European Green Mutual Fund Performance: A Comparative Analysis with their Conventional and Black Peers

Abstract

We conduct the first comparative analysis of the financial performance of European green, black (fossil energy and natural resource) and conventional mutual funds. Based on a unique dataset of 175 green, 259 black and 976 conventional mutual funds, the investigation contrasts the financial performance of the three dissimilar investment orientations over the 1991–2014 period. Over the full sample period, green mutual funds significantly underperform relative to conventional funds, while no significant risk-adjusted performance differences between green and black mutual funds could be established during the same period. Environmentally friendly investment vehicles display a significant exposure to small cap and growth stocks, while black funds are more exposed to value stocks. Remarkably, the green funds’ risk-adjusted return profile progressively improves over time until no difference in the performance of the green and the conventional classes could be discerned. Further evidence suggests that the green funds are beginning to significantly outperform their black peers, especially over the 2012–2014 investment window.

Full text

European Green Mutual Fund Performance: A Comparative Analysis with
their Conventional and Black Peers
GBENGA IBIKUNLE*
University of Edinburgh,
Centre for Responsible Banking and Finance, University of St Andrews and
1
Fondazione European Capital Markets Cooperative Research Centre (ECMCRC), Pescara
TOM STEFFEN
University of Edinburgh,
Macquarie University and
Capital Markets Cooperative Research Centre (CMCRC), Sydney
Abstract
We conduct the first comparative analysis of the financial performance of European green,
black (fossil energy and natural resource) and conventional mutual funds. Based on a unique
dataset of 175 green, 259 black and 976 conventional mutual funds, the investigation
contrasts the financial performance of the three dissimilar investment orientations over the
1991-2014 period. Over the full sample period, green mutual funds significantly
underperform relative to conventional funds, while no significant risk-adjusted performance
differences between green and black mutual funds could be established during the same
period. Environmentally friendly investment vehicles display a significant exposure to small
cap and growth stocks, while black funds are more exposed to value stocks. Remarkably, the
green funds’ risk-adjusted return profile progressively improves over time until no difference
in the performance of the green and the conventional classes could be discerned. Further
evidence suggests that the green funds are beginning to significantly outperform their black
peers, especially over the 2012–2014 investment window.
JEL classification: F30, G11, G15, G23, M14
Keywords: Green mutual funds, black mutual funds, conventional mutual funds, socially
responsible investments, risk-adjusted returns
* Corresponding author: University of Edinburgh Business School, 29 Buccleuch Place, Edinburgh EH8 9JS,
United Kingdom; e-mail: Gbenga.Ibikunle@ed.ac.uk; phone: +44 (0) 1316515186.
We thank an anonymous referee and the Finance Section Editor, Gary S. Monroe, for their constructive and
helpful feedback. We are grateful to Jo Danbolt, Bert Scholtens, Wenxuan Hou, Kenneth Amaeshi and
Francisco Ascui for helpful comments. All remaining errors are our own.

2
“Investment must be sustainable – delivering value not just financially, but also in social,
environmental and developmental terms… Our aim is to generate a critical mass of socially
responsible investors, entrepreneurs and businesses prepared to uphold social and
environmental principles… Increasingly, investors understand that embracing social,
economic and environmental responsibility does not mean sacrificing investment returns.”
UN Secretary General, Ban Ki Moon, speaking at the New York Stock Exchange on July 24th
2013
Introduction
The foregoing quote credited to a serving UN Secretary General underscores the growing
bullish mood regarding sustainable investing, or socially responsible investing/investment
(SRI) as it is more popularly known. Sustainable investing is generally defined as an
investment approach that considers environmental, social and responsible corporate
governance criteria in order to yield long-term competitive financial utility, as well as
favourable societal effect. For decades, following the work on optimal portfolio theory by
Markowitz (1952), the overwhelming assumption has been that investing in a restricted
universe of stocks implies the concession of the standard risk-reward optimisation.
Proponents of this argument therefore assume that SRI is based on convictions, such as
religious beliefs, generating additional non-financial compensations and not necessarily
optimising financial rewards (see Renneboog et al. 2008). However, with the increasing
popularity of SRI products since the 1980s, other evidence suggests that SRI investors are as
keen as conventional investors to make money on their investments (see for example,
Statman 2000). According to the Global Sustainable Investment Alliance, the global market
share in 2014 of SRI relative to total managed assets amounts to 30.2% ($21.4 trillion). More
than one in every six dollars under professional management in the United States at the start
of 2014 is invested in SRI portfolios – some $6.57 trillion. With over $13 trillion, twice the
number of investments in the U.S. and a regional market share of over 58%, Europe has
become the undisputed leader in SRI assets under management. This level of investment
suggests that SRI portfolios yield financially competitive returns.

3
In this paper, our analysis of sustainable investment performance focuses on green mutual
funds as a sub-set of SRI.
1
A green mutual fund is defined as one that makes investments
based on a sole commitment to environmental principles and engagements. A green mutual
fund therefore selects companies demonstrating exceptional environmentally friendly
conduct and/or low environmental impact. Companies selected by green mutual funds would
include those demonstrating exceptional environmentally friendly conduct and low
environmental impact, an involvement in natural resource protection, energy efficiency
projects, clean technology or alternative and renewable energy, as well as other
environmentally friendly pursuits.
We test a central hypothesis that the expected returns on green mutual funds are not different
in statistical terms from those of conventional mutual funds. This would imply that the
environmental responsibility of the stocks contained in the green funds is not priced.
Furthermore, given that environmental investment options have increased steadily over the
past two decades, and green investors and fund managers alike are becoming more
experienced, we expect to find the performance of green mutual funds improving steadily
over time relative to their conventional peers.
The second proposition that we test relates to comparative analysis of the performance of the
green and conventional funds against black funds. A black fund is defined as a mutual fund
investing in carbon intensive equities of entities involved in the exploitation and depletion of
our natural resources and natural capital. This definition also explicitly comprises
corporations involved in the extraction, facilitation, transportation, storage, processing, sale
and use of natural resources, and thus reaches from upstream drilling companies to

4
downstream utilities. Companies in the value and supply chain of the fossil fuel industry (oil,
gas and coal), the mining of minerals, ferrous, non-ferrous, and precious metals, as well as
other raw materials would therefore be included in a black mutual fund. Recent events, such
as the divestment from coal by the French financial services multinational AXA and the
Norwegian Sovereign Wealth Fund, suggest that the awareness of climate change impact has
led to attempts by some investors to withdraw from undertakings with significant ties to
fossil fuels (see Ansar et al. 2013), or at the very least price in the externalities of fossil fuel
consumption. Governments around the world and at the United Nations level have enacted
legislations to reduce greenhouse gas (GHG) emissions from fossil fuel sources. As part of
the efforts to reduce GHG emissions, market mechanisms have been established to price the
hitherto unpriced cost of fossil fuel consumption (see among others Daskalakis et al. 2011).
Increasingly, investors are demanding that oil majors address the question that between 60 –
80% of their unexplored assets could become stranded, while multiple stakeholders are
campaigning for outright fossil fuel divestment (see Ansar et al. 2013). We propose that the
impact of these risk factors will lead to reduced risk-adjusted returns for black funds, such
that green and conventional funds will start to outperform black funds over time.
An implicit assumption in our analysis is that the investment driver for SRI investors is
similar to that of non-SRI investors – financial utility. This assumption is consistent with the
SRI financial performance analysis literature (see for example, Climent and Soriano 2011).
Notwithstanding the established approach employed, this paper makes significant
contributions to the literature. This paper is the first to conduct a comparative financial
performance analysis on European green, conventional and black mutual funds. Based on a
unique dataset of 175 green, 259 black and 976 conventional mutual funds, the investigation
contrasts the financial performance of the three dissimilar investment orientations over the

5
1991-2014 period applying a CAPM-based methodology. We find that the green funds’ risk-
adjusted return profile improves over time until no statistical difference in the performance of
the green and conventional classes can be identified. Additionally, we confirm that in recent
years green funds significantly outperform their black peers; the underperformance of black
funds appears to be linked with increasing environmental risk factors. We also report firm
exposure asymmetry between green and black funds such that green mutual funds show a
significant exposure to small cap and growth stocks, while black funds are more exposed to
defensive traditional value stocks.
The catalyst of investments in green mutual funds could be behavioural, with respect to
investors’ ethical convictions, or purely economic (Renneboog et al. 2008). Early
environmental endeavours such as renewable energy and energy efficiency developments
supported by both private and public entities appear to have mostly been driven by non-
financial utility, as a result of social and political pressure. For example, some forms of
energy generation such as wind and solar were rarely competitive with conventional energy
generation. However, over time renewable energy technologies for instance have established
themselves both as competitive sources of energy, and as critical components of national
energy mixes (see Aguirre and Ibikunle 2014). They have, to a large extent, become
economically viable alternatives to established fossil fuel energy sources. In addition to this,
the environmental orientation of businesses can translate into advantages such as the
exploitation of revenue enhancing and cost reducing low carbon investment opportunities
(Ambec and Lanoie 2008; Porter and Linde 1995). Consequently, investment in green
portfolios is increasingly justified by qualified economic and financial arguments. In this
paper, we avoid focusing on behavioural considerations and non-financial utility driving

6
investment in green funds. Rather, we focus only on the financial performance of the three
fund classes enumerated in the preceding paragraph.
While Luther et al. (1992) are the first to examine the performance of ethical UK unit trusts
over an index benchmark, the beginning of comparative SRI mutual fund studies is marked
by Hamilton et al. (1993), who conduct a simple regression analysis comparing the risk-
adjusted return profile of socially responsible investments (SRI) and conventional mutual
funds. Since then other studies have followed suit, however overall one notices that most
focus on the U.S. mutual fund market, rest upon relatively small sample sizes, and analyse
short time spans (Kreander et al. 2005). Furthermore, only very few dissect the different SRI
dimensions and thus focus on the broad effect of the environmental, social, and governance
(ESG) principles on mutual fund performance (see Renneboog et al. 2008). The blending of
multiple ethical notions under the concept of SRI mutual funds (Galema et al. 2008), and the
on-going promising development of environmentally conscious investment vehicles, favour a
dissection of the various SRI components to allow for isolated studies. The anticipated
strengthening of the principles behind green investment vehicles by substantiated self-
contained economic advantages gives ground to expect intriguing insights through the
adoption of a more differentiated and focused analysis. Specifically, the environmental
dimension of mutual funds, which is of particular interest to this study, has only been picked
up by White (1995), Climent and Soriano (2011) and Ito et al. (2013), who again majorly
focus on the United States. The literature on purely green mutual fund studies is therefore
very limited.
Climent and Soriano (2011) find that for the full period from 1987 to 2009, U.S.
environmental funds underperform their conventional matched counterparts. This is

7
consistent with White (1995), who finds that environmental mutual funds underperform the
general U.S. market (S&P 500) and the U.S. SRI Domini index. Interestingly however, for
the more recent time period from 2001 to 2009, Climent and Soriano (2011) find that no
significant differences in the risk-adjusted returns of the green fund portfolio, compared with
the more generic SRI and conventional mutual funds, can be determined. The authors suggest
that green funds underwent a catch-up phase and do not necessarily now come at a cost to
investors. The initial underperformance of green mutual funds can possibly be explained by a
constrained investment set, therefore it may be inappropriate to use a broad measure such as
the S&P 500 (White 1995). After adjusting the model to an environmentally friendly
benchmark such as the FTSE KLD Global Climate 100 Index, Climent and Soriano (2011)
could not identify any statistical performance differences between the green and conventional
mutual funds. Likewise, one can assume that the environmental investment opportunities, and
therefore the green stock universe, will grow over time, allowing investors to achieve returns
similar to conventional funds. In consequence, it is reasonable to focus on the results of the
more recent sample period for which green, SRI and conventional funds can be considered as
equally established investment vehicles.
Ito et al. (2013) apply the single factor CAPM model, and show that the Jensen (1968) alpha
is statistically significant for only very few SRI and green funds. The authors also employ an
innovative and benchmark-independent technique, simultaneously handling fund risk and
return, to identify possible changes in the outcomes. They suggest that, in terms of
consistency, their approach is superior to the frequently applied CAPM-based analysis.
Applying the dynamic mean-variance model developed by Briec and Kerstens (2009), Ito et
al. (2013) find that SRI funds significantly outperform their conventional counterparts in the
U.S. and especially the EU, while the green funds demonstrate slightly inconclusive results

8
with equivalent or slightly superior risk-adjusted returns. In comparison to the overall longer
term (2000 – 2009), the performance of the environmentally friendly funds decreases in the
U.S. over the more recent time period of 2006 – 2009. The authors link this unexpected
finding to a liquidity shortage caused by the global financial crisis, posing a greater threat to
environmentally related businesses, as investors misevaluate the long-term benefits of
responsible investments.
Other studies, such as Derwall et al. (2005:52), classify stocks within self-composed equity
portfolios based on “the economic value a company adds relative to the waste it generates
when creating that value” (eco-efficiency), thereby isolating the environmental aspect of
social responsibility. Derwall et al. (2005) show that the high-ranked portfolio significantly
outperforms its low-ranked counterpart, for firms in the U.S. from 1993 to 2003. Thus, no
penalty is incurred for holding a green portfolio. While Derwall et al. (2005) acknowledge
that direct investigations of historic mutual fund returns yield interesting insights into the
practical implications of SRI and green investments, they highlight the limitations of these
studies by pointing out distortionary influences such as the fund management’s skill,
undisclosed holdings, and diverging screening methods. Furthermore, mutual fund studies
impede the determination of a SRI/green premium or penalty due to the symbiotic effects of
stock holdings occurring in the portfolios of both SRI/green and conventional mutual funds.
2
Derwall et al. (2005) also challenge the origins of the eco-efficiency premium and ask how it
fits into the classical finance theory. It is difficult to explain the eco-efficiency premium with
the help of the classical risk versus return argument, and this may suggest a mispricing by the
market. A lower eco-efficiency ranking rather implies a higher corporate risk, thus
incentivising investors to require a higher return.

9
Ziegler et al. (2007) investigate both the implications of the broader sustainability dimensions
and the specific environmental aspect on European stocks between 1996 and 2001 using self-
composed equity portfolios. Applying a CAPM-based methodology, Ziegler et al. (2007) find
that an industry’s average environmental performance has a significantly positive impact on
stock performance, while an industry’s average social performance has a significantly
negative influence on stock performance. The relative social or relative green corporate
conduct of a firm within its industry however has no significant effect on financial market
performance. According to Ziegler et al. (2007), a buy-and-hold strategy focusing on clean
industries with a good average environmental performance generates a superior portfolio
value, whereas the same strategy focusing on industries with an exceptional average social
performance decimates portfolio value. Selecting stocks according to their relative social or
environmental performance within a given industry (also known as best-in class approach)
will not yield any positive abnormal returns. However, no negative abnormal returns will be
experienced by this approach either. Thus, exposure to corporations with heightened
environmental or social commitments does not come at a cost, and can therefore be
increased. Subsequently, Ziegler et al. (2011) show that composing a portfolio by taking a
long position in European companies with established corporate climate impact disclosures,
and shorting those with no disclosure practices, pays off. The same holds for U.S. energy
firms.
Interestingly, Ziegler et al. (2007) note that corporations with the highest environmental
performance often demonstrate a lower social performance (e.g. banking and insurance). This
may explain why SRI mutual fund studies in general yield confounding and still inconclusive
results. The disparity in the green and social rankings of companies may suggest that the
abovementioned negative financial impacts of socially conscious investments cancel out the

10
positive financial effects of environmentally oriented investments. Furthermore, Galema et al.
(2008) suggest that the inconclusiveness of the results may also be due to negative
interrelations between the sub-dimensions of SRI mutual funds (e.g. environmental
considerations vs. stakeholder relations), which may offset one another. SRI aggregates the
different ESG dimensions, which possibly have confounding effects thereby prohibiting an
explicit superior/inferior performance of sustainable investments.
Overall, it is still not clear whether environmentally responsible investments allow investors
to generate comparable or superior risk-adjusted returns. Clarification on this issue is
important given the increased environmental awareness and activism of some investors and
stakeholder societies in general. Furthermore, the recent and increasingly vociferous
campaign against fossil fuel investment is bound to increase the riskiness of black fund
investments, thus necessitating an examination of the risk-adjusted performance of black
funds against their conventional and green peers. This paper’s main contributions are in these
two aforementioned respects. To begin with, this is the first paper to conduct a comparative
performance analysis of green mutual funds against their conventional and black peers.
Second, the competitiveness of black fund investments in relation to green funds is examined
against the backdrop of increased societal and investor environmental awareness over time.
In addition, the green fund sample size of 175 is by far the largest to have been examined in
any study focused on the comparative performance of green mutual funds and their peers.
Potentially, this allows for drawing statistically stronger insights into the analysis of the
performance of green mutual funds. To our knowledge only Climent and Soriano (2011)
conduct a similar fund level analysis, focusing on environmental mutual funds in the US.
Their study contrasts the performance of 7 green, 14 matched SRI and 28 matched
conventional investment vehicles. Other studies mainly investigate the broader SRI fund

11
universe. For example, Renneboog et al. (2008) examine the performance of 440 SRI and
16,036 conventional mutual funds.
The remainder of this paper is structured as follows: the following data section describes the
mutual fund data set employed in this study; the third section sets out the methodology used;
the fourth section presents the results from our empirical analysis; and the fifth section
concludes.
Data
In this section, we outline the data collection process of the three mutual fund classes and the
market benchmarks used to conduct the empirical analysis. In addition, the data quality
control process is presented in some detail. The return data collected for the mutual funds is
the Total Return Index, which includes dividends (net income) and other distributions
realised over a given period of time by assuming that all cash distributions are reinvested.
Mutual fund dataset
The sample period of the comparative performance analysis covers the time span from
January 1991 to June 2014, incorporating 282 months or approximately 23 years of fund
data. Table I summarises the main mutual fund selection criteria and quality screens applied
to compose the final data set. In order to evaluate the performance of solely green mutual
funds and compare them to their black and conventional peers, the three distinct classes are
segmented from the overall mutual fund universe. The Thomson Reuters EIKON fund
screener allows us to narrow down the global fund universe and focus only on the fund
classes that are of interest. We target funds domiciled in the European Economic Area (EEA)
and Switzerland (CH)
3
. We also include the British administration areas of Gibraltar,

12
Guernsey, Isle of Man and Jersey, given their close connections with the EU. Finally, we
include funds from Andorra and Monaco for similar reasons. For comparability with previous
US-based studies, we also follow Climent and Soriano (2011) in employing only open-ended
mutual funds, labelled as primary funds, and whose asset class is equity.
INSERT TABLE I ABOUT HERE
Green Mutual Funds
EIKON’s theme/strategy screen allows us to narrow down the universe of interest to ethical
funds, but there is no similar option for a purely green fund classification in the database. The
ethical filter, however, helps to substantially reduce the fund population to only incorporate
the entities of potential relevance for the performance analysis. Yet this broader screen
includes all mutual funds which are categorised as ethical investments to some extent and
thus focus on at least one aspect of the ESG principles. Hence, green mutual funds are
captured by the ethical screen but are mingled with other funds. We therefore manually filter
the funds to exclude non-green mutual funds by reviewing the official investor documents
and fund holdings received from publicly available mutual fund databases, or the issuers
themselves. The majority of the green mutual funds are selected based on the clear
descriptions of the investment policies in the official fund documents. In cases where no
detailed description/information is available, the funds are excluded in order to ensure the
quality of the data.
4
No evaluation of ‘how green is green’ is undertaken, and the paper does
not intend to participate in the discussion questioning the environmentally friendliness of
these funds as long as the description provided in the database explicitly shows that the fund
invests exclusively in green stocks. Finally, the EIKON ethical screen is tested for
completeness. In order to ensure that green mutual funds that are not labelled as ethical are, at
best, all included in the final sample, the complete European equity mutual fund universe is
searched for environmentally related catchwords.
5
Further examination of fund documents is

13
then conducted. The applied screening process results in the identification of 175 active,
liquidated and merged green mutual funds domiciled in 21 different countries, with the first
one having been issued in 1984. 113 of these funds are ‘Live’ and the remaining 62 are
‘Dead’.
Black Mutual Funds
Analogously to the procedure for green mutual fund selection, the primary equity mutual
fund universe is sifted for fossil energy and natural resource funds. The completeness of the
sample is further enhanced and assured through a manual search of the entire European
equity mutual fund universe for specific fossil energy and natural resource related keywords.
6
In order to ensure that we build a purely black mutual fund sample, the official investor
documents and fund holdings are scrutinised. The applied screening process identifies 259
active, liquidated and merged black mutual funds domiciled in 21 different countries, with
the first one being issued in 1965. There are 150 ‘Live’ and 109 ‘Dead’ black mutual funds in
the sample.
Conventional Mutual Funds
With regard to Carhart (1997), the fund population is set to solely include diversified equity
mutual funds. For this reason, the Lipper Global Classification Scheme is utilised to
exclusively account for funds not being overly restricted by their investment policy. Mutual
funds, which apply sectoral and/or specific geographical constraints, are therefore excluded.
7
The first phase of the screening process leads to the reduction of the equity mutual fund
universe to roughly 7,000 entities. However, in order to improve the integrity of the data,
through the application of the data quality checks described in the next section our final
sample is made up of 976 active, liquidated or merged conventional mutual funds, domiciled

14
in 23 countries. The first fund was issued in 1959. Of these funds, 586 are ‘Live’ and 390 are
‘Dead’.
Data Quality Assurance
For all three mutual fund classes, the quality of the data is assured by accounting for several
probable sources of distortions. Firstly, consistent with Renneboog et al. (2008), fixed-
income, money-market, mixed and balanced funds are excluded. Guaranteed, protected,
alternative strategy and absolute return funds are also excluded from the final sample (see
Bauer et al. 2005).
8
At this point, it should be noted that the samples enclose only funds listed
as ‘primary’ by the EIKON screener and are cleaned manually for same-class or multi-
country listings. Following Statman (2000), the youngest or the smallest of the daughter
funds are excluded from the samples. Secondly, mutual funds for which no or limited data is
available via Datastream are excluded from the three fund families of interest. Likewise,
mutual funds with less than 12 months of data are eliminated (cf. Bauer et al. 2005; Climent
and Soriano 2011). Thirdly, not only active mutual funds but also liquidated and merged
mutual funds are included, in order to avoid a survivorship bias in the final sample (see
Brown et al. 1992). Following Bauer et al. (2005), all funds closed between 1991 and 2014
are added back to the samples. The respective data is collected from the Datastream ‘Dead’
mutual fund dataset. In addition, the return data of dead funds is included in the samples until
they are merged or liquidated. Accordingly, the total return index (RI) data of the dead
mutual funds is cleaned for stale price data to solely include return information up to the
point at which the fund is liquidated.
Market Benchmarks and Factor Portfolios

15
The factor portfolios are acquired from the Kenneth R. French data library. The data file
contains the four factor proxies (MKT, SMB, HML, MOM) and the risk-free rate discussed
in the methodology section. Due to the majorly international investment orientation of the
mutual funds contained in the three samples, this paper relies on the Kenneth R. French
global factors to conduct the main analysis. However, half of the conventional mutual funds
have a European investment orientation
9
(see Table II). In a second step several robustness
investigations, using the European factor portfolios, are performed in order to account for
potential distortions. The risk-free rate is the U.S. one-month T-bill rate for both the global
and the European factor portfolios. Additionally, due to a possible small company effect
experienced by the green mutual funds (see Luther et al. 1992; Gregory et al. 1997), the
analysis is extended by using alternative market proxies – a small cap market proxy (FTSE
Global Small Cap Index) and specialty indices. For instance, the green mutual fund
estimations are repeated using the S&P Global Alternative Energy Index as market
benchmark. Correspondingly, the black mutual fund regressions are computed once more
based on the S&P Global Natural Resources Index.
10
Methodology
Previous studies in this literature stream apply either of two approaches in comparing the
performances of ethical and conventional funds. The first is a comparison of the means of
groups, and the second is a matched-pair analysis approach; in this paper, we present results
for the former for two main reasons. Firstly, in the matching procedure a large amount of
valuable monthly return data is lost. Even though matched funds are very similar in age and
size, the matched-pairs return data does not overlap completely and thus the non-contrasted
monthly returns are wasted. Given this loss of vital data, our aim of comparing the evolution
of mutual funds returns over time is not best served by using the matched-pair analysis.

16
Secondly, for the three classes, the end of period (2014) total asset value is not fully available
for merged and liquidated funds via the Datastream dead mutual fund file, while the return
data is fully retrievable. Including merged or liquidated funds in the matching procedure is
therefore not possible (as end-of-period fund size is a matching criteria), and hence, if we
apply the matched-pairs analysis, the study will suffer from survivorship bias.
11
Our
econometric methodology is based on unbalanced random panel data regressions. The
approach stems from the CAPM-based (see Sharpe 1964; Lintner 1965) Jensen (1968) alpha
measure, which captures the risk-adjusted average abnormal return in excess of a market
benchmark. However, given criticisms that the 1-factor CAPM framework does not
sufficiently explain the expected stock returns (see as an example, Fama and French 1992),
we also employ the multi-factor framework as proposed by Carhart (1997), which is a further
extension of both the original CAPM and the Fama and French (1992) 3-factor model. Over
time, the model has become the standard for evaluating mutual fund performance in finance
literature. For an introduction to the single CAPM, please refer to Jensen (1968).
If the return, r, on fund i in month t is given as:
ri,t = ln(RIi,t) – ln(RIi,t-1), (1)
then, the excess return (rei,t ) of fund i in month t is computed by subtracting the risk-free rate
from the monthly fund return as in (2):
rei,t = ri,t – rf,t (2)
The excess market return (rm,t – rf,t) is also calculated by subtracting the monthly risk-free rate
from the respective market return. The market return is calculated on a value-weighted
regional (or global) market portfolio.
The Carhart (1997) 4-factor model takes the following form:
(3)
titmomMOMithml
HML
itsmbSMBitftmMKTiitfti rrrrrrr ,,,,
,
,,,,,,, )( 

17
where
MKT
i,

is the coefficient measuring the market-risk exposure of fund i;
corresponds to the coefficient measuring the small firm effect of fund i, is the return
spread between a small cap portfolio and a large cap portfolio at time t; is the
coefficient measuring the value premium of fund i, is the difference in return between a
value stock portfolio (high book-to-market ratio) and a growth stock portfolio (low book-to-
market ratio) at time t; is the coefficient measuring the momentum impact of fund i,
and is the difference in return between a portfolio of past 12 months winners and a
portfolio of past 12 months losers at time t. Momentum is included in order to account for the
effect of differences in investment strategy on performance (see Carhart 1997). The intercept
, also called Jensen’s (1968) alpha, encapsulates the out- or under-performance of fund i
relative to the factor portfolios. As an extension to the Carhart (1997) 4-factor model, we
control for performance differences between the fund classes by implementing a dummy
variable framework. The performance of the individual fund classes is contrasted against
each other through the use of dummy variables controlling for the ‘green’, ‘black’ or
‘conventional’ classification of the funds, in order to determine an eventual over- or
underperformance by one group; the dummy augmented model is as follows:
(4)
where is the coefficient measuring the effect of the affiliation to class x on fund i, and
is a dummy variable taking the value 1 if the fund belongs to class x (green, black or
conventional) and 0 otherwise. The CAPM-based regressions are also modelled to include the
dummy variables as shown in Equation (5):
(5)
SMBi,

tsmb
r,
HML
i,

thml
r,
MOMi,

tmom
r,


i
ti
class
CLASSitmomMOMithml
HML
itsmbS MBitftmMKTiitfti x
Drrrrrrr ,,,,,
,
,,,,,,, )( 
classi ,


Dx
class
ti
class
CLASSitftmMKTiitfti x
Drrrr ,,,,,,, )( 

18
Finally, in order to avoid the under-estimation of standard errors which could lead to
overconfidence, and to account for residual correlation across the different funds, we also
employ Beck and Katz’s (1995) Panel Corrected Standard Errors (PCSE). This method
involves using a panel OLS estimation procedure to determine parameter estimates, but
replacing the OLS standard errors with PCSE. This allows for the violation of the assumption
that is iid (in Equations 3–5); thus can be contemporaneously correlated and
heteroscedastic across the instruments, and time series autocorrelation within the regressors is
also permitted.
Results and Discussion
Summary statistics
Table II reports the summary statistics pertaining to the green, black and conventional mutual
fund samples. With 175 entities, the green sample is the smallest of the three fund types.
Furthermore, green funds are the youngest by average fund age (9 years) and are also the
smallest by average fund size, with just $73.5M of assets under management, compared to
the black (average age: 11 years) and conventional (average age: 12 years) funds with sample
sizes (fund average total asset value) of 259 ($134.8M) and 976 ($221.1M) respectively. For
the greater part, the green (82%) and black (88%) mutual funds adopt an international
investment approach, whereas the geographical scope of the conventional sample is
accurately balanced between a generic global and European orientation (50% each).
12
INSERT TABLE II ABOUT HERE
From 1991 to 2014 the green mutual funds earned an average annualised return of 4.06%,
which is lower than the 4.53% and 5.38% return gained on their black and conventional peers
respectively. While the black funds (18.56%) are the most risky, the annualised standard
ti,

ti,


19
deviations suggest that the green mutual funds (17.47%) are more volatile than their
conventional counterparts (16.21%). One reason for the relatively less risky nature of the
conventional funds is the fact that there is very little or no restriction in the fund equity
investment avenues. Thus, managers enjoy the benefits of full portfolio diversification.
CAPM (Single-factor) Regression Results
Global Benchmark
In Table III, we present the CAPM results for each of the three mutual fund samples using
the Kenneth R. French global factor as market benchmark. The very broad global market
proxy is used since a significant proportion of the fund classes have mainly international
investment orientations, as shown in Table II.
13
The first observation from the results is that
the green and conventional fund classes significantly underperform the market benchmark
over the full period of investigation; both risk-adjusted estimates of -7.28% for the green
funds and -3.05% for the conventional funds are statistically significant at a 0.01 level.
Notably, the result obtained for the black funds (-2.48%) is inconclusive and not statistically
significant. The high beta of all three classes is related to the selection of a very broad global
market proxy, which slightly overstates the sensitivity of the fund returns to the market risk.
We subsequently address this through the application of a narrower market benchmark (see
Table IV). Still on the global benchmark, interestingly, with a beta of 1.14, the green mutual
funds are the most sensitive to market risk, implying high correlation with the market and
heightened risks due to greater volatility (as evidenced by the standard deviation estimates in
Table II) and reduced diversification opportunities. Other studies confirm that renewable
energy stocks often rank in the high-beta segment (see Henriques and Sadorsky 2008; Bohl et
al. 2015). Furthermore, the model best fits the green mutual fund returns (R2ADJ = 0.73). This
is surprising as the global market proxy from the Kenneth R. French data library is applied,

20
which is initially expected to best explain the diversified conventional mutual fund return
variations. The model is least suitable to explain the performance behaviour of the black
mutual funds (R2ADJ = 0.55), which seems realistic as the fossil energy and natural resource
industries are influenced by a number of other economic and political factors as well.
In Panel B of Table III, the performances of all three mutual fund portfolios are contrasted
against each other using dummy variables, as detailed in Equation (5). The results suggest
that neither green nor conventional mutual funds significantly underperform their black peers
over the 1991-2014 period. Interestingly, the black mutual funds are unable to significantly
outperform their green counterparts over the full period. We anticipated statistically
significant superior abnormal risk-adjusted returns of the black class, as the period of
investigation includes the early pre-Kyoto time span from 1991 to 1998, an epoch
characterised by the fossil energy boom. However, this initial intuition cannot be empirically
confirmed. The findings also hint at the conventional funds outperforming their green
counterparts, with the estimated coefficient measuring the green funds’ underperformance (-
3.62%) being statistically significant at the 0.05 level.
Even though the black class is the only class which does not significantly underperform the
broad global market index, there is no imperative inversion of the argument requiring the
black class to then outperform the green and conventional mutual fund class. While the black
mutual funds perform better than the conventional mutual funds compared to a broad global
market index, it is the conventional fund class, and not the black class, which significantly
outperforms the green class at the 0.05 level of statistical significance. Indications as to the
possible reasons may be found in the explanatory power of the global market factor, which is
a less appropriate proxy for the abnormal black mutual fund returns (R2ADJ = 0.55) than for

21
the other two classes (R2ADJ = 0.73 and 0.65). Additionally, green ( = 1.14) and
conventional ( = 1.08) funds are more sensitive to market risk than the black funds (
= 1.02), explaining their inferior performance compared to the global market index.
These findings underline the intuition that black mutual fund returns are influenced by
various other economic and political factors as well, not captured by the standard equity
index.
INSERT TABLE III ABOUT HERE
European Benchmark
Now we re-estimate the CAPM and Equation (5) using the less broad Stoxx Europe 600 Index
over the 2001-2014 period; the results are presented in Table IV.
14
The main observation in
Panel A is that the results obtained in Table III are largely supported, with the overall
performance of all fund samples improving slightly. The outperformance of the conventional
over the green mutual funds also decreases to -2.67%, and is now only statistically significant
at the 0.10 level. Overall, the R2ADJ increases for all three classes, while the change is most
obvious for the conventional funds (0.70). This indicates that the European investment scope
of half of the conventional mutual funds significantly affects the estimation outcomes. While
the sensitivity to the market exposure has decreased for the three classes compared to the
results in Table III, the pecking order remains identical to the previous findings.
INSERT TABLE IV ABOUT HERE
As mentioned by Climent and Soriano (2011), the estimations of the CAPM regression using
a broad market proxy may distort the results, as the environmentally friendly screens
substantially reduce the investment set available to green mutual funds. Additionally, the
development of the low carbon and renewable energy industry was driven by an overall
global bullish stock market, rising prices of high technology shares and soaring oil prices
MKT

MKT

MKT


22
from the turn of the millennium until the global financial crisis (see Henriques and Sadorsky
2008; Kumar et al. 2012; Bohl et al. 2015; Sadorsky 2012; Inchauspe et al. 2015; Bohl et al.
2013; Managi and Okimoto 2013). The worsening state of the global economy thereafter
lands the industry in substantial difficulties. According to Bohl (2015:194): “fierce
competition and excess supply from Asian manufacturers have taken their toll on the sector's
profit margins since the late 2000s. […] Following the outbreak of the global financial and
economic crisis, prices of alternative energy stocks plunged as quickly as they had risen,
resulting in an almost hump-shaped performance pattern.” This development has a
potentially negative effect on the performance of the green mutual fund class. In order to test
for these presumptions, the analysis is repeated using the S&P Global Alternative Energy
Index and the results are presented in Table V. As the index was only introduced in 2003, the
period of investigation has to be reduced. As shown in Table V, the green fund portfolio now
tends to demonstrate superior abnormal risk-adjusted returns of 3.58%, although they are not
significantly different from the environmental index. Also, there is no evidence of significant
performance differences between the mutual fund categories any longer. Interestingly, the
index does not substantially increase the estimation of the R2ADJ for the green fund portfolio
(0.75), which contradicts the findings on the U.S. market by Climent and Soriano (2011).
However, Bauer et al. (2005) report similar results, noticing that standard indices are more
suitable for explaining SRI fund returns. In any case, the usefulness of a green speciality
index to compare the financial performance of three distinct mutual fund classes is
questionable. All three investment orientations pursue very diverse goals, yet all are assumed
to share the same financial performance maximisation ambition, and therefore the only
objective reference value for an inter-class comparison is a diverse standard equity index.
INSERT TABLE V ABOUT HERE
Small Cap Effect

23
With regard to the small company effect established by Luther et al. (1992) and Gregory et
al. (1997), the sole purpose of the utilisation of the FTSE Global Small Cap Index is to
specify whether the green fund portfolio experiences an enhanced tendency towards small
capitalisation stocks. The investigation is limited to the 1994-2014 period, and the results are
presented in Table VI. Most remarkably, with a value of 0.51, the green mutual fund sample
exhibits by far the highest R2ADJ, while it remains at equally low levels for the black (0.31)
and conventional (also 0.31) class. Equivalently, the green mutual funds are the most exposed
to the market sensitivity measure βMKT, of the small cap index, showing a value of 0.67.
INSERT TABLE VI ABOUT HERE
Multi-factor Regression Results
Tables VII and VIII summarise the results of the estimation of Equations (3) and (4). Table
VII shows the estimation results of using a global market proxy, while Table VIII presents
the outcome of applying a European benchmark.
INSERT TABLES VII AND VIII ABOUT HERE
In contrast to the 1-factor model, all mutual fund classes significantly underperform the
market benchmark from 1991 to 2014. The main difference to the CAPM results is found in
the significantly inferior risk-adjusted returns of the black mutual funds. However, the three
additional factors do not notably increase the R2ADJ of the model for any of the three classes,
although, with a value of 0.68 (Table VIII), the European multi-factor model is better able to
explain the behaviour of the conventional mutual fund returns. Corresponding to the findings
of the CAPM-based computations, the green mutual funds experience the highest exposure to
market risk for the global ( = 1.15) as well as European ( = 0.99) proxy. The
lower European beta estimations can be attributed to the less comprehensive stock universe
used to compute the European market proxy. Contemplating the global factor estimations, it
MKT

MKT


24
is interesting to note that the green ( = 0.38) as well as black ( = 0.41) mutual funds
experience a significantly enhanced exposure to small cap stocks, which corresponds to
earlier findings. In general, responsible investment-related screening processes are more
inclined to exclude large capitalisation stocks (see Cortez et al., 2012).
Analogous to Bauer et al. (2005), Cortez et al. (2009) and Cortez et al. (2012), the green fund
portfolio shows a statistically significant tendency towards growth stocks in both the global
and European scenarios; shows values of -0.09 and -0.22 at the 0.1 and 0.01 levels
respectively. The results thus confirm earlier studies, suggesting that responsible funds have a
tendency towards fast growing and small cap companies as they often invest in
environmental and clean tech avant-gardes (cf. Luther et al. 1992; Gregory et al. 1997;
Kreander et al. 2005). Moreover, value stocks often bear larger environmental liabilities
(Cortez et al. 2012). By contrast, black mutual funds are significantly invested in value stocks
with a coefficient of 0.2 for the global benchmark. In line with these findings, Bauer et
al. (2005:1762) note that “the high proportion of growth stocks may lie in the exclusion of
traditional value sectors like chemical, energy and basic industries”. These sectors are
particularly ignored by green funds, but actively included in the portfolio holdings of black
funds. The stocks of the avoided sectors are often considered as defensive equities, and tend
to experience less market risk and, therefore, show lower market betas than aggressive
stocks. Naturally, black funds show a higher exposure to high book-to-market defensive
equities (for example gas, electric and utility stocks). The fact that green mutual funds
exclude defensive traditional value stocks from their portfolios may also help to explain the
higher market sensitivity ( ), leading to higher return volatility and inferior risk-adjusted
returns.
SMB

SMB

HML

HML



MKT

25
The green trend may also help to explain the growth stock bias experienced by
environmentally conscious stocks over the last two decades. This argument is supported by
the fact that, between 2002 and 2008, the green funds are heavily exposed to growth stocks.
15
The period was characterised by rising oil prices – which are widely accepted as a driver of
the financial performance of renewable energy companies as high fossil energy prices incite
the use of cleaner alternatives (see Henriques and Sadorsky 2008) – and heightened social,
political and economic climate change action (see Aguirre and Ibikunle 2014). Examples of
significant events during the period include the introduction of Phase I of the EU-ETS or the
Kyoto Protocol entering into force, thus leading to the introduction of Kyoto market
mechanisms such as the Clean Development Mechanism and International Emissions
Trading. These events potentially led to an enhanced demand for clean stocks, driving up
their market value. Again, this confirms the ‘sin stock theory’ (see Galema et al. 2008; Hong
and Kacperczyk 2009) and the consequences of an excess demand for responsible stocks, and
a shortfall in demand for ‘sin’ stocks, on their pricing and returns. Specifically, Hong and
Kacperczyk (2009) show higher book-to-market ratios and higher excess returns for the
disgraced stocks.
It is also important to note that the momentum factor has significant implications for the
black mutual fund returns ( = 0.11 in Table VII). This coefficient estimate seems
realistic in light of the defensive stock exposure of black funds (amongst others, gas, electric
and utility stocks), given that these stocks experience lower variability and are less sensitive
to market fluctuations. Hence, their performance is more stable during times of economic
hardship and less impacted by major economic cycles. For this reason it is more likely that
‘good follows good’ for the winner stocks in the black fund portfolio.
MOM


26
As with the CAPM-based regressions, neither the green nor the conventional mutual funds
significantly underperform their black counterparts over the 1991-2014 period. Nevertheless,
the evidence of an outperformance of the green by the conventional fund portfolio remains
statistically significant, with values of -3.34% (Table VII) and -2.51% (Table VIII) at the
0.05 and 0.1 levels respectively. The underperformance of green funds may be explained by
the fact that sectors stigmatised as being pollutive are ignored, while others characterised as
‘clean’ are over-weighted (see also Climent and Soriano 2011). Consistent with the classical
portfolio theory, the discussed industry concentration and sectoral avoidance bias lead to a
restricted investment universe bringing along reduced diversification opportunities. The
negative implications of the latter on financial performance therefore help to explain the
significant performance differences between the conventional and the green fund portfolios.
Considering the overall picture based on a longer time series of fund returns, our main
proposition that the performance of green funds is not different from other fund types is not
supported. We next examine whether the evidence on performance evolution is consistent
with our expectations.
Evolution of Fund Performance
Table IX outlines the multi-factor regression outcomes for two reduced sample periods
(following Bauer et al. 2005; Renneboog et al. 2008; Climent and Soriano 2011). The full
period of investigation (1991–2014) is divided into two shorter sub-periods (1991–2002;
2003–2014). This analysis is of specific interest to the study, as a change in the financial
performance of environmentally friendly funds is expected to have occurred as a result of
important social, political and economic-related developments affecting investments in green
and black funds. The sub-periods used are exogenously determined. The full period is divided
in January 2003 in order to account for the European Union’s adoption of the first emissions

27
trading directive, later entering into force in 2005, marking a milestone for the environmental
lobby. The move led to the creation of linked market mechanisms for pricing carbon
emissions across the EEA.
16
There are several interesting observations in this set of results. The first observation is that
the R2ADJ increases significantly for the second twelve-year period, suggesting that the global
factor portfolio data fits the model better over the more recent time span. Nevertheless, it is
important to note that the majority of green funds are launched after 2003. Accordingly, the
results of the first sub-period need to be treated with caution, as the return data is retrieved
from 42 funds only (launched between 1984–2003), while the sample substantially increases
thereafter to a total of 175 green mutual funds. The second observation is that green funds
significantly underperform the black ( = -6.73%) and conventional ( = -4.47%)
funds over the 1991–2002 period at the 0.10 and 0.05 levels respectively, whereas only the
conventional fund portfolio shows a significantly superior financial performance over the
more recent time span at the 0.05 level of statistical significance ( = -3.45%).
Specifically, no significant differences in abnormal risk-adjusted returns between the green
and black classes can be reported. In general, the performance differences between the three
fund classes are less important between 2003 and 2014, suggesting that the returns of the
three fund classes converge over time.
INSERT TABLE IX ABOUT HERE
Thirdly, the growth stock bias of green mutual funds appears to have only developed more
recently. The HML coefficient estimate of -0.23 is significant at the 0.01 level of statistical
significance, thus implying a significant bias towards low book-to-market stocks from 2003
to 2014. This is consistent with the expectation of a recent growth in renewable energy
GREEN

GREEN

GREEN


28
investment and an increasing set of environmental business opportunities. Black mutual
funds experience a significant exposure to value stocks (0.61 at the 0.01 level of statistical
significance) from 1991 to 2002, which then transforms into a significant growth stock bias
of -0.52 at the 0.01 level of statistical significance for the 2003–2014 period. As the fossil
fuels pass their climax, constituent firms of black mutual funds may have to adjust their
investment orientation towards emerging specialised small cap high-growth businesses
involved in the fossil energy value chains, in order to sustain their returns. This may involve
large cap firms acquiring the share capital or, indeed, the direct operations of more nimble
and smaller firms with operations in similar sectors of the economy. An example of this
approach includes the recent acquisition of a 25% stake in Cuadrilla Resources by Centrica, a
major gas exploration and supply corporation.
17
Green mutual funds have a significantly
negative momentum exposure of -0.09 during the first period. Green stocks thus belong to the
group of loser stocks for that period. Back then, the environmental industry was still in its
infancy and it seemed to be more challenging to break the cycle of bad performance.
Contrarily, but analogous to the full period analysis, black funds show a significantly positive
momentum exposure of 0.12 over the more recent sub-period. The underlying black stocks
thus belong to the group of winner stocks for the entire sample period, not least because of
the oil price boom from 2003 onwards.
Finally, in order to better test our main propositions, we extend the sub-period analysis to
cover six other moving windows on a comparative basis, as shown in Table X. The results in
Table X are very interesting indeed, as they document an astounding continuous
improvement of the green mutual fund performance compared to the performance of their
black, as well as conventional, counterparts. The growth of the fossil energy and natural
resource industries was long assumed to be steady and certain, not least because of the

29
burgeoning wealth of the growing populations in the emerging economies, national and
international mobility escalations and the on-going globalisation. All supported the
exponential development of fossil fuel usage, which was only questioned over the past few
years. From 1991 onwards, the risk-adjusted returns of environmentally friendly mutual
funds transitioned from showing traces of slight underperformance (-2.69%) to demonstrating
evidence of a significant outperformance over their black peers. For the most recent period of
2012-2014, the green fund portfolio outperforms its black counterpart with a value of about
14.36%; the result is statistically significant at the 0.05 level. While green funds prosper
during the transition to a cleaner economy, black funds thrived during the fossil energy and
natural resource age and capitalised on the success of the latter. Black funds performed well
in the past, but the fossil energy era is approaching its end and will be most likely supplanted
by the renewable energy age in the future; assuming scientific evidence continues to strongly
underscore the need to reduce greenhouse gas emissions. Equally, over the entire sample
period, the inferior performance of the green relative to the conventional fund portfolio
progressively improves, from significant underperformance of -3.34% at the 0.05 level, to no
remaining evidence of statistically significant performance differences between 2012 and
2014. The initial substantially inferior returns can be ascribed to the more restricted stock
universe, which ought to have expanded over the years, bringing with it an enlarged
investment set and diversification benefits.
INSERT TABLE X ABOUT HERE
The green mutual fund performance slowly but steadily improves up to 2007. The following
four years were characterised by the global financial crisis (2007-2009) and the Eurozone
crisis (2009-2011), the effects of which began to deflate by the end of 2012. The crisis
situation caused a liquidity problem as suggested by Ito et al. (2013) which, combined with
declining stringency of environmental regulations and a reduced emphasis on climate change

30
issues due to weakened economic activity, resulted in limited financing activities for
environmentally oriented ventures. However, in partial agreement to Nofsinger and Varma
(2014), who find evidence that socially responsible mutual funds show superior financial
performance during the years of crisis compared to their conventional counterparts, our
results suggest that over the 2007 - 2014 period the green mutual funds gain in strength, as
shown by the diminution of the significant underperformance compared to conventional
mutual funds from the 0.05 level (2003-2014) to the 0.1 level of statistical significance. From
the beginning of the economic recovery in 2011 onwards the green mutual funds then
considerably improve to finally outperform their black counterparts at the 0.05 level, and to
substantially reduce their inferior performance compared to the conventional class to an
insignificant level, with only a coefficient difference estimate of -0.62%. With regard to the
black versus conventional fund portfolio comparison, the black mutual funds’ performance
continuously deteriorates to significantly underperform the conventional class at the 0.01
level over the most recent years. Strikingly, the black mutual funds’ excess returns
continuously decrease over the full 1991-2014 period and, much recently, show significantly
inferior behaviour to both the green (-12.69%, at 0.05 level) and the conventional (-13.23%,
at 0.01 level) mutual fund classes.
Thus both of our main hypotheses are supported by the results detailing the evolution of the
comparative performances of the three fund classes. We contend that the increasing
awareness of the riskiness of investment in black funds will lead to the demand for higher
returns by investors. Similarly, as the need for inducing emission-constrained economies
becomes more apparent, investment opportunities in this sector will increase across new and
existing industries, and along with it the understanding of green investing. These in turn will
lead to improvements in the performance of green funds, such that the risk associated with

31
the reduced investment universe will shrink. The signs are indeed mounting that the required
development towards a greener economy will leave its traces on the private sector,
companies’ asset valuations and business models. Firms active in carbon intensive industries
therefore face augmented obstacles in the present and the future in the form of regulatory,
political, economic, financial and social pressures. The above results constitute the most
significant evidence yet from financial market data of a change of tendency in energy-related
ventures, conceivably stimulated by the gradual transition from a fossil energy era to an
emission-constrained and greener one.
Perhaps the most important implication of the results is that the risk-adjusted returns yielded
by conventional mutual funds are not statistically different from those obtained by investing
in green mutual funds instead. Given the results in this paper, green mutual funds could be
viewed as a financially rational and standard investment vehicle. Thus we provide the first
body of evidence to support the predictions of Climent and Soriano (2011: 285) that:
“…as fund managers and investors gain experience with green-orientated investment and
investment opportunities increase, we may find returns approaching those obtained on
conventional funds.”
Conclusion
This paper comparatively examines the performances of black, conventional and green
European mutual funds over time. Although our approach conforms, by and large, to the
recent trend in the general SRI literature area (see Capelle-Blancard and Monjon 2012), the
results obtained are of greater significance because our focus is on the financial performance
of a sub-set of SRI funds, which is currently under-researched – green mutual funds.

32
Furthermore, we provide the first financial markets-based evidence of what appears to be a
changeover in European green and black mutual fund performance to the advantage of the
former. While it is possible that this evolution in mutual fund performances is driven by an
underlying transition from a fossil fuel age into an emission-constrained one, this study does
not provide a definitive proof of such transition. We find that the green, as well as the black
and conventional, mutual funds show significant negative risk-adjusted abnormal returns
when contrasted with a broad global market benchmark. This is consistent with expectations,
as mutual funds in general experience a reduced investment universe, and specifically green
and black mutual funds are subject to investment restrictions, which negatively impact their
financial performance. Interestingly, the black fund class is the only one not to reveal
significant negative single-factor alpha estimates when compared to the broad equity indices.
Standing out from the competition, black mutual funds likely benefited from the fossil energy
and natural resource age, and the associated supportive ambient conditions, which have
shaped the world economy over the past century.
Remarkably, when it comes to the inter-fund class comparison, the black fund portfolio is
unable to significantly outperform its conventional and green counterparts. Yet, when we
contrast the green mutual funds with the conventional investment vehicles, the latter show a
significantly superior performance. This is related to the variance in the explanatory power of
the global market index for the different fund types. These findings underscore the intuition
that black mutual fund returns are influenced by various other economic and political factors
too, not properly captured by the standard equity index. These may include socio-political
unrest in major oil producing countries that are not well mainstreamed into the global
economy. The findings also strengthen the assumption that a restricted investment set limits
the green funds’ diversification endeavours and negatively impacts the financial performance

33
of the class. Over the full period of investigation, green mutual funds experience a significant
small company effect. Small cap and growth stocks face less environmental risks and
presumably the funds’ holdings are tilted towards innovative environmental pioneers (see
Cortez et al. 2012; Kreander et al. 2005). Likewise, the green fund portfolio is highly exposed
to growth stocks. Among other explanations, the environmentally oriented stock prices most
likely soared due to an excess demand triggered by the environmental trend ultimately
resulting in disproportionate market values. A subsequent adjustment to founded stock price
levels would have negatively impacted the green funds’ performance. Conversely, black
funds show a tendency towards high book-to-market defensive value stocks.
The looming end of the fossil fuel and natural resource age and the impending renewable
energy era suggest that, possibly, the performance of green and black mutual funds has
substantially changed over the period of investigation. Our findings reveal that the
performance of black mutual funds diminishes over time and, most recently, evidence
indicates a significant underperformance of the black funds when compared to their green
and conventional peers. Correspondingly, green mutual fund performance progressively
improves such that no significant performance differences between the conventional and the
green class could be established. While, over the 1991–2014 period, a statistically significant
advantage could be obtained by investing in conventional mutual funds, lately investors are
not penalised for investing in green portfolios instead. Equally, investors do not pay a
premium for choosing green over black mutual funds. The results imply that, in practice,
investment specialists can enhance their exposure to environmentally friendly investments
without sustaining a loss in risk-adjusted returns.

34
In reference to discussions in previous studies on how to explain a superior financial
performance of green investment vehicles, the question arises: is this a mispricing story? In
the early days of investment in SRI stocks, financial markets likely overestimated the risks
faced by clean stocks, and underestimated the environmental risk confronted by carbon-
intensive stocks. This is a classic case of information asymmetry in financial markets. Such
an occurrence is usually due to the lack of transparency in the market as well as the dearth of
information. In this case, it is more likely that the unavailability of adequate information
about green investments, and ignorance about the externalities caused by carbon intensive
investments, combined to give rise to the mispricing of both green and black stocks. Together
with an undervaluation of the environmentally related revenues and profitable opportunities,
green stocks are potentially mispriced during the early years of their emergence.
Our efforts to compile comprehensive and representative fund samples notwithstanding, the
significance of our findings is limited by the data and only valid for our particular period of
investigation and chosen geographic region. Moreover, uneven fund class distributions over
time call for caution when interpreting the results, especially at the beginning of our period of
investigation. Hereafter, it is important to expand the study to different time periods and
distinct geographic areas with focalised in-depth investigations of more recent time frames,
including market downturns. Linking the performance differences between green and black
mutual funds to explanatory factors – fossil fuel prices, environmental regulations and
policies, technological innovations, investors’ environmental awareness and investment
objectives – would add to the understanding of fund class behaviour. Examining the funds’
portfolio holdings would also provide invaluable insights into the inner workings of the three
fund classes.

35
Despite the limitations of this study, the findings of this paper demand attention. The findings
indicate that we possibly see a mispricing story by the market during the early years. Green
funds underperform the conventional funds largely during the first fifteen years of their
existence. Roughly, over the last five years or thereabouts, the green funds appear to post
equivalent performances when compared to the conventional funds, and also significantly
outperform the black funds during the same period. Hence, the investors’ awareness of a
reduced risk exposure of the green funds compared to their black peers is eventually
enhanced over time, leading to the elimination of possible mispricing of green and black
stocks. This should lead to a general downward adjustment of the required rate of return for
green stocks and an upward adjustment for black stocks, with investors requiring a
compensation for the heightened risk. Indeed, de Haan et al. (2012) show that investors
receive a premium for holding a portfolio of dubious environmental reputation. The
compensation for the higher level of recognised risk that investors assume when they
purchase black stocks should be reflected in the cost of capital for individual firm projects.
This in turn could shrink the investment universe for carbon-intensive firms, leading to the
ushering in of an emission-constrained global economy. For example, as the cost of capital
for oil exploration activities begins to rise relative to profits, firms are likely to become more
prudent when making investment decisions. They may choose to pursue mainly proven oil
reserves rather than prospecting for new ones. Consequently the volume of oil exploration
projects may reduce and, with less products in circulation, prices will rise, thus leading to an
increased search for alternatives: green energy.

36
Notes
1
This approach has already been well established in the literature (see as examples, Climent
and Soriano 2011; Ito et al. 2013; Ziegler et al. 2007).
2
Please note that the congruence of the portfolio holdings of the three distinct mutual fund
classes has not been tested due to limited information on portfolio stock contents. However,
while it is possible that the diversified conventional mutual funds include stocks contained in
both the green and black mutual funds, it is highly unlikely that the congruence will be
significant enough to bias our results. This is because each conventional mutual fund usually
contains about 100 – 150 stocks on average and the inclusion of one or two black and green
stocks each will be enough to capture the gains of diversification. The diversification benefits
in the funds should ensure that no one sector dominates returns. Furthermore, given the
commonality in the two non-conventional mutual fund classes, fund returns are unlikely to be
significantly boosted in the long-term by including several stocks from the same industry in
one conventional mutual fund.
3
The EEA includes EU countries and also Iceland, Liechtenstein and Norway. Membership
of this economic bloc allows the non-EU countries access to the EU’s single market.
However, Switzerland is neither a EU nor an EEA member but is also part of the single
market based on other subsisting treaties.
4
The official fund documents such as the ‘Key Investor Information Document’ (KIID), the
prospectus, the sales brochure or the annual/half-year reports are scanned to identify the true
investment objective of each and every single ethical mutual fund. In addition, in the case
that uncertainty persists, the concerned fund’s portfolio composition and top holdings are
revised so as to increase the level of confidence of the fund class allocation. The documents
are received from the publicly available Morningstar or Fundsquare mutual fund database. In
the case that the official mutual fund investor documents have only been available in a
language other than English, French or German, the translation of the relevant text passage
has been performed with the help of Google Translate, and the subsequent green
inclusion/abandon decision trusts the tool to yield the correct translation of the generic
meaning of the information. We also obtain information from the issuers themselves. The
mutual fund emitters’ declarations are believed to be accurate.
5
The fund names of all primary European equity mutual funds are browsed for keywords
such as ‘renewable’, ‘green’, ‘eco’, ‘efficiency’, ‘water’, ‘solar’, ‘wind’, ‘biomass’,
‘environment’ and ‘climate’ as well as their respective synonyms and counterparts in other
European languages.
6
The Lipper Global Classification Schemes screen is used to sift for funds classified under
the following categories: ‘Commodity Energy’, ‘Equity Sector Natural Resource’, ‘Equity
Sector Utilities’, ‘Commodity Industrial Metals’ and last but not least ‘Commodity Precious
Metals’.
7
The sample is defined to solely comprise mutual funds with a diversified investment
strategy through the application of the Lipper filters ‘Equity Europe’, ‘Equity Eurozone’ and
‘Equity Global’.
8
This exclusion controls for short selling and other alternative trading strategies. Short
selling restrictions were implemented in Europe, if at all, for very limited periods and would
have the highest impact only on these excluded funds. As summarised by Beber and Pagano
(2013), for the period of the global financial crisis, most short selling constraints were lifted

37
within several months following their imposition. Furthermore, it is generally accepted that
the majority of open-ended mutual funds engage in long only investment strategies.
9
The European benchmark is employed for robustness. We aim to account for potential
distortions, and also confirm the appropriateness of use and the results of the global
benchmark.
10
In the same spirit, the single factor computations are repeated employing the S&P Global
Natural Resources Index; the results are not presented but are available on request. While the
force of expression of all previous indices on the black mutual fund returns is relatively low,
the natural resources benchmark notably improves the black class’s R2ADJ. Nonetheless, it
should be noted that the amelioration might be ascribed to the considerably reduced time
span for which index data is available (2009–2014). In line with prior estimations, no
performance differences between the black fund portfolio and the applied market proxy can
be identified. An inter-class interpretation of the regression outcomes is avoided due to the
aforementioned line of reasoning.
11
Nevertheless, we also conduct an analysis based on the matched-pair approach using a
reduced sample of funds, with no qualitative difference in the inferences drawn. This is not
surprising because the sample size of each of the investigated three mutual fund classes
allows for correction of possible distorting fund characteristics such as fund size, age,
management, and investment policy. If they exist, the biases are expected to average out.
Thus, as suggested by the matched-sample robustness analysis, the significance of the study
outcomes is not affected. The results from this additional analysis are not presented but are
available on request.
12
Further analysis, conducted to examine whether regional orientation of funds is a factor
influencing performance, suggests that the orientation is not a significant factor for our
samples. This is not surprising given the high degree of commonality among global financial
markets.
13
We also conduct narrower estimations using mutual funds with only global investment
focus for both the 1-factor and multi-factor models. The results obtained are qualitatively
similar. This is not surprising since most of the funds have a global investment focus.
14
We also conduct narrower estimations using only mutual funds with a European
investment focus for both the 1-factor and multi-factor models. Although the statistical
significance of coefficients obtained from the estimations is generally reduced on account of
smaller sample sizes, the overall inferences drawn from the results are qualitatively
unchanged. Furthermore, using the Kenneth R. French data library European market portfolio
does not yield qualitatively different results; however, the R2ADJ values are slightly lower.
15
: -0.51*** (-4.33) - Please note that these results are not shown in any of the tables
included in this paper.
16
We also conduct a Chow-type parameter stability test by obtaining the residual sum of
squares for the panel regressions; the results suggest that there are no breakpoints at all the
periods tested.
17
See “Centrica buys into Cuadrilla’s Lancashire fracking licence” in the June 13, 2013
edition of Financial Times.
HML


38
References
Aguirre, M., & Ibikunle, G. (2014). Determinants of renewable energy growth: A global
sample analysis. Energy Policy, 69, 374-384.
Ambec, S., & Lanoie, P. (2008). Does It Pay to Be Green? A Systematic Overview. The
Academy of Management Perspectives, 22(4), 45-62.
Ansar, A., Caldecott, B., & Tilbury, J. (2013). Stranded assets and the fossil fuel divestment
campaign: what does divestment mean for the valuation of fossil fuel assets? Stranded
Assets Programme. Oxford: Smith School of Enterprise and the Environment,
University of Oxford.
Bauer, R., Koedijk, K., & Otten, R. (2005). International evidence on ethical mutual fund
performance and investment style. Journal of Banking & Finance, 29(7), 1751-1767.
Beber, A., & Pagano, M. (2013). Short-Selling Bans Around the World: Evidence from the
2007–09 Crisis. The Journal of Finance, 68(1), 343-381.
Beck, N., & Katz, J. N. (1995). What to do (and not to do) with Time-Series Cross-Section
Data. The American Political Science Review, 89(3), 634-647.
Bohl, M. T., Kaufmann, P., & Siklos, P. L. (2015). What drove the mid-2000s explosiveness
in alternative energy stock prices? Evidence from U.S., European and global indices.
International Review of Financial Analysis, 40, 194-206.
Bohl, M. T., Kaufmann, P., & Stephan, P. M. (2013). From hero to zero: Evidence of
performance reversal and speculative bubbles in German renewable energy stocks.
Energy Economics, 37, 40-51.
Briec, W., & Kerstens, K. (2009). Multi-horizon Markowitz portfolio performance
appraisals: A general approach. Omega, 37(1), 50-62.
Brown, S., Goetzmann, W., Ibbotson, R., & Ross, S. (1992). Survivorship bias in
performance studies. Review of Financial Studies, 5(4), 553-580.
Capelle-Blancard, G., & Monjon, S. (2012). Trends in the literature on socially responsible
investment : looking for the keys under the lamppost. [Aufsatz in Zeitschriften,
Article in journal]. Business ethics : a European review, 21(3, (6)), 239-250.
Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance,
52(1), 57-82.
Climent, F., & Soriano, P. (2011). Green and Good? The Investment Performance of US
Environmental Mutual Funds. Journal of Business Ethics, 103(2), 275-287.
Cortez, M., Silva, F., & Areal, N. (2009). The Performance of European Socially Responsible
Funds. Journal of Business Ethics, 87(4), 573-588.
Cortez, M. C., Silva, F., & Areal, N. (2012). Socially Responsible Investing in the Global
Market: The Performance of US And European Funds. International Journal of
Finance & Economics, 17(3), 254-271.
Daskalakis, G., Ibikunle, G., & Diaz-Rainey, I. (2011). The CO2 Trading Market in Europe:
A Financial Perspective. In A. Dorsman, W. Westerman, M. B. Karan, & Ö. Arslan
(Eds.), Financial Aspects in Energy: A European Perspective (pp. 51-67). Berlin
Heidelberg: Springer.
de Haan, M., Dam, L., & Scholtens, B. (2012). The drivers of the relationship between
corporate environmental performance and stock market returns. Journal of
Sustainable Finance & Investment, 2(3-4), 338-375.
Derwall, J., Guenster, N., Bauer, R., & Koedijk, K. (2005). The Eco-Efficiency Premium
Puzzle. Financial Analysts Journal, 61(2), 51-63.
Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The
Journal of Finance, 47(2), 427-465.
Galema, R., Plantinga, A., & Scholtens, B. (2008). The stocks at stake: Return and risk in
socially responsible investment. Journal of Banking & Finance, 32(12), 2646-2654.

39
Gregory, A., Matatko, J., & Luther, R. (1997). Ethical Unit Trust Financial Performance:
Small Company Effects and Fund Size Effects. Journal of Business Finance &
Accounting, 24(5), 705-725.
Hamilton, S., Jo, H., & Statman, M. (1993). Doing Well while Doing Good? The Investment
Performance of Socially Responsible Mutual Funds. Financial Analysts Journal,
49(6), 62-66.
Henriques, I., & Sadorsky, P. (2008). Oil prices and the stock prices of alternative energy
companies. Energy Economics, 30(3), 998-1010.
Hong, H., & Kacperczyk, M. (2009). The price of sin: The effects of social norms on
markets. Journal of Financial Economics, 93(1), 15-36.
Inchauspe, J., Ripple, R. D., & Trück, S. (2015). The dynamics of returns on renewable
energy companies: A state-space approach. Energy Economics, 48, 325-335.
Ito, Y., Managi, S., & Matsuda, A. (2013). Performances of socially responsible investment
and environmentally friendly funds. [General Paper]. J Oper Res Soc, 64(11), 1583-
1594.
Jensen, M. C. (1968). The Performance of Mutual Funds in the period 1945–1964. The
Journal of Finance, 23(2), 389-416.
Kreander, N., Gray, R. H., Power, D. M., & Sinclair, C. D. (2005). Evaluating the
Performance of Ethical and Non-ethical Funds: A Matched Pair Analysis. Journal of
Business Finance & Accounting, 32(7-8), 1465-1493.
Kumar, S., Managi, S., & Matsuda, A. (2012). Stock prices of clean energy firms, oil and
carbon markets: A vector autoregressive analysis. Energy Economics, 34(1), 215-226.
Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in
Stock Portfolios and Capital Budgets. The Review of Economics and Statistics, 47(1),
13-37.
Luther, R. G., Matatko, J., & Corner, D. C. (1992). The Investment Performance of UK
“Ethical” Unit Trusts. Accounting, Auditing & Accountability Journal, 5(4), 57-70.
Managi, S., & Okimoto, T. (2013). Does the price of oil interact with clean energy prices in
the stock market? Japan and the World Economy, 27, 1-9.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Nofsinger, J., & Varma, A. (2014). Socially responsible funds and market crises. Journal of
Banking & Finance, 48(0), 180-193.
Porter, M. E., & Linde, C. v. d. (1995). Toward a New Conception of the Environment-
Competitiveness Relationship. The Journal of Economic Perspectives, 9(4), 97-118.
Renneboog, L., Ter Horst, J., & Zhang, C. (2008). The price of ethics and stakeholder
governance: The performance of socially responsible mutual funds. Journal of
Corporate Finance, 14(3), 302-322.
Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock
prices of clean energy and technology companies. Energy Economics, 34(1), 248-255.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under
Conditions of Risk. The Journal of Finance, 19(3), 425-442.
Statman, M. (2000). Socially Responsible Mutual Funds. Financial Analysts Journal, 56(3),
30-39.
White, M. A. (1995). The performance of environmental mutual funds in the United States
and Germany: is there economic hope for green investors? In Research in Corporate
Social Performance and Policy (Vol. 1, pp. 324-344): Emerald Group Publishing.
Ziegler, A., Busch, T., & Hoffmann, V. H. (2011). Disclosed corporate responses to climate
change and stock performance: An international empirical analysis. Energy
Economics, 33(6), 1283-1294.

40
Ziegler, A., Schröder, M., & Rennings, K. (2007). The effect of environmental and social
performance on the stock performance of european corporations. Environmental and
Resource Economics, 37(4), 661-680.

41
TABLE I
Fund sample selection criteria and quality screens
Criteria
Green MF
Black MF
Conventional
MF
A Open-ended MF
√
√
√
B Primary funds
√
√
√
C Equity
√
√
√
D Debt, mixed and balanced funds
X
x
x
E Guaranteed, protected, alternative
strategies and absolute return funds
X
x
x
F Same class, double country listing
X
x
x
G Domicile
Europe, EEA +
CH
Europe, EEA
+ CH
Europe, EEA +
CH
H Geographical scope
Europe/Global
Europe/Global
Europe/Global
I Screens
Green
Black
Diversified
J Asset status
A, L, M
A, L, M
A, L, M
Total # of funds
175
259
976
# of countries
21
21
23
This table illustrates the sample selection criteria and quality screens applied to mutual fund sample selection.
“√” illustrates that the final sample of the associated fund class adheres to the respective criteria. “x” illustrates
that the final sample of the associated fund class excludes funds possessing the respective criteria. In Row J, A,
L and M correspond to Active, Liquidated and Merged respectively. EEA and CH refer to the European
Economic Area and Switzerland respectively.

TABLE II
Summary statistics of mutual fund classes
MF class
Mean return
(%)
Standard deviation
(%)
# of
MFs
Average
size
Average fund age
(years)
Geographical investment scope of the
MF (%)
Green (1)
4.06
17.47
175
$73.5 M
9
Domestic: 9 (5%)
Europe: 20 (11%)
Global: 143 (82%)
Other: 3 (2%)
Black (2)
4.53
18.56
259
$134.8M
11
Domestic: 3 (1%)
Europe: 17 (6%)
Global: 227 (88%)
Other: 12 (5%)
Conventional
(3)
5.38
16.21
976
$221.1 M
12
Domestic: /
Europe: 489 (50%)
Global: 487 (50%)
The Table provides summary statistics on all three fund classes. The size and geographical scope are represented as of 01/06/2014. The geographical investment scope of the
MF (%) reports the domestic, regional or global investment focus within each class in percentages (%). The average fund returns are calculated for each month based on an
equally weighted portfolio of all funds and then averaged out over the whole sample period. The mean return (%) is inclusive of any distributions. Both the mean return as
well as the corresponding standard deviation are annualised. All figures are denominated in US$. Sample period: 31.01.1991-30.06.2014. ‘Other’ in column 7 refers to
narrower geographical investment scopes such as “Asia-Pacific”, “Emerging Markets”, “Nordic”, “Scandinavian” etc.

TABLE III
Empirical results for 1-factor (CAPM) Regressions using the Kenneth R. French data library-sourced global market factor
Panel A

MKT

ADJ
R2
Green (1)
-7.28***
(-3.64)
1.14***
(46.09)
0.73
Black (2)
-2.48
(-1.08)
1.02***
(27.76)
0.55
Conventional (3)
-3.05***
(-3.93)
1.08***
(75.47)
0.65
Panel B
GREEN

BLACK

CONV

Black vs. (1) & (3)
-2.89
(-1.37)
/
0.76
(0.35)
Conventional vs. (1) & (2)
-3.62**
(-2.55)
-0.75
(-0.35)
/
This Table reports the results of the CAPM-based random effects panel least squares seemingly unrelated regressions (SUR), using a global market factor. Panel A presents
the estimation results for the single-factor CAPM equation, while Panel B presents the results for Equation (5) with dummies applied as appropriate for each estimation. The
global MKT factor portfolio collected from the Kenneth R. French data library is used as market proxy to measure the risk-adjusted returns of the green, black and
conventional mutual funds. α measures the risk-adjusted abnormal return relative to the applied market proxy. βMKT measures the effect of the MKT factor. In Panel B, the
over- and underperformance hypotheses on the ‘comparative classes’ (green, black and conventional) are determined by regressing the excess mutual fund returns of all three
fund classes jointly against the independent variable and thereby including two dummy variables, each one accounting for one of the three classes. This way a potential over-
or underperformance of two of the three classes compared to the remaining third class can be identified. All α measures and the fund class δs in the table are annualised and
in percentage terms. The t-statistics are depicted in parentheses and derived from panel corrected standard errors (PCSE). *, ** and *** correspond to statistical significance
at 10%, 5% and 1% levels respectively. Sample period is 31.01.1991-30.06.2014.

TABLE IV
Empirical results for 1-factor (CAPM) Regressions using the Stoxx Europe 600
Panel A

MKT

ADJ
R2
Green (1)
-4.85**
(-2.31)
0.94***
(42.81)
0.74
Black (2)
-0.83
(-0.30)
0.85***
(25.31)
0.59
Conventional (3)
-2.44***
(-3.99)
0.91***
(104.67)
0.70
Panel B
GREEN

BLACK

CONV

Black vs. (1) & (3)
-2.88
(-1.22)
/
-0.22
(-0.09)
Conventional vs. (1) & (2)
-2.67*
(-1.71)
0.22
(0.09)
/
This Table reports the results of the CAPM-based random effects panel least squares seemingly unrelated regressions (SUR), using a regional European market factor. Panel
A presents the estimation results for the single-factor CAPM equation, while Panel B presents the results for Equation (5) with dummies applied as appropriate for each
estimation. The Stoxx Europe 600 Index is used as market proxy to measure the risk-adjusted returns of the green, black and conventional mutual funds. α measures the risk-
adjusted abnormal return relative to the applied market proxy. βMKT measures the effect of the MKT factor. In Panel B, the over- and underperformance hypotheses on the
‘comparative classes’ (green, black and conventional) are determined by regressing the excess mutual fund returns of all three fund classes jointly against the independent
variable and thereby including two dummy variables, each one accounting for one of the three classes. This way a potential over- or underperformance of two of the three
classes compared to the remaining third class can be identified. All α measures and the fund class δs in the table are annualised and in percentage terms. The t-statistics are
depicted in parentheses and derived from panel corrected standard errors (PCSE). *, ** and *** correspond to statistical significance at 10%, 5% and 1% levels respectively.
Sample period is 28.02.2001-30.06.2014.

TABLE V
Empirical results for 1-factor (CAPM) Regressions using the S&P Global Alternative Energy
Panel A

MKT

ADJ
R2
Green (1)
3.58
(1.41)
0.76***
(38.24)
0.75
Black (2)
3.14
(1.11)
0.71***
(22.96)
0.61
Conventional (3)
2.09
(0.87)
0.64***
(25.58)
0.61
Panel B
GREEN

BLACK

CONV

Black vs. (1) & (3)
1.14
(0.42)
/
0.22
(0.07)
Conventional vs. (1) & (2)
0.92
(0.48)
-0.22
(-0.07)
/
This Table reports the results of the CAPM-based random effects panel least squares seemingly unrelated regressions (SUR), using a specialty index as the market factor.
Panel A presents the estimation results for the single-factor CAPM equation, while Panel B presents the results for Equation (5) with dummies applied as appropriate for each
estimation. The S&P Global Alternative Energy Index is used as market proxy to measure the risk-adjusted returns of the green, black and conventional mutual funds. α
measures the risk-adjusted abnormal return relative to the applied market proxy. βMKT measures the effect of the MKT factor. In Panel B, the over- and underperformance
hypotheses on the ‘comparative classes’ (green, black and conventional) are determined by regressing the excess mutual fund returns of all three fund classes jointly against
the independent variable and thereby including two dummy variables, each one accounting for one of the three classes. This way a potential over- or underperformance of
two of the three classes compared to the remaining third class can be identified. All α measures and the fund class δs in the table are annualised and in percentage terms. The
t-statistics are depicted in parentheses and derived from panel corrected standard errors (PCSE). *, ** and *** correspond to statistical significance at 10%, 5% and 1% levels
respectively. Sample period is 31.12.2003-30.06.2014.

TABLE VI
Empirical results for 1-factor (CAPM) Regressions using the FTSE Global Small Cap Index
Mutual Fund Classes

MKT

ADJ
R2
Green (1)
-5.98**
(-2.41)
0.67***
(25.25)
0.51
Black (2)
-0.94
(-0.31)
0.41***
(13.67)
0.31
Conventional (3)
-2.55
(-1.03)
0.42***
(17.05)
0.31
This Table reports the results of the CAPM-based random effects panel least squares seemingly unrelated regressions (SUR), using a global small cap index as the market
factor. The FTSE Global Small Cap Index is used as market proxy to measure the risk-adjusted returns of the green, black and conventional mutual funds. α measures the
risk-adjusted abnormal return relative to the applied market proxy, and βMKT measures the effect of the MKT factor.
All α measures and the fund class δs in the table are annualised and in percentage terms. The t-statistics are depicted in parentheses and derived from panel corrected standard
errors (PCSE). *, ** and *** correspond to statistical significance at 10%, 5% and 1% levels respectively. Sample period is 31.01.1994-30.06.2014.

TABLE VII
Empirical results for Multi-factor Regressions using the Kenneth R. French data library-sourced global factors
Panel A

MKT

SMB

HML

MOM

ADJ
R2
Green (1)
-7.46***
(-4.15)
1.15***
(47.97)
0.38***
(6.46)
-0.09*
(-1.76)
0.03
(1.17)
0.74
Black (2)
-4.00**
(-1.97)
1.05***
(27.77)
0.41***
(4.91)
0.2***
(2.83)
0.11***
(2.66)
0.57
Conventional (3)
-3.40***
(-4.24)
1.08***
(72.26)
0.08**
(2.50)
0.03
(1.13)
0.02
(1.08)
0.65
Panel B
GREEN

BLACK

CONV

Black vs. (1) & (3)
-2.69
(-1.30)
/
0.67
(0.32)
Conventional vs. (1) & (2)
-3.34**
(-2.43)
-0.67
(-0.32)
/
This table reports the results of the Carhart (1997) four-factor model-based random effects panel least squares seemingly unrelated regressions (SUR), using global factors.
Panel A presents the estimation results for Equation (3), while Panel B presents the results for Equation (4) with dummies applied as appropriate for each estimation. The
global factor portfolios collected from the Kenneth R. French data library are used as factors to measure the risk-adjusted returns of the green, black and conventional mutual
funds. α measures the risk-adjusted abnormal return relative to the applied proxies. βMKT, βSMB, βHML and βMOM measure the effects of the MKT, SMB, HML and MOM
factors, where SMB corresponds to the return spread between a small cap portfolio and a large cap portfolio, HML is the difference in return between a value stock portfolio
and a growth stock portfolio, and MOM is the difference between a portfolio of the past 12 months’ winners and a portfolio of the past 12 months’ losers. In Panel B, the
over- and underperformance hypotheses on the ‘comparative classes’ (green, black and conventional) are determined by regressing the excess mutual fund returns of all three
fund classes jointly against the independent variables and thereby including two dummy variables, each one accounting for one of the three classes. This way a potential over-
or underperformance of two of the three classes compared to the remaining third class can be identified. All α measures and the fund class δs in the table are annualised and
in percentage terms. The t-statistics are depicted in parentheses and derived from panel corrected standard errors (PCSE). *, ** and *** correspond to statistical significance
at 10%, 5% and 1% levels respectively. Sample period is 31.01.1991-30.06.2014.

TABLE VIII
Empirical results for Multi-factor Regressions using the Kenneth R. French data library-sourced European factors
Panel A

MKT

SMB

HML

MOM

ADJ
R2
Green (1)
-5.54***
(-3.43)
0.99***
(52.18)
0.37***
(8.12)
-0.22***
(-5.02)
0.01
(0.64)
0.75
Black (2)
-3.59*
(-1.84)
0.91***
(29.28)
0.39***
(5.46)
-0.05
(-0.77)
0.10***
(2.75)
0.59
Conventional (3)
-2.37***
(-4.60)
0.93***
(113.46)
0.06***
(3.08)
-0.12***
(-6.84)
0.00
(0.02)
0.68
Panel B
GREEN

BLACK

CONV

Black vs. (1) & (3)
-2.00
(-0.98)
/
0.52
(0.24)
Conventional vs. (1) & (2)
-2.51*
(-1.88)
-0.51
(-0.24)
/
This table reports the results of the Carhart (1997) four-factor model-based random effects panel least squares seemingly unrelated regressions (SUR), using European
factors. Panel A presents the estimation results for Equation (3), while Panel B presents the results for Equation (4) with dummies applied as appropriate for each estimation.
The European factor portfolios collected from the Kenneth R. French data library are used as factors to measure the risk-adjusted returns of the green, black and conventional
mutual funds. α measures the risk-adjusted abnormal return relative to the applied proxies. βMKT, βSMB, βHML and βMOM measure the effects of the MKT, SMB, HML and MOM
factors, where SMB corresponds to the return spread between a small cap portfolio and a large cap portfolio, HML is the difference in return between a value stock portfolio
and a growth stock portfolio, and MOM is the difference between a portfolio of the past 12 months’ winners and a portfolio of the past 12 months’ losers. In Panel B, the
over- and underperformance hypotheses on the ‘comparative classes’ (green, black and conventional) are determined by regressing the excess mutual fund returns of all three
fund classes jointly against the independent variables and thereby including two dummy variables, each one accounting for one of the three classes. This way a potential over-
or underperformance of two of the three classes compared to the remaining third class can be identified. All α measures and the fund class δs in the table are annualised and
in percentage terms. The t-statistics are depicted in parentheses and derived from panel corrected standard errors (PCSE). *, ** and *** correspond to statistical significance
at 10%, 5% and 1% levels respectively. Sample period is 31.01.1991-30.06.2014.

TABLE IX
Empirical sub-period analysis results for Multi-factor regressions using the Kenneth R. French data library-sourced global factors
Panel A

MKT

SMB

HML

MOM

ADJ
R2
JAN 1991 – DEC 2002
Green (1)
-9.63***
(-2.75)
0.91***
(21.06)
0.36***
(5.50)
-0.05
(-0.78)
-0.09**
(-2.38)
0.57
Black (2)
-7.14***
(-2.61)
0.92***
(15.39)
0.48***
(5.62)
0.61***
(7.75)
0.01
(0.25)
0.36
Conventional (3)
-2.54
(-1.48)
0.96***
(26.52)
0.17***
(3.13)
0.00
(0.00)
0.00
(0.00)
0.50
Panel B
GREEN

BLACK

CONV

Black vs. (1) & (3)
-6.73*
(-1.69)
/
-2.36
(-0.60)
Conventional vs. (1) & (2)
-4.47**
(-2.07)
2.41
(0.60)
/
Panel C

MKT

SMB

HML

MOM

ADJ
R2
JAN 2003 – DEC 2014
Green (1)
-7.41***
(-3.95)
1.20***
(42.42)
0.36***
(4.26)
-0.23***
(-2.69)
0.05
(1.33)
0.76
Black (2)
-3.86
1.19***
0.34***
-0.52***
0.12**
0.65

(-1.63)
(26.27)
(2.67)
(-3.83)
(2.10)
Conventional (3)
-3.49***
(-3.70)
1.13***
(60.04)
-0.05
(-0.97)
-0.08
(-1.47)
0.00
(0.21)
0.72
Panel D
GREEN

BLACK

CONV

Black vs. (1) & (3)
-1.96
(-0.74)
/
1.54
(0.56)
Conventional vs. (1) & (2)
-3.45**
(-2.14)
-1.52
(-0.56)
/
This table reports the results of the Carhart (1997) four-factor model-based random effects panel least squares seemingly unrelated regressions (SUR), using global factors.
Panels A and C present the estimation results for Equation (3), while Panels B and D present the results for Equation (4) with dummies applied as appropriate for each
estimation. The global factor portfolios collected from the Kenneth R. French data library are used as factors to measure the risk-adjusted returns of the green, black and
conventional mutual funds. α measures the risk-adjusted abnormal return relative to the applied proxies. βMKT, βSMB, βHML and βMOM measure the effects of the MKT, SMB,
HML and MOM factors, where SMB corresponds to the return spread between a small cap portfolio and a large cap portfolio, HML is the difference in return between a
value stock portfolio and a growth stock portfolio, and MOM is the difference between a portfolio of the past 12 months’ winners and a portfolio of the past 12 months’
losers. In Panels B and D, the over- and underperformance hypotheses on the ‘comparative classes’ (green, black and conventional) are determined by regressing the excess
mutual fund returns of all three fund classes jointly against the independent variables and thereby including two dummy variables, each one accounting for one of the three
classes. This way a potential over- or underperformance of two of the three classes compared to the remaining third class can be identified. All α measures and the fund class
δs in the table are annualised and in percentage terms. The t-statistics are depicted in parentheses and derived from panel corrected standard errors (PCSE). *, ** and ***
correspond to statistical significance at 10%, 5% and 1% levels respectively. Sample period is 31.01.1991-30.06.2014.

TABLE X
Empirical comparative analysis results for Multi-factor regressions using the Kenneth R. French data library-sourced global factors
This Table reports the results of the extended Carhart (1997) four-factor model-based (Equation 4) random effects panel least squares seemingly unrelated regressions (SUR).
The global factor portfolios collected from the Kenneth R. French data library are used as factors to measure the risk-adjusted returns of the green, black and conventional
mutual funds. α measures the risk-adjusted abnormal return relative to the applied proxies. βMKT, βSMB, βHML and βMOM measure the effects of the MKT, SMB, HML and MOM
factors, where SMB corresponds to the return spread between a small cap portfolio and a large cap portfolio, HML is the difference in return between a value stock portfolio
and a growth stock portfolio, and MOM is the difference between a portfolio of the past 12 months’ winners and a portfolio of the past 12 months’ losers. The over- and
underperformance hypotheses on the ‘comparative classes’ (green, black and conventional) are determined by regressing the excess mutual fund returns of all three fund
classes jointly against the independent variables and thereby including two dummy variables, each one accounting for one of the three classes. This way a potential over- or
underperformance of two of the three classes compared to the remaining third class can be identified. All α measures and the fund class δs in the table are annualised and in
percentage terms. The t-statistics are depicted in parentheses and derived from panel corrected standard errors (PCSE). *, ** and *** correspond to statistical significance at
10%, 5% and 1% levels respectively. Sample period is 31.01.1991-30.06.2014.
Panel A
GREEN

91-14
GREEN

95-14
GREEN

99-14
GREEN

03-14
GREEN

07-14
GREEN

11-14
GREEN

12-14
Black vs. (1) & (3)
-2.69
(-1.30)
-2.64
(-1.26)
-3.39
(-1.47)
-1.96
(-0.74)
-0.29
(-0.08)
7.82
(1.51)
14.36**
(2.37)
Conventional vs. (1) & (2)
-3.34**
(-2.43)
-3.31**
(-2.37)
-3.28**
(-2.18)
-3.45**
(-2.14)
-3.79*
(-1.88)
-4.27
(-1.63)
-0.62
(-0.25)
Panel B
BLACK

91-14
BLACK

95-14
BLACK

99-14
BLACK

03-14
BLACK

07-14
BLACK

11-14
BLACK

12-14
Green vs. (2) & (3)
2.75
(1.30)
2.70
(1.26)
3.50
(1.47)
2.00
(0.74)
0.29
(0.08)
-7.30
(-1.51)
-12.69**
(-2.37)
Conventional vs. (1) & (2)
-0.67
(-0.32)
-0.69
(-0.32)
0.11
(0.05)
-1.52
(-0.56)
-3.51
(-0.93)
-11.29**
(-2.42)
-13.23***
(-2.58)