Environmental, social and governance risk exposures of mutual funds

Abstract

Frequent natural disasters caused by climate change and the COVID-19 pandemic have increased global awareness of sustainability issues with a consequent focus on sustainable finance. This study disaggregates the exposures of mutual funds to environmental, social, and governance risks using data from 18,648 investment funds. We find that investment in technology and financial firms and herding behavior support an environmental strategy but not necessarily governance or social responsibility. Further, funds with longer-tenured managers are less sustainability-focused, possibly entrenched from an era before ESG became a societal concern.

Full text

Vol:.(1234567890)
Journal of Asset Management (2025) 26:316–332
https://doi.org/10.1057/s41260-025-00401-7
ORIGINAL ARTICLE
Environmental, social andgovernance risk exposures ofmutual funds
ChristineHelliar1 · BarbaraPetracci2· NongnuchTantisantiwong3
Revised: 15 January 2025 / Accepted: 2 March 2025 / Published online: 17 April 2025
© The Author(s) 2025
Abstract
Frequent natural disasters caused by climate change and the COVID-19 pandemic have increased global awareness of
sustainability issues with a consequent focus on sustainable finance. This study disaggregates the exposures of mutual
funds to environmental, social, and governance risks using data from 18,648 investment funds. We find that investment in
technology and financial firms and herding behavior support an environmental strategy but not necessarily governance or
social responsibility. Further, funds with longer-tenured managers are less sustainability-focused, possibly entrenched from
an era before ESG became a societal concern.
Keywords Sustainability· ESG risk management· Inequality in environmental, social, and governance impacts· Fund
management
Introduction
As a trend, environmental (E), social (S), and governance
(G) concerns have become an essential part of business in
the move to a sustainable future (Starks 2021). The United
Nations’ Sustainable Development Goals (UN SDGs), the
United Nations’ Principles for Responsible Investing (UN
PRI), and other global concerns are challenging the asset
management industry to consider E, S, and G factors in
investment portfolios, changing the trend in investment strat-
egies (Hoepner etal. 2021). Mutual fund managers have
risen to this challenge by integrating E, S, and G factors into
their investment practices. Globally, ESG-focused funds now
account for 10% of fund assets (Reuters 2021), with $6.1
trillion investments in ESG-labeled funds, but the exponen-
tial growth has fueled concerns about the unethical practice
of greenwashing, with little disclosure of the separate E, S,
and G of funds.
Many studies examine socially responsible investment
(SRI) funds, which, as Laurel-Fois (2018) notes, are funds
that integrate sustainability into investment management
practices, integrating ESG factors (Risi etal. 2020). For
example, Van Duuren etal. (2016) show that asset managers
integrate ESG factors in their investment processes and use
this information to identify any unsustainable management
strategies of investee companies. Madhavan etal. (2021)
analyze how investment managers take into consideration
ESG factors by selecting companies with attractive ESG
profiles.
Notably, however, previous research pays little attention
to the disaggregation of E, S, and G risk exposures, focusing
more on the generic strategy of ESG risk exposure (Hübel &
Scholz 2020). As a contribution, this paper disaggregates the
E, S, and G profiles of investment portfolios and finds that
the E dimension is more sensitive to style factors than the S
or the G dimensions. By delineating between environmental,
social, and governance concerns rather than examining ESG
collectively we build upon sustainability in the investment
industry to examine E, S, and G factors separated out. As
Edmans (2023) notes, good governance leads to the E and
the S in investee companies.
Asset allocation decisions and fund management strate-
gies are important, and investors should know that the E, S,
and G risks of their investments are being managed, with
mutual funds following their mandates. Funds that adopt
an ESG strategy need to understand climate change action
and manage their exposure to environmental risk by meas-
uring the GHG emissions of all their investee companies.
Further, funds need an understanding of the social impact
* Christine Helliar
christine.helliar@unisa.edu.au
1 University ofSouth Australia, Adelaide, SA, Australia
2 Bologna University, Bologna, Italy
3 CIMB Thai Bank, Bangkok, Thailand
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317Environmental, social andgovernance risk exposures ofmutual funds
of their investments, especially with the recent focus on
modern slavery (Christ & Helliar 2021), as well as the gov-
ernance of the businesses they invest in. Each of these has
implications for the strategy adopted by fund managers. In
practice, the mutual fund industry has a mix of investment
objectives across funds, and these differences expose funds
to different types and degrees of risk, including E, S, and G
risk. Nevertheless, fund managers may adopt certain strate-
gies influenced by national and local practices. Investment
trends may be driven by the fund's domicile and national
and cross-country frameworks, with many funds investing,
in particular, in US securities.
The literature is replete with the risk and performance of
ESG funds (Utz & Wimmer 2014) and comparisons with
conventional funds (Kreander etal. 2005), but there is little
on the asset allocations of funds (Joliet & Titova 2018). The
impact of asset allocation and fund management features on
ESG risk exposure has not been examined in a systematic
fashion, with little research on how portfolio managers
devise their portfolios (Erragragui & Lagoarde-Segot
2016). In addition, Risi etal. (2020) note that large financial
intermediaries, such as fund managers, vary significantly
in how they adopt ESG strategies because, as Patel (2018)
notes, ESG investment styles attract different investors. The
managers of mutual funds integrating E, S, and G factors
into their investment analysis and allocation decisions may
require better stock-picking skills and management skills,
with multi-tasking reducing the focus on any particular
investment fund (Agarwal etal. 2023). The long-term
success of fund managers in running a fund and how long
they remain as managers may result in different investment
decisions and, thus, different exposures to E, S, and G risk.
We analyze whether a fund’s E, S, and G risk exposure is
related to how the fund is managed. Fund managers may
change from time to time, and this change may impact the
funds’ decision-making and asset allocations.
As a contribution, we first use univariate analysis
regarding asset allocations, followed by regression analysis
that examines the relationship between disaggregated E, S,
and G risk exposures and fund characteristics. To the best of
our knowledge, this research is the first to analyze the most
popular equity fund investee companies, finding that many
funds invest in a few well-known stocks, such as Amazon.
com Inc., Alphabet Inc., and Apple Inc., irrespective of
fund strategy. In particular, we find that ESG fund asset
allocations may not necessarily be driven by E, S, and G
investment objectives but by herding behavior. ESG funds
seem to invest in popular investments rather than searching
for the best companies on E, S, and G principles. Further, we
find that the management characteristics of funds influence
E, S, and G risk scores, with funds employing the same
fund manager over a longer period being less focused on
E, S, and G risk exposure. Another contribution is finding
that, although most funds invest in technology and financial
stocks as well as health care, exposures to E, S, and G risk
are specific to investment in certain sectors. For example,
investments in technology companies reduce environmental
risk exposure but not social or governance risk exposure.
Thus, fund managers may focus on the environment but
not necessarily on social or governance factors. Overall,
these results provide insightful information for E, S, and
G dimensions. Funds should promote the E, S, and G
agenda through their investment activities and not through
greenwashing (Joliet & Titova 2018; Candelon etal. 2021).
The remainder of this paper is outlined as follows. The
next section outlines the prior literature to draw hypotheses
for our investigation, identifying three main categories
of studies: cross-country frameworks, trends in investor
behavior, and managerial characteristics. This section is
followed by the methodology section, which outlines the
methods employed in this research. The next section reports
the results and their implications for fund management
practice. The robustness check of our findings is also
provided. The final section concludes the paper.
Literature review andhypothesis
development
Cross‑country regulatory frameworks
Across the globe, regulators are concerned about investment
management businesses. For example, in Australia in 2021,
concerns were raised by the House of Representatives
Standing Committee on Economics that accused two funds
of misleading investors by having similar investments
despite having different fund objectives (Micallef 2021).
This is especially so as regulators demand that funds
disclose far more data about their investments, especially
sustainable investments (see, for example, the Sustainable
Finance Disclosure Regulation at the European level).
Joliet and Titova (2018) examine investment strategy
focused on the geographical scope of funds and find that
ESG performance is weakly related to asset allocation
decisions for ESG funds with a US focus. They note that
the lower ESG quality of US-oriented funds is due to lower
disclosure levels and policies of US companies relative
to European companies. They also show that ESG funds
investing more in the US assign greater importance to the
ESG rating of investee companies than similar conventional
funds.
With a US focus, in May 2022, the US Securities and
Exchange Commission (SEC) announced rules to protect
societal concerns and prevent fund managers from
misleading investors when claiming that their funds focus on
ESG issues. This is to ensure that they accurately describe
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318 C.Helliar et al.
their investments and declare that at least 80% of their assets
are in accordance with their declared strategy. In addition,
funds may need to disclose the aggregated greenhouse gas
(GHG) emissions of their investee companies (SEC 2022a,
b).
In general, the domicile of funds reflects their legal
environment, the development level of the stock market and
the fund market (Lozano etal. 2006; Ferreira etal. 2013;
Rathner 2013) as well as the screening most commonly used
(Goldreyer and Diltz 1999). In the US, the use of negative
screens and shareholder activism are common (Revelli &
Viviani 2015); in contrast, in continental Europe, funds tend
to use positive screens (Renneboog etal. 2011) or “best-in-
class” strategies (Revelli & Viviani 2015). US-domiciled
funds’ negative screening reduces diversification benefits
as some sectors are excluded from portfolios, and asset
allocations are concentrated in a limited number of
sectors. This may play a role in asset allocation decisions
(Renneboog etal. 2008; Leite & Cortez 2014), especially as
funds often perform better in a country with a strong legal
environment and strict law enforcement (Cox & Schneider
2010; Richardson & Cragg 2010). ESG risk exposure may,
therefore, be impacted by the fund domicile. However, to
date, there has been no comparison of ESG risk exposure
between funds domiciled in the US and those domiciled
elsewhere and whether there is a US investment focus.
According to the literature, investing in US securities and
US domicile may expose funds to more ESG risk, so we set
out our first hypothesis as follows:
H1 Investments in US securities and US domicile increase
E, S, and G risk exposures
Trends ininvestor behavior
The UN PRI requires the integration of ESG factors in
investment decisions. With global concerns about ESG
issues and the demand for sustainable investment, mutual
funds have recently incorporated ESG factors into allocation
decisions affecting investor behavior. The UN PRI prompts
funds to adopt more of an ESG focus (Rasche etal. 2012)
and has seen growth in the ESG fund industry over the last
few decades (Arjaliès, 2010; Fang and Foucart 2014). Today,
investors are concerned with:
“…extra-financial considerations, such as concerns
regarding the ethical, religious, social, governance,
or environmental impacts of the entities that investors
are looking to invest in” (Slager 2015, p. 393).
The literature shows that specialist-ESG investment
practices have become similar to traditional ones. Folqué
etal. (2021) investigate ESG risks and distinguish between
funds that apply only negative filters and those that
apply more advanced ESG strategies such as corporate
engagement and impact investment (Casalini & Vecchi
2023; Vogeley etal. 2023). Similarly, Ferriani and Natoli
(2021) analyze investors’ preferences during the COVID-19
pandemic, finding that investors are turning to lower ESG
risk and more sustainability-focused funds. An ESG focus
by fund managers is becoming normal in the trend of fund
asset allocation with possibly little differentiation between
ESG and conventional funds. Hence, it may be that ESG
risk has already been considered in the investment decision
process of funds, but importantly, environmental, social,
and governance dimensions are different in scope, material
issues, and risk assessment methodology, so specialization
may be needed in disaggregating the three dimensions.
This paper contributes to the literature by examining the
investment practice behavior of investment managers
concerning exposure to environmental risk, social risk, and
governance risk separately.
Investor convention is to use beta to measure portfolio
risk, which shows how volatile a fund’s returns might be
compared to the overall market’s returns. However, this does
not reflect any ESG-related risk to which funds are exposed,
such as climate change, the health of people, or any related
governance risk. As a result, a higher rate of return may
be obtained at the cost of ignoring ESG risk exposure. If,
as Markowitz etal. (2012) document, ESG fund manager
behavior is to try to align their practices with conventional
fund managers to show that their ESG funds are similar,
this may result in greater exposure to ESG risk and lead
to herding behavior. Indeed, Statman (2000) argues that
ESG funds are similar to traditional funds as their socially
responsible or ESG mandate has no value, emphasizing that
value is driven by the risk profile. For example, some studies
find that ESG funds tend to have better average sustainability
scores than other funds (Joliet & Titova 2018), but they
often focus on negative rather than positive attributes,
eliminating “risk” but not including “good” (Ferruz etal.
2012). Funds may, thus, vary in their investment strategy
and again, as Ielasi etal. (2018) note, sustainability is not
the same as an E, S, or G dimension separately. Sustainable
investment is, by definition, an investment that creates
positive and unharmful impacts on E, S, and G dimensions,
but, in practice, investor behavior seems to focus more on
the environment rather than on social or governance issues.
Thus, an investment strategy to promote a sustainable
environment may be different from an investment strategy
focusing on positive impacts on society or promoting good
governance practices in business.
Although investor behavior with a focus on ESG-rated
securities may lower risk exposure, there is often a greater
exposure to smaller companies (Luther & Matatko 1994;
Gregory & Whittaker 2007). Indeed, Schröder (2004) shows
that very few ESG funds focus on large-capitalization stocks
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319Environmental, social andgovernance risk exposures ofmutual funds
and, of these, they are concentrated in the US. In Australia,
Humphrey & Lee (2011) document that an investment
strategy of positive screening biases funds to selecting
larger investee firm stocks, while negative screens result
in the selection of smaller firm stocks. Joliet and Titova
(2018) note that smaller capitalization stocks have higher
ESG risk exposure, often because their ESG disclosure is
less, and the integration of ESG into business operations
can be relatively costly for small firms. Thus, larger investee
companies are more likely to have better E, S, and G scores,
making these popular with investment managers and, hence,
would possibly lower the E, S, and G risk of funds investing
in these assets. Irrespective of whether the focus is on
ESG or the separate dimensions, Statman (2000) provides
evidence that an ESG focus reduces diversification. Ooi
and Laibcygier (2013) show that fully diversified portfolios
are not possible for ESG-focused funds and that industry
classifications explain portfolio returns. Thus, there may be
implications for risk scores regarding ESG factors, where
a more restricted investment universe results in less size
dispersion of investee companies that increases ESG risk
exposures, in contrast to ESG-rated securities reducing risk
exposure leading to our second hypothesis:
H2 Investments in ESG-rated securities, more ESG assets
under management (AUM) and greater size dispersion
decrease E, S, and G risk exposures.
Nevertheless, investment managers may have
differentiation strategies as some studies show that ESG-
focused funds are more growth-oriented (Gregory &
Whittaker 2007; Cortez etal. 2012), but others find they
are more value-oriented (Bauer etal. 2006) or less value-
oriented (Humphrey etal. 2016), with Hoepner etal. (2011)
finding that Islamic ESG funds focus on growth stocks.
Bessler etal. (2022) show that growth funds have better
timing skills and higher alphas than value-orientated and
blended-style funds. In contrast, Joliet & Titova (2018) find
that when comparing ESG scores, asset holdings are more
economically significant for value funds than growth funds.
Overall, investor behavior may focus on value or growth
investment styles, but having less style dispersion reduces
diversification, albeit with an ESG focus. As the literature
has mixed views, we model both value and growth and also
style dispersion as our third hypothesis:
H3 Investments concentrated in growth or value securi-
ties and limited style dispersion increase E, S, and G risk
exposures.
There may also be a trend toward certain sectors. Hall
and McVicar (2013) find that managers have style-specific
investment skills. For example, some funds may focus their
investment strategies on technology (Kirzner & Uhlaner
2013). In contrast, sustainability-focused funds with an
ESG investment objective, as Ielasi etal. (2018) note,
invest less in financial firms. Brière etal. (2017) examine
US funds’ exposure across ten sectors and find that three
sectors account for around half of the portfolio holdings.
For ESG funds, technology companies account for 21% of
the fund allocation, health care 15%, and financials 13%; for
conventional funds, the proportion invested in technology
is 19%, financial companies 15%, and health care 11%. In
contrast, utilities make up just 2% of the asset allocation.
Notably, ESG funds have a higher proportion of their
investment in health care, which is not surprising as good
health and well-being are sustainability issues (UN SDG
No. 3). However, the health care sector is more aligned with
societal concerns around sustainability, but for technology
and financial firms, of which investments form a large part
of fund allocations, exposure to E, S, and G risk is not
commonly known, so we set out the next hypothesis as
follows:
H4 Investments in technology or financial companies
decrease E, S, and G risk exposures.
Another feature of investment management is the
concept of herding, where popular investment strategies are
replicated by other funds. For example, Choi and Sias (2009)
find evidence of institutional industry herding, where buying
the securities of companies in certain sectors one quarter
leads to around 40% of funds buying securities within those
same sectors in the next quarter. Thus, there is evidence of
some sectors and securities being popular at any one time,
and this may reflect that E, S, and G factors are not driving
portfolio asset allocations at all. As noted above, three
sectors dominate asset management strategies, and with a
trend of more scrutiny from the investment industry, herding
behavior may reduce E, S, and G risk exposures. We set out
Hypothesis 5 as follows:
H5 Investments in popular securities decrease E, S, and G
risk exposures.
Managerial characteristics
A focus on E, S, and G dimensions by funds may be because
of management features that impact investment decision-
making. Fund managers play a significant role in fund invest-
ment decision-making (Muñoz etal. 2014) and, as Statman
(2000) notes, funds analyzing ESG information may incur
more costs, analyzing data across both financial and ESG
parameters, with a consequent increase in the management
fees. Further, Cici etal. (2010) analyze the experience of
fund managers before joining the investment industry with
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320 C.Helliar et al.
fund families exploiting that talent for successful outcomes.
Goldman etal. (2016) report that the average tenure of a
fund manager is 6 years, and it may be that a longer tenure
embeds an ESG focus and enables managers to concentrate
on long-term rather than short-term performance measures.
Thus, a higher management fee and longer-tenured manag-
ers may reduce E, S, and G risk exposure, as set out in our
next two hypotheses:
H6 Higher management fees decrease E, S, and G risk
exposures.
H7 Experienced management decreases E, S, and G risk
exposures.
Control factors
As some factors, such as fund size and fund age, are impor-
tant considerations in the fund management literature (Mal-
lin etal. 1995; Bauer etal. 2005; Kreander etal. 2005; Gil-
Bazo etal. 2010; Sievänen etal. 2013; Capelle-Blancard and
Monjon, 2012; Nofsinger & Varma 2014), we use these as
control factors. For example, Goldman etal. (2016) note that
the average age of their fund sample is just 11 years. Finally,
the literature documents that equity investment performance
is related to systemic risk (beta) as well as the idiosyncratic
risk associated with portfolio characteristics, such as port-
folio size. To control for the effect of fund type (ESG funds
versus conventional funds) together with the effects of fund
age, systemic risk (beta) and fund size, we include these
variables in the model to ensure that our findings on E, S,
and G risk exposures are not due to these factors. The next
section now outlines the research methods undertaken in
this study.
Methodology
Starting from a comprehensive dataset of 112,484 funds and
applying the approach of Helliar etal. (2022), we obtain a
representative sample of 18,648 funds as of the end of June
2020. Figure1 outlines our research design with a series
of steps, first selecting our sample of ESG funds and then
matching them with conventional funds. We then analyze the
portfolio holdings of funds before we apply the generalized
linear modeling (GLM) approach to examine how invest-
ment strategies and fund managers’ experience impact E, S,
and G risk scores after controlling for fund type (ESG versus
conventional), size, age, and beta.
More precisely, we use the Morningstar database
for our data because, as Chang etal. (2020) note, the
Morningstar sustainability ratings are based on data from
Sustainalytics that has enormous credibility within the
investment community and influences investor decision-
making. Candelon etal. (2021) also note that the ESG
scores in Morningstar cover over 90% of funds’ securities
holdings and that the fund scores are normalized over the
Fig. 1 A schematic representation of the study design
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321Environmental, social andgovernance risk exposures ofmutual funds
aggregation of the firm-level ratings. They also note that,
unlike other data sources such as MSCI, Morningstar’s
sustainability ratings do not have any industry biases. We
use the Morningstar fund database and select funds located
in countries with at least one ESG fund to avoid a selection
bias problem that may be caused by including countries that
only have conventional funds that are less aware of ESG risk.
Our starting sample is composed of 112,484 funds1 with an
average fund age of about 10 years and represents the global
fund industry incorporating funds with a potential mix of
ESG risk scores as outlined below.
First, any outliers are removed from the sample by
removing all funds with extreme values, such as percentages
greater than 100%, and the dataset is cleaned by removing
funds with missing data on criteria such as management
and fund allocation. Where funds have the same Fund ID
in the same currencies across different tranches, we choose
the largest, oldest fund tranche for each Fund ID, reducing
our sample size to 45,457 funds (Step I).2 Seven criteria
are used to match funds. The first criterion is performance,
represented by the fund’s profitability and a risk measure
(the fund beta). The second criterion is management
attributes reflected by features such as management fees, the
tenure and retention of fund managers, and the success ratio.
The third criterion is the investment strategy, including cash
holdings, US equity investments, the percentage in the top
10 holdings, a growth-focused investment, a value-focused
investment, as well as size and investment style dispersions.
The fourth criterion is based on the sectoral spread proxied
by the percentage of investment of each fund in different
sectors. Our last three matching criteria are fund size and age
(widely applied in the literature) and the P/B ratio to reflect
the fund growth. The data for all these criteria are obtained
from the Morningstar Database (Steps II and III).
Following Helliar etal. (2022), we score each of 45,457
funds and then match each ESG fund to a non-ESG fund
with an index score +/−0.025 of the index score of an ESG
fund up to 5 funds above or below the score. Where the 5th
(+5) conventional fund has the same index score as other
funds (+6 etc.), all funds with the same value are included in
the sample; thus, in such a case, more than five conventional
funds will be selected to avoid a bias selection problem.
Our final sample is composed of 18,648 funds with a mix of
surviving and dead funds: 2,638 are ESG funds and 16,010
are conventional funds (Steps IV and V).
Next, we retrieve Sustainalytics data that includes
scores measuring ESG risk and some ESG investment
information of funds. The Portfolio Environment Risk
score (Env Risk Score) is “the asset-weighted average of
the company environmental risk scores for the covered
holdings in a portfolio displayed as a number between 1 and
100.”The lower the score, the better. The Portfolio Social
and Governance risk scores (Soc Risk Score and Gov Risk
Score) are calculated in a similar fashion for social risk and
governance risk, respectively. In addition to the scores, for a
robustness test, we also download the percentile ranks within
each risk score category for each fund. The percentage of
AUM covered by ESG reflects the percentage of AUM with
an ESG rating from Sustainalytics.
The individual investee holdings data for each of the
18,648 funds was downloaded from Morningstar as of
Table 1 Investment allocation of a fund downloaded from Morning-
star
This table shows the investment allocation on one fund as
downloaded from Morningstar
Investment %
Cash & Cash Equivalents 17.39
Quanta Services Inc. 7.17
Fuji Electric Co Ltd 5.54
Cheniere Energy Inc. 4.90
First Solar Inc. 4.02
Trane Technologies PLC 3.97
National Grid PLC 3.97
Vestas Wind Systems A/S 3.93
Daikin Industries Ltd 3.91
Linde PLC 3.86
Mitsubishi Electric Corp 3.78
Prysmian SpA 3.71
Rockwell Automation Inc. 3.68
MasTec Inc. 3.64
Air Products & Chemicals Inc. 3.63
Georg Fischer AG 3.49
ABB Ltd 3.38
Valmont Industries Inc. 3.23
Johnson Controls International PLC 3.09
Hitachi Ltd 2.77
Oil Search Ltd 2.66
Constellium SE A 2.34
Emerson Electric Co 1.94
ISE Ltd 0.00
1 We select ESG funds as a proxy for funds with lower ESG risk
exposure. Following Candelon et al. (2021), we select ESG funds
using a discrete selection process based on keywords such as “social,”
“environment,” “green,” “Catholic,” and “Islamic” where the name of
the fund was not clear, we sourced the fund’s brochures and docu-
ments to verify the ESG credentials. Our sample of ESG funds was
matched with non-ESG funds so our final sample includes both ESG
and conventional funds (Helliar etal. 2022).
2 Some funds share the same Fund ID but are in different currencies.
For each currency, we choose the largest, oldest fund to be in the rep-
resentative sample.
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322 C.Helliar et al.
June 30th, 20203 or the latest reporting date for dead funds,
including the name of the investee company, its SECID
code, and the percentage of each fund invested in it. Table1
shows an example of a conventional fund’s investee holding
data downloaded from Morningstar. The table shows
that, for this particular fund, 17% is held in cash and cash
equivalents and that 23 securities make up the fund AUM.
Across the sample, the funds vary in the amount of cash held
and the number of investments—some funds hold thousands
of investee company securities, and some only have one
or two investments. Across our sample funds, on average,
ESG (conventional) funds hold around 156 (126) securities,
and the average amount held in cash and cash equivalents
is about 6.19% (4.23%). Similar to our sample, Goldman
etal. (2016) report that the average number of assets in their
mutual funds is 144, ranging from just 11 to over 3,000.
Next, we create a new variable representing another
investment strategy of funds, herding investment. Thus, we
calculate the ratio of common investee companies to the
total securities for each fund—so-called % Invest in Popular
Securities.4 First, we focus on every fund's top 10 holdings
to identify the 50 most common investment choices. We then
compile a list of all the investee companies included in the
portfolios held by at least 100 funds in our sample. We then
analyze the Sustainalytics E, S, and G risk scores separately,
using a GLM estimator,5 as outlined in Model 1:6
where the scorei, the E, S, or G risk score of fund i, is our
dependent variable; εi indicates a zero-mean idiosyncratic
stochastic error term; where βi is the coefficient for our vari-
ables; in particular, β1 is the coefficient of the percentage of
investment in US equity (% Invest in US Equity), β2 is the
coefficient of the dummy equal to one if the fund domicile is
(1)
score
i
=𝛼+𝛽
1
%Invest in US Equity +𝛽
2
US domicile
i
+𝛽
3
Invtrend
i
+𝛽
4
Managementi
+𝛽
5
Investment Objective
i
+𝛽
6
Fund Size
i
+𝛽
7
Fund Age
i
+𝛽
8
Beta
i
+𝜀
i
the US, β3 and β4 are vectors of the coefficients for the vari-
ables in “Invtrend” and “Management” vectors, respectively.
Our “Invtrend” is a vector of variables representing the
fund investment strategy. Investment trends investigated in
Model 1 include the percentage of ESG-rated securities (%
Invest in ESG Securities), the percentage of AUM held in
ESG-rated securities (% of AUM in ESG Securities), invest-
ment dispersion across different firm sizes (Size Disper-
sion), the percentage of investment using a growth strategy
(Growth Investment) and a value strategy (Value Investment),
dispersion of investment styles employed by the fund man-
ager (Style Dispersion)7, the percentage of investment in the
technology industry (% Invest in Technology Firms) and in
the financial industry (% Invest in Financial Firms), the per-
centage of investment in popular securities (% Invest in Pop-
ular Securities). Our “Management” is a vector of variables,
including Management Fee, the manager tenure (Manager
Tenure), the manager retention over 5 years (Manager 5Y
Retention)8 and the success ratio of the fund (Management
5Y Success Ratio).9 We then replace the scores with E, S,
and G risk rankings for robustness checks. The next section
outlines the results.
3 This research requires data retrieved at a point of time, instead of
several snapshots, in order to avoid data repetition issues and to allow
the impact of fund age on the fund’s risk exposure and controlling the
risk condition associated with the period. Note that our investigation
focuses on the impact of investment strategy, which is related to static
investment objectives, and the characteristics of fund management.
4 The most popular securities coincide with the most 50 investee
companies held by our sample funds. This variable was built by the
authors.
5 Newton-Raphson algorithm with Marquardt steps is implemented.
The GLM estimator is chosen as it is more flexible than the OLS esti-
mator. More precisely, a normal distribution for the dependent vari-
able is not required and the choice of the relationship is not linked to
the choice of random component.
6 Fund Size coincides with the natural logarithm of the total amount
of money managed as a standalone portfolio across share classes,
while Fund Age is the natural logarithm of the number of months
since the date on which the fund began its operation.
7 Size and Style Dispersion have values of 0, 1, and 2.
8 The percentage of a firm's portfolio managers who worked there
during the past five calendar years. The calculation ignores a manager
moving from fund to fund within the same organization. The calcula-
tion is done at the branding level.
9 The percentage of a branding company's open-ended mutual funds
with a Morningstar Category rank of less than 50 over the 5-year
period through the previous month's end.
Results
Table2 reports the descriptive statistics for our sample and
shows that the social risk score is higher than the govern-
ance risk score, and the environment risk score is the lowest,
similar to the risk ranks. This evidence shows that funds are,
on average, less exposed to environmental risk and more
exposed to governance and social risk. Table2 shows that
our investigation has a good spread of funds, with around
three quarters of funds domiciled in countries other than
the US and some funds investing all or nothing in US stocks
and highlights that funds have about 95% of AUM in ESG-
rated stocks. Funds invest more in technology companies
but less in financial services, although these two sectors are
important, with around a sixth of investments in technology
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323Environmental, social andgovernance risk exposures ofmutual funds
stocks and around an eighth in financial services. The per-
centage invested in popular securities is fairly low, around
3%. With regard to management attributes, managers have
been employed for around 8 years and around three quarters
have a tenure longer than five. The fund beta is around 1. In
addition, the fund investment strategies are similarly split
between growth-focused and value-focused.
Table3 shows the top 50 investee companies held by the
sample of funds10 and demonstrates that well-known com-
panies’ securities are held by our funds, such as Alphabet
Inc., Amazon.com Inc., and Apple Inc. We analyze these
securities by Russell Sector (Table4), showing that the top
3 sectors are technology, health care, and financial services.
At the Russell subsector level, technology includes informa-
tion technology, and at the more granular Russell industry
level, it includes computer services, software, and systems,
with about 53% of the technology sector (8 of 15) concen-
trated in this Russell industry. For health care, at the Russell
subsector level, pharmaceuticals & biotech comprise 8 of
the 12 healthcare companies, and at the Russell industry
level, pharmaceuticals comprise 7 of the 8. For financial
services, at the Russell subsector level, all 6 firms belong to
Table 2 Descriptive statistics of
the sample funds
This table shows the descriptive statistics for our funds. All the data was downloaded from Morningstar.
N. Mean Std Dev min Max
Dependent variables
Env Risk Score 16,121 5.1851 5.0142 0.2000 65.3300
Soc Risk Score 16,121 9.8631 4.3672 3.7400 65.7000
Gov Risk Score 16,121 8.1174 4.3545 2.8100 63.2900
Env Risk Rank 16,014 47.4211 27.9972 1.0000 100.0000
Soc Risk Rank 16,014 47.6697 27.5633 1.0000 100.0000
Gov Risk Rank 16,014 47.5173 27.7357 1.0000 100.0000
Independent variables
% Invest in US Equity 18,648 32.8793 37.4902 0.0000 100.0000
US Domicile 18,648 0.2100 0.4073 0.0000 1.0000
% Invest in ESG Securities 18,648 76.2564 22.3489 0.0000 99.7537
% of AUM in ESG Securities 18,648 94.7618 9.8293 0.0000 100.0000
Size Dispersion 18,648 1.3556 0.5290 0.0000 2.0000
Growth Investment 18,648 30.0850 16.7578 0.0000 90.0900
Value Investment 18,648 28.8725 18.0119 0.0000 95.6500
Style Dispersion 18,648 1.5717 0.5077 0.0000 2.0000
%Invest in Technology Firms 18,648 17.6134 12.1620 0.0000 98.6600
% Invest in Financial Firms 18,648 12.0955 7.5787 0.0000 47.3900
% Invest Popular Securities 18,648 2.9413 4.1930 0.0000 41.1765
Management Fee 18,648 0.9067 0.5449 0.0000 3.5200
Manager Tenure 18,648 7.8130 3.4830 0.0500 66.6700
Manager 5Y Retention 18,648 0.7635 0.1261 0.1100 1.0000
Management 5Y Success Ratio 18,648 0.5010 0.1498 0.0000 1.0000
Control variables
Investment Objective 18,648 0.1415 0.3485 0.0000 1.0000
Fund Size 18,648 18.7921 2.3071 −0.0300 26.6300
Fund Age 18,648 118.7129 100.2047 0.0667 1090.1000
Beta 18,648 1.0221 0.1870 −0.4300 4.2500
10 We also compared ESG fund holdings against non-ESG funds
and found that seventy percent of the investments are common across
both types of funds, with 35 investee companies held by both; these
securities include Alphabet Inc., Amazon.com Inc., Apple Inc., and
Microsoft Corp.. The ESG funds’ investee companies include invest-
ments such as L’Oreal SA and Vestas Wind Systems A/S. The non-
ESG funds' investee companies include Taiwan Semi-conductor’s
ADR, which is in addition to Taiwan Semi-conductor Manufacturing
Co. Ltd shares held by both types of funds, Berkshire Hathaway Inc.
and British American Tobacco PLC. The top 50 holdings across ESG
and non-ESG comprised of 63 investee companies in total, 49 top
holdings for ESG funds (excluding the Alphabet Inc. duplication) and
48 for conventional funds (excluding Alphabet Inc. and Taiwan Semi-
conductor Manufacturing Co. Ltd duplication). In our regression, we
use the 63 investee companies to ensure that we do not have a bias
toward any particular type of fund.
Footnote 10 (Continued)
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324 C.Helliar et al.
electronics, and at the Russell industry level, semiconductors
and components comprise 4 of the 6.
Table5 compares the sector classification of the sample
of the most popular securities and follows a similar pattern
to Table2, except that it is more pronounced in technology
and financial services: around 30% of the fund holdings are
in technology, around a fifth in health care and 12% in finan-
cial services.
Tables6 and 7 show the popularity of certain securi-
ties by analyzing those held by at least 100 funds, num-
bering 217 securities in total. The 217 investee companies
are shown in Table6, and they are all in Table3. These
popular investments are also in the three popular sectors
of technology, financial services, and health care (Table7);
117 of the 217 investee companies (54%) belong to these
three sectors. Many of these companies are well-known
household names, such as Amazon.com Inc. and Facebook
Inc., as well as AstraZeneca PLC and Pfizer Inc., that have
become household names since the COVID-19 pandemic.
Our analysis in Tables4 and 7 confirms the results in the
previous literature, such as Brière etal. (2017), who show
that technology companies account for the most fund alloca-
tion, followed by health care and financial services. How-
ever, these three sectors have different commitments regard-
ing the single dimensions of sustainability. On the one hand,
the mean sustainability scores of our top 50 firms selected
by both ESG and conventional funds and belonging to the
technology sector appear to be more balanced: 2, 7, and
7 for the environment, social, and governance dimensions,
respectively. On the other hand, the sustainability of similar
firms belonging to the healthcare sector is mainly driven by
the social dimension: the mean scores are equal to 1, 12, and
7 for the environment, social, and governance dimensions,
respectively. If we focus on the financial services sector,
the environmental dimension is neglected completely. We
get similar results if we consider the investee companies
selected by at least 100 funds. This additional analysis con-
firms the formulation of our fourth hypothesis only focused
on the technology and financial sectors, chosen as E, S, and
G risk is not commonly known.
After describing the main aspects of the asset alloca-
tion of our sample funds, we run the GLM regression to
identify the determinants of environmental, social, and
governance risks. The results shown in Table8 reject our
first hypothesis. Funds with more investments in US equi-
ties have less environmental and governance risk exposure,
possibly because: (1) more US companies are now focused
on climate change issues and risk issues but not so focused
on health and safety, modern slavery in the supply chain or
other social issues (see Cai etal. 2016); and (2) the govern-
ance of US firms is stronger than before, with CSR involve-
ment ingrained in US culture (Maignan and Ralston 2002).
Funds domiciled in the US also have less E, S, and G risk,
Table 3 The top 50 investee company holdings
This table shows the top 50 investee companies held by all our
sample funds
* Apple Inc. included both Class A and Class C shares
Top 50 holdings
Accenture PLC Class A
Adobe Inc.
Agilent Technologies Inc.
AIA Group Ltd
Alibaba Group Holding Ltd ADR
Allianz SE
Alphabet Inc. A
Amazon.com Inc.
American Water Works Co. Inc.
Apple Inc.
ASML Holding NV
AstraZeneca PLC
Danaher Corp.
Ecolab Inc.
Enel SpA
Facebook Inc. A
GlaxoSmithKline PLC
Intel Corp.
Johnson & Johnson
Koninklijke DSM NV
LG Chem Ltd
Linde PLC
Lonza Group Ltd
L'Oreal SA
Mastercard Inc. A
Microsoft Corp.
Nestle SA
Novartis AG
Novo Nordisk A/S B
NVIDIA Corp.
Orsted A/S
PayPal Holdings Inc.
Ping An Insurance (Group) Co. of China Ltd Class H
Procter & Gamble Co.
Roche Holding AG Dividend Right Cert.
Salesforce.com Inc.
Samsung Electronics Co. Ltd
Sanofi SA
SAP SE
Schneider Electric SE
Siemens AG
SK Hynix Inc.
Taiwan Semi-conductor Manufacturing Co. Ltd
Tencent Holdings Ltd
Tesla Inc.
The Home Depot Inc.
Thermo Fisher Scientific Inc.
UnitedHealth Group Inc.
Vestas Wind Systems A/S
Visa Inc Class A
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325Environmental, social andgovernance risk exposures ofmutual funds
and as the sign of US domicile and %Investin US Equity are
both negative (except for the social score), this may provide
support for Ferreira etal. (2017), who find an informational
advantage for institutional investors if they invest domesti-
cally. Thus, US-domiciled funds would have an information
advantage when investing in US stocks.
In examining our second hypothesis, more investments
in ESG-rated securities do not make funds less exposed to
E, S, and G risk. Importantly, the signs of coefficients for %
Invest in ESG securities (+) and % AUM in ESG securities
(−) are opposite, but the size of the coefficient for the for-
mer is much smaller than the coefficient for the latter; so a
fund may not be more ESG by investing in more ESG-rated
securities by quantity than a fund investing less in ESG-rated
securities but where the total AUM is higher. Thus, funds
driven by E, S, and G investments focus on the total AUM
rather than quantity. In addition, funds that are more diversi-
fied in terms of the size of investee companies have lower
environmental, social, and governance risk exposures, while
a more diverse investment style increases social and govern-
ance risk scores but is irrelevant to the environmental risk
score. Thus, it is important to analyze the E, S, and G sepa-
rately as risk exposures vary across the three dimensions.
If we continue to focus on our third hypothesis, although
the coefficient for funds investing in growth stocks is not
significant, funds investing in value stocks have higher envi-
ronmental and governance risk exposures with no difference
in social risk scores.
In answer to our fourth hypothesis, more investment in
the technology industry decreases environmental and social
risk exposure but increases governance risk. Recall from
Tables3-7 that popular securities are mainly in the tech-
nology industry. Many funds nowadays invest in green or
climate change technologies, and technology development
is believed to help increase the efficiency of production and
resource use, affecting the environment and society. With
regard to the finance industry, there is no significant rela-
tionship with environmental risk exposure, but social and
governance risks increase. Notably, technology and finan-
cial firms have had their governance called into question,
featuring prominently in the disclosure of poor practices
or greenwashing incidents, as seen in recent news (Forbes
Magazine 2019; Lodhia and Mitchell 2022; Reuters 2023).
An environmental focus is more prominent in investment
strategy, followed by a focus on social factors, with govern-
ance being unimportant.
We find support for our fifth hypothesis, whereby more
investment in popular securities, reflecting herd behavior,
relates to less environmental, social, and governance risk
exposure. Thus, it may be that the whole fund industry
is being driven by an E, S, and G focus, with common
securities reducing E, S, and G risk.
With regard to H6 and H7, Table8 has some interest-
ing findings regarding fund management characteristics
and E, S, and G risk exposure. A higher management fee
is not significant for E, S, and G risk exposure, reflecting
that although investors may perceive that investment in E,
S, and G-focused companies would incur higher search and
management costs, a higher management fee cannot be seen
as a signal for better E, S or G performance. Table8 also
shows that the longer the average fund manager has stayed
with the fund and the higher the percentage of managers
that have stayed within the fund family, the higher a fund’s
E, S, and G risk. Thus, the more that managers experience
the retention process, the more the fund family may become
Table 4 Top 3 sectors of the top 50 investee companies
This table shows the top 3 Russell sectors of the top 50 investee companies, disaggregated into Russell subsector and Russell industry
Russell sector (9) N. companies % Russell subsector (17) N.
companies
% Russell industry (n.27) N.
companies
%
Technology 15 30.00 Information Technology 9 18.00 Computer Services Software
and Systems
8 16.00
Health care 12 24.00 Pharmaceuticals & Biotech 8 16.00 Pharmaceuticals 7 14.00
Financial Services 6 12.00 Electronics 6 12.00 Semiconductors and
components
4 8.00
Table 5 Sector analysis of the top 50 investee companies
This table shows the sector analysis for the top 50 investee
companies. Two securities for Taiwan Semi-conductor were included
as the shares and the ADR and Apple Inc. had two classes of shares
Russell Sector N. %
Technology 15 30.00
Health care 12 24.00
Financial Services 6 12.00
Consumer Discretionary 4 8.00
Materials & Processing 4 8.00
Producer Durables 3 6.00
Utilities 3 6.00
Consumer Staples 2 4.00
Energy 1 2.00
Total 50
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326 C.Helliar et al.
Table 6 Investments held by at least 100 funds
Company name
ABB Ltd Baxter International Inc. Daikin Industries Ltd JD.com Inc. Mitsubishi UFJ Financial
Group Inc.
Reckitt Benckiser Group
PLC
Taiwan Semi-conductor
Manufacturing Co. Ltd
Abbott Laboratories Bayer AG Danaher Corp Jiangsu Hengrui
Medicine Co. Ltd
Morgan Stanley Recruit Holdings Co. Ltd Takeda Pharmaceutical
Co. Ltd
AbbVie Inc. Becton, Dickinson and
Co.
Danone SA Johnson & Johnson Naspers Ltd Regeneron
Pharmaceuticals Inc.
TAL Education Group
Accenture PLC Berkshire Hathaway Inc. Dassault Systemes SE JPMorgan Chase & Co.. National Grid PLC Reliance Industries Ltd TC Energy Corp..
Adidas AG BHP Group Ltd Deutsche Boerse AG Kakao Corp. NAVER Corp. RELX PLC Teleperformance SE
Adobe Inc. BHP Group PLC Deutsche Post AG KDDI Corp. NCsoft Corp. Rio Tinto PLC Tencent Holdings Ltd
Adyen NV BNP Paribas Deutsche Telekom AG Keyence Corp. Nestle SA Roche Holding AG Tesla Inc.
Agilent Technologies Inc. BP PLC Diageo PLC Kingspan Group PLC NetEase Inc. Roper Technologies Inc. Texas Instruments Inc.
AIA Group Ltd Bristol-Myers Squibb
Company
eBay Inc. Koninklijke Ahold
Delhaize NV
Netflix Inc. Royal Bank of Canada The Home Depot Inc.
Air Liquide SA British American
Tobacco PLC
Ecolab Inc. Koninklijke DSM NV New Oriental Education
& Technology Group
Inc.
Royal Dutch Shell PLC The Toronto-Dominion
Bank
Alibaba Group Holding
Ltd
Broadcom Inc. Eli Lilly and Co. Koninklijke Philips NV Newmont Corp. S&P Global Inc. The Walt Disney Co.
Allianz SE Brookfield Asset
Management Inc. Class
A
Enbridge Inc. Kweichow Moutai Co.
Ltd
NextEra Energy Inc. Salesforce.com Inc. Thermo Fisher Scientific
Inc.
Alphabet Inc. Canadian National
Railway Co
Enel SpA LafargeHolcim Ltd Nike Inc. Samsung BioLogics Co.
Ltd
Total SE
Amazon.com Inc. Canadian Pacific Railway
Ltd
Equinix Inc. Lam Research Corp. Nintendo Co. Ltd Samsung Electronics
Co. Ltd
Toyota Motor Corp.
American Tower Corp. Capgemini SE Essity AB LG Chem Ltd Nippon Telegraph &
Telephone Corp
Samsung SDI Co. Ltd UBS Group AG
American Water Works
Co. Inc.
Celltrion Inc. Facebook Inc. Linde PLC Novartis AG Sandvik AB Unilever NV
Amgen Inc. Charter Communications
Inc.
Fidelity National
Information Services
Inc.
Logitech International
SA
Novo Nordisk A/S Sanofi SA Unilever PLC
Anglo American PLC Chevron Corp FinecoBank SpA London Stock Exchange
Group PLC
NVIDIA Corp. SAP SE Union Pacific Corp.
Ansys Inc. China Construction Bank
Corp.
Franco-Nevada Corp Lonza Group Ltd Oracle Corp. Sberbank of Russia PJSC UnitedHealth Group Inc.
Anthem Inc. China Merchants Bank
Co. Ltd
Fresenius Medical Care
AG & Co. KGaA
L'Oreal SA Orsted A/S Schneider Electric SE Verizon Communications
Inc.
Aon PLC China Mobile Ltd Gilead Sciences Inc. Lowe's Companies Inc. Partners Group Holding
AG
ServiceNow Inc. Vertex Pharmaceuticals
Inc.
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327Environmental, social andgovernance risk exposures ofmutual funds
This table shows the 217 investee companies that are held by at least 100 funds
Table 6 (continued)
Company name
Apple Inc. Cigna Corp Givaudan SA Luxshare Precision
Industry Co. Ltd
PayPal Holdings Inc. Shin-Etsu Chemical Co.
Ltd
Vestas Wind Systems A/S
ASM International NV Cisco Systems Inc. GlaxoSmithKline PLC LVMH Moet Hennessy
Louis Vuitton SE
PepsiCo Inc. Shopify Inc. Vinci SA
ASML Holding NV Citigroup Inc. HDFC Bank Ltd Mastercard Inc. Pfizer Inc. Siemens AG Visa Inc.
AstraZeneca PLC Coca-Cola Co. Hong Kong Exchanges
and Clearing Ltd
MediaTek Inc. Philip Morris
International Inc.
Sika AG Wal - Mart de Mexico
SAB de CV
AT&T Inc. Comcast Corp. Housing Development
Finance Corp Ltd
Medtronic PLC Ping An Insurance
(Group) Co. of China
Ltd
SK Hynix Inc. Walmart Inc.
Autodesk Inc. Compagnie de Saint-
Gobain SA
Hyundai Mobis Co. Ltd Meituan PJSC Lukoil SoftBank Group Corp. Wheaton Precious Metals
Corp.
AXA SA Crown Castle
International Corp.
Hyundai Motor Co. MercadoLibre Inc. Procter & Gamble Co. SolarEdge Technologies
Inc.
Wolters Kluwer NV
Bank of America Corp CSL Ltd Iberdrola SA Merck & Co Inc. Prosus NV Sony Corp. Worldline SA
Bank of Nova Scotia CVS Health Corp. Infineon Technologies
AG
Microsoft Corp. Prudential PLC Symrise AG Wuliangye Yibin Co Ltd
Barrick Gold Corp Daiichi Sankyo Co Ltd Intel Corp. Midea Group Co Ltd Qualcomm Inc. Synopsys Inc. Zurich Insurance Group
AG
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328 C.Helliar et al.
complacent, and thus, the E, S and G risk exposure of their
funds increases.
With regard to our control variables, Table8 highlights
that newer funds have less E, S, and G risk exposure. Smaller
funds are less exposed to E and S risk, but fund size is not
related to G risk exposure, and market risk is not associated
with E or G risk, but funds with lower market risk have
higher S risk.
Robustness tests
To verify the robustness of our findings, we repeated the
same regressions using the environmental, social, and gov-
ernance risk rankings for the funds instead of the risk scores.
Table9 reports these results. The three dependent variables
of the model are the environmental, social, and governance
riskrankings. The three variables are percent rankings from
Table 7 Sector analysis of the 217 investments held by at least 100
funds
This table shows the breakdown by sector of the investee companies
in which at least 100 funds invest
Russell Sector N. %
Technology 44 20.28
Financial Services 38 17.51
Health care 35 16.13
Consumer Discretionary 26 11.98
Materials & Processing 21 9.68
Consumer Staples 16 7.37
Producer Durables 14 6.45
Utilities 13 5.99
Energy 10 4.61
Table 8 Regression results for
ESG scores
This table shows the regression results where the dependent variables are the E, S, and G scores. 10%, 5%,
and 1% significance levels are indicated by ***, **, and *, respectively.
Dependent variables Env risk score Soc risk score Gov risk score
Independent variables Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error
Constant Term 4.3711** 0.2412 3.8692** 0.1546 3.8479** 0.1809
% Invest in US Equity −0.0013** 0.0004 0.0017** 0.0001 −0.0009** 0.0002
US Domicile −0.2178** 0.0401 −0.1085** 0.0122 −0.1326** 0.0158
% Invest in ESG Securities 0.0073** 0.0012 0.0024** 0.0003 0.0031** 0.0004
% of AUM in ESG Securities −0.0433** 0.0036 −0.022** 0.002 −0.0257** 0.0024
Size Dispersion −0.0600* 0.0303 −0.0287** 0.0098 −0.0429** 0.013
Growth Investment −0.0022 0.0015 0.0003 0.0005 −0.001 0.0006
Value Investment 0.0048** 0.0013 0.0006 0.0004 0.0016** 0.0006
Style Dispersion −0.0069 0.027 0.0262** 0.0083 0.0224* 0.011
% Invest in Technology Firms −0.0069** 0.0023 −0.0013* 0.0007 0.0054** 0.0008
% Invest in Financial Firms −0.0033 0.0027 0.0065** 0.0008 0.0096** 0.0011
% Invest in Popular Securities −0.0521** 0.0049 −0.0067** 0.001 −0.0075** 0.0014
Management Fee 0.0524 0.027 −0.0134 0.0099 −0.0214 0.0128
Manager Tenure 0.0149** 0.0026 0.0074** 0.0014 0.0096** 0.0017
Manager 5Y Retention 0.8591** 0.1605 0.3064** 0.0494 0.3762** 0.0666
Management 5Y Success Ratio −0.047 0.0904 −0.0521 0.0308 −0.0618 0.0404
Controlling factors
Investment Objective 0.0791 0.0516 −0.0339* 0.0155 −0.0096 0.0203
Fund Size 0.0157* 0.0068 0.0045* 0.0019 0.0025 0.0025
Fund Age 0.0008** 0.0001 0.0003** 0.0001 0.0005** 0.0001
Beta 0.0204 0.0687 −0.1219** 0.0271 −0.0622 0.0342
Statistics
N. observations 16,121 16,121 16,121
Log-likelihood −47,397 −45,764 −45,656
LR statistic 3,217.97 1,845.87 1,981.49
Prob(LR statistic) 0.0000 0.0000 0.0000
Akaike info criterion 5.8826 5.68 5.6666
Schwarz criterion 5.8922 5.6896 5.6762
Hannan-Quinn criterion 5.8858 5.6832 5.6698
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329Environmental, social andgovernance risk exposures ofmutual funds
1 to 100. The fund ranked first has the lowest risk expo-
sure. The first rank is the most environmental-friendly for
the environmental rank, having the greatest positive impact
on society for the social rank, and investing most or all in
well-governed and transparent companies for the governance
rank. Thus, ESG funds tend to have a better rank (the coef-
ficient for ESG funds is negative in all models in Table9).11
Comparing the results in Table8 with those in Table9, over-
all, the rankings provide more prominent results than the
risk scores for each environmental, social, and governance
dimension. Apart from a few changes in signs, the robust-
ness of the results shows that a fund’s investment strategies
can play a role in reducing the E, S, and G rankings of a
fund, while, unlike the result for E, S, and G risk scores, a
rigorous selection process, reflected in a higher management
fee and a longer tenure of management, can improve a fund’s
E, S, and G rankings.
Conclusion
ESG investing has become an investor trend in the fund
industry, driven by the UN PRI and other global initiatives.
Importantly, there has been no systematic investigation of
the separate E, S, and G dimensions, and we contribute to
the literature by disaggregating the ESG factors into the E,
S, and G dimensions. As noted by previous literature, E,
S, and G factors are not the same (see Ielasi etal. 2018;
Hübel & Scholz 2020), and our analysis shows that E, S,
and G dimensions are not equal and that sustainability is
Table 9 Regression results for ESG ranks
This table shows the regression results where the dependent variables are the E, S, and G rankings. 10%, 5%, and 1% significance levels are
indicated by ***, **, and *, respectively
Dependent variables Env risk rank Soc risk rank Gov risk rank
Independent variables Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error
Constant Terms 3.5943** 0.1295 2.7347** 0.1312 2.817** 0.1328
% Invest in US Equity −0.0018** 0.0001 0.0015** 0.0001 −0.0005** 0.0001
US Domicile 0.0739** 0.0124 −0.2214** 0.0124 −0.0161 0.0118
% Invest in ESG Securities 0.0011** 0.0002 0.0017** 0.0002 0.0002 0.0002
% of AUM in ESG Securities 0.007** 0.0012 0.0113** 0.0012 0.0078** 0.0012
Size Dispersion −0.1776** 0.0088 −0.2072** 0.0092 −0.2510** 0.009
Growth Investment −0.0088** 0.0005 0.0042** 0.0005 −0.0012* 0.0005
Value Investment 0.0026** 0.0004 0.0067** 0.0004 0.0057** 0.0004
Style Dispersion −0.0203** 0.0094 0.0264** 0.0092 0.0573** 0.0092
% Invest in Technology Firms −0.0033** 0.0005 −0.0038** 0.0004 0.0048** 0.0004
% Invest in Financial Firms −0.0085** 0.0006 0.0068** 0.0006 0.0181** 0.0006
% Invest in Popular Securities −0.0184** 0.0013 −0.0048** 0.0011 0.0026* 0.001
Management Fee −0.0031 0.0082 −0.0996** 0.0086 −0.0548** 0.0085
Manager Tenure −0.0045** 0.0015 −0.0064** 0.0016 −0.0041** 0.0015
Manager 5Y Retention 0.1018** 0.0385 0.0839* 0.0374 0.0973** 0.0371
Management 5Y Success Ratio −0.1244** 0.0298 0.0581 0.03 0.031 0.029
Controlling Factors
Investment Objective −0.0803** 0.0134 −0.3422** 0.0153 −0.356** 0.0143
Fund Size 0.0089** 0.002 0.0061** 0.002 0.0088** 0.0021
Fund Age −0.0001 0.00004 0.0002** 0.00004 0.0003** 0.00004
Beta 0.0765** 0.0271 −0.2524** 0.0253 −0.0887** 0.0276
Statistics
N. observations 18,434 18,434 18,434
Log-likelihood −74,222 −74,399 −73,739
LR statistic 4,183.15 3,135.09 5,039.50
Prob(LR statistic) 0.0000 0.0000 0.0000
Akaike info criterion 9.2722 9.2943 9.2119
Schwarz criterion 9.2818 9.3039 9.2214
Hannan-Quinn criterion 9.2754 9.2975 9.215
11 The results are available from the authors on request.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

330 C.Helliar et al.
more often related to the environment than either social
or governance factors. For example, our results indicate
that investment in US stocks and US domicile is good for
the environment and governance but not so for the social
dimension. Our results for sectoral bias show that funds
concentrate their investments in the same three sectors
of technology, finance, and health care, with a particular
focus on technology companies, but the E, S, and G risks
are different. Investments in technology firms reduce funds’
exposure to environmental and social risks but increase
their governance risk exposure. Investments in financial
firms, although reducing environmental and social risks,
increase governance risk. We also find that investing in
more ESG-rated securities by quantity is not reflected in
E, S, and G risk exposures, but more ESG-rated securities
in total AUM reflects a higher concern for E, S, and G. We
also find that investing in popular securities, following a
herding strategy, affects the E, S, and G risk exposure
differently. Our univariate analysis shows that, despite the
ESG trend, many funds invest in well-known stocks, such as
Amazon.com Inc., Alphabet Inc., and Apple Inc., regardless
of the strategy (Utz & Wimmer 2014). Consequently, the
asset allocations of funds may not be driven by E, S, or
G investment objectives but rather by herd behavior, and
funds seem to invest in popular stocks instead of seeking out
companies that excel in E, S, or G principles. Future research
could examine whether this is because popular securities
are often technology companies such as Alphabet Inc.
and Apple Inc. In addition, we contribute to the literature
by showing that funds with a higher management fee are
associated with better social and governance rankings but by
showing no significant relationship with the environmental
risk ranking, implying that search costs and fees could
decrease S and G risk exposure. We also find that older
funds tend to have higher E, S, and G risk exposure than
younger ones, entrenching a conventional market approach.
Notably, our study has some limitations as we employ
cross-sectional data of funds retrieved from Morningstar:
it is composed of funds of different ages. We used a single
data snapshot to avoid repeating samples and overlapping
values because the model includes ‘fund age’ and various
5-year management factors. Further studies could use
panel data to examine whether funds change their asset
allocation over time, especially whether the E dimension
has become more important and entrenched within the
industry than the S and G dimensions. In addition, we
did not analyze passive versus active funds and how this
feature can impact E, S, and G risk. This would be a
fruitful avenue for future research.
To conclude, funds need to make clear to what extent
they are following an E, S, or G mandate rather than a
generic ESG mandate, as these are not necessarily the same.
Our findings could assist fund managers in planning their
strategy to achieve the fund’s objectives. For example, an
environmental focus suggests that they should invest more
in US securities and technology stocks, while a social focus
suggests that they should invest less in US securities and
avoid investments in financial companies. Further studies
should provide a deeper analysis of the alignment between
ESG funds instead of E, S, or G funds. With further analysis
and future research, the sustainability of our planet and
society may be assured.
Funding Open Access funding enabled and organized by CAUL and
its Member Institutions.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
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permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Publisher's Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Christine Helliar is an emeritus professor at the University of South
Australia, having worked in London at EY, Morgan Stanley, Citigroup
and Hong Kong and Shanghai banks. She has been a Director of
Research at The Institute of Chartered Accountants of Scotland and a
Research Director of the Australian Accounting Standards Board. She
was previously Dean of the School of Business at the University of
Dundee, Head of School of Commerce at UniSA, Chair of the British
Accounting and Finance Association (BAFA) and Chair of the Finance
Research Network (FIRN) in Australia. Her research interests cover the
circular economy, business applications of technology such as block-
chain, AI and machine learning, as well as risk and controls. She has
over 100 publications, over 30 grants, has been an editor of a journal
and is on a number of editorial boards.
Barbara Petracci is an Associate professor at Bologna University
Italy. She has extensive publications in corporatefinance. Her research
interests include corporate governance, sustainability, andfund
management industry.
Dr. Nongnuch Tantisantiwong is Head of Enterprise Risk and Infra-
structureat CIMBThai Bank Bangkok Thailand, previouslyworked
at Ministry of Finance (Thailand) and in UK academia. She has had
an academic career in the UK with a successful publication recordand
over 10 national and internationalgrants. Her research interests cover
financial economics, derivatives,risk modelling, machine learning,
banking, fundinvestment and sustainability.She has extensive bank-
ing industry experience coveringcredit and enterprise riskmodelling
and management, corporate andsustainabilitystrategies, and environ-
mental, social and governance risk management.
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