See right through our ESG approach
There is growing interest by investors to “do the right thing” by using their influence to pressure companies to improve their approach to environmental, social and governance (ESG) issues. But a major challenge is how to minimize the potential costs imposed by ESG constraints on portfolios and overcome the persistent sparsity of ESG data resulting from companies’ non-reporting. In this study, we propose a quantitative approach to integrating ESG into portfolios that is expected to deliver comparable performance to non-ESG portfolios and is capable of classifying companies based on ESG even when they do not disclose sufficient data. The approach is particularly suitable for quantitative portfolios with large numbers of positions and many small exposures. In such portfolios, one can generally identify companies with bad ESG metrics and swap them out for companies with similar expected future returns and better ESG scores. This allows the manager to efficiently tilt the entire portfolio towards better ESG companies without the need to employ detailed ESG analysis of individual firms.
Key Findings
- Classification of companies should be performed using ESG items material to their specific industry.
- Our innovative Good Minus Bad (GMB) ESG factor can be used to extend ESG classification to non-reporting companies, expanding the universe by over 200%. This approach helps to overcome one of the most challenging obstacles to ESG portfolio construction: the lack of available ESG data.
- With our quantitative approach, which combines the material ESG items and ESG expansion, the companies with better ESG metrics have higher valuations than lower-scoring companies but comparable future returns. We find that financial analysts misprice the returns of good ESG companies by expecting their higher valuations to continue, in much the same way that good-quality companies often enjoy both current higher valuations and potentially higher future returns.
Find more at qma.com/esg
INTEGRATING ESG IN
PORTFOLIO CONSTRUCTION
An innovative data lens gives a wider view on sustainable investing.
There is growing interest by investors to “do the right thing” by using their influence to pressure companies to improve their
approach to environmental, social and governance (ESG) issues. But a major challenge is how to minimize the potential
costs imposed by ESG constraints on portfolios and overcome the persistent sparsity of ESG data resulting from companies’
non-reporting. In this study, we propose a quantitative approach to integrating ESG into portfolios that is expected to deliver
comparable performance to non-ESG portfolios and is capable of classifying companies based on ESG even when they do not
disclose sufficient data. The approach is particularly suitable for quantitative portfolios with large numbers of positions and
many small exposures. In such portfolios, one can generally identify companies with bad ESG metrics and swap them out for
companies with similar expected future returns and better ESG scores. This allows the manager to efficiently tilt the entire
portfolio towards better ESG companies without the need to employ detailed ESG analysis of individual firms.
Key Findings
Classification of companies should be performed using ESG items material to their specific industry.
Our innovative Good Minus Bad (GMB) ESG factor can be used to extend ESG classification to non-reporting
companies, expanding the universe by over 200%. This approach helps to overcome one of the most challenging
obstacles to ESG portfolio construction: the lack of available ESG data.
With our quantitative approach, which combines the material ESG items and ESG expansion, the companies with
better ESG metrics have higher valuations than lower-scoring companies but comparable future returns. We find that
financial analysts misprice the returns of good ESG companies by expecting their higher valuations to continue, in
much the same way that good-quality companies often enjoy both current higher valuations and potentially higher
future returns.
RESEARCH IN BRIEF
RESEARCH BRIEF
For professional investors only.
All investments involve risk, including the possible loss of capital.
Variable
N
Mean
Std. Dev.
10th
Pctl.
25th
Pctl.
50th
Pctl.
75th
Pctl.
90th
Pctl.
Number of
material ESG Items
20955
1.439
2.550
0.000
0.000
0.000
2.000
5.000
Number of
ESG items
23007
5.767
7.399
0.000
0.000
1.000
13.000
14.000
Material ESG score
7766
0.304
0.308
0.000
0.000
0.250
0.500
0.750
ESG score -
All items
15445
0.283
0.335
0.000
0.000
0.154
0.444
1.000
Future annual excess
return
23007
0.017
0.490
-0.453
-0.199
0.002
0.203
0.465
Market value ($mil.)
23007
5867
21569
179
353
1018
3310
11074
Book/Market
22999
0.631
2.070
0.120
0.271
0.499
0.823
1.225
Introduction
Even as the popularity of ESG investing has grown, investors
have continued to struggle with an essential challenge:
incorporating ESG factors into portfolio construction. Adding
ESG considerations typically means that the universe of
investment candidates becomes more restricted or that additional
restraints are placed on the portfolios. In either case, short-term
returns may suffer.
Our extensive literature review convincingly shows that firms
with better ESG scores tend to have a lower cost of capital and
enjoy higher valuations than firms with inferior ESG scores.
1
This
suggests that firms with better ESG metrics should be expected
to experience lower future returns. However, while some studies
document lower future returns for better ESG firms, some show
higher returns, and still others demonstrate no meaningful
difference. A major shortcoming of all these studies, however,
is that because ESG disclosures are voluntary, data is frequently
incomplete due to non-reporting. In addition, a lack of uniform
standards hinders comparability across companies, and low
correlations among data vendors’ company ratings adds more
noise, further hampering the portfolio construction process.
To address these issues, we first leverage recent work by the
Sustainability Accounting Standards Board (SASB) and suggest
an approach using material ESG items. Second, we propose
a new ESG factor-loading methodology that expands the
universe to non-reporting companies. Using our innovative ESG
approach, we find that companies with better ESG scores have
higher valuations but similar returns to those with poor ESG
scores.
Plotting ESG Data on the Materiality Map
Our first task was to establish a numeric framework for ESG issues
to allow meaningful comparisons across firms. SASB has created a
Materiality Map™ that maps 30 ESG issues to 79 industries based
on evaluations by analysts specialized in each industry. Utilizing
this industry-specific materiality map, we first mapped each of
SASB’s 30 material items to one or more of 52 Bloomberg raw
data items and aligned each of the 79 industries with one of the
157 MSCI GICS sub-industries.
We then created a numeric sub-score for each ESG data item. For
items consisting of a “Yes” or “No” response corresponding to a
positive or negative ESG impact, we assigned +1 or -1, reversing
the score to -1 or +1 when “Yes” was a negative (such as a product
recall). For unscaled numeric metrics such as carbon emissions, we
manually scaled them by market capitalization. We then ranked
all scaled numeric items into two groups — above and below
median — and again assigned a +1 and -1 sub-score, reversing the
score if the item was negative (such as above-median emissions).
This mapping and scoring system allowed us to classify a firm as
having good ESG metrics (with at least six material items and at
least 50% being positive) or bad ESG metrics (with at least six
material items but fewer than 20% being positive), with the rest
classified as either neutral or missing. To determine the benefit of
using the SASB materiality mapping, we then followed the same
classification procedure using not only the material items but all
disclosed ESG items.
ESG Scores and Future Returns
We analyzed the Bloomberg ESG data from December 2008
through December 2015 for Russell 3000
®
and S&P 500
companies. ESG scores were constructed for December of each
year (e.g., 12/2009), using data identified from the previous
year (e.g., 2008), while stock performance was examined for the
following year (e.g., 2010). For stock returns, we used the CRSP
database, using the buy-and-hold return on stocks from January
through December minus the buy-and-hold return of similar
stocks in terms of size, book-to-market ratio and momentum.
As shown in Table 1, for the Russell
®
universe we had over 23,000
annual observations of market values and subsequent returns
for the eight years we studied. Strikingly, though, only 7,766,
or roughly a third, were from companies reporting six or more
material ESG items. The average company reported fewer than
two material ESG items. As for the S&P universe, we observed
better ESG data availability, with the average company reporting
over four material items and almost 80% reporting at least one
material item. Both the Russell 3000
®
and S&P 500 had about
four times more ESG than material ESG items.
Table 1
Summary Statistics for Russell 3000
®
The table reports summary statistics for all observations on the portfolio formation dates of
12/2008-2015. Source: QMA, Bloomberg ESG data, CRSP database, Compustat Point-In-Time
database, Russell 3000
®
Index, S&P 500 Index, SASB. The Russell
®
Indices are trademarks/
service marks of the Frank Russell Company. Russell
®
is a trademark of the Frank Russell
Company.
Past performance is not a guarantee or a reliable indicator of
future results. All investments involve risk, including the possible loss
of capital.
Summary Statistics for S&P 500
1
ESG Literature Review, QMA, 6/2018,
https://www.qma.com/assets/pdf/QMA_ESG_Literature_Review_June2018.pdf
Variable
N
Mean
Std. Dev.
10th
Pctl.
25th
Pctl.
50th
Pctl.
75th
Pctl.
90th
Pctl.
Number of
material ESG Items
3419
4.436
3.542
0.000
2.000
4.000
6.000
9.000
Number of
ESG items
3871
15.691
6.198
12.000
13.000
14.000
19.000
24.000
Material ESG score
3037
0.387
0.290
0.000
0.143
0.400
0.600
0.750
ESG score -
All items
3694
0.347
0.203
0.083
0.154
0.333
0.500
0.619
Future annual excess
return
3871
0.041
0.311
-0.265
-0.115
0.028
0.173
0.338
Market value ($mil.)
3871
27536
46604
3981
6805
12638
26420
60163
Book/Market
3871
0.517
0.549
0.128
0.234
0.395
0.654
1.014
Table 2 demonstrates the average annual excess return for firms
with metrics classified as good, bad, neutral or missing according
to both the material ESG items and all items. Interestingly, the
companies with good metrics according to material items showed
a strong tendency to outperform the bad, with an average excess
return of 1.7% (vs. -1.4% for companies with bad metrics),
although a subsequent t-test did not reveal statistical significance.
It’s important to note that our classification of good and bad
ESG firms according to the material items resulted in a very small
percentage of the population, about 1.4% and 2%, respectively.
This confirmed the need for expansion of the classification to
non-reporting firms.
Table 2
Panel A: Russell 3000
®
SASB material items
All ESG items
Mean
N
Mean
N
Good ESG
0.017
482
0.038
1069
Bad ESG
-0.014
334
0.046
3777
Neutral ESG
0.045
6950
0.048
10599
Missing
0.005
15241
-0.042
7562
Panel B: S&P 500
The table reports average annual excess returns in the year immediately following the
portfolio formation which occurs every December from 2008-2015.
Source: QMA, Bloomberg ESG data, CRSP database, SASB, Russell 3000
®
Index, S&P 500
Index. The Russell
®
Indices are trademarks/service marks of the Frank Russell Company.
Russell
®
is a trademark of the Frank Russell Company.
Past performance is not a guarantee or a reliable indicator of
future results. All investments involve risk, including the possible loss
of capital.
A very different picture emerged when we classified companies
using all available ESG items. The companies with good ESG
metrics had a tendency to underperform the bad, with excess
returns of 3.8% and 4.6%, respectively, although statistical
significance was not reached. Similar results were found in S&P
500 (i.e., just the largest) companies. When material items were
used, companies with good metrics outperformed the bad (1.3%
vs. -.1% bps), and when all ESG items were used, companies with
bad metrics outperformed good ones (4.5% vs. 3.5%).
Expanding the ESG Classification
To expand the ESG classifications to non-reporting Russell 3000
®
companies, we followed a procedure similar to the one used in
pairs trading. We first created for each December a Good Minus
Bad (GMB) ESG factor, which is the value-weighted return of the
companies with good ESG metrics, according to the SASB
material items, minus the value-weighted return of the companies
with bad ESG metrics
. We then ran an ordinary least squares
(OLS) regression of the monthly returns of both reporting and
non-reporting companies on the five Fama-French factors plus the
GMB ESG factor over the previous five years. Firms with positive
loadings on the GMB ESG factor were considered good, and
firms with negative loadings were considered bad.
As shown in Table 3, our method allowed for an increase of
240% (from 625 to 2,131) in the number of companies with good
ESG metrics and an increase of 206% (from 510 to 1,561) in the
number with bad ones. In addition, in the expanded universe,
firms with positive loadings on GMB indeed showed a higher
average ESG score (0.34) than those with negative loadings (0.26),
and the difference was statistically significant (p=0.0001). This
further corroborated our new ESG expansion methodology.
Table 3
Summary Statistics Russell 3000
®
—
Analysis Based on SASB Material Items
The table reports summary statistics for all observations on the portfolio formation dates of
12/2008-2015.
Source: QMA, Bloomberg ESG data, CRSP database, Compustat Point-In-Time database, SASB
IBES, Russell 3000
®
Index.
The Russell
®
Indices are trademarks/service marks of the Frank Russell Company. Russell
®
is
a trademark of the Frank Russell Company.
Past performance is not a guarantee or a reliable indicator of
future results. All investments involve risk, including the possible loss
of capital.
SASB material items
All ESG items
Mean
N
Mean
N
Good ESG
0.013
392
0.035
859
Bad ESG
-0.001
113
0.045
1121
Neutral ESG
0.041
2532
0.037
1714
Missing
0.059
834
0.074
177
Observations with significant negative loadings on GMB
Variable
N
Mean
Std. Dev.
10th
Pctl.
25th
Pctl.
50th
Pctl.
75th
Pctl.
90th
Pctl.
t-statistic
2131
-2.496
0.482
-3.184
-2.721
-2.370
-2.131
-2.018
Future annual
excess return
2131
-0.032
0.653
-0.604
-0.285
-0.042
0.195
0.536
Material ESG score
625
0.257
0.276
0.000
0.000
0.200
0.500
0.667
ESG score - All items
896
0.238
0.257
0.000
0.077
0.154
0.351
0.556
Book/Market ratio
2131
1.425
13.721
0.194
0.359
0.662
1.155
2.080
Analysts' average B/M
1497
0.836
4.638
0.150
0.282
0.476
0.777
1.206
Analysts' average
implied return
1497
1.334
2.299
1.022
1.091
1.172
1.305
1.571
Market value ($mil.)
2131
5002
19678
24
91
729
3609
11098
Observations with significant positive loadings on GMB
Variable
N
Mean
Std. Dev.
10th
Pctl.
25th
Pctl.
50th
Pctl.
75th
Pctl.
90th
Pctl.
t-statistic
1561
2.535
0.523
2.040
2.158
2.374
2.764
3.276
Future annual
excess return
1561
0.027
0.441
-0.338
-0.126
0.022
0.180
0.399
Material ESG score
510
0.339
0.302
0.000
0.000
0.333
0.500
0.750
ESG score - All items
944
0.331
0.343
0.000
0.000
0.214
0.524
1.000
Book/Market ratio
1561
0.789
0.956
0.201
0.381
0.602
0.888
1.325
Analysts' average B/M
1237
0.612
0.532
0.176
0.321
0.531
0.770
1.063
Analysts' average
implied return
1237
1.211
2.281
0.994
1.045
1.101
1.189
1.328
Market value ($mil.)
1561
10220
35573
111
298
1005
4041
18164
When the annual excess return was analyzed, the expanded
universe of companies with good ESG metrics outperformed
the expanded universe with bad metrics by close to six percentage
points (2.7% to -3.2%), and the difference was statistically
significant (p=0.0021). Nevertheless, when the returns of the
good and bad ESG companies were analyzed on an annual
basis, we found that more variation existed, with the bad actually
outperforming in four of the eight years. Thus, although we show
statistically better performance of good ESG firms during the
entire period, practically speaking, we should probably think of
the two groups as having equivalent returns. Still, these results
show the benefit of using the expanded ESG universe.
In addition, Table 3 shows an average book-to-market ratio of
0.79 for companies with good ESG metrics and 1.43 for bad,
confirming previous findings that companies with good ESG
metrics enjoy higher valuations (M/B ratios). If current valuations
of the good ESG firms are higher, why do they not have lower
future excess returns?
To address this counterintuitive finding, we analyzed the future
valuations and implied rates of return by some of the most
informed investors — sell-side research analysts. The ratio of
book value per share to average target price (from IBES) showed
an average of 0.84 for bad ESG firms and 0.61 for good ESG
firms, indicating that analysts expected a higher future valuation
on the good firms. Meanwhile, the average implied rate of return
(12-month target price divided by current price) was higher for
bad firms than good firms at 33% and 21%, but this difference
was not statistically significant (p=0.1642). Thus, while security
analysts price firms with good ESG metrics at higher valuations
than bad ones, they seem to misprice their future returns
somewhat by expecting those higher valuations to continue.
Conclusions
This study provides two major contributions to the efforts to
integrate ESG into portfolio construction. We provide evidence
that using only material ESG items in each industry is preferable
to using all disclosed ESG items. We further show that it is
possible to expand the classification of non-ESG-reporting
firms into good and bad ESG groups using our innovative GMB
ESG factor. This expansion allows us to increase the number
of good and bad companies by over 200% while preserving the
characteristics and return patterns of the original good and bad
ESG firms. Using our ESG approach, we find that companies
with better ESG scores have similar returns to those with poor
ESG scores, suggesting it should be possible to systematically tilt
a portfolio toward better-scoring companies without detracting
from performance. As quantitative investment processes are
likely the most efficient at incorporating sparse ESG data, our
study provides an innovative and potentially powerful approach
for quant investors to do well through doing good.
Sources: QMA, Bloomberg ESG data, CRSP database, Russell 3000 Index, S&P 500 Index. SASB, IBES. Compustat Point-In-Time database.
For professional investors only. All investments involve risk, including the possible loss of capital. Past performance is not a guarantee or a reliable
indicator of future results.
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Copyright 2018 QMA. All rights reserved. QMA-20180716-187.
A
UTHORS
Roy Henriksson, PhD, Chief
Investment Officer
Joshua Livnat, PhD, C
PA, Managing Director and Head of
Research
Patrick Pfeife
r, CFA, Vice President and Senior Quantitative Analyst
Margaret Stumpp, PhD, Senior Advisor
F
OR MORE INFORMAT
ION
To learn more about QMA’s capabilities, please contact Gavin Smith,
PhD, Managing Director and Client Portfolio Manager, at
gavin.smith@qma.com or 973-367-4569.
ABOUT QMA
Serving investors since 1975, QMA targets superior risk-adjusted
returns by combining research-driven quantitative investment
processes built on economic and behavioral foundations with judgment
from experienced market practitioners. Ultimately, each portfolio
is constructed to meet the individual financial needs of the client.
An independent boutique backed by the capabilities of one of the
world’s largest asset managers, QMA is the quantitative equity and
global multi-asset solutions business of PGIM, the global investment
management businesses of Prudential Financial, Inc. Today, we manage
approximately $127 billion* in assets for a wide range of global clients.
*As of 6/30/2018.
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