Even in our 
parsimonious model, the NPV effects are not insignificant, with a range of +0.5% 
to -0.4%, and they could easily be amplified by using more aggressive adjustment 
factors. The next step from a practical application point of view would be to 
integrate such changes into the portfolio construction process, which we leave 
outside of this relatively brief case study. 
 
Variations 
Investors could experiment with non-
static WACC adjustment factors 
The approach presented above shows how an ESG risk premium can be added 
to the WACC used as the discount rate in typical DCF model. As mentioned 
above, it is a simplified framework that could be built upon in many ways. Most 
obviously, investors could experiment with non-static WACC adjustment factors, 
such as those delivered through a factor optimization process. Investors could 
also segment WACC values into buckets, such as high, medium and low, and 
apply micro-adjustments to each. From an ESG perspective, investors could also 
consider using MEI-specific unmanaged risk scores to drive the WACC 
adjustment signal.  
 
Conclusion – reinterpreting risk 
A simple yet potent integration method  This chapter has shown that incorporating an ESG risk premium into the WACC 
value used to discount a company’s FCF can potentially lead to significant 
changes in the valuation of that company, and may provide investors with a more 
holistic risk signal. Adjusting a company’s WACC to capture unmanaged ESG 
risk is a simple yet potent method of integrating ESG information into financial 
analysis. The extent to which material ESG factors are already embedded in 
conventional WACC values is a deeply interesting and evolving question in the 
responsible investment community, and one that Sustainalytics is likely to 
further explore.

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18 | P a g e  
 
Smart beta ESG 
 
Combining ESG scores and fundamental inputs 
Blending ESG factors and fundamental 
inputs in a rules-based portfolio 
construction process 
A third example of how investors could integrate the ESG Risk Ratings into their 
investment process is by blending ESG factors with fundamental inputs in a 
rules-based portfolio construction process, or what we refer to as smart beta 
ESG. Smart beta AUM have grown rapidly in recent years, up more than 630% 
from USD 136bn in 2007 to USD 1tn in 2017.31 And as Sustainalytics, Aberdeen 
Standard Life and the University of Oxford found in a 2018 market study, a 
growing number of smart beta investors are combining conventional factor 
exposures with ESG information to differentiate their offerings and improve 
performance.32 
Provoking further thinking about smart 
beta ESG 
The possibilities for harnessing the ESG Risk Ratings in a smart beta strategy 
appear countless. The approach that we outline below is comparatively 
straightforward and is meant to provoke further thinking about how investors 
could blend ESG scores with financial ratios in a structured, rules-based manner.  
 
Unpacking our approach 
Constituents are drawn from the FTSE 
100 index and reweighted 
Our approach focuses on the FTSE 100 index.33 Constituents are drawn from the 
index at the beginning of each portfolio year and reweighted according to their 
performance in a basic model that scores companies on two financial ratios and 
their ESG Risk Ratings score. The Sustainalytics’ Smart Beta ESG FTSE 100 
sample portfolio thus (largely) consists of the same constituents as the FTSE 
100 but the constituent weights are altered to reflect companies’ fundamentals 
and ESG risk profile. An overview of our approach is offered in Figure 9.  
 
Portfolio construction process 
Companies were scored against their 
sector peers 
The precise portfolio construction process followed seven steps. First, 
companies were drawn from the FTSE 100 index as of December 31 of the year 
preceding each portfolio year (e.g. December 31, 2013 for the 2014 portfolio 
year, etc).34 We ran the sample portfolio from 1 January 2014 to 31 December 
2018 for a total of five years or 60 months.  
Second, companies for which ESG Risk Rating scores were available35 were 
grouped according to their Sustainalytics sector36 and inverse ranked according 
to their ESG Risk Rating score.37 To illustrate, ESG Risk Rating scores for the 19 
Consumer Goods companies in the benchmark in the 2014 portfolio year ranged 
from 36.9 for Associated British Foods to 10.7 for Kingfisher. Associated British 
Foods was thus assigned an inverse rank of 1 and Kingfisher an inverse rank of 
19.38

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19 | P a g e  
Figure 9: Overview of smart beta ESG portfolio construction process 
 
Source: Sustainalytics 
The fundamental score is driven by 
dividend yield and return on assets 
Third, final ESG scores were calculated by dividing ESG inverse ranks by the 
number of companies in each sector. To carry on with the earlier example, 
Associated British Foods has a final ESG score of 0.053 (1/19 = 0.053), and 
Kingfisher 1 (19/19 = 1).  
In the fourth step, steps two and three were repeated for dividend yield.39 The 
dividend yield was calculated as of 1 January 2014 for the 2014 portfolio year, 
as of 1 January 2015 for the 2015 portfolio year, etc.40  
In the fifth step, steps two and three were repeated for return on assets.41 As 
with dividend yield, return on assets was calculated as of 1 January 2014 for the 
2014 portfolio year, as of 1 January 2015 for the 2015 portfolio year, etc.42 
We selected dividend yield and return on assets as inputs for the fundamental 
score because they are broadly applicable across industries, although there are 
clearly many other candidate ratios that could be substituted.43 
 
Summing the scores 
The fundamental score and final ESG 
score are equally weighted 
In the sixth step, we calculated companies’ fundamental score (dividend yield 
score + return on assets score) and their total score by summing their final ESG 
score and fundamental score. 
 
 
ESG score
ESG risk ratings 
score
Fundamental score
Dividend yield 
ratio
Return on 
assets ratio
Inputs
Portfolio  
weight

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20 | P a g e  
Portfolio weight is based on a 
company’s total score 
In the seventh and final step, companies’ portfolio weight was calculated by 
dividing their total score by the sum of all total scores. For instance, the highest 
weighted company in the 2014 portfolio year, Persimmon PLC, had a portfolio 
weight of 1.9%, which was derived from its total score of 2.7 against a total 
portfolio score of 144.7 (2.7/144.7 = 1.9%). 
 
Holdings analysis 
Average active weights range from 
+1.55% to -6.05% 
In our approach, all FTSE 100 companies for which ESG scores were available 
ended up in the final portfolio, irrespective of their ESG or fundamental score. In 
other words, companies did not have to meet specified performance hurdles. 
Companies with lower total scores were underweighted in the portfolio; 
companies with higher total scores were overweighted. In the most recent 
(2018) portfolio year, portfolio weights ranged from 1.83% (National Grid PLC) 
to 0.12% (Barclays).  
Active weights, which are the difference between a company’s portfolio and 
index weight, varied significantly.44 Average active weights (i.e. the average of 
active weights calculated across all five portfolio years) ranged from +1.55% 
(Persimmon Plc) to -6.05% (HSBC Holdings). In other words, relative to the FTSE 
100 index, Persimmon Plc is the most overweighted holding in the Sustainalytics 
Smart Beta ESG FTSE 100 sample portfolio and HSBC Holdings is the most 
underweighted. Figure 10 shows a selection of holdings from the sample 
portfolio that demonstrate the range in average active weights. 
Figure 10: Range of average active weights in the Sustainalytics Smart Beta ESG FTSE 100 sample portfolio 
 
Source: Sustainalytics, Bloomberg Financial, L.P.45 
 
Returns analysis 
Comparing the returns of the sample 
portfolio and benchmark 
Figure 11 shows how the Smart Beta ESG FTSE 100 sample portfolio would have 
performed against the FTSE 100 index from a total return perspective during a 1 
January 2014 to 31 December 2018 investment period. The portfolio had an 
active return of 3.4%, posting a total return of 24.5% compared to 21.1% for the 
benchmark.46 However, the sample portfolio was slightly more volatile than the 
benchmark, with a standard deviation of daily returns of 13.4 compared to 13.0 
for the FTSE 100 index. 
The sample portfolio had a slightly 
higher Sharpe ratio 
The Smart Beta ESG FTSE 100 sample portfolio had a higher Sharpe ratio than 
the benchmark over the full holding period (0.36 vs 0.32), indicating slightly 
improved risk-adjusted returns. 
Company name
Sector
Average portfolio 
weight
Average index 
weight
Average active 
weight
Persimmon
Industrials & Infrastructure
1.90%
0.34%
1.55%
Marks & Spencer Group
Consumer Goods
1.26%
0.37%
0.89%
Sky
Technology, Media and Telecom
1.10%
0.57%
0.53%
BP
Extractives and Utilities
0.96%
4.87%
-3.91%
HSBC Holdings
Financials
0.85%
6.90%
-6.05%

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21 | P a g e  
 
Figure 11: Smart Beta ESG FTSE 100 Sample Portfolio vs FTSE 100 index 
 
Source: Sustainalytics, Bloomberg Financial, L.P.47 
 
Attribution analysis 
Interaction effects accounted for the 
bulk of the sample portfolio’s active 
return 
Figure 12 breaks down the sample portfolio’s active return of 3.4% into 
allocation, currency, interaction and selection effects using a conventional 
performance attribution model.48 This analysis shows that interaction effects 
accounted for the bulk of the sample portfolio’s outperformance (2.64%), 
followed by selection effects (0.70%), currency effects (0.05%) and allocation 
effects (0.01%). 
 
Figure 12: Attribution analysis of the Smart Beta ESG FTSE 100 Sample Portfolio 
vs FTSE 100 index 
 
Source: Sustainalytics, Bloomberg Financial L.P. 
 
 
80
90
100
110
120
130
140
150
Indexed returns (1 Jan 2014 = 100)
Smart Beta ESG FTSE 100 Sample Portfolio
FTSE 100 index
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Allocation
effect
Currency
effect
Interaction
effect
Selection
effect
Total attribution

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22 | P a g e  
Rolls-Royce Holdings had the highest 
positive total attribution 
Figure 13 delves deeper and shows the securities with the five largest positive 
and negative contributions to the sample portfolio’s active return. The security 
with the most significant positive contribution was Rolls-Royce Holdings, with a 
total attribution of 1.5%.  
The effect of Rolls-Royce Holdings’ 
underperformance was dampened 
With an ESG Risk Rating score of 29.9, Rolls-Royce Holdings is a medium risk 
ESG firm and is on the cusp of slipping into the high-risk category. It also has a 
below average fundamentals score, due largely to its low dividend yield ratio. 
While the company posted a total return of -28.4% during the investment period, 
one of the lowest of any FTSE 100 constituent, it was underweighted in 
Sustainalytics’ sample portfolios by 20 bps, on average, relative to the FTSE 100 
index. The sample portfolio’s emphasis on dividend yield, return on assets and 
ESG 
scores 
helped 
dampen 
the 
effect 
of 
Rolls-Royce 
Holdings’ 
underperformance during the investment period. 
 
Figure 13: Contributions to active return of the Smart Beta ESG FTSE 100 Sample 
Portfolio 
 
Source: Sustainalytics, Bloomberg Financial, L.P. 
Provident Financial had the largest 
negative total attribution 
At the other extreme, Provident Financial had the largest negative total 
attribution (-1.3%). Provident Financial is a medium ESG risk firm, with an ESG 
Risk Rating score of 22.6, but the company performed relatively well in our 
fundamental assessment. As a result, the company was overweighted in the 
sample portfolios by an average of 30 bps relative to the benchmark. However, 
the company posted a total return of -72.5% during the investment period, the 
lowest of any FTSE 100 constituent. In this particular case, the focus on dividend 
yield, return on assets and, to a lesser extent, ESG scores, resulted in a drag on 
the sample portfolio’s performance. 
 
 
-2%
-1%
0%
1%
2%
Rolls-Royce Holdings
Anglo American
Admiral Group
Mondi
3i group
Sports Direct International
Mediclinic International
Compass Group
HSBC Holdings
Provident Financial
Total attribution

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23 | P a g e  
 
Does the ESG score add incremental value? 
We constructed two additional 
portfolios to test the incremental 
benefit of each score input 
Perhaps a more intriguing attribution question, particularly for investors who are 
accustomed to smart beta ESG strategies, is the incremental benefit of the ESG 
score and fundamental score in the performance of Sustainalytics’ sample 
portfolio. In other words, how would the sample portfolio have performed if one 
of these scores were removed? 
To answer this question, we created two additional portfolios: the Fundamental 
sample portfolio, which is driven exclusively by companies’ fundamental score, 
and the ESG sample portfolio, which is driven exclusively by companies’ ESG 
score. These portfolios follow the same construction methodology as the main 
portfolio (i.e. the Smart Beta ESG FTSE 100 sample portfolio) but they rely on a 
single input. The performance of these additional sample portfolios is shown in 
Figure 14. 
Figure 14: Comparing financial returns across sample portfolios 
 
Source: Sustainalytics, Bloomberg Financial, L.P. 
The ESG sample portfolio had the 
highest total return 
The additional sample portfolios (also) outperformed the benchmark, with an 
active return of 3.1% (Fundamental) and 4.1% (ESG). However, only the latter 
added incremental value against the Smart Beta ESG FTSE 100 sample portfolio 
(i.e. the ESG sample portfolio outperformed the main sample portfolio, but the 
fundamental sample portfolio did not). While we would not expect an alternative 
indexing strategy driven exclusively by ESG data to necessarily outperform 
across all markets and investment periods, in our analysis the marginal benefit 
of the ESG signal was much more significant than that of the fundamental signal. 
Indeed, the ESG sample portfolio outperformed the Fundamental sample 
portfolio by nearly a full percentage point (25.1% vs 24.2%).   
The sample portfolios had a higher 
Sharpe ratio than the benchmark 
The Sharpe ratio of all three sample portfolios exceeded that of the FTSE 100 
index, albeit marginally. The main and ESG sample portfolios both had a Sharpe 
ratio of 0.36 over the full holding period, compared to 0.35 for the Fundamental 
sample portfolio and 0.32 for the benchmark. 
The ESG sample portfolio had the 
lowest volatility 
The sample portfolios all had a higher standard deviation of daily returns than 
the benchmark, indicating that their outperformance came at the expense of an 
increase in volatility. Once again, we find that the ESG sample portfolio is the top 
performer. Its standard deviation of daily returns, 13.3, is the lowest among the 
three sample portfolios, although it is higher than that of the benchmark (13.0). 
 
 
Measure
FTSE 100 
Index
Smart Beta ESG FTSE 100 
Sample Portfolio
Fundamental
Sample Portfolio
ESG
Sample Portfolio
Total return
21.1%
24.5%
24.2%
25.1%
Active return
-
3.4%
3.1%
4.1%
Standard deviation
13.0
13.4
13.5
13.3
Sharpe ratio
0.32
0.36
0.35
0.36

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24 | P a g e  
ESG as a standalone factor? 
Although based on a limited investment period of 5 years, this analysis suggests 
that ESG scores could potentially be used as a standalone factor in smart beta 
ESG strategies. Indeed, in the aforementioned 2018 market study conducted by 
Sustainalytics, Aberdeen Standard Life and the University of Oxford, we found 
that a growing number of investors are experimenting with ESG data as a source 
of risk premium.49 
 
Variations 
There are numerous ways to design 
and implement smart beta ESG 
The smart beta ESG approach introduced above is a simplified strategy that is 
meant to provoke ideas about how the ESG Risk Ratings could be blended with 
financial ratios in a rules-based indexing strategy. Clearly there are numerous 
variations to this exercise. First and foremost, investors could explore different 
combinations of fundamental inputs, including those with recognized risk 
premia such as volatility, momentum and quality.  
Investors could also consider overlaying specific ESG criteria or performance 
hurdles into the security selection process. For instance, investors could remove 
from the investable universe companies that are experiencing, or have (recently) 
experienced, a Category 4 or 5 controversy.50  
Ensuring the portfolio is industry 
neutral 
Another possibility would be to ensure that the portfolio is industry neutral 
relative to the benchmark, which is comparable to a best-in-class approach. This 
constraint would presumably constrain the ESG signal, recognizing, as 
mentioned earlier, that industries have vastly different ESG risk profiles. But it 
could potentially help reduce tracking error.  
Other variations include using a different weighting scheme, for example by 
overweighting the ESG score relative to the fundamental score or comparing 
ESG Risk Rating scores across industries rather than against same-sector peers 
as we did. 
 
Conclusion – blending portfolio inputs 
Our findings demonstrate the potential 
of smart beta ESG 
This chapter offers a basic framework for blending unmanaged risk scores with 
financial ratios in a rules-based portfolio construction process. The returns 
analysis of the three sample portfolios – the Smart Beta ESG FTSE 100 sample 
portfolio, the Fundamental sample portfolio and the ESG sample portfolio – 
suggests that smart beta ESG can potentially help investors improve 
performance against market benchmarks. Given the perceived advantages of 
smart beta strategies over active and conventional passive strategies,51 and the 
growing materiality of ESG factors, we expect a growing number of investors to 
explore smart beta ESG in the future.

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Best-in-class strategies 
 
Selecting top ESG performers 
All industries are typically eligible with 
BIC strategies  
Best-in-class (BIC) typically involves building a concentrated portfolio of top ESG 
performers using ESG ranking hurdles. It is distinct from negative screening in 
that all industries are typically eligible for inclusion in the investment universe, 
although many investors combine BIC with negative screens that remove 
companies that meet specific screening criteria.  
 
Layers of information 
We used three considerations to set 
up a BIC strategy 
As shown in Figure 15, three considerations that are often part of BIC strategy 
formulation relate to industry, market and company-specific measure(s) used to 
determine best-in-class. More filters lead to greater specificity and more 
concentrated strategies, but a smaller number of qualifying companies. 
 
Figure 15: Three layers of information in BIC strategy formulation 
 
Source: Sustainalytics 
 
Industry 
BIC strategies can be run across or 
within industries 
The first layer relates to industry boundaries. BIC strategies are typically run 
across industries, but they can also be deployed within specific sectors. The ESG 
Risk Ratings cover companies in 138 unique subindustries and can thus be 
deployed in highly specialized BIC strategies. 
 
 
Best-in-class
Industry
Market
Company-
specific

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26 | P a g e  
 
Market 
There is a trade-off between precision 
and universe size 
Like the industry layer, the market layer involves boundary setting. Should the 
strategy consider companies across all geographies? Or should it target 
companies based in specific countries or regions? There is a trade-off between 
precision and sample size: country-specific BIC strategies may be more exact, 
but by definition they will restrict the size of the investment universe.   
 
Company-specific 
The ESG Risk Ratings offer a plethora 
of metrics for use in BIC strategies 
The third layer relates to how “best-in-class” is determined. In the context of the 
ESG Risk Ratings, top-of-mind measures might include overall unmanaged risk 
scores, management scores and exposure scores. Moving up the precision 
curve, more tailored metrics might include MEI-specific unmanaged risk scores, 
which could be applied on an industry-specific basis, and management 
indicators that underlie MEI analysis, such as employee turnover rates or carbon 
intensity metrics. 
In addition to the measure used to define BIC, there is a question of how high to 
set the bar. Commonly used thresholds include the top 10%, 40% or 50% of 
performers from a starting investment universe (for example, the top 10% of 
companies from the FTSE All-World Index based on overall unmanaged risk 
scores). The threshold decision depends on many factors, such as the effects 
on portfolio diversification of applying more stringent thresholds. 
 
A wide range of BIC strategies 
Strategies range from the micro to the 
broad 
The versatility of the ESG Risk Ratings allows investors to pursue any number of 
BIC strategies, from the micro (for instance, German automobile manufacturers 
with a top decile score on the product and service safety programmes indicator) 
to the narrow (North American banks and insurance firms with a top decile 
unmanaged risk score on the Business Ethics MEI) to the broad (global 
companies with top percentile unmanaged risk scores by sector).  
 
Tracking error 
Tracking error limitations can 
constrain BIC 
Tracking error is an important discussion point in BIC strategies, as 
implementing BIC can lead to significant deviation from conventional 
benchmarks, including market cap-weighted indices.52 As a result, tracking error 
constraints, such as those sometimes imposed by asset owner mandates, can 
limit the degree to which managers can apply BIC strategies. 
Some variants of BIC relax tracking error constraints or remove them entirely, 
thus allowing managers greater flexibility to deviate from market indices. Such 
strategies, which are typically found in dedicated funds, allow for deeper and 
more prolonged ESG integration across their portfolio companies. 
 
Financial performance 
We contribute to a large and growing 
body of literature 
There is a large and growing body of literature that investigates whether BIC 
strategies are associated with market outperformance.53 Sustainalytics seeks to 
contribute to this discussion through rigorous empirical testing of the ESG Risk

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27 | P a g e  
Ratings, the results of which were first introduced in the Volume 1 white paper.54 
Below we summarize our findings as they pertain to BIC analysis.  
 
Conventional vs momentum 
8 x 3 x 2 = 48 unique BIC strategies 
Our empirical evaluation of the ESG Risk Ratings included the development and 
testing of 48 discrete BIC strategies. They were based on eight MEIs across 
three markets and included two distinct types of BIC (conventional BIC and BIC 
momentum).55   
A 30% ranking hurdle 
While the first two inputs (MEIs and markets) are straightforward, further 
explanation is needed to differentiate the BIC types. The first is a conventional 
BIC that uses a 30% ranking hurdle (i.e. the top 30% of companies operating in 
industries where the MEI was relevant were selected to the portfolio). 
Performance was based on companies’ MEI-specific unmanaged risk scores. 
The BIC momentum portfolios select 
companies improving the most 
The second type is BIC momentum. Companies were selected to these 
portfolios based on the improvement in their MEI-specific unmanaged risk score, 
rather than their absolute score. The intent was to capture those companies 
making the most rapid gains in their MEI performance, not necessarily those with 
the lowest level of unmanaged risk in absolute terms. As with the conventional 
BIC portfolios, the momentum portfolios used a 30% ranking hurdle and were run 
across all industries for which the MEI was relevant. Figure 16 provides a 
summary of the BIC portfolio inputs. 
 
Figure 16: Overview of BIC portfolio inputs 
 
Source: Sustainalytics 
 
Investment period 
Different investment period for each 
type of BIC strategy 
The investment period for the conventional BIC portfolios ran from January 2010 
to June 2018, for a total of 102 months. The constituents were refreshed on 31 
December of the year preceding each portfolio year (e.g. December 31, 2017 for 
the 2018 portfolio year), and performance measurement began on 1 January of 
each portfolio year.56 Constituents were selected based on their MEI-specific 
unmanaged risk score in the year preceding each portfolio year. The BIC 
portfolios were market-cap weighted.  
 
 
MEI
Market
Type
Business Ethics
APAC
Conventional
Carbon - Own Operations
Europe
Momentum
E&S Impact of Products and Services
North America
Emissions, Effluents and Waste
ESG Integration – Financials
Human Capital
Occupational Health and Safety
Resource Use

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BIC momentum looks at year over year 
improvements 
The BIC momentum portfolios were tested from January 2011 to June 2018, for 
a total of 90 months.57 As with the regular BIC portfolios, the constituents were 
refreshed on 31 December of the year preceding each portfolio year, but the 
criterion was the improvement in MEI-specific unmanaged risk scores. The BIC 
momentum portfolios were also market-cap weighted. 
 
Benchmark 
We used the Carhart model to 
determine abnormal returns 
An important consideration in any empirical test is determining the benchmark 
against which to measure the abnormal return of a portfolio. Looking at equity 
markets, the Capital Asset Pricing Model (CAPM) has traditionally been used for 
this purpose. It was later replaced by multi-factor models, such as the so-called 
Fama/French three-factor model. This, in turn, was superseded by an augmented 
four-factor version, which is now known as the Carhart model.58 Over the past 
decade, in both academic literature and the quant practitioners’ world, this model 
has become the quasi standard for measuring risk-adjusted abnormal returns, 
i.e. alpha. Therefore, we used it to evaluate the performance of our ESG Risk 
Ratings-based investment strategies.59 
 
Results 
Momentum strategies outperformed 
conventional BIC 
Of the 48 BIC portfolios analyzed for the purposes of this paper, 21 (44%) were 
found to have statistically significant positive alpha, with annualized alpha 
ranging from 11.8 to 2.3. Figure 17 shows the three best performing strategies 
by region.  
Figure 17: Top three strategies by region, Carhart model-based alphas in % p.a.*
 
*Gold columns: statistically significant at 1% level; grey columns: 5% level; teal columns: 10% level; Solid colours indicate BIC momentum strategies; diagonal 
stripes indicate conventional BIC.                                                                                                                                                                                             Source: Sustainalytics 
 
 
11.8
10.6
9.8
8.8
7.4
7.3
3.8
2.8
2.3
0
2
4
6
8
10
12
14
Carbon - Own
Operations
E&S Impact of
Products and
Services
Emissions,
Effluents &
Waste
E&S Impact of
Products and
Services
Human
Capital
Carbon - Own
Operations
Human
Capital
Carbon - Own
Operations
Human
Capital
APAC                                                       Europe                                                  North America
Alpha in % p.a.

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Momentum strategies outperformed 
conventional BIC 
Three takeaways are immediately clear. First, the BIC momentum strategies, 
represented in Figure 17 by the bars with solid colours, outperformed the 
conventional BIC portfolios. Of the nine strategies shown in Figure 17, all but two 
(Carbon – Own Operations in Europe and Human Capital in North America) are 
momentum-based. This finding may indicate that, in the context of BIC 
strategies, the trendline in companies’ ESG performance is more closely 
associated with alpha generation than companies’ absolute (i.e. current) ESG 
performance. 
Of the BIC strategies created for this 
study, those in APAC performed best  
Second, for the BIC strategies used in this study, the APAC region had the best 
performance. The three top APAC strategies have an average alpha of 10.7, 
compared to 7.8 for Europe and 3.0 for North America. This relationship also 
holds up across the full sample of 48 BIC strategies.60 These findings may 
indicate that some markets are more mature at pricing in ESG-related factors 
than others, although it is beyond the scope of this paper to review this question 
in detail. 
Carbon – Own Operations is relatively 
pervasive 
Finally, Carbon – Own Operations is the only MEI that shows up as a top 
performing strategy across all three regions. This may indicate that carbon-
related risks, such as those posed by energy transition, are more systematic than 
risk factors captured in other MEIs.  
 
Conclusion – delivering value through BIC 
Our results point to many possibilities 
for using the ESG Risk Ratings in BIC 
BIC strategies are a staple among the growing multitude of approaches for 
integrating ESG information into portfolio decision-making, and the ESG Risk 
Ratings are well-suited to contribute new innovations in this long-favoured 
integration technique.  
Of the 48 BIC portfolios that we evaluated in our empirical testing, 21 (44%) were 
found to have statistically significant positive alpha, ranging from 11.8 to 2.3 on 
an annualized basis. While further research is needed to answer some of the 
questions raised by our results, we believe they are encouraging and point to 
many advantageous possibilities for using the ESG Risk Ratings in BIC strategy 
development.

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Screening techniques 
 
Narrowing the investable universe 
Screening is the most common ESG 
integration technique 
Screening is typically understood to involve eliminating companies or entire 
industries from an investor’s investable universe based on specific ESG criteria. 
Common examples include removing companies with products connected to 
tobacco, alcohol, gambling, cluster munitions, animal testing or child labour. 
Negative screening is the most common ESG integration technique, accounting 
for approximately 36% of all ESG integration strategies.61 
Screening can be based on an 
investment logic 
Motivations for screening can range from ethical concerns to financial value 
considerations.62 An emblematic example is screening companies with 
exposure to thermal coal. Of course, ethical and investment motivations are not 
mutually exclusive, and investors can have different reasons for removing the 
same companies from their investable universe. 
 
Effect on returns 
Does screening imperil returns? 
The extent to which screening affects the risk/return characteristics of a 
portfolio is a longstanding question in the responsible investing (RI) community, 
particularly in the context of stripping out entire industries. While we do not wish 
to revisit this debate, it is worth mentioning that modern portfolio theory’s 
assumption that constraining the investment universe necessarily leads to 
underperformance has recently come under assault from an empirical point of 
view. 
Maintaining sufficient portfolio 
diversification  
Among several recent studies questioning the risk/return effects of screening, 
Jeremy Grantham of GMO investments presented long-term data that showed 
removing entire sectors from the investable universe of a US equity investor 
would have yielded only minor effects (i.e. less than 50 bps) on annualized 
absolute returns stretching back to 1989.63 Although this type of analysis is 
sensitive to changes in time frame, the methodology for measuring returns and 
other factors, these findings are consistent with a growing body of evidence 
suggesting that sufficient portfolio diversification can be achieved while 
screening out specific sectors.64

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Screening with the ESG Risk Ratings 
Various ways to deploy the ESG Risk 
Ratings in screening strategies 
The ESG Risk Ratings could be used to facilitate portfolio screening in many 
different ways. Due to their cross-sector comparability, the ESG Risk Ratings are 
well-suited for screening at the universe level. The MEI structure of the ratings 
also provides opportunity for thematic screening. Examples include: 
▪ 
Removing companies with an overall unmanaged risk score in the severe 
ESG risk category; 
▪ 
Removing companies with an overall unmanaged risk score above a specific 
threshold, such as companies with a top decile overall unmanaged risk score 
within their respective subindustry; 
▪ 
Removing entire subindustries with an average unmanaged risk score above 
a specific threshold, such as subindustries with a top quartile average 
unmanaged risk score; 
▪ 
Removing companies with an unmanaged risk score in the severe ESG risk 
category on a particular MEI, such as Data Privacy and Security. 
Clearly, these strategies would have varied diversification and tracking error 
effects, and investors would need to balance these considerations with the 
screening rationale in portfolio construction.  
 
Unmanageable risk 
That portion of a company’s risk 
exposure that cannot be managed 
away 
While the screening strategies listed above are intuitive and could succeed in 
reducing portfolio-level ESG risk, we demonstrate below an alternative approach 
that is based on unmanageable risk.65 Unmanageable risk is that portion of a 
company’s ESG risk exposure that cannot be managed away through relevant 
policies, programmes or initiatives. Referring once again to the decomposition 
of the ESG Risk Ratings in Figure 2, unmanageable risk is the difference between 
a company’s exposure to a given MEI and that portion of the exposure that is 
manageable (i.e. manageable risk). 
Coal companies cannot fully manage 
away carbon emission risks without 
exiting the industry 
To unpack this concept in practice, let us consider the example of the coal 
industry and the MEI of Carbon – Products and Services. Coal companies cannot 
fully manage the carbon emission risks of coal without exiting the coal business. 
Coal will continue to emit carbon when burned, irrespective of management 
initiatives, such as efficiency improvements, that may reduce these emissions. 
The only option to fully manage the carbon emission risks of coal would be to 
diversify out of the coal business. Indeed, in Sustainalytics’ view, 60% of the Coal 
subindustry’s exposure to the Carbon – Products and Services MEI is 
unmanageable.  
The long-term shift to sustainability 
A screening strategy based on unmanageable risk therefore targets 
subindustries whose constituent firms may struggle, relatively speaking, in 
adjusting to a broad, long-term shift to a more sustainable economy.

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Implementing a screened portfolio 
The sample portfolio is based on the 
FTSE All-World index 
To test the effects that screening based on unmanageable risk could potentially 
have on both portfolio ESG risk and portfolio returns, we created a sample 
portfolio (the Sustainalytics Unmanageable Risk Screened Sample Portfolio). 
The portfolio seeks to mirror the FTSE All-World index except that constituents 
operating in subindustries with a significant amount of unmanageable risk are 
excluded. 
Average unmanageable risk values 
range from 7.9 to 0 
The precise portfolio construction process followed three steps. First, we 
determined which subindustries to screen out by calculating each subindustry’s 
average unmanageable risk. Average manageable risk is calculated as the 
difference between a subindustry’s average exposure score and the average 
overall manageable risk of all companies in that subindustry. At the company 
level, overall manageable risk is defined as the amount of ESG exposure a 
company can actually manage after accounting for structural issues, such as 
inherent negative externalities (e.g. the health risks of smoking).66 
Average unmanageable risk values range from a high of 7.9 (Integrated Oil & 
Gas) to a low of 0 (Real Estate Services), with an overall average of 2.6 and a 
standard deviation of 1.7. For the sample portfolio we set a threshold of 5: any 
subindustry with an average unmanageable risk score of 5 or higher was 
eliminated from the investable universe. As shown in Figure 18, this approach 
screens out companies in 18 subindustries. 
 
Figure 18: Subindustries screened using average unmanageable risk 
 
Source: Sustainalytics67 
Constituents taken from the 
benchmark on 31 December  
Second, we took constituents from the FTSE All-World index as of the close of 
31 December before each portfolio year (2014 – 2018) and removed those that 
were classified in the 18 screened subindustries.68 On average, 418 constituents 
were removed each year as a result of the screen. 
 
 
Screened subindustries
Average 
Exposure score
Average 
Manageable risk
Average 
Unmanageable risk
Integrated Oil & Gas
75.5
67.6
7.9
Oil & Gas Exploration and Production
71.8
64.3
7.5
Oil & Gas Refining and Marketing
57.1
50.2
7.0
Coal
56.5
49.6
6.9
Conglomerates
63.1
56.2
6.9
Airlines
49.5
42.7
6.8
Agricultural Chemicals
63.7
57.4
6.4
Tobacco
32.2
26.0
6.2
Diversified Chemicals
55.6
50.0
5.6
Specialty Chemicals
54.0
48.6
5.4
Diversified Metals Mining
72.0
66.6
5.4
Aerospace and Defence
56.5
51.1
5.4
Precious Metals Mining
71.2
65.8
5.3
Gold
69.6
64.4
5.2
Aluminum
68.9
63.7
5.2
Commodity Chemicals
45.1
40.0
5.2
Construction Materials
50.4
45.4
5.0
Oil & Gas Storage and Transportation
47.1
42.1
5.0

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33 | P a g e  
The sample portfolio had an average 
of 2,258 names 
In the final step, we reweighted the remaining benchmark constituents on a 
market cap-weighted basis. The sample portfolio had an average size of 2,258 
names compared to 2,889 for the FTSE All-World index.69 
 
Figure 19: Comparing returns and ESG scores 
 
Source: Sustainalytics, Bloomberg Financial, L.P., Vanguard70 
 
Results 
The sample portfolio achieved a 3.1% 
reduction in ESG risk 
As shown in Figure 19, the Unmanageable Risk Screened Sample Portfolio has 
an ESG Risk Rating score of 22.9, which represents the sum of the weighted ESG 
Risk Rating scores for all companies in the portfolio. This represents a 3.1% 
reduction from the benchmark’s ESG score of 23.6. This outcome comes as no 
surprise as the 18 subindustries that are absent in the sample portfolio contain 
many of the highest ESG risk companies in Sustainalytics’ coverage universe.  
The sample portfolio had a positive 
active return of 5.7% 
In addition to outperforming from an ESG risk point of view, the sample portfolio 
had a positive active return of 5.7% relative to the benchmark over the full 
investment period, based on a total return of 32.9% compared to 27.2% for the 
FTSE All-World index.71 
The sample portfolio also outperformed from a risk-adjusted point of view, with 
a Sharpe ratio of 0.54 over the full holding period compared to 0.45 for the 
benchmark.  
The sample portfolio is underweight 
Oil & Gas stocks 
The outperformance of the sample portfolio should be interpreted in the context 
of global oil prices, which declined from USD 97.49 per barrel at the start of the 
investment period to USD 33.62 about two years later.72 Although prices began 
recovering at this point, they are still well below their January 2014 levels and 
this trend almost certainly aided the performance of the sample portfolio, which 
is significantly underweight Oil & Gas stocks.73 
An increase in volatility 
Similar to other sample portfolios, the outperformance of the Unmanageable 
Risk Screened Sample Portfolio came at the expense of an increase in volatility. 
As shown in Figure 19, the portfolio’s standard deviation of daily returns was 
16.6 over the investment period, compared to 15.4 for the benchmark. 
Measure
FTSE All-World 
Index
Unmanageable Risk Screened 
Sample Portfolio
ESG Risk Rating Score
23.6
22.9
ESG risk reduction 
-
-3.1%
Total return
27.2%
32.9%
Active return
-
5.7%
Standard deviation
15.4
16.6
Sharpe ratio
0.45
0.54

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Figure 20: Unmanageable Risk Screened Sample Portfolio vs FTSE All-World 
index 
 
Source: Sustainalytics, Bloomberg Financial, L.P., Vanguard74 
 
Attribution 
Allocation effects accounted for most 
of the sample portfolio’s active return 
As shown in Figure 21, allocation effects (5.44%) and interaction effects (5.32%) 
had the most significant positive contributions to the sample portfolio’s 
outperformance. The significant allocation effect was expected, as the sample 
portfolio has removed companies in 18 subindustries from the benchmark, or 
approximately 418 companies in each portfolio year on average. Currency 
effects had a modest positive contribution (0.50%), while selection effects had 
a significantly negative contribution (-5.61%). 
 
Figure 21: Attribution analysis of the Unmanageable Risk Screened Sample 
Portfolio vs FTSE All-World index 
 
Source: Sustainalytics, Bloomberg Financial L.P., Vanguard75 
 
 
90
100
110
120
130
140
150
160
Indexed returns (1 Jan 2014 = 100)
Unmanageable Risk Screened Sample Portfolio
FTSE All-World index
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
Allocation
effect
Currency
effect
Interaction
effect
Selection
effect
Total attribution

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Variations 
The ESG Risk Ratings offer many 
possibilities for screening strategies 
The screening case study described above is clearly one of many that could be 
facilitated with the ESG Risk Ratings. As previously mentioned, investors could 
consider employing screening strategies at the universe level using overall 
unmanaged risk scores or score categories. Investors could also consider 
delving into MEI-level analysis and screening companies based on their 
performance on singular themes or issues, such as climate change. Portfolio 
screening is likely to remain a staple in the RI marketplace, and the ESG Risk 
Ratings appear to us to be well-suited to support a new generation of screening 
strategies. 
 
Conclusion – screening subindustries 
Contributing to the ongoing 
conversation 
The results presented in this chapter provide initial evidence about the potential 
ESG risk reduction effects and outperformance benefits of screening strategies 
that harness the concept of unmanageable risk. The sample portfolio developed 
in this chapter, which removes companies in industries that may struggle, 
relatively speaking, in adjusting to a long-term pivot to sustainability, achieved 
an ESG risk reduction of 3.1%, a positive active return of 5.7% and a significantly 
improved Sharpe ratio (0.54 vs 0.45) compared to the benchmark over the full 
investment period. We hope the results contributed in this chapter contribute to 
ongoing conversations in the RI community about the risk/return effects of 
screening.

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Thematic investing 
 
Maximizing impact, minimizing risk 
Selecting investments from the top 
down 
Thematic investing typically involves taking a top-down view of the economy and 
selecting investments that will benefit if the investor’s predictions develop as 
expected. Themes can be general, such as macroeconomic shifts or 
demographic changes, or targeted around individual topics, such as obesity, 
widening income gaps or specific technologies, such as blockchain.  
Many important investment themes 
have an ESG angle 
Many of the most dynamic trends taking place in today’s economy are ESG-
related. The explosion in renewable energy technologies (new renewable energy 
capacity hit 157 gigawatts in 2017, more than double new fossil fuel capacity),76 
the surge of electric vehicles, the proliferation of water scarcity solutions, clean 
tech and the rise of healthy eating are prominent examples of top-down ESG-
related investment themes. 
Positive impact and ESG risk 
minimization 
The thematic investing approach that we developed for this white paper 
combines two main elements: exposure to upside thematic opportunities that 
deliver positive social or environmental impact, and ESG risk minimization. A 
schematic of our approach is provided in Figure 22.  
Figure 22: Overview of thematic investing framework 
 
 
Source: Sustainalytics 
 
 
Starting universe of companies
Investment theme filter
Companies with 
thematic revenue exposure
Low ESG risk companies with thematic 
revenue exposure
ESG risk filter
Market cap filter
Sample portfolio

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Two information streams  
The benefit of two information 
streams 
The rationale of this approach is that it seeks to give investors exposure to 
upside ESG themes, but in a way that minimizes portfolio ESG risk. It is based on 
the recognition that companies that perform well in one area do not always 
perform well in the other. 
High ESG risk companies can sell 
products or services with a 
sustainability upside 
For instance, the ESG Risk Ratings offer a comprehensive ESG assessment and 
signal how companies are performing relative to their global subindustry peers 
on ESG issues that are material for that subindustry. But there is no guarantee 
that low ESG risk firms offer products or services with environmental or social 
upside. 
On the other hand, it is entirely possible for high ESG risk companies to sell 
products or services that deliver positive environmental or social impacts. For 
instance, many of the companies positioned to benefit on the renewable energy 
theme are utilities, a subindustry that has inherently high ESG risk exposure.  
The approach that we developed is an integrated impact strategy that targets 
top performers in both areas. 
 
Constructing a sample portfolio 
We ran the sample portfolio for a total 
of 60 months 
To test how the thematic investing strategy that we developed for this white 
paper would have performed from a financial return standpoint we created a 
sample portfolio (the Sustainable Products Low ESG Risk Sample Portfolio) and 
ran it from 1 January 2014 to 31 December 2018 for a total of five years or 60 
months. 
A concentrated basket of 27 
companies 
The construction process followed three steps.  
▪ 
We first scanned Sustainalytics’ Sustainable Products Research database 
and selected all companies in the maximum revenue exposure category for 
any theme.77 
▪ 
Next, we overlayed ESG Risk Rating scores and filtered out companies with 
a score in the medium, high or severe risk categories. 
▪ 
Finally, to improve comparability against the FTSE All-World index, we 
imposed a minimum market cap threshold of USD 2bn.78  
The end result is a concentrated basket of 27 mid and large cap companies with 
low or negligible ESG risk and significant upside exposure to positive impact ESG 
themes. Companies in the sample portfolio were equally weighted and 
constituents were held constant across the five-year investment period. 
Portfolio weights were rebalanced on 31 December prior to each portfolio year. 
 
Unpacking security selection 
The security selection process in 
action 
Figure 23 illustrates our security selection process. The themes in the first 
column are taken from Sustainalytics’ Sustainable Products Research database. 
Companies in the second column are a sample of those that have maximum

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38 | P a g e  
revenue exposure to the respective theme (i.e. 50-100% of their total revenue) 
and pass the first step in the security selection process.79 
Three steps in the construction of the 
sample portfolio 
As shown in the third column, companies that pass the first step in the security 
selection process often exhibit significant differences in their ESG Risk Rating 
scores. For instance, of the three companies in Figure 23 with maximum revenue 
exposure to the energy efficiency theme, only one (Signify NV) has an ESG Risk 
Rating score in the negligible or low category. 
The fourth column displays companies’ market cap category.80 As previously 
mentioned, we imposed a size constraint to improve comparability against the 
benchmark. Thus, relatively small firms with low ESG risk, such as Frasers 
Commercial Trust in the green buildings theme, were ineligible for inclusion in 
the sample portfolio. 
Figure 23: Selection process for the Sustainable Products Low ESG Risk Sample Portfolio 
 
Source: Sustainalytics 
 
Results 
The sample portfolio had a positive 
active return of 26.6% 
Figure 24 compares the sample portfolio against the FTSE All-World index from 
1 January 2014 to 31 December 2018.81 During this investment period the 
Sustainable Products Low ESG Risk Sample Portfolio had a positive active return 
of 26.6%, with a total return of 53.9% compared to 27.2% for the benchmark. 
However, as expected, the sample portfolio was significantly more volatile with 
a standard deviation of return of 19.6 compared to 15.4 for the FTSE All-World 
index.  
In addition to delivering a large positive active return, the sample portfolio also 
outperformed from a risk-adjusted return perspective. The Sustainable Products 
Low ESG Risk Sample Portfolio posted a Sharpe ratio of 0.71 across the full 
holding period, which is 26 points higher than the benchmark’s ratio (0.45). 
Theme
Companies with maximum 
revenue exposure
ESG Risk 
Rating category
Market cap 
category
Portfolio 
candidate?
Monolithic Power Systems
High
Mid cap
Nordic Semiconductor ASA
Medium
Small cap
Signify NV
Low
Mid cap
Frasers Commercial Trust
Low
Small cap
SOHO China Ltd
Medium
Mid cap
Unibail-Rodamco SE
Negligible
Large cap
Getlink
Negligible
Mid cap
Stagecoach Group PLC
Low
Small cap
Tesla Inc
High
Large cap
Engie Brasil Energia S.A.
Low
Mid cap
Nordex SE
Medium
Small cap
RusHydro PJSC
High
Mid cap
American Water Works Co Inc
Medium
Large cap
CITIC Limited
Severe
Large cap
Xylem Inc
Low
Large cap
Water
Energy efficiency
Green buildings
Green transport
Renewable energy

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Figure 24: Sustainable Products Low ESG Risk Sample Portfolio vs FTSE All-
World index 
 
Source: Sustainalytics, Bloomberg Financial, L.P., Vanguard82 
 
Attribution 
Interaction and selection effects were 
the major drivers of the sample 
portfolio’s active return 
Attribution analysis shows that interaction effects (25.5%) and selection effects 
(14.9%) had the most significant positive contribution to the sample portfolio’s 
active return. Allocation effects accounted for -0.8% and, given the diversified 
geographic nature of the sample portfolio, currency effects contributed -12.9% 
in attribution.  
 
Figure 25: Attribution analysis of the Sustainable Products Low ESG Risk Sample 
Portfolio vs FTSE All-World index 
 
Source: Sustainalytics, Bloomberg Financial, L.P., Vanguard 
 
Active subindustry weights 
The sample portfolio is heavily 
overweight REITs 
For the sake of simplicity, we did not hedge the industry exposure of the sample 
portfolio relative to the benchmark. And as it turned out, the sample portfolio had 
a significantly different industry composition than the benchmark. Figure 26 
shows the average active weights of the Sustainable Products Low ESG Risk 
80
90
100
110
120
130
140
150
160
170
180
Indexed returns (1 Jan 2014 = 100)
Sustainable Products Low ESG Risk Sample Portfolo
FTSE All-World index
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Allocation
effect
Currency
effect
Interaction
effect
Selection
effect
Total attribution

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Sample Portfolio by subindustry. The sample portfolio is heavily overweight 
REITs, which comprise 29.6% of the sample portfolio but only 1.8% of the FTSE 
All-World index.83 The large active bet on REITs is a result of two factors: a) REITs 
are well-represented in Sustainalytics’ Sustainable Products Research database; 
and b) REITs tend to have relatively low ESG Risk Rating scores.84 Investors could 
address these active industry bets by setting industry caps to ensure sector 
neutrality against the benchmark. 
 
Figure 26: Active subindustry weights in the Sustainalytics Sustainable Products 
Low ESG Risk Sample Portfolio 
 
Source: Sustainalytics, Bloomberg Financial, L.P., Vanguard 
 
Variations 
Opportunities for innovation 
The thematic investing approach that we conceived for this paper is illustrative 
and could be built upon in many ways. For instance, investors could consider 
restricting the strategy to specific impact themes, such as health or green 
buildings. The security selection process could also be refined by imposing more 
aggressive (or relaxed) ESG risk hurdles. And as mentioned above, investors 
could also set industry caps to ensure sector neutrality against the benchmark. 
Another option would be to incorporate financial ratios, such as return on assets, 
or market ratios, such as the price to earnings ratios, in the selection process, 
similar to the smart beta ESG strategy discussed above. 
 
Conclusion – achieving impact with low ESG risk 
The potential of an integrated impact 
strategy 
Marrying the ESG Risk Ratings with Sustainalytics’ recently launched Sustainable 
Products Research could provide investors with exposure to low ESG firms that 
are well-positioned to benefit, from a revenue perspective, as demand escalates 
for products and services that deliver positive impact. It is essentially an 
integrated impact strategy. While the sample portfolio’s large active return and 
Sharpe ratio of 0.71 compared to the FTSE All-World index should be interpreted 
with caution, we believe the results showcase the potential of combining these 
research streams in portfolio construction.  
0%
5%
10%
15%
20%
25%
30%
REITs
Paper and Pulp
Renewable Power Production
Real Estate Management
Electrical Equipment
Real Estate Services
Water Utilities
Highways and Railroads
Industrial Machinery
Sustainalytics subindustry

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41 | P a g e  
 
Engagement and voting 
 
Identifying engagement targets  
ESG issues such as climate change 
are key engagement topics 
Corporate engagement and voting have long been considered important 
components of an overarching RI strategy. While governance issues often 
dominate investors’ engagement agendas, environmental and social issues, 
notably climate change, have moved up the priority list in recent years.85  
The goals and motivations of engagement and voting are as varied as the 
investors that are engaging on ESG issues. Some investors see engagement and 
voting primarily as a tool to influence the behaviour of portfolio companies, with 
a view to improving their social and environmental performance. Others 
emphasize the long-term financial value creation that can accrue from active 
ownership. In many cases, these motivations are intertwined.86 
The MEI structure of the ESG Risk 
Ratings maps to engagement themes 
Sustainalytics believes that the ESG Risk Ratings could be used to inform 
investors’ engagement and voting practices in several ways. From a top-down 
perspective, the MEI structure of the ratings can be used to unpack broad 
engagement themes. For example, Figure 27 shows the MEIs that fall under four 
common engagement themes: risk management, climate change, human rights, 
and conduct and culture.87 Investors can explore the various components of 
these MEIs and feed the findings into their engagement topic selection process. 
Figure 27: Mapping MEIs to common engagement themes 
 
Source: Sustainalytics 
 
 
Risk 
Management
Data Privacy 
and Security
Human 
Capital
Occupational 
Health & 
Safety
Product 
Governance
Resilience
Climate 
Change
Carbon –
Own 
Operations
Carbon –
Product and 
Services
Emissions 
Effluents and 
Waste
Land Use and 
Biodiversity
Human 
Rights
Community 
Relations
Human 
Rights
Human 
Rights –
Supply Chain
Conduct and 
Culture
Bribery and 
Corruption
Business 
Ethics
Engagement 
theme
MEIs

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42 | P a g e  
 
Pinpointing engagement targets 
Carbon emissions from company 
operations face tightening regulations 
The ESG Risk Ratings could also be used to identify engagement targets. In the 
discussion below, we use the Carbon – Own Operations MEI to show how such 
a selection process could unfold in practice. We use this MEI as a case study 
because corporate strategizing around climate change, including risk 
identification, reporting and emissions measurement, has become a dominant 
engagement topic in recent years.88 
 
Carbon – Own Operations MEI at the subindustry level 
Some subindustries face higher risks 
than others 
Of the 138 subindustries represented in the ESG Risk Ratings, 79 (57%) are 
exposed to the Carbon – Own Operations MEI, which is to say that the issue is a 
material concern for the 2,418 companies in these 79 subindustries. However, 
the precise exposure of these companies is far from uniform, and the ESG Risk 
Ratings contain numerous quantitative metrics that can be used to further 
differentiate companies and subindustries based on their level of exposure.  
One approach is to target subindustries that have a relatively high average 
unmanaged risk score on the Carbon – Own Operations MEI. Figure 28 shows 
the five subindustries that float to the top in this analysis.89 These subindustries 
constitute a reasonable starting universe for investors looking to engage on 
carbon issues within company operations. 
 
Figure 28: Targeting subindustries in the Carbon – Own Operations MEI 
 
Source: Sustainalytics 
Some investors may prefer targeting 
subindustries with a performance gap 
The grey dots in Figure 28 show each subindustry’s average unmanaged risk 
score on the Carbon – Own Operations MEI, while the height of the gold bar 
captures the variation in company-specific scores. Thus, while the typical Oil & 
Gas Exploration and Production company has an unmanaged risk score of 7.0, 
the values range from a high of 10.9 (Panoro Energy ASA) to a low of 2.5 (DNO 
ASA). Some investors may prefer targeting subindustries that have a wide range 
of unmanaged risk scores as they may contain a deeper contrast in management 
practices. 
 
 
7.0
6.8
6.2
5.5
5.4
0
2
4
6
8
10
12
Oil & Gas
Exploration and
Production
Steel
Independent
Power Production
and Traders
Construction
Materials
Agricultural
Chemicals
Unmanaged Risk - Carbon Own 
Operations

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Narrowing the signal 
Selecting engagement targets using 
the management gap 
Carrying on from the analysis above, the ESG Risk Ratings could be used to 
identify company-specific engagement targets in a transparent and rules-based 
manner. The approach that we sketch below is based on the management gap, 
which is the difference between a company’s manageable risk and managed 
risk.90 The management gap unmasks those companies that have the highest 
(manageable) exposure to, and the worst management of, a given MEI. This 
technique targets companies that a) have high risk exposure; b) can mitigate 
risks through relevant policies and programmes; and c) have not, for whatever 
reason, implemented such policies or programmes, as reflected in management 
and events indicators. 
 
Company level 
A refined list of engagement targets 
Figure 29 shows the five companies from the five at-risk subindustries (Oil & Gas 
Exploration and Production, Steel, Independent Power Production and Traders, 
Construction Materials and Agricultural Chemicals) with the highest 
management gap score on the Carbon – Own Operations MEI. These firms 
constitute a refined basket of engagement targets for investors looking to 
engage on carbon issues.  
Figure 29: Targeting companies on the Carbon – Own Operations MEI 
 
Source: Sustainalytics 
 
Enhancing the engagement process 
Using the ESG Risk Ratings could 
improve the engagement process 
itself 
In addition to helping investors identify engagement targets, the ESG Risk 
Ratings could enhance the engagement process itself, for example through the 
pieces of data and informed judgement the rating is built upon, including 
indicator-level information. Our 2016 report on engagement offers an instructive 
backdrop to the different ways that investors sometimes develop their 
engagement practices.91 
 
Conclusion – harnessing ESG scores in engagement 
Many different possibilities to use the 
ESG Risk Ratings in engagement 
The ESG Risk Ratings could contribute to investors’ engagement and voting 
strategies in myriad ways. The MEI-based structure of the ratings lines up with 
the thematic nature of engagement, and quantitative metrics, including 
unmanaged risk and the management gap, could potentially help investors 
identify potential engagement targets in a transparent and rules-based manner. 
 
 
Engagement target
Country
Subindustry
Management Gap, 
Carbon - Own 
Operations
Unmanaged 
Risk, 
Carbon - Own 
Operations
Unmanaged Risk 
Category, Carbon -
Own Operations
Panoro Energy ASA
Norway
Oil & Gas Exploration and Production
10.3
10.9
Severe
Parsley Energy Inc
United States   
Oil & Gas Exploration and Production
10.0
10.6
Severe
Extraction Oil & Gas Inc
United States
Oil & Gas Exploration and Production
9.8
10.4
Severe
Diamondback Energy Inc
United States
Oil & Gas Exploration and Production
9.6
10.2
Severe
Africa Oil Corp
Canada
Oil & Gas Exploration and Production
9.1
9.7
Severe

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44 | P a g e  
 
Conclusion 
 
Summarizing results, looking ahead 
Creating value for Sustainalytics’ 
clients 
In this paper we have sought to aid Sustainalytics’ clients by reviewing seven 
potential use cases of the ESG Risk Ratings: industry tilts, WACC adjustments, 
smart beta ESG, BIC strategies, screening techniques, thematic investing and 
corporate engagement. For five of these use cases, we developed sample 
portfolios to explore the investment value proposition of the ratings. While the 
results of any financial backtest are subject to inherent limitations and require 
careful interpretation, we believe the portfolio results presented throughout this 
study underscore the potential of the ESG Risk Ratings to create incremental 
investment value for Sustainalytics’ clients. The portfolio results offered in this 
report are summarized in Figure 30.  
Figure 30: Overview of portfolio results 
 
Source: Sustainalytics, Bloomberg Financial, L.P., Vanguard92 
 
Future direction 
A much longer journey 
While this paper concludes the ESG Risk Ratings white paper series, in many 
ways it marks the beginning of a much longer journey centred on quantitative 
testing of Sustainalytics’ ratings and data products. We look forward to 
continuing our quantitative investigations and working with Sustainalytics’ 
clients to identify further applications of the ESG Risk Ratings. Top of mind 
research questions include:  
▪ 
testing sample portfolios based on Sustainalytics’ data over longer holding 
periods; 
▪ 
updating the correlation coefficient between WACC values and ESG Risk 
Rating scores; 
▪ 
investigating the use of an optimizer in setting WACC adjustment factors; 
Chapter
Sample portfolio
ESG Risk 
Rating score
Total 
return
Active 
return
Standard 
deviation
Sharpe 
ratio
Alpha % 
p.a.
Industry tilts
Conservative tilt
23.3
29.2%
2.0%
15.9
0.48
-
Industry tilts
Moderate tilt
23.1
29.5%
2.3%
16.1
0.49
-
Industry tilts
Aggressive tilt
22.8
30.4%
3.2%
16.3
0.50
-
Smart beta ESG
Smart beta ESG FTSE 100
-
24.5%
3.4%
13.4
0.36
-
Smart beta ESG
Fundamental
-
24.2%
3.1%
13.5
0.35
-
Smart beta ESG
ESG
-
25.1%
4.1%
13.3
0.36
-
BIC strategies
Carbon - Own Operations, APAC, BIC-MOM
-
-
-
-
-
11.8
BIC strategies
E&S Impact of Products and Services, APAC, BIC-MOM
-
-
-
-
-
10.6
BIC strategies
Emissions, Effluents & Waste, APAC, BIC-MOM
-
-
-
-
-
9.8
BIC strategies
E&S Impact of Products and Services, Europe, BIC-MOM
-
-
-
-
-
8.8
BIC strategies
Human Capital, Europe, BIC-MOM
-
-
-
-
-
7.4
BIC strategies
Carbon - Own Operations, Europe, BIC
-
-
-
-
-
7.3
BIC strategies
Human Capital, North America, BIC-MOM
-
-
-
-
-
3.8
BIC strategies
Carbon - Own Operations, North America, BIC-MOM
-
-
-
-
-
2.8
BIC strategies
Human Capital, North America, BIC
-
-
-
-
-
2.3
Screening techniques
Unmanageable risk screened
22.9
32.9%
5.7%
16.6
0.54
-
Thematic investing
Sustainable products low ESG risk
-
53.9%
26.6%
19.6
0.71
-

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45 | P a g e  
▪ 
unpacking regional BIC strategies into country-specific BIC strategies; 
▪ 
combining the ESG Risk Ratings with recognized risk premia such as 
volatility, momentum and quality in a smart beta ESG strategy; 
▪ 
exploring long/short strategies, building off the foundation from Volume 1 
in the ESG Risk Ratings white paper series; and 
▪ 
marrying the ESG Risk Ratings with other Sustainalytics’ products in 
portfolio construction (e.g. Sustainalytics’ country risk research in a multi-
asset portfolio). 
We look forward to exploring these and other research questions in future 
publications.

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46 | P a g e  
Endnotes 
1 
The authors wish to thank their Sustainalytics colleagues Clark Barr, Ita Demyttenaere, Palla Ellemann, Roxana Dobre, Alison Gray, 
Cheryl Gustitus, Michael Jantzi, Francesco Mazzeo, Shazia Naik, Hanna Roberts, Diederik Timmer, Victor Ursulescu, Martin Vezér, 
Claudia Volk and Megan Wallingford for their assistance in preparing this report. We would like to extend a special note of thanks to our 
colleague Dr. Hendrik Garz for his continued guidance and support throughout the writing of this report. 
2 
Garz, H. and Volk, C. (2018), The ESG Risk Ratings, Moving up the Innovation Curve, White Paper – Volume 1, Sustainalytics, accessed 
(02.04.2019) at: https://marketing.sustainalytics.com/acton/attachment/5105/f-0b6c/1/-/-/-/-
/SustainalyticsESGRiskRatings_WhitePaperVolumeOne_October%202018.pdf 
3 
Morrow, D., Syed, M. and Burress, S. (2018), The ESG Risk Ratings, Exploring the Internet Software and Services subindustry, White 
Paper – Volume 2, Sustainalytics, accessed (02.04.2019) at: https://marketing.sustainalytics.com/acton/attachment/5105/f-0ba7/1/-/-
/-/-/SustainalyticsESGRiskRating_WhitePaperVolumeTwo.pdf 
4 
To be considered material in the ESG Risk Ratings, an ESG issue must have the potential to have a significant impact on the financial 
value of a company and, hence, the financial risk and return profile of an investment in the company. It is important to distinguish the 
ESG Risk Ratings’ use of materiality as a concept from narrower legal or accounting-focused definitions. More formally, an ESG issue is 
considered material within the ESG Risk Ratings’ framework if its presence or absence in financial reporting is likely to influence the 
decisions made by a reasonable investor. For more information, see Volume 1 in the ESG Risk Ratings white paper series. 
5 
Some risks are manageable, like the risk of on-the-job injuries, which can be managed through establishing stringent safety procedures, 
having emergency response plans, safety drills, promoting a safe culture, etc. Some risks, on the other hand, are not fully manageable, 
such as the carbon emissions of airplanes in flight. For more information, see Volume 1 in the ESG Risk Ratings white paper series. 
6 
This is a simplified discussion that leaves several concepts of the ESG Risk Ratings, including beta, unexamined. For a more detailed 
explanation of the methodology behind the ESG Risk Ratings, see Volume 1 in the ESG Risk Ratings white paper series. 
7 
Based on ESG data as of March 6, 2019. 
8 
Friede, G., Busch, T. and Bassen, A. (15.12.2015), “ESG and financial performance: aggregated evidence from more than 2000 empirical 
studies,” Journal of Sustainable Finance & Investment, accessed (02.04.2019) at: 
https://www.tandfonline.com/doi/abs/10.1080/20430795.2015.1118917 
9 
As proxied by the Vanguard VWRL ETF, which tracks the FTSE All-World index. Information about VWRL can be found at: 
https://www.vanguardinvestor.co.uk/investments/vanguard-ftse-all-world-ucits-etf-usd-
distributing?intcmpgn=equityglobal_ftseallworlducitsetfusddistributing_fund_link 
10 
Industry weights in the benchmark and three tilt scenarios as of January 1, 2018. Benchmark weights accessed from Bloomberg 
Financial L.P. through the Vanguard ETF VWRL. We adopt the Industry Classification Benchmark (ICB) methodology, which is used by 
the FTSE All-World index. ESG scores as of March 6, 2019. 
11 
We used a simple average of subindustry-specific unmanaged risk scores and mapped the scores from the 138 subindustries in 
Sustainalytics’ industry classification system to the 10 ICB industries. 
12 
Index weight approximated through the Vanguard VWRL ETF, sourced from Bloomberg Financial L.P. and as of January 1, 2018. 
13 
Equals 0.5% due to rounding. 
14 
As of January 1, 2018. 
15 
As the revised weights under all 3 scenarios slightly exceeded 100%, we divided the remainder by three and applied it to the three 
industries with a negative tilt (Utilities, Basic Materials and Oil & Gas) thus further reducing their weight. 
16 
After 150%, the amplifiers begin to reduce the portfolio weight of high-risk industries to 0, which is effectively a screening strategy.

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17 
Constituents taken from the Vanguard VWRL ETF, which tracks the FTSE All-World index. 
18 
As proxied by the Vanguard VWRL ETF, which tracks the FTSE All-World index. 
19 
Of the 3,072 companies in the FTSE All-World index as of 1 January 2018, ESG Risk Rating scores were available for 2,926 companies. 
20 
Based on 2,926 companies from the starting benchmark universe of 3,072 companies (as of 1 January 2018) that are part of the ESG 
Risk Ratings Coverage universe. 
21 
Total returns, standard deviation and Sharpe ratio for the FTSE All-World index approximated using the Vanguard VWRL ETF. Total 
returns, standard deviation and Sharpe ratio calculated using daily returns in USD from January 1, 2014 to December 31, 2018. ESG 
scores as of March 6, 2019. 
22 
Bloomberg defines the Sharpe ratio as follows: A risk-adjusted measure that calculates the excess performance with respect to the risk 
free rate (in our case the yield 3 months linked to the currency), per unit of volatility over the time frame. Performance is measured as 
mean return. The higher the Sharpe ratio, the better the historical risk-adjusted performance. 
23 
Hargrave, M. (10.02.2019), “Weighted average cost of capital – WACC definition,” Investopedia, accessed (02.04.2019) at: 
https://www.investopedia.com/terms/w/wacc.asp 
24 
See our discussion about pricing in Volume 1 of the ESG Risk Ratings white paper series, available at:  
https://marketing.sustainalytics.com/acton/attachment/5105/f-0b6c/1/-/-/-/-
/SustainalyticsESGRiskRatings_WhitePaperVolumeOne_October%202018.pdf 
25 
WACC values were taken from Bloomberg Financial, L.P. on 15 November 2018. WACC values represent an estimate of a firm’s 
potential costs of funding sources. 
26 
ESG data as of 6 March 2019. WACC data as of 15 November 2018. 
27 
We used sectors to improve statistical significance. The 138 subindustries in Sustainalytics’ industry classification system currently 
map to six sectors: Consumer Goods, Extractives and Utilities, Financials, Healthcare and Chemicals, Industrials & Infrastructure and 
Technology, Media and Telecom. 
28 
ESG data as of 6 March 2019. WACC data as of 15 November 2018. 
29 
FCF data were taken from Bloomberg Financial, L.P. on 3 March 2019. FCF data refer to Bloomberg Financial, L.P. consensus forecasts 
for the 12 months ending 31 December 2020. 
30 
WACC values are estimates compiled by Bloomberg Financial, L.P. and were taken on 15 November 2018. FCF data were taken from 
Bloomberg Financial, L.P. on 3 March 2019. FCF data refer to Bloomberg Financial, L.P. consensus forecasts for the 12 months ending 
31 December 2020. ESG data as of March 6, 2019. 
31 
Morningstar Direct, accessed (04.02.2018) at: https://www.morningstar.com/products/direct. Morningstar uses the phrase strategic 
beta instead of smart beta. Strategic beta is slightly more expansive than what is typically referred to as “smart beta”. 
32 
Morrow, D., Rodrigues, A. and Mooij, S. (2017), Smart beta and ESG: Promoting sustainability in smart beta investment strategies, 
accessed (04.02.2019) at: https://www.sustainalytics.com/esg-research/thematic-reports/smart-beta-esg/ 
33 
The FTSE 100 is a market cap-weighted index of UK-listed blue chip companies. For more information, see 
https://www.ftse.com/Analytics/Factsheets/Home/DownloadSingleIssue?issueName=UKX&IsManual=False. We selected this index 
instead of the FTSE All-World due to the smaller number of constituents and data requirements in our simplified smart beta ESG model. 
34 
Constituents taken from the Vanguard VUKE ETF, which tracks the FTSE 100 index. For more information about the VUKE ETF, see 
https://americas.vanguard.com/institutional/mvc/detail/etf/overview?portId=9509&assetCode=EQUITY##overview

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35 
This screen resulted in the removal of 9 constituents representing 6.8% of the index in 2014, 6 constituents representing 8.6% of the 
index in 2015, 2 constituents representing 3.9% of the index in 2016, 2 constituents representing 0.8% of the index in 2017 and 3 
constituents representing 1.1% of the index in 2018. 
36 
While the ESG Risk Ratings are computed at the subindustry level, we used the broader sector grouping to increase sample size. The six 
sectors are: Consumer Goods, Extractives and Utilities, Financials, Healthcare and Chemicals, Industrials & Infrastructure, Technology, 
Media and Telecom. 
37 
ESG Risk Rating scores as of March 6, 2019. They were held constant during the investment period, i.e. we did not use historical ESG 
scores. 
38 
Inverse ranks were necessary because we sought to assign more “points” to superior performers, and in the ESG Risk Ratings model, 
lower scores are superior, i.e. they indicate lower levels of unmanaged risk. 
39 
Dividend yield data sourced from Bloomberg Financial L.P. on March 19, 2019. Dividend yield defined as the sum of gross dividend per 
share amounts that have gone ex-dividend over the prior 12 months, divided by the current stock price. 
40 
Companies for which dividend yield data was unavailable were assigned their sector average. 
41 
Return on assets data sourced from Bloomberg Financial L.P. on March 19, 2019. Return on assets gives an idea as to how efficient 
management is at using its assets to generate earnings. It is defined as (Trailing 12M Net Income / Average Total Assets) * 100. 
42 
Companies for which return on assets data was unavailable were assigned their sector average. 
43 
These include: price to earnings ratio, and return on equity. 
44 
As approximated by the Vanguard VUKE ETF. 
45 
Index weights approximated by the Vanguard VUKE ETF. Portfolio and index weights are average weights calculated over the 5 year 
investment period (i.e. 2014 – 2018). 
46 
Portfolio and index returns computed on Bloomberg Financial L.P. Portfolio and benchmark returns calculated using daily returns in 
GBP. 
47 
Calculated using daily returns in GBP. 
48 
Attribution analysis conducted using Bloomberg Financial L.P.’s attribution model. 
49 
Morrow, Rodrigues and Mooij (2017), op. cit. 
50 
Sustainalytics Controversies Research, corporate website, last accessed (04.02.2019) at: http://www.sustainalytics.com/controversies-
research/. 
51 
In some ways smart beta sits in a sweet spot between active and passive. For more information, see Morrow, Rodrigues and Mooij 
(2017), op. cit. 
52 
Vezér, M., David, T., Ranney, K. and Morrow, D. (2017), How investors integrate ESG: A typology of approaches, IRRC Institute and 
Sustainalytics, accessed (04.02.2019) at: https://www.sustainalytics.com/esg-research/thematic-reports/how-investors-integrate-esg-
typology-irrci/ 
53 
Friede, Busch and Bassen (2015), op. cit. 
54 
The BIC analysis presented in this report is a sample of a much larger empirical discussion from Volume 1. Concepts not covered here 
but touched on in Volume 1 include using the ESG Risk Ratings in a long/short strategy, alternative portfolio weightings, and MEI-
specific alphas.

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55 
From a starting universe of 20 MEIs, these were the 8 that met our data availability requirements. 
56 
The fact that the ESG Risk Ratings were developed as a completely new approach/methodology, a true innovation, made it unavoidable 
that we would face some data constraints in back-testing. For example, new indicators were introduced to assure that we were able to 
measure how well a company is able to manage its financially material ESG issues. Obviously, there was no track record for these new 
indicators. Similarly, the second dimension of the rating, the exposure dimension, was also newly developed and introduced with the 
ESG Risk Ratings, which meant an absence of historical data points. We dealt with this situation by (1) backfilling historical data gaps as 
feasible, and (2) making simplifying assumptions or reducing completeness requirements. We are planning to continue our back-testing 
efforts and will present further results in lockstep with the progress we make in backfilling further data gaps. 
57 
For the momentum strategies, the portfolio performance analysis started in January 2011, instead of January 2010, reflecting the need 
to calculate an annual change number as a prerequisite for portfolio formation. This reduces the portfolio return observation period to 
90 months (from 102 months). 
58 
See Carhart, M. M. (1997), On Persistence in Mutual Fund Performance, The Journal of Finance, Vol. 52, pp. 57–82. 
59 
In technical terms, the Carhart α is nothing else than the intercept (α) of the following four factor regression: 
𝑅𝑡−𝑅𝑓𝑡= 𝛼+ 𝛽1 ∗𝑅𝑀𝑅𝐹𝑡+ 𝛽2 ∗𝑆𝑀𝐵𝑡+ 𝛽3 ∗𝐻𝑀𝐿𝑡+ 𝛽4 ∗𝑈𝑀𝐷𝑡+ 𝜀𝑡  
𝑅𝑡 is the monthly return of an investment strategy portfolio and 𝑅𝑓𝑡 is the risk-free rate in month t. RMRF is the difference between 
market return and the risk-free rate, SMB is the difference in return between a small-cap portfolio and a large-cap portfolio, HML is the 
difference in return between a portfolio of high book-to-market stocks and one of low book-to-market equities and UMD is the difference 
in return between equities with upward and downward share price momentum. We used monthly factor return data from Kenneth 
French’s data library (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). 
60 
Average alpha by region across all 48 strategies are 6.8 for APAC, 5.8 for Europe and 1.3 for North America. 
61 
Global Sustainable Investment Alliance, The 2016 Global Sustainable Investment Review (2017), accessed (02.04.2019) at: 
http://www.gsi-alliance.org/wp-content/uploads/2017/03/GSIR_Review2016.F.pdf 
62 
Vezér, David, Ranney and Morrow (2017), op. cit. 
63 
Grantham, J. (13.06.2018), “The mythical peril of divesting from fossil fuels,” The London School of Economics and Political Science 
and the Grantham Research Institute on Climate Change and the Environment, accessed (02.04.2019) at: 
http://www.lse.ac.uk/GranthamInstitute/news/the-mythical-peril-of-divesting-from-fossil-fuels/ 
64 
Trinks, A., Scholtens, B., Mulder, M. and Dam, L. (04.04.2018), “Fossil fuel divestment and portfolio performance,” Ecological Economics, 
accessed (02.04.2019) at: https://www.sciencedirect.com/science/article/pii/S0921800917310303 
65 
The full definition is as follows: Material ESG Risk inherent from the intrinsic nature of the products or services of a company and/or the 
nature of a company’s business, which cannot be managed by the company if the company continues to offer the same type of 
products or services and remains in the same line of business. For example, a coal company cannot fully manage the carbon emission 
risks of coal without exiting the coal business, as coal will continue to emit carbon when burned, regardless of a company’s 
management initiatives. The only option to fully manage this risk would be to diversify out of the coal business. This risk cannot be 
meaningfully modelled by assessment of management indicators and is therefore regarded as unmanageable. 
66 
It is calculated as the sum of each MEI’s exposure multiplied by that MEI’s manageable risk factor. MRFs essentially seek to answer the 
following question, “How much of the risk associated with a given ESG issue can a company reasonably manage, given its current 
business model and the current business environment?”. MRFs are set at the subindustry level and are MEI-specific. The higher the 
MRF, the greater the amount of ESG risk companies in a given subindustry can manage on a given MEI. See Volume 1 for a more 
complete discussion. 
67 
Based on ESG data as of March 6, 2019. 
68 
Constituents taken from the Vanguard VWRL ETF, which tracks the FTSE All-World index.

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69 
As approximated using the Vanguard VWRL ETF, which tracks the FTSE All-World index. Sourced from Bloomberg Financial L.P. 
70 
Total returns, standard deviation and Sharpe ratio for the FTSE All-World index approximated using the Vanguard VWRL ETF. Total 
returns, standard deviation and Sharpe ratio calculated using daily returns in USD from January 1, 2014 to December 31, 2018. ESG 
scores as of March 6, 2019. 
71 
As approximated using the Vanguard VWRL ETF, which tracks the FTSE All-World index. Sourced from Bloomberg Financial L.P. 
72 
Macrotrends (2019), Crude oil prices – 70 year historical chart, Macrotrends, accessed (02.04.2019) at: 
https://www.macrotrends.net/1369/crude-oil-price-history-chart Prices refer to West Texas Intermediate. 
73 
The sample portfolio had a 0.8% allocation to Oil & Gas companies, as some Oil & Gas-related subindustries, such as Oil & Gas Drilling 
and Oil & Gas Equipment, were not screened. 
74 
Calculated using daily returns in USD. Index returns approximated using the Vanguard VWRL ETF, which tracks the FTSE All-World index. 
75 
The FTSE All-World index proxied by the Vanguard VWRL ETF, which tracks the FTSE All-World index, in attribution analysis. 
76 
Chestney, N. and Doyle, A. (05.04.2018), “Solar power eclipsed fossil fuels in new 2017 generating capacity: U.N.,” Reuters, accessed 
(02.04.2019) at: https://www.reuters.com/article/us-renewables-investment/solar-power-eclipsed-fossil-fuels-in-new-2017-generating-
capacity-u-n-idUSKCN1HC1NC 
77 
The maximum revenue category in Sustainalytics’ Sustainable Products Research is 50-100%. For more information, see 
https://www.sustainalytics.com/sustainable-products-research/ 
78 
The FTSE All-World index tracks the performance of mid and large cap stocks. Market cap data sourced from Bloomberg as of March 
27, 2019. 
79 
We show only 15 firms (3 each for 5 themes) for the sake of simplicity. In total, 249 firms covering 11 themes passed the first stage of 
the security selection process. 
80 
Market cap data sourced from Bloomberg as of March 27, 2019. 
81 
Index returns proxied using the Vanguard VWRL ETF, which tracks the FTSE All-World index. 
82 
Calculated using daily returns in USD. 
83 
As approximated using the Vanguard VWRL ETF, which tracks the FTSE All-World index. Based on average weights over all 5 portfolio 
years. 
84 
The REITs subindustry has an average ESG Risk Rating score of 18.7, which is the 28th lowest out of the 138 subindustries in 
Sustainalytics’ classification system. 
85 
Institutional Investors Group on Climate Change (IIGCC) (2019), Corporate engagement, accessed (02.04.2019) at: 
http://www.iigcc.org/publications/category/Corporate-Climate-Risk-Management 
86 
Dimson, E., Karakas, O. and Li. X. (17.12.2012), “Active ownership,” Review of Financial Studies, accessed (02.04.2019) at: 
http://www.people.hbs.edu/kramanna/HBS_JAE_Conference/Dimson_Karakas_Li.pdf 
87 
Hermes (2018), Public engagement report Q3 2018, Hermes, accessed (20.02.2019) at: https://www.hermes-investment.com/ukw/wp-
content/uploads/sites/80/2018/10/public-engagement-report-q3-2018.pdf 
88 
Hermes (2018), op. cit.

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We excluded Airlines and Shipping as they have a relatively high amount of unmanageable risk on the Carbon – Own Operations MEI 
(60% and 40% respectively). 
90 
The management gap score is the difference between manageable risk and managed risk. It is the amount of risk that a company could 
manage but does not have initiatives in place yet to manage. For more details, see Volume 1 of the ESG Risk Ratings white paper series, 
available at:  https://marketing.sustainalytics.com/acton/attachment/5105/f-0b6c/1/-/-/-/-
/SustainalyticsESGRiskRatings_WhitePaperVolumeOne_October%202018.pdf 
91 
O’Sullivan, N. and Gond, J.P. (2016), Engagement: Unlocking the black box of value creation, Sustainalytics and Cass Business School, 
University of London, accessed (20.02.2019) at: https://marketing.sustainalytics.com/acton/attachment/5105/f-08d0/1/-/-/-/-
/Thematic%20Research%202016%2006%20Engagement%20Unlocking%20the%20black%20box%20of%20value%20creation.pdf 
92 
Total returns calculated using daily returns during a holding period from 1 January 2014 to 31 December 2018. The benchmark for the 
sample portfolios in the industry tilts, screening techniques and thematic investing chapters was the Vanguard VWRL ETF, which tracks 
the FTSE All-World index. The benchmark for the sample portfolios in the smart beta ESG chapter was the FTSE 100 index. For the BIC 
strategies chapter, only the top three sample portfolios per region are shown (i.e. those from Figure 17). Alpha % p.a. calculated using 
the Carhart model. See the BIC strategies chapter for further details.