(Ir)responsible Investing: Revisiting the Effects of ESG Performance on Portfolio Returns.

(1)

(Ir)responsible Investing: Revisiting the Effects of

ESG Performance on Portfolio Returns.

Master Thesis

MSc. Finance

January 11

th

_{, 2021}

Author: N.P.A. van Tilborg

Email: n.p.a.van.tilborg@student.rug.nl

Student ID: S2305534 Supervisor: Dr. A. Dalò

(2)

Abstract

The sin stock anomaly where stocks belonging to “sin” industries such as alcohol, tobacco, gambling and weapons yield abnormal returns above the market return was observed decades ago. Presently, this anomaly fits into the broader focus of investors on factors besides returns, such as ESG, and indications exist that high ESG might result in excess returns. In this paper we investigate if ESG and returns are related. In order to investigate this, we create decile portfolios based on the Refinitiv ESG Combined Scores of the individual stocks and test for the presence of alpha in these portfolios using different models in the period 2003-2019. Throughout the models and after robustness checks, it seems that actually the lowest ESG portfolio is the only one to yield a significantly positive alpha.

(3)

1. Introduction

Investors face increasing pressure to carefully select their stocks. This is especially true for institutional investors such as pension funds whom besides being expected to deliver good returns are expected to behave ethically and select their portfolio accordingly. This increasing interest in socially responsible investing (SRI) has pushed (institutional) investors to focus on other preferences beyond returns such as performance on environmental, social and governance (ESG) issues.

The increased interest in ESG investing becomes apparent from the large inflow of capital. According to the Global Sustainable Investment Alliance (GSIA), a collaboration between seven (inter)nation sustainable investment organisations, the global market for sustainable investment assets is valued at $30.7 trillion at the beginning of the year, an increase of 34% in just two years. (GSIA, 2018)

This growth is still ongoing as the Financial times reported. In 2020 in the period January 1st_{until July 30}th_{ETFs focusing on ESG pulled in $38bn, a record, in new capital.} (Nauman, 2020)

The debate on the responsibility of investors for the effects their investments has been going on for decades, where previously the focus was mainly on the question if investors should avoid investing in stocks that had negative effects on society such as tobacco, alcohol, gambling and weapons collectively known as “sin stocks” or “vice stocks”. A commonly used argument against excluding these stocks from a portfolio has long been the high returns these sin stocks offer compared to the market, which has been dubbed the sin premium. The excess return of sin stocks has been extensively research in the past, and it is generally accepted that these stocks do offer excess returns.

On the other hand, research into returns of stocks with high ESG scores has recently become increasingly popular, as scholars suggest that besides avoiding sin stocks, investors should invest in stocks that have an above average ESG score. Thus far, results into the performance have been ambiguous with some suggesting these high ESG stocks yield significantly better results while others find the opposite, besides some studies find no significant effects be it positive or negative. In the literature review, these studies are explored more thoroughly.

(5)

In this paper we use Fama and French’s (2015) five factor model to to test for the presence of alpha in order to investigate if there is a link between ESG performance and portfolio returns.

Does ESG performance affect portfolio returns?

In the next section we will examine the literature that has already been published on this subject. In section 3 the methodology and model will be explained, while in section 4 an overview of the data will be presented. In section 5 the results of the analysis will be presented, while in section 6 we will check the robustness of our results. a conclusion from these results is drawn in section 7 followed by a discussion in section 8.

2. Literature Review.

As mentioned, the relationship between ESG and performance was first studied in the context of abnormal returns for sin stocks. One of the first to observe and give an explanation for the sin stock anomaly was Merton (1987). He argues that the excess returns on sin stocks can be explained by their susceptibility to being mispriced, since there are few analysts reports on these stocks because many investors are avoiding these stocks resulting in a lower demand. Another factor that can explain the anomaly is the high(er) risk of litigation already being factored into the price. The issue of mispricing also surfaces in a study by Mǎnescu (2011), in which she concludes that there is an effect of ESG factors on stock return but that these effects are not sufficiently incorporated in stock prices.The sin stock anomaly was also observed by Salaber (2007) who contributed to the field by finding that in Europe, the excess returns of sin stocks are larger in traditionally protestant countries when compared to catholic nations. This is explained by a larger “sin aversion” being present in protestants, resulting in them demanding higher returns as a form of compensation for holding on to these stocks.

The sin stock anomaly is not just limited to European markets. Hong and Kacperczyk (2009) found a similar anomaly when they focused on US sin stocks. They created an equally weighted portfolio of sin stocks and used both Fama and French’s (1993) three-factor model and the Carhart (1997) four-factor model to evaluate returns and compare them to a market benchmark. They concluded that the higher returns for sin stocks can be mostly attributed to the lack of interest from large institutional investors.

In terms of stocks with high ESG scores there is some evidence that these stocks also provide an excess return. For example, Sahut and Pasquini-Descomps (2015) found that the

(6)

not find similar evidence for the US and Switzerland which is exemplary for the ambiguous results when studying the relationship between ESG performance and returns.

This is also exemplified in a study by Doreitner Utz and Wimmer (2013) that found a significant positive relationship between ESG performance and returns in the long run on both the European and the North American market. However, in the same study the effect of ESG on returns is much weaker in Japan.

La Torre et al. (2020) examine the relationship between ESG scores and returns of stocks listed on the Eurostoxx50 index. They found some weak results, for some of the stocks listed on the Eurostoxx50 index, there is a small significant relation between ESG and returns. For the majority of the firms however, this was not the case.

Despite the previous literature almost unanimously suggesting that sin stocks yield excess returns there is some recent literature that seems to reject this and find that ESG performance does not affect returns. Richey (2017) analysed US sin stocks and confirmed the results of most previous studies in that under the CAPM, the three- and four-factor models, sin stocks seem to provide a significant positive alpha indicating excess returns for the sin stocks. However, when using the Fama French (2015) five-factor model by including the investment and the profitability factor, the alpha becomes insignificant suggesting there is no abnormal return on sin stocks when controlling for these two additional factors.

Another study that could not establish a link between low ESG stocks and excess returns was by Hoeper and Zeume (2014) who analysed the performance of the Vice Fund, an investment funds based entirely on sin stocks. They did not find abnormal returns for the Vice Fund. Which they link to literature regarding ethical funds that also fail to establish a connection between fund ethics and returns. This would imply that funds regardless of their ESG characteristics are generally priced correctly and thus offer no opportunity to outperform the market since any benefits from the stock selection are already priced into the fund.

This conclusion is also interesting when used in a different context, Humphrey et al. (2012) investigated the consequences of implementing corporate social performance (CSP) strategies on financial performance and risk. They analysed a set of UK firms with differing ESG characteristics and found that for their sample there was no significant effect of ESG on performance or risk, implying that implementing a CSP strategy does not give a significant benefit nor cost in terms of returns.

(7)

In a study that compares performance of ESG indices against the performance regular MSCI indices by Jain, Deep Sharma and Srivastava (2019) found no evidence that the indices based on high ESG scores yield significantly different returns compared to the MSCI indices.

Besides evidence suggesting that ESG scores have a positive or neutral effect on returns, there is also evidence that high ESG levels might negatively impact stock performance.

Chawana (2014) analysed the South African market and specifically the returns of a dedicated SRI index. When comparing the performance of this index to other indices it seems that investors actually pay a premium for their SRI investments.

In addition, Auer and Schuhmacher (2016) found that whereas investors focussing on high ESG scores are able to achieve similar returns to regular portfolios in North America and in the Asia and Pacific region, European investors seem to be paying a price for their responsible investments.

In a study by Friede et al. (2015) that examined the existing literature on the link between ESG and financial performance they addressed the varying conclusions of different studies. When aggregating the results, they found however that, overall, studies suggest that there is a positive relation between ESG and financial performance. They do not however that this relationship is more evident for individual firms than portfolio studies.

Given the results obtained in the existing literature we expect that ESG performance does affect returns. We therefore hypothesise that ESG does have an effect on portfolio returns.

3. Methodology

The models used in the studies mentioned in the previous section have been updated and extended. Given the ambiguous results in studies into the link between high ESG scores and returns, and the slightly older studies claiming excess returns for sin stocks, in this study we will revisit these outcomes using a more modern model as introduced by Fama and French (2015). This five-factor model is an extension of the Fama French (1993) three-factor model and the Carhart (1997) four-factor model that has often been used in previous literature to explain anomalies in returns.

One of the first and most common models used to determine required returns is the capital asset pricing model (CAPM). This model finds its roots in modern portfolio theory as devised by Markowitz (1952), where a link is established between (portfolio) risk and expected

(8)

returns. This implies that for a given level of risk the expected returns are maximised, and in order to increase those returns, an investor needs to take on more risk. This model was used as the basis for William Sharpe (1964) to create the capital asset pricing model. The CAPM adds to modern portfolio theory by distinguishing between systematic and non-systematic (i.e. specific) risk. These two types of risk affect investors differently since with proper diversification it is possible to eliminate specific risk while systematic risk is unavoidable. The CAPM is expressed as:

!_!,#$ _{= #}

!+ %!!%$ + &&

Here !_!,#$ _{represents portfolio p’s excess returns in a given period t which is defined as} the portfolio return minus the risk-free rate, where #_! denotes the risk adjusted returns also referred to as the pricing anomaly. The portfolio beta is indicated as %_! while the market risk premium (MRP) is included as !_%$_.

The risk adjusted return is used to measure a portfolio’s performance compared to the market. Under the efficient market hypothesis (EMH) alpha would equal zero since with all available information already reflected in prices, it will not be possible to systematically outperform the market. The beta measures the systemic risk of a stock or a portfolio compared to that of the market, where the market beta equals one implies similar risk as the market and a beta greater than one implies a higher risk compared to the market portfolio, whereas the opposite is true for a beta smaller than one.

Even though the CAPM is one of the most commonly taught and used models, it does come with some limitations. In practice it is often inaccurate when predicting returns in comparison to realised returns. Due to these shortcomings, scholars have worked on improving the CAPM’s accuracy by including more factors. One of the most used of these models was developed by Fama and French (1993) who created a three-factor model (FF3) by adding the firm size and the book to value ratio to the CAPM. In this model firm size is expressed in the difference in returns between small and large firms. This factor is included to account for the more volatile nature of small firms compared to larger firms resulting in a higher upside potential for smaller firms. This factor is known as the “Small-Minus-Big” or SMB factor of the model. The final factor is known as the “High-Minus-Low” (HML) and is included to account for the fact that returns for high book-to-market value stocks differ from those of low

(9)

book-to-market value stock (also known as growth stocks) When adding the size factor and the value premium to the CAPM we obtain the Fama and French three-factor model:

!_!,#$ _{= #}

!+ %!!%$ + %'()'() + %*(+*(+ + &&

Here SMB represents the size factor where the %_'() is the corresponding factor-beta. HML is the size factor which has a factor-beta presented as %_*(+.

Even though the Fama-French three-factor model is more accurate in predicting returns than the CAPM, it still is not always able to fully explain portfolio returns. Research into additional factors that might explain the returns of stocks or portfolios continued and this eventually led to Carhart (1997) to further expand the Fama-French three-factor model to include a fourth factor, the momentum. This factor accounts for difference in past performance of stocks. Carhart’s study builds on the assumption that it is possible for investors to benefit from trends, encouraging investors to buy stocks that have performed well over the last months, while selling stocks that are down on performance. The momentum factor is derived by subtracting the excess return of stocks that went down from the excess returns of stocks whose value went up. This gives the following model:

!_!,#$ _{= #}

!+ %!!%$ + %'()'() + %*(+*(+ + %(,((,( + &&

The additional momentum factor is expressed as MOM which has a corresponding coefficient expressed as %_(,(. The latest contribution to the multi-factor model is again by Fama and French (2015) who expanded it to become the five-factor model. They included two “quality factors”. Firstly the profitability factor which is included on the basis that more profitable firms tend to realise higher returns. Additionally the model includes the investment factor that factors in the amount of earnings which are reinvested, which should lead to higher returns. Unlike the Carhart four-factor model, the Fama-French five-factor model does not include a momentum factor. Ultimately, this leads to the following model:

!_!,#$ _{= #}

(10)

Here RMW (robust minus weak) is the expression of the profitability factor with %_-(. being the corresponding beta. The investment factor is expressed as Conservative Minus Aggressive (CMA) with the corresponding beta depicted as %_/(0.

While these models help to measure the performance of a given portfolio, in order to test the hypothesis, we first need to create portfolios that are based on the ESG performance of the stocks comprising the portfolio.

4. Data

To measure the ESG performance of stock we have chosen to use the Refinitiv ESG Combined Scores. This score measures a firms ESG performance over the three pillars of ESG: Environmental, Social and Governance. Each of these pillars consists of different topics. In total over 450 ESG measures are collected and combined into scores for each individual pillar and an overall ESG Score. These scores however do not take into account the attention generated by scandals a company might be involved in. This effect is measured in the ESG Controversies Score. Combining the ESG and ESG Controversies Scores results in the Combined ESG Score which is equal to the ESG Score if there are no controversies and is lower than the ESG Score when such controversies do arise.

To construct the portfolios, using DataStream, we first obtain the market capitalisation expressed in US dollars and the ESG Combined Scores with all of the components used in deriving this score (ESG Score, Controversies, Environmental Pilar, Social Pilar and Governance Pilar) for all stocks that were scored in the period June 30th_{2003 until June 30}th 2019, where we obtain these scores on a yearly basis. Based on the ESG Combined Score at the end of June for each year we create ten portfolios based on deciles, where portfolio 1 contains the 10% of stocks with the highest ESG Combined Scores, portfolio 2 the second best 10%, etc. Subsequently, for all the stocks included in the portfolio we obtain the prices at the end of each month included in our sample period, and using these prices we calculate the monthly returns for each individual stock using:

!_1,# = 3 41,#

4_1,#235 − 1

Where !_1,# is the return of stock it time t, 4_1,# refers to the price of stock i at time t.

(11)

The available data differs considerably for each year with the number of available firms ranging from 867 firms in 2003 up to 6,259 different firms in 2018. A complete overview of the number of firms available each year can be found in appendix A.

In order to prevent small cap stocks to disproportionally affect the portfolio returns, all the stocks are value weighted based on their market capitalisation each year at the end of June, where the weight of a single stock is equal to the market capitalisation of that stock, divided by the sum of market capitalisations of all stocks included in the portfolio. Using the weighted returns, we can then calculate the monthly portfolio returns for each of the portfolios. The portfolios are rebalanced each year at the end of June to encapsulate the changes in ESG performance and changes in (relative) market capitalisation. Value weighting also better reflects typical investors behaviour in the sense that investors tend to invest in firms they are familiar with which typically have a large market capitalisation.

To test if high ESG portfolios do outperform low ESG portfolios, we create an eleventh portfolio which consists of a long position in the highest scoring portfolio and a short position in the lowest ESG scoring portfolio. If this were to be the case, this eleventh portfolio should give a significantly positive alpha.

4.1 Data overview

Table 1 presents a general description of the dataset. The data appears to be slightly unbalanced given the different number of observations for the different ESG scoring components. However, this should not affect the results of our research since for the construction of our portfolios, only the ESG combined score is used.

(12)

Table 1:

Descriptive Statistics Raw Data July 2003- June 2020

VARIABLES Mean SD Min Max Obs.

'89:; /<8=!> (@9>8ℎBC) 0.008 1.101 -0,690 1.136 1,597,256 (D!;<8 1D4 (E'F @B>. ) 6.801 47.591 0.000 11,389.304 127,116 I'J 19@KL><M Score 39.094 19.144 0.000 93.610 68,933 I'J 19>!9N<!OL<O Score 91.927 21.534 0.000 100.000 68,912 I'J ':9!< 40.398 20.288 0.000 95.070 68,933 I>NL!9@<>8DB PLBBD! Score 30.499 28.671 0.000 99.100 68,792 '9:LDB PLBBD! Score 40.775 23.063 0.052 98.920 68,792 J9N<!>D>:< PLBBD! Score 47.439 22.692 0.140 99.380 68,925

Where Stock returns are defined as (3!,# / 3!,#&')-1, Market cap is the value of outstanding stocks on June 30th _{expressed in US Dollars. All scores related to ESG and its pillars are as provided by Refinitiv and}

obtained from Thompson Reuters’ DataStream

Using this data, we then construct ten portfolios based on the ESG scores at the end of June for each year in the period 2003-2019. The descriptive statistics of the ten resulting portfolios are presented in Table 2 on the next page where #1 refers to the portfolio based on the highest decile of ESG scores and #10 to the portfolio based on the lowest decile of ESG scores.

(13)

Table 2:

Descriptive Portfolio Statistics

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 VARIABLES !"#$ &_! 0.0060 0.0073 0.0061 0.0074 0.0072 0.0085 0.0092 0.0095 0.0107 0.0098 -0.0037 '"!, 0.0376 0.0378 0.0387 0.0372 0.0382 0.0433 0.0414 0.0412 0.0426 0.0397 0.0152 !#( &_! 0.1201 0.1130 0.1079 0.0902 0.1081 0.1158 0.1346 0.1385 0.1358 0.1285 0.0381 !)$ &_! -0.1664 -0.1453 -0.1537 -0.1544 -0.1421 -0.1845 -0.1773 -0.1818 -0.1735 -0.1653 -0.0468 Mean Market Cap 24.085 17.245 15.199 15.152 13.083 10.155 7.195 5.850 4.986 4.189 14.137 Mean ESG Combined Score 74.560 62.102 54.152 47.538 41.357 36.013 30.655 25.343 19.521 11.077 42.818 Where Mean )! represents the average monthly portfolio return where the monthly portfolio return is calculated as the sum of all weighted

returns of the stocks comprising the portfolio. ;"! represents the portfolio standard deviation. <=> )! and <?@ )! represent the maximum and minimum values of monthly portfolio returns respectively. Whereas Mean Market Cap refers to the average market capitalisation in millions of US dollars of the firms comprising the portfolio. The Mean ESG Combined score is the average ESG Combined Score of the stocks included in

the portfolio.

(14)

On first inspection, it seems that stocks in the lower ESG scoring portfolios have a higher average return than those in the higher ranked portfolios. Another interesting observation is that, based on the table, there is a relation between market capitalisation and the ESG Combined score, where the firms included in the highest scoring portfolios have on average a much larger market capitalisation than those in the lower end, with the difference between portfolio number 1 and 10 being almost a factor six.

The data for the factors is obtained from Kenneth R. French’s webpage where we use the global factors since our portfolios are not limited to specific regions and contain stocks from all over the world. For the risk free rate we use the one month US Treasury bill rate.

To see if these observations are actually meaningful and significant, we will present the results of our regressions in the next section.

5. Results

To obtain the different Beta’s we will use Ordinary Least Squares (OLS) estimations. In order to prevent the possible presence of heteroskedasticity affecting the results we use White's heteroskedasticity consistent standard errors.

To test our hypothesis that ESG scores do affect returns we run the regressions for the different models (CAPM, Fama French 3 factors, Carhart 4 factors and the Fama French Five factor model). The results thereof can be found in the subsequent tables where #11 relates to the portfolio with a long position in portfolio #1 and a short position in #10.

First, table 3 presents the results for the CAPM. What immediately stands out is that both of the “extreme” portfolios yield significant alphas where interestingly, the highest ESG scoring portfolio yields a negative alpha of 0.488%, whereas the worst portfolio in terms of ESG does yield a significantly positive alpha of 0.2825%. This implies that a portfolio consisting of the 10% worst ESG scoring stocks outperform the market by 0.2825 percent point.

When adding the SMB and HML factors the results from the CAPM seem to hold. Again, the number 1 portfolio has a significantly negative alpha, albeit a lot lower at -0.106%. As under CAPM, the worst ESG portfolio has an alpha of 0.299% at the 90% significance level. This in turn means that the difference portfolio does yield a significantly lower alpha of -0.355% compared of the individual portfolios of which it is comprised.

(15)

Table 3:

CAPM Results July 2003 – June 2020

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 VARIABLES !_! 0.182*** 0.829*** 0.835*** 0.859*** 0.825*** 0.861*** 0.942*** 0.915*** 0.910*** 0.908*** -0.727*** (0.0688) (0.0315) (0.0279) (0.0290) (0.0254) (0.0230) (0.0375) (0.0313) (0.0324) (0.0343) (0.0875) # -0.448** -0.129 -0.00831 -0.145 0.0140 -0.0312 0.0324 0.129 0.158 0.282** -0.729** (0.206) (0.104) (0.101) (0.102) (0.0978) (0.0889) (0.130) (0.112) (0.112) (0.138) (0.282) Observations 204 204 204 204 204 204 204 204 204 204 204 R-squared 0.080 0.864 0.868 0.876 0.872 0.902 0.844 0.868 0.866 0.808 0.429

Results are obtained using an OLS time-series regression on the CAPM. Where !_! refers to the coefficient for the market risk premium. # represents the outperformance of the portfolio (in %).

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

(16)

Table 4:

Fama French Three-Factor Results July 2003 – June 2020

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 VARIABLES !_! 0.00259 0.857*** 0.851*** 0.886*** 0.856*** 0.864*** 0.956*** 0.932*** 0.917*** 0.924*** -0.922*** (0.00185) (0.0311) (0.0298) (0.0325) (0.0287) (0.0237) (0.0394) (0.0324) (0.0334) (0.0361) (0.0365) !_"#$ 0.00238 -0.130*** -0.0873* -0.110** -0.143*** -0.0338 -0.0571 -0.0142 0.0289 0.0579 -0.0556 (0.00388) (0.0466) (0.0475) (0.0431) (0.0450) (0.0415) (0.0542) (0.0452) (0.0481) (0.0607) (0.0611) !_%#& 0.998*** 0.0374 0.0373 0.00797 0.0369 0.0345 0.00859 -0.0706 -0.0805* -0.174*** 1.172*** (0.00294) (0.0469) (0.0473) (0.0522) (0.0401) (0.0476) (0.0625) (0.0498) (0.0460) (0.0508) (0.0511) # -0.106*** -0.132 -0.00624 -0.156 0.00911 -0.0235 0.0283 0.103 0.134 0.229* -0.335** (0.00925) (0.104) (0.102) (0.104) (0.0958) (0.0893) (0.129) (0.111) (0.110) (0.135) (0.137) Observations 204 204 204 204 204 204 204 204 204 204 204 R-squared 0.998 0.869 0.871 0.879 0.879 0.902 0.844 0.870 0.868 0.818 0.845

Results are obtained using an OLS time-series regression on the Fama and French (1993) Three Factor Model. Where !_! refers to the coefficient for the market risk premium, !_"#$ is the parameter for the return long in small stocks and short in large cap stocks. !_%#& is the coefficient corresponding to the return of a portfolio long in high book-to-market value and short in low book-to-market value shares. # represents the

outperformance of the portfolio (in %) Robust standard errors in parentheses

(17)

Next we further expand our analysis by adding the momentum factor as suggested by Carhart (1997). The results of this can be found in table 5 on the next page. The additional momentum factor does not appear to be significant for many of the portfolios. The results with regards to the alpha seem to comply with those obtained from the other models. Again, it seems that the portfolio scoring highest on ESG is the only portfolio with a significant alpha whereas the worst portfolio in terms of ESG performance is the only that yields a significantly positive alpha.

Finally, we test the Fama French 5 factor model by including the Robust Minus Weak and the Conservative Minus Aggressive factors. As with the previously added factors, the corresponding betas mostly turn out to be statistically not different from zero. However, in terms of alpha we again observe that the only significant alphas are yielded by the best and worst ESG scoring portfolio, where the highest ESG portfolio does not meet the market benchmark while the lowest scoring portfolio again has a positive alpha of 0.296%. This implies that the portfolio constructed out of the percentile of lowest ESG scoring stocks does outperform the market by almost 0.3 percent point. Subsequently, the portfolio consisting of the top ESG scoring decile does underperform, where the difference with the market return is -0.106 percent.

The results of the regressions seem to a relationship between ESG performance and returns. Surprisingly however, it seems that that the best performing portfolio in terms of ESG is also the only portfolio that has a negative alpha.

The negative alpha for the high ESG scoring portfolio could be an indication that the stocks that comprise it are overpriced and hence do underperform in terms of returns. This could be explained by investors preferring high ESG scoring stocks where the resulting increased demand pushes prices past their natural levels. Contrarily, the positive alpha for the worst ESG scoring portfolio might reflect investor’s reluctance to invest in bad ESG stocks which drives down prices, resulting in under-priced assets. This is in line with the results of Hong and Kacperczyk (2009), even when using the Fama French five factor model. An important note to this is that the worst ESG scoring decile portfolio is not necessarily comprised of only sin stocks, since these are defined as stocks belonging to specific industries whereas in our portfolios, the selection is made not on industry but overall ESG scores. The implications are however similar, the under-pricing of sin/low ESG assets can possibly be attributed to a lack of (institutional) investors interest.

(18)

Table 5:

Carhart Four-Factor Results July 2003 - June 2020

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 VARIABLES !_! 0.00200 0.847*** 0.836*** 0.875*** 0.848*** 0.859*** 0.949*** 0.919*** 0.898*** 0.904*** -0.902*** (0.00195) (0.0329) (0.0325) (0.0333) (0.0300) (0.0257) (0.0423) (0.0344) (0.0328) (0.0377) (0.0381) !_"#$ 0.00253 -0.128*** -0.0834* -0.107** -0.141*** -0.0327 -0.0554 -0.0111 0.0338 0.0631 -0.0606 (0.00386) (0.0457) (0.0458) (0.0418) (0.0446) (0.0412) (0.0535) (0.0439) (0.0450) (0.0573) (0.0580) !_%#& 0.997*** 0.0168 0.00621 -0.0144 0.0206 0.0259 -0.00454 -0.0951* -0.119** -0.215*** 1.212*** (0.00308) (0.0477) (0.0477) (0.0555) (0.0389) (0.0525) (0.0611) (0.0511) (0.0483) (0.0511) (0.0517) !_#'# -0.00209 -0.0363 -0.0546* -0.0394 -0.0286 -0.0151 -0.0231 -0.0431 -0.0676** -0.0720* 0.0699* (0.00165) (0.0401) (0.0278) (0.0280) (0.0230) (0.0235) (0.0369) (0.0413) (0.0323) (0.0388) (0.0390) # -0.105*** -0.125 0.00554 -0.147 0.0153 -0.0202 0.0333 0.112 0.148 0.245* -0.350** (0.00925) (0.105) (0.103) (0.105) (0.0969) (0.0899) (0.131) (0.113) (0.110) (0.136) (0.137) Observations 204 204 204 204 204 204 204 204 204 204 204 R-squared 0.998 0.871 0.874 0.881 0.880 0.903 0.845 0.871 0.872 0.822 0.848

Results are obtained using an OLS time-series regression on the Carhart (1997) Four Factor Model. Where !! refers to the coefficient for the market risk premium, !_"#$ is the parameter for the return long in small stocks and short in large cap stocks. !_%#& is the coefficient corresponding to the return of a portfolio long in high book-to-market value and short in low book-to-market value shares. !_#'# is the

coefficient related to the momentum factor while # represents the outperformance of the portfolio (in %). Robust standard errors in parentheses

(19)

Table 6:

Fama French Five-Factor Model Results July 2003 – June 2020

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 VARIABLES !_! 0.00312 0.857*** 0.837*** 0.875*** 0.848*** 0.861*** 0.932*** 0.923*** 0.900*** 0.887*** -0.884*** (0.00202) (0.0303) (0.0298) (0.0327) (0.0272) (0.0243) (0.0362) (0.0325) (0.0332) (0.0339) (0.0342) !_"#$ 0.00251 -0.128** -0.0947* -0.123*** -0.138*** -0.0333 -0.0734 -0.00516 0.0337 0.0463 -0.0438 (0.00394) (0.0519) (0.0501) (0.0464) (0.0463) (0.0441) (0.0590) (0.0495) (0.0512) (0.0598) (0.0602) !_%#& 0.996*** 0.0393 0.0737 0.0269 0.0683 0.0442 0.0647 -0.0287 -0.0186 -0.0677 1.064*** (0.00343) (0.0569) (0.0521) (0.0615) (0.0524) (0.0557) (0.0786) (0.0574) (0.0553) (0.0553) (0.0554) !_(#) 0.00132 0.0149 -0.0560 -0.0836 0.0184 -0.000364 -0.115 0.0384 0.00614 -0.105 0.106 (0.00473) (0.0793) (0.0682) (0.0737) (0.0700) (0.0608) (0.0944) (0.0814) (0.0755) (0.0905) (0.0908) !*#+ 0.00536 -0.00875 -0.122 -0.0557 -0.115 -0.0345 -0.184 -0.155* -0.221** -0.364*** 0.369*** (0.00639) (0.0914) (0.0760) (0.0837) (0.0940) (0.0751) (0.118) (0.0880) (0.0956) (0.0971) (0.0981) # -0.107*** -0.136 0.0228 -0.124 0.0141 -0.0201 0.0817 0.105 0.153 0.296** -0.403*** (0.00947) (0.0986) (0.101) (0.107) (0.0918) (0.0916) (0.124) (0.109) (0.107) (0.133) (0.135) Observations 204 204 204 204 204 204 204 204 204 204 204 R-squared 0.998 0.869 0.873 0.881 0.880 0.902 0.848 0.872 0.873 0.830 0.855

Results are obtained using an OLS time-series regression on the Fama and French (2015) Five Factor Model. Where !_! refers to the coefficient for the market risk premium, !"#$ is the parameter for the return long in small stocks and short in large cap stocks. !%#& is the coefficient

corresponding to the return of a portfolio long in high book-to-market value and short in low book-to-market values. !(#) represents the parameter of the return of a portfolio long in robust profit shares and short in weak profit shares. !_*#+ is the expression of the coefficient of the

return of a portfolio long in conservative investment portfolio and short in aggressive investment portfolio. While # represents the outperformance of the portfolio (in %).

(20)

6. Robustness

To test the robustness of our results we will repeat the regressions of the Fama French Five Factor model but this time using equally weighted portfolios, in order to test whether or not the results might be biased due to the increased influence of larger firms in value weighted portfolios. The results of these regressions can be found in Appendix B

The results for the equally weighted portfolios seem to mostly match those of the value weighted portfolio, the main difference being that for the #10 portfolio, there is no significant alpha, and hence no advantage in investing in low ESG scoring stocks. For the best scoring portfolio in terms of ESG, there still is a significant alpha of -0.107%. This is in part in line with the hypothesis that ESG does have an effect on portfolio returns.

7. Conclusion

Given the results obtained from our estimation there seems to be evidence for some sort of relationship between ESG and portfolio returns. Both the highest and lowest ESG scoring portfolios and subsequently the portfolio long in the high ESG and short in the low ESG portfolios generate significant alphas across the different models used for our estimations. For the remaining portfolios however, there seems to be no outperformance in any direction.

Starting with the high ESG portfolio, using the CAPM the alpha is estimated at -0.45% which is a lot lower than the alpha of -0.11% that results from the other multi-factor model estimations. The fact remains that this negative alpha is sustained throughout the different estimation models implying that there is a significant negative effect of ESG on portfolio returns.

For the lowest ESG portfolio however, thing are quite different. Starting at the CAPM where alpha is estimated at 0.28%. This number slightly decreases (but remains significant) when estimating the Fama and French three-factor model and the Carhart four-factor model and even increases to a value of 0.30% when estimated using the Fama and French five-factor model. This seems to be in line with previous literature regarding sin anomaly, where sin stocks do outperform the market. However, this result could not be replicated in the robustness check which used equally weighted portfolios. Also, as noted earlier, low ESG does not necessarily equate to sin stocks given its definition of being limited to specific industries.

(21)

Overall, we can conclude that ESG does have an effect on portfolio returns. One should however take caution with this conclusion since the effect appears to only be significant at the extremes of ESG performance. Moreover, the results are limited and provide a basis for further research, the details of which will be discussed in the next section.

8. Discussion

The results of this paper should be carefully interpreted and invite for further research into this topic. In our setup we made no distinction between regions and opted for a global approach. It would be interesting to see if there are differences between regions in terms of the relation between ESG and financial performance. This could for example be due to differences in local preferences or focus on non-financial characteristics of portfolios.

Another option for further research is into the difference between industries previous studies often focussed specifically on vice industries and found that these tend to give excess returns, ignoring any of the ESG characteristics of the firms active in these industries. By taking a broader approach looking beyond industry but fo

Another area to further adres would be the effect of ESG on returns during times of crisis. Do the more long-term oriented high ESG shares outperform low ESG shares during economic downturn as suggested by some studies, and do these results hold when using the relatively new Fama and French (2015) five-factor model. Or are sin stock which tend to be often associated with fast moving consumer goods less dependent on economic cycles?

Finally, in this paper we used the ESG Combined Score as a criterium in constructing our portfolio, it would be interesting to see if results would differ for the different pillars of ESG, can alpha be achieved by selecting stocks on the basis of their performance in on of the ESG pillars instead of focusing on overall ESG performance.

In conclusion ESG does affect portfolio returns, but more questions remain unanswered and invite further research into this area.

(22)

9. Apendix

Apendix A: Number of Firms Where Total refers to the total number of firms available for each given year. #1 up until #11 refer to the number of firms included in each respective portfolio for each given year. Year Total #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 2003 867 86 86 86 87 87 87 87 87 87 87 173 2004 1,583 159 159 159 158 158 158 158 158 158 158 317 2005 1,988 198 199 199 199 199 199 199 199 199 198 396 2006 2,009 201 200 201 201 201 201 201 201 201 201 402 2007 2,127 213 213 212 212 212 213 213 213 213 213 426 2008 2,530 253 253 253 253 253 253 253 253 253 253 506 2009 2,971 297 297 297 297 297 297 297 297 297 298 595 2010 3,459 346 346 346 346 346 345 346 346 346 346 692 2011 3,501 350 350 350 350 350 351 350 350 350 350 700 2012 3,574 358 358 358 358 357 357 357 357 357 357 715 2013 3,710 371 371 371 371 371 371 371 371 371 371 742 2014 3,823 383 383 382 382 383 382 382 382 382 382 765 2015 4,556 455 455 455 455 456 456 456 456 456 456 911 2016 5,286 529 529 529 529 529 529 528 528 528 528 1,055 2017 5,645 564 564 565 565 564 564 565 565 565 564 1,128 2018 6,258 626 626 626 626 626 626 626 625 625 626 1,252 2019 6,206 620 620 620 620 621 621 621 621 621 621 1,241

(23)

Apendix B:

Fama French Five-Factor Model Results July 2003 – June 2020 Equally Weighted Portfolios

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 VARIABLES "_! 0.00312 0.889*** 0.876*** 0.900*** 0.888*** 0.904*** 0.883*** 0.906*** 0.876*** 0.871*** -0.868*** (0.00202) (0.0383) (0.0387) (0.0439) (0.0415) (0.0422) (0.0417) (0.0422) (0.0377) (0.0402) (0.0403) "_"#$ 0.00251 0.0280 0.0814 0.0610 0.138** 0.174*** 0.228*** 0.240*** 0.278*** 0.253*** -0.250*** (0.00394) (0.0663) (0.0681) (0.0624) (0.0647) (0.0600) (0.0566) (0.0533) (0.0551) (0.0574) (0.0575) "_%#& 0.996*** 0.141* 0.176** 0.140 0.135 0.146* 0.156* 0.133 0.0931 0.154** 0.843*** (0.00343) (0.0792) (0.0767) (0.0851) (0.0848) (0.0860) (0.0822) (0.0844) (0.0787) (0.0772) (0.0768) "_'#( 0.00132 0.0109 -0.0387 -0.0530 -0.0120 -0.0473 -0.0748 -0.0412 0.000914 -0.0511 0.0524 (0.00473) (0.0938) (0.0946) (0.0879) (0.0882) (0.0846) (0.0838) (0.0758) (0.0786) (0.0799) (0.0797) ")#* 0.00536 -0.137 -0.184* -0.150 -0.216** -0.195* -0.221** -0.167* -0.191* -0.291*** 0.296*** (0.00639) (0.109) (0.110) (0.113) (0.108) (0.102) (0.101) (0.0994) (0.107) (0.0950) (0.0959) # -0.107*** -0.150 -0.00206 -0.0483 -0.0378 0.0425 0.0833 0.121 0.0773 0.147 -0.254* (0.00947) (0.126) (0.128) (0.128) (0.123) (0.121) (0.123) (0.121) (0.118) (0.132) (0.133) Observations 204 204 204 204 204 204 204 204 204 204 204 R-squared 0.998 0.849 0.849 0.859 0.865 0.882 0.880 0.893 0.884 0.871 0.865

Results are obtained using an OLS time-series regression on the Fama and French (2015) Five Factor Model. Where "! refers to the coefficient for the market risk premium, "_"#$ is the parameter for the return long in small stocks and short in large cap stocks. "_%#& is the coefficient

corresponding to the return of a portfolio long in high book-to-market value and short in low book-to-market values. "_'#( represents the parameter of the return of a portfolio long in robust profit shares and short in weak profit shares. "_)#* is the expression of the coefficient of the

return of a portfolio long in conservative investment portfolio and short in aggressive investment portfolio. While # represents the outperformance of the portfolio (in %).

(24)

10. References

- Auer, B. R., & Schuhmacher, F. (2016). Do socially (ir) responsible investments pay? New

evidence from international ESG data. The Quarterly Review of Economics and Finance, 59, 51-62.

- Broadstock, D. C., Chan, K., Cheng, L. T., & Wang, X. (2020). The role of ESG performance

during times of financial crisis: Evidence from COVID-19 in china. Finance research letters, 101716.

- Carhart, C. (1997). On persistence in mutual fund performance, Journal of Finance, 45(5), 57-82.

- Chawana, M. (2014). Socially responsible investing returns: Evidence from South Africa,

2004-2012. Journal of Economic and Financial Sciences, 7(1), 103-126.

- Dorfleitner, G., Utz, S., & Wimmer, M. (2013, October). Where and when does it pay to be good? A global long-term analysis of ESG investing. In 26th Australasian Finance and

Banking Conference.

- Fama, E.F., French, K.R. (1993). Common risk factors in the returns on stocks and bonds,

Journal of Financial Economics, 33(1), 3-56.

- Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial

Economics, 116(1), 1-22.

- Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: aggregated

evidence from more than 2000 empirical studies. Journal of Sustainable Finance &

Investment, 5(4), 210-233.

- Global Sustianable Investment Alliance (2018) Global Sustainable Investment Review 2018,

Retrieved from

http://www.gsi-alliance.org/wp-content/uploads/2019/03/GSIR_Review2018.3.28.pdf [Accessed October 13, 2020] - Hoepner, A. G. F. and Zeume, S. (2014) ‘Fiduciary Duty and ‘Sin Stocks’: is vice really

nice?’ In J. P. Hawley, A. G. F. Hoepner, K. L. Johnson, J. Sandberg, and E. J. Waitzer (eds.)

Handbook of institutional investment and fiduciary duty:181-206 .Cambridge: Cambridge

University Press.

- Hong H., and Kacperczyk M. (2009). The Price of Sin: the Effects of Social Norms on Markets, Journal of Financial Economics, 93(1), 15-36.

- Humphrey, J. E., Lee, D. D., & Shen, Y. (2012). Does it cost to be sustainable?. Journal of

Corporate Finance, 18(3), 626-639.

- Jain, M., Sharma, G. D., & Srivastava, M. (2019). Can sustainable investment yield better financial returns: A comparative study of ESG indices and MSCI indices. Risks, 7(1), 15.

- Mǎnescu, C. (2011). Stock returns in relation to environmental, social and governance

performance: Mispricing or compensation for risk?. Sustainable development, 19(2), 95-118. - Merton, R.C. (1987). A Simple Model of Capital Market Equilibrium with Incomplete

Information. The Journal of Finance, 42(3), 483-510.

- Nauman, B. (2020, September 14) ESG surges as investors search for better corporate citizens. Financial Times, Retrieved from:

(25)

https://www.ft.com/content/20f6c929-2fbf-47d5-- Nofsinger, J., & Varma, A. (2014). Socially responsible funds and market crises. Journal of

Banking & Finance, 48, 180-193.

- Singh, A. (2020). COVID-19 and safer investment bets. Finance research letters, 101729.

- Torre, M. L., Mango, F., Cafaro, A., & Leo, S. (2020). Does the ESG Index Affect Stock

Return? Evidence from the Eurostoxx 50. Sustainability, 12(16), 6387.

- Richey, G. (2017), "Fewer reasons to sin: a five-factor investigation of vice stock returns", Managerial Finance, Vol. 43 No. 9, pp. 1016-1033.

- Sahut, J.-M., Pasquini-Descomps Hélène, Cohendet, P., & Mazouz, B. (2015). Esg impact on market performance of firms: international evidence. Management International, 19(2), 40– 63.

- Salaber, J. (2013). Religion and returns in Europe, European Journal of Political Economy, 32(1), 149-160.

(Ir)responsible Investing: Revisiting the Effects of ESG Performance on Portfolio Returns.