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ESG Impact on Market Performance of Firms: International Evidence

May 2015
Management international 19(2)

DOI:10.7202/1030386ar

Authors:

Jean-Michel Sahut

Ecole de Commerce IDRAC

Helene Pasquini-Descomps

University of Applied Sciences and Arts Western Switzerland – Geneva

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.

ESG DATA

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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.

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

. 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.

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.

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.

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

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.

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.

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,

or even destroy a thus-far successful

company.

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

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

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

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).

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

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

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.

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

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

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

on various ESG thematic. More details about how Covalence

http://www.ammannsteiner.ch/

http://business-school.exeter.ac.uk/research/areas/centres/xfi/research/famafrench/files/

http://www.jasonhsu.org/research-data.html

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/

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.

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

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

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.

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

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.

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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

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

4. Governa nce, Commi tments, and Engage ment

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

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

Loca l Hiring

EC7

Indir ect Economic Impacts

Infras tru cture s

GRI 3.1 EC8

Indir ect Economic Impacts

Indirect Economic Impac ts

EC9

Indir ect Economic Impacts

Pricing / Needs

EC9

Indir ect Economic Impacts

Inte lle ctual Propert y Rights

EC9

Mater ia ls

EN1, EN2

Energy

EN3, EN4, EN5, EN6, EN7

Wat er

Water Mana gement

EN8, EN9, EN10

Biodi vers ity

EN11, EN12

Emiss ions, Effluent s, and Was te

Emiss ions

EN16, EN17, EN18, EN19, EN20

Emiss ions, Effluent s, and Was te

Was te Management

EN21, EN22, EN24, EN25

Emiss ions, Effluent s, and Was te

Poll ution

EN23

Products and Servi ces

Environme ntal Impa cts of Product s

EN26, EN27

Compl ia nce

Compl iance

EN28

Trans port

Environme ntal Impa ct of Trans port

EN29

Employme nt

LA1, LA2

Employme nt

Employee Bene fits

LA3, LA15

Lab or/Mana gement Relati ons

Trade Unions

LA5

Occupa tional Health and Safety

Hea lth and Safety

LA6, LA7, LA8, LA9

Train ing and Educati on

Traini ng and Educati on

LA10, LA11, LA12

Diversi ty and Equal Opport unit y

Diversi ty and Equal Opportu nity

LA13

Inves tment and Procureme nt Pra ctices

Huma n Rights Policy

HR1, HR2, HR3, HR10, HR11

Non-dis crim ina tion

Dis crimina ti on

HR4, LA14

Child Labor

HR6

Forced and Compuls ory Labor

Forced Labor

HR7

Security Pra ctices

HR8

Indige nous Rights

HR9

Loca l Communiti es

SO1

Loca l Communiti es

Huma nitari an Action

SO1

Corrupt ion

SO2, SO3, SO4

Public Pol icy

Lobbyi ng Practic es

SO5

Public Pol icy

Contri butions to Polit ical Pa rties

SO6

Anti-Compe titive Behavi or

Competition

SO7

Compl ia nce

Socia l Complia nce

SO8

Awards

Awards , Reports and Comme nts

Cust omer Heal th and Safety

Product Safety

PR2

Product and Service Lab eli ng

Product Lab eling

PR4

Market ing Communica tions

PR6, PR7

Cust omer Priva cy

PR8

Compl ia nce

Product Compl iance

PR9

Socia l Impact s of Products

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

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

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%

-1%

RM.RF 100% 69% (***) 28% (***)

-35%

(

***

)

-4%

-2%

-1%

-2%

-3%

HML 100% 51% (***)

-57%

(

***

)

-9%

(

)

-1%

-2%

-1%

SMB 100%

-68%

(

***

)

-10%

(

)

-1%

-4%

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%

2% (.)

-1%

-2%

-1%

RM.RF 100% 43% (***) 39% (***)

-48%

(

***

)

-2%

(

)

-1%

2% (.)

-1%

HML 100% 18% (***)

-47%

(

***

)

3% (*)

SMB 100%

-14%

(

***

)

-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%

(

)

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

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

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.

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

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

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.

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

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.

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.

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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