Double Bottom-Line? – The Financial
Performance of Green Energy ETFs
1
Abstract
2
Contents
1. Introduction ... 3
2. Literature Review... 6
3. Research Method ... 11
3.1. Sample ... 12 3.2. Performance ... 144. 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
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:
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.
5
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.
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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.
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.
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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.
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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.
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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
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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−1𝑃𝑡
)
( 1 )
where 𝑅
𝑙𝑜𝑔is 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.
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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,𝑡being the daily ETF return at time t, 𝑟
𝑖,𝑡the daily one-month T-bill rate at time t used as
risk-free rate,
𝛼
𝑓the average annualised four-factor adjusted return for fund f, 𝛽
𝑀𝐾the estimate of the
market factor, 𝑟
𝑖𝑛𝑑𝑒𝑥,𝑡the return of the applicable benchmark, and 𝜀
𝑓,𝑡being 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
𝑆𝑀𝐵𝑟
𝑡𝑆𝑀𝐵+ 𝛽
𝐻𝑀𝐿𝑟
𝑡𝐻𝑀𝐿+ 𝜀
𝑓,𝑡( 3 )
with 𝑟
𝑓,𝑡being the daily ETF return at time t, 𝑟
𝑖,𝑡the daily one-month T-bill rate at time t used as
risk-free rate,
𝛼
𝑓the average annualised four-factor adjusted return for fund f, 𝛽
𝑀𝐾the estimate of the
market factor,
𝑟
𝑖𝑛𝑑𝑒𝑥,𝑡the return of the applicable benchmark,
𝛽
𝑆𝑀𝐵the estimate of the size factor,
𝑟
𝑡𝑆𝑀𝐵the size factor return at time t, 𝛽
𝐻𝑀𝐿
the estimate of the value factor, 𝑟
𝑡𝐻𝑀𝐿the return of the value
factor at time t, and 𝜀
𝑓,𝑡being 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:
15
𝑟
𝑡𝑆𝑀𝐵the size factor return at time t, 𝛽
𝐻𝑀𝐿the estimate of the value factor, 𝑟
𝑡𝐻𝑀𝐿the return of the value
factor at time t, 𝛽
𝑀𝑂𝑀the estimate of the momentum factor, 𝑟
𝑡𝑀𝑂𝑀the return of the momentum factor
at time t, and 𝜀
𝑓,𝑡being 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
thand/or
12
thorder 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
thorder 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.
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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
−1𝜇
𝐾
)
−1𝛼̂ ′ 𝛴̂
−1𝛼̂
( 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
𝐾is 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.
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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
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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
2for 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)
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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.
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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
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
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
2when 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
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
1band 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
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
1cis 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
1cannot 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.
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
2astating 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
2bin 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
2cis 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.
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
2is 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.
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.
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
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.
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.
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.
32
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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
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
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
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)
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