Socially Responsible Fund Performance in Europe

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Socially Responsible Fund Performance in Europe

An analysis of performance and investment strategy

Elise Helene von Hirsch

June 9, 2020

Economics Program Department of Economics

University of Oslo

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Preface

This thesis marks the end of my MSc. degree in Economics at the University of Oslo. I want to thank my supervisor, Jin Cao, for his excellent guidance and support throughout the process. This thesis would not be possible without you.

I also want to thank my colleagues at KLP Asset Management for their time and help. It has been a pleasure working with you and the experience has been enriching. I especially want to thank Jeanett Bergan, the head of Responsible Investments, for the research topic. My internship at KLP has given me great insights about socially responsible investments that have been useful when writing this thesis.

Elise Helene von Hirsch June 2020

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Abstract

In this paper, I analyze the question of whether European socially responsible mutual funds outperform or underperform conventional funds between 2010 and 2020.

Further, I investigate whether the funds have different investment strategies. By employing a matched-pair approach, I match funds based on size and age. I construct three socially responsible portfolios; a pooled socially responsible portfolio, a ESG portfolio and a environmental portfolio. Then, I construct a matched conventional and difference portfolio for each socially responsible portfolio. By applying the Carhart four factor model, I investigate whether there is a difference in performance and investment strategy. To test the robustness of my results I consider management fees, different benchmarks, model specification, sub-periods and ethical rating. My main results show that there is not a statistically significant difference in risk-adjusted performance between socially responsible and conventional funds. Environmental funds underperformed conventional funds between 2011 and 2014, and outperformed between 2017 and 2020. I find that there is a difference in investment strategy between socially responsible and conventional funds; socially responsible funds are more growth-oriented and exposed to large stocks.

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1 Introduction

Socially responsible investing¹ (SRI) has become increasingly important for investors and customers. Responsible investing takes into account ethical considerations as well as traditional corporate financial performance. This thesis investigates the performance and investment strategy of socially responsible European funds investing in the European market.

There is substantial literature evaluating the performance of socially responsible funds, but most research focus on the US and UK markets. My contribution to the literature is that I analyze the less-explored European market. Most studies view SRI funds as a homogenous group, and ignore the fact that these funds can have different objectives and approaches to incorporating ethical factors. By divding the socially responsible funds into sub-groups based on ethical issue focus, I am able to explore the heterogeneity of SRI funds. I also contribute to the literature by using the most recent data on performance, between January 2010 and January 2020. Calculations and analysis have been carried out using Microsoft Excel and the statistical software STATA (version 16).

The empirical questions are:

• Is there a difference in risk-adjusted returns between socially responsible and conventional funds?

• Do socially responsible and conventional funds follow different investment strategies?

I will also answer these questions for both subgroups of SRI funds.

The thesis is structured as follows: Chapter 2 explains what socially responsible investments are, the history, drivers and approaches. In chapter 3, I review previous literature on performance and investment strategy of SRI funds. Chapter 4 discusses the different asset pricing models considered. Chapter 5 describes the data and I discuss some potential biases. Chapter 6 describes the methods used; matched-pair approach, fund portfolio construction, model specification, factor portfolio construction, diagnostics and summary statistics. Chapter 7 is split into two parts; main findings and robustness tests. In chapter 8, I discuss my results and compare them to previous literature. A general conclusion

1Socially responsible investing in this thesis refers to all ”ethical” investing.

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follows in chapter 9. The results from the diagnostics and robustness tests can be found in the appendix.

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2 Background

Between 2012 and 2018, assets managed on the European socially responsible market has doubled in value from 252 to 496 billion euros (Statista, 2019a). During this period, the amount of responsible investing funds domiciled in Europe increased from 1584 to 2816 funds (Statista, 2019b).

Socially responsible funds can have different ethical issue strategy focuses and approaches to incorporating ethical considerations into their investments. ESG, which is one of the fastest- growing areas of investments, refers to taking environmental, social and governance factors into account in investment decisions (Sarkar et al., 2015). Environmental funds mainly focus on environmental factors, such as climate change and global warming. Religious funds invest in a manner that reflects their religious beliefs and values.

2.1 History

Responsible investing is not a new phenomenon, its roots can be traced back to the 16th century with religious groups. Quakers and Methodists are considered the first ethical investors (Bauer et al., 2005). They wanted their investments to reflect their beliefs and values. Through negative screening, they avoided investing in so-called ”sin stocks”.

Modern socially responsible investments dates back to the 1970’s as a result of the political climate. Activism around the Vietnam war in the 1970’s and Apartheid in the 1980’s raised awareness among investors. In the 1980’s, ethical investing started focusing on the environment as a reaction to climate disasters such as the Exxon Valdez oil spill in Alaska.

Awareness around climate change continued growing in the 1990’s, and so did the focus on SRI investments (Liu, 2020).

2.2 Drivers

The United Principles for Responsible Investment (UNPRI) lists three main drivers for ESG investments; materiality, client demand and regulation (United Principles for Responsible Investment, 2020).

• Materiality

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There is a growing acknowledgement that ESG factors affect risk and investor returns.

Poor policies can result in controversies, which can lead to lower returns. Investing in companies that have robust policies is a way of mitigating such risks. Fund managers tend to want to avoid reputational risk as it may lead to scrutiny from their beneficiaries. Beneficiaries can also put pressure on the managers to sell their stocks, which could decrease stock returns.

• Client Demand

The demand for ethical investments has increased these past years. This is caused by acknowledgement that ESG factors influence returns and reputation, and an increased focus on the impact companies have (United Principles for Responsible Investment, 2020). A survey conducted in 2018 found that the majority of high net worth millennials regard ESG as an important factor in making investment decisions.

It also found that a majority of millennials want to invest in a way that reflect their values (MSCI ESG Research LLC, 2020). Millennials represent a large demographic group, which will have increased wealth in the future as they grow older. Thus, we can expect that the demand for ESG investments will increase. It is not only millennials who are increasingly becoming interested in ESG, interest among the general population has also increased these last years. In the past, women were more likely than men to value ESG in their investments. This has now changed, and the difference between the genders has decreased. Women do not invest as much as men, but we can expect that this will change. This untapped market can also lead to an increase in demand for ethical investments (Esposito, 2020).

• Regulation

Regulations on responsible investments have increased after the financial crisis in 2008. There is a growing recognition among regulators that the financial sector has a responsibility to reach climate goals and prevent tax evasion, among other things (United Principles for Responsible Investment, 2020).

2.3 Approaches

The most common approach to ethical investments in Europe is to exclude companies

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sectors that do not align with their ethical values. Sectors that are regarded as unethical can thus be excluded; for example tobacco, alcohol, gambling, pornography, oil sand and coal. Another common strategy of excluding companies is based on behaviour; violating human and labour rights, damage to the environment, corruption and money laundering among other things. Fund managers can also engage in positive screening, which means that they invest in companies that are exceptional at ESG in an industry. This strategy is referred to as best-in-class. Fund managers can invest in firms that score high on ESG metrics, have strong policies, or that produce renewable energy (United Principles for Responsible Investment, 2020).

Responsible investors can partake in active ownership by dialogue, encouraging companies to disclose more, improve their practices and policies. Investors can vote against the board if they aren’t satisfied with the management’s performance or corporate governance. There are many small investors, who may not necessarily have a lot of influence by themselves, but by grouping together with others they may be able to influence big corporations and move them in the right direction. Using the press is also an effective tool to put pressure on companies to change.

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3 Literature Review

There is a substantial amount of literature on socially responsible mutual fund performance, most of which focus on the US and UK market. Mallin et al. (1995) studied the performance of ethical and conventional funds in the UK between 1986 and 1993. They used a matched pair approach, matching funds based on size and age. They test two hypotheses; ethical funds underperform the market, and that the performance of ethical funds does not differ from conventional funds’ performance. The performance of the funds is evaluated using the excess return to variability measure (Sharpe, 1966), the excess return to non-diversifiable risk (Treynor, 1965), and the differential return with risk measured by beta (Jensen, 1968).

Their results showed no statistically significant difference in performance between ethical and conventional funds.

Kreander et al. (2005) also used a matched pair approach to study fund performance between 1995 and 2001. Their sample included UK, Swedish, German and Dutch funds.

They used three different performance measures; Sharpe, Treynor and Jensen. Overall, their results show that ethical funds performed worse than the benchmarks, but the performance was similar when the risk-adjusted performance measures were considered.

They concluded that there was not a significant difference in performance between ethical and conventional funds. They find that management fees is a significant variable in explaining the Jensen (1968) measure.

Another study aimed at evaluating performance using indices is by Cort´ez et al. (2009).

They analyzed the performance of ethical funds from seven European countries between 1996 and 2007; Austria, Belgium, France, Germany, Italy, the Netherlands and the UK.

They divide the investment types into three categories; global equity, Europe/Eurozone equity and Euro balanced. Three performance measures are used; a unconditional measure of performance, the Jensen’s alpha from the CAPM, a partial conditional model which allows beta to be time-varying, and lastly a fully conditional measure that also allows a time-varying alpha. The latter measure reflects the possibility that economic conditions can affect performance. They analyze performance at individual and aggregate level by using an equally weighted portfolio of funds for each country and fund category. To test

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robustness, they use a conventional benchmark and a SRI indice². Surprisingly they found that the explanatory power was higher when using conventional benchmarks. This suggests that conventional benchmarks are better than SRI indices in explaining ethical performance.

They conclude that SRI funds perform as well as conventional and SRI indices. As they mention in their paper, these results are not in line with neo-classical theory. Socially responsible funds often perform negative screenings which limits their investment universe, which in turn limits their potential for diversification. Less diversification should in theory result in lower risk-adjusted returns.

Other studies have used multi-factor models as well, the most common one being the Carhart four factor model ³. Bauer et al. (2005) studied German, UK and US ethical mutual fund performance and investment strategy between 1990 and 2001. In addition to using a conditional model, they use the Carhart four factor model. They used a matched pair approach matching on size and age. Through this method, they created ethical, conventional and difference portfolios. They did not find a statistically significant difference in performance between ethical and conventional funds. However, their findings suggest that the funds differ in investment strategies and are differently exposed to the factor portfolios. By looking at sub-periods, they find that ethical funds went through a catching- up phase. Ethical funds began by underperforming and over time their performance caught up to conventional funds. Another interesting finding, was that ethical benchmarks did a poor job explaining ethical performance compared to conventional benchmarks. The same conclusion was reached when analyzing the performance in the Canadian and Australian market using the same model (Bauer et al., 2007, 2004).

Lean et al. (2015) study socially responsible funds from Europe and North America between 2001 and 2011. They employ the Carhart four factor model and the Fama French three factor model to evaluate performance. They find that European and North American socially responsible funds outperform the market benchmark. When using the four-factor alpha from the Carhart model, they found that North American SRI funds perform better than European SRI funds. In addition to this, they find little evidence of performance persistence, but they do find more evidence of persistence for the European funds than

2A socially responsible indice is a benchmark which re-weighs or excludes companies based on ESG criteria. The aim of the indice is to represent the most common ESG investment approaches.

3The Carhart four factor model will be explain in detail in chapter 4.

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the North American ones. Finally, they conclude that European SRI funds have higher downside risk than North American SRI funds. They also find that return fluctuation of European SRI funds is higher than for the North American ones.

There are not many studies that investigate heterogeneity of socially responsible funds, those that do focus on environmental funds. Mu˜noz et al. (2014) analyze performance of socially responsible mutual funds in the US and Europe between 1994 and 2013. They compare the performance of green funds to other socially responsible funds and conventional funds. In addition to this, they compare managerial abilities, particularly during crisis periods. To evaluate fund performance, they use the Carhart four factor model. For managerial abilities, they use a combination of Treynor (1965) and the Carhart model developed by Lu (2005). By using a matched pair approach, they match by investment objective, inception date and the total net assets under management. For each socially responsible portfolio, they build a matched conventional and difference portfolios. They distinguish between global and domestic investment aims. Their results suggest that green funds do not perform worse than other socially responsible funds or conventional funds, even when controlling for crisis market periods. Generally, they conclude that green fund managers are unable to successfully follow stock-picking or timing strategies despite having a smaller investment universe. They do however find that European global fund managers are able to follow the size strategy, i.e. invest in small stocks. During crises, European fund managers appear to have worse managerial abilities. During non-crisis market periods, green European fund managers successfully follow the value strategy in the European market. In the global market, European fund managers are able to follow the size strategy.

Another study on green funds was conducted by Climent and Soriano (2011). They studied green mutual funds in the US between 1987 and 2009. Using a matched pair approach, they match each green mutual fund to an equally weighted portfolio of four conventional funds by age, fund size and investment objectives as matching criteria. Each green fund is also matched to an equally weighed portfolio of two SRI funds using the same criteria.

Two models were used to evaluate differences in performance; the CAPM and the Carhart four factor model. They find that environmental funds underperform conventional funds between 1987 and 2009, and explain that this is due to lack of diversification. Another

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possible explanation is poor management in green funds. However, when they look at a shorter time-period, between 2001 and 2009, they find that there is not a statistically significant difference in risk-adjusted returns between green and SRI or conventional funds.

They argue that the value of green funds may have increased because of increased demand.

When applying a green benchmark as the market proxy, they find no difference in return between green and conventional funds.

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4 Theory

In this section, the four models used to evaluate fund performance are presented. The Carhart four factor model is the main model that will be used, while the other models are used in the robustness tests.

4.1 CAPM

The capital asset pricing model (CAPM) shown in equation (1) is a one-factor model used to evaluate the performance of investments (Sharpe, 1964)

E[R_it]−R_{F t} =a_i+b_i(R_{M t}−R_{F t}) +e_it (1)

where E[R_it] is the expected return of portfolio i at timet. R_Ft is the return of the risk free rate at time t. R_M_,t is the return of the market at time t. a_i is the risk-adjusted return of portfolio i at time t. The coefficient b_i is the market coefficient. _i,t is the error term of portfolio i at time t.

The model only takes into account the market risk premium, (R_Mt−R_Ft). The excess expected return, (E[R_it]−R_Ft), is the expected return of a portfolio minus the risk free rate. The market factor is the market return minus the risk free return. The Jensen’s alpha, a_i, can be interpreted as performance evaluating. If the Jensen’s alpha is positive, the portfolio outperforms the market, and if it is negative, it underperforms the market. If the market factor captures all of the variation in expected returns, the intercept a_i is equal to zero, which means that the portfolio performs exactly like the market. The market coefficient is a measure of the volatility or systematic risk of the portfolio compared to the market (Sharpe, 1964). It can also be interpreted as sensitivity towards the market.

If the market coefficient is equal to one, the portfolio is as volatile as the market. If it is smaller than 1, it is less volatile than the market, and if it is larger than 1, it is more volatile than the market (Fama and French, 2015). The market factor and alpha has the same interpretation in the following expansions of the CAPM.

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4.2 Fama French Three and Five Factor Model

The Fama French three factor model shown in equation (2) was developed in 1992. The model includes two additional dimensions of risk or sensitivities. Fama and French argued that there were many issues with the CAPM.

Rit−RF t =ai+bi(RM t−RF t) +siSM Bt+hiHM Lt+eit (2)

where E[R_it] is the expected return of portfolio i at timet. R_Ft is the return of the risk free rate at time t. R_M_,t is the return of the market at time t. a_i is the risk-adjusted return of portfolio i at timet. SMB_t is the difference in returns between small cap and large cap portfolios at time t. HML_t is the difference in returns between value and growth stocks portfolios at time t. _i,t is the error term of portfolioi at timet. The coefficientsb_i, s_i, and h_i are the factor coefficients of the respective three risk factors for portfolioi.

Fama and French found that stocks with small market capitalization tend to outperform stocks with large market capitalization. Market capitalization of a stock is the current market price per share times the number of outstanding shares. Large stocks tend to be more mature and less risky than small stocks. SMB, small minus big, is the return on a diversified portfolio of small stocks minus the return on a portfolio of big stocks. This factor measures the historic excess return of small companies compared to big companies.

The coefficient on SMB can be interpreted as exposure to small stocks. If the coefficient s_i is positive, the portfolio is more exposed to small stocks than large stocks. Conversely, if the coefficient is negative, the portfolio is more exposed to large stocks than small stocks (Fama and French, 1992).

They also found that value stocks tend to outperform growth stocks. Value stocks are stocks with a high book-to-market equity ratio. They argue that this value premium is a compensation for bearing risk. Value stocks tend to be in distress, they have low potential for growth, and are more risky. Growth stocks on the other hand have a higher potential for growth, and a low book-to-market ratio. They found that there is a premium associated with relative distress and that including this factor provides a better explanation of performance of value stocks than the CAPM (Fama and French, 1998). HML, high minus low, is the difference between the returns on diversified portfolios of high and low

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book-to-market stocks (Fama and French, 1992). If the coefficient h_i is positive, the portfolio is more value-oriented. On the other hand, if it is negative, the portfolio is more growth-oriented, i.e. invested in stocks that have a low book-to-market equity ratio. One interpretation of value stocks is that the market undervalues them, these stocks are usually in distress and are riskier than growth stocks. Growth stocks have good prospects for return in the future and the market overvalues them, hence the book-to-market equity ratio is low.

In 2014, Fama and French expanded their model to include two more risk factors, shown in (3).

R_it−R_{F t} =a_i+b_i(R_{M t}−R_{F t}) +s_iSM B_t+h_iHM L_t+r_iRM W_t+c_iCM A_t+e_it (3)

where E[R_it] is the expected return of portfolio i at timet. R_Ft is the return of the risk free rate at time t. R_M_,t is the return of the market at time t. a_i is the risk-adjusted return of portfolio i at time t. SMB_t is the difference in returns between small cap and large cap portfolios at time t. HML_t is the difference in returns between value and growth stocks portfolios at time t. RMW_t is the difference in returns between robust and weak profitability stock portfolios at time t. CMA_t is the difference in returns between conservative and aggressive stock portfolios at time t. _i,t is the error term of portfolio i at time t. The coefficients b_i, s_i, h_i, r_i, and c_i are the factor coefficients of the respective five risk factors for portfolio i.

Their results suggested that their previous three factor model could have problems when portfolios had strong tilts towards stocks with high or low profitability (Fama and French, 2015). They argue that the previous model was missing two factors that could explain variation in average return, namely profitability and investment.

RMW, robust minus weak, is the difference between the returns on diversified portfolios of stocks with robust and weak profitability. If the coefficient ri is positive, the portfolio is more exposed to stocks with robust profitability. If it is negative, the portfolio is more exposed to stocks with weak profitability. Stocks with robust profitability tend to have higher returns than those with weak profitability.

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The last factor CMA, conservative minus aggressive, is the difference between the returns on diversified portfolios of stocks with low and high investment policies (Fama and French, 2015). This factor is a proxy for investment activity. Companies with a high percentage of change in total assets are aggressive, meaning they have high investment policies.

Conservative companies have low or negative changes in total assets, i.e. low investment policies. Their results suggested that conservative stocks tend to outperform aggressive stocks.

4.3 Carhart Four Factor Model

The Carhart four factor model was introduced by Carhart in 1997, and is shown in equation (4).

R_i,t−R_F,t =a_i+b_i(R_M,t−R_F,t) +s_iSM B_t+h_iHM L_t+m_iM OM_t+e_i,t (4)

where E[R_it] is the expected return of portfolio i at timet. R_Ft is the return of the risk free rate at time t. RM,t is the return of the market at time t. ai is the risk-adjusted return of portfolio i at timet. SMB_t is the difference in returns between small cap and large cap portfolio at time t. HMLt is the difference in returns between value and growth stock portfolios at time t. MOM_t is the difference in returns between momentum and contrarian stock portfolios at time t. i,t is the error term of portfolio i at time t. The coefficients b_i, s_i,h_i and m_i are the factor coefficients of the respective four risk factors for portfolio i.

The model expands on the Fama French three factor model, and includes the momentum factor MOM. Momentum refers to the tendency for assets that have had higher past returns to continue rising, and conversely for poor-performing assets to continue declining.

Carhart states that funds that are more exposed to momentum stocks do not do so because the fund managers successfully follow momentum strategies, but because some funds are coincidentally invested in a large portion of last year’s high-performing stocks (Carhart, 1997). If the MOM coefficient m_i is positive, the portfolio is more exposed to momentum stocks than contrarian stocks. Momentum stocks, which are also referred to as ”winner stocks”, are stocks that have high prior return.

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5 Data

All of the fund data is collected from Morningstar Direct⁴, and the factors are collected from the Fama French website (French, 2020).

5.1 Sample selection

My sample consists of open-ended European equity funds with at least 12 months of data during my sample period. This screening criterion is made to ensure that the funds are similar in order to easily compare performance.

Some funds have multiple share classes, and oldest share-class denotes the share class in the fund that has the longest history. This criteria prevents me from having duplicate funds. To enhance comparability, my sample only includes funds with a main investment area in Europe and domicile in Europe. Investment area denotes the geographic area which the fund focuses its investments in.

After this screening process, my initial sample contains 1721 unique funds, 297 of which are socially responsible funds and 1424 are conventional funds. Socially responsible funds can have four different ethical issue focuses; ESG, environmental, Shariah and religious.

This tag identifies the primary ethical investment focus of a fund. My sample includes 266 ESG funds ⁵, 27 environmental funds and 4 Shariah funds. One issue that can arise is that there may be some funds that are socially responsible but are not marked as such in the data set.

5.2 Survivorship bias and Outliers

Survivorship bias is the tendency for inactive funds to be excluded from a sample. There are two reasons why funds become inactive; either they perform poorly or their market value is so small that the fund isn’t profitable. Excluding dead funds will thus create a bias and will overstate performance. Excluding dead funds would lead to an underrepresentation of underperforming funds. A fund that disappears can either be merged into another fund or cease to exist entirely. Attrition may be different for funds with different objectives, as

4Morningstar Direct is an investment analysis platform.

5ESG funds take into account environmental, social and governance factors into the investment process.

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I have in my sample, which may differentially affect the measured performance. Lastly, other variables may be correlated with attrition, which means that it can create spurious correlation between these variables and performance (Elton et al., 1996). My sample is survivorship-bias free, I include funds that become inactive during my sample period.

Their returns are included until they become inactive. The data set is unbalanced as I have both newly started funds and funds that become inactive during my sample period.

The ratio of inactive funds in my sample is presented in table 1.

I have identified very few outliers in the data set. I have chosen to keep them as it is difficult to assess whether they are in fact real returns or not. The inclusion of the outliers did not affect the average return or the regression results in a big magnitude.

The coefficients from the regressions remained almost identical, and it did not affect my conclusions.

5.3 Variables

I have time series data with monthly frequency between January 2010 and January 2020.

The factors are collected from the Fama French website (French, 2020). The proxy for the risk free rate provided by Fama and French is the US one month treasury-bill. This is in line with Fama and French (2017) and Lean et al. (2015), who also used the US T-bill as a proxy when analyzing the European market. This means that I am looking at the European market from a US investor’s standpoint. As all of the factors provided by Fama and French are in USD, all other variables are also measured in USD to avoid exchange rate fluctuations. A description of how the factors are created can be found in chapter 6.

The market proxy used in the main results is MSCI Europe, and it was chosen because it is the most commonly used benchmark in my sample. In the robustness tests, I use the benchmark from the Fama French website, STOXX Europe 600 and MSCI Europe Custom ESG. STOXX Europe 600 is also a commonly used benchmark in my sample. The ESG indice was chosen as its parent benchmark is MSCI Europe, which I used in the main regressions. This indice is based on ESG screening criteria selected by Northern trust.

The screening criteria excludes companies that do not comply with UN global Compact Principles, have business related to tobacco and controversial weapons, involved in mining

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and extraction of thermal coal or thermal coal based power generation (MSCI, 2019).

In one of the robustness tests, I use Morningstar’s sustainability rating. Funds are ranked and divided into five groups based on normal distribution. The funds are then rated from

”high” to ”low”, where ”high” is the best.

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6 Method

6.1 Matched-Pair Approach

I use a matched-pair approach, matching each socially responsible fund to two conventional funds. My two matching criteria are size and age. A pair is matched if the inception date does not differ by more than 12 months and the average fund size does not differ by more than three percentiles. This method generates many potential matches, and a match is made for the smallest difference in average fund size. These strict matching criteria ensure that the matched pairs are similar in size and age, and thus minimizes age and size effects on returns. There is a consensus that these factors should be taken into account when comparing fund performance (Climent and Soriano, 2011). A stricter criteria would create too few matches and result in many socially responsible funds being excluded and too many duplicate conventional funds. Widening the criteria would create worse matches and the potential for age and size effects on returns would increase. A downside to the strict criteria, is that it does result in a few duplicate conventional funds. One bias that may arise is called the home-bias, which describes the tendency for funds to over-weigh domestic stocks (Bauer et al., 2005). Evidence from previous studies suggests that this is due to information advantages as it is more costly to operate from far away (Leite and Cortez, 2014). As all of the domiciles are geographically close, some of this bias will be mitigated. Matching based on domicile would create fewer matches and I would have to widen the matching criteria, which in turn would lead to higher age and size effects.

In the end, I have 291 SRI funds, which means that six of the SRI funds did not get a match based on these criteria. This method is in line with previous studies on SRI fund performance, which usually include two to three conventional matches per SRI fund.

6.2 Fund Portfolio Construction

I employ the same method as Bauer et al. (2005) to construct my fund portfolios. I have panel data of returns for the funds. I construct an equal weighted SRI portfolio by taking the average returns of all the SRI funds each month during the sample period. The same step is repeated for the conventional portfolio. The difference portfolio is constructed by subtracting the returns of the conventional funds from the matched SRI funds, and taking the average of these differences in each period. Returns are included until the funds

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become inactive, and the portfolios are then re-weighted.

As mentioned earlier, socially responsible funds have different ethical issue focuses. My sample contains ESG, environmental and Sharia funds. It includes three Sharia funds, and there are periods in which none of the funds are active. I have therefore decided not to construct a Sharia portfolio. For the ESG and environmental portfolios, I apply the same method described above and construct SRI portfolios, matched conventional and difference portfolios for both subgroups. Note that the pooled SRI and all subgroup portfolios have different conventional and difference portfolios.

6.3 Carhart Four Factor Model

A commonly used model in explaining ethical fund performance, is the Carhart four factor model. Prior research also used CAPM, but this has been widely criticized (Fama and French, 1992, 1998). Other models, Fama French three and five factor, were considered but they have been criticized for not including the widely accepted momentum factor. In addition to this, as I will mention later when performing diagnostics tests, the Fama French five factor model failed to satisfy more OLS assumptions than any other model.

I apply the Carhart four factor model shown in equation (5) on my socially responsible, conventional and difference portfolios. This is an unconditional model, i.e. I assume that the factor coefficients and the four-factor alpha are constant over time. When evaluating whether there is a difference in performance and investment strategy, I am mainly interested in the difference portfolios. The four-factor alpha in the difference portfolio tells us whether there is a difference in risk-adjusted performance between the socially responsible and conventional portfolios. The factors mimic the underlying risk in return related to volatility to the market, size, value and momentum. The factors can also be interpreted as investment strategy, and can thus tell us whether there is a difference in investment strategy between the portfolios. The alphas on the conventional and socially responsible portfolios tells us whether the portfolios outperform or underperform the market. Lastly, the factor coefficients tells us about the risk exposures holding everything else constant.

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R_i,t−R_F,t =a_i+b_i(R_M,t−R_F,t) +s_iSM B_t+h_iHM L_t+m_iM OM_t+e_i,t (5) where R_i,t −R_F_,t is the excess return, which is the return of a portfolio i at timet minus the proxy for the risk free rate at time t. a_i is the risk-adjusted return of a portfolio at time t. RM,t −RF,t is the excess return of the market at time t, i.e. the return of the market minus the risk free rate. SMB_t is the difference in returns between small cap and large cap portfolios at time t. HMLt is the difference in returns between value and growth stocks portfolios at time t. MOM_t is the difference in returns between momentum and contrarian stock portfolios at time t. i,t is the error term of portfolio i at time t. The coefficients b_i, s_i,h_i and m_i are the factor coefficients of the respective four risk factors for portfolio i.

6.4 Factor Portfolio Construction

The factor portfolios SMB, HML and MOM are collected from the Fama French website (French, 2020). The market factor is the excess return of the MSCI Europe indice.

To construct the SMB and HML portfolios, Fama and French begin by constructing six portfolios. The portfolios are constructed by sorting European stocks on market capitalization and book-to-market equity ratios. The breakpoints for book-to-market are 30th and 70th percentiles. Value stocks have high book-to-market equity ratios and growth stocks have low ratios. Stocks between the 30th and 70th percentiles are neutral. For market capitalization, big stocks are the top 90 percent and small stocks are those in the bottom 10 percent.

As we can see from equation (6), the SMB portfolio is equal to the difference in average returns of diversified small stock and big stock portfolios. The small stock portfolio is the average of small value, small neutral and small growth portfolios. The big portfolio is constructed in the same way, it is the average of big value, big neutral and big growth portfolios.

SM B = 1/3(Small Value + Small Neutral + Small Growth)

−1/3(Big Value + Big Neutral + Big Growth)

(6)

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The HML factor, shown in equation (7), is equal to the difference in average returns of diversified value and growth portfolios. The value portfolio is the average of a small value and a big value portfolio, while the growth portfolio is the average of a small growth and a big growth portfolio.

HM L= 1/2(Small Value + Big Value)−1/2(Small Growth + Big Growth) (7)

Lastly, to construct the MOM factor, Fama and French first construct six value weight portfolios. European stocks are sorted by size and lagged momentum return, specifically cumulative return the prior 12 months. The breakpoints for size is the same as before. The breakpoints for momentum are in the 30th and 70th percentiles of the lagged momentum returns of the big stocks in the Europe. The momentum stocks are in the 70th percentile and the contrarian stocks are the 30th percentile. Momentum stocks have had high returns in the past, while contrarian stocks have had low returns. Equation (8) shows the construction of the momentum portfolio, also called WML by Fama and French.

The momentum factor is the difference in average returns of diversified momentum and contrarian portfolios.

M OM = 1/2(Small High + Big High)−1/2(Small Low + Big Low) (8)

6.5 Diagnostics tests

The results for the diagnostics tests can be found in the appendix. To summarize, I have issues with heteroskedasticity, serial correlation and non-normality of residuals in some of the portfolios. To correct for heteroskedasticity and serial correlation, I use Newey-West errors with four lags. I have conducted the diagnostics tests using other models and using the ESG and environmental portfolios and have similar results. Some of the models have other issues as well, the Fama French five factor model has very strong negative correlation between HML and RMW. These results are not presented in the thesis, but are available upon request.

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6.6 Summary Statistics

Table 1 provides summary statistics for the funds. We see that there is not a big difference in average return between the socially responsible and conventional portfolios. The ESG portfolio has a higher average return than the environmental portfolio. This may in part be explained by the fact that ESG funds are slightly older than environmental funds, but most importantly they are much larger. The average size of the ESG funds is approximately three times larger than the environmental. The conventional portfolios also differ in average return which suggests size and age effects on returns. The differences in average size and age between the SRI and conventional portfolios is small, which minimizes the age and size effects on returns. Lastly, we see that a larger fraction of conventional funds are inactive compared to the SRI funds. The ratios are approximately the same as for the whole population.

Table 1: Summary statistics

Variable Obs Mean Std. Dev. Min Max Nb. of funds Average Size Average Age Inactive

SRI portfolio 121 0.624 4.725 -12.521 12.155 288 259 297 497 153.5 4.5 %

Conventional portfolio 121 0.615 4.745 -12.625 12.374 576 253 612 191 153.5 9.9 % Difference portfolio 121 0.010 0.293 -0.927 0.620

ESG portfolio 121 0.635 4.724 -12.353 12.228 260 277 946 178 157.2 3.8 %

Conventional portfolio 121 0.624 4.742 -12.619 12.364 520 271 588 422 157.2 9.8 % Difference portfolio 121 0.011 0.281 -0.993 0.597

Environmental portfolio 121 0.477 4.864 -14.510 11.391 25 92 650 041 120.6 8.0 % Conventional portfolio 121 0.505 4.793 -12.541 12.471 50 93 137 625 120.8 12.0 % Difference portfolio 121 0.010 0.908 -2.726 2.483

Note: Table 1 reports summary statistics for the pooled SRI portfolio, ESG portfolio and environmental portfolio, and their respective matched conventional portfolio and difference portfolio. Mean, standard deviation, minimum and maximum for gross return. Average age is measured in months, gross return and size is measured in USD. Inactive is the percentage of inactive funds in the portfolio at the end of the sample period.

As we can see from figure 1, the gross return of the difference portfolio varies around zero. There is not a big difference in gross returns between the SRI and the conventional portfolio. We cannot see whether the SRI portfolio underperforms or outperforms the conventional portfolio.

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Figure 1: Gross return

−10010Gross return

2010 2012 2014 2016 2018 2020

Date

Ethical portfolio Conventional portfolio Difference portfolio

Note: Figure 1 shows monthly gross returns of the SRI, conventional and difference portfolios between 2010 and 2020.

Figure 2 looks almost identical to figure 1. This is reasonable as ESG funds constitute a majority of SRI funds in Europe.

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Figure 2: Gross return of ESG portfolio

−10010Gross return

2010 2012 2014 2016 2018 2020

Date

ESG portfolio Conventional portfolio Difference portfolio

Note: Figure 2 shows monthly gross returns of the ESG, conventional and difference portfolios between 2010 and 2020.

Figure 3 shows the gross return of the environmental portfolio and its matched conventional portfolio and the difference portfolio. There seems to be a larger difference in performance between environmental funds and conventional funds than ESG funds. It also appears that environmental funds underperformed conventional funds at the begin of the sample period, but started catching up and perhapse even outperforming during the last years. I will investigate this further in the robustness tests by looking at sub-periods.

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Figure 3: Gross return of Environmental portfolio

−20−10010Gross return

2010 2012 2014 2016 2018 2020

Date

Environmental portfolio Conventional portfolio Difference portfolio

Note: Figure 3 shows monthly gross returns of the environmental, conventional and difference portfolios between 2010 and 2020.

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7 Empirical Results

All of the results are estimated using OLS with Newey-West standard errors with four lags to correct for serial correlation and heteroskedasticity. This section begins with a description of the investment strategies and performance of the socially responsible and conventional portfolios. By using the difference portfolio, I investigate whether performance and investment strategies differ between the portfolios. Next, I investigate the performance and investment strategies of the ESG and environmental portfolios. Lastly, I perform five robustness tests by using net returns, other models, different benchmarks, different time periods, and ethical rating.

7.1 Main Findings

Table 2 presents the results of applying the Carhart four factor model to the pooled socially responsible, conventional and difference portfolios. Both the SRI and conventional portfolios are more volatile than the market as the market coefficients are positive and statistically significant. The coefficient on SMB is positive and statistically significant for both portfolios, which means that they are more exposed to small stocks than big stocks.

As mentioned earlier, small stocks are riskier and tend to have higher returns. The third factor, HML tells us whether the portfolio is more growth- or value-oriented. The coefficient on HML is negative and significant for both portfolios, which means that they are more growth-oriented. The coefficient on momentum is positive but not statistically significant.

Lastly, the four factor alphas, which measure risk-adjusted return are not statistically significant. This implies that neither of the portfolios outperform or underperform the market.

The difference portfolio is used to examine differences in performance and investment style. First, the four-factor alpha is negative but not statistically significant. This implies that there is not a statistically significant difference in risk-adjusted returns between the socially responsible and conventional portfolios.

Only two of the factors are significant; SMB and HML. Since the sign is negative on the SMB coefficient, it can be interpreted as the SRI portfolio being more exposed to large stocks than the conventional portfolio. The HML coefficient is negative as well, which

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means that the SRI portfolio is more growth-oriented than the conventional portfolio. The SMB and HML coefficients are statistically significant at a 1 percent significance level.

There is no difference in volatility to the market or exposure to momentum stocks.

Table 2: Results from Carhart Four Factor Model using the pooled SRI portfolio.

(1) (2) (3)

Variables SRI Conventional Difference

Market 1.038*** 1.034*** 0.000 (0.008) (0.009) (0.005) SMB 0.289*** 0.369*** -0.076***

(0.029) (0.031) (0.014) HML -0.098*** -0.038* -0.072***

(0.020) (0.019) (0.010)

MOM 0.010 0.023 -0.014

(0.017) (0.017) (0.009)

Alpha 0.015 -0.004 -0.026

(0.033) (0.035) (0.023)

Obs. 121 121 121

Adj. R² 0.994 0.994 0.451

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Table 2 reports the regression results of the pooled SRI portfolio and its matched conventional portfolio and difference portfolio. The results are estimated using OLS with Newey West standard errors with four lags to correct heteroskedasticity and serial correlation. The market factor is equal to the MSCI Europe benchmark minus the risk free rate from the Fama French website. Alpha is the risk-adjusted return.

The results from the SRI subgroups are presented in table 3 below. The results for the ESG subgroup are similar to the ones in table 2. The difference in performance between the ESG and conventional portfolios is not statistically significant. The ESG portfolio is more exposed to large stocks and more growth-oriented than the conventional portfolio.

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Both the SMB and HML factor coefficients are significant at a 1 percent level. There is no statistically significant difference in market sensitivity or exposure to momentum stocks.

The alpha is negative but not statistically significant, which implies that there is not a statistically significant difference in performance between the portfolios.

Next, we look at the environmental subgroup. The market and SMB coefficients are statistically significant for both the environmental and conventional portfolio. The environmental and conventional portfolios are more volatile than the market, and they are more exposed to small stocks than large stocks. The HML factor is only significant for the environmental portfolio, and the negative sign implies that the portfolio is more exposed to growth stocks.

From the difference portfolio, we see that there is only a statistically significant difference in the HML factor. The factor coefficient is significant at a 1 percent significance level.

The environmental portfolio is more exposed to growth stocks. The four factor alpha is negative but not statistically significant, which means that there is no difference in risk-adjusted performance between the environmental and conventional portfolios. The explanatory power of the model is quite low for the environmental difference portfolio. As we will see later in the robustness tests, this is due to the factor coefficients and alpha changing over time.

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Table 3: Results from Carhart using the SRI subgroups.

ESG Environmental

Variables ESG Conventional Difference Environmental Conventional Difference

Market 1.037*** 1.035*** -0.001 1.052*** 1.025*** 0.014

(0.008) (0.009) (0.004) (0.020) (0.010) (0.025)

SMB 0.278*** 0.369*** -0.085*** 0.442*** 0.359*** 0.081

(0.029) (0.032) (0.013) (0.053) (0.036) (0.058)

HML -0.096*** -0.042** -0.067*** -0.079* 0.006 -0.106***

(0.019) (0.019) (0.010) (0.042) (0.026) (0.039)

MOM 0.010 0.025 -0.015 -0.009 -0.013 -0.002

(0.017) (0.016) (0.009) (0.031) (0.029) (0.035)

Constant 0.028 0.002 -0.020 -0.143 -0.060 -0.080

(0.031) (0.034) (0.018) (0.093) (0.054) (0.118)

Obs. 121 121 121 121 121 121

Adj. R² 0.994 0.994 0.493 0.971 0.989 0.0457

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Table 3 reports the regression results of the ethical subgroups using the Carhart four factor model. The results are estimated using OLS with Newey West standard errors with four lags to correct heteroskedasticity and serial correlation.

The market factor is equal to the MSCI Europe benchmark minus the risk free rate from the Fama French website.

7.2 Robustness tests

To test the robustness of my results, I use net returns, different benchmarks, different models, sub-periods and ethical rating. The results for all of the robustness tests can be found in the appendix.

7.2.1 Net return

Socially responsible management can be expensive and may therefore result in higher management fees. Surprisingly enough, the conventional funds in my sample have higher management fees than the SRI funds.

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The regression results using net returns for the SRI portfolio is presented in table 11. The four-factor alpha for the SRI and conventional portfolios are both negative and significant, which means that both of the portfolios underperform the market. The other coefficients are of similar magnitude and sign as in the main results. The HML coefficient for the conventional portfolio is less statistically significant than before. As we can see, the magnitude of the SMB and HML coefficients have increased slightly. The results confirm the conclusions from the main findings. The SRI portfolio is more exposed to large stocks and more growth-oriented than the conventional portfolio. Even when taking management fees into account, there is not a statistically significant difference in performance.

Table 12 shows the results for the ESG and environmental portfolios. The main difference from the results using gross returns, is that the four-factor alpha for the ESG and conventional portfolios are negative and statistically significant. Both of the portfolios underperform the market, but there is not a statistically significant difference in performance between the ESG and conventional portfolios. Some coefficients change slightly in magnitude, but the conclusion remains the same. The ESG portfolio is more exposed to large stocks and more growth-oriented than the conventional portfolio.

The results for the environmental portfolio are also similar when using net returns. There is not a statistically significant difference in performance between the environmental and conventional portfolio. The only statistically significant difference in investment strategy is the exposure to growth stocks. When including management fees, both the environmental and conventional portfolios underperform the market.

7.2.2 Benchmarks

Choosing the wrong benchmark may lead to a benchmark error, and thus produce inaccurate results. A benchmark error occurs when a wrong proxy is used for the true market portfolio (Lee and Lee, 2006). I use three additional benchmarks to test robustness; STOXX Europe 600, the Fama French benchmark provided on their website (French, 2020), and MSCI Europe Custom ESG. STOXX Europe was chosen as it is a benchmark that is commonly used in my sample. Note that the ESG indice has a shorter time period than the other indices. The shorter time period may also influence the conclusions.

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Table 13 presents the results using the Carhart model with three different benchmarks on the SRI portfolio. The adjusted R-squared are similar to the ones when using the MSCI Europe indice. We do however see that it seems like the SRI indice may do a better job at explaining the difference portfolio.

We begin by comparing the results from using MSCI Europe to STOXX Europe 600.

The magnitude, sign and significance remains the same for all coefficient except for the HML. The HML coefficient is no longer significant for the conventional fund, but it is negative as before. The conclusion remains the same, the SRI portfolio is more exposed to large stocks and more growth-oriented than the conventional portfolio. There is no difference in performance between the two portfolios. Second, we look at the results using the Fama French benchmark. The results are similar to using MSCI Europe, but some coefficients change magnitude and sign. The MOM and four-factor alpha become negative but remain insignificant. The conclusions remain the same when using the Fama French benchmark. Lastly, we look at the results using the MSCI Europe Custom ESG indice.

Some coefficients change slightly in magnitude, and the HML coefficient is no longer significant for the conventional portfolio. However, the conclusions remain the same. Using different indices does not change the results significantly for the difference portfolio.

The results for the ESG portfolio using these benchmarks is shown in table 14. Again, some coefficients change slightly in magnitude. However, the conclusions still stand. Table 15 presents the results for the environmental portfolio. The results are similar when using the STOXX benchmark. When using the Fama benchmark however, the environmental and conventional portfolios underperform the market. The four-factor alphas are statistically significant at a 10 percent level. The conventional portfolio also underperforms when we use the MSCI ESG indice. Nonetheless, the conclusions are still the same as in the main results. Note that the ESG indice does a better job at explaining the difference portfolio for the difference portfolio.

7.2.3 Model Specification

I apply three different models to test robustness; CAPM, Fama French three and five factor models. As mentioned earlier, there are some issues with these models.

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First, we look at the results for the SRI portfolio shown in table 16. In contrast to the Carhart model, the market beta from the CAPM for the SRI and conventional portfolios are smaller than 1, which suggests that the portfolios are less volatile than the market.

The four-factor alpha is positive and significant only for the SRI portfolio, which means that the portfolio outperforms the market. There is not a statistically significant difference in risk-adjusted returns.

Both the Fama French three and five factor models have negative and statistically significant alphas for the difference portfolio. This is in stark contrast of the results from using the Carhart model. The four factor alphas are not large, which means that the difference in risk-adjusted in performance is not big. The SMB and HML factor coefficients are negative and significant for the difference portfolio using both models. This means that the conclusion about investment strategy remains the same as when using the Carhart model.

The Fama French five factor model has two more risk factors, RMW and CMA. Only the RMW factor coefficient is statistically significant, and suggests that the ethical portfolio is more exposed to stocks with robust profitability compared to the conventional portfolio.

Note that both the SRI and conventional portfolios are more exposed to aggressive stocks than conservative stocks.

Table 17 presents the results of applying the three models on the ESG portfolio. The results are similar to the ones described above for the pooled SRI portfolio. When using the CAPM, also the conventional portfolio outperforms the market. When applying the Fama French five factor model, only the ESG portfolio outperforms the market. There is not a statistically significant difference in performance between the ESG and conventional portfolios.

Next, we look at the results from the environmental portfolio shown in 18. The conclusion is the same when using the CAPM model; there is no statistically significant difference in performance. The adjusted R-squared is negative which suggests that the model does a very poor job at explaining the difference in performance. The alpha is statistically significant and negative for the environmental portfolio when applying the Fama French three and five factor models, which means that it underperforms the market. The conclusion about difference investment strategy and performance remains the same. Lastly, we see that

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none of the factor coefficients are statistically significant in the difference portfolio using the Fama French five factor model.

7.2.4 Sub-periods

I apply the Carhart four factor model for three time periods; 2011-2014, 2014-2017 and 2017-2020. Shorter time periods will have lower attrition rates⁶. As mentioned earlier, funds tend to have low returns before becoming inactive. Considering shorter time periods will also allow us to examine performance over time.

We begin by looking at the results for the pooled SRI portfolio shown in table 19. The four-factor alphas for the difference portfolios are statistically insignificant for all time periods, which means that there is not a statistically significant difference in risk-adjusted returns between the SRI and conventional portfolio. Between 2011 and 2014, there was not a statistically significant difference in exposure to the SMB factor. This however changed over time, and the factor coefficient for SMB is significant for the two last time periods.

The four-factor alpha is negative and statistically significant for the conventional portfolio between 2017 and 2020, which indicates that it underperforms the market.

Table 20 presents the results of the ESG portfolio using three time periods. There is not a statistically significant difference in performance in any of the periods, and none of the portfolios underperform the market. The difference in investment strategy is the the same for the last two periods. Between 2011 and 2014, the momentum factor coefficient from the difference portfolio was also statistically significant. This indicates that the ESG portfolio was less exposed to momentum stocks than the conventional portfolio.

The results for the environmental portfolio are quite interesting. From the four factor alphas in the difference portfolio, we see that the environmental portfolio began by underperforming their conventional counterpart between 2011 and 2014. The four-factor alpha is quite high, which means that it is economically significant, and it is significant at a 1 percent significance level. It seems like the environmental portfolio went through a catching up phase, as there was not a statistically significant difference in performance between 2014 and 2017. During the last periods, 2017 to 2020, the environmental portfolio outperformed

6Attrition rate is estimated as the number of dead funds at the end of a period divided by the number

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the conventional portfolio. This result is of statistical and economic significance, as the four factor alpha is significant at a 5 percent significance level and of quite high in magnitude.

The difference in investment strategy has also changed during these time periods. From 2011 to 2014, the environmental portfolio was more exposed to small stocks than the conventional portfolio. This difference was insignificant between 2014 and 2017, the only significant difference during this time was the exposure to HML. The environmental portfolio was more exposed to growth stocks than their conventional counterpart. In the last period, we see that the momentum factor coefficient also becomes statistically significant. The environmental portfolio was more exposed to growth stocks and less exposed to momentum stocks.

7.2.5 Ethical rating

The Morningstar sustainability rating ranks funds by their historical sustainability score.

The funds are ranked and then assigned a group based on a normal distribution. There are 36 funds in the ethical portfolio that have a Morningstar sustainability rating of below average or low. Having a low sustainability score may suggest that these funds do not take ethical issues seriously and may be a sign of greenwashing. One issue is that 54 of the funds do not have a sustainability rating. I will only look at funds that have a sustainability score of average or above. In this sample, the average score for socially responsible funds is ”above average”, and for the conventional funds it is ”average”. The score for the conventional funds is the same as in the initial sample.

Table 22 presents the results of the pooled SRI portfolio. The results do not differ significantly and the conclusion remains the same. The results for the ESG and environmental portfolios are shown in table 23. The conclusion about performance remains the same for both the ESG and environmental portfolios. The most important difference is that the SMB factor coefficient is significant for the environmental difference portfolio. The environmental portfolio is more exposed to small stocks than the conventional portfolio.

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8 Discussion

Generally, my results show that there is not a statistically significant difference in risk- adjusted performance between the socially responsible and conventional portfolios. This is in line with previous research, most of which has concluded that there is no difference in performance. In theory, one would expect socially responsible funds to have lower returns since their investment universe is narrower. In Europe, the most common strategy to incorporate ethical factors is to exclude companies or entire sectors, which should significantly limit diversification (Statista, 2018). It seems like the socially responsible funds manage to diversify, which may indicate that they still have a large enough investment universe after screening. On the other hand, socially responsible companies can be less exposed to litigation and reputational risk than other companies, which should have a positive effect on SRI fund performance.

I find that the SRI portfolio is more volatile than the market, and that there are growth and size effects when comparing the socially responsible portfolio to the benchmark. This is in line with Lean et al. (2015) who find similar results for the European market. However, they also find that the European SRI funds outperform the market, I find that there is not a statistically significant difference in performance between the SRI portfolio and the market. Further, they find that SRI funds are more exposed to contrarian stocks than the market while the momentum factor is not significant in my findings.

All robustness tests except for one confirmed my conclusion about insignificant difference in performance. The results from the Fama French three and five factor models show that the conventional portfolio outperforms the SRI and ESG portfolios. The magnitude of the coefficients is small, which means that there is not a big difference in performance.

These models do not include the widely accepted momentum factor, which means that they may be missing a risk factor. The results from the environmental portfolio shows no statistically significant difference in performance for both models.

The results on investment strategy all had the same conclusion; the socially responsible portfolio is more growth-oriented and more exposed to large stocks than their conventional counterpart. This is in line with previous research, Bauer et al. (2005) finds that ethical

Socially Responsible Fund Performance in Europe