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The question of how and why investors take into account Corporate Social Responsibility (CSR) activities of firms when making their investment decision is highly relevant for research on CSR disclosure and CSR investments as well as for firms themselves. This study investigates how news-based scores in environmental, social, and corporate governance (ESG) may have influenced the monthly stocks' market return in Switzerland, the US, and the UK during the 2007–2011 period. Our model is a multifactor linear model, consisting of the classic four-factors (Fama-French's three factors and momentum), plus a fifth factor, the EGS score, which represents the potential of the ESG to explain monthly returns during the observed period. By linear regression, we find that the variation of the overall ESG score is not significant in the US and Switzerland for the observed stocks. In the UK however, the change in the overall ESG score is a significant and slightly negative factor of the observed stocks' monthly performance in the 2007–2010 period. Using the same model, we also study if the changes in sub-categories of ESG ratings (namely, governance, economic, environment, labor, human rights, society, and products) could explain the monthly market return. We find that the changes in sub-category ratings exhibit a small but significant impact on the stock's performance during limited periods or on limited sectors, which varies among the countries. Finally, to explore a possible non-linear influence of the ESG score over monthly returns, we use a non-parametric model for Switzerland during the 2007–2011 period. The non-parametric kernel regression shows that the function linking a stock's performance to its ESG-score changes is probably non-linear.
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ESG Impact on Market Performance of Firms:
International Evidence
(Working Paper, June 17, 2014)
Jean-Michel SAHUT
IPAG Business School, Paris
Hélène PASQUINI-DESCOMPS
HEC Geneva
Abstract:
The question of how and why investors take into account Corporate Social Responsibility
(CSR) activities of firms when making their investment decision is highly relevant for
research on CSR disclosure and CSR investments as well as for firms themselves. This study
investigates how news-based scores in environmental, social, and corporate governance
(ESG) may have influenced the monthly stocks’ market return in Switzerland, the US, and the
UK during the 2007–2011 period. Our model is a multifactor linear model, consisting of the
classic four-factors (Fama-French’s three factors and momentum), plus a fifth factor, the EGS
score, which represents the potential of the ESG to explain monthly returns during the
observed period. By linear regression, we find that the variation of the overall ESG score is
not significant in the US and Switzerland for the observed stocks. In the UK however, the
change in the overall ESG score is a significant and slightly negative factor of the observed
stocks’ monthly performance in the 2007–2010 period. Using the same model, we also study
if the changes in sub-categories of ESG ratings (namely, governance, economic, environment,
labor, human rights, society, and products) could explain the monthly market return. We find
that the changes in sub-category ratings exhibit a small but significant impact on the stock’s
performance during limited periods or on limited sectors, which varies among the countries.
Finally, to explore a possible non-linear influence of the ESG score over monthly returns, we
use a non-parametric model for Switzerland during the 2007–2011 period. The non-
parametric kernel regression shows that the function linking a stock’s performance to its ESG-
score changes is probably non-linear.
Keywords: ESG; rating; governance, performance; return; kernel regression.
We would like to thank Professor Chris Mallin of Norwich Business School for her review and constructive
comments on an earlier version of this paper.
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1. INTRODUCTION
Socially responsible investment (SRI) consists of introducing criteria related to sustainability
into investment decisions, in contrast to classic investment that focuses solely on financial
criteria. Sustainability criteria are usually organized around three themes: environmental,
social/society and corporate governance (ESG). The first form of SRI is the exclusion of
certain sectors such as weapons, alcohol, and tobacco for religious or moral purposes and can
be traced back to the 18th century. The exclusion-based strategies now incorporate exclusions
based on recent international standards and norms and still apply to more than half of SRI in
Europe. In addition, the modern form of SRI uses various positive screening strategies such as
the “best-in-class” approach, which favors companies that are better rated according to ESG
criteria than other companies in the same sector (Cf. Appendix A). In addition, active
strategies such as sustainability-themed funds or shareholder rights usage to direct a corporate
strategy are also growing in popularity.
SRI in all its forms has experienced growing popularity in the last decade
1
. This interest
comes mainly from institutional investors, as public funds undergo further moral pressure
toward sustainability from communities and legislators. The popularity of responsible
investment has grown even more following the 2007 financial crisis that shattered the
confidence of investors in financial markets and traditional investments, while triggering
many new policies and rules. SRI proved to be a safer investment during dropping markets,
while rewarding investors with a certain moral satisfaction, thus emerging as a seductive
alternative investment portfolio approach. It is still unclear, however, how ESG criteria are
linked to a firm’s market performance, which is the main question of this study. The question
of how and why investors take into account Corporate Social Responsibility (CSR) activities
of firms when making their investment decision is highly relevant for research on CSR
disclosure and CSR investments as well as for firms themselves.
The academic world has been actively studying the field of modern SRI since the 1990s. This
long lasting interest is fuelled by the growth in SRI and a lack of a clear consensus despite
numerous studies. Historically, evaluation of SRI studies was hindered by a lack of theory,
data, and methodology (McWilliams and Siegel; 1999, Margolis et al.; 2007). Recently, ESG-
related data have become more accessible and standardized, and successful methodologies
have been identified. As a result, more and more papers offering sound theoretical framework
as well as strong associated results are being published, mainly focused on the American
market. But, given large variations in the empirical results, some authors warn that there is no
conclusive evidence regarding the relationship between ESG and financial performance of
companies (Ioannou and Serafeim; 2011, Orlitzky; 2013).
Therefore, our research question is how the individual company’s market and financial
performance are related to ESG criteria. The last financial crisis showed the SRI potential to
reduce the risk of an investment through better long-term management of a company, and this
perspective seems more and more attractive to investors.
Our hypothesis is that companies with high ESG scores have a lower residual risk and
therefore show a higher performance. We also believe that only ESG information that is
publicly available will reflect positively in the market price as investors associate this with
lower residual risk and higher goodwill.
1
According to US SIF, assets under SRI strategies went from $2.1 bn in 1999 to $3.7 bn in 2002. EURO SIF
claims a 1.7€ bn in 2005, coming to 11.7 € bn in 2011 which includes norm-based screening since 2009.
3
We then propose an original econometric study of the monthly market performance related to
ESG criteria for major companies in Switzerland; the US, and the UK between 2007 and
2011.
2
Our approach, in order to include ESG into a company’s market price, is a linear
model using Carhart four-factors plus ESG criteria, as well as a non-parametric model for
kernel regression on the same variables.
Our results show that the variation of the global ESG score is a significant but slightly
negative factor of a stock’s monthly performance in the UK, but is not significant in the US or
Switzerland. The changes in sub-categories ratings (for instance, governance, environment,
and labor) exhibit a small but significant influence over the stock’s performance only during
limited periods or on limited sectors, which varies among the countries. Moreover, the non-
parametric regression shows that the response of market performance related to ESG is
nonlinear, which could be explained in various ways.
In fact, these results provide valuable information for asset managers looking to include ESG
criteria into their portfolio strategy and for companies to understand the influence of ESG
news–based ratings on their market price.
This study also contributes to the literature on corporate social responsibility showing how
ESG criteria are linked to a firm’s market performance, with a new methodological approach,
and the non-parametric response of performance to ESG criteria may open a new way of
research to better understand the complexity of this relationship (Orlitzky; 2013).
2. CSR AND FINANCIAL PERFORMANCE
Academia seeks actively to demonstrate a connection between the various ESG criteria and
financial performance, and an increasing number of studies have been devoted to this topic
over the past ten years. It is important to make a distinction between studies on the financial
performance of a firm or stock related to ESG, and studies on the overall performance of an
SRI portfolio or fund (Renneboog et al.; 2008, and Galema et al.; 2008). In this second
category of researches, studies compare the performance of SRI funds to non-SRI funds.
Instead, they do not take into account the SRI funds’ heterogeneity. Moreover, the practices of
fund management significantly differ in the world (Sandberg et al., 2009). Almost all SRI
funds in the US use negative screening criteria, which is far from being the case in Europe. In
Europe, the best-in-class approach –where the leading companies with regard to ESG criteria
from all industries are included in the portfolio – is the norm. But the best-in-class approach is
often considered at the cutting-edge of SRI (Statman and Glushkov, 2009). Few studies try to
overtake these limits. For example, Capelle-Blancard and Monjon (2011) use a different
approach, by looking into the determinants of the financial performance among the SRI funds.
They demonstrate that a higher screening intensity reduces the risk-adjusted return. However,
this result is significant only for sector-specific screening criteria; transversal screening
criteria do not necessarily lead to poor diversification, and so, do not reduce financial
performances.
For all these reasons, our study relates to the first category, and we will therefore focus our
review primarily on those, i.e., studies that explore the link between a firm’s ESG
commitment and its stock’s performance. While a valuable contribution in many aspects, the
2
The UK period is 2007 to 2010 only, as we did not have the four-factors for the year 2011 at the time of the
study.
4
studies on SRI funds or constructed portfolio require additional theories on the construction
and management of the portfolio that prevents relating those results solely with the
performance of the individual stocks. For a single company, the stock’s market performance
should adjust to the corporate’s operational and financial performance, at least in the semi-
strong form of the efficient-market hypothesis. Therefore, we will first explore why ESG
could signal a change in the financial performance for a corporate.
2.1 Linking Social Responsibility and Corporate Performance
Regarding the definition of a “responsible” company, a theory often mentioned is the
stakeholder theory of R.E. Freeman (1984). His theory of modern management says that the
managers of a company must take into account all stakeholders, that is to say, employees,
civil society, and suppliers in their investment decisions and not just shareholders. Although
the stakeholder theory has laid a framework in the methods of corporate social responsibility
(for instance ISO 26000 on Global Reporting Initiative uses methods similar to those
suggested by Freeman), it does not, however, provide information about the relative
performance of a company applying ESG principles in relation to its peers.
Therefore, several studies tried to identify and evaluate these effects and show that CSR
activities can create opportunities for firms to increase image or sales (Albuquerque et al.,
2012), to attract or motivate employees (Balakrishnan et al. 2011), to lower the costs of
capital (El Ghoul et al. 2011), to reduce the “residual risk” (Sharfman and Fernando, 2008), or
to anticipate “best practices” (Eccles et al. 2012).
A prevailing view on the positive impact of ESG activities is to enhance a firm’s image—let
us call it the “ESG advertising” effect. From a marketing perspective, adopting a policy of
sustainability would provide costs and benefits similar to those of an advertising campaign.
Waddock and Graves (1997) demonstrated a strong relationship between a company's
reputation (according to the list of most admired by Fortune magazine) and its ratings in
social responsibility. The impact of ESG advertising seems bigger for firms whose clients are
individuals, rather than other firms. A survey for Switzerland from Birth et al. (2008)
surveyed the 300 largest Swiss companies on their CSR communication; 81% of respondents
claimed to direct their communication toward customers and 62% point out that their primary
objective is customer loyalty. In addition, a recent work (Albuquerque et al., 2012)
demonstrates that ESG is a strategic product sold to clients by a company, and that this
product is bringing more positive revenues the sooner it will be created, with late followers
receiving less value from it.
In the same way, Porter and Kramer (2011) showed that CSR could become part of a
company's competitive advantage if it is approached in a strategic way. In particular, societal
concerns can yield productivity benefits to a company; society benefits because employees
and their family become healthier, and the firm minimizes employees absences and lost of
productivity”. Moreover, a global survey of 1,122 corporate executives suggests CEOs
perceived that businesses benefit from CSR because it increases attractiveness to potential and
existing employees (Economist, 2008). These findings have been confirmed by the researches
of Battacharya et al. (2008) and Balakrishnan et al. (2011). These last researchers use a
laboratory experiment to show how corporate giving to charity motivates employees. They
highlight a double effect: a strong altruism effect and a signaling effect. Firstly, even when
employees cannot be remunerated for their actions, employee contributions to employers
significantly increase as the level of corporate giving increases. Secondly, when employees
can be remunerated for their actions, employee contributions initially increase as the level of
corporate giving increases.
5
Among the reasons why ESG should lead to increased performance for a firm, a widely
accepted theory in SRI is the “cost of capital” reduction. The prevailing opinion is that the
costs incurred by the establishment of a socially responsible structure in a company are offset
by a decrease in its cost of capital. In view of this, Mackey et al. (2007) postulates that
responsible behavior is a “product” sold by companies to socially responsible investors; but is
this product a profitable one for a company? Previous studies tend to believe that the impact
of investors’ opinion on the cost of capital is not a significant one. Angel and Rivoli (1997)
demonstrated through an analysis based on the CAPM that the impact of a boycott of
shareholders on the cost of capital of a company would probably be small if less than 65% of
the shareholders were boycotting the firm. Similarly, Teoh, Welch, and Wazzan’s (1999)
study on the largest shareholder boycott in South Africa shows minimal impact on securities.
With SRI investments reaching about 12% of all institutional investment in the US as of 2010,
this could be a bone of contention. However, a recent analysis from El Ghoul et al. (2011),
using accounting models on American firms, reveals a constantly lower cost of capital for
firms with high SRI ratings (KLD rating), bringing a renewed interest to the cost of capital
theory.
Another common theoretical position around ESG and firms’ performance is the residual
risk’s “information effect.” Several authors (Kurtz, 2005; Sharfman and Fernando, 2008)
argue that the ratings of a company on non-accounting parameters tell us about how the
company controls the risks it faces. Therefore, high ESG ratings would mean lower residual
risk for such companies compared to the market. This paradigm is tightly linked to the well-
known reputational risk. The media in the last 10 years have evolved tremendously and the
propagation of news, both good and bad, is now extremely fast. A reputation risk issue on
ESG criteria could affect the company market price,
3
or even destroy a thus-far successful
company.
4
The risk reduction effect of ESG is not to be neglected, as reputation risk arises as a
major threat for companies today.
One last group of principles concerns what could be called the “best practices’ anticipation
theory. Porter (1991) explains, about environmental regulations, that the costs arising from the
implementation of a sustainable structure are offset in time by improving business
productivity. This anticipation theory claims two type benefits: first, sustainable companies
should also have a better distribution of costs in relation to upgrading to future regulations.
This could be measured, for instance, by the stability of cash flows over time, in contrast to
other companies increased spending to adapt to new regulations in target years. Secondly,
companies putting in place regulations before others are the leaders in best practices, they are
more advanced and forward thinking compared to their peers, which should lead to an
increase in its wealth and the wealth of its shareholders. This is what Garriga and Melé (2004)
call the instrumental theory of corporate social responsibility, further supported in a recent
paper from Eccles et al. (2012), who explains from a management standpoint how mandatory
innovation in products, processes, and business models in sustainable firms leads to better
performance.
In contrast, let us now review some theories on how high ESG standards could negatively
affect a firm’s performance. One can reply to the stakeholder theory that the primary purpose
3
Apple’s Foxconn scandal on labor conditions may have cause share prices to drop 5% when it was announced,
taking all other factors into account. http://seekingalpha.com/article/926801-did-foxconn-bring-down-apple-
stock
4
Following the Jan.2013 horsemeat scandal, the French company Spanghero filed for bankruptcy in April 2013
http://www.huffingtonpost.fr/2013/04/19/viande-cheval-spanghero-place-liquidation-judiciaire_n_3115675.html
6
of a business is solely to increase the wealth of its shareholders (Friedman, 1962), and any
other purpose diverting the firm from this purpose will make it less effective. Some work such
as Mackey et al. (2007) and Graff, Zivin, and Small (2005) argue that a shareholder expects
from a firm to maximize its wealth without ESG constraints, and that ESG engagement should
be done separately, by for instance giving to charity. A shareholder investing in a firm with
ESG constraints makes a consumption choice where the charity portion is going to the firm,
hence he expects a lower cost of capital from the firm. This model should lead to neutral
effect for the performance of firms with high ESG ratings, but it does not account for the risk
reduction effect of ESG.
Another branch bringing controversy are the recent studies on “sin stocks.” Hong and
Kacperczyk (2006) and Statman and Glushkov (2008) studied “sin stocks” (tobacco, weapons,
alcohol) and found that they shows superior performance to the same extent as companies
highly praised by socially responsible investors. Consequently, they argue that, contrary to
common belief, social responsibility efforts as such are not reflected in the share price.
To summarize, setting-up an ESG program within a firm has some costs that the firm expects
to be compensated by an advertising effect, more stable revenues from loyal clients, and a
possibly lower cost of capital, i.e., lower expected return from investors. In the process, the
company might as well lower its risk and perform better, because considering all of its
stakeholders will bring a broader view of its risks and processes. Our first hypothesis is
therefore:
We expect a slightly positive relationship between yearly ESG ratings of a firm and its
yearly financial performance. (H1.a )
This concept of synergies created within a firm by engaging with stakeholders, whether it is
clients, business partners, or employees, is not quite new. It could be considered as part of the
goodwill priced on top of the book value by investors. Therefore, when a positive ESG score
or news is published, we should observe higher demand, growth, and higher market prices for
the corresponding firm as investors should recognize this added value and lower residual risks.
This additional value and lower residual risk should be reflected in a stocks market model as a
positive alpha of the alpha of the stock.
We expect a slightly positive relationship between monthly ESG ratings of a firm and its
monthly risk-adjusted market performance. (H1.b)
This is consistent with the findings of Gompers, Ishii, and Metrick (2003) who found that
low-rated companies in terms of governance had a risk-adjusted performance below average.
A study by Russo and Fouts (1997) also showed that, after adjusting for the most probable
parameters (size, growth, media, finance, and others), companies with better environmental
scores had a better-than-average performance. More recently, Edmans (2007) also found,
taking into account the parameters of the model of Carhart four-factors (market risk, size,
style, and momentum) that companies ranked by Fortune among the one hundred most-
desirable employers outperformed the average.
Finally, a few excellent meta-analyses have been performed on SRI studies that summarize
the findings in the domain and provide a good overview of the methods used. The synthesis
work carried out by Orlitzky et al. (2003) and more recently, Margolis et al. (2007) for
instance, concludes that there is, in general, a slightly positive relationship between ESG and
financial performance of companies, although less so over the last decade. However, given
large variations in the empirical results, some authors warn that there is no conclusive
evidence regarding this correlation and emphasize that explanations for the link are complex
(Ioannou and Serafeim; 2011, Orlitzky; 2013).
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2.2 Measuring the financial and CSR performances
Indeed, though the link between a firm’s market performance and ESG criteria has been much
discussed in recent literature, the empirical results, however, are often inconclusive. This lack
of consistency in the results may be explained by the multiplicity of data and methodologies
used among studies. Specially, the strength of the link between financial and CSR
performances depends on the way the two performances are measured and numerous
moderating variables (Gramlich and Finster 2013). With support from the above-mentioned
meta-analyses and additional ones cited below, we review the methods used in previous
studies leading to significant results and summarize our findings below.
There is no doubt that the model used in the studies to evaluate a firm’s performance plays a
central role. We can distinguish first between studies that assess the market performance
(stock market returns) and the accounting financial performance of a company. In general,
accounting models more often bring significant, positive results than market models. An
example of an accounting model is the Ohlson (1995) model with ROE, ROA, and Tobin’s q
variables. The major problem with accounting models is the number of samples, as it is
limited to yearly or quarterly observations that may be hard find for long periods (over ten
years). For market models, the simple CAPM model has been progressively abandoned in the
profit of multifactor models such as Fama and French, Fama and MacBeth and Carhart (1997)
models. Regressions on such multifactor models generally lead to significant positive results,
whereas CAPM-based models bring little results.
Logic would suggest that working on the most recent practicable data with the longest
possible observation period would provide a certain significance during statistics tests;
however, the availability of ESG data might limit the ability of the researchers. Revelli and
Viviani’s (2013) recent meta-analysis shows that an observation period of less than 5 years
tends to show negative coefficients, whereas 5 to 10 years of data usually bring the most
positive results. They also record that having an observation panel of more than 100 samples
will greatly increase the significance. Nonetheless, the most common practical issue causing
discrepancies in results might be the sampling frequency. Orlitzky et al. (2003) believe it to be
the main cause of variance among studies in corporate social responsibility.
It should be emphasized that each of the three categories of ESG scores, whether it is
environment, society, or governance, brings overall positive results regarding accounting
performance. However, if we speak about market or fund performance, the results vary
greatly with the selected category, which could explain why previous findings argue that
stock market rewards are rarely observable at the aggregate level. Hence, we can expect, if
using a market model, that ratings in different subcategories could bring a neutral, negative, or
a positive influence. Therefore, we add the following hypothesis to our study: Environmental,
Social/Society or Governance factors do not affect market performance in the same
proportion (H2)
The most studied ESG category is by far governance, whose positive effect brings a
consensus among studies (Orlitzky et al., 2003); second is environment, while society factors
are the less studied. Horváthová’s (2010) meta-analysis on ecological studies warns that a
simple correlation coefficient will bring more negative results when linking performance to
ecological factors. Therefore it seems appropriate to rely on advanced econometric methods
instead. She also warns that a positive link is found more frequently in common law countries
than in civil law countries, which bring us to our next topic.
Concerning the country of observation, there seems to be a difference in the results obtained
in the US and other countries. Studies in the US bring positive results more often, while non-
US studies lead to neutral results. An attempt to justify these discrepancies is the activism of
8
US pension funds toward sustainability. An interesting study would be to compare emerging
markets, as well as the influence of the legal system toward ESG results across categories as
Horváthová (2010) did, but this can be made difficult as most data providers focus on
developed countries.
To summarize our findings, to provide certain significance during statistics tests, a study
should make the choice of an accounting model or a multifactor market model as a base for
their performance model. If a market model is used, we should break down the ESG
observation into sub-categories, as the aggregated score would lead to no result. The
observation period should be over 5 years or at least 100 samples. There might be a need to
resample the data according to previous studies if no significant results can be found. Finally,
we should expect less positive results in non-US studies that in US ones.
3. METHODOLOGY
3.1 Models
We propose below an original study of over 200 large US, UK, and Swiss companies, based
on the availability of ESG scores and Fama-French factors. Our study on the performance of
companies will compare their ESG ratings available from Covalence with their market
performance adjusted for various factors during the 2007–2011 period. We measure the
change in the market value of a stock using a five-factor linear market model derived from
Carhart’s model (Carhart 1997). Carhart’s model explains a stock’s market performance
contains the Fama-French three factors HML (Fama and French, 1993), namely the market’s
excess return (RM-RF), the small firm’s excess return SMB, and the growth firms excess
return HML. In addition, Carhart’s four-factors model adds the momentum factor WML to
model the market trend anomaly. Our hypothesis to add our fifth factor, called ESG, is that
the ESG score variations could explain partly the stocks’ performance, as it would represent
the overall opinion of investors about a corporate’s ability to lower its risks and anticipate
trends. We expect a neutral or slightly positive relationship between ESG ratings and adjusted
market performance (Hypothesis H1.b).
(Model 1)
with
StockReturn = monthly company stock’s performance
RF = monthly risk free rate
(RM-RF) = monthly performance of the Market Index, minus RF
SMB = difference in performance between small and large companies (by market
capitalization)
HML = difference in performance between growth and mature companies
WML = differential performance between companies with a positive or negative trend over
the past month
ESG = monthly change in ESG overall score or sub-score see details in section 4-DATA
In addition, we want to test if the relation with each factor is indeed be linear. In case of the
four-factors, the wide recognition of those factors might have shaped the response in a linear
way. However, in case of the ESG score, we believe that the positive variations or negative
9
variations may not affect the stocks in the same way, and that the magnitude of the change in
ESG score might affect the stock’s performance in a non-linear way. To test the form of this
response without constraint, we conduct a non-parametric regression on the five factors of the
first model.
(Model 2)
where
f1 to f5 are functions that will be identified during the regression to minimize the error under constraints.
In parametric regression, we must determine the functions f(x) from the start. In non-
parametric regression, no hypothesis is made about form of the f(x) functions, instead, it is
deduced from the data themselves. The objective of the kernel regression is to find a non-
linear relation i.e., f(x) between two random variables, in our case (StockReturn-RF) and each
other variable of the model. As in ordinary least squares (OLS), a weighted sum of the
(StockReturn-RF) observations is used to obtain the fitted values. An important parameter
when fitting the curve to observation is the bandwidth, which provides smoothing so that only
some level variation will affect the fitting, and “noise” variation, on the contrary, will not
affect it. We estimate the unknown regression function using Nadaraya-Watson kernel
implemented in the R “np” package that uses automatic (data-driven) bandwidth selection.
3.2 Dependent and Independent Variables
The stock market return (StockReturn) is computed monthly for each stock based on month-
end close prices by Telekurs. For Switzerland, the risk-free rate (RF) and four factors (RM-
RF, SMB, HMW, and WML) are available until 2011 on the Amman-Steiner website.
5
RF is
the Swiss Franc call money rate from Factset and the market return is a constructed portfolio
bringing returns very similar to the Swiss performance index (SPI). The UK four-factors are
taken from the University of Exeter’s
6
website, available until 2010 at the time of our study.
RF (risk-free rate) is the monthly return on three-month UK Treasury bills, while RM is the
total return computed on the FT All-Share Index. The four factors for the US are available on
the Jason Hu website
7
until June 2011 where RF also represents the yield of three-month US
Treasury bills. More details on the construction of the factors are available on the respective
websites.
Concerning our ESG variable, it corresponds to the change in the Global EthicalQuote® score
(hereafter global score or rating) between the beginning and the end of the observation period.
It can also correspond to the change in each of the respective sub-scores of one the following
sub-category (governance, economic, environment, labor, human rights, society, products), as
we will test those variables successively.
The Global EthicalQuote® score and the score in each sub-category are monthly news-based
ratings provided by Covalence
8
on various ESG thematic. More details about how Covalence
5
http://www.ammannsteiner.ch/
6
http://business-school.exeter.ac.uk/research/areas/centres/xfi/research/famafrench/files/
7
http://www.jasonhsu.org/research-data.html
8
Covalence SA is a limited company based in Geneva, Switzerland, founded in 2001. They provide ESG ratings,
news and data of the world’s largest companies to investors, as well as reputation research and benchmarks to
corporations. http://www.covalence.ch/
10
computes those ratings and how they link to the Global Reporting Initiative (GRI) are
available in our data section.
3.3 Control Variables
To take into account the specificities of the companies, we considered two control variables
commonly used for the analysis of results within the same market: firm size and sector. In our
sample, however, the 11 firms are among medium or large within their respective markets. In
a study on common stock returns, Banz (1981) has shown that smaller firms have higher
returns, but this effect is not distinctive between medium and large firms. Since our sample
only consists of medium and large firms, we tend to believe that the parameter influencing the
stock returns will not play differently relative to the size factor; therefore, we disregard this
factor in our market model.
Concerning the sector variable, we will split our sample in the US and UK according to their
sectors, as presented in Table 1. As our sample for Switzerland is too small to consider each
sector individually, we decided instead to group the firms into the three themed groups that
are detailed below. The rationale for the first group is that it seems that those firms that are
selling consumer products directly to individuals are more impacted by ESG activities (Eccles,
2012), so we want to see if their market prices are differently influenced by ESG news. We
also segregate banks and insurance as a special group because of the indirect influence of the
assets holdings.
Insert Table 1
4. ESG DATA
Our first study sample consists of 618 monthly observations of change in ESG ratings,
corresponding market parameters on 11 stocks for Switzerland from 2007 to 2011. Our
second study sample consists of 1,335 monthly observations of change in ESG ratings and
corresponding financial parameters on 32 UK firms, with observation range from year 2007 to
2010. Our last study sample consists of 8,039 monthly observations of change in ESG ratings
and corresponding financial parameters on 189 US firms, with observations ranging from
2007 to 2011.
In each case, the ESG variable corresponds to the change in the ESG ratings. ESG ratings
available nowadays can be categorized as compliance-based ratings and news-based ratings,
this study’s ratings following the second category. The compliance-based ratings depend on
the compliance of a firm with respect to some pre-defined rules; for instance, CO2 emissions,
the presence of external auditors, the disclosure of a code of business conduct and ethics.
They often follow the Global Reporting Initiative (GRI) directives, which has set a standard
set of rules for firms to comply with. The rating is then computed depending on how the firm
is complying with the rules. Such data are found, for instance, on Thomson Reuters’s
ASSET4 or CSRHub. The news-based scores, on the other hand, are based on positive and
negative news concerning a company found in newspapers and other media and which
contains keywords in relation to environment, society, and governance; for instance, trials,
charities, and NGO activities. Regardless of the method chosen to create the ratings, the
awarded ESG scores are classified by most providers according to large categories of ideals,
often in the number of three (ESG) or four (ecological, corporate governance, community, i.e.,
contribution to society, and humanitarian, i.e., non-operating employees). An overall ESG
score that aggregates all categories is usually available.
11
The compliance-based and news–based rating systems each have certain advantages and
disadvantages. The first method seems easier to assess because it is following a grid of
specific criteria, but the exact knowledge of what is required to comply with a rule gives
companies the freedom to simulate good conduct by, for instance, disclosing a code of
conduct which is in fact not followed internally. Another problem is that it offers only a
qualitative but not a quantitative appreciation, so it may not allow to compare companies that
both comply with the same criterion. Finally, compliance rules rely on a yearly evaluation,
which makes it hard for re-assessment during the year.
News-based scores have the advantage of being re-assessed more often, as they are based on
new communicated by the media and may therefore come from several sources external to the
company that may provide different opinions in an ad-hoc manner. The major drawback is the
media’s over-exposure of big companies and client-facing businesses relative to others. Large
companies will be drowned in a flood of accusation by some organizations or conversely, the
media will extensively cover their good deeds, while smaller companies will remain in the
shadows and often without a realistic score. To address this issue, advanced news-based
scores compute the media exposure and adjust the ratings accordingly.
Here are more details on how the ESG scores from Covalence are calculated. The score is
obtained by comparing the amounts of positive and negative information collected on the
Web, i.e., by subtracting daily the negative information from the positive information. When a
majority of negative information is observed, the score then becomes a negative number.
S = score = A - B
With A = positive information (or ethical bids)
B = negative information (or ethical demands)
To overcome the bias due to media exposure and size, a rate representing the total volume of
information affecting the company score is introduced into the formula.
Media exposure adjustment:
V = volume = A + B R = rate = S / V
Final score = S * R
An erosion factor of 2% per month gives less importance to old news as compared to the
latest ones. The final score takes into account results performed by several human analysts
specialized in ESG.
A text encoded in the database must also be attached to one or two criteria among the fifty
“criteria for business contribution to human development” listed below. Those criteria follow
the dimensions of the GRI’s sustainability reporting and are distributed among seven
dimensions. This allows Covalence to compute the sub-score for each dimension, namely:
A_Governance, B_Economic, C_Environment, D_Labor, E_HumanRights, F_Society,
G_Products. Table 2 summarizes the groups and the criteria belonging to it.
The availability of sub-ratings in each of the seven ESG dimensions, on top of the global
score, will allow us to test which group may have an influence on the stock’s excess return
(Hypothesis H2).
Insert Table 2
12
5. RESULTS
5.1 Descriptive Statistics
The descriptive statistics of our first sample (market Model 1 and 2) is summarized in the
table below for each country. For Switzerland, we have 618 observations for each variable
over the period 2007–2011 and 8,039 for the US on the same period. We have 1,335
observations in the UK between 2007 and 2010. The stock excess returns range between -53%
and +49% in Switzerland, -63% and +90% in the UK, and -78 and +260% in the US. The
ESG ratings experience a higher range of variations (e.g., Switzerland, between -4,500% and
180%) than the other dependent variables (e.g., Switzerland, min - 15% and max 12%).
Insert Table 3
We test positively for normality by drawing histograms, where the high kurtosis can be noted
for the ESG scores. Heteroscedasticity is tested negatively by using a plot of each of our
independent variables against the square of the residual, showing no pronounced pattern. The
multi-colinearity between the Carhart four-factors’ and the ESG scores’ change is low with
VIF indices below 2. The Pearson correlation between the excess stock return and the
variables are shown in Table 2. In the overall sample, the four-factors are, as expected, highly
correlated with the stock’s excess return. For Switzerland and the UK stocks excess return is
also correlated to the global ESG score, positively for Switzerland, and negatively for the UK.
The US does not display any significant correlation between the stock’s excess return and the
global score, but a positive one with the labor sub-score changes. Despite the high correlation
between the four-factors for all countries and ESG scores for Switzerland, the VIF indices are
low and below 3 for all coefficients.
Insert Table 4
5.2 Model 1 Analysis
We run our regression toward Model 1 in R, with results presented below. As expected, the
market premium RM-RF shows the highest positive significance toward the stock’s
performance. The other classic factors also display a various degree of significance with an
expected negative coefficient for SMB since all of our firms are large-cap and an expected
positive coefficient for our firm since our stocks are value stocks, confirming global findings
on Fama-French models. The momentum factor seems slightly negative for Switzerland. Our
first model linear regression shows a slightly positive relationship between the EthicalQuote
global score and the market performance; however, it is not significant. The coefficient factor
for the ESG Global score change over stock’s market performance is 0.004, which is very
small. A bigger sample might be required to confirm such a small effect in a significant
manner.
Insert Table 5
To explore the influence of each ESG subcategory individually, we then regress for a linear
model consisting of four factors, and the score changes in each of the seven subcategories.
The figures are presented below. For Switzerland, economic news expectedly demonstrates a
positive relation to stock market performance. The overall sample exhibits a significant
negative relation between labor score changes and the stock’s excess return changes. This
small negative impact of labor ratings over the whole period, which might confirm
13
Friedman’s (1970) concern that business should focus on profit only, but this effect tends to
disappear in recent years as we later explore regression by year. Labor rating results from
positive and negative news concerning the labor practices and decent work, such as
employment and employee benefits, trade unions, health and safety at work, training and
education, and diversity (see Table 2 for equivalent GRI criteria). A bivariate Granger
causality test with a one-period shift shows a highly significant probability that it is the
labor’s score change that is causing the changes in market value. We also consistently
measure the impact of ESG news-based ratings to be smaller in comparison with market
premium and smaller than SMB, HML, and MOM factors.
For the US and UK, only the market premium and momentum factors show a high degree of
significance. Society news demonstrates a statistically significant relation in the UK over the
whole period, but the factor’s coefficient seems too small to be meaningful.
Insert Table 6
In order to further explore the relationship with each ESG subcategory, we observe each
category’s score per year. For Switzerland, over year 2011, the environment score exhibits a
positive and significant (P < (t) 0.05) influence over the stock market’s performance, while
the labor score’s significant negative coefficient only seems to apply to the year 2008. Those
results suggest that some factors may be more influential during some periods or context, as,
for instance, 2008’s sensitivity to labor when the financial crisis began. For labor, this could
mean that positive news concerning the employee benefits of employment are perceived
negatively in the markets during a crisis or more probably that negative news, such as lay-offs,
are still perceived as a positive sign that the business is restructuring, which might be
challenged. 2011’s sensitivity to environmental questions might have been triggered by the
Fukushima Daiichi nuclear disaster or by the 2011 proposal for a new regulation from the
Swiss federal office to cut CO2 emission, which was finally rejected. The environment
category in our news-based score contains news related to materials, energy, water
management, biodiversity, emission and waste, pollution, ecological impact of products and
transports.
Changes in the society score also show a significant positive coefficient for year 2008. A
bivariate Granger causality test for each variable with respect to the stock’s excess return does
not enable us to conclude on the direction of causality.
The UK sample demonstrates a negative, but significant, coefficient over the year 2009 for
the society score (local communities, humanitarian action, corruption and lobbying, etc.) has a
negative significant relation with market performance, which might be a reaction to the
lingering recovery and the MP expenses scandal causing defiance toward anything but
economic value. The economic score, which gathers new related to economic performance
and social factors, such as wages, local sourcing and hiring, and property rights has a positive
significant relation with market performance for 2010, but the causality is not confirmed by
the Granger test. Therefore, it is unknown if the firms improved their socio-economics
because of better performances that usual, or the firms with a higher socio-economic score
were having better market performances. The US sample shows a positive significant
coefficient toward society score changes in 2007 and a slightly negative one for the year 2009
regarding product changes (product safety and labeling, product social impact, consumer
privacy, etc.), but both impacts are very small. As we will see later with our split by sector,
products score shows a significant positive relation to the technology sector in the whole
period.
14
Insert Table 7
As described in our methodology section, we then split our sample by sector and groups in
order to control for a possible industry effect. The application of our linear model for each
sector/group shows the following results: The influence of market premium RM-RF is still the
highest significant factor, and the other three factors show their previous significance over the
period. For Switzerland, the client-facing groups show a positive significant factor toward
human rights. Banks and financial firms seems positively influenced by society and
negatively by labor changes, while the rest of the industry seems oriented toward economic
ESG news. For the UK, oil and gas shows a highly significant positive factor for environment
news, which links to the oil split affair. For banks, there seems to be a negative link toward
society, while media has a very positive one. The travel industry seems to have a negative link
with labor.
For the US, we find that financial services seem negatively influenced by society score
changes, while oil and gas are neutral toward such changes. Retail seems influenced
negatively by economic changes and telecom by labor changes. Technology, however, seems
positively influenced by product changes and telecom by governance and economic.
Insert Table 8
5.3 Model 2 Analysis
The functions obtained with a non-parametric kernel regression for each parameter over the
whole Swiss sample are as follows:
f1 = positive linear function of RM-RF, a confirmation or a consequence of CAPM
f3 = positive linear function of HML with almost flat slope
f5 = the function of ESG Global Ethical quote score seems to be flat until a certain
amount of positive change in score. It then becomes positive linear but with a cap, i.e.,
past a certain threshold, the ESG score has little additional influence on market
performance.
This could mean that ESG-related information is of importance to investors but that investors
may be unable to distinguish between “virtuous” companies and those that are “very virtuous.”
Insert Figure 1
Non-parametric regression over the ESG score in each category shows highly nonlinear
functions for B_Economic and C_Labor score changes, which could require further
confirmation on bigger sample or different markets.
The functions obtained with a non-parametric kernel regression for each parameter over the
whole sample are presented in Figure 2 below. The shape of the function displayed for each
ESG factors does not seems significant. Since the non-parametric regression is sensitive to the
bandwidth, a more detailed regression could be conducted using a non-automatic bandwidth
to better tailor the variation of the data sample.
Insert Figure 2
15
6. CONCLUSION
Our research question was how the individual company’s market and financial performance
are related to ESG criteria. We tried to identify the influence of ESG ratings on a firm’s
market performance in Switzerland, the UK, and the US, with two linear and nonlinear
models.
In theory, a good ESG rating should signal firms with lower residual risks and therefore
increase their market value as demand and valuation would adjust accordingly. We tested
monthly stock’s excess performance over a five-year period for several Swiss, US, and UK
companies and their related news-based ratings in various ESG categories. We find a neutral
or slightly negative relationship with the overall rating for the UK but not for the US or
Switzerland. Our results regarding the sub-categories scores highlight that the link with such
scores and market performance is highly dependent on the year and sector. Those results
could be a sign that investors do not recognize ESG ratings variation as a flag of a
lower/higher residual risk, except for some periods where the market is sensitive to specific
conditions. Only under those conditions would the prices adjust to the better/worst perception
of the risk of the firm, which could be an interesting topic to expand in the field of behavioral
finance. We also consistently measured the impact of ESG news-based ratings on the stock’s
market return to be smaller than the Fama-French and momentum factors. Our results should,
however, be considered with care as our sample only consists of hundreds of firms and as
such, should be extended to a larger number of firms and a longer observation period in
order to confirm the link with theory. The kernel regression for Switzerland displays a
nonlinear relation for news-based ratings toward the market over the whole period, which
could be taken into account and may lead to further studies using a non-linear relationship.
To conclude, the stakeholder theory (Freeman, 1984) postulates that there are some benefits
for firms to improve their ESG ratings as this could increase their performance. But we show
that this link, however, is still not fully understood and recognized by the market, as it will not
sanction the overall monthly increase or decrease of ESG ratings, except during specific,
contextual periods. It is an interesting result for a firm’s management who might want to
expose their good deeds in those contextual periods when there is exposure regarding that
factor, for instance, when there is discussion on new regulation that the firm is already
compliant with. It is also interesting for public policy maker regulators to know that the
market does not clearly sanction negative or positive ESG efforts yet and that firms or
investors, despite being favorably minded toward sustainability, might need further incentives
from them. This study also contributes to the literature evaluating the relationship between
financial and CSR performances, and the non-parametric response of performance to ESG
criteria may open a new way of research to better understand the complexity of this
relationship (Orlitzky; 2013). Moreover, it would be interesting to further study the link
between an ESG news-based rating and market performance with regard to a larger sample
and other countries, as well as study the link between those returns and financial performance
using accounting models over the same period.
16
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Table 1: Sectors of the empirical study
In order to study if environmental, social, and corporate governance (ESG) scores have a specific impact on a particular
sector, the firms in our study were sorted by sectors. US and UK firms where divided using ICB supersectors. In Switzerland,
as the number of sample was too small, we grouped the ICB supersectors in three custom groups by type of activity:
Consumer facing, Bank and Insurance, Industry and other.
Sectors for US & UK Number of compani es Sector groups for Switzerland :
UK US
Automobi le s & Parts 1 4
Banks 5 5 GROUP I - Consumer facing
Bas ic Resource s 3 7 Food & Beverages Nestlé S.A.
Chemi cals 6 Personal & House hold Goods
Compa gnie Financie re Richemont SA
Construction & Materia ls 3
Fina nci al Services 12 GROUP II Banks & Insurance
Food & Beverages 3 14 Banks UBS AG
Hea lth Care 2 13 Credit Suiss e Group
Indus trial Goods & Services 1 17 Insura nce Swis s Re AG
Insurance 1 7 Financial Services Juli us Bär Gruppe AG
Media 3 11
Oil & Gas 1 1 4 GROUP III - Industry & Others
Personal & House hold Goods 3 12 Health Care Novartis AG
Reta il 4 22 Roche Holding AG
Technology 19 Indus trial Goods & Services ABB Ltd.
Telecommunicat ion 2 4 Chemica ls Syngenta AG
Travel & Lei sure 1 9 Const ruction & Materi al s Holc im Ltd.
Utiliti es 2 10
Grand Total 32 189 Total 11
19
Table 2: Methodology of Covalence score
GRI (Global Reporting Initiative) is one of the most renowned standards for sustainability reporting, The news-based scores
from Covalence are grouped under seven categories, the GRI dimension. Each dimension covers specific criteria, which
correspondence to the GRI guidelines G3.1 is provided below. The news-based scores are computed for the seven categories,
and a Global score that aggregate all seven dimensions is provided as well.
GRI Dimension
GRI Aspect id Crit eria name Referenc es to GRI G3.1
Governanc e
Governanc e
4. Governa nce, Commi tments, and Engage ment
Unite d Nati ons Polic y
Unite d Nati ons Polic y
Commit ments to Exte rnal Init ia tive s
Commit ments to Exte rnal Initia tive s
Part 2. 4
Stake holder Engagem ent
Stake holder Engagem ent
Economic Performanc e
Fiscal Contri buti ons
EC1
Economic Performanc e
Socia l Sponsors hip
EC1
Economic Performanc e
Public Fundi ng
EC4
Market Pres ence
Wages
Market Pres ence
Loca l Sourcing
EC6
Market Pres ence
10
Loca l Hiring
EC7
Indir ect Economic Impacts
11
Infras tru cture s
GRI 3.1 EC8
Indir ect Economic Impacts
12
Indirect Economic Impac ts
EC9
Indir ect Economic Impacts
13
Pricing / Needs
EC9
Indir ect Economic Impacts
14
Inte lle ctual Propert y Rights
EC9
Mater ia ls
15
Mater ia ls
EN1, EN2
Energy
16
Energy
EN3, EN4, EN5, EN6, EN7
Wat er
17
Water Mana gement
EN8, EN9, EN10
Biodi vers ity
18
Biodi vers ity
EN11, EN12
Emiss ions, Effluent s, and Was te
19
Emiss ions
EN16, EN17, EN18, EN19, EN20
Emiss ions, Effluent s, and Was te
20
Was te Management
EN21, EN22, EN24, EN25
Emiss ions, Effluent s, and Was te
21
Poll ution
EN23
Products and Servi ces
22
Environme ntal Impa cts of Product s
EN26, EN27
Compl ia nce
23
Compl iance
EN28
Trans port
24
Environme ntal Impa ct of Trans port
EN29
Employme nt
25
Employme nt
LA1, LA2
Employme nt
26
Employee Bene fits
LA3, LA15
Lab or/Mana gement Relati ons
27
Trade Unions
LA5
Occupa tional Health and Safety
28
Hea lth and Safety
LA6, LA7, LA8, LA9
Train ing and Educati on
29
Traini ng and Educati on
LA10, LA11, LA12
Diversi ty and Equal Opport unit y
30
Diversi ty and Equal Opportu nity
LA13
Inves tment and Procureme nt Pra ctices
31
Huma n Rights Policy
HR1, HR2, HR3, HR10, HR11
Non-dis crim ina tion
32
Dis crimina ti on
HR4, LA14
Child Labor
33
Child Labor
HR6
Forced and Compuls ory Labor
34
Forced Labor
HR7
Security Pra ctices
35
Security Pra ctices
HR8
Indige nous Rights
36
Indige nous Rights
HR9
Loca l Communiti es
37
Loca l Communiti es
SO1
Loca l Communiti es
38
Huma nitari an Action
SO1
Corrupt ion
39
Corrupt ion
SO2, SO3, SO4
Public Pol icy
40
Lobbyi ng Practic es
SO5
Public Pol icy
41
Contri butions to Polit ical Pa rties
SO6
Anti-Compe titive Behavi or
42
Competition
SO7
Compl ia nce
43
Socia l Complia nce
SO8
Awards
44
Awards , Reports and Comme nts
Cust omer Heal th and Safety
45
Product Safety
PR2
Product and Service Lab eli ng
46
Product Lab eling
PR4
Market ing Communica tions
47
Market ing Communica tions
PR6, PR7
Cust omer Priva cy
48
Cust omer Priva cy
PR8
Compl ia nce
49
Product Compl iance
PR9
Socia l Impact s of Products
50
Socia l Impacts of Products
Socie ty
Product Res ponsibi lit y
Covalence EthicalQuote Criteria © Covalence SA 2012
The EthicalQuo te index aggregates tho usands of do cuments gathered o nline from vario us sources and classified acco rding to 50 sust ainability criteria inspired by the Global
Repo rting Initiative's G3.1 sustainabilit y reporting guidelines, as well as by the experience accumulat ed by Co valence since 2001. These criteria co ver the econo mic, social,
enviro nmental and gov ernance impacts o f companies . The Global Reporting Initiative (GRI) is a non-pro fit organizatio n that prom otes econo mic, environm ental and social
sust ainability. GRI provides all companies and organizatio ns with a com prehensive sustainabilit y reporting framework that is widely used around the world.
Governanc e,Co mmitment s,
and Engagemen t
Economic
Environm enta l
Lab or Pra cti ces and De cen t
Work
Huma n Rights
20
Table 3:
Descriptive statistics
Performance Measurement Model for January 2007 to December 2011. Stock’s return is log-return computed from the
monthly market observations of the close price from Telekurs. RF is the three -month T-bill returnfor US and UK, and call
money rate for Switzerland. RM-RF is the excess return on Fama and French's (1993) market proxy. SMB and HML are
Fama and French's factors for size and value. MOM is the Carhart’s factor for momentum. Our ESG factors represent the
variation in the news-based score on environmental, social/societal and governance criteria. The ESG Global Chng
represents the variation in the overall score, while the seven other ESG (category) Chng represent the changes for the score
computed only in one of the seven GRI dimension measured by Covalence.
CH 2007-2011 Count Mean Standard
Deviation Kurtosis Skewness Minimum Maximum Range
StockReturn-RF 618 -0.010 0.095 4.70 0.28 -0.53 0.49 1.02
RM-RF 618 -0.004 0.044 0.32 -0.23 -0.11 0.12 0.23
SMB 618 0.001 0.028 0.05 0.21 -0.06 0.09 0.15
HML 618 -0.002 0.022 -0.05 -0.13 -0.06 0.05 0.11
MOM 618 0.005 0.033 6.34 -1.68 -0.15 0.08 0.23
ESG Global Chng 618 0.026 0.717 144.34 6.67 -7.38 11.69 19.08
ESG A_Governance Chng 618 0.001 0.443 113.05 7.36 -2.40 6.56 8.96
ESG B_Economic Chng 618 0.008 0.251 103.46 5.39 -2.16 3.77 5.92
ESG C_Environment Chng
618 0.039 0.395 185.32 12.45 -0.88 6.59 7.47
ESG D_Labor Chng 618 -0.098 1.979 457.44 -19.53 -45.56 12.81 58.37
ESG E_Human Rights Chng
618 0.001 1.318 221.14 -8.06 -24.59 12.83 37.42
ESG F_Society Chng 618 -0.032 1.312 170.82 -3.44 -20.74 18.17 38.91
ESG G_Product Chng 618 -0.015 0.421 415.75 -18.35 -9.48 2.05 11.53
UK 2007-2010 Count Mean Standard
Deviation Kurtosis Skewness Minimum Maximum Range
StockReturn-RF 1'335 0.003 0.107 10.25 0.81 -0.63 0.90 1.53
RM-RF 1'335 0.002 0.054 -0.23 -0.45 -0.14 0.10 0.24
SMB 1'335 -0.002 0.045 5.65 1.15 -0.12 0.19 0.30
HML 1'335 -0.004 0.032 4.60 1.46 -0.07 0.11 0.19
MOM 1'335 0.006 0.064 6.79 -1.85 -0.27 0.14 0.41
ESG Global Chng 1'335 -0.049 1.249 689 -24.34 -38.10 4.68 42.78
ESG A_Governance Chng 1'335 0.018 0.873 490 18.84 -8.75 23.23 31.98
ESG B_Economic Chng 1'335 -0.020 2.890 789 -18.84 -90.39 50.66 141.06
ESG C_Environment Chng
1'335 -0.042 1.315 808 -25.43 -42.19 9.74 51.93
ESG D_Labor Chng 1'335 -0.178 5.149 782 -23.30 -163.34 63.64 226.98
ESG E_Human Rights Chng
1'335 -0.104 2.377 817 -26.86 -76.24 9.84 86.08
ESG F_Society Chng 1'335 -0.318 8.603 947 -29.76 -286.18 26.86 313.04
ESG G_Product Chng 1'335 -0.081 2.377 511 -18.39 -61.94 31.44 93.39
US 2007-2011 Count Mean Standard
Deviation Kurtosis Skewness Minimum Maximum Range
StockReturn-RF 8'039 0.005 0.112 65.74 3.00 -0.78 2.60 3.38
RM-RF 8'039 0.004 0.055 0.48 -0.66 -0.17 0.10 0.27
SMB 8'039 0.005 0.024 -0.36 0.51 -0.03 0.07 0.10
HML 8'039 -0.005 0.039 1.17 0.15 -0.12 0.11 0.22
MOM 8'039 0.000 0.071 9.22 -2.34 -0.35 0.13 0.48
ESG Global Chng 8'039 0.146 6.615 7'408 84.58 -32.08 581.12 613.20
ESG A_Governance Chng 8'039 0.096 3.986 5'027 65.81 -37.80 316.28 354.08
ESG B_Economic Chng 8'039 0.063 2.376 2'670 41.15 -65.82 157.57 223.38
ESG C_Environment Chng
8'039 0.060 1.860 2'334 43.82 -20.84 103.29 124.13
ESG D_Labor Chng 8'039 0.023 3.473 2'447 38.97 -58.96 226.74 285.70
ESG E_Human Rights Chng
8'039 -0.137 10.824 6'227 -75.32 -909.12 86.73 995.85
ESG F_Society Chng 8'039 -0.012 4.375 5'567 -69.22 -357.37 30.56 387.93
ESG G_Product Chng 8'039 0.097 5.580 7'299 83.54 -41.23 488.38 529.61
21
Table 4: Correlation between variables
Performance Measurement Model for January 2007 to December 2011. Stock’s return is log-return computed from the
monthly market observations of the close price from Telekurs. RF is the three -month T-bill returnfor US and UK, and call
money rate for Switzerland. RM-RF is the excess return on Fama and French's (1993) market proxy. SMB and HML are
Fama and French's factors for size and value. MOM is the Carhart’s factor for momentum. Our ESG factors represent the
variation in the news-based score on environmental, social/societal and governance criteria. The ESG Global Chng
represents the variation in the overall score, while the seven other ESG (category) Chng represent the changes for the score
computed only in one of the seven GRI dimension measured by Covalence.
CH Stock
Retu rn-R F
RM - R F HML SMB MOM ESG
Gl oba l
Chng
ESG
A_Gove rna
nce Chng
ESG
B_Econ omi
c Chng
ESG
C_Enviro n
me nt Chng
ESG
D_La bor
Chng
ESG
E_Hum an
Ri ghts
Chng
ESG
F_So ciety
Chng
ESG
G_Produ ct
Chng
Stock Return -RF 100% 60%(***) 30%(***) -33%(***) -36% (***) 7% (.) 0% 4% -3% -6% 2% 5% 3%
RM - R F 100% 32% -42% -50% 7%(.) 2% 0% -7% 0% -1% 2% 1%
HML 100% -33% -29% 2% 5% 1% 2% 5% -7%(.) 5% 6%
SMB 100% 19% 5% -3% 7% (.) 8%( *) 0% -3% 1% - 1%
MOM 100% -6% -3% 3% 1% 3% 4% -5% -3%
ESG G lob al Chng 100% 27%(***) 16%(***) 4% 9%( *) 3% 10%( *) 9%( *)
ESG A_G overna nce Ch ng 100% 15%(**) 3% 8%(*) 2% 6% 14%(**)
ESG B _Econom ic Chng 100% 2% 16%(***) 4% 13%(**) 3%
ESG C_En vironme nt Chn g 100% 1% 2% -16%(***) 2%
ESG D _Labo r Chng 100% 1% 1% 0%
ESG E_H uma n Ri ghts Chng 100% 9%(*) -1%
ESG F _Socie ty Chng 100% 5%
ESG G _Product Chn g 100%
n=618 Si gnif . code s: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
UK Stock
Retu rn-R F
RM - R F HML SMB MOM ESG
Gl oba l
Chng
ESG
A_Gove rna
nce Chng
ESG
B_Econ omi
c Chng
ESG
C_Enviro n
me nt Chng
ESG
D_La bor
Chng
ESG
E_Hum an
Ri ghts
Chng
ESG
F_So ciety
Chng
ESG
G_Produ ct
Chng
Stock Return .RF 100% 51% (***) 39% (***) 23% (***)
-30%
(
***
)
-15%
(
***
)
-2%
-1%
-2%
3%
-1%
1%
-1%
RM.RF 100% 69% (***) 28% (***)
-35%
(
***
)
-4%
-2%
-1%
-2%
3%
-3%
0%
0%
HML 100% 51% (***)
-57%
(
***
)
-9%
(
**
)
0%
0%
0%
-1%
-1%
-2%
-1%
SMB 100%
-68%
(
***
)
-10%
(
**
)
2%
4%
3%
-1%
1%
-4%
2%
MOM 100% 12% (***) 2% -2% -2% -1 % 1% 0% -1%
ESG G lob al Chng 100% 2% 2% 6% (*) 1% 1% 11% (***) 1%
ESG A_G overna nce Ch ng 100% 1% 1% -1% 1% 0% -2%
ESG B _Econom ic Chng 100% 3% 1% 0% 2% 1%
ESG C_En vironme nt Chn g 100% 1% 0% 0% 1%
ESG D _Labo r Chng 100% 3% 0% 0%
ESG E_H uma n Ri ghts Chng 100% -1% 0%
ESG F _Socie ty Chng 100% 0%
ESG G _Product Chn g 100%
n=1335 S igni f. cod es: 0 '***' 0.001 '**' 0.01 '* ' 0.05 '.' 0.1 ' ' 1
US Stock
Retu rn-R F
RM - R F HML SMB MOM ESG
Gl oba l
Chng
ESG
A_Gove rna
nce Chng
ESG
B_Econ omi
c Chng
ESG
C_Enviro n
me nt Chng
ESG
D_La bor
Chng
ESG
E_Hum an
Ri ghts
Chng
ESG
F_So ciety
Chng
ESG
G_Produ ct
Chng
Stock Return .RF 100% 55% (*** ) 28% ( ***) 22% ( ***)
-33%
(
***
)
-1%
1%
1%
-1%
2% (.)
-1%
-2%
-1%
RM.RF 100% 43% (***) 39% (***)
-48%
(
***
)
-2%
(
*
)
1%
1%
-1%
2% (.)
-1%
0%
-1%
HML 100% 18% (***)
-47%
(
***
)
3% (*)
0%
0%
0%
1%
0%
1%
0%
SMB 100%
-14%
(
***
)
-1%
-2%
-2%
-1%
2% (*)
1%
2%
-2%
MOM 100% 1% -2% -1% 0% 1% 2% (.) -1% 0%
ESG G lob al Chng 100% 1% 4% (**) 1% 0% 0% 1% 0%
ESG A_G overna nce Ch ng 100% 1% 0% 0% 3% (*) 1% 0%
ESG B _Econom ic Chng 100% 0% 0% 0% 1% 0%
ESG C_En vironme nt Chn g 100%
-2%
(
.
)
0%
0%
0%
ESG D _Labo r Chng 100% 0% 0% 0%
ESG E_H uma n Ri ghts Chng 100% 0% 0%
ESG F _Socie ty Chng 100% 0%
ESG G _Product Chn g 100%
n=8039 S igni f. cod es: 0 '***' 0.001 '**' 0.01 '* ' 0.05 '.' 0.1 ' ' 1
22
Table 5: Regression results for Model 1
Performance Measurement Model for January 2007 to December 2011. Stock’s return is log-return computed from the
monthly market observations of the close price from Telekurs. RF is the three -month T-bill returnfor US and UK, and call
money rate for Switzerland. RM-RF is the excess return on Fama and French's (1993) market proxy. SMB and HML are
Fama and French's factors for size and value. MOM is the Carhart’s factor for momentum. The ESG Global Chng factor
represents the variation in the overall news-based score on environmental, social/societal and governance criteria
StockReturn-RF ~ RM-RF + S MB + HML + MOM + ESG Global Chng
CH Estimate Std. Error t-value Pr(>|t|) VIF
RM-RF 1.084 *** 0.0881 12.3130 <2e-16 1.6037
SMB -0.244 * 0.1215 -2.0100 0.0448 1.2994
HML 0.411 ** 0.1541 2.6650 0.0079 1.2098
MOM -0.196 . 0.1079 -1.8160 0.0699 1.3805
ESG Global Chng 0.004 0.0043 0.8310 0.4062 1.0138
(Intercept) -0.004 0.0031 -1.2970 0.1951
Signif. codes: Pr(>|t|) 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error : 0 .07548 on 6 12 degrees of freedom
Multiple R-squared: 0.3 75, Adjusted R-s quared: 0.3699
F-statist ic: 73.44 on 5 an d 612 DF, p -va lue: < 0.0000
Residuals : Min 1Q Median 3Q M ax
-0.35965 -0.04201 -0.0018 0.0398 0.3060
UK Estimate Std. Error t value Pr(>|t|) VIF
RM-RF 0.969 *** 0.0640 15.1440 < 2e-16 1.9392
SMB 0.024 0.0765 0.3130 0.7543 1.9393
HML -0.115 0.1239 -0.9300 0.3523 2.6133
MOM -0.211 *** 0.0564 -3.7330 0.0002 2.1385
ESG Global Chng -0.010 *** 0.0020 -4.8290 0.0000 1.0160
(Intercept) 0.001 0.0025 0.2600 0.7949
Signif. codes: Pr(>|t|) 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error : 0 .09026 on 1 329 degrees of freedom
Multiple R-squared: 0.2 93, Adjusted R- squ ared: 0.2904
F-statist ic: 110.2 on 5 an d 1329 DF, p- value: < 2.2e-16
Residuals:
Min 1Q Media n 3Q Max
-0.4927 - 0.0438 -0.0005 0.0408 0. 755 8
US Estimate Std. Error t value Pr(>|t|) VIF
RM-RF 1.005 *** 0.0241 41.7790 < 2e-16 1.6075
SMB 0.070 0.0471 1.4930 0.1350 1.1836
HML 0.072 * 0.0318 2.2740 0.0230 1.3845
MOM -0.126 *** 0.0178 -7.0810 0.0000 1.4666
ESG Global Chng 0.000 0.0002 0.2830 0.7770 1.0024
(Intercept) 0.001 0.0011 1.0900 0.2760
Signif. codes: Pr(>|t|) 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error : 0 .09356 on 8 033 degrees of freedom
Multiple R-squared: 0.3 061 ,Adjusted R -sq uared: 0.3057
F-statist ic: 708.7 on 5 an d 8033 DF, p- value: < 2.2e-16
Residuals:
Min 1Q Media n 3Q Max
-0.6242 -0.04328 -0 .00256 0. 0403 2.44116
23
Table 6: Regression results for Model 1 - Sub-scores
Performance Measurement Model for January 2007 to December 2011. Stock’s return is log-return computed from the
monthly market observations of the close price from Telekurs. RF is the three -month T-bill returnfor US and UK, and call
money rate for Switzerland. RM-RF is the excess return on Fama and French's (1993) market proxy. SMB and HML are
Fama and French's factors for size and value. MOM is the Carhart’s factor for momentum. Our ESG factors represent the
variation in the news-based score on environmental, social/societal and governance criteria. ESG (category) Chng represent
the changes for the score computed only in one of the seven GRI dimension measured by Covalence.
24
CH Estimate Std.Error tvalue Pr(>|t|) VIF
RM-RF 1.087 *** 0.087765 12.38 <2e-16 1.600978
SMB -0.255 * 0.121912 -2.089 0.0371 1.313569
HML 0.423 ** 0.155375 2.721 0.0067 1.235752
MOM -0.195 . 0.107964 -1.809 0.0709 1.390157
ESG A_G over nan ce Chng -0.006 0.007012 -0.823 0.4107 1.051974
ESG B_E cono mic Chng 0.023 . 0.01251 1.873 0.0615 1.075129
ESG C_E nvir onm ent Chng 0.003 0.007842 0.323 0.7469 1.046932
ESG D_L abor Ch ng -0.003 * 0.001558 -2.112 0.0351 1.034534
ESG E_H uman .Ri ghts Chng 0.002 0.002324 0.937 0.3491 1.021631
ESG F_S ocie ty Chng 0.002 0.00239 0.661 0.5086 1.06963
ESG G_P rodu ct Chng 0.004 0.007288 0.546 0.585 1.02624
(Intercept) -0.004 0.003087 -1.409 0.1594
Signif. codes: Pr(>|t|) 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07529 on 606 degrees of freedom
Multiple R-squared: 0.3841, Adjusted R-squared: 0.3729
F-statistic: 34.36 on 11 and 606 DF, p-value: < 2.2e-16
Residuals: Min 1Q Median 3Q Max
-0.35766 -0.04021 -0.00118 0.03955 0.30425
UK Estimate Std. Error t value Pr(>|t|) VIF
RM-RF 0.959 *** 0.06482 14.801 < 2e-16 1.947188
SMB 0.036 0.07756 0.47 0.639 1.951354
HML -0.093 0.1254 -0.743 0.458 2.620275
MOM -0.224 *** 0.05701 -3.922 0.0000925 2.139914
ESG A_G over nan ce Chng -0.001 0.002865 -0.382 0.703 1.003457
ESG B_E cono mic Chng 0.000 0.0008656 -0.532 0.595 1.003316
ESG C_E nvir onm ent Chng -0.001 0.001902 -0.412 0.68 1.002886
ESG D_L abor Ch ng 0.000 0.0004861 0.471 0.638 1.004173
ESG E_H uman .Ri ghts Chng 0.000 0.001052 -0.089 0.929 1.00196
ESG F_S ocie ty Chng 0.000 0.0002909 0.189 0.85 1.003889
ESG G_P rodu ct Chng 0.000 0.001051 -0.253 0.8 1.001766
(Intercept) 0.001 0.002561 0.523 0.601
Signif. codes: Pr(>|t|) 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.09122 on 1323 degrees of freedom
Multiple R-squared: 0.2811,Adjusted R-squared: 0.2752
F-statistic: 47.04 on 11 and 1323 DF, p-value: < 2.2e-16
Residuals: Min 1Q Median 3Q Max
-0.491 55 -0.043 81 -0.0 0108 0 .04 055 0.747 87
US Estimate Std. Error t value Pr(>|t|) VIF
RM-RF 1.004 *** 0.02406 41.713 < 2e-16 1.60753
SMB 0.071 0.0471 1.516 0.1295 1.185605
HML 0. 073 * 0.03182 2.297 0.0216 1.382455
MOM -0.126 *** 0.01776 -7.108 1.28E-12 1.468276
ESG A_G over nan ce Chng 0.000 0.000262 0.348 0.7279 1.001718
ESG B_E cono mic Chng 0.000 0.0004394 0.862 0.3887 1.000742
ESG C_E nvir onm ent Chng 0.000 0.0005613 -0.886 0.3759 1.00075
ESG D_L abor Ch ng 0.000 0.0003007 0.983 0.3256 1.001574
ESG E_H uman .Ri ghts Chng 0.000 0.00009648 0.272 0.7858 1.001517
ESG F_S ocie ty Chng 0 .00 0 . 0.0002386 -1.776 0.0758 1.000549
ESG G_P rodu ct Chng 0.000 0.000187 -0.244 0.807 1.000373
(Intercept) 0.001 0.001084 1.091 0.2753
Signif. codes: Pr(>|t|) 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.09356 on 8027 degrees of freedom
Multiple R-squared: 0.3066,Adjusted R-squared: 0.3057
F-statistic: 322.7 on 11 and 8027 DF, p-value: < 2.2e-16
Residuals: Min 1Q Median 3Q Max
-0.624 26 -0.0 4328 -0.00 26 0.04035 2.44102
StockReturn-RF ~ RM-RF + SMB + HML + MOM + ESG A_Governance Chng + ESG B_Economic.Chng + ESG
C_Environment.Chng + ESG D_Labor.Chng + ESG E_Human.Rights.Chng + ESG F_Society.Chng + ESG
G_Product.Chng
25
Table 7: Regression results for Model 1 - Sub-scores per year
Performance Measurement Model for January 2007 to December 2011. Stock’s return is log-return computed from the
monthly market observations of the close price from Telekurs. RF is the three -month T-bill return for US and UK, and call
money rate for Switzerland. RM-RF is the excess return on Fama and French's (1993) market proxy. SMB and HML are
Fama and French's factors for size and value. MOM is the Carhart’s factor for momentum. Our ESG factors represent the
variation in the news-based score on environmental, social/societal and governance criteria. ESG (category) Chng represent
the changes for the score computed only in one of the seven GRI dimension measured by Covalence.
CH 2007 2008 2009 2010 2011
Estimate Estimate Estimate Estimate Estimate
RM-RF 1.18 *** 0.96 *** 0.98 * 1.49 *** 0.95 ***
SMB -0.02 -0.55 . -0.37 -0.19 -0.51
HML 0.24 0.03 0.12 0.51 . 0.66 *
MOM -0.44 -0.09 -0.40 0.12 -0.20
ESG A_Governance.Chng -0.001 -0.025 . -0.004 -0.019 0.000
ESG B_Economic.Chng 0.009 0.047 0.086 . - 0.023 0.023
ESG C_Environment.Chng 0.004 -0.055 -0.068 -0.032 0.085 *
ESG D_Labor.Chng 0.036 -0.005 * 0.006 -0.012 0.014
ESG E_Human.Rights.Chng -0.008 0.009 0.003 0.001 -0.031
ESG F_Society.Chng 0.001 0.018 * -0.005 0.012 -0.019
ESG G_Product.Chng 0.025 0.116 . -0.059 -0.010 0.004
(Intercept) -0.01 -0.02 0.00 0.00 -0.01
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
UK 2007 2008 2009 2010
Estimate Estimate Estimate Estimate
RM-RF 1.18 *** 0.83 *** 1.01 *** 1.14 ***
SMB -0.19 0.04 0.10 0.05
HML -0.53 -0.02 -0.29 -0.62 .
MOM -0.16 -0.19 . -0.30 * 0.05
ESG A_Governance.Chng -0.015 -0.004 -0.014 0.002
ESG B_Economic.Chng -0.016 . -0.001 0.000 0.029 *
ESG C_Environment.Chng 0.007 0.000 -0.014 0.013
ESG D_Labor.Chng 0.002 0.001 0.000 0.001
ESG E_Human.Rights.Chng 0.002 0.006 0.014 -0.001
ESG F_Society.Chng 0.020 0.000 -0.017 *** 0.000
ESG G_Product.Chng -0.002 0.000 -0.001 0.017
(Intercept) -0.01 0.00 -0.01 0.00
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
US 2007 2008 2009 2010 2011
Estimate Estimate Estimate Estimate Estimate
RM-RF 0.94 *** 1.10 *** 0.92 *** 1.00 *** 0.90 ***
SMB -0.08 0.05 0.10 0.01 0.08
HML -0.17 0.16 * 0.10 0.08 0.14
MOM -0.12 . -0.02 -0.17 *** 0.04 -0.30
ESG A_Governance.Chng 0.000 -0.001 0.000 0.003 0.000
ESG B_Economic.Chng 0.001 0.002 0.000 0.001 0.001
ESG C_Environment.Chng 0.000 0.000 -0.006 0.002 0.004
ESG D_Labor.Chng 0.002 0.000 0.001 -0.001 0.001
ESG E_Human.Rights.Chng -0.001 0.000 0.000 0.000 0.001
ESG F_Society.Chng 0.000 ** -0.002 0.002 -0.001 -0.001
ESG G_Product.Chng 0.001 0.000 -0.008 ** 0.000 0.001
(Intercept) 0.00 0.00 0.00 0.00 0.00
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
StockReturn-RF ~ RM-RF + SMB + HML + MOM + ESG A_Governance Chng + ESG B_Economic.Chng + ESG
C_Environment.Chng + ESG D_Labor.Chng + ESG E_Human.Rights.Chng + ESG F _Society.Chng + ESG G_Product.Chng
26
Table 8: Regression results for Model 1 – Sectors and Sector’s groups
Performance Measurement Model for January 2007 to December 2011. Stock’s return is log-return computed from the
monthly market observations of the close price from Telekurs. RF is the three -month T-bill return for US and UK, and call
money rate for Switzerland. RM-RF is the excess return on Fama and French's (1993) market proxy. SMB and HML are
Fama and French's factors for size and value. MOM is the Carhart’s factor for momentum. Our ESG factors represent the
variation in the news-based score on environmental, social/societal and governance criteria. ESG (category) Chng represent
the changes for the score computed only in one of the seven GRI dimension measured by Covalence. In this regression, US
and UK firms where divided using ICB supersectors. In Switzerland, as the number of sample was too small, we grouped the
ICB supersectors in three custom groups by type of activity: Consumer facing, Bank and Insurance, Industry and other.
27
CH -by sectors
Estimate Estimate Estimate
RM-RF 1.04 *** 1.32 *** 1.01 ***
SMB -0.25 - 0.42 . -0.18
HML -0.19 1.52 *** 0.01
MOM 0.24 - 0.88 *** 0.13
ESG A_Governance Chng -0.025 -0.017 -0.018 *
ESG B_Economic Chng 0.014 0.001 0.085 ***
ESG C_Environment Chng -0.008 0.083 0.001
ESG D_Labor Chng 0.035 -0.005 ** 0.005
ESG E_Human.Rights Chng 0.210 * 0.029 0.001
ESG F_Society Chng 0.003 0.018 * - 0.002
ESG G_Product Chng 0.038 0.002 0.002
(Intercept) 0.00 -0.01 -0.01 .
UK - by sectors
Estimate Estimate Estima te Estima te Estimate Estimate Estimate Estim ate Estimate
RM.RF 3.05 ** 0.99 *** 1.98 *** 0.86 *** 0.66 *** 1.04 ***
SMB 2.47 * 0.31 -0.01 -0.19 -0.57 *** 0.23
HML -2.35 0.64 . -0.40 -0.37 -0.66 * -0.90 .
MOM -0.16 - 0.85 *** 0.02 -0.17 -0.32 ** 0.05
A_Governance.Chng NA -0.004 -0.199 0.017 0.002 0.070
B_Economic.Chng -0.008 0.050 . -0.008 0.055 0.061 0.000
C_Environment.Chng 0.004 -0.001 0.037 -0.010 0.085 -0.014
D_Labor.Chng -0.126 0.007 0.001 0.004 -0.002 -0.012
E_Human.Rights.Chng NA 0.002 0.000 -0.001 0.003 NA
F_Society.Chng NA -0.010 * 0.036 -0.013 0.081 0.797
G_Product.Chng NA 0.052 0.001 -0.029 0.002 0.028 *
(Intercept) -0.01 0.00 0.01 0.01 0.00 -0.01
Estimate Estimate Estima te Estima te Estimate Estimate Estimate Estim ate Estimate
RM.RF 0.66 . 0.78 *** 0.96 *** 0.64 *** 0.83 *** 0.70 *** 0.89 *** 0.58 *
SMB 0.41 0.13 -0.73 ** 0.05 0.18 -0.10 -0.03 -0.17
HML 1.06 -0.47 . 0.23 - 0.51 * -0.21 0.69 . -0.71 -0.04
MOM -0.30 0.06 0.00 0.01 -0.15 0.10 -0.17 -0.03
A_Governance.Chng 1.195 -0.543 -0.069 *** -0.022 0.001 -0.301 -0.005 NA
B_Economic.Chng -0.972 0.367 0.182 0.002 -0.002 -0.162 -0.008 -0.068
C_Environment.Chng -1.159 NA 0.113 ** -0.006 0.328 0.460 -0.012 . -0.063
D_Labor.Chng 0.017 -0.007 -0.019 0.000 -0.003 - 0.001 -0.021 *** 0.050
E_Human.Rights.Chng NA -0.049 0.014 0.005 0.004 -0.015 0.005 NA
F_Society.Chng 0.252 0.241 * 0.002 0.000 0.056 0.257 -0.001 0.266
G_Product.Chng -0.218 . - 0.018 0.005 0.000 -0.073 0.059 0.000 0.054
(Intercept) 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
US - by se ctors
Estimate Estimate Estima te Estima te Estimate Estimate Estimate Estim ate Estimate
RM-RF 1.42 *** 1.14 *** 1.59 *** 1.00 *** 1.11 *** 1.43 *** 0.61 *** 0.88 *** 1.05 ***
SMB 0.76 -1.34 *** -0.09 0.42 . 0.42 - 0.43 -0.34 ** -0.45 *** - 0.08
HML 0.22 2.23 *** -0.41 * 0.26 -0.06 0.48 . -0.02 - 0.16 . 0.16 *
MOM -0.84 *** -0.46 *** -0.25 * -0.41 *** -0.17 -0.24 0.05 0.04 -0.13 ***
ESG A_Governance Chng 0.047 0.000 0.001 -0.021 0.023 -0.003 -0.001 0.001 0.001
ESG B_Economic Chng -0.018 0.187 0.000 0.046 0.017 0.000 0.001 -0.019 0.001
ESG C_Environment Chng -0.011 0.034 0.003 -0.001 -0.002 - 0.020 0.000 -0.005 . 0.001
ESG D_Labor Chng 0.002 0.000 0.000 0.003 -0.010 -0.004 0.001 -0.001 0.001
ESG E_Human.Rights Chng 0.010 0.003 0.007 -0.036 0.001 0.030 0.000 0.001 0.000
ESG F_Society Chng 0.060 -0.006 - 0.002 0.011 -0.007 0.000 0.001 -0.002 0.000
ESG G_Product Chng 0.018 -0.005 -0.010 - 0.001 -0.056 -0.010 * 0.001 0.001 0.002
(Intercept) 0.01 0.01 0.00 0.00 -0.01 -0.01 0.00 0.00 0.00
Estimate Estimate Estima te Estima te Estimate Estimate Estimate Estim ate Estimate
RM.RF 1.11 *** 1.22 *** 1.27 *** 0.75 *** 0.79 *** 1.17 *** 1.05 *** 0.69 *** 0.73 ***
SMB -0.34 0.32 . -0.16 0.01 0.61 *** 0 .33 ** -0.15 1.35 *** -0.30 **
HML 0.68 *** 0.04 -0.50 *** 0.29 ** 0.10 -0.31 *** -0.62 ** 0.52 * -0.36 * **
MOM -0.37 *** 0.03 0.03 -0.02 -0.17 *** -0.02 0.07 -0.64 *** 0.10 **
A_Governance.Chng -0.028 0.008 0.000 0.003 0.000 0.003 0.013 * 0.001 0.000
B_Economic.Chng 0.339 0.004 0.009 . -0.003 -0.003 * 0.001 0.051 * -0.024 . 0.002
C_Environment.Chng -0.014 0.000 -0.002 0.001 0.000 -0.002 -0.001 0.000 0.000
D_Labor.Chng 0.121 0.004 0.001 0.000 0.000 0.001 -0.025 * 0.015 -0.004
E_Human.Rights.Chng 0.060 -0.001 0.001 -0.003 0.000 0.002 -0.010 0.000 -0.003
F_Society.Chng 0.005 0.005 0.000 * 0.003 -0.003 0.001 0.001 0.000 -0.013
G_Product.Chng 0.016 0.000 0.006 0.001 -0.004 0.016 * -0.013 -0.004 . 0.000
(Intercept) 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 -0.01 *
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Personal & Household Goods
Food & Beverages
Grou p II
Banks
Insurance
Financial Services
Industrial Goods & Services
Construction & Materials
Health Care
Chemicals
Stoc kRet urn-R F ~ RM-R F + SMB + HM L + MOM + ES G A_G over nanc e Ch ng + ESG B_E conom ic.C hng + ES G C_ Envi ronm ent. Chng +
ESG D_La bor.C hng + ES G E_ Huma n.Ri ghts .Chn g + ESG F _Soc iety .Chn g + ESG G_Pr oduct .Chn g
Grou p I Grou p II I
Food & Health Care Industrial
Insurance Media Oil & Gas Personal & Retail Technology Telecommun
Automobiles B anks B asic Resources Chemicals Construction & Financial
Travel & Utilities
Automobiles
& Parts
Banks Basic Resources Chemicals
Construction &
Materials
Financial
Services
Food &
Beverages
Health Care
Industrial
Goods &
Technology Telecommun
ication
Travel &
Leisure UtilitiesInsurance Media Oil & Gas
Personal &
Household
Goods
Retail
28
Figure 1: Regression results for Model 2
Non-linear functions for the Performance Measurement Model for January 2007 to December 2011. Model 2 is a non-linear
Market model to explain StockReturn-RF based on functions of the variables hereafter Stock’s return is log-return computed
from the monthly market observations of the close price from Telekurs. RF is the three -month T-bill return for US and UK,
and call money rate for Switzerland. RM-RF is the excess return on Fama and French's (1993) market proxy. SMB and HML
are Fama and French's factors for size and value. MOM is the Carhart’s factor for momentum. Global Chng factor represent
the variation in the news-based score on environmental, social/societal and governance criteria. The graphs represent the
estimated functions of the StocksReturn-RF(y-axis) depending of the variable in the x-axis.
29
Figure 2: Regression results for Model 2 - Sub-scores
Non-linear functions for the Performance Measurement Model for January 2007 to December 2011. Model 2 is a non-linear
Market model to explain StockReturn-RF based on functions of the variables hereafter Stock’s return is log-return computed
from the monthly market observations of the close price from Telekurs. RF is the three -month T-bill return for US and UK,
and call money rate for Switzerland. RM-RF is the excess return on Fama and French's (1993) market proxy. SMB and HML
are Fama and French's factors for size and value. MOM is the Carhart’s factor for momentum.The variable(Category) Chng
represent the variation in the news-based score computed only in one of the seven GRI dimension measured by Covalence.
The graphs represent the estimated functions of the StocksReturn-RF(y-axis) depending of the variable in the x-axis.
30
Appendix A
Fig. A1 - Socially Responsible Investment – Acknowledged Strategies
Fig. A2 - US and European SRI Growth –US SIF 2012 Executive Summary report, EURO SIF 2012 report
SRI in the US IN $bn 1997 1997 1999 2003 2005 2007 2010 2012
639 1'185 2'159 2'323 2'290 2'711 2'069 3'744
SRI in Europe in €bn 2005 2007 2009 2011
1'768 4'066 *7'375 *11'661
* includes norm-based screening since 2009 - 2009 988bn-2011 2'346bn
Type Strategies Definition
Negative
Screening
Exclusion Exclusion of certain sectors such as weapons etc.
Norm-based screening Exclusion based on compliance with international standard and norms
Positive
screening
ESG Integration Integration of ESG criteria to classic Financial analysis
Best-in-Class Selection or Weighting of stocks according to ESG criteria
Active
Investment
Themed Funds Funds with a theme focused on Sustainability , e.g. Green energy, Health, etc.
Engagement Voting Active Ownership through share voting on ESG topics
Impact Investment Investing for a clear ESG impact e.g. Microfinance, local business funds, etc...
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