Evaluating The Performance and Style of European Socially Responsible Funds
Abstract
In this thesis, I examine the performances of 128 European SRI funds and 1037 European conventional funds. I also analyze whether the performances of SRI funds are related to the features of the screening process. I find evidence that only the conventional funds are able to outperform the market. Further, I show that only sustainability-themed screening strategy increases financial performance, while best-in-class, exclusion and combination strategy have no impact. Last, when the criteria of SRI selection process is based on the ESG criteria, I find that its impact on funds’ performance is not significant.
Name : Ira Ayundari Clarissa Student Number : 11255900
Supervisor : Shivesh R. Changoer University : University of Amsterdam Faculty : BSc Economics and Business Track : Economics and Finance
Statement of Originality
This document is written by Ira Ayundari Clarissa, who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
1. Introduction
Social, governance, environmental, traditional ethical issues, have increased the social awareness of people, including investors (Domini, 2001). This lead to the creation and development of Socially Responsible Investment (SRI) funds 1 . SRI is an investment process that integrates
social, environmental, and ethical considerations into investment decision making (Renneboog et al., 2008). Some of SRI funds do not hold shares in firms, which operate in the alcohol, pornography and tobacco industries, while others forbid the purchase of equities of firms with poor environmental track records (Kreander et al., 2005). SRI funds distinguish themselves as it pays attention to those issues, while the other type of investments overlooked them. Some argue that SRI funds offer lower returns to investors because socially responsible investors are willing to give up on their portfolio return so they can align their investment with their social preferences (Riedl, 2017). Other suggest the opposite, claiming that SRI funds need to compensate the investor for holding a non-optimally diversified portfolio. Some argue that SRI provides a higher return by holding less liquid assets (Bollen, 2007) and a good corporate citizenship that will increase firm’s productivity and profitability (McGuire et al., 1988). Motivated by these two opposing views, I examine the performance of SRI funds, relative to conventional funds, conventional and SRI indices. I also examine the effect of different type of screening on the performance of the funds and evaluate the impact of different factor of ESG investment strategies to see how each factor impacts the financial returns.
To examine the performance of SRI funds, relative to conventional funds, conventional and SRI indices, I analyze the socially screened funds return to see what are the performances of 128 SRI funds over the period of 2013-2018. I do so by employing the elaborated multi-factors model that control size, book-to-market, momentum, and local biases. The results show a positive difference between the return of the conventional and SRI funds, in favor of conventional funds. However, the difference turns out to be statistically insignificant. I then compare the performance of funds that use a particular type of screen to the funds that use another type of screens, intending to answer the question of how do the different SRI screening strategies affect the return of the ethical funds. I find that Sustainability-themed strategy has a positive and significant impact on
SRI funds performances. While Best-in-class and Exclusion strategy has an insignificant impact on the SRI funds’ performance.
Next, I perform a multiple regression with the selection criteria as one of the variables to answer the question of how do SRI funds selection process affect the return of the SRI funds. My results suggest that SRI funds that focus Environment and Social issue have better performance but not significantly than the funds that focus on the Environmental, Social, and Governance issues altogether. Funds that care about governance issue underperform but also not significantly relative to the fund that focuses on the ESG issue altogether.
My study contributes to the existing literature in four ways. First, it analyzes the SRI funds return performance of recent SRI funds. The result of some previous European studies may have become outdated as they use the sample of funds ranging from 1996-2008. I believe that this analysis is important, because the characteristic of SRI funds may have changed over time. Based on EUROSIF (2016), there is a shift in European SRI assets from equities to fixed income. Second, my analysis is based on the sample of European Funds, while most of the previous studies are more restricted to the US market (see e.g., Bauer et al., 2005; Schueth et al., 2003; Goldreyer et al., 1999). In the aftermath of financial crisis, the European Commission proposed a policy measure, which is Shareholders Rights Directive. It aims to encourage investors to be more responsible and companies will have to step up the ESG communication plan as part of their investor relations program (Eurosif, 2016). The European legislative developments will support the future growth of European SRI, thus this analysis is important to give further input on how European SRI funds add value to investors. The studies of Schröder (2004) and Bauer et al. (2005) focus only on funds from two European markets (Germany and Switzerland). Kreander et al. (2005) and Cortez et al. (2009) examine the performance of SRI funds from several European countries, however, they do not control size, book-to-market, momentum and local bias effect. Third, I analyze the effect of different screening strategies on the performance of SRI funds. To the extent of my knowledge, only Leite et al. (2014) investigate the SRI funds’ performance according to the screen filters using European funds. However, they focus on internationally oriented SRI funds where this study comprises global and international funds. Fourth, I evaluate the impact of the different factor of ESG investment strategies to see how each factor (Environmental, Social, and Governance) impacts the financial returns.
This paper is organized as follows. The ‘Theoretical Framework’ section provides a review of the relevant literature. The ‘Hypotheses’ section describe my early expectation from this study. The ‘Research Design’ section discusses the data collection process and describes the methodology. The ‘Results and Analysis’ section presents empirical results and discussion of the main finding. The ‘Sensitivity Analysis’ section evaluate the data with different models and variables, and the last section concludes.
2. Theoretical Framework
2.1 Socially Responsible Investment
Socially Responsible Investment is a very general term, defined as an investment strategy that includes social, ethical and environmental values as the screening criteria (Cowton, 1999). SRI has become increasingly popular, according to Sustainable Investment Alliance (GSIA) in the beginning of 2016, Global SRI asset has reached $22.89 trillion and Europe accounts to 53% of these assets. This indicates that investors are actively looking for environmental, climate and other social related assets. Investors may have optimistic risk-return expectations for SRI another possible motive could be that investors hold SRI to boost their social image or reputation (Riedl et al., 2017). GSIA reports that demographic changes as a result of the intergenerational transfer of wealth, suggest a shift to young investors, results in a greater demand for the socially responsible investment. The report also shows that not only is this demand driven by individual investors, but pension and endowments funds are also starting to show interest, with Europe leading the way. According to 2017 Wealth and Worth report, 76% of millennial consider their investment decisions to be a way to express their social, political and environmental values and 88% look at company’s impact in these areas as important consideration when they make investment decisions. Investors want to stay away from investing in company that selling potentially harmful products such as tobacco, alcohol, guns and adult entertainment products (Trinks et al., 2017) and SRI helps socially responsible investors to filter their investment portfolio according to their social and ethical principles (Domini, 2001).
Incorporating the social criteria in the investment screening process may come at cost. It is argued that this is because SRI limits the investment universe (Bauer et al., 2005). The expected
returns on socially responsible portfolio are lower compared to the conventional portfolio (Hamilton et al., 1993) and investors especially those that have limited resources cannot optimally diversify their portfolio. This may be a problem since the screening process constraint the investment universe even before any security analysis begins. For instance, a fund that totally focus on environmental issue will avoid investment in recourses extraction or energy production industries and the more ethical issue investors try to cast, the more areas will not be eligible for investment. According to Modern Portfolio Theory, restricting the investment universe is disadvantageous for beating the market and diversification may be hindered as social criteria eliminates or favors certain industries (Sauer, 1997). Other than that, Investor limiting their selection to companies with social screens would end up excluding stable blue chip and attractive investment opportunities. Also, social screens eliminate larger firms from the investment universe, the remaining firms are smaller thus have higher volatility in return. (Sauer, 1997). Even though there are potential cost of investing in SRI funds, it is not clear if SRI are actually bad investment. First, because SRI promotes investors loyalty, Bollen (2007) find that socially responsible investor to be more loyal than investors in conventional funds. Given the long term commitment of investors, SRI funds can invest in less liquid asset thus provide higher average returns (Amihud, 2002). Second, socially responsible and environmentally responsible firms are less likely to be subjected to environmentally and product liability fine and lawsuits. This means that these firms are less likely to pay costly settlements which will affect firms stock prices (Bloomberg, 2014). Third because good corporate citizenship may attract and retain good employees. It is argued that employee loyalty benefits a firm by improving productivity, innovation, lowering production cost and further improve profitability. (McGuire et al., 1988). In other words, SRI serves a tool to reflects company’s relationship with the society, which will filter out the excellent companies regarding their social contribution.
2.2 Prior Findings on Performance
Because of the contrasting ideas of weather the socially responsible funds are good or bad investment, several studies has examined the financial performance of SRI funds relative to a particular market benchmark or sample of conventional non-screened fund. Most of the research focused on the US and UK funds. Statman (2000) focuses on US SRI and non SRI funds and
finds no significance difference in performance of the funds over his sample time frame (1990-1998). This is consistent with the findings of previous researches (e.g Hamilton et al., 1993; Reyes at al., 1998; Goldreyer et al., 1999). Mallin et al. (1995) focus on British investment Funds and find that UK SRI funds outperform the conventional funds. Gregory et al. (1997) critiques those findings as Mallin et al. (1995) did not control for size effect2. Gregory et al. (1997) then finds non-significant evidence that UK SRI funds actually underperform the conventional ones after controlling for size
Bauer et al. (2002) who focus on a sample of 32 British, 16 German and 55 US SRI funds extend the work of Gregory et al. (1997) by using a more developed multifactor CAPM. The model has four factors. The first three factors are the Fama-French model and the fourth factors controls the effect of momentum. Employing this model, Bauer et al. (2002) find evidence that German and US SRI funds underperform the market index and their matched sample conventional funds. They also show that UK SRI funds slightly outperform. However, none of these findings are statistically significant.
Kreander et al. (2000) extend the work of Mallin et al. (1995) in to a European settings. They use the Jensen’s alpha, Sharpe ratio and Treynor ratio to measure the performance of 40 European funds from seven countries. The statistical test shows that the Sharpe and Treynor ratio of the non-ethical funds are slightly higher but non-significant. The result suggest that the European SRI funds perform similarly to that of unscreened peers. However, the authors may not use the appropriate benchmark as they used the MSCI world index. According to Schroder (2004) this choice of benchmark may be poor since several funds in the sample concentrate their investment in their home country. Another shortcoming from the study is that the authors do not use a small cap market index to deal with the size bias of the SRI funds.3 Cortez et al. (2009) also analyse the performance of European SRI funds. They use the conditional and unconditional CAPM single index model as performance measurement. The authors find that European SRI are not able to outperform the screened and unscreened indices.
2UK SRI funds tend to invest more heavily on in smaller capitalization firm.
3SRI funds are significantly more exposed to small caps than conventional funds (Luther and Matatko, 1994;Gregory et al., 1997;
Most of the literature agree that SRI funds have similar performance with the conventional funds (see Leite et al., 2014; Renneboog et al., 2008; Scholtens, 2005; Goldreyer et al., 1999; Hamilton et al., 1993).
2.3 Screening Strategy
Almost all of the previous research mentioned above ignore the effect of different types of screening have on the funds’ performance outcome. The use of different screening strategies might lead to different performance pattern. Nofsinger et al., (2012) evaluated the performance of SRI funds based on the type of screening used. They reported that different types of screens impact fund performance differently. In terms of screening process, Eurosif (2016) define classification of 7 SRI approaches in Europe, those are; Exclusion, Norm-based screening, Best-in-Class, Sustainability themed, ESG and Engagement and voting. Cortez et al. (2009) compared the performance SRI funds that employ best-in-class strategy with the ones that do not. They find evidence that best-in-class funds display better performance than funds that use other type screening criteria. However, the difference is not statistically significant. Best-in-Class is one of the positive strategies, where investors have the opportunity to pick companies that have the best ESG score in a particular sector. As with negative screening, funds exclude companies that manufacture products that are repulsive, such as weapons, alcohol and tobacco products. Positive screen avoid factor biases that comes with negative screening, thus less diversification loss. Nofsinger et al., (2012) find outperformances of funds that employ positive screen rather than negative screen. Barnett et al., (2006) also show that only sector specific screen or exclusion of sin stock seem to decrease financial return of funds. In line with these two findings, Kemp et al., (2007) find that investors can a remarkably high abnormal returns by implementing positive screening strategy but not with negative screening approach.
Another way which investor can screen their investment is through ESG screening. Investors asses the funds selection process based on their environmental, social and governance criteria. Recently, the world becomes increasingly focus on ensuring environmental sustainability and climate change risks is now firmly on the political agenda (JP Morgan, 2016). Through environmental screening investor look at the impact of company’s activity on the environment (waste management, level of carbon dioxide, etc.). King et al., (2001) find a positive relationship
between environmental screening criteria and financial performance. Porter et al., (1995) show that pollution management is able to reduce cost and increase efficiency. Social screening look at the impact of a company’s activity on customers, employees, stockholders or on society in general focusing on human rights, international labor standards, etc. Dobbin et al., (2010) find weak evidence that there is a relationship between gender diversity and stock performance. Governance concerns on the way in which a company conducts the day to day business, this include the relationship between company’s stockholders, management and board of director. Core et al, (1999) find a better governance leads to a better stock return performance.
3. Hypotheses
Some argue investing in SRI funds may come with cost as it reduces potential return and panelizes investor with the cost of ethical diversification. While other argue that ethical funds will outperform the conventional funds, as Cumming (2000) states that with SRI, both social and financial goals are achieved through long term commitment to social behavior, which minimizes externalities to the firm. Yet others argue that because market are efficient, it is impossible for investor to gain superior performance through expert stock selection or market timing. Because I support the view that market are efficient, my prediction is that there no significance difference in the performance of the SRI and conventional funds.
Hypothesis 1 : SRI funds and conventional funds do not significantly differ in terms of risk-adjusted return
Since I expect that SRI and conventional funds do not significantly differ in terms of risk-adjusted return, I also do not expect to find that the ESG score of funds have an effect on its return. This lead to my second hypothesis.
Hypothesis 2 : There is no relationship between SRI fund’s return and its ESG score
Investors can limit their investment to a list of companies that share the same ethical value with them. Investors may decide to negatively screen sectors that include or substantially involved in
an objectionable investment. As investors have less selection of stocks, the idiosyncratic risk of the portfolio might increase. This will affect the return and performance of the portfolio. Another way to screen portfolio is through positive screening, where investors invest in companies with a commitment and strong record of responsible business practice. Investor can do selection of stocks of companies that perform best against a defined set of ESG criteria. Best-in-class and Sustainability themed strategy are two of positive strategies. Applying positive screens could lead to possible bias toward some industries (Kemp et al., 2007). This bias comes from the high concentration of some SRI industries in a portfolio. If these industries are expected to have lower expected return then it would have an impact on the performance on a positively screened investment. Because of those reasons I believe the implementation of these different type of screening strategy will result in different SRI funds performances, thus I predict the following hypothesis.
Hypothesis 3 : Implementation of best-in-class, exclusions, Sustainability themed and other types of screening has a significant effect on the performance figures of SRI funds
Environmental, social, and Governance (ESG) is a set of standards for company’s operation that investors use to screen their potential investment. These are the factors that consider companies’ ethical impact and sustainable practices. With these criteria, investors can choose investment that matches their own value. Environmental criteria look at company’s energy use. Social criteria look at company’s business relationship. Whereas, governance look at the company’s leadership, executive pay and shareholders’ right. However, no fundamental reason to expect the these types of funds have different financial performances.
Hypothesis 4 : Environment, Social and Corporate Governance oriented SRI funds do not differ in terms of performance
4. Research Design
4.1 Method
To test the first hypothesis, I use the following model:
RSRI/RCONV = α+β0MRP + β1SMB +β2HML + β3WML + β4LOC + ε
Where:
RSRI/RCONV = equally weighted quarterly return of the SRI funds or conventional funds minus risk free rate, measured by the one-month Euribor rate for the period of 2013-2018
MRP = market risk premium factor, measured by the MSCI AC Europe return index minus the one-month Euribor rate
SMB = size factor, measured by the difference of the MSCI AC Europe Small Cap index and MSCI AC Europe Large Cap index for the period of 2013-2018
HML = value factor, measured by the difference of the MSCI AC Europe Value index and MSCI AC Europe Growth index for the period of 2013-2018
WML = momentum factor, measured by the return difference between top 6 and
bottom 6 sectors of the Dow Jones STOXX 600 Super Sector Indices for the period of 2013-2018
LOC = local factor, measured by the return difference between local country market index and MCSI AC Europe index for the period of 2013-2018
Ε = error term
The first factor is market risk factor, measured, as in Banegas et al., (2013), by the MSCI AC Europe return index minus the one-month Euribor rate, The size factor, SMB (small minus big) is calculated, as in Banegas et al., (2013) as the difference of the MSCI All Country (AC) Europe Small Cap index and the MSCI AC Europe Large Cap index. The value factor, HML (high minus low) is calculated, as in Banegas et al., (2013), as the difference in total return of the MSCI AC Europe Value index and the MSCI AC Europe Growth index. The momentum factor, WML (winners minus losers) constructed from 18 different Dow Jones STOXX 600 Super
Sector Indices that has a lifespan throughout the sample. The factor computed as the return difference between top 6 sectors and bottom 6 sectors. 6 out of 24 is chosen to create the same factor weight as Carhart (1997). Where the top sectors and the bottom sectors equally consisted of 30% of the selected sectors.
Several literature recognize that other factors besides market affect portfolio returns ( Cahart, 1997; Elton et al., 1996; Fama and French 1993). Recent evidence also indicates that multi factor model are a more useful characterization of portfolio returns than single-index models ( Cortez et al., (2009). For this reason I use multi factor models to evaluate the performance for both SRI and conventional funds. Furthermore, previous studies have shown home bias for international funds, either SRI or conventional (Bauer et al., 2006; Chan et al., 2005; Gregory et al., 2007). According to Engström (2003), due to information advantage, fund managers may prefer to invest in local stocks. Since my sample funds do not only consist of global SRI funds but also international SRI funds, I include LOC to control for potential home bias. As I use the funds from different European countries, I may need to take into account the potential home bias. The local factor is measured as the return difference between the local country market index weighted and the European market index MCSI AC Europe index. I expect the difference between α for both SRI and conventional funds to be not significantly different from zero.
To test the effect of ESG score on the excess returns, I use the following model :
RSRI = α + β0ESGSCR + β1AGE + β2SIZE + β3VOL + β4MFEE + β5EQU + β6BAL+ β7GLO +ε
Where:
RSRI = equally weighted quarterly return of the SRI funds
ESGSCR = the ESG score of the SRI funds ranging from 0-100 per May 2018 AGE = the age of the SRI funds in years since the date of incorporation SIZE = the fund’s size in millions of euros per May 2018
VOL = the standard deviation that measured how much the fund’s return varies around its mean
MFEE = the cost shareholders paid for management and administrative services per May 2018
EQU = equal to 1 if the SRI fund is equity fund and 0 otherwise BAL = equal to 1 if the SRI fund is balanced fund and 0 otherwise GLO = equal to 1 if the SRI fund is global fund and 0 otherwise OTH = the default dummy if EQU BAL and GLO equal zero
ε = error term
RSRI represents the equally weighted quarterly return of the 128 European SRI funds over the period of 2013-2018. The MorningStar uses ESGSCR as the a measurement of how well the SRI funds are managing their environmental, social and governance (ESG) relative to their category peers. According to MorningStar, funds with higher ESG ratings tend to have higher quality holdings. This refer to the funds’ risk adjusted return, volatility, exposure to healthy companies. Morningstar gives a scale from 0 the lowest to 100 for the highest ESG score. Some control variables are used, AGE denotes the age of the SRI funds in years since the date of incorporation. SIZE indicates the fund’s size in millions of euros. VOL represent that standard deviation of the SRI funds, it measure the risk of a fund that is an investor’s only holdings. MFEE represents the costs shareholders paid for management and administrative services over the fund’s prior fiscal year. I use a set of dummy variables to identify fund’s type: EQU, BAL, GLO. Based on my hypothesis, I expect β0 to be not significantly different from zero.
I include AGE to address the learning effect in SRI funds identified by Bauer et al. (2008). I include SIZE as it may also effect fund performance as in Chen et al., (2004). I include VOL as a measure for the risk of the fund and MFEE following Renneboog et al. (2008) who shows that these two variables are able to explain the performance of SRI funds. I include dummy variable to identify type of funds following Capelle-Blancard (2014).
To test the effect of different screening strategy on the excess return, I will use the following regression model:
RSRI = α+ β0SBIC + β1SSUS + β2SEXC + β3SCOM + β4AGE + β5SIZE + β6VOL + β7MFEE
Where:
SBIC = equal to 1 if the SRI fund uses the best-in-class screening strategy and 0 otherwise
SSUS = equal to 1 if the SRI fund uses the sustainability-themed screening strategy and 0 otherwise
SEXC = equal to 1 if the SRI fund uses the exclusion screening strategy and 0 otherwise SCOM = equal to 1 if the SRI fund uses the combination of the screening strategies and
0 otherwise
ε = error term
RSRI, AGE, SIZE, VOL, MFEE, EQU, BAL AND GLO are defined as in previous model. SBIC, SSUS, SEXC, and SCOM are the dummies for screening strategy. SBIC will be equal to one if the fund use the best-in-class screening strategy and 0 otherwise. SSUS is a variable equals to one if the funds use the Sustainability-themed strategy and 0 otherwise. Sexc will be equal to 1 if the fund use the exclusion strategy and 0 otherwise. SCOM equals to one if the fund use combination of screening strategy and 0 otherwise. I use the same control variables (AGE, SIZE, VOL, MFEE, EQU, BAL and GLO) as the previous model. Based on my hypothesis, I expect β0, β1, β2, and β3 to be significantly different from zero.
To examine the effect of screening process on the SRI funds’ performance, the model of the SRI returns leads to following regression:
RSRI = α + β0ENV + β1SOC + β2GOV + β3AGE + β4SIZE + β5VOL + β6MFEE + β7EQU
+ β8BAL + β9GLO + ε
Where:
ENV = equal to 1 if the SRI fund focuses on environmental issue and 0 otherwise SOC = equal to 1 if the SRI fund focuses on social issue and 0 otherwise
GOV = equal to 1 if the SRI fund focuses on governance issue and 0 otherwise
ESG = reference whether the fund focuses about environmental, social and governance issues altogether
ε = error term
RSRI, AGE, SIZE, VOL, MFEE, EQU, BAL AND GLO are defined as in previous model. ESG is a set of three dummy variables: Environment, Social and Corporate Governance. All the dummies are equal to one if the fund focus on Environmental, Social, and Corporate Governance issues respectively and 0 otherwise. The control variables use are similar to the previous model (AGE, SIZE, VOL, MFEE, EQU, BAL and GLO). I expect β0, β1, β2, and β3 to be not
significantly different from zero. 4.2 Data Selection
My main objective is to evaluate the differences in the performance between SRI and conventional funds in the main European market, so I focus my research on the specific Euro-Area country. A eurozone country is a member state of the European union that has adopted the euro as its sole legal tender. As of October 2015, the eurozone comprises the following EU member states: Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxemburg, Malta, the Netherlands, Portugal, Slovakia, Slovenia, and Spain. Eurozone companies include any company located in the Eurozone or which conducts significant business activity in the Eurozone.
For the Eurozone companies, I obtain the list of SRI funds from MorningStar. MorningStar provides quarterly return of SRI funds from the year of 2013-2018 along with the day of incorporation, country location, fund’s size and standard deviation. My sample includes a total of 128 socially responsible investment, 109 from the MorningStar Eurozone Large-cap category (LARGE-CAP), 16 funds are selected from MorningStar Eurozone Mid-cap category (MEDIUM-CAP), 3 funds are from the MorningStar Eurozone Small-cap classification (SMALL-CAP). To be included in the sample, funds are required to have at least 16 quarterly observations and falls into active fund category. For each of these SRI funds, I verify the SRI fund’s investment policy and screening criteria through information available in the MorningStar Europe or from the individual funds’ prospectuses whenever necessary.
Then, I collect quarterly return for the conventional funds that are domiciled in the 19 countries from the DataStream. After filtering the data from missing values, my final sample consist of 1036 conventional funds. No fund ceased operations during the sample period, thus both SRI and conventional fund samples do not suffer from survivorship bias. Last, I collect the datas necessary for the construction of 5 factor model are all collected from the DataStream.
4.3 Descriptive statistics
Table I presents the summary statistics of the SRI and conventional funds
Variable Obs Mean Std. Dev. Min Max Skewness Kurtosis
RSRI 128 1.509 0.779 -1.055 4.134 -0.650 5.210
RCONV 1,037 1.622 1.120 -0.470 8.831 1.321 6.744
This table presents the summary statistics based on equally weighted portfolio of SRI and conventional funds. Number of observation, mean excess return, standard deviation, outliers, skewness and kurtosis are reported for the period of Jan 2013 - March 2018
The results show a positive mean excess returns for both type of funds. The descriptive statistics implies that on average the excess return of the SRI funds are lower than the excess return of the conventional funds. This could lead to the conclusion that SRI funds underperform the unscreened funds. However, the difference is less than 0.2%. A t-test for comparing two means using independent samples when the populations have unequal variances shows a p value that is greater than 0.05. Thus, both mean excess returns do not significantly differ at significance level of 5%. Looking at the value of skewness and kurtosis, as with most fund return data, the hypothesis for normality is rejected.
Table II presents the summary statistics of the SRI funds characteristics. (Return, Age, Size, Volatility, Management Fee and funds’ ESG score)
Variable Obs Mean Std. Dev. Min Max
RSRI 128 2.509 0.779 -0.055 5.134
AGE 128 10.828 6.092 1 34
VOL 128 13.82414 1.555 11.18 21.1
MFEE 128 1.162 0.551 0.08 2.3
ESGSCR 128 56.179 2.736 49 61
This table presents the number of observation, mean, standard deviation, and outliers for the return of SRI funds (in percentage) and the control variables; Age is measured in years, since the date of incorporation, Size is measured in Millions of Euro, Volatility represents that standard deviation (in percentage) of the SRI funds, management fee (%) represents the costs shareholders paid for management and administrative services and ESG score is measured on a scale of 0-100.
The characteristics of the 128 SRI funds are reported in Table II. In my sample, fund age (AGE) varies between 1 years and 34 years, with mean of 10 years. While asset size (SIZE) ranges from 0.97 million of euros to 5,946.12 million of euros. Volatility (VOL) ranges from 11.18% to 21.1%. Management fee (MFEE) ranges between 0.08% and 2.3%. The SRI funds’ ESG score (ESGSCR) ranges from 49 the lowest and 61 the highest.
Table III presents descriptive statistics about the capitalization, screening strategy, nature and type of the SRI funds
Variable Obs Proportion
CAPITALIZATION LARGE-CAP 128 0.851 MEDIUM-CAP 128 0.125 SMALL-CAP 128 0.023 SCREENING BEST-IN-CLASS 128 0.687 EXCLUSION 128 0.570 COMBINATION 128 0.476 SUSTAINABILITY 128 0.179
NATURE ESG 128 0.649 ENVIRONMENT 128 0.181 SOCIAL 128 0.102 CORPORATE GOVERNANCE TYPE 128 0.068 EQUITY 128 0.546 BALANCE 128 0.210 GLOBAL 128 0.226 OTHER 128 0.023
The table documents the characteristic of the 128 SRI funds. Capitalization represents the criteria of the funds based on MorningStar category (large-cap, medium-cap, and small-cap. Screening reports the percentage of funds which are identified to be using best-in-class, exclusion, Sustainability themed or combination of screening criteria. The next column represents the nature of the funds. Percentage of funds that are conscientious to the Environmental, Social, Corporate Governance or all of these issues (ESG) are reported.
The array of screens of the SRI funds vary a lot, most of them combine the best-in-class with the exclusion approach. The most common exclusions of the sample are linked to weapon followed by tobacco and nuclear energy. Very few focus exclusively on the environmental protection, human rights, and labor standards, about 17% of the sample. Most of the SRI funds from the sample care about the Environmental, Social and Governance (ESG) issues but few focus on one specific topic. About 54% of the SRI sample funds are classified as equity funds. Very small portion of the sample are bond funds. About one fourth of the investment diversify their assets outside Europe.
Based on Table IV, Exclusion strategy (SEXC) and Combination strategy (SCOM) are highly positively correlated. Suggesting that, the funds that use combination strategy are more likely to use a combination of exclusion screens. For instance, the practice of screening process that includes the exclusion of firms related to weapons, tobacco, animal testing, and pornography. The result also suggest Global funds (GLO) and Equity funds (EQU) are highly negatively correlated. The two variables have a negative correlation of -0.596. Suggesting that, if the funds
This table presents the correlations between the variables used throughout the analysis. RSRI indicates equally weighted quarterly return of SRI funds for the period 2013-2018. AGE, SIZE, MFEE, VOL, ESGSCR, GLO, EQU, BAL and OTH are control variables. The screening strategy SBIC, SSUS, SEXC and SCOM are dummy variables, with value one if the fund use the corresponding strategy. The funds nature ENV, SOC, GOV and ESG are dummy variables.
Control Variables Screening Strategy Selection Criteria Type
RSRI AGE SIZE MFEE VOL ESGSCR SBIC SSUS SEXC SCOM ESG ENV SOC GOV GLO EQU BAL OTH
RSRI 1 AGE 0.269 1 SIZE 0.194 0.210 1 MFEE -0.039 0.141 -0.027 1 VOL -0.049 -0.039 -0.251 -0.029 1 ESGSCR -0.151 0.098 0.077 -0.236 0.072 1 SBIC 0.138 -0.028 -0.179 -0.217 0.017 0.096 1 SSUS 0.210 0.050 0.005 0.034 -0.103 -0.126 -0.317 1 SEXC -0.112 -0.011 0.132 0.132 0.024 0.146 -0.107 -0.253 1 SCOM 0.132 -0.003 -0.100 0.006 -0.015 0.008 0.343 0.042 0.520 1 ESG 0.018 0.007 -0.027 -0.261 0.102 0.147 0.447 -0.097 -0.114 0.097 1 ENV -0.072 0.010 -0.116 0.213 -0.107 0.013 -0.295 0.035 0.290 0.062 -0.479 1 SOC 0.129 0.010 0.024 -0.021 -0.059 -0.135 -0.089 0.305 -0.126 0.008 -0.355 -0.131 1 GOV -0.143 -0.114 0.039 0.008 -0.056 -0.122 -0.057 -0.138 0.039 -0.090 -0.275 0.080 -0.030 1 GLO 0.104 0.067 0.354 0.113 -0.120 0.008 -0.046 -0.010 -0.127 -0.217 -0.136 -0.020 0.129 0.141 1 EQU -0.178 0.129 -0.256 -0.285 0.053 0.096 0.014 0.017 0.025 0.114 0.071 -0.005 -0.034 -0.149 -0.594 1 BAL 0.057 -0.149 0.112 0.189 0.037 -0.156 -0.118 0.107 0.063 -0.071 -0.021 0.095 0.020 0.053 -0.005 -0.568 1 OTH 0.125 -0.038 0.014 0.132 -0.061 0.032 0.128 -0.072 0.145 0.1624 0.039 -0.074 0.033 -0.064 -0.083 -0.170 -0.080 1
are primarily invested in stocks the funds are more likely to focus on stocks around the world excluding the investor home country. The mean of the Variation Inflation Factor of all the independent variables is 1.83 and EQU has the highest VIF value, which is 3.61, implying the problem of multicollinearity is not present in the data.
5. Results and Analysis
5.1 Funds Performance
Table V presents the regression results of the multi factor model
Variable Alpha Market SMB HML WML Loc Rsquared
RSRI 1.412 0.532** 0.292 0.237 0.674 -0.345 0.611 (0.168) (0.015) (0.550) (0.457) (0.253) (0.540) RCONV 2.492*** 0.976*** -0.245** -0.060 0.139 -0.009 0.975 (0.000) (0.000) (0.031) (0.472) (0.298) (0.948) DIFFERENCE -1.080 -0.443* 0.538 0.297 0.534 -0.336 0.277 (0.329) (0.068) (0.200) (0.425) (0.294) (0.583)
This table presents regression estimates for equally weighted portfolio of funds and R-squared computed using the 5 factor Fama French model with robust standard error for both SRI and conventional funds. Value in bracket is the p-value. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level
The alpha shows that only conventional funds are able to outperform the market. This finding is not consistent with my first hypothesis, where I expect both funds to have similar performance in terms of risk-adjusted return. However, I find no statistically significance difference in performance (alpha) between SRI and conventional funds.
The market betas, which represents the risk from owning the fund in general are positive for both funds, implying that SRI and conventional funds move in the same direction as the market. However, the difference in market betas is negative. This results implies that the conventional funds carry higher risk than the SRI funds. This is consistent with Gregory et al (1997) and Kreander et al (2005), who show that SRI funds are less affected by the market risk.
The size factor (SMB) for conventional funds show a negative value (p-value < 5%), implying that the non-screened funds are predominantly large-cap stocks. While the size factor for SRI funds shows a positive value, the funds appear to be driven more by small cap return.
For both SRI and conventional funds the coefficients on the book-to-market (HML) and momentum (WML) factors are statistically insignificant. This evidence is consistent with the findings of Bauer et al. (2007) for Canadian funds, and Renneboog et al. (2008) for European funds. The coefficient on the local factor (LOC) for both SRI and conventional funds is not significant. This is the opposite of the findings by Gregory et al., (2007) and Leite et al., (2014), who find that ethical funds are heavily invested in local stocks.
5.2 ESG Rating
Table VI presents the regression results of the ESG Score model
Variable Model 1 Model 2 Model 3
CONSTANT 4.717 5.381*** 6.016*** (0.019) (0.007) (0.001) AGE 0.035*** 0.044*** (0.002) (0.000) SIZE 0.000** 0.000 (0.038) (0.166) VOL 0.006 0.003 (0.890) (0.935) ESGSCR -0.039 -0.057 -0.058 (0.261) (0.270) (0.153) TYPE EQU -0.655*** (0.001)
BAL -0.263* (0.008) GLO -0.335** (0.031) R-SQUARED 0.023 0.142 0.204 F-STATISTIC 1.280 4.75*** 4.090***
This table presents result from OLS regression with robust standard error of the SRI mean return on SRI funds ESG score. In Model 1, only the dummies of the funds nature are regressed. In Model 2, control variables (age, size, volatility, and management fee) are added into the regression. Model 3 shows the regression with the whole control variables, adding fund type into the regression. Value in bracket is the p-value. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level
I find a negative but insignificant coefficient on ESGSCR. This finding is not surprising given the results in table IV where the difference in alpha is negative. Although the results are consistent with those presented earlier, I cannot reject the hypothesis that there is no relationship between SRI fund’s excess return and its ESG score. I recognize that using the same ESG score data for the whole sample period may cause some bias. In addition, looking at funds ESG score, it appears that the score are around the same values and have a very small range.
5.3 Screening strategy
Table VII presents the regression results of the screening strategy model
Variable Model 1 Model 2 Model 3
CONSTANT 2.268*** 1.566** 2.164*** (0.000) (0.047) (0.006) AGE 0.027 0.035*** (0.016) (0.005) SIZE 0.000*** 0.000** (0.002) (0.018)
VOL 0.020 0.016 (0.693) (0.742) MFEE -0.018 -0.148 (0.888) (0.281) SCREENING SBIC 0.254 0.277 0.211 (0.177) (0.117) (0.216) SEXC -0.153 -0.219 -0.223 (0.414) (0.168) (0.166) SSUS 0.467** 0.429** 0.437** (0.019) (0.034) (0.041) SCOM 0.184 0.259 0.269 (0.301) (0.125) (0.108) TYPE EQU -0.579** (0.004) BAL -0.187 (0.309) GLO -0.283* (0.091) R-SQUARED 0.097 0.210 0.260 F-STATISTIC 4.520*** 3.350*** 3.230***
This table presents result from OLS regression with robust standard error of the SRI mean return (RSRI) on a number of characteristics of the SRI screening process. In Model 1, only the dummies of screening strategy are regressed. In Model 2, control variables (AGE, SIZE, VOL, and MFEE) are added into the regression. Model 3 shows the regression with the whole control variables, adding fund type (EQU, GLO and BAL) into the regression. Value in bracket is the p-value. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level
There are some interesting findings. First, it appears that the sustainability-themed funds significantly positively affect the mean return of SRI funds and perform better than the Best-in-class strategy and the other two types of screens. This finding is consistent with Eurosif (2016) who show pursuing climate-related opportunities, water and waste management, healthcare and education on the SRI funds screening process has positive effect on the funds performances. Possible explanation is that as investors increasingly observe whether a company are concerned on the risk related to the sustainability issues may account for company’s market value.
Second it turns out that best-in-class fund do not perform better than the other types of screening strategy. This finding is not consistent with Kempf et al., (2007). They show that best-in-class strategy leads to a higher performance than other screening strategy.
Third, it appears that there is no significant cost or benefit from imposing negative screens and combining screens which is not consistent with the lack of diversification hypothesis. This hypothesis is based on the idea that with exclusion strategy SRI funds use only a subset of the total investment universe, thus implementing this strategy should have negatively affect the funds return. Also, with exclusion strategy socially responsible investors are more likely to exclude larger and well-established companies from their portfolio, whereas the best companies in each sector are likely larger.
Thus my hypothesis that the implementation of best-in-class, exclusions, Sustainability themed and other types of has a significant effect on the performance figures of SRI funds is rejected. As only the sustainability-themed screening criteria have a significant positive effect on the SRI funds mean return.
5.4 Selection criteria
Table VIII presents the regression results of the funds nature model
Variable Model 1 Model 2 Model 3
CONSTANT 2.518*** 2.256*** 2.874*** (0.000) (0.003) (0.000) AGE 0.029** 0.039*** (0.010) (0.001) SIZE 0.000* 0.000 (0.086) (0.251) VOL -0.004 -0.006 (0.913) (0.886) MFEE -0.087** -0.222 (0.500) (0.112) NATURES ENV -0.090 -0.042 0.002 (0.609) (0.834) (0.990) SOC 0.215 0.208 0.230 (0.184) (0.0149) (0.124) GOV -0.297 -0.256 -0.308 (0.125) (0.218) (0.105) TYPE EQU -0.680*** (0.003) BAL -0.231 (0.197)
GLO -0.352* (0.060)
R-SQUARED 0.086 0.126 0.195
F-STATISTIC 3.830** 2.780*** 2.750***
This table presents result from OLS regression with robust standard error of the SRI mean return (RSRI) on a number of characteristics of the SRI selection criteria. In Model 1, only the dummies of the funds nature are regressed. In Model 2, control variables (AGE, SIZE, VOL and MFEE) are added into the regression. Model 3 shows the regression with the whole control variables, adding fund type ( EQU, BAL and GLO) into the regression. Value in bracket is the p-value. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level
I find that, as the number of control variables increase the regression coefficient for ENV becomes positive. However, the value of the coefficient is really small and not statistically significant. Hence, I conclude that companies do not have to encounter a trade-off between eco-efficiency and financial performance (Derwall et al., 2005). The coefficient on SOC is positive in each model, but not significant, which indicates that investor activism on company’s business relationship has no impact on SRI financial performance. This finding is not consistent with Edmans (2011), who show that companies with high employees satisfaction earns higher risk adjusted return above the industry benchmark.
The regression coefficient on GOV is negative in each model and it is not statistically significant. This result contradicts with the study by Gompers et al., (2003) who find that firm with a strong shareholders right have higher return on equity, net profit margin, and Tobin’s Q, but support Bauer et al., (2004), who find a weaker excess return on corporate governance strategy.
The coefficient on ENV, SOC, and GOV indicate that the selection process (ESG) might not have an effect on the SRI funds return. Therefore, the hypothesis that Environment, Social and Governance oriented SRI funds do not differ in terms of performance (mean return) is not rejected.
6. Sensitivity Analysis 6.1 Asset Pricing Model
There is some disagreement among financial economist about which asset pricing morel is most accurate. Some support the CAPM advanced by Sharpe (1964) and Lintner (1965), while others advocate more elaborate models such as the 3-factor model proposed by Fama and French (1993) or the 4-factor model proposed by Carhart (1997). I therefore rerun the regression using 3 different asset pricing models.
Table IX presents the regression results of the CAPM, 3-factor, and 4-factor model
Variable SRI Conventional Difference
ALPHA 2.270** 2.169*** -0.100
(0.026) (0.000) (0.923)
MARKET 0.741*** 1.000*** 0.261*
(0.000) (0.000) (0.073)
R-SQUARED 0.522 0.963 0.102
This table presents regression estimates for equally weighted portfolio of funds and R-squared computed using the CAPM model with robust standard error for both SRI and conventional funds. Value in bracket is the p-value. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level
Table X presents the regression results of the 3-factor
Variable SRI Conventional Difference
ALPHA 1.885* 2.603*** -0.718
(0.071) (0.000) (0.478)
(0.000) (0.000) (0.054) SMB 0.294 -0.217** 0.511 (0.359) (0.017) (0.127) HML 0.401* -0.004 0.406 (0.084) (0.935) (0.112) R-SQUARED 0.575 0.974 0.238
This table presents regression estimates for equally weighted portfolio of funds and R-squared computed using the 3-factor model with robust standard error for both SRI and conventional funds. Value in bracket is the p-value. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level
Table XI presents the regression results of the 4-factor model
Variable SRI Conventional Difference
ALPHA 1.407 2.492*** -1.085 (0.145) (0.000) (0.297) MARKET 0.653** 0.979*** -0.325 (0.002) (0.000) (0.074) SMB 0.156 -0.249*** 0.405 (0.659) (0.008) (0.282) HML 0.147 -0.062 0.210 (0.577) (0.370) (0.497)
WML 0.597 0.138 0.459
(0.265) (0.264) (0.419)
R-SQUARED 0.600 0.975 0.263
This table presents regression estimates for equally weighted portfolio of funds and R-squared computed using the 4-factor model with robust standard error for both SRI and conventional funds. Value in bracket is the p-value. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level
These results from the CAPM and 3-factor model are not consistent with the previously used 5-factor model. Here both SRI and conventional funds are able to outperform the market. In all models, SRI funds have lower exposure to market risk and in terms of comparability it appears that there is no significance difference in alpha of SRI and conventional funds. These results are consistent with those presented earlier.
7. Conclusion
Using the database of 128 European SRI funds, I analyze European SRI funds financial performance along with its screening and selection criteria. The result presents some interesting insights. First, I find that no significant differences between SRI funds risk-adjusted return and conventional funds. This suggest that investors do not have to pay a higher cost for investing in SRI funds. This contradicts with the utility maximization principle, which consider that SRI will underperform because the screening process reduces the investment universe and inhibit wealth maximization. I also find that socially responsible funds that employ positive screens outperform the funds that use exclusion strategy. This is in line with the (lack of) diversification hypothesis, where with exclusion strategy investors are more likely to exclude large and well established firms. Moreover, I also find that funds selection process might not have an impact on the SRI funds mean return. Investors can choose funds that focus either on Environment, Social and Governance or these issues all together without sacrificing the potential return of their portfolio. All in all, SRI funds seem to have no firm disadvantage in terms of their abnormal return when compared to conventional funds. Their risk-adjusted return is similar to conventional funds.
8. Limitation
The results of this study should be interpreted with caution as it has some limitations and issues. First, I only use 128 SRI funds while the size for the conventional funds reaches 1037 funds. The small sample size could explain the lack of statistical significance in my research. Future research could use more data for longer period to get more precise estimates. Second, for the SRI funds analysis the multi factor model has a higher R-squared then single factor model, however more coefficients are significant for the later. This is surprising since I expect that multifactor models are superior in explaining the funds returns. Third, I do not evaluate the performance of SRI funds during crisis period while it is important for investor to know how well the SRI funds perform when the market is not stable. The reason is that I have limited access to the data sources. MorningStar only provide SRI funds return for the period of 2013-2018 and other resources only provide information for paying customers. Further research can also look at the European SRI performance in periods of crisis or when there is downturns in the market.
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