the effect of portfolio performance using social
TRANSCRIPT
The effect of portfolio performance using social responsibility screens
Master Thesis
Author: Donny Bleekman BSc. (927132)
Supervisor: dr. P. C. (Peter) de Goeij
Study program: Master Finance
December 2013
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Abstract
This study investigates the effect of portfolio performance between July 2002 and December 2011
using social responsibility scores from ASSET4. Based on ASSET4 scores equal- and value-weighted
portfolios were created and tested for abnormal returns using the Carhart (1997) four-factor model.
Although the estimated alphas are statistically insignificant, I observe systematic differences in the
factor loadings between high-rated and low-rated socially responsible companies. On average, high-
rated socially responsible companies have lower market betas than low-rated socially responsible
companies. High-rated socially responsible companies have a negative loading on the SMB factor,
while low-rated socially responsible companies have a positive loading on the SMB factor. Low-rated
socially responsible companies have a greater negative loading on momentum than high-rated
socially responsible companies.
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Contents
1. Introduction .................................................................................................................................... 3
2. Literature Survey & Hypothesis Development .............................................................................. 5
2.1 Literature Survey ........................................................................................................................ 5
2.2 Hypothesis Development ........................................................................................................... 10
3. Data & Methodology .................................................................................................................... 13
3.1 Data ............................................................................................................................................. 13
3.2 Methodology............................................................................................................................... 14
4. Empirical findings .......................................................................................................................... 16
4.1 Descriptive Statistics ................................................................................................................... 16
4.2 Portfolio Results ......................................................................................................................... 19
5. Conclusion ..................................................................................................................................... 24
6. References ..................................................................................................................................... 26
7. Appendix ....................................................................................................................................... 28
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1. Introduction
Socially responsible investment (SRI) is becoming widely spread among institutional global investors
and has drawn much attention among researchers in recent years. According to the US SIF
Foundation in 2012 the assets engaged in sustainable and responsible investing practice represent
11.3 percent of the $33.3 trillion in total assets under management tracked by Thomson Reuters
Nelson.1 The last decade the number of SRI related studies have increased substantially. However,
the results from these studies do not give a clear picture, whether it pays off to invest socially
responsible.
Derwall et al. (2005) report that a best-in-class portfolio that score high on “eco-efficiency scores”
will return a four-factor alpha of 4.15 percent per year over the period 1995-2003. Kempf and
Osthoff (2007) show that environment, employee, and community screening using the KLD dataset
will earn statistically significant higher four-factor alphas for high-rated portfolios. However, they do
not observe statistical significant outperformance of high-rated companies over low-rated companies
for the diversity, human rights, and product screen. Statman and Glushkov (2009) used the same
dataset as Kempf and Osthoff (2007) and found that stocks with high social responsibility ratings
performed generally better than stocks with low social responsibility ratings. However, none of the
results were statistically significant except for the employee screen. Galema et al. (2008) who also
used the KLD dataset only found outperformance for the high-rated community portfolio. Studies
that covered the European market even tell a less convincing story. Van de Velde et al. (2005) as well
as Brammer et al. (2006) did not find any statistically significant outperformance of high-rated
socially responsible companies over low-rated companies.
In order to extent the literature on SRI I used the ASSET4 dataset from Thomson Reuters for my
thesis. To my knowledge the ASSET4 dataset has not been used for SRI related studies. The
advantage of using the ASSET4 dataset is that it covers the global market instead of just one
particular region. The ASSET4 dataset assigns scores to companies based on four pillars: corporate
governance, economic, environment, and social. Moreover, ASSET4 provides an overall indicator that
combines all four pillar scores into one score that measures overall SRI performance. With these five
scores I have constructed several equal- and value-weighted portfolios. The first portfolio consists of
the top 10 percent of companies that perform the best based on one of the five scores. The second
portfolio consists of the bottom 10 percent of companies that perform the worst based on one of the
five scores. In addition, for each pillar score and the overall score high-low portfolios were
constructed in which I go long in the top ten percent and short in the bottom ten percent for each
1 2012 Report on Sustainable and Responsible Investing Trends in the United States, see
http://www.ussif.org/content.asp?contentid=40
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score. These portfolios were tested for abnormal return using the Carhart (1997) four-factor model
covering the period July 2002 till December 2011. The results are further tested by performing some
robustness checks. First, I test for any regional bias by considering the US and European market
separately. Second, I check whether the estimated alphas remain the same, if I apply different cut-
offs. Third, I apply best-in-class screening to overcome a possible bias towards some economic
sectors.
Results from the equal-weighted portfolios show that investors can earn positive abnormal returns, if
they invest in companies that score high on corporate governance and economic. The opposite is
true for screening companies on environment and social scores. Low-rated environment and social
companies earn positive abnormal returns and high-rated environment and social companies earn
negative abnormal returns. However, the estimated alphas are statistically insignificant. Using value-
weighted portfolios, regional samples, different cut-offs, and best-in-class screening do not result in
any statistically significant outperformance using SRI. In this study I do observe statistically significant
differences in the factor loadings of the high- and low-rated portfolios. On average high-rated socially
responsible companies have lower market betas than low-rated socially responsible companies. Also,
high-rated socially responsible companies have a negative loading on the SMB factor, while low-rated
socially responsible companies have a positive loading on the SMB factor. Low-rated socially
responsible companies have a greater negative loading on momentum than high-rated socially
responsible companies. These findings are robust for regional and sector bias.
The remainder of this thesis proceeds as follows. In section 2, previous literature on SRI is covered
and I develop the hypotheses I want to test. Section 3 gives a description of the data and present the
methodology for portfolio formation as well as the model to test the hypotheses. In section 4, I
present the descriptive statistics and the empirical results from my regression analyses. I conclude in
section 5.
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2. Literature Survey & Hypothesis Development
2.1 Literature Survey
There has been an increase in the attention towards social responsible investment (SRI) related
academic studies. Renneboog et al. (2008) provide a full-scale overview of some of the most recent
studies on SRI found in literature. This section will cover the most recent studies on portfolio
performance.
Van de Velde et al. (2005) conducted a study about the return of SRI portfolios using Vigeo corporate
social responsibility scores. The sample consisted of European firms covering the period from 2000 to
2003. Portfolio construction was based on a company’s relative sustainability performance within the
sector it operates in. A sustainability score greater than one standard deviation above the sector
mean meant it would be placed in the “Best” portfolio. A sustainability score between the sector
mean and one standard deviation meant it would be placed in the “Good” portfolio. In similar
fashion, “Bad” and “Worst” portfolios were constructed. Although they observed that firms with low-
sustainability scores under-performed the market and firms with high-sustainability scores out-
performed the market, their findings are not statistically significant most likely due to the relative
short time horizon.
Brammer et al. (2006) have studied the relationship between corporate social performance and stock
returns for a sample of UK listed companies. They used three measures of social performance
(community, environmental, and employee) from the Ethical Investment Research Service (EIRIS)
database. The indicator of employee responsibility relates to six measures based on health and safety
systems, systems for employee training and development, equal opportunities policies, equal
opportunities systems, systems for good employee relations, and systems for job creation and
security. The environment indicator is based on three measures: quality of environmental policies,
environmental management systems, and environmental reporting. The community indicator is
measured as one single variable. Each measure is normalized to a score that runs from zero to three,
and then summed to generate an overall CSR score of zero to nine. Their results show that firms with
higher social performance scores have significantly lower average returns than the FTSE benchmark.
Moreover, the portfolio that consisted of 17 firms with the lowest score on every social performance
measure yielded a positive return of 8 percent, outperforming 20 percent of the FTSE benchmark.
Brammer et al. (2006) concluded that standard risk models (CAPM and Fama-French (1993)) and
industry effects do not explain the low returns of firms that score the highest with respect to social
performance. However, their findings might be due to the fact that the sample period is rather small.
In conclusion, they argue that, in line with Navarro (1988), companies that have high expenditures on
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corporate social activities underperform compared to companies that have low expenditures on
corporate social activities.
In contrast to Brammer et al. (2006), Derwall et al. (2005) show evidence that SRI can be profitable
for investors. They obtained data on “eco-efficiency” scores from Innovest and combined this with
the CRSP database. Eco-efficiency is defined as the ratio of the value that a firm adds to the waste
that it generates. On the basis of these eco-efficiency scores two yearly-rebalanced portfolios were
constructed: the low-ranked (high-ranked) portfolio consisted of companies making up the 30
percent of total capitalization lowest rated (highest) by Innovest. The high-ranked portfolio
performed substantially and statistically significant better than the low-ranked portfolio, even after
controlling for differences in market sensitivity, investment style, or industry-specific factors. They
also constructed portfolios based on “best-in-class” analysis for practical portfolio construction. The
best-in-class portfolio outperformed the worst-in-class portfolio by about 3 percent with the best-in-
class portfolio having a lower volatility than the worst-in-class portfolio. This difference persisted
when using the Carhart (1997) four-factor model. Furthermore, when transaction costs are
incorporated, the return gap widened even further because the worst-in-class portfolio turnover rate
was higher than the best-in-class portfolio. The best-in-class portfolio outperformed the worst-in-
class portfolio annually by 2.05 percent in a CAPM framework (not statistically significant) and by
4.25 percent in a Carhart (1997) four-factor framework (statistically significant at the ten percent
level).
Edmans (2011) investigated the relationship between employee satisfaction and long-run stock
returns. A value-weighted portfolio was constructed consisting of companies that were included in
the “100 Best Companies to Work For in America”. This portfolio earned a four-factor alpha of 3.5
percent per year from 1984 to 2009 in excess of the risk-free rate. Moreover, the “Best Companies”
portfolio still earned a statistically significant alpha of 2.1 percent annually while controlling for
industry effects. The results remained the same when controlling for firm characteristics, different
weighting methodologies, and the exclusion of outliers. According to Edmans (2011) these results
suggest that the market fails to incorporate intangible assets (in this case employee satisfaction) fully
into stock valuations.
Hong and Kacperczyk (2009) studied another aspect of SRI screening, namely the exclusion of sin
stocks (also called negative screening). Sin stocks are publicly traded firms that are involved in
producing alcohol, tobacco, and gambling. Hong and Kacperczyk (2009) studied the performance of
sin stocks on the American market over the period 1965-2003. An equal-weighted portfolio (long in
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sin stocks and short in comparable industry stocks) yields a CAPM alpha of 25 basis points that is
statistically significant at the 10 percent level. The Fama-French (1993) model and Carhart (1997)
model both yield an alpha of 26 basis points per month. They also performed cross-sectional
regressions which controlled for firm characteristics and well-known determinants of expected
returns (market size, past return, and market-to-book ratio). Again, they found that sin stocks
outperformed their comparables by 29 basis points per month. They mention two reasons for their
findings. First, following along the lines of Merton (1987) prices of neglected stocks will be lower
relative to their fundamental value because of limited risk sharing. Second, sin stocks face higher
litigation risk. Due to the fact that these sin stocks are shunned by large institutional investors they
showed that there is a significant price effect in the range of 15-20 percent.
Fabozzi et al. (2008) also investigated sin stocks in an international context and found similar results.
Their sample consisted of companies from 21 countries (including the US). They classified companies
that obtained more than 30 percent of their revenue from six sin product categories (alcohol,
tobacco, defense, biotech, gambling, and adult services) as sin stocks. The average company from the
sin portfolio produced an average annual return of 19.02 percent compared to 7.87 percent return
on the market. Similar to Hong and Kacperczyk (2009) they conclude that this outperformance of sin
stocks is due to the fact that they are shunned away by the average investor. Moreover, since sin
industries have high barriers to entry (strict ordinances, rules, regulations, and multi-jurisdictional
laws that control sin industries), companies operating in these industries, which have managed to
survive despite these barriers, should be compensated with monopolistic rents.
Salaber (2007) conducted another study about the performance of sin stocks. She used a sample of
18 European countries with different legal and cultural environments and she wanted to test
whether these aspects have any influence on the return of sin stocks. First, she tested whether sin
stocks exhibit higher risk-adjusted returns than other stocks in Protestant countries only. This is
because Protestants are less willing to promote sin compared to Catholics. She also constructed a
portfolio that was long in sin stocks and short in non-sin stocks. In Catholic countries she observed no
abnormal returns whereas in Protestant countries the long-short portfolio had a statistically
significant alpha of 5.7 percent using the Carhart (1997) four-factor model. Second, she hypnotized
that in countries with high litigation risk you should observe higher risk-adjusted returns. She showed
that a long-short sin portfolio returns a significant alpha of 5.5-5.8 percent annually in countries with
high litigation risk. Her third hypothesis is whether sin stocks in countries with high excise taxation on
beer have higher risk-adjusted returns than both non-sin stocks and sin stocks in countries with
lower excise taxes. When looking at the sin portfolio there is a statistically significant difference in
the risk-adjusted return between the low-excise group and the high-excise group.
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Many researchers have used ratings from KLD Research & Analytics for analyzing social responsibility
on stock returns. This is mainly because KLD has the longest track record when it comes to social
ratings. KLD uses two broad criteria to evaluate a company: qualitative and exclusionary criteria.
Qualitative criteria (community, corporate governance, diversity, employee relations, environment,
human rights, and products) are used for positive and best-in-class screening policies. Exclusionary
criteria (involvement in alcohol, tobacco, gambling, military, nuclear power, and firearms) are used
for negative screening policies.
Kempf and Osthoff (2007) created several portfolios based on negative and positive screening
policies from KLD. Similar to Hong and Kacperczyk (2009) and Fabozzi et al. (2008) they found that sin
stocks perform better than non-sin stocks in their sample. However, the difference in return is not
statistically significant, because Hong and Kacperczyk (2009) and Fabozzi et al. (2008) used a longer
data sample and a narrower definition of sin stocks. Kempf and Osthoff (2007) also used positive
screens; high-rated portfolios consisted out of the top 10 percent with respect to a screen
(community, diversity, employee relations, environment, human rights, and products) and low-rated
portfolios consisted of the bottom 10 percent. Their results show that a strategy long in the high-
rated portfolio and short in the low-rated portfolio, when using the community or the employee
relations screens, yielded a statistically significant alpha of 4.55 percent and 5.98 percent
respectively. When all positive screens were combined, the portfolio yielded an alpha of 4.46 percent
and when the negative screen was added alpha increased to 4.8 percent. The best-in-class method
(selecting stocks in the top 10 percent per industry) showed the highest performance with an alpha
of about 5 percent per year. As in Derwall et al. (2005) including transaction costs did not change the
performance of the long-short strategies. Increasing the cut-off points lowered the abnormal return
leading to the conclusion that investors should only focus on the best stocks with respect to socially
responsible screens.
According to the view of Galema et al. (2008) the observed difference between theory and empirical
research on SRI performance comes from misinterpretation of the results stemming from two
possible errors. First, financial performance is calculated while controlling for systematic risk.
However, the empirical measure used by researchers does not fully capture systematic risk. SRI and
non-SRI firms with equal risk levels may have different book-to-market ratios, because there is excess
demand for SRI stocks. This would imply that exposure to the book-to-market ratio factor is
independent of the risk profile of the underlying cash flows. As a consequence, the trade-off
between SRI and financial performance is only partly captured by the book-to-market ratio. Second,
the use of aggregate social responsibility ratings may confound existing relationships between
individual dimensions of SRI and returns. They formed portfolios based on positive and negative
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screens on six SRI dimensions of KLD during the period 1992-2006. First, they tested if these
portfolios could deliver excess return by using the Carhart (1997) four-factor model in a GMM
framework as in Mackinlay and Richardson (1991) and Clare et al. (1997). Estimating portfolio returns
in a GMM framework has the advantage of relying on weaker assumptions compared to OLS. In
contrast to Kempf and Osthoff (2007), they do not find any risk-adjusted out- or underperformance.
Second, Galema et al. (2008) ran cross-sectional regressions to investigate the impact of SRI scores
on excess returns. Only the employee relations screen had a statistically significant positive effect on
excess return of 84 basis points annually. Moreover, they noticed when they looked at the
subscreens of employee relations that only concern subscreens have a statistically significant effect
on excess return, but they were not consistent in sign. Therefore, they concluded that adding the
subscores of screens leads to confounding effects. Lastly, they ran book-to-market regressions to
measure whether a stock that has a low book-to-market ratio scores high on one of the SRI ratings.
They found that diversity and environment have a negative relationship with the book-to-market
ratio and governance a positive relationship with the book-to-market ratio. Therefore, Galema et al.
(2008) argue that SRI is reflected in demand differences between SRI and non-SRI stocks.
Statman and Glushkov (2009) also studied the performance of companies that are listed on the S&P
500 using KLD ratings. They also used the best-in-class method to control for industry effects. Unlike
Kempf and Osthoff (2007) they excluded companies that had no strength or concern indicators. Per
KLD rating they ranked all companies by their best-in-class scores. Then they divided the companies
into three groups of approximately the same number of stocks. An equal-weighted yearly rebalanced
long-short portfolio was constructed that was long in top-third group and short in the bottom-third
group. The returns were benchmarked against CAPM, Fama-French (1993) three factor model, and
the four-factor model of Carhart (1997). Stocks with high social responsibility ratings performed
generally better than stocks with low social responsibility ratings. However, most of the results were
statistically insignificant except for the employee screen. As in Hong and Kacperczyk (2009) shunning
controversial stocks is bad for performance. A portfolio long in accepted stocks and short in
controversial stocks yielded a yearly excess return of -3.34 percent (CAPM), -2.62 percent (3-factor
model), and -3.34 percent (4-factor model). As in Galema et al. (2008), they found that applying both
positive and negative screenings the effect of positive screens on return are offset by the effect of
negative screens.
Derwall et al. (2011) constructed two yearly rebalanced portfolios using the KLD data running from
1992 till 2008. The first portfolio consisted of only sin stocks as in Hong and Kacperczyk (2009). The
second portfolio consisted of the top 30 percent of stocks that KLD ranks the highest with an
employee-relations score. The abnormal return of both portfolios were measured by estimating the
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Carhart (1997) four-factor model over four different time periods (1992-2002, 1992-2004, 1992-
2006, and 1992-2008). The annualized abnormal return of the first portfolio ranges from 2.58
percent to 2.86 percent over the different time horizons and is always statistically significant. The
annualized abnormal return of the second portfolio is 5.62 percent over the 1992-2002 period and
4.55 percent over the 1992-2004 period. Both abnormal returns are statistically significant at the 10
percent level. The annualized abnormal return over the 1992-2006 and 1992-2008 period are 2.94
percent and 2.81 percent respectively. However, they are not statistically significant at the 10
percent level.
2.2 Hypothesis Development
My thesis focuses on the risk-adjusted performance of portfolios based on SRI screening. In
particular, to my knowledge, the ASSET4 database has not been used for this topic. Past studies have
focused on mutual fund performance or used other databases. The advantage of using the ASSET4
dataset is that it has ESG (Environmental, Social and Corporate Governance) scores of multiple
markets. The KLD dataset only has information from the US market and the Vigeo corporate social
responsibility scores used by Van de Velde et al. (2005) only covers the European market. In contrast,
the ASSET4 dataset covers both these markets and this increases the sample size. ASSET4 has four
pillar scores which are updated annually. These pillar scores are aggregated into one score that
reflects overall ESG performance. I am interested in whether portfolios based on ESG criteria can
earn abnormal return after adjusting for common risk factors. Therefore, the topic of my thesis is:
The effect of portfolio performance using social responsibility screens
I investigate this topic by means of 5 hypotheses. Kempf and Osthoff (2007) and Statman and
Glushkov (2009) showed that applying multiple screens yields a positive abnormal return. The
aggregated score of the ASSET4 database is a combination of all four pillar scores and based on this
score I test whether SRI will result in a positive abnormal return.
Hypothesis 1: High social responsible stocks have higher risk-adjusted returns than low socially
responsible stocks based on the overall ASSET4 rating.
The first pillar in the ASSET4 database concerns the economic performance of a company. Economic
performance is measured by client loyalty, performance, and shareholders loyalty. Fornell et al.
(2006) show that companies that score high on customer satisfaction outperform the market.
Performance as described by ASSET4 relates to the financial performance (for example, return on
equity and profit margin) of a company. Shareholder loyalty relates to the ability of management to
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retain its shareholders. For example, one of the measures used is the dividend payout ratio. If a
company decides to lower its dividend payout ratio, this may result in shareholders selling their stake
in the company. A lower dividend payout ratio thus negatively affects a company’s economic
performance and return. Therefore, my second hypothesis is:
Hypothesis 2: Stocks that score high on economic performance have higher risk-adjusted returns
than stocks that score low on economic performance based on the economic ASSET4 rating.
The second pillar in the ASSET4 database is the environmental performance of a company.
Environmental performance is measured by resource reduction, emission reduction, and product
innovation. Derwall et al. (2005) show that companies with high eco-efficiency scores substantially
and statistically significant outperform those companies with low eco-efficiency scores. Moreover,
Kempf and Osthoff (2007) show that a portfolio consisting out of 10 percent of all stocks with the
highest rating on environment generates an abnormal return of 3.6 percent annually. Therefore, my
third hypothesis is:
Hypothesis 3: Stocks that score high on environmental performance have higher risk-adjusted
returns than stocks that score low on environmental performance based on the environment ASSET4
rating.
The third pillar in the ASSET4 database is the social performance of a company. Social performance is
measured by employment quality, health & safety, training & development, diversity, human rights,
community, and product responsibility. Studies by Kempf and Osthoff (2007), Statman and Glushkov
(2009), and Edmans (2011) have showed that stocks with high employee scores have an abnormal
return that is positive and statistically significant. Empirical studies on diversity suggest that there is
no statistically significant relation between stock return and diversity. Kempf and Osthoff (2007) did
find a positive relation between diversity and stock return. However, their results were not
statistically significant. Empirical studies on human rights show different results. Kempf and Osthoff
(2007) show that there is a positive relation between human rights and stock return, while Statman
and Glushkov (2009) show a negative relation. In both studies the relation between human rights and
stock return was statistically non-significant. Studies by Kempf and Osthoff (2007), Statman and
Glushkov (2009), and Galema et al. (2008) have showed that stocks with best community scores have
an abnormal return that is positive. However, only the results of Kempf and Osthoff (2007) are
statistically significant. Kempf and Osthoff (2007), Statman and Glushkov (2009), Galema et al.
(2008), and Van de Velde et al. (2005) found no statistically significant relation between stock return
and product quality. All in all, most empirical studies show that it is possible to achieve positive
abnormal returns by screening stocks on social performance.
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Hypothesis 4: Stocks that score high on social performance have higher risk-adjusted returns than
stocks that score low on social performance based on the social ASSET4 rating.
The fourth pillar in the ASSET4 database is the corporate governance performance of a company.
Corporate governance performance is measured by board structure, compensation policy, board
functions, shareholders rights, and vision & strategy. Empirical studies by Statman and Glushkov
(2009), Galema et al. (2008), and Van de Velde et al. (2005) found no statistically significant relation
between good corporate governance and stock return. Therefore, my fifth hypothesis is:
Hypothesis 5: Stocks that score high on corporate governance performance have the same risk-
adjusted returns as stocks that score low on corporate governance performance based on the
corporate governance ASSET4 rating.
First, I will test these hypotheses using the raw sample returns. Next, I will run Carhart (1997)
regressions to estimate the risk-adjusted returns in order to test the hypotheses more formally.
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3. Data & Methodology
3.1 Data
My main data resource is the ASSET4 database. ASSET4 rates companies against over 750 individual
data points2, which are combined into over 250 key performance indicators (KPIs). These KPIs are
aggregated into a framework of 18 categories grouped within 4 pillars (Economic, Environmental,
Social, and Corporate Governance) that are integrated into a single overall score.3
Figure 1: The ASSET4 ESG framework
KPIs, Categories, Pillars and Overall Score are equally weighted calculations of relative company
performance, the benchmark being the ASSET4 company universe. These ratings are normalized to
position the score between 0 and 100 percent. It expresses the value in units of standard deviation of
that value from the mean value of all companies in the ASSET4 universe. The data comes from
publicly available information, including sustainability/CSR reports, company websites, annual
reports, proxy filings, NGO as well as news of all major providers. In addition, CO2 data is sourced
from the Carbon Disclosure Project. The ESG data is typically updated on an annual basis
Using Thomson Reuters Datastream I have extracted companies that have available data on each of
the four pillar scores (ECNSCORE, ENVSCORE, SOCSCORE, and CGVSCORE) and the overall score
2 For an overview of each individual data point see the ASSET4 ESG Data Glossary which is available on the
website of Thomson Reuters: http://extranet.datastream.com/data/ASSET4%20ESG/documents/ASSET4_ESG_DATA_GLOSSARY_april2013.xlsx 3 http://extranet.datastream.com/data/ASSET4%20ESG/documents/ASSET4_ESG_Methdology_FAQ_0612.pdf
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(A4IR) over the period 2002-2011. Next to the scores I have downloaded the monthly return and the
market capitalization of each stock from Datastream. As in Fama and French (1992) I have excluded
financials from the sample. The total sample consists out of 562 companies from 25 countries, 10
economic sectors, and 45 industries. For each score I have created two equal-weighted and two
value-weighted portfolios. The following formula calculates the return for equal-weighted portfolios:
represents the return of an portfolio p in month t. k is the total number of companies in portfolio
p. is the return of company i in month t. The following formula calculates the return for value-
weighted portfolios:
Again, represents the return of an portfolio p in month t. k is the total number of companies in
portfolio p. is the weight of company i in portfolio p in July of year τ. is the return of company
i in month t.
The first equal-weighted portfolio consists of the top 10 percent of companies that perform the best
based on one of the five scores. The second equal-weighted portfolio consists of the bottom 10
percent of companies that perform the worst based on one of the five scores. The two value-
weighted portfolios were created in similar fashion. All portfolios are yearly rebalanced in July. In
addition, for each pillar score and the overall score high-low portfolios were constructed in which I go
long in the top ten percent and short in the bottom ten percent for each score.
3.2 Methodology
To measure the performance of the high-rated, low-rated, and the high-low portfolios, I employ the
Carhart (1997) four-factor model. It controls for the impact of the market risk, the size factor, the
book-to-market factor, and the momentum factor on returns. To control for these common factors, I
estimate the following regression:
The dependent variable is the monthly return of portfolio i in month t in excess of the risk-free rate.
The independent variables are the returns of four factor portfolios. denotes the excess
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return of the global market portfolio over the risk-free rate. denotes the return difference
between a small and a large capitalization portfolio in month t. denotes the return difference
between a high and a low book-to-market portfolio in month t. A stock with a low book-to-market
ratio is referred to as a growth stock, while a high book-to-market ratio is referred to as a value
stock. denotes the return difference between portfolios of stocks with high and low returns
over the past twelve months. α denotes the abnormal return of the portfolio i. , , , and are
the factor loadings and stands for the idiosyncratic return. The risk-free rate and the excess return
of the market portfolio, the size factor, the value factor, and the momentum factor were taken from
the Kenneth R. French data library using the global factors.
Robustness checks
In order to further test my hypotheses I perform three robustness checks. First, I test whether the
results in previous regressions differ, if I look at different regions. Fama and French (2012) show that
researchers can use the global four-factor model to explain the returns on global portfolios as long as
the portfolios do not have a strong tilt toward stocks with very small market capitalization or toward
the stocks of a particular region. My sample is heavily tilted toward the US (260 stocks) and Europe
(267 stocks). As before, I employ the Carhart (1997) four-factor model only now using the US factors
for the US sample and the European factors for European sample. Again, the US factors and the
European factors were taken from the Kenneth R. French data library. Second, I analyze if the risk-
adjusted returns of the portfolios differ choosing various cut-offs. I recreate the portfolios by
selecting the top 20 (30) percent stocks and the bottom 20 (30) percent stocks for both the equal-
weighted and the value-weighted portfolios. Third, due to the nature of their business some
companies have lower score than other companies and create bias towards some industries. For
example, oil companies tend to have lower scores relative to other companies with respect to
environment. I recreate portfolios using best-in-class screening based on Thomson Reuters Business
Classification. Thomson Reuters classifies companies in ten sectors: Basic Materials, Consumer
Cyclicals, Consumer Non-Cyclicals, Energy, Financials, Healthcare, Industrials, Technology,
Telecommunications Services, and Utilities. For the best-in-class approach the companies are divided
into the ten sectors and then ranked according to their ASSET4 scores. Next, for each score the top
10 percent of companies that perform the best within a sector based on one of the five scores are
selected and put in the high-rated portfolio. Similar, for each score the bottom 10 percent of
companies that perform the worst within a sector based on one of the five scores are put in the low-
rated portfolio.
16
4. Empirical findings
4.1 Descriptive Statistics
Table 1 presents the descriptive statistics of the equal-weighted and the value-weighted portfolios
created from the 562 companies that I consider. The monthly mean returns of the high-rated
portfolios are lower than the low-rated portfolios in all screens except for the corporate governance
screen (both equal- and value-weighted) and the value-weighted economic screen. The monthly
mean returns are not corrected for any exposure to common risk factors. Not surprisingly, the
monthly standard deviations of return of the high-rated portfolios are lower than the low-rated
portfolios in all screens except for the corporate governance screen (both equal- and value-
weighted) and the value-weighted economic screen. The high-low strategy has negative monthly
mean returns for the aggregate, environment, and social screen for both the equal- and value-
weighted portfolios. Only the corporate governance and economic screen have positive monthly
mean returns for both the equal- and value-weighted portfolios. The monthly standard deviations of
return of high-low portfolios are systematically lower than both the high-rated and low-rated
portfolios. Furthermore, table 1 presents the t-statistics and the corresponding p-values of the
difference in mean test between the high- and low-rated portfolios. Only the social screen of the
value-weighted portfolios shows a statistically significant difference between the high-rated and low-
rated portfolio mean returns. For all the other screens I cannot conclude that the difference between
the mean returns is greater than zero.
Table 2 presents the distribution of companies per country and economic sector. As noted in part 3.2
my sample consists mostly out of US companies (260) and European companies (267). The sectors
consumer cyclicals and industrials are the two largest sectors in my sample and therefore a possible
bias as described in part 3.2 could be present.
17
Table 1: Summary Statistics
Portfolio Mean return (%) St. dev. (%) Minimum (%) Maximum (%) Difference of means
t-statistic p-value
Panel A: Equal-weighted portfolios Aggregate
High-rated 0.59 4.81 -14.63 15.02
Low-rated 0.84 6.95 -22.13 26.46
High-Low -0.25 3.12 -15.16 8.14 -0.839 (0.404)
Corporate Governance
High-rated 0.72 5.17 -15.72 14.56
Low-rated 0.54 5.45 -14.18 18.93
High-Low 0.18 2.66 -5.98 6.65 0.734 (0.465)
Economic
High-rated 0.60 5.10 -14.81 13.07
Low-rated 0.74 8.81 -25.27 41.22
High-Low -0.14 4.81 -31.73 10.84 -0.308 (0.759)
Environment
High-rated 0.48 5.46 -15.73 14.10
Low-rated 0.85 6.68 -21.89 24.23
High-Low -0.37 2.88 -11.02 6.67 -1.377 (0.171)
Social
High-rated 0.36 5.07 -12.49 11.36
Low-rated 0.84 6.62 -21.26 22.84 High-Low -0.48 2.93 -11.33 9.10 -1.756 (0.082)
Panel B: Value-weighted portfolios Aggregate
High-rated 0.40 4.50 -13.43 14.82
Low-rated 0.61 5.23 -16.13 15.44
High-Low -0.21 2.67 -6.97 7.64 -0.835 (0.405)
Corporate Governance
High-rated 0.38 4.53 -12.83 13.97
Low-rated 0.14 4.72 -12.80 9.50
High-Low 0.24 3.40 -8.06 9.48 0.723 (0.471)
Economic
High-rated 0.49 4.62 -13.67 13.69
Low-rated 0.44 6.28 -13.63 20.59
High-Low 0.05 3.19 -13.37 8.55 0.168 (0.867)
Environment
High-rated 0.28 4.77 -14.42 14.75
Low-rated 0.64 5.53 -19.01 15.92
High-Low -0.36 2.79 -8.03 7.05 -1.383 (0.169)
Social
High-rated 0.20 4.62 -12.73 12.91
Low-rated 0.75 5.51 -19.63 16.13 High-Low -0.55 2.74 -8.06 8.84 -2.128** (0.036)
18
Table 1 (continued)
Portfolio Mean return (%) St. dev. (%) Minimum (%) Maximum (%)
Panel C: Fama-French Portfolios Market
0.48 4.99 -19.46 11.42
SML
0.22 1.67 -5.12 3.84
HML
0.20 1.60 -4.79 4.34
MOM 0.26 4.01 -23.89 9.32
Note: This table summarizes for each screen the monthly mean return, the monthly standard deviation of return, the lowest and highest observed return in a month and the difference of means of the high- and low-rated portfolios. The difference of means test represents the t-statistics and the corresponding p-values are in parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% level. Panel A shows the summary statistics of equal-weighted portfolios. Panel B shows the summary statistics of value-weighted portfolios. Panel C shows the summary statistics of the Fama-French global portfolios.
Table 2: Country and Sector Composition
Country Number of Companies Economic Sector Number of Companies
Australia 3
Basic Materials 58
Austria 7
Consumer Cyclicals 126
Belgium 9
Consumer Non-Cyclicals 60
Brazil 1
Energy 40
Canada 7
Financials 0
Denmark 11
Healthcare 55
Finland 13
Industrials 113
France 35
Technology 56
Germany 25
Telecommunications Services 22
Greece 6
Utilities 32
Hong Kong 4 Ireland 6 Israël 1 Italy 14 Japan 17 Mexico 1 Netherlands 13 Norway 9 Portugal 2 Spain 13 Sweden 25 Switzerland 24 Taiwan 1 United Kingdom 55 United States 260
Note: This table shows the number of companies from each country and economic sector as specified by Thomson Reuters Business Classification.
4.2 Portfolio Results
Table 3 gives the results of the monthly abnormal return of the equal-weighted portfolios for each
screen. The estimated alphas are positive for the high-rated portfolios of the aggregate, corporate
governance, and economic screen. In contrast, the estimated alphas of the high-rated portfolios of
the environment and social screen are negative. The low-rated portfolios of the aggregate,
environment, and social screen show positive estimated alphas, while the low-rated portfolios of the
corporate governance and economic screen show negative estimated alphas. The estimated alphas
of the high-low portfolios of the corporate governance and the economic screen are positive, while
the estimated alphas of the environment and social screen are negative. The estimated alpha of the
aggregate high-low portfolio is positive, but very close to zero. In line with Galema et al. (2008) and
Statman and Glushkov (2009), since the aggregate score is a combination of all four scores, it is
possible that the positive abnormal returns of the corporate governance and economic screen are
offset by the negative abnormal returns of the environment and social screens in the aggregate
screen. To give an indication of economic significance: if an investor had gone long in the high-rated
corporate governance portfolio and short in the low-rated corporate governance portfolio, the
investor would have earned a monthly abnormal return of 0.278 percent which is equal to 3.387
percent on an annual basis. The high-low economic portfolio would have earned an abnormal return
of 1.791 percent annually over the period 2002-2011. In contrast, the high-low portfolios of the
environment and social screen would have returned an annual abnormal return of -2.268 and -3.111
percent respectively. The aggregate high-low portfolio only delivered an abnormal return of 0.120
percent annually. However, all estimated alphas are not statistically significant. Looking at the
market factor loadings, I observe that on average high-rated socially responsible companies have
market betas lower than one. The low-rated portfolios of the aggregate, environment, and social
screens have on average slightly higher market betas than one. The low-rated corporate governance
portfolio has a similar market beta as the high-rated corporate governance portfolio. The low-rated
economic portfolio has a rather high market beta of 1.233. Furthermore, looking at the SMB factor I
observe that on average high-rated socially responsible companies have a negative loading on the
SMB factor. In contrast, low-rated socially responsible companies have a positive loading on the SMB
factor. Thus, the high-rated socially responsible companies tend to be bigger with respect to market
capitalization than low-rated socially responsible companies. Also, the HML factor loadings are
positive for all the high- and low-rated portfolios indicating that these portfolios consist mostly out of
value stocks. High-rated socially responsible companies tend to have lower loadings on the HML
factor compared to low-rated socially responsible companies. Remarkably, all portfolios have a
negative loading on the momentum factor. The negative momentum factor loading is greater for the
low-rated portfolios than for the high-rated portfolios.
20
Table 4 presents the estimation results of the four-factor Carhart (1997) model of the value-weighted
portfolios for each screen. The estimated alphas are negative for all of the high-rated portfolios. The
high-low portfolios show the same direction as with the equal-weighted portfolios except that the
aggregate high-low portfolio is negative. As with equal-weighted portfolios, all the estimated alphas
are not statistically significant except for the high-low portfolio of the social screen which we also
observed in table 1. Looking at the market factor loadings of the value-weighted portfolios I observe
that these loadings are lower compared to the equal-weighted portfolios. The market factor loadings
of the high-rated portfolios of the aggregate, economic, and social screens are lower than the low-
rated portfolios which I also observed in the equal-weighted portfolios. As with the equal-weighted
portfolios high-rated socially responsible companies tend to be large companies. In contrast to the
equal-weighted portfolios, the exposure to the HML factor is much lower and in most portfolios not
statistically significant. The momentum factor loading loses much of its statistical significance across
all portfolios. For all the high-rated portfolios I cannot conclude that the momentum factor loading is
statistically different from zero. I observe that there is only a difference between the momentum
factor loading of the high- and low-rated portfolios of the aggregate and economic screen.
Robustness checks
Table 5 and 6 (see Appendix) show the performance of the equal-weighted portfolios of the US and
European sample respectively. In both samples the estimated alphas of all the portfolios are
statistically insignificant. Also, high-rated socially responsible companies have higher market risk than
low-rated socially responsible companies. When I compare the US sample with the European sample,
I observe that the European portfolios have much lower market risk exposure than the US portfolios.
With respect to the SMB factor loadings I observe that the low-rated portfolios in the US sample are
tilted towards small companies. The same holds on average for the European sample, but not as
strong as in the US sample. The observed difference between the SMB factor loadings of the low- and
high-rated portfolios in both samples are the same as in the total sample (see table 3). Moreover, in
Europe low-rated portfolios have higher factor loadings on HML than the high-rated portfolios and
thus the European low-rated portfolios consist of companies that have higher book-to-market ratios
than the European high-rated portfolios. The negative momentum factor loadings observed in table 3
persist in both the US and European sample.
Table 7 (see Appendix) shows the estimated alphas of equal-weighted and value-weighted portfolios
for various cut-offs. In case of the equal-weighted portfolios increasing the cut-off from 20 to 30
percent, increases the monthly abnormal return of the high-rated aggregate portfolio by 0.245
percent, the high-rated environment portfolio by 0.302 percent, and the high-rated social portfolio
21
by 0.383 percent. In contrast, for the value-weighted portfolios the estimated alphas of the high-
rated portfolios remain negative for all cut-offs. Also, for the value-weighted portfolios the estimated
alphas of the low-rated portfolios remain positive for all cut-offs. However, changing the cut-off of
the top and bottom portfolios do not result in any statistically significant alphas except for the value-
weighted social high-low portfolio with a cut-off of 10 percent which was already observed in table 4.
Table 8 (see Appendix) gives the results of the performance of the equal-weighted portfolios using
best-in-class screening. Again, none of the portfolios show any statistically significant alphas. The
market factor loadings are the same as observed in table 3. Overall, in contrast to table 3, the SMB
factor loses statistical significance with best-in-class screening of the corporate governance,
economic, environment, and social screen. The HML factor loadings are positive for the high- and
low-rated portfolios, but only for the economic screen can I conclude that the high-rated portfolio
has a statistically significant lower HML factor loading than the low-rated portfolio. As in previous
results, the MOM factor loading is negative for the low- and high-rated portfolios. Table 9 (see
Appendix) gives the results of the performance of the value-weighted portfolios using best-in-class
screening. The estimated alphas of the value-weighted portfolio using best-in-class screening do not
differ from the estimated alphas that I have found in table 4. Although the estimated alphas of the
low-rated aggregate portfolio and the high-rated economic portfolio are different in sign compared
to table 4, the differences are small and not statistically significant. Moreover, as in table 4 all the
estimated alphas are statistically insignificant except for the high-low social portfolio. Again, using
best-in-class screening results in lower market exposure with value-weighted portfolios than best-in-
class screening with equal-weighted portfolios. On average high-rated socially responsible companies
have negative loading on SMB. For the aggregate, environment, and social screen the high-rated
portfolios have higher HML factor loadings. The momentum factor loadings are the same as observed
in table 4.
22
Table 3: The Performance of Equal-weighted Portfolio Returns by Social Responsibility Characteristic, August 2002-December 2011
Alpha Market SMB HML MOM adj. R-sq
Aggregate
High-rated 0.082 (0.688)
0.818*** (0.000)
-0.212* (0.064)
0.217* (0.079)
-0.107* (0.075)
0.816
Low-rated 0.073 (0.788)
1.061*** (0.000)
0.485*** (0.005)
0.510*** (0.004)
-0.381*** (0.000)
0.848
High-Low 0.010 (0.965)
-0.243*** (0.000)
-0.697*** (0.000)
-0.294 (0.106)
0.274*** (0.000)
0.572
Corporate Governance
High-rated 0.176 (0.456)
0.848*** (0.000)
-0.134 (0.281)
0.316** (0.042)
-0.151** (0.012)
0.803
Low-rated -0.102 (0.670)
0.857*** (0.000)
0.379** (0.012)
0.293* (0.068)
-0.214*** (0.001)
0.803
High-Low 0.278 (0.286)
-0.009 (0.888)
-0.513*** (0.006)
0.022 (0.894)
0.063 (0.453)
0.075
Economic
High-rated 0.073 (0.744)
0.901*** (0.000)
-0.200 (0.121)
0.041 (0.767)
-0.052 (0.385)
0.802
Low-rated -0.075 (0.828)
1.233*** (0.000)
0.477* (0.060)
0.774*** (0.001)
-0.691*** (0.000)
0.853
High-Low 0.148 (0.563)
-0.332*** (0.000)
-0.678*** (0.002)
-0.733*** (0.000)
0.639*** (0.000)
0.728
Environment
High-rated -0.060 (0.803)
0.911*** (0.000)
-0.231* (0.094)
0.221 (0.152)
-0.135** (0.032)
0.790
Low-rated 0.131 (0.653)
1.040*** (0.000)
0.249 (0.168)
0.493*** (0.007)
-0.286*** (0.001)
0.809
High-Low -0.191 (0.436)
-0.128** (0.028)
-0.480*** (0.003)
-0.272* (0.092)
0.151* (0.095)
0.230
Social
High-rated -0.117 (0.608)
0.852*** (0.000)
-0.335*** (0.009)
0.151 (0.286)
-0.127** (0.037)
0.796
Low-rated 0.146 (0.588)
1.068*** (0.000)
0.266 (0.138)
0.306 (0.102)
-0.304*** (0.000)
0.845
High-Low -0.263 (0.244) -0.216*** (0.000) -0.602*** (0.000) -0.155 (0.320) 0.177** (0.018) 0.393
Note: This table summarizes for each screen the monthly abnormal return, factor loadings, and the adjusted R² of a portfolio strategy using the Carhart four-factor model. The high-rated (low-rated) portfolio consists of the 10% of all stocks with the highest (lowest) rating. The high-low portfolio is a trading strategy going long in the high-rated and short in the low-rated portfolio. The portfolios are equal-weighted. P-values are in parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% level.
23
Table 4: The Performance of Value-weighted Portfolio Returns by Social Responsibility Characteristic, August 2002-December 2011
Alpha
Market
SMB
HML
MOM
adj. R-sq
Aggregate
High-rated -0.054 (0.797)
0.772*** (0.000)
-0.438*** (0.000)
0.201* (0.098)
-0.034 (0.543)
0.787
Low-rated 0.053 (0.816)
0.885*** (0.000)
0.298** (0.041)
-0.178 (0.218)
-0.163*** (0.005)
0.803
High-Low -0.106 (0.634)
-0.113** (0.049)
-0.736*** (0.000)
0.380** (0.019)
0.129** (0.035)
0.305
Corporate Governance
High-rated -0.031 (0.886)
0.790*** (0.000)
-0.494*** (0.000)
-0.030 (0.821)
-0.021 (0.791)
0.764
Low-rated -0.308 (0.292)
0.727*** (0.000)
-0.130 (0.481)
-0.027 (0.901)
-0.034 (0.641)
0.586
High-Low 0.277 (0.394)
0.063 (0.414)
-0.364* (0.081)
-0.002 (0.991)
0.014 (0.880)
0.000
Economic
High-rated -0.001 (0.995)
0.829*** (0.000)
-0.331*** (0.004)
0.061 (0.617)
0.013 (0.818)
0.790
Low-rated -0.110 (0.710)
1.005*** (0.000)
-0.096 (0.654)
0.074 (0.715)
-0.287*** (0.000)
0.786
High-Low 0.109 (0.692)
-0.177** (0.024)
-0.235 (0.248)
-0.013 (0.944)
0.300*** (0.001)
0.285
Environment
High-rated -0.194 (0.357)
0.817*** (0.000)
-0.497*** (0.000)
0.234* (0.099)
-0.026 (0.681)
0.784
Low-rated 0.090 (0.750)
0.911*** (0.000)
0.037 (0.829)
-0.032 (0.872)
-0.131* (0.081)
0.735
High-Low -0.283 (0.255)
-0.093 (0.210)
-0.534*** (0.001)
0.267 (0.189)
0.105 (0.257)
0.139
Social
High-rated -0.244 (0.240)
0.792*** (0.000)
-0.510*** (0.000)
0.206* (0.086)
-0.047 (0.374)
0.800
Low-rated 0.232 (0.361)
0.952*** (0.000)
0.199 (0.171)
-0.434** (0.024)
-0.163** (0.012)
0.795
High-Low -0.476** (0.028)
-0.161*** (0.007)
-0.709*** (0.000)
0.640*** (0.000)
0.116 (0.121)
0.377
Note: This table summarizes for each screen the monthly abnormal return, factor loadings, and the adjusted R² of a portfolio strategy using the
Carhart four-factor model. The high-rated (low-rated) portfolio consists of the 10% of all stocks with the highest (lowest) rating. The high-low portfolio
is a trading strategy going long in the high-rated and short in the low-rated portfolio. The portfolios are value-weighted. P-values are in parentheses. *,
**, *** indicate statistical significance at the 10%, 5%, and 1% level.
24
5. Conclusion
My first hypothesis states that high socially responsible stocks have higher risk-adjusted returns than
low socially responsible stocks based on the overall ASSET4 rating. Since the estimated alphas of the
high-low portfolio are statistically insignificant as well as the alphas of the high- and low-rated
portfolios, I cannot conclude that high-scoring social responsible stocks have higher risk-adjusted
returns than low-scoring social responsible stocks. The same conclusion holds for hypothesis 2 and 3.
There is no statistically significant difference between the risk-adjusted returns of high-scoring and
low-scoring stocks based on economic and environment screens. My fourth hypothesis states that
stocks that score high on social performance have higher risk-adjusted returns than stocks that score
low on social performance based on the social ASSET4 rating. I conclude that when value-weighted
portfolios are created, stocks that score low on social performance have higher risk-adjusted returns
than stocks that score high on social performance based on the social ASSET4 rating which is the
opposite what my fourth hypothesis states. This conclusion is robust when best-in-class screening is
applied, but not robust for equal-weighted portfolios, regional bias, and different cut-offs. As found
in previous studies I observe that stocks that score high on corporate governance performance have
the same risk-adjusted returns as stocks that score low on corporate governance performance.
Therefore, I cannot reject my fifth hypothesis. Furthermore, in this thesis I observe statistically
significant differences in the factor loadings of the high- and low-rated portfolios. On average high-
rated socially responsible companies have lower market betas than low-rated socially responsible
companies. Also, high-rated socially responsible companies have a negative loading on the SMB
factor, while low-rated socially responsible companies have a positive loading on the SMB factor.
Low-rated socially responsible companies have a greater negative loading on momentum than high-
rated socially responsible companies. These findings are robust for regional and sector bias.
Future research
As noted by Kempf and Osthoff (2007), Van de Velde et al. (2005), and Brammer et al. (2006) in order
to derive statistically significant alphas it is important to have a large number of observations. In five
to ten years the number of companies in the ASSET4 universe as well as the number of years under
observation will have increased that could lead to different conclusions found in this thesis. Future
research could also focus on different time-horizons. It is common that returns and factor loadings
can change as time passes. For example, it would be interesting to see if the current and past
financial crises have any effect on portfolio performance. Lastly, different methodologies can be used
with the ASSET4 dataset. In this research I have created high- and low-rated portfolios based on the
four pillar scores and the aggregated score. As shown in figure 1, these pillars are based on 18
25
categories and more than 250 key performance indicators. Future researchers may want to create
portfolios based on the categories or key performance indicators and find statistically significant out-
or underperformance. Another different methodology could be to change the weighting scheme of
the value-weighted portfolios. The weights of the stocks in a portfolio are now set in July and kept
constant until the next year, when the portfolio is rebalanced. It would be interesting to see what
would happen to portfolio performance, if the weights can change on a monthly basis. Lastly, in most
studies on SRI the holding period of a portfolio is set to one year. Again, it would be interesting to see
what would happen to portfolio performance, if the holding period is changed.
26
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7. Appendix
Table 5: The Performance of Equal-weighted Portfolio Returns by Social Responsibility Characteristic, August 2002-December 2011, US sample
Alpha Market SMB HML MOM adj. R-sq
Aggregate
High-rated 0.140 (0.495)
0.886*** (0.000)
-0.023 (0.810)
0.160 (0.138)
-0.066* (0.068)
0.808
Low-rated 0.302 (0.343)
1.174*** (0.000)
0.923*** (0.000)
0.166 (0.208)
-0.350*** (0.000)
0.840
High-Low -0.162 (0.562)
-0.288*** (0.003)
-0.947*** (0.000)
-0.006 (0.972)
0.283*** (0.000)
0.623
Corporate Governance
High-rated 0.313 (0.160)
0.894*** (0.000)
0.020 (0.841)
0.256* (0.058)
-0.110** (0.010)
0.804
Low-rated 0.217 (0.461)
1.223*** (0.000)
0.607*** (0.000)
0.117 (0.402)
-0.278*** (0.000)
0.843
High-Low 0.097 (0.695)
-0.329*** (0.001)
-0.588*** (0.000)
0.138 (0.258)
0.168** (0.015)
0.533
Economic
High-rated 0.073 (0.751)
0.933*** (0.000)
0.043 (0.718)
0.029 (0.796)
0.036 (0.451)
0.761
Low-rated 0.090 (0.807)
1.392*** (0.000)
0.772*** (0.000)
0.429** (0.015)
-0.510*** (0.000)
0.848
High-Low -0.017 (0.955)
-0.459*** (0.000)
-0.729*** (0.000)
-0.400*** (0.005)
0.546*** (0.000)
0.751
Environment
High-rated 0.004 (0.986)
0.965*** (0.000)
0.052 (0.638)
0.212** (0.050)
-0.082* (0.052)
0.819
Low-rated 0.207 (0.465)
1.050*** (0.000)
0.413*** (0.003)
0.137 (0.301)
-0.135** (0.048)
0.786
High-Low -0.203 (0.385)
-0.084 (0.235)
-0.361*** (0.002)
0.076 (0.562)
0.052 (0.310)
0.153
Social
High-rated -0.031 (0.878)
0.894*** (0.000)
-0.022 (0.840)
0.057 (0.614)
-0.061 (0.160)
0.810
Low-rated 0.291 (0.325)
1.220*** (0.000)
0.554*** (0.000)
0.018 (0.913)
-0.143* (0.091)
0.821
High-Low -0.322 (0.195) -0.326*** (0.000) -0.576*** (0.000) 0.039 (0.814) 0.081 (0.165) 0.493
Note: This table summarizes for each screen the monthly abnormal return, factor loadings, and the adjusted R² of a portfolio strategy using the Carhart
four-factor model. The high-rated (low-rated) portfolio consists of the 10% of US stocks with the highest (lowest) rating. The high-low portfolio is a
trading strategy going long in the high-rated and short in the low-rated portfolio. The portfolios are equal-weighted. P-values are in parentheses. *, **,
*** indicate statistical significance at the 10%, 5%, and 1% level.
29
Table 6: The Performance of Equal-weighted Portfolio Returns by Social Responsibility Characteristic, August 2002-December 2011, European sample
Alpha Market SMB HML MOM adj. R-sq
Aggregate
High-rated 0.082 (0.785)
0.575*** (0.000)
-0.269* (0.055)
0.109 (0.585)
-0.190*** (0.007)
0.667
Low-rated -0.078 (0.819)
0.737*** (0.000)
0.301 (0.141)
0.616*** (0.005)
-0.380*** (0.000)
0.765
High-Low 0.160 (0.595)
-0.161** (0.014)
-0.570** (0.011)
-0.507*** (0.005)
0.190** (0.032)
0.436
Corporate Governance
High-rated 0.143 (0.676)
0.595*** (0.000)
-0.179 (0.251)
0.176 (0.466)
-0.316*** (0.000)
0.657
Low-rated 0.024 (0.940)
0.662*** (0.000)
0.398** (0.021)
0.263 (0.246)
-0.259*** (0.002)
0.701
High-Low 0.118 (0.633)
-0.066 (0.318)
-0.577*** (0.000)
-0.087 (0.656)
-0.056 (0.405)
0.175
Economic
High-rated 0.069 (0.826)
0.700*** (0.000)
-0.298* (0.050)
-0.087 (0.670)
-0.118* (0.075)
0.680
Low-rated -0.418 (0.351)
0.738*** (0.000)
0.450* (0.054)
0.867*** (0.006)
-0.641*** (0.000)
0.756
High-Low 0.487 (0.130)
-0.038 (0.564)
-0.748*** (0.000)
-0.954*** (0.000)
0.524*** (0.000)
0.644
Environment
High-rated 0.109 (0.755)
0.669*** (0.000)
-0.252* (0.088)
0.059 (0.791)
-0.198** (0.026)
0.655
Low-rated 0.034 (0.926)
0.754*** (0.000)
0.237 (0.295)
0.473* (0.053)
-0.422*** (0.000)
0.747
High-Low 0.076 (0.792)
-0.085 (0.160)
-0.489*** (0.005)
-0.414** (0.023)
0.224*** (0.001)
0.333
Social
High-rated -0.084 (0.776)
0.584*** (0.000)
-0.310** (0.015)
0.108 (0.561)
-0.197*** (0.002)
0.678
Low-rated -0.053 (0.884)
0.737*** (0.000)
0.438** (0.012)
0.532** (0.022)
-0.370*** (0.005)
0.765
High-Low -0.031 (0.915) -0.153*** (0.007) -0.748*** (0.000) -0.424*** (0.009) 0.173 (0.132) 0.472
Note: This table summarizes for each screen the monthly abnormal return, factor loadings, and the adjusted R² of a portfolio strategy using the
Carhart four-factor model. The high-rated (low-rated) portfolio consists of the 10% of European stocks with the highest (lowest) rating. The high-
low portfolio is a trading strategy going long in the high-rated and short in the low-rated portfolio. The portfolios are equal-weighted. P-values are
in parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% level.
30
Table 7: Estimated Alphas of Equal-weighted and Value-weighted Portfolio Returns by Social Responsibility Characteristic for various Cut-offs, August 2002-December 2011
Equal-weighted Value-weighted
10% 20% 30% 10% 20% 30%
Aggregate
High-rated 0.082 (0.688)
0.088 (0.652)
0.333 (0.259)
-0.054 (0.797)
-0.041 (0.826)
-0.035 (0.852)
Low-rated 0.073 (0.788)
0.142 (0.595)
0.147 (0.574)
0.053 (0.816)
0.143 (0.527)
-0.036 (0.876)
High-Low 0.010 (0.965)
-0.054 (0.766)
0.186 (0.207)
-0.106 (0.634)
-0.185 (0.328)
0.001 (0.996)
Corporate Governance
High-rated 0.176 (0.456)
0.096 (0.683)
0.063 (0.827)
-0.031 (0.886)
-0.051 (0.809)
-0.031 (0.878)
Low-rated -0.102 (0.670)
0.025 (0.912)
0.015 (0.947)
-0.308 (0.292)
-0.115 (0.612)
-0.097 (0.641)
High-Low 0.278 (0.286)
0.071 (0.755)
0.048 (0.841)
0.277 (0.394)
0.063 (0.805)
0.066 (0.777)
Economic
High-rated 0.073 (0.744)
0.123 (0.560)
0.085 (0.749)
-0.001 (0.995)
-0.022 (0.905)
-0.015 (0.936)
Low-rated -0.075 (0.828)
-0.022 (0.936)
0.041 (0.879)
-0.110 (0.710)
0.033 (0.887)
0.038 (0.870)
High-Low 0.148 (0.563)
0.145 (0.393)
0.044 (0.801)
0.109 (0.692)
-0.055 (0.768)
-0.052 (0.758)
Environment
High-rated -0.060 (0.803)
0.004 (0.987)
0.306 (0.234)
-0.194 (0.357)
-0.064 (0.721)
-0.057 (0.754)
Low-rated 0.131 (0.653)
0.162 (0.571)
0.172 (0.518)
0.090 (0.750)
0.164 (0.534)
0.161 (0.509)
High-Low -0.191 (0.436)
-0.158 (0.458)
0.134 (0.415)
-0.283 (0.255)
-0.229 (0.292)
-0.218 (0.231)
Social
High-rated -0.117 (0.608)
-0.085 (0.688)
0.303 (0.198)
-0.244 (0.240)
-0.091 (0.628)
-0.050 (0.776)
Low-rated 0.146 (0.588)
0.139 (0.615)
0.117 (0.666)
0.232 (0.361)
0.164 (0.494)
0.054 (0.820)
High-Low -0.263 (0.244) -0.224 (0.283) 0.187 (0.300) -0.476** (0.028) -0.254 (0.172) -0.104 (0.543)
Note: This table summarizes for each screen the monthly abnormal return of a portfolio strategy using the Carhart four-factor model for different cut-offs. The high-rated (low-rated) portfolio consists of the 10%, 20% or 30% of all stocks with the highest (lowest) rating. The high-low portfolio is a trading strategy going long in the high-rated and short in the low-rated portfolio. The portfolios in the left panel are equal-weighted and the portfolios in the right panel are value-weighted. P-values are in parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% level.
31
Table 8: The Performance of Equal-weighted Portfolio Returns by Social Responsibility Characteristic using Best-in-Class Screening, August 2002-December 2011
Alpha Market SMB HML MOM adj. R-sq
Aggregate
High-rated 0.212 (0.339)
0.931*** (0.000)
0.112 (0.371)
0.382*** (0.007)
-0.187*** (0.001)
0.840
Low-rated 0.020 (0.933)
1.076*** (0.000)
0.378** (0.011)
0.420*** (0.004)
-0.265*** (0.000)
0.864
High-Low 0.192 (0.189)
-0.145*** (0.000)
-0.267*** (0.009)
-0.037 (0.692)
0.078** (0.036)
0.281
Corporate Governance
High-rated 0.191 (0.441)
0.887*** (0.000)
0.095 (0.519)
0.321* (0.052)
-0.273*** (0.000)
0.805
Low-rated -0.114 (0.617)
0.856*** (0.000)
0.252* (0.073)
0.284* (0.076)
-0.189*** (0.001)
0.805
High-Low 0.305 (0.153)
0.032 (0.582)
-0.157 (0.248)
0.037 (0.788)
-0.084 (0.169)
0.021
Economic
High-rated 0.065 (0.793)
0.945*** (0.000)
0.133 (0.332)
0.232 (0.238)
-0.303*** (0.000)
0.823
Low-rated -0.126 (0.667)
1.139*** (0.000)
0.318 (0.103)
0.595*** (0.003)
-0.437*** (0.000)
0.848
High-Low 0.191 (0.332)
-0.194*** (0.000)
-0.186 (0.250)
-0.363*** (0.004)
0.133** (0.026)
0.353
Environment
High-rated 0.047 (0.857)
0.915*** (0.000)
0.088 (0.558)
0.531*** (0.001)
-0.250*** (0.003)
0.803
Low-rated 0.081 (0.751)
1.002*** (0.000)
0.194 (0.204)
0.436*** (0.004)
-0.240*** (0.000)
0.827
High-Low -0.035 (0.828)
-0.087*** (0.009)
-0.107 (0.305)
0.094 (0.282)
-0.010 (0.899)
0.046
Social
High-rated 0.212 (0.369)
0.912*** (0.000)
0.066 (0.606)
0.373*** (0.006)
-0.202*** (0.003)
0.824
Low-rated 0.103 (0.677)
1.014*** (0.000)
0.222 (0.167)
0.359** (0.035)
-0.242*** (0.000)
0.847
High-Low 0.108 (0.516) -0.103** (0.011) -0.156 (0.233) 0.014 (0.908) 0.040 (0.414) 0.093
Note: This table summarizes for each screen the monthly abnormal return, factor loadings, and the adjusted R² of a portfolio strategy using the Carhart four-factor model. The high-rated (low-rated) portfolio consists of the 10% of all stocks with the highest (lowest) rating within each sector. The high-low portfolio is a trading strategy going long in the high-rated and short in the low-rated portfolio. The portfolios are equal-weighted. P-values are in parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% level.
32
Table 9: The Performance of Value-weighted Portfolio Returns by Social Responsibility Characteristic using Best-in-Class Screening, August 2002-December 2011
Alpha Market SMB HML MOM adj. R-sq
Aggregate
High-rated -0.088 (0.660)
0.777*** (0.000)
-0.548*** (0.000)
0.167 (0.203)
-0.055 (0.379)
0.805
Low-rated -0.024 (0.911)
0.897*** (0.000)
0.234* (0.078)
-0.117 (0.432)
-0.111** (0.041)
0.818
High-Low -0.064 (0.735)
-0.120*** (0.010)
-0.782*** (0.000)
0.284* (0.051)
0.056 (0.405)
0.375
Corporate Governance
High-rated 0.006 (0.980)
0.774*** (0.000)
-0.506*** (0.000)
0.066 (0.615)
-0.032 (0.711)
0.755
Low-rated -0.355 (0.173)
0.763*** (0.000)
-0.118 (0.406)
0.170 (0.372)
0.025 (0.725)
0.666
High-Low 0.361 (0.187)
0.011 (0.839)
-0.389** (0.024)
-0.105 (0.535)
-0.057 (0.604)
0.029
Economic
High-rated 0.018 (0.930)
0.810*** (0.000)
-0.381*** (0.001)
0.100 (0.438)
0.034 (0.626)
0.785
Low-rated -0.017 (0.947)
0.882*** (0.000)
-0.314* (0.095)
-0.080 (0.636)
-0.104 (0.241)
0.745
High-Low 0.035 (0.875)
-0.073 (0.202)
-0.067 (0.717)
0.179 (0.274)
0.138** (0.029)
0.063
Environment
High-rated -0.186 (0.390)
0.786*** (0.000)
-0.493*** (0.000)
0.325** (0.033)
-0.025 (0.682)
0.774
Low-rated 0.074 (0.762)
0.889*** (0.000)
0.010 (0.944)
-0.092 (0.605)
-0.105* (0.073)
0.775
High-Low -0.259 (0.199)
-0.103* (0.090)
-0.504*** (0.000)
0.417** (0.019)
0.080 (0.164)
0.213
Social
High-rated -0.161 (0.449)
0.816*** (0.000)
-0.468*** (0.000)
0.192 (0.184)
-0.094 (0.130)
0.797
Low-rated 0.213 (0.388)
0.874*** (0.000)
0.152 (0.267)
-0.238 (0.211)
-0.160*** (0.005)
0.784
High-Low -0.375* (0.063) -0.057 (0.265) -0.620*** (0.000) 0.430*** (0.009) 0.065 (0.306) 0.243
Note: This table summarizes for each screen the monthly abnormal return, factor loadings, and the adjusted R² of a portfolio strategy using the Carhart four-factor model. The high-rated (low-rated) portfolio consists of the 10% of all stocks with the highest (lowest) rating within each sector. The high-low portfolio is a trading strategy going long in the high-rated and short in the low-rated portfolio. The portfolios are value-weighted. P-values are in parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% level.