• No results found

Double Bottom-Line? – The Financial Performance of Green Energy ETFs

N/A
N/A
Protected

Academic year: 2021

Share "Double Bottom-Line? – The Financial Performance of Green Energy ETFs"

Copied!
78
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

Double Bottom-Line? – The Financial

Performance of Green Energy ETFs

(2)

1

Abstract

(3)

2

Contents

1. Introduction ... 3

2. Literature Review... 6

3. Research Method ... 11

3.1. Sample ... 12 3.2. Performance ... 14

4. Results ... 17

4.1. Green Energy ETFs ... 17

4.2. Conventional Equity ETFs ... 19

4.3. Traditional Energy ETFs ... 20

4.4. Combined Results ... 21

4.5. Evaluation ... 28

5. Conclusion ... 30

References ... 32

Appendix A – ETF List... 36

Appendix B – Beta Estimates Figures ... 40

Appendix C – Individual Regression Results ... 46

Appendix D – CAPM Regression Results ... 70

Appendix E – 3-Factor Model Regression Results ... 73

(4)

3

1. Introduction

Climate change has become a predominant subject across industries due to its possible

detrimental effects on society. Resulting from the ever-increasing carbon dioxide emissions, global

temperatures increased to nearly one degree Celsius above expected levels (NASA, 2018). As a result,

on the 12th of December 2015, the Paris agreement (COP21) was signed by 195 parties to reduce the

risks of climate change with the goal of keeping temperature increases below 2-degree Celsius.

However, this far, one could only observe mediocre results. One of the largest sources of greenhouse

gas emissions is the energy sector, based on burning fossil fuels to produce electricity (United States

Environmental Protection Agency, 2019). One might argue that a switch to renewable energy would

help reaching the goals agreed on during the COP21, however, unfortunately, this transition is far from

being made. The United States, for example, is far from reaching its 2020 target share of renewable

sources, with currently only around 11% of the country’s energy being produced through renewable

sources (U.S. Energy Information Administration, 2018).

With companies not only being viewed as the main cause for climate change, but also as a

potential solution for the transition towards a more sustainable interaction with the environment

(Scherer & Palazzo, 2008), there is an increased need for funds to flow to environmentally friendly

companies. As Ceres (2014) found, the amount of funds required to be devoted to sustainable sectors

must be considerably large. This is primarily due to the relatively high initial costs for sustainable

energy sources compared to traditional sources. Companies often have difficulties raising such high

amounts internally. Therefore, they require external providers of funds, which are mainly raised

through three different channels: Bank lending, market debt, and market equity (Campiglio, 2016). As

Wüstenhagen’s (2007) results indicate that the renewable energy transition can be stimulated by

increasing the attractiveness of new energy sources through the financial participation of individuals,

market equity seems to be of high importance regarding both raising funds and creating market

acceptance of renewable energy. Market equity is generally obtained by retail or institutional investors

seeking a part of ownership in a company. Commonly used vehicles are “the purchase of shares of

publicly listed companies, or through private equity investment” (Campiglio, 2016, p. 1). However,

investors main motivation remains to realise high risk-adjusted returns, whereas assisting with the

energy transition might not be investors’ top priority. Therefore, this paper aims to answer the

following research question:

(5)

4

This paper focuses on exchange-traded funds (ETFs) as investment vehicle to acquire

ownership of publicly listed companies. ETFs are popular investment vehicles, which grew to have

“$2.5 trillion in assets under management (AUM) in the United States ($3.5 trillion globally),

accounting for about 35% of the volume in U.S. equity markets” (Ben-David, Franzoni, & Moussawi,

2018, p. 2471). In 1993, the SPDRs ETF was the first to be traded in the United States, tracking the

S&P 500 Index. The most common ETF providers include names like Vanguard, iShares, and SPDR

(Elton, Gruber & Souza, 2019). Most ETFs are passive investment funds aiming to mimic some

underlying index and therefore are often compared to mutual funds. The main difference to mutual

funds is that ETFs are traded on stock exchanges, where they can be traded throughout the trading day.

Mutual funds, on the contrary, are only priced once a day at market closure. Advantages of ETFs which

led to an increased popularity include increased liquidity, diversification benefits, low costs, as well

as the provision of access to investment strategies such as leveraging and short selling, which have

been unavailable to most investors (Ben-David, Franzoni, & Moussawi, 2018). Kostovetsky (2003)

additionally states that index funds are outperformed by ETFs when it comes to management and

transaction fees as well as tax advantages. Elton, Gruber, & Souza (2019) further find that when offered

to solely invest in ETFs or mutual funds, over one and three years, investors would earn higher returns

by exclusively investing in ETFs. Through these considerable advantages of ETFs compared to

conventional share ownership, ETFs have the potential to ease investors’ access and thus increase

financial participation in sustainable energy companies.

(6)

5

(7)

6

2. Literature Review

Revelli and Viviani (2015) combine literature on SRI and found that initial literature on

environmentally friendly investing mainly focused on the relationship between environmental and

financial performance at a corporate level. SRI literature can mainly be divided into two camps: The

first viewpoint, as seen in Friedman’s (1962) paper, suggests that managers focusing on environmental

performance sacrifice valuable time and thus lose focus of the financial performance of the company.

This will result in increased costs to monitor environmental performances and decreased focus on

maximising profits of the firm, leading to an inferior financial performance of the firm (Walley &

Whitehead, 1994). The second viewpoint, advocated by Freeman and Evan’s (1990) stakeholder view,

contrarily argues that an increase in environmental performance could lead to a better use of company

resources, potentially increasing competitive advantage. This will ultimately lead to an improved

financial performance compared to firms ignoring environmental concerns.

In line Friedman’s (1962) viewpoint, Molina-Azorin et al. (2009) state that firms taking

environmental performance into account are focussing less on core business tasks than firms ignoring

environmental efficiency. Initially, when governments began directing companies to increase their

environmental consciousness, managers saw this additional task as a burden leading to increased costs

for the company. This in turn would make the firm less competitive compared to firms not incurring

such costs (Ambec & Lanoie, 2008). Confirming these results, studies such as Di Giuli and

Kostovetsky (2014), and Wagner (2005) find that an increase in environmental conduct leads to

decreased financial performance. Therefore, to best meet shareholders’ goal of profit maximisation,

firms should restrict their environmental costs to the legally required minimum. Fernando et al. (2017)

conclude that an increase in a firm’s environmental consciousness does not increase shareholder value.

Therefore, institutional investors avoid investing in environmentally conscious firms and prefer to

invest in firms who are impartial to environmental involvement.

(8)

7

evidence by suggesting that a better environmental conduct can provide firms with, inter alia, lower

risk management costs as well as increased profits through, for example, augmented possibilities for

product differentiation. Marti-Ballester (2015) agrees by finding that greener firms can profit from

creating more attractive products, being more appealing to potential employees, and having more

efficient operational processes. Likewise, bad environmental performance will lead some investor to

shun from investing. This is mainly because some investors are environmentally conscious and

actively avoid investing in firms with low environmental performance. Therefore, less investors will

be inclined to invest (Fernando et al., 2017), resulting in an increase of equity financing costs for the

firm. Another effect of a reduced investor pool is that the risks of owning stocks in the company cannot

be shared among as many investors as one might wish (El Ghoul et al., 2018). Although literature

provides contradicting results, several meta-analyses, such as Dixon-Fowler et al., 2013, find a positive

link between environmental and financial performance at a corporate level.

(9)

8

responsible investing outperforms conventional fund portfolios. The same has been found by Gil-Bazo

et al. (2010) for green mutual funds, which is in line with Derwall et al. (2005) concluding that

portfolios with higher environmental concerns have higher returns than portfolios disregarding such

concerns. Ziegler et al. (2011) argue the same by examining a portfolio constructed by buying

European firms which disclose their environmental conduct and shorting firms not disclosing them. A

possible explanation for this outperformance is that by investing in green funds, investors might profit

form a downside protection regarding environmental risks for the firm (Henke, 2016). This

explanation has been used on several occasions to explain the performance similarity of green funds

and conventional funds in recent years. Most of the studies find that green funds in aggregate either

underperformed or performed similarly to their conventional counterpart, research focusing on more

recent time spans find that green funds in aggregate perform similar to conventional funds. A

repeatedly cited paper in this regard by Climent and Soriano (2011) argues that over the period from

2001 until 2009, American green funds did not under- nor outperform conventional funds. This finding

holds for extended periods when using a green market benchmark. In their review study, Renneboog

et al. (2008) agree by stating that overall, there is no statistically significant difference between the

financial performance of socially responsible investing versus investing in conventional funds. A

conclusion would be that investors are not penalised by investing in green portfolios but are neither

rewarded with additional returns. For a sample period from 1991 until 2014, Ibikunle and Steffen

(2017) conclude similar results when testing for the last 5 years. However, when considering the first

15 years, green funds seem to considerably underperform their conventional counterparts. When

ignoring more recent time periods and solely looking at the full observation horizon, Climent and

Soriano (2011) agree that American green funds are outperformed by conventional funds which seems

to be further accentuated for European green funds. This underperformance has been observed since

early research on green fund performance (White, 1995). Therefore, this paper aims to determine if

such underperformance can also be found when comparing green energy ETFs to conventional equity

ETFs by hypothesising:

H0

1

: Conventional equity ETFs outperform green energy ETFs.

(10)

9

the Carhart (1997) multifactor model. Despite that “26 of 81 renewable energy funds […] significantly

outperform the clean energy benchmark, none of them achieve a positive and significant risk-adjusted

return when adopting […] conventional indexes as benchmarks” (Marti-Ballester, 2019, p.

1117-1118). The results of both studies would suggest that investing in green energy funds is financially less

appealing to investors than investing in conventional equity funds. This would translate in investors

paying a premium for investing in green energy funds.

After comparing the performance of green energy funds to conventional equity funds, investors

might be wondering how green energy funds perform within the energy sector. Differences between

the financial performance of green and traditional energy funds should be expected, since American

green energy stocks seem to correlate more with technology related stocks than with oil prices, whereas

fossil fuel energy firms correlate more with oil prices (Chang et al., 2020). Reboredo (2015)

additionally illustrates that increases in prices for oil might result in investors’ capital flowing to green

energy rather than traditional energy. This would mean that a rise in oil prices corresponds to a boost

in green energy investments. Moreover, increased governmental involvement in fossil fuel energy

companies lead to higher volatility levels of green energy investments compared to traditional energy

investments (e.g., Wen et al, 2014, Zhang & Du, 2017). This increased volatility might also be due to

differences in characteristics of green and traditional energy firms. Green energy firms for example

tend to be smaller, growth-oriented firms compared to the more value-oriented traditional energy firms.

This growth orientation of green energy firms however has led these firms to become more competitive

through their new energy technologies, making green energy an attractive alternative to traditional

energy. This development is further accelerated by recent movements against the fossil fuel industry

and in favour of green energy (Ibikunle & Steffen, 2017). Due to their high volatility profile and

considerable differences from traditional energy funds, this paper examines the performance of green

energy funds in comparison to traditional energy by hypothesising:

H0

2

: Traditional energy ETFs outperform green energy ETFs.

(11)

10

(12)

11

3. Research Method

To determine if conventional equity ETFs outperform green energy ETFs, each ETF’s

individual performance is assessed using three different benchmarks. Additionally, joint significance

is assessed for each ETF category. ETF categories are then compared based on the individual ETF

performances and joint significance. The three benchmarks applied are market proxies for the three

ETF categories: green energy, traditional energy and conventional equity. To combine results to

ultimately answer the first hypothesis if conventional equity ETFs outperform green energy ETFs,

three sub hypotheses are added. To reject the first hypothesis, all the following sub hypotheses must

be rejected.

𝐻0

1𝑎

: Conventional equity ETFs significantly outperform the green energy market

benchmark more often than green energy ETFs.

𝐻0

1𝑏

: Conventional equity ETFs significantly outperform the traditional energy market

benchmark more often than green energy ETFs.

𝐻0

1𝑐

: Conventional equity ETFs significantly outperform the conventional equity

market benchmark more often than green energy ETFs.

The more, to determine if traditional energy ETFs outperform green energy ETFs, the same

method is used: after assessing the performance of each ETF, the two ETF categories are compared

using benchmarks for each one of the three ETF category markets. To reject the second main

hypothesis that traditional energy ETFs outperform green energy ETFs the following three sub

hypotheses need to be rejected:

𝐻0

2𝑎

: Traditional energy ETFs significantly outperform the green energy market

benchmark more often than green energy ETFs.

𝐻0

2𝑏

: Traditional energy ETFs significantly outperform the traditional energy market

benchmark more often than green energy ETFs.

𝐻0

2𝑐

: Traditional energy ETFs significantly outperform the conventional equity market

benchmark more often than green energy ETFs.

(13)

12

3.1. Sample

To determine the sample of exchange-traded funds, a list of energy funds is acquired from the

widely used ETF database:

www.etfdb.com/etfs/

. Energy ETFs categorised as ‘YieldCos’, ‘Wind

Energy’, ‘Solar Energy’, ‘Cleantech’, and ‘Clean Energy’ are considered as being green energy ETFs.

Traditional energy ETFs are defined as ETFs categorised as ‘Unconventional Oil & Natural Gas’, ‘Oil

Equipment & Services’, ‘Oil & Gas Exploration & Production’, ‘Nuclear Energy’, ‘Natural Gas’,

‘MLP’, ‘Energy Infrastructure’, and ‘Coal’. Additionally, since they do not solely invest in sustainable

energy, ETFs categorised as ‘Broad Energy’ which include both black energy and green energy

equities are considered as being traditional energy ETFs. For comparing green energy ETFs to

conventional equity ETFs, the top 100 equity ETFs based on AUM are taken from the same database.

To alleviate a possible survivorship bias several inactive ETFs are added to the sample. The resulting

sample consists of 173 green energy, traditional energy and conventional equity ETFs listed in US

dollars and traded on at least one stock exchange in the United States of America. Descriptive ETF

statistics per category are depicted in Table 1. Each fund only belongs to one of the three ETF

categories. Whereas no ETF is limited by the geographical reach of firms included, only the

conventional equity ETFs may include firms across all sectors. The traditional energy ETFs are

restricted to the energy sector whereas green energy ETFs are further restricted to solely invest in green

energy firms. A detailed list of all ETFs used per category can be found in Appendix A. Indices used

as benchmarks for green energy, traditional energy, and conventional equity markets are the ‘MSCI

Global Alternative Energy Index’, ‘MSCI World Energy Index’, and ‘MSCI World Index’. These

benchmarks are all characterised as float-adjusted market capitalisation weighted and are widely used

in academic research such as by Marti-Ballester (2019). Table 2 provides a more detailed description

of the benchmarks, including, inter alia, the number of constituents and market capitalisation.

Table 1. Summarising Statistics

(14)

13

Using the corresponding tickers, monthly price data for each ETF and benchmark is obtained

through the Thomson Reuters EIKON/Datastream database. The sample period of the data stretches

from January 2010 to December 2019 for which at least 24 monthly observations are required. All

ETFs not fulfilling these criteria are dropped from the sample. The final sample consists of 11 green

energy ETFs, 59 traditional energy ETFs, and 103 conventional equity ETFs. For further calculations,

monthly logarithmic returns are calculated for the ETFs’ monthly prices and the three different

benchmark returns as follows:

𝑅

𝑙𝑜𝑔

= 100 ∗ 𝑙𝑛 (

𝑃𝑡−1

𝑃𝑡

)

( 1 )

where 𝑅

𝑙𝑜𝑔

is the logarithmic return of the ETF or benchmark, P is the monthly price of the ETF or

benchmark, t is the month, and ln is the natural logarithm.

Table 2. Benchmark Descriptions

Benchmark Launch Date Constituents Mkt. Cap. (USD millions) Description MSCI Global Alternative Energy Index January 20, 2009

69 176,399.17 "The [...] index includes developed and emerging market large, mid and small cap companies that derive 50% or more of their revenues from products and services in Alternative energy."

MSCI World Energy Index

September 15, 1999

54 1,113,990.64 "The [...] index is designed to capture large and mid cap segments across 23 Developed Markets countries. All securities in the index are classified in the Energy sector as per the Global Industry Classification Standard (CIGS®)"

MSCI World Index

March 31, 1986

1,067 44,996,280.71 "The […] index captures large and mid cap representation across 23 Developed Markets countries. With 1.607 constituents, the index covers approximately 85% of the free float-adjusted market capitalization in each country."

Note. This table describes the indices used as market benchmarks. The first column shows the full name of the index. The second column indicates the initial launch date of the index. The third column specifies the number of constituents tracked by the index. The market capitalisation of the index in millions of US Dollars is shown in column four. The fifth column provides a short description of the index. Information in this table is copied from the factsheets corresponding to the indices. These factsheets have been retrieved from the MSCI website (www.msci.com) on October 19, 2020.

(15)

14

3.2. Performance

To estimate the financial performance of each green energy, traditional energy and

conventional equity ETF, common practice is followed (e.g., Chen & Scholtens, 2018, Marti-Ballester,

2019) by assessing the Jensen’s (1968) alpha of each fund through the Carhart (1997) 4-factor model.

Initially, Jensen’s (1968) alpha was defined as a fund’s outperformance from what is predicted by the

CAPM. The CAPM (Sharpe, 1970) was developed to enable investors to assess and compare funds’

risk-adjusted performance with the following equation:

𝑟

𝑓,𝑡

− 𝑟

𝑖,𝑡

= 𝛼

𝑓

+ 𝛽

𝑀𝐾

(𝑟

𝑖𝑛𝑑𝑒𝑥,𝑡

− 𝑟

𝑖,𝑡

) + 𝜀

𝑓,𝑡

( 2 )

with 𝑟

𝑓,𝑡

being the daily ETF return at time t, 𝑟

𝑖,𝑡

the daily one-month T-bill rate at time t used as

risk-free rate,

𝛼

𝑓

the average annualised four-factor adjusted return for fund f, 𝛽

𝑀𝐾

the estimate of the

market factor, 𝑟

𝑖𝑛𝑑𝑒𝑥,𝑡

the return of the applicable benchmark, and 𝜀

𝑓,𝑡

being the fund’s error term at

time t.

However, Fama-French (1996) found that the CAPM model lacks the ability to account for

common market anomalies, such as the size and the value anomaly. Their 3-factor model accounts for

these anomalies, which are of special importance for the funds in our sample. This is mainly due to

green companies being on average smaller and more growth oriented than their value-oriented

fossil-heavy counterparts (Ibikunle & Steffen, 2017). The 3-factor model is defined as follows:

𝑟

𝑓,𝑡

− 𝑟

𝑖,𝑡

= 𝛼

𝑓

+ 𝛽

𝑀𝐾

(𝑟

𝑖𝑛𝑑𝑒𝑥,𝑡

− 𝑟

𝑖,𝑡

) + 𝛽

𝑆𝑀𝐵

𝑟

𝑡𝑆𝑀𝐵

+ 𝛽

𝐻𝑀𝐿

𝑟

𝑡𝐻𝑀𝐿

+ 𝜀

𝑓,𝑡

( 3 )

with 𝑟

𝑓,𝑡

being the daily ETF return at time t, 𝑟

𝑖,𝑡

the daily one-month T-bill rate at time t used as

risk-free rate,

𝛼

𝑓

the average annualised four-factor adjusted return for fund f, 𝛽

𝑀𝐾

the estimate of the

market factor,

𝑟

𝑖𝑛𝑑𝑒𝑥,𝑡

the return of the applicable benchmark,

𝛽

𝑆𝑀𝐵

the estimate of the size factor,

𝑟

𝑡𝑆𝑀𝐵

the size factor return at time t, 𝛽

𝐻𝑀𝐿

the estimate of the value factor, 𝑟

𝑡𝐻𝑀𝐿

the return of the value

factor at time t, and 𝜀

𝑓,𝑡

being the fund’s error term at time t.

The Carhart’s (1997) 4-factor model further extends the Fama-French (1996) 3-factor model

with a fourth factor, the momentum factor:

(16)

15

𝑟

𝑡𝑆𝑀𝐵

the size factor return at time t, 𝛽

𝐻𝑀𝐿

the estimate of the value factor, 𝑟

𝑡𝐻𝑀𝐿

the return of the value

factor at time t, 𝛽

𝑀𝑂𝑀

the estimate of the momentum factor, 𝑟

𝑡𝑀𝑂𝑀

the return of the momentum factor

at time t, and 𝜀

𝑓,𝑡

being the fund’s error term at time t.

Chen and Scholtens (2018) are followed in collecting data on size (SMB), book-to-market

(HML), and momentum (MOM) factors as well as the U.S. monthly T-bill rate as risk-free rate from

the Kenneth R. French data library. Where Fama and French (1996, p. 55) describe the size factor as

“the difference between the return on a portfolio of small stocks and the return on a portfolio of large

stocks (SMB, small minus big)”, and the book-to-market factor as “the difference between the return

on a portfolio of high-book-to-market stocks and the return on a portfolio of low-book-to-market

stocks (HML, high minus low)”. The momentum anomaly, discovered by Jegadeesh and Titman

(1993) is accounted for by the momentum factor constructed by taking the difference between winner

and loser stocks (Boodie, Kane, & Marcus, 2018).

An ordinary least squares (OLS) regression is run, for which Marti-Ballester (2019) is followed

in using the standard errors corrected by the Newey-West (1987) procedure. This choice is due to

evidence found of heteroskedasticity in error terms in some of our models - tested using the White’s

test. Additionally, conducting a Breusch-Godfrey test with 6 and 12 lags showed evidence of 6

th

and/or

12

th

order autocorrelation in error terms of some of our models. The Newey-West standard errors take

both issues into account. Specifying the number of lags to 12 corrects for any form of

heteroskedasticity and autocorrelation issues up until the 12

th

order of the error terms in the models.

Applying equation (4) allows to assess an ETF’s financial performance in excess of the benchmark by

examining the estimates of the constant in our model, 𝛼

𝑓

, representing the average annualised

four-factor risk-adjusted return for the ETF. A positive alpha value represents an ETF’s ability to

outperform the benchmark applied and a negative alpha represents an ETF’s underperformance

compared to the benchmark. However, both a positive and a negative alpha value need to be

statistically significant to deduct these conclusions. In case of a statistically insignificant alpha, the

equation represents a similar return of the ETF compared to the benchmark.

(17)

16

The main difference from running the regression in function (4) is the possibility of testing for several

dependent variables at a time. Therefore, the GRS test allows to draw conclusions at a portfolio level.

This ultimately allows to determine and compare the performance of the different fund categories.

Using the estimates from equation (4), the GRS multivariate test statistic used to determine joint

significance is as follows:

𝐽 =

(𝑇−𝑁−𝐾)

𝑁

∗ (1 + 𝜇

𝐾

′ Ω

−1

𝜇

𝐾

)

−1

𝛼̂ ′ 𝛴̂

−1

𝛼̂

( 5 )

With J being the GRS test statistic, 𝑇 the number of observations, 𝑁 the number of funds, and 𝐾 the

number of factors.

𝜇

𝐾

is a vector of factor means,

Ω is the factors’ estimated variance-covariance

matrix,

𝛼̂ are the individual regression intercepts and 𝛴̂ is the variance-covariance matrix of these

intercepts.

(18)

17

4. Results

To answer the research question if green energy ETFs underperform conventional equity and/or

traditional energy ETFs, in chapter 4.1 green energy ETFs’ performances are assessed by analysing

average alpha estimates and the number of significantly out- and underperforming ETFs for each

market proxy. In chapter 4.2 and 4.3 the same procedure is implemented to assess conventional equity

ETFs’ and the traditional energy ETFs’ performances. Chapter 4.4 compares the different ETF

categories with each other based on average percentage of significant outperformance. To confirm

interpretations of summary regression statistics, GRS test results are also interpreted. The results help

to answer the sub hypotheses and ultimately the two main hypotheses if green energy ETFs are

outperformed by conventional equity and/ or traditional energy ETFs. Chapter 4.5 discusses results

more critically; limitations are presented and ideas for further research are provided. Graphical

representation of the beta estimates can be found in Appendix B, and all the individual Carhart

regression results are presented in Appendix C.

4.1. Green Energy ETFs

(19)

18

traditional energy market as for the green energy market. Additionally, with no outperformance and 2

green energy ETFs underperforming the conventional equity benchmark one would suggest that green

energy ETFs are not able to outperform the conventional equity market.

Negative mean alpha estimates are observed for each of the three market proxies applied. This

suggests that even if outperformance is detected, this outperformance is on average rather low.

However, this suggestion is not reliable since average standard deviations for the alpha estimates seem

to be rather high. Looking at the mean R

2

for each of the market proxies, it is observable that models

with the most explanatory power for green energy ETFs employ the ‘MSCI Global Alternative Energy

Index’ as market proxy. Even though this seems obvious, one can also observe that using the ‘MSCI

World Index’ as a market proxy results in models with higher explanatory power than models using

the ‘MSCI World Energy Index’. This is rather unexpected, since it seems reasonable to assume that

the traditional energy market benchmark would be a better market proxy for green energy ETFs, than

the ‘MSCI World Index’ representing the whole equity market. This suggests a disconnection between

green energy ETFs and the traditional energy market.

Table 3. Carhart Regression Summary Statistics for Green Energy ETFs

Alpha Benchmark SMB HML MOM R2 MSCI Global Alternative Energy

Mean -0.050 0.647 0.359 0.008 -0.008 0.585 Std. Dev. 1.275 0.343 0.265 0.371 0.359 0.272 Max. 1.078 1.331 0.662 0.826 0.739 0.818 Min. -3.034 -0.010 -0.206 -0.435 -0.324 0.035 No. of +/0/- significance (6/4/1) (9/2/0) (3/8/0) (0/10/1) (6/4/1)

MSCI World Energy

Mean -0.386 0.605 0.517 -0.292 -0.021 0.352 Std. Dev. 1.149 0.323 0.319 0.520 0.344 0.171 Max. 0.710 1.076 1.094 0.991 0.717 0.576 Min. -3.003 -0.042 -0.058 -0.790 -0.268 0.027 No. of +/0/- significance (2/8/1) (9/2/0) (4/7/0) (0/8/3) (1/10/0) MSCI World Mean -0.996 1.084 0.799 0.191 0.074 0.525 Std. Dev. 0.927 0.602 0.364 0.300 0.298 0.260 Max. 0.036 1.998 1.590 0.977 0.706 0.857 Min. -2.961 -0.126 0.246 -0.124 -0.171 0.028 No. of +/0/- significance (0/9/2) (9/2/0) (7/4/0) (1/10/0) (0/9/2)

(20)

19

4.2. Conventional Equity ETFs

Average alpha and beta estimates for each of the factors using conventional equity ETFs are

presented in Table 4. At a 10% level, it is observable that a considerable number of conventional equity

ETFs statistical significantly outperform each of the benchmarks applied. Namely 89 of the ETFs

outperform the green energy market benchmark, 76 the traditional energy market benchmark, and 68

the conventional equity market benchmark. None of the conventional equity ETFs significantly

underperforms proxies for the green energy and the traditional energy market. Nonetheless, 18 of the

ETFs significantly underperform the conventional equity market proxy. Risk-adjusted returns similar

to the benchmark, shown by non-significant alpha estimates, can be found for 24 conventional equity

ETFs for the green energy market benchmark, 27 for the traditional energy benchmark, and 17 for the

conventional equity benchmark. With none of the ETFs underperforming both energy market proxies

and a lot of ETFs overperforming, the results suggest that conventional equity ETFs considerably

outperform the green energy and the traditional energy markets.

(21)

20

Table 4. Carhart Regression Summary Statistics for Conventional Equity ETFs

Alpha Benchmark SMB HML MOM R2 MSCI Global Alternative Energy

Mean 0.911 0.356 -0.086 -0.284 -0.199 0.430 Std. Dev. 0.499 0.138 0.516 0.437 0.256 0.113 Max. 3.705 1.219 2.472 0.867 0.287 0.625 Min. -0.699 -0.166 -1.059 -2.513 -2.138 0.048 No. of +/0/- significance (89/24/0) (98/4/1) (15/49/39) (2/65/36) (1/85/17) MSCI World Energy

Mean 0.691 0.547 -0.069 -0.609 -0.084 0.559 Std. Dev. 0.412 0.216 0.506 0.524 0.309 0.148 Max. 2.891 1.786 2.400 1.012 0.321 0.948 Min. -0.520 -0.251 -1.046 -3.198 -2.762 0.045 No. of +/0/- significance (76/27/0) (99/2/2) (17/46/40) (1/81/21) (1/97/5) MSCI World Mean 0.149 0.955 0.184 -0.154 -0.003 0.783 Std. Dev. 0.330 0.346 0.508 0.394 0.203 0.206 Max. 1.042 3.231 2.645 0.871 0.443 0.976 Min. -0.652 -0.287 -0.902 -2.15 -1.61 0.04 No. of +/0/- significance (68/17/18) (99/3/1) (40/38/25) (10/58/35) (11/90/2) Note. This table provides summary statistics for equation (4) for conventional equity ETFs, using as benchmark the ‘MSCI Global Alternative Energy Index’, the ‘MSCI World Energy Index’, and the ‘MSCI World Index’. Depicted are the average alpha estimates (Alpha), the average beta estimates of the excess return of the benchmark (Benchmark), size factor (SMB), value factor (HML) and momentum factor (MOM), and the average estimated coefficient of determination (R2). Presented are the mean, standard deviation, maximum, minimum, and the number of ETFs

showing positive, no, and negative statistical significance at an alpha level of 10%.

4.3. Traditional Energy ETFs

(22)

21

when compared to the traditional energy market. Nonetheless, no traditional energy ETF is able to

outperform the traditional energy benchmark.

Mean alpha estimates are negative and thus rather low for each of the market proxies. This is

of no surprise since for each benchmark, a considerable number of ETFs underperform, resulting in

low average alpha estimates. However, for the same alpha estimates, high standard deviations with

values above 1 can be observed. Regarding mean coefficients of estimates (R

2

), exhibiting higher

estimates, models using the MSCI World Energy Index as market proxy are most efficient to explain

the excess return of traditional energy ETFs, whereas the green energy benchmark is the least efficient.

Table 5. Carhart Regression Summary Statistics for Traditional Energy ETFs

Alpha Benchmark SMB HML MOM R2 MSCI Global Alternative Energy

Mean -0.476 0.373 0.261 0.413 -0.242 0.283 Std. Dev. 1.331 0.423 1.559 0.821 0.821 0.094 Max. 1.501 1.711 6.002 2.509 3.663 0.416 Min. -5.806 -1.350 -6.732 -2.902 -3.432 0.054 No. of +/0/- significance (0/45/14) (49/6/4) (18/39/2) (25/31/3) (10/31/18) MSCI World Energy

Mean -0.672 0.801 0.097 -0.135 0.035 0.647 Std. Dev. 1.131 1.189 1.365 0.505 0.324 0.264 Max. 1.385 4.745 4.655 1.259 1.071 0.98 Min. -5.630 -4.425 -6.744 -2.926 -0.902 0.056 No. of +/0/- significance (0/40/19) (51/4/4) (23/32/4) (2/47/10) (4/51/4) MSCI World Mean -1.184 1.046 0.074 0.621 0.073 0.501 Std. Dev. 1.055 1.328 0.298 1.110 0.420 0.164 Max. 0.463 5.46 0.706 4.613 1.334 0.728 Min. -7.19 -4.673 -0.171 -4.824 -0.69 0.056 No. of +/0/- significance (0/24/35) (51/4/4) (32/22/5) (39/16/4) (7/50/2) Note. This table provides summary statistics for equation (4) for traditional energy ETFs, using as benchmark the ‘MSCI Global Alternative Energy Index’, the ‘MSCI World Energy Index’, and the ‘MSCI World Index’. This table presents the average alpha estimates (Alpha), the average beta estimates of the excess return of the benchmark (Benchmark), size factor (SMB), value factor (HML) and momentum factor (MOM), and the average estimated coefficient of determination (R2). Presented are the mean, standard deviation, maximum, minimum, and the number of ETFs showing positive, no, and negative statistical significance at an alpha level of 10%.

4.4. Combined Results

(23)

22

This suggests that out of the three benchmarks, the most suitable benchmark was chosen to represent

each ETF category’s market. The more, besides for their own benchmark, green energy ETF and

traditional energy ETF models exhibit a higher mean R

2

when applying the conventional equity market

proxy than their energy counterpart’s market proxy. This means that for example for traditional energy

ETFs it is more accurate to use the conventional market benchmark than the green energy market

benchmark.

Figure 1. Alpha Estimates Using ‘MSCI Global Alternative Energy’ Benchmark

(24)

23

Figure 2. Alpha Estimates Using the ‘MSCI World Energy’ Benchmark

Applying the traditional energy market proxy, Figure 2 depicts that conventional equity alpha

estimates on average exhibit higher values than green energy alpha estimates. This is in line with the

summarised regression results from Table 6 showing that 74% of conventional equity ETFs

significantly outperform the traditional energy market proxy, while only about 18% of green energy

ETFs do so. At a portfolio level, the GRS test (Table 7) shows that these interpretations seem to hold.

With a significant mean alpha estimate of -0.124, the green energy portfolio seems to perform worse

than the conventional equity portfolio, exhibiting a significant mean alpha of 0.691. This implies that

on average, green energy ETFs underperform and conventional ETFs outperform the traditional energy

market proxy. This suggests the rejection of hypothesis H0

1b

and thus, conventional equity ETFs do

significantly outperform the traditional energy benchmark more often than green energy ETFs.

Figure 3. Alpha Estimates Using the ‘MSCI World’ Benchmark

(25)

24

summarised alpha estimates in Table 6, showing that whereas none of the green energy ETFs is able

to outperform the market benchmark, 66% of conventional equity ETFs outperform. This

interpretation holds for GRS mean alpha estimates in Table 7. With significant mean alpha estimates

of -0.799 and 0.149, the green energy portfolio performs considerably worse than the conventional

equity portfolio. Therefore, H0

1c

is not rejected and thus conventional equity ETFs outperform the

conventional equity benchmark more often than green energy ETFs. Combining these sub-hypotheses

outcomes leads to an answer to the first main hypothesis:

H0

1

: Conventional equity ETFs outperform green energy ETFs.

The test results and interpretations do not lead to any of the three sub hypotheses being rejected.

This shows that conventional equity ETFs outperform green energy ETFs on all the market proxies

and, therefore,

H0

1

cannot be rejected, suggesting that conventional equity ETFs outperform green

energy ETFs.

Table 6. Carhart 4-Factor Model Summary Alpha Statistics per ETF Category

MSCI Global Alternative

Energy

MSCI World

Energy MSCI World Green Energy ETFs Positive 54.5% (6) 18.2% (2) 0.0% (0) No sign. 36.4% (4) 72.7% (8) 81.8% (9) Negative 9.1% (1) 9.1% (1) 18.2% (2) Traditional Energy ETFs

Positive 0.0% (0) 0.0% (0) 0.0% (0) No sign. 76.3% (45) 67.8% (40) 40.7% (24) Negative 23.7% (14) 32.2% (19) 59.3% (35) Conventional Equity ETFs

Positive 86.4% (89) 73.8% (76) 66.0% (68) No sign. 23.3% (24) 26.2% (27) 16.5% (17) Negative 0.0% (0) 0.0% (0) 17.5% (18) Note. This tables provides Carhart 4-factor model summary alpha statistics for green energy, traditional energy, and conventional equity ETFs using as benchmarks the ‘MSCI Global Alternative Energy Index’, the ‘MSCI World Energy Index’, and the ‘MSCI World Index’. For each ETF category and benchmark, the table provides percentages of the number of ETFs with positive, no, and negative statistical significance at a 10% level. The number of funds is presented in parentheses.

(26)

25

55% of green energy ETFs do. These interpretations are supported by GRS test results in Table 7,

which show that both the green and traditional energy portfolios show results jointly significantly

different from zero. However, whereas the traditional energy portfolio exhibits a negative mean alpha

value, the green energy portfolio exhibits a positive mean alpha value. This suggests a rejection of

sub-hypothesis H0

2a

stating that traditional energy ETFs significantly outperform the green energy market

benchmark more often than green energy ETFs.

The more, with relatively more green energy alpha estimates being positive than traditional

energy alphas, Figure 2 shows that previous interpretations hold for the traditional benchmark as well.

Regarding statistically significant alpha estimates, Table 6 shows that about 18% of green energy ETFs

outperform the MSCI World Energy Index, whereas none of the traditional energy ETFs significantly

outperforms. This seems to remain true on an aggregate level, because GRS test results in Table 7

show significant mean alpha estimates of -0.124 for green energy and -0.664 for traditional energy.

This indicates that green energy ETFs in general perform better than traditional energy ETFs when

assessed with the traditional energy benchmark. This implies the rejection of

H0

2b

in stating that

traditional energy ETFs significantly outperform the traditional energy benchmark more often than

green energy ETFs.

Interestingly, Figure 3 does not show a clear distinction between alpha levels of traditional and

green energy ETFs. Even though it might seem that green energy ETFs perform somewhat better,

regression estimates and GRS test results are needed for clarification. The summarised regression

estimate results in Table 6 show that, even though none of the energy ETFs significantly outperforms

the conventional equity benchmark, less green energy ETFs (18.2%) underperform the ‘MSCI World

Index’ than traditional energy ETFs (59.3%). This shows that green energy ETFs are more prone to

outperform the conventional equity market benchmark than traditional energy ETFs. Further

clarification is provided by the GRS test showing that green energy ETFs have a significant mean

alpha estimate of -0.799 and traditional energy ETFs of -1.186. This shows that traditional energy

ETFs on average perform worse than their green energy counterparts. Thus, H0

2c

is rejected in stating

that traditional energy ETFs outperform the conventional equity benchmark more often than green

energy ETFs. Combing the results of the three sub hypotheses leads to an answer to the second main

hypothesis:

H0

2

: Traditional energy ETFs outperform green energy ETFs.

(27)

26

than green energy ETFs. Combining these results suggests that traditional energy ETFs are not able to

outperform their green energy counterparts and thus H0

2

is rejected. Interestingly, it is also observable

that none of the traditional energy ETFs outperforms any of the benchmarks. Comparing this to

conventional equity ETFs of which a considerable number outperform the benchmarks, provides

additional evidence to the conclusions made about the two main hypotheses.

Table 7. Carhart 4-Factor Model GRS Test Results

Mean Alpha Mean SE Mean adj. R2

MSCI Global Alternative Energy

Green Energy 0.249*** 0.456 0.622 Traditional Energy -0.465*** 0.839 0.245 Conventional Equity 0.911*** 0.348 0.406 MSCI World Energy

Green Energy -0.124*** 0.600 0.356 Traditional Energy -0.664*** 0.535 0.635 Conventional Equity 0.691*** 0.310 0.541 MSCI World Green Energy -0.799*** 0.517 0.807 Traditional Energy -1.186*** 0.709 0.479 Conventional Equity 0.149*** 0.222 0.774 Note. This table provides Carhart 4-factor model GRS summary statistics for each portfolio using as benchmarks the ‘MSCI Global Alternative Energy Index’, ‘MSCI World Energy Index’, and the ‘MSCI World Index’. This table presents mean alpha estimates, mean standard errors (Mean SE), and mean adjusted R2 (mean adj. R2). Statistically significant estimates are

denoted by *, **, *** for levels of 10, 5, and 1 percent.

(28)

27

Carhart 4-factor model and the 3-factor model are known to estimate performance more accurately,

this result does not provide enough evidence to dispute previous interpretations. Further, GRS joint

significance robustness was also checked by testing for the period from January 2014 until December

2019 using the Carhart 4-factor model. This timespan was chosen due to the findings by Ibikunle and

Steffen (2017). They find that compared to other funds, green funds display an improved financial

performance for the last 5 years of the sample period. This suggests that green funds financial

performance has changed during recent years. Therefore, robustness is checked by running a GRS test

for the last 5 years of the sample period. As shown in Appendix F, this test for a reduced time period

shows similar results than for the full sample GRS test. Besides for the ‘MSCI Global Alternative

Energy’ benchmark, where both traditional and green energy ETFs do not show alpha values which

are jointly significantly different from zero, test results are similar to previous results. For the

remaining benchmarks, green energy ETFs do not exhibit risk-adjusted returns significantly different

from zero, traditional energy ETFs display significantly negative mean performance and conventional

equity ETFs significantly positive performance. This complies with previous GRS test results and thus

agrees with conclusions that conventional equity ETFs perform better and traditional energy ETFs

perform worse than green energy ETFs.

(29)

28

However, at the investor’s portfolio level investing in the energy sector might be advantageous.

Most investors already own some conventional equities and might wish to diversify their portfolio by

investing in the energy sector. Due to the ability to expose investors to a whole market through one

single investment, ETFs are often considered as the go to vehicle. The results of this paper show that

investors receive higher risk-adjusted returns when choosing to invest in green instead of traditional

energy. This can especially benefit environmentally conscious investors previously avoiding

investment in the energy sector due to the involvement in fossil fuels. However, investors should be

aware that an ETF investment in green energy does not substitute for investing in traditional energy

ETFs (Zang & Du, 2017). Besides the difference in financial performance, the ETFs react opposingly

to changes in oil prices (Reboredo, 2015), and green energy ETFs seem to correlate more with

technology stocks (Chang et al., 2020).

4.5. Evaluation

(30)

29

characterised as the more profitable energy companies. This would bias the results and render

conclusions based on the environmental friendliness of these companies obsolete.

(31)

30

5. Conclusion

With the energy sector being one of the biggest greenhouse gas emitters, a drastic switch to

green energies is of upmost importance to fulfil the goals set during the COP21. This switch requires

a large amount of funds, which is especially high for sustainable energy sources due to the high initial

costs involved (Ceres, 2014). With their numerous advantages, ETFs can be considered as an appealing

investment vehicle for making funds flow from investors to energy companies. With investor’s main

motivation remaining financial performance, interest in such investments is dependent on risk-adjusted

returns. However, due to a lack of academic research on the financial performance of green energy

ETFs, investors might feel uniformed and avoid investing in green energy through these vehicles. To

facilitate the flow of funds, this paper aims to close the knowledge gap on the financial performance

of green energy ETFs. To clarify this financial performance, green energy ETFs are compared to both

conventional equity and traditional energy ETFs. A detailed dissection and analysis of current existing

financial theories leads to two hypotheses this paper aims to answer, namely, if conventional equity

ETFs and/or traditional energy ETFs outperform green energy ETFs. To assess financial performance,

each ETF’s performance is determined by Jensen’s alpha levels estimated through running individual

Carhart 4-factor OLS regressions. Regressions are run using three different market proxies: the ‘MSCI

Global Alternative Energy Index’ for the green energy market, the ‘MSCI World Energy Index’ for

the traditional energy market, and the ‘MSCI World Index’ for conventional equity market.

Summarising the individual ETFs’ estimates allows a preliminary comparison between the different

fund categories. To determine if these interpretations hold at a portfolio level, joint significance for

each ETF category is tested by conducting a GRS test using the same Carhart 4-factor model and

market proxies.

(32)

31

performance. The results thus agree with findings of Climent and Soranio (2011) that conventional

funds outperform green funds. By focusing on ETFs, this paper shows that Reboredo, Quintela, and

Otero’s (2017) findings that alternative energy mutual funds outperform their conventional funds can

be applied to ETFs as well. However, within the energy sector, results suggest a different

interpretation. Finding that green energy funds outperform traditional funds is in line with Freeman

and Evan’s (1990) stakeholder view. This means that Kempf and Osthoff’s (2007) findings that

socially responsible fund portfolios outperform conventional portfolios holds for ETFs within the

energy sector. Ibikunle and Steffen’s (2017) findings for the period from 2012 until 2014 that green

funds outperform black funds persist when considering energy ETFs from 2010 until 2019. All in all,

the results agree with Marti-Ballester (2019) finding that green energy mutual funds are outperformed

by conventional equity funds, but disagree with the conclusion that investors need to pay a premium

for environmentally conscious investing within the energy sector.

(33)

32

References

Ambec, S., & Lanoie, P. (2008). Does it pay to be green? A systematic overview. Acadamy of

Managent. 22 (4), 45–62.

Ben-David, I., Franzoni, F., & Moussawi, R. (2018). Do ETFs increase volatility? The Journal of

Finance, 73(6), 2471-2535.

Bodie, Z., Kane, A., & Marcus, A. J. (2018). Investments (11th edition). McGraw-Hill Education.

Campiglio, E. (2016). Beyond carbon pricing: The role of banking and monetary policy in financing

the transition to a low-carbon economy. Ecological Economics, 121, 220-230.

Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1),

57-82.

Ceres (2014). Investing in the clean trillion: Closing the clean energy investment gap. Ceres.

Chang, C. L., Ilomäki, J., Laurila, H., & McAleer, M. (2020). Market timing with moving averages

for fossil fuel and renewable energy stocks. Energy Reports, 6, 1798–1810.

Chen, X., & Scholtens, B. (2018). The urge to act: A comparison of active and passive socially

responsible investment funds in the United States. Corporate Social Responsibility and

Environmental Management, 25(6), 1154–1173.

Climent, F., & Soriano, P. (2011). Green and good? The investment performance of US environmental

mutual funds. Journal of Business Ethics, 103(2), 275–287.

Derwall, J., Guenster, N., Bauer, R., & Koedijk, K. (2005). The eco-efficiency premium puzzle.

Financial Analysts Journal, 61, 53–64.

Di Giuli, A., & Kostovetsky, L. (2014). Are red or blue companies more likely to go green? Politics

and corporate social responsibility. Journal of Financial Economics, 111, 158-180.

Dimson, E., Karakas, O., & Li, X. (2015). Active ownership. Review of Financial Studies, 28(12),

3225-3268.

Dixon-Fowler, H., Slater, D., Johnson, J., Ellstrand, A., & Romi, A. (2013). Beyond “Does it pay to

be green?” A meta-analysis of moderators of the CEP-CFP relationship. Journal of Business

Ethics, 112, 353-366.

(34)

33

Elton, E. J., Gruber, M. J., & de Souza, A. (2019). Passive mutual funds and ETFs: Performance and

comparison. Journal of Banking & Finance, 106, 265–275.

Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. The Journal

of Finance, 51(1), 55–84.

Fernando, C. S., Sharfman, M. P., Uysal, V. B. (2017). Corporate environmental policy and

shareholder value: Following the smart money. Journal of Financial and Quantitative Analysis,

52, 2023-2051.

Freeman, R. E., & Evan, W. M. (1990). Corporate governance: a stakeholder interpretation. Journal of

Behavioral Economics, 19, 337-359.

Friedman, M. (1962). Capitalism and freedom. University of Chicago Press.

Gibbons, M. R., Ross, S. A., & Shanken, J. (1989). A test of the efficiency of a given portfolio.

Econometrica, 57(5), 1121.

Gil-Bazo, J., Ruiz-Verdu, P., & Santos, A. P. (2010). The performance of socially responsible mutual

funds: The role of fees and management companies. Journal of Business Ethics, 94(2), 243–263.

Hartzmark, S. M., & Sussman, A. B. (2019). Do investors value sustainability? A natural experiment

examining ranking and fund flows. The Journal of Finance, 74(6), 2789-2837.

Henke, H. M. (2016). The effect of social screening on bond mutual fund performance. Journal of

Banking & Finance, 67, 69-84.

Ibikunle, G., & Steffen, T. (2017). European green mutual fund performance: A comparative analysis

with their conventional and black peers. Journal of Business Ethics, 145(2), 337–355.

Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for

stock market efficiency. The Journal of Finance, 48(1), 65–91.

Jensen, M. C. (1968). The performance of mutual funds in the period 1945–1964. The Journal of

Finance, 23(2), 389–416.

Kempf, A., & Osthoff, P. (2007). The effect of socially responsible investing on portfolio performance.

European Financial Management, 13(5), 908–922.

(35)

34

Lesser, K., Rößle, F., & Walkshäusl, C. (2016). Socially responsible, green, and faith-based investment

strategies: screening activity matters!. Finance Research Letters, 16:171–8.

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.

Marti-Ballester, C. P. (2015). Can socially responsible investment for cleaner production improve the

financial performance of Spanish pension plans? Journal of Cleaner Production, 106, 466-477.

Marti-Ballester, C. (2019). The role of mutual funds in the sustainable energy sector. Business Strategy

and the Environment, 28(6), 1107-1120.

Molina-Azorín, J. F., Claver-Cortes, E., Lopez-Gamero, M. D., & Tarí, J. J. (2009). Green

management and financial performance: a literature review. Management Decision, 47,

1080-1100.

NASA (2018). Global surface temperature. Retrieved August 18, 2020, from:

https://climate.nasa.gov/vital-signs/global-temperature/

Newey, W. K., & West, K. D. (1987). Hypothesis testing with efficient method of moments estimation.

International Economic Review, 28, 777–787.

Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and renewable energy stock

prices? Energy Economics, 48, 32–45.

Reboredo, J. C., Quintela, M., & Otero, L. A. (2017). Do investors pay a premium for going green?

Evidence from alternative energy mutual funds. Renewable and Sustainable Energy Reviews, 73,

512–520.

Renneboog, L., Horst, J. T., & Zhang, C. (2008). Socially responsible investments: Institutional

aspects, performance, and investor behavior. Journal of Banking and Finance, 32(9), 1723–1742.

Revelli, C., & Viviani, J. L. (2015). Financial performance of socially responsible investing (SRI):

What have we learned? A meta‐analysis. Business Ethics: A European Review, 24(2), 158–185.

Rudd, A. (1981). Social responsibility and portfolio performance. California Management Review, 23,

55-61.

Scharpe, W. F. (1970). Portfolio theory and capital markets. McGraw-Hill College.

(36)

35

United States Environmental Protection Agency (2019). Global greenhouse gas emissions data.

Retrieved August 18, 2020, from

https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data

U.S. Energy Information Administration (2020). U.S. primary energy consumption by energy source,

2019.

Retrieved

November

08,

2020,

from

https://www.eia.gov/energyexplained/

renewable-sources/

Wagner, M. (2005). How to reconcile environmental and economic performance to improve corporate

sustainability: corporate environmental strategies in the European paper industry. Journal of

Environmental Management, 76, 105-118.

Walley, N., & Whitehead, B. (1994). It's not easy being green. Harvard Business Review, 72, 46-51.

Wen, X., Guo, Y., Wei, Y., & Huang, D. (2014). How do the stock prices of new energy and fossil

fuel companies correlate? Evidence from China. Energy Economics, 41, 63–75.

White, M. A. (1995). The performance of environmental mutual funds in the United States and

Germany: Is there economic hope for green investors?. Research in Corporate Social Performance

and Policy, supplement 1, 323–344.

Wüstenhagen, R., Wolsink, M., & Bürer, M. J. (2007). Social acceptance of renewable energy

innovation: An introduction to the concept. Energy Policy, 35(5), 2683-2691.

Zhang, G., & Du, Z. (2017). Co-movements among the stock prices of new energy, high-technology

and fossil fuel companies in China. Energy, 135, 249–256.

(37)

36

Appendix A – ETF List

Green Energy ETFs

Ticker ETF Name

YLCO Global X YieldCo & Renewable Energy Income ETF FAN First Trust ISE Global Wind Energy Index Fund TAN Invesco Solar ETF

PZD Invesco Cleantech ETF

QCLN First Trust NASDAQ Clean Edge Green Energy Index Fund PBD Invesco Global Clean Energy ETF

ICLN iShares global Clean Energy ETF

GRID First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index Fund SMOG VanEck Vectors Low Carbon Energy ETF

PWND PowerShares NASDAQ OMX Clean Edge Global Wind Energy Index Fund (Inactive) KWT VanEck Vectors Solar Energy ETF (Inactive)

Note. This table presents a list of green energy ETFs with their corresponding ticker. Inactive ETFs are denoted as inactive in parentheses behind the ETF's name.

Traditional Energy ETFs

Ticker ETF Name

FRAK VanEck Vectors Unconventional Oil & Gas ETF XES SPDR S&P Oil & Gas Equipment & Services ETF OIH VanEck Vectors Oil Services ETF

IEZ iShares U.S. Oil Equipment & Services ETF PXJ Invesco Dynamic Oil & Gas Services ETF DUG ProShares Ultra Short Oil & Gas

DDG Short Oil & Gas ProShares ERY Dixerion Daily Bear 2X Shares IXC iShares Global Energy ETF IYE iShares U.S. Energy ETF

XLE Energy Select Sector SPDR Fund VDE Vanguard Energy ETF

XOP SPDR S&P Oil & Gas Exploration & Production ETF IEO iShares U.S. Oil & Gas Exploration & Production ETF PXE Invesco Dynamic Energy Exploration & Production ETF PXI Invesco DWA Energy Momentum ETF

DIG ProShares Ultra Oil & Gas

ERX Dixerion Daily Energy Bull 2X Shares FILL iShares MSCI Global Energy Producers ETF FENY Fidelity MSCI Energy Index ETF

GUSH Dixerion Daily S&P Oil & Gas Exploration & Production Bull 2x Shares DRIP Dixerion Daily S&P Oil & Gas Exploration & Production Bear 2x Shares CRAK VanEck Vectors Oil Refiners ETF

JHME John Hancock Multi-Factor Energy ETF FTXN First Trust Nasdaq Oil & Gas ETF USAI American Energy Independence ETF

(38)

37 MLPI UBS E-TRACS Alerian MLP Infrastructure ETN MLPG UBS E-TRACS Alerian Natural Gas MLP Index Fund AMLP Alerian MLP ETF

MLPY Morgan Stanley Cushing MLP High Income Index ETN MLPA Global X MLP ETF

AMU ETRACS Alerian MLP Index ETN IMLP iPath S&P MLP ETN

ATMP Barclays ETN+ Select MLP ETN

MLPX Global X MLP & Energy Infrastructure ETF MLPC Miller/Howard Fundamental MLP ETN ZMLP Direxion Zacks MLP High Income Shares AMZA InfraCap MLP ETF

MLPB ETRACS Alerian MLP Infrastructure Index ETN AMUB ETRACS Alerian MLP Index ETN

MLPO Credit Suisse S&P MLP Index ETN

MLPE C-Tracks Miller/Howard MLP Fundamental Index ETN Series B BMLP Dorsey Wright MLP Index ETN

ENFR Alerian Energy Infrastructure ETF EINC VanEck Vectors Energy Income ETF KOL VanEck Vectors Coal ETF

RYE Invesco S&P 500 Equal Weight Energy ETF FXN First Trust Energy AlphaDEX Fund

CHIE Global X MSCI China Energy ETF ERUS iShares MSCI Russia ETF

PSCE Invesco S&P SmallCap Energy ETF TPYP Tortoise North American Pipeline Fund

PKN Invesco Powershares Global Nuclear Energy Portfolio ETF (Inactive) UBN ETRACS CMCI Energy TR ETN (Inactive)

IOIL Index IQ Global Oil Small Cap ETF (Inactive) DCNG iPath Seasonal Natural Gas ETN (Inactive)

Note. This table presents a list of traditional energy ETFs with their corresponding ticker. Inactive ETFs are denoted as inactive in parentheses behind the ETF's name.

Conventional Equity ETFs

Ticker ETF Name

SPY SPDR S&P 500 ETF IVV iShares Core S&P 500 ETF VTI Vanguard Total Stock Market ETF VOO Vanguard S&P 500 ETF

QQQ Invesco QQQ

VEA Vanguard FTSE Developed Markets ETF IEFA iShares Core MSCI EAFE ETF

VWO Vanguard FTSE Emerging Markets ETF VUG Vanguard Growth ETF

IWF iShares Russell 1000 Growth ETF

IEMG iShares Core MSCI Emerging Markets ETF VTV Vanguard Value ETF

EFA iShares MSCI EAFE ETF

(39)

38 IJR iShares Core S&P Small-Cap ETF

IWM iShares Russell 2000 ETF

VGT Vanguard Information Technology ETF IWD iShares Russell 1000 Value ETF XLK Technology Select Sector SPDR Fund VO Vanguard Mid-Cap Index ETF

USMV iShares Edge MSCI Min Vol USA ETF IVW iShares S&P 500 Growth ETF

VB Vanguard Small Cap ETF

ITOT iShares Core S&P Total U.S. Stock Market ETF VYM Vanguard High Dividend Yield ETF

VEU Vanguard FTSE All-World ex-US ETF VXUS Vanguard Total International Stock ETF IWB iShares Russell 1000 ETF

XLV Health Care Select Sector SPDR Fund EEM iShares MSCI Emerging Markets ETF DIA SPDR Dow Jones Industrial Average ETF SCHX Schwab U.S. Large-Cap ETF

IWR iShares Russell Midcap ETF

IXUS iShares Core MSCI Total International Stock ETF SCHF Schwab International Equity ETF

QUAL iShares Edge MSCI USA Quality Factor ETF VV Vanguard Large Cap ETF

XLF Financial Select Sector SPDR Fund GDX VanEck Vectors Gold Miners ETF SCHB Schwab U.S. Broad Market ETF IVE iShares S&P 500 Value ETF

XLY Consumer Discretionary Select Sector SPDR Fund SDY SPDR S&P Dividend ETF

MDY SPDR S&P MidCap 400 ETF VT Vanguard Total World Stock ETF

XLP Consumer Staples Select Sector SPDR Fund VBR Vanguard Small Cap Value ETF

IWP iShares Russell Midcap Growth ETF RSP Invesco S&P 500® Equal Weight ETF DVY iShares Select Dividend ETF

ACWI iShares MSCI ACWI ETF SCHD Schwab US Dividend Equity ETF VGK Vanguard FTSE Europe ETF

SCHG Schwab U.S. Large-Cap Growth ETF XLU Utilities Select Sector SPDR Fund DGRO iShares Core Dividend Growth ETF

MTUM iShares Edge MSCI USA Momentum Factor ETF VHT Vanguard Healthcare ETF

(40)

39 SCHA Schwab U.S. Small-Cap ETF

EWJ iShares MSCI Japan ETF

VXF Vanguard Extended Market VIPERs ETF IWV iShares Russell 3000 ETF

IUSG iShares Core S&P U.S. Growth ETF MGK Vanguard Mega Cap Growth ETF

GSLC Goldman Sachs ActiveBeta U.S. Large Cap Equity ETF IWO iShares Russell 2000 Growth ETF

IBB iShares Nasdaq Biotechnology ETF VOE Vanguard Mid-Cap Value ETF

SPLV Invesco S&P 500® Low Volatility ETF FVD First Trust Value Line Dividend Index SPYG SPDR Portfolio S&P 500 Growth ETF SCZ iShares MSCI EAFE Small-Cap ETF ESGU iShares ESG MSCI USA ETF EFG iShares MSCI EAFE Growth ETF VOT Vanguard Mid-Cap Growth ETF IWN iShares Russell 2000 Value ETF SPDW SPDR Portfolio World ex-US ETF IHI iShares U.S. Medical Devices ETF TQQQ ProShares UltraPro QQQ

ARKK ARK Innovation ETF

SCHE Schwab Emerging Markets Equity ETF OEF iShares S&P 100 ETF

IJK iShares S&P MidCap 400 Growth ETF SCHV Schwab U.S. Large-Cap Value ETF SCHM Schwab US Mid-Cap ETF

IUSV iShares Core S&P U.S. Value ETF SPLG SPDR Portfolio S&P 500 ETF

GDXJ VanEck Vectors Junior Gold Miners ETF IYW iShares U.S. Technology ETF

NOBL ProShares S&P 500 Aristocrats VFH Vanguard Financials ETF EFV iShares MSCI EAFE Value ETF

ALFI AlphaClone International ETF (Inactive)

UBM ETRACS CMCI Industrial Metals TR ETN (Inactive) PTM ETRACS CMCI Long Platinum TR ETN (Inactive) GDXX ProShares Ultra Gold Miners ETF (Inactive)

(41)

40

(42)
(43)
(44)
(45)
(46)

Referenties

GERELATEERDE DOCUMENTEN

This is easy to understand if we know that the more crosstalk is present, the more power we need to increase the data rate and so the less effective that power becomes, leading to

This is easy to understand if we know that the more crosstalk is present, the more power we need to increase the data rate and so the less effective that power becomes, leading to

Fassbender in 2006 on the issue of due process, where it was held that the Security Council has to respect the fundamental rights and freedoms when carrying out all

If the thin-centred ideology of populism attached itself once to the full ideology of fascism, that does not mean that current Western European right-wing populists are

H3 SiCng on furniture made of warm material will lead to the experience of hospitality H4 SiCng on furniture made of warm material will lead to the experience of physical

Alhoewel ondernemingen door het toestaan van Papillon- en zusjes-fiscale eenheden in EU- en verdragssituaties wellicht minder worden belemmerd te gaan opereren buiten de

H2: The level of absorptive capacity of an energy incumbent positively moderates the relationship between green acquisitions and firm performance, such that incumbents with

This table reports the names of the six different ETFs with the S&P 500 as underlying index and publishes the levels of exposure, launch dates and