ESMA document number: 50-165-2146
Publication date: 23 May 2022
TRV 
Risk 
Analysis 
The drivers of the costs and 
performance of ESG funds

ESMA Report on Trends, Risks and Vulnerabilities – Risk Analysis 
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ISBN 978-92-95202-55-9, doi: 10.2856/260634, ISSN 2599-8749, EK-05-22-066-EN-N 
European Securities and Markets Authority (ESMA) 
Risk Analysis and Economics Department 
201-203 Rue de Bercy 
75012 Paris 
FRANCE 
risk.analysis@esma.europa.eu

ESMA TRV Risk Analysis 
23 May 2022 
3 
 
Investor Protection 
The drivers of the costs and 
performance of ESG funds 
Contact: natacha.mosson@esma.europa.eu1 
 
Summary 
Investment funds that include environmental, social and governance (ESG) features have 
grown rapidly over the last years. ESMA recently determined that ESG equity undertakings 
for collective investment in transferable securities (UCITS), excluding exchange-traded funds,
were cheaper and better performers in 2019 and 2020 compared to non-ESG peers. The
reasons behind this relative cheapness and outperformance of ESG funds are of particular 
interest. Understanding the cost and performance dynamics may bring insights for the overall
fund industry on how to make funds more affordable and profitable for retail investors. This 
study builds on past analyses by assessing whether portfolio composition can help to 
understand the cost and performance differentials between ESG and non-ESG funds. It
identifies several differences between the two categories of funds, with ESG funds being more 
oriented toward large caps and developed economies, and it demonstrates that these factors 
are correlated with lower ongoing costs. However, even after controlling for fund 
characteristics and differences in portfolio exposures, ESG funds remain statistically cheaper
and better performing than non-ESG peers between April 2019 and September 2021. Further 
research is thus needed to identify the other factors driving these cost and performance 
differences. 
 
Introduction 
In a context of increased retail participation in 
capital markets2, the question of the costs and 
performance of investment products is key to 
preserve investors’ confidence. These two 
elements are strongly linked as costs can 
significantly impact the net final return of an 
investment.3 Clear and complete information in 
this area is crucial to allow investors to make 
 
1 
 The author thanks Claudia Guagliano, Steffen Kern, Adrien Amzallag and Julien Mazzacurati for helpful comments and 
suggestions. All remaining errors are my own. 
2 
 See for example ESMA (2022b), Report on Trends, Risks and Vulnerabilities, No 1. 
3 
 The 2022 ESMA annual statistical report on performance and costs of retail investment products in the EU showed that an 
investment of EUR 10 000 in a hypothetical retail portfolio composed of equity (40 %), bond and mixed funds (30 % each) 
between 2011 and 2020 would have returned approximately EUR 18 000 in gross terms and around EUR 15 400 after 
deduction of costs. 
4 
 ESMA (2022a), Annual statistical report on performance and costs of retail investment products in the EU. 
5 
 Originally, ESMA (and other European supervisory authorities) received a request from the European Commission to 
publish recurrent reports on the cost and performance of retail investment, insurance and pension products (Request sent 
to ESAs in October 2017 to report on the cost and past performance of the main categories of retail investment, insurance 
and pension products). Following the review of its regulation, ESMA is now explicitly mandated to analyse the costs and 
charges of retail financial services and products (see Article 9 of Regulation (EU) No 1095/2010 of the European Parliament 
and of the Council of 24 November 2010 establishing a European Supervisory Authority (European Securities and Markets 
Authority), amending Decision No 716/2009/EC and repealing Commission Decision 2009/77/EC). 
informed 
decisions 
and 
to 
foster 
their 
participation in capital markets.  
With the aim to provide regular information on the 
costs and performance of investments across the 
European Union (EU), ESMA publishes every 
year an annual statistical report displaying the 
costs and past performance of retail investment 
products under its remit.4 5

ESMA TRV Risk Analysis 
23 May 2022 
4 
 
Following the continuous growth of ESG funds 
over the past years6 (Chart   1), a specific analysis 
of those funds was introduced in the last two 
editions of the report.  
The reports showed that ESG equity UCITS 
(excluding exchange-traded funds) were on 
average cheaper and performed better compared 
to non-ESG peers in 2019 and 2020. Indeed, in 
2020 the gross annual performance7 of ESG 
retail equity UCITS (exchange-traded funds 
excluded) stood at 3.3 % compared to 0.8 % for 
the non-ESG peers. ESG equity UCITS were also 
cheaper with total costs of 1.5 % in 2020 
compared to 1.8 % for non-ESG equivalents.8 
While the relative cheapness of ESG funds was 
also demonstrated on the French9 and Austrian 
markets10, the reasons behind the difference are 
unclear, as opposite factors can come into play. 
As highlighted in Darpeix and Mosson (2021), 
ESG funds might bear additional costs related, 
first, to the analysis of extra-financial criteria, and 
 
6 
 See for example ESMA (2022b), Report on Trends, 
Risks and Vulnerabilities, No 1 or ESMA (2022a), 
Annual statistical report on performance and costs of 
retail investment products in the EU. 
7 
 The gross annual performance is computed as the 
geometric mean of the four annual performances 
obtained at the end of each quarter. 
8 
 For further details see ESMA (2022a), Annual statistical 
report on performance and costs of retail investment 
products in the EU. 
9 
 Darpeix, P.-E. and Mosson, N. (2021), ‘Costs and 
performance of funds incorporating a non-financial 
approach marketed in France between 2012 and 2018’, 
AMF risk and trend mapping, AMF France. 
second, to the procurement of a label and the 
production of ex post extra-financial reporting, 
which might be costly. Finally, the popularity of 
ESG funds might incentivise fund managers to 
increase the fees, especially if ESG fund 
investors 
are 
more 
concerned 
with 
the 
environmental 
or 
social 
impact 
of 
their 
investments than the level of fees (compared to 
other investors). On the other hand, following an 
ESG strategy might reduce the investment 
universe and lead to lower research costs. 
Besides, the increasing popularity of ESG funds 
could lead to a decrease in fees from economies 
of scale. Since ESG funds are on average more 
recent, their pricing eventually reflects the 
gradual reduction of fees over time (Chart   2). 
 
Understanding how ESG funds can charge lower 
fees and outperform non-ESG funds is key as it 
could provide some insights on how to make the 
overall fund industry more affordable and 
profitable for retail clients.  
Previous analyses that assessed the difference 
of costs and performance between ESG and non-
ESG funds notably took into consideration the 
10  FMA (2021), ‘Market study on fees charged by Austrian 
retail funds’. 
11  We observe for ESG funds a drop in the average 
ongoing costs in December 2019 and January 2020. 
This decrease can be explained by the significant 
number of ESG funds that are included in the sample 
for the first time in December 2019. 355 new ESG funds 
integrate the sample in December 2019, whereas for 
the period May 2019 – September 2021 (December 
2019 excluded) an average of 30 new ESG funds are 
added to the sample each month. Also in November 
2019, the proportion of ESG funds with ongoing costs 
lower or equal to 1 % is 35 % but this share increases 
to 40 % in December 2019 and to 43 % in January 
2020. 
Chart   1 
 
ESG fund assets 
ESG funds grow almost continuously since 2019 
 
Note: Assets under management of EU-domiciled ESG funds by type 
of fund (EUR billion) and share of ESG fund (in terms of assets under 
management, in percent, right-hand scale). Both retail and institutional 
funds are included. 
Sources: Morningstar, ESMA. 
Chart   2 
 
Average ongoing costs of equity UCITS 
Decrease of costs over time 
 
Note: Average ongoing costs of EU equity UCITS, in percent.11 
Sources: Morningstar, ESMA. 
0 %
5 %
10 %
15 %
20 %
 0
 200
 400
 600
 800
1 000
1 200
1 400
1 600
1 800
Nov-19 Mar-20 Jul-20 Nov-20 Mar-21 Jul-21 Nov-21
Mixed
Equity
Bonds
Alternative
Total ESG share (right-hand scale)
1.1
1.15
1.2
1.25
1.3
1.35
1.4
1.45
1.5
1.55
Apr-19
Sep-19
Feb-20
Jul-20
Dec-20
May-21
Overall
Non-ESG funds
ESG funds

ESMA TRV Risk Analysis 
23 May 2022 
5 
 
inception date of the funds (to control for the 
relative youth of ESG funds), the size of the funds 
(to control for the growing popularity of ESG 
funds and the fact that they are on average 
larger) or the share of passive funds among ESG 
and non-ESG funds.12 They also demonstrated 
that ESG funds remain cheaper even after 
including these considerations. 
This article builds on past analyses with the aim 
to test whether additional drivers could be 
identified to explain the cost and performance 
differential between ESG and non-ESG funds. In 
particular, the goal is to assess the extent to 
which portfolio characteristics impact the ongoing 
costs and gross performance of funds. 
First, we investigate whether the size of 
underlying issuers of securities plays a significant 
role in the differences observed between ESG 
and non-ESG funds. Research suggests that 
ESG rating can be biased in favour of large cap 
firms.13 Less communication from small cap firms 
on ESG issues may be one reason for this bias. 
For instance, more than 80 % of very large 
European Economic Area firms (i.e. firms whose 
market 
capitalisation 
is 
greater 
than 
EUR 20 billion) have a CO2 emissions reduction 
target, but this proportion falls to 2 % for small 
and medium-sized European Economic Area 
firms (i.e. firms whose market capitalisation is 
below EUR 200 million)14. This bias in favour of 
large cap firms could translate into a greater 
exposure to large caps for ESG funds. Yet, large 
caps are generally associated with higher 
liquidity, which might lower the trading costs for 
funds more exposed to these companies. 
Similarly, the ESG ratings can also be biased in 
favour of developed economies, because of 
lower ESG disclosures among emerging market 
 
12  A European Fund and Asset Management Association 
study showed that the share of passive funds was 
higher among ESG funds compared to non-ESG peers 
(see EFAMA (2021), ‘ESG investing in the UCITS 
market, a powerful and inexorable trend’, Market 
Insights, No 4). 
13  Boffo, R. and R. Patalano (2020), ‘ESG Investing: 
Practices, progress and challenges’, OECD Paris. 
14  See ESMA (2021b), Report on Trends, Risks and 
Vulnerabilities, No 2, p. 44. 
15  Boffo, R. and R. Patalano (2020), ‘ESG Investing: 
Practices, progress and challenges’, OECD Paris. 
16  See for example ESMA (2022a), Annual statistical 
report on the performance and costs of EU retail 
investment products. 
17  The choice was made here to focus on UCITS funds to 
maximise the homogeneity of the sample and avoid 
potential biases. 
18  The analysis focuses on the April 2019–September 
2021 period. At the time of the extraction (December 
companies.15 Investing in emerging markets 
might also be associated with higher costs if, for 
instance, the currency of the purchased asset is 
different from the fund’s currency (this purchase 
might then require a currency hedging). 
Regarding performance more specifically, one of 
the 
main 
hypotheses 
to 
explain 
the 
outperformance of ESG funds was the different 
sectoral allocation.16 For instance, ESG funds are 
more exposed to the healthcare sector, which 
performed well during the peak of the COVID-19 
crisis. However, this hypothesis has not been 
statistically proven yet; this analysis intends to fill 
this gap. 
Portfolio exposures were left out of previous 
analyses, assessing their impact is the main 
contribution of this study. By analysing the factors 
driving the costs and performance of investment 
funds, the aim of this article is to improve our 
understanding of this market.  
The following analyses rely on an initial database 
containing 9 866 equity UCITS17 funds domiciled 
in the EU and operating at the time of the 
extraction 
(December 
2021). 
The 
dataset 
contains several funds’ characteristics and time 
series variables that were extracted on a monthly 
basis between January 2019 and October 
2021.18 Several filters were then applied to only 
keep funds that could be included in at least one 
regression analysis19 and to remove suspicious 
observations 
(negative 
ongoing 
costs 
or 
observations 
reported 
before 
the 
fund’s 
inception).20 The final sample is composed of 
6 528 equity UCITS funds. 
2021), data for the fourth quarter of 2021 were 
incomplete. We observe a significant improvement in 
the coverage (i.e. the number of funds we can analyse) 
between 2019 and 2021. However, the number of funds 
reporting information in the first quarter of 2019 appears 
low (less than 1 400 observations for January 2019 
compared to a monthly average of almost 2 800 funds 
for the three other quarters). For these reasons, we 
decided to exclude the first quarter of 2019 and the last 
quarter of 2021 from the analysis. 
19  This requires for funds to have, at least once between 
April 2019 and September 2021, all the variables listed 
in the equation on page 7. The information (especially 
on the portfolio composition) is reported by investment 
funds to Morningstar on a voluntary basis. Since there 
is no obligation, some funds do not report and are then 
excluded from our sample. 
20  Also, in order to maximise the sample size, the missing 
information on costs has been replaced for each fund 
with the last known cost figure until a new figure is 
reported.

ESMA TRV Risk Analysis 
23 May 2022 
6 
 
Differences 
in 
portfolio 
composition 
In our first analysis, we compare the aggregate 
exposures of ESG and non-ESG fund portfolios 
with several allocation categories.21 
Large caps versus small caps22 
Between April 2019 and September 2021, non-
ESG funds have on average increased their 
exposure to large caps and reduced their 
exposure to small caps, whereas the exposure of 
ESG funds remained broadly unchanged. In 
September 2021, ESG funds remained more 
exposed to large caps and less exposed to small 
caps compared to non-ESG funds.  
 
The COVID-19 crisis might have led to ‘flight to 
quality/liquidity’ behaviours, with fund managers 
divesting their small caps participations to 
increase their exposure to large caps. 
Value stocks versus growth stocks23 
Both ESG and non-ESG funds reduced their 
exposure to value and growth stocks between 
April 2019 and September 2021. As of 
September 2021, the exposure to value stocks is 
greater for non-ESG funds, whereas the 
exposure to growth stocks is greater for ESG 
funds (Table 2). 
 
21  For this study, we rely on the Morningstar definition of a 
sustainable investment fund. Morningstar classifies the 
following strategies as sustainable investment: ESG 
integration, 
ESG 
company 
engagement, 
impact 
investing and thematic investing. This definition 
excludes funds that only employ ‘exclusions’, which are 
identified via norm-based screening and the exclusion 
of specific activities/sectors. See Morningstar (2019), 
‘Morningstar sustainable attributes: Framework and 
definitions for “sustainable investment” and “employs 
exclusions” attributes’. 
Geographical exposure 
The geographical focus of ESG funds shifted 
towards developed economies between 2019 
and 2021. During this period, ESG funds 
increased their exposure to North America and 
Europe and decreased their exposure to other 
regions. Conversely, non-ESG funds significantly 
reduced their exposure to Europe and mainly 
increased their exposure to North America and 
Asia. 
On an aggregate level, the exposure to 
developed economies24 was similar between 
ESG and non-ESG funds in April 2019. However, 
22  This classification relies on the Morningstar style box, 
see Morningstar (2018), ‘Morningstar Style Box 
Methodology’, Morningstar Methodology Paper. 
23  This classification relies on the Morningstar style box, 
see Morningstar (2018), ‘Morningstar Style Box 
Methodology’, Morningstar Methodology Paper. 
24  The exposure to developed economies is the sum of 
exposures to North America, developed Europe, the 
United Kingdom, Japan, developed Asia, Australia and 
New Zealand. 
Table 1 
The exposure of equity funds to small and large caps 
Exposure of ESG funds broadly unchanged 
April 2019 
September 2021 
Non-
ESG 
 
ESG 
Non-
ESG 
 
ESG 
Large caps 
62 % <*** 67 % 
65 % 
<** 
67 % 
Small caps 
14 % >*** 
9 % 
12 % >*** 
9 % 
Note: Average of EU equity UCITS individual exposure to large and 
small caps as of April 2019 and September 2021. The symbols “<” and 
“>” indicate whether the exposure is greater for ESG or non-ESG 
funds. The stars represent the significance level of the differences
which is reported as follows: 0.01 (***), 0.05 (**), 0.1 (*). For instance, 
the symbol “<***” indicates that the exposure is greater for ESG funds 
than for the non-ESG funds and the difference is significant at the 1 %
confidence level. 
Sources: Morningstar, ESMA. 
Table 2 
Exposure of equity funds to value and growth stocks 
ESG funds less exposed to value stocks 
April 2019 
September 2021 
Non-
ESG 
 
ESG 
Non-
ESG 
 
ESG 
Value 
29 % 
 
29 % 
27 % 
>*** 
21 % 
Growth 
39 % 
>* 
38 % 
30 % 
<*** 
31 % 
Note: Average of EU equity UCITS individual exposure to value and 
growth stocks as of April 2019 and September 2021. The symbols “<” 
and “>” indicate whether the exposure is greater for ESG or non-ESG 
funds. The stars represent the significance level of the differences
which is reported as follows: 0.01 (***), 0.05 (**), 0.1 (*). For instance, 
the symbol “<***” indicates that the exposure is greater for ESG funds 
than for the non-ESG funds and the difference is significant at the 1 %
confidence level. 
Sources: Morningstar, ESMA. 
Table 3 
Average geographical exposure of equity funds 
Equity funds highly exposed to Europe 
April 2019 
September 2021 
ESG 
Non-
ESG 
ESG 
Non-
ESG 
Africa Middle 
East 
1.5 % 
1.1 % 
0.6 % 
1.2 % 
Asia 
18.5 % 
22.2 % 
16.8 % 
22.9 % 
Australia & New 
Zealand 
1.1 % 
1.4 % 
1.0 % 
1.2 % 
Europe 
46.9 % 
46.0 % 
48.3 % 
43.3 % 
North America 
29.4 % 
27.3 % 
32.0 % 
29.5 % 
South America 
2.7 % 
1.9 % 
1.3 % 
2.0 % 
Note: Average of EU equity UCITS individual geographical exposure 
as of April 2019 and September 2021. The dark green colour indicates 
the largest geographical exposure, the light green colour indicates the
next-largest geographical exposure, while the yellow colour indicates 
the third-largest geographical exposure. 
Sources: Morningstar, ESMA.

ESMA TRV Risk Analysis 
23 May 2022 
7 
 
ESG funds became more exposed to this area 
during the 2019–2021 period and the difference 
is statistically significant in September 2021. 
Sectoral exposures25 
Between April 2019 and September 2021, UCITS 
equity funds increased their exposure to 
communication services26 (the magnitude is 
greater for non-ESG funds) and healthcare 
stocks (the rise was more important for ESG 
funds). Both types of funds partially divested their 
consumer defensive, energy and financial stocks, 
but in all cases the magnitude was greater for 
ESG funds.  
Low-cost ESGs: Multitude 
of drivers 
In order to provide more statistical evidence to the 
stylised facts presented above, we regress funds’ 
ongoing costs and gross performance on several 
characteristics (e.g. domicile, management style, 
type of clients targeted or size) and on the 
 
25  This classification relies on the Morningstar global 
equity classification structure. 
26  Morningstar includes in this sector ‘Companies that 
provide communication services using fixed-line 
networks or those that provide wireless access and 
services. This sector also includes companies that 
provide internet services such as access, navigation 
and internet-related software and services.’ For further 
details, see Morningstar global equity classification 
structure. 
27  Not all sectors were included in the regression to avoid 
collinearity issues. The real estate sector was namely 
exposure of funds.27 The equation to be 
estimated on the overall sample is the following. 
𝑌𝑖,𝑡
= 𝛼0 +  𝛼1 𝐸𝑆𝐺𝑖,𝑡+ 𝛼2 𝑑𝑎𝑡𝑒𝑡+ 𝛼3 𝑐𝑙𝑖𝑒𝑛𝑡𝑠𝑖
+ 𝛼4 𝑚𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡𝑖+ 𝛼5 𝑓𝑒𝑒𝑑𝑒𝑟𝐹𝑂𝐹𝑖
+ 𝛼6 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑 𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑒𝑠𝑖,𝑡+ 𝛼7 𝑑𝑜𝑚𝑖𝑐𝑖𝑙𝑒𝑖
+ 𝛼8 𝑙𝑎𝑟𝑔𝑒 𝑐𝑎𝑝𝑠𝑖,𝑡 +  𝛼9 𝑣𝑎𝑙𝑢𝑒 𝑠𝑡𝑜𝑐𝑘𝑖,𝑡  
+ 𝛼10 𝑢𝑡𝑖𝑙𝑖𝑡𝑦 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡 +  𝛼11 𝑏𝑎𝑠𝑖𝑐 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙𝑠 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡
+  𝛼12 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡
+ 𝛼13 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑐𝑦𝑐𝑙𝑖𝑐𝑎𝑙 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡
+  𝛼14 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑑𝑒𝑓𝑒𝑛𝑠𝑖𝑣𝑒 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡
+ 𝛼15 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡 +  𝛼16 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡
+ 𝛼17 ℎ𝑒𝑎𝑙𝑡ℎ𝑐𝑎𝑟𝑒 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡
+ 𝛼18 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑎𝑙 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡 +  𝛼19 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑠𝑒𝑐𝑡𝑜𝑟𝑖,𝑡
+ 𝛼20 𝑎𝑔𝑒𝑖,𝑡 +  𝛼21 log (𝑛𝑒𝑡 𝑎𝑠𝑠𝑒𝑡𝑠)𝑖,𝑡
+ 𝛼22 𝐴𝑠𝑠𝑒𝑡 𝑀𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡 𝐶𝑜𝑚𝑝𝑎𝑛𝑦𝑖+ 𝜀𝑖,𝑡 
The variables are defined as follows. 
─ 
𝑌𝑖,𝑡 is the annual ongoing costs or the monthly 
gross performance. 
─ 
𝐸𝑆𝐺𝑖,𝑡 is a dummy variable taking the value of 
1 if a fund is an ESG fund at a given date.28 
This is our main variable of interest. 
─ 
𝑑𝑎𝑡𝑒𝑡 is a time dummy identifying the month 
and year of the observation. This variable 
controls for the decrease of fees over time 
and for the volatility of performances during 
the period under review. 
─ 
𝑐𝑙𝑖𝑒𝑛𝑡𝑠𝑖 is a time invariant dummy variable 
which equals 1 if the fund is sold to 
institutional clients. This variable controls for 
the cost difference between retail and 
institutional funds. 
─ 
𝑚𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡𝑖 is a time invariant dummy 
variable equal to 1 if the fund is passively 
managed. This variable controls for the cost 
difference between passive and active funds. 
─ 
𝑓𝑒𝑒𝑑𝑒𝑟𝐹𝑂𝐹𝑖 is a time invariant dummy 
variable equal to 1 if the fund is a feeder fund 
or a fund of funds (FoF). This variable 
controls for the higher costs of funds 
investing in other funds. 
─ 
𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑 𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑒𝑠𝑖,𝑡 is a continuous time 
series variable indicating the share of the 
removed from the explanatory variables. For this 
reason, we also removed from the regressions the 
share of small caps (highly correlated to the share of 
large caps) and the share of growth stocks (also highly 
correlated to the proportion of value stocks). The 
correlation between the remaining continuous variables 
stands between – 0.4 and 0.4. The generalised 
variance inflation factor was also controlled and shows 
no multicollinearity issue. 
28  Contrary to the other dummy variables in the equation 
the ESG variable can change over time to reflect the 
potential change of strategy. 
Table 4 
Average sectoral exposure of equity funds 
Equity funds highly exposed to technology 
April 2019 
September 2021 
ESG 
Non-
ESG 
ESG 
Non-
ESG 
Basic materials 
6.4 % 
7.2 % 
5.9 % 
7.0 % 
Communication 
3.3 % 
2.7 % 
6.9 % 
7.6 % 
Consumer 
cyclical 
11.5 % 
12.9 % 
10.8 % 
12.2 % 
Consumer 
defensive 
9.0 % 
7.8 % 
6.7 % 
6.9 % 
Energy 
3.4 % 
5.2 % 
1.8 % 
4.0 % 
Financials 
16.2 % 
16.0 % 
13.2 % 
14.4 % 
Healthcare 
10.8 % 
10.5 % 
13.0 % 
11.8 % 
Industrials 
15.3 % 
12.8 % 
16.6 % 
12.8 % 
Real Estate 
4.0 % 
5.5 % 
3.7 % 
4.0 % 
Technology 
16.5 % 
16.8 % 
18.1 % 
16.6 % 
Utilities 
3.7 % 
2.6 % 
3.4 % 
2.6 % 
Note: Average of EU equity UCITS individual sectoral exposure as of 
April 2019 and September 2021 (share of each sector in percent). The 
dark green colour indicates the first sectoral allocation, the light green 
colour indicates the second sectoral allocation, while the yellow colour 
indicates the third sectoral allocation. 
Sources: Morningstar, ESMA.

ESMA TRV Risk Analysis 
23 May 2022 
8 
 
fund’s 
portfolio 
exposed 
to 
developed 
economies. The inclusion of this variable is a 
novelty compared to previous studies. 
─ 
𝑑𝑜𝑚𝑖𝑐𝑖𝑙𝑒𝑖 is a time invariant dummy variable 
identifying the fund’s domicile. This variable 
controls for the heterogeneity of costs across 
countries.29 
─ 
𝑙𝑎𝑟𝑔𝑒 𝑐𝑎𝑝𝑠𝑖,𝑡 is a continuous time series 
variable indicating the share of the fund’s 
portfolio exposed to large caps. The inclusion 
of this variable is a novelty compared to 
previous studies. 
─ 
𝑣𝑎𝑙𝑢𝑒 𝑠𝑡𝑜𝑐𝑘𝑖,𝑡 is a continuous time series 
variable indicating the share of the fund’s 
portfolio exposed to value stocks. The 
inclusion of this variable is a novelty 
compared to previous studies. 
─ 
The sectoral exposures are continuous time 
series variables indicating the share of the 
fund’s portfolio exposed to each sector. The 
inclusion of those variables is a novelty 
compared to previous studies. 
─ 
𝑎𝑔𝑒𝑖,𝑡 is a continuous time series variable 
calculated as the difference between the 
observation date and the fund’s inception 
date. This variable controls for the gradual 
reduction of fees over time, leading recent 
funds to be on average cheaper. 
─ 
log (𝑛𝑒𝑡 𝑎𝑠𝑠𝑒𝑡𝑠)𝑖,𝑡 is a continuous time series 
variable reflecting the logarithm of the fund’s 
net assets. This variable controls for the 
costs/performance differences due to the 
fund’s size. 
─ 
𝐴𝑠𝑠𝑒𝑡 𝑀𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡 𝐶𝑜𝑚𝑝𝑎𝑛𝑦𝑖 
is 
a 
time 
invariant dummy variable identifying the 
fund’s management company. This variable 
controls for the different pricing policies 
across management companies. 
For some of the independent variables, there is a 
strong presumption of their expected effect. 
─ 
𝑬𝑺𝑮𝒊,𝒕. Previous analyses demonstrated that 
ESG funds are cheaper. 
 
29  For further details, see ESMA (2022a), Annual 
statistical report on performance and costs of retail 
investment products in the EU. 
30  The 
2022 
ESMA 
annual 
statistical 
report 
on 
performance and costs of retail investment products in 
the EU shows that an investment of EUR 10 000 in a 
hypothetical retail portfolio composed of equity (40 %), 
bond and mixed funds (30 % each) between 2011 and 
2020 would have returned approximately EUR 18 000 
in gross terms and around EUR 15 400 after deduction 
of costs. The hypothetical retail investor consequently 
pays around EUR 2 600 in costs. Comparatively, a 
─ 
𝒄𝒍𝒊𝒆𝒏𝒕𝒔𝒊. We expect funds sold to institutional 
investors to be cheaper than retail funds, 
consistent with the findings of the ESMA 
annual statistical report on performance and 
costs of retail investment products in the 
EU.30 
─ 
𝒎𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕𝒊. We expect passive funds to 
be cheaper than active funds, consistent with 
the findings of ESMA’s annual statistical 
report on performance and costs of retail 
investment products in the EU. 
─ 
𝒇𝒆𝒆𝒅𝒆𝒓𝑭𝑶𝑭𝒊. We expect FoFs and feeder 
funds to be more expensive as the investors 
indirectly bear the costs of several layers of 
funds. 
─ 
𝒅𝒆𝒗𝒆𝒍𝒐𝒑𝒆𝒅 𝒆𝒄𝒐𝒏𝒐𝒎𝒊𝒆𝒔𝒊,𝒕. 
As 
explained 
previously, we expect funds that are more 
exposed to developed economies to be 
cheaper. 
─ 
𝒍𝒂𝒓𝒈𝒆 𝒄𝒂𝒑𝒔𝒊,𝒕. As explained previously, we 
expect funds that are more exposed to large 
caps to be cheaper. 
─ 
𝒂𝒈𝒆𝒊,𝒕. We expect older funds to be more 
expensive, in keeping with the decreasing 
trend of fees. 
─ 
𝐥𝐨𝐠 (𝒏𝒆𝒕 𝒂𝒔𝒔𝒆𝒕𝒔)𝒊,𝒕. We expect larger funds to 
be cheaper due to economies of scale. 
The model is estimated through pooled Ordinary 
Least Square (pooled OLS), with standard errors 
clustered at the individual level. 
As highlighted in the previous section, ESG funds 
were on average less exposed to small caps and 
were 
more 
oriented 
towards 
developed 
economies. These divergences might indeed 
contribute to lowering the fees of ESG funds. 
However, the regression on the overall sample 
(Model (1) of Table 5) shows that ESG funds 
remained cheaper even when controlling for the 
differences in the portfolio composition. All else 
being equal, an ESG fund appears less 
expensive than a non-ESG fund by 0.080 
percentage points (or 8.0 basis points31). 
hypothetical institutional investor with a similar portfolio 
would only have paid around EUR 1 600 in costs. 
31   This figure is consistent with previous findings. The 
2022 ESMA annual statistical report on performance 
and costs of retail investment products in the EU 
showed that ESG funds were less costly compared to 
non-ESG peers across the four quarters of 2020. The 
difference of total expense ratio oscillates between 8.0 
and 8.8 basis points. The AMF study demonstrated that 
a fund share class sold in France, claiming non-financial

ESMA TRV Risk Analysis 
23 May 2022 
9 
 
Table 5 
Regression analysis of the costs for equity funds 
ESG funds remained cheaper 
Pooled OLS  
Dependent variable: Ongoing costs 
 
Standard 
model 
 
(1) 
Model with 
interaction 
terms 
(2) 
 
Model with 
tracking 
error 
(3) 
 
ESG 
‒ 0.080 *** 
‒ 0.118 * 
‒ 0.076 *** 
Institutional  
‒ 0.436 *** 
‒ 0.437 *** 
‒ 0.459 *** 
Passive  
‒ 0.645 *** 
‒ 0.646 *** 
‒ 0.626 *** 
FoF  
0.390 *** 
0.391 *** 
0.347 *** 
Developed 
‒ 0.003 *** 
‒ 0.003 *** 
‒ 0.003 *** 
Large caps 
‒ 0.002 *** 
‒ 0.002 *** 
‒ 0.001 *** 
Value 
‒ 0.001 ** 
‒ 0.001 ** 
‒ 0.001 
Utilities 
‒ 0.002 
‒ 0.002 
‒ 0.003 * 
Materials 
0.001 
0.001 
0.0002 
Communication 
‒ 0.0005 
‒ 0.0005 
‒ 0.001 
Consumer cyclical 
‒ 0.001 
‒ 0.001 
0.0001 
Consumer 
defensive 
‒ 0.002 
‒ 0.002 
‒ 0.002 * 
Energy 
0.003 *** 
0.003 *** 
0.002 ** 
Financials 
0.0002 
0.0002 
0.001 
Healthcare 
0.001 
0.001 
0.001 
Industrials 
0.002 ** 
0.002 ** 
0.002 ** 
Technology 
0.0004 
0.0004 
‒ 0.0003 
Age 
0.021 *** 
0.021 *** 
0.022 *** 
Size 
‒ 0.007 * 
‒ 0.007 * 
‒ 0.003 
Age * ESG 
 
0.001 
 
Developed * ESG 
 
0.0001 
 
Large * ESG 
 
0.0003 
 
Tracking error 
 
 
0.032 *** 
Intercept 
1.498 *** 
1.502 *** 
1.245 *** 
Observations 
96 146 
96 146 
67 180 
R2 
0.655 
0.655 
0.674 
Note: ‘ESG’ is a dummy taking the value of 1 if a fund is an ESG 
fund at a given date. ‘Institutional’ is a dummy taking the value of 1 
if the fund targets institutional clients. ‘Passive’ is a dummy taking 
the value of 1 if the fund is passively managed. ‘FoF’ is a dummy 
taking the value of 1 if a fund is a fund of fund or a feeder fund. 
‘Developed’ indicates the share of the portfolio invested in 
developed economies. 'Large caps’ indicates the share of the 
portfolio invested in large caps. ‘Value stocks’ indicates the share 
of the portfolio invested in value stocks. ‘Utilities’, ‘Materials’, 
‘Communication’, ‘Consumer cyclical’, ‘Consumer defensive’, 
‘Energy’, ‘Financials’, ‘Healthcare’, ‘Industrials’ and ‘Technology’ 
indicate the share of the portfolio invested in each sector. ‘Age’ 
measures the age of the fund from its inception date expressed in 
years. ‘Size’ represents fund size in terms of net assets in 
logarithmic terms. ‘Tracking error’ is the yearly tracking error. The 
date, domicile and Asset Management Company variables are 
hidden from the results. Significance levels are reported as follows: 
(0.01 (***), 0.05 (**), 0.1 (*)). 
Sources: Morningstar, ESMA. 
 
Previous analyses (e.g. Darpeix and Mosson 
(2021) or ESMA (2022a)) demonstrated the 
relative cheapness of passive funds, of funds 
targeting institutional clients or larger funds, and 
the current study confirms those results once 
more. Conversely, FoFs and older funds are 
 
characteristics (through its name) and having a label 
appears less expensive (in terms of total expense ratio) 
than a "conventional" fund share class by 10.4 basis 
points in 2019. The spread was smaller in the case of 
Austrian funds as the funds investing in accordance 
with the Austrian Ecolabel 49 were less expensive (in 
terms of ongoing costs) by 4 basis points compared to 
all retail funds in 2020. 
32  The calculation of the tracking error requires first to 
compute for each fund the monthly return in excess of 
associated with higher costs. As expected, 
increasing exposure to developed economies or 
large caps is correlated with lower costs. 
Model (2) of Table 5 presents the results of a 
regression using the same variables but adding 
interaction terms. Since ESG funds are on 
average more recent, assessing whether the 
effect of age on costs is different for ESG and 
non-ESG funds is of interest. However, this 
interaction term is not significant, implying that 
the effect of age on costs is similar between ESG 
and non-ESG funds. Likewise, increasing the 
exposures to large caps or developed economies 
does not have a different impact, in terms of 
costs, for either ESG or non-ESG funds. 
Finally, Model (3) of Table 5 adds the yearly 
tracking error32 as an explanatory variable. We 
observe that the distribution of the tracking error 
is shifted downward for ESG funds (Chart   3) and 
that the distribution of the tracking error for ESG 
funds is statistically different at the 1 % 
confidence level from the distribution of the 
tracking error for non-ESG funds. We then test 
whether this difference explains the cost 
differential between ESG and non-ESG funds. 
the prospectus benchmark. The annual tracking error is 
then obtained by taking the standard deviation of twelve 
consecutive excess returns. 
33  The smallest adjacent value is obtained by the following 
formula: 𝑄1 − 
3
2 (𝑄3 −𝑄1), while the largest adjacent 
value is obtained by the following formula: 𝑄3 + 
3
2 (𝑄3 −
𝑄1), where Q1 corresponds to the first quartile and Q3 
to the third quartile. 
Chart   3 
 
Distribution of tracking error for equity funds 
ESG funds show a lower distribution 
 
Note: Distribution of the yearly tracking error for EU equity UCITS. All 
yearly tracking errors ending between April 2019 and September 2021 
are pooled. The extremities of the box plots represent the smallest and 
largest adjacent values33, the bottom of the box represents the first 
quartile, the middle line the median and the top of the box represents 
the third quartile. 
Sources: Morningstar, ESMA. 
0
0.5
1
1.5
2
2.5
3
3.5
4
Non-ESG funds
ESG funds

ESMA TRV Risk Analysis 
23 May 2022 
10 
 
Model (3) of Table 5 shows that the coefficient of 
the tracking error variable is positive and 
significant, meaning that a higher tracking error is 
correlated with higher ongoing costs. However, 
even after the inclusion of this variable, ESG 
funds remain associated with lower costs. 
Given the growing number of funds that 
converted to ESG34, we created a detailed ESG 
variable to distinguish funds that have included 
ESG characteristics since their launch and funds 
that included ESG features into their existing 
strategy. Funds identified as ESG in the quarter 
following their creation are considered as funds 
created as ESG (‘Created ESG’). Funds 
identified as ESG more than a quarter after their 
launch are considered as converted funds 
(‘Converted ESG’). Since the information on the 
ESG characteristics was retrieved from January 
2019, it is not possible to assess whether a fund 
created before 2019 was created as ESG or not. 
To solve this issue, we created a third category to 
classify funds identified as ESG and created 
before 2019 (‘Older ESG’). 
Model (1) of Table 6 shows a significant 
difference between the ongoing costs of those 
categories of ESG funds. Funds created as ESG 
appear to be the cheapest, followed by funds that 
converted to ESG. Hence, a fund created as ESG 
appears less expensive than a non-ESG fund by 
0.279 percentage points. The difference between 
costs of funds created as ESG and funds that 
converted to ESG is significant at the 1 % 
confidence level. The smallest difference with 
non-ESG funds can be observed for ESG funds 
created before 2019. 
Interestingly, when we restrict the sample to 
converted funds only, we observe a difference in 
cost between the period where they were 
identified as non-ESG and the period where they 
were identified as ESG. Before converting, the 
average cost for these funds is 1.1 % compared 
to 1.0 % after their conversion. However, it is 
difficult to say whether the decrease in fees is 
related to the change of strategy or to the broader 
decrease of fees over time (this gradual reduction 
of fees can be observed on Chart   2). 
We also distinguish ESG funds according to their 
strategies and put them into two categories: 
impact funds35 and funds following other ESG 
strategies. 
 
34  See Losavio, E. (2021), ‘ESG demand prompts more 
than 250 European funds to change tack‘, Financial 
Times. 
35  According to Morningstar, impact funds employ 
‘strategies that seek to make a measurable impact 
Table 6 
Regression analysis of the costs for equity funds 
Funds created as ESG are cheaper 
Pooled OLS  
Dependent variable: Ongoing costs 
 
Model with the 
different ESG 
funds 
(1) 
Model with the 
different ESG 
strategies 
(2) 
Created ESG 
‒ 0.279 *** 
 
Older ESG 
‒ 0.046 *** 
 
Converted ESG 
‒ 0.117 * 
 
Impact funds 
 
‒ 0.092 *** 
Other ESG 
 
‒ 0.073 *** 
Institutional  
‒ 0.447 *** 
‒ 0.437 *** 
Passive  
‒ 0.646 *** 
‒ 0.641 *** 
FoF  
0.390 *** 
0.389 *** 
Developed 
‒ 0.003 *** 
‒ 0.003 *** 
Large caps 
‒ 0.002 *** 
‒ 0.002 *** 
Value 
‒ 0.001 ** 
‒ 0.001 ** 
Utilities 
‒ 0.002 
‒ 0.002 
Materials 
0.001 
0.001 
Communication 
‒ 0.001 
‒ 0.0005 
Consumer cyclical 
‒ 0.001 
‒ 0.001 
Consumer 
defensive 
‒ 0.002 * 
‒ 0.002 * 
Energy 
0.003 *** 
0.003 *** 
Financials 
0.0002 
0.0002 
Healthcare 
0.001 * 
0.001 
Industrials 
0.002 ** 
0.002 ** 
Technology 
0.001 
0.0004 
Age 
0.021 *** 
0.021 *** 
Size 
‒ 0.009 ** 
‒ 0.007 * 
Intercept 
1.528 *** 
1.531 *** 
Observations  
96 146 
95 479 
R2 
0.657 
0.653 
Note: ‘Created ESG’ is a dummy taking the value of 1 if the fund was
created as an ESG fund. ‘Older ESG’ is a dummy taking the value of
1 if the fund is identified as ESG but created before 2019. ‘Converted
ESG’ is a dummy taking the value of 1 if the fund was repurposed as
an ESG fund. ‘Impact fund’ is a dummy taking the value of 1 if the
fund is identified as an impact fund. ‘Other ESG’ is a dummy taking
the value of 1 if the fund follows other ESG strategies. For all the
ESG variables, the relation is with respect to non-ESG funds.
‘Institutional’ is a dummy taking the value of 1 if the fund targets
institutional clients. 'Passive’ is a dummy taking the value of 1 if the
fund is passively managed. ‘FoF’ is a dummy taking the value of 1 if
a fund is a fund of fund or a feeder fund. ‘Developed’ indicates the
share of the portfolio invested in developed economies. ‘Large caps’
indicates the share of the portfolio invested in large caps. ‘Value
stocks’ indicates the share of the portfolio invested in value stocks.
‘Utilities’, 
‘Materials’, 
‘Communication’, 
‘Consumer 
cyclical’,
‘Consumer 
defensive’, 
‘Energy’, 
‘Financials’, 
‘Healthcare’,
‘Industrials’ and ‘Technology’ indicate the share of the portfolio
invested in each sector. ‘Age’ measures the age of the fund from its
inception date expressed in years. ‘Size’ represents fund size in
terms of net assets in logarithmic terms. The date, domicile and
Asset Management Company variables are hidden from the results.
Significance levels are reported as follows: (0.01 (***), 0.05 (**),
0.1 (*)). 
Sources: Morningstar, ESMA. 
 
Model (2) of Table 6 shows that both impact 
funds and funds following other ESG strategies 
are cheaper than non-ESG funds. Impact funds 
appear to be the cheapest category (the 
difference between impact funds and funds 
alongside financial return on specific issue areas 
through their investments’. See Morningstar (2019), 
‘Morningstar sustainable attributes: Framework and 
definitions for “sustainable investment” and “employs 
exclusions” attributes’.

ESMA TRV Risk Analysis 
23 May 2022 
11 
 
employing other ESG strategies is also significant 
at the 1 % confidence level). 
ESG 
outperformance: 
Beyond sectoral effects 
The second part of the analysis focuses on the 
monthly gross performance36 and investigates 
the drivers behind the outperformance of ESG 
funds. The hypothesis tested is whether the 
different sectoral exposures might explain the 
outperformance of ESG funds. Indeed, as 
highlighted in the previous sections, ESG funds 
increased some of their exposure to sectors that 
performed well at the peak of the COVID-19 crisis 
(e.g. the healthcare sector). 
Model (1) of Table 7 shows that the inclusion of 
sectoral exposures does not change the previous 
result: ESG funds are indeed associated with a 
larger gross performance. All else being equal, 
an ESG funds outperformed a non-ESG funds by 
0.057 percentage points on average. 
The type of clients targeted or the investment in 
other funds does not seem to impact the gross 
performance. Besides, the gross performance of 
passive funds is not statistically different from the 
performance of active funds. 
Moreover, a fund with more large caps or value 
stocks in its portfolio is associated with a lower 
gross performance on average.37 Some sectors 
such as energy, financials, industrials or 
technology are on average correlated with a 
greater 
performance, 
while 
the 
consumer 
defensive 
and 
utilities 
sectors 
appear 
to 
negatively impact the funds’ performance. 
However, ESG funds still outperformed non-ESG 
funds when we control for these sectoral 
exposures. 
 
36  Morningstar defines the gross performance as the 
‘return an investor would have received had they not 
paid any expenses. The calculation of gross return 
adjusts the monthly total return for the share class by 
the share class level fees prevailing at that time’. 
Table 7 
Regression analysis of EQ fund performance 
ESG funds performed better 
Pooled OLS  
Dependent variable: Monthly gross performance 
 
Standard 
model 
 
 
 
(1) 
Model with 
tracking 
error 
 
 
(2) 
Model with 
tracking error 
and 
interaction 
term 
(3) 
ESG 
0.057 *** 
0.038 
0.328 *** 
Institutional  
‒ 0.017 
‒ 0.002 
‒ 0.004 
Passive  
‒ 0.046 
0.037 
0.023 
FoF  
‒ 0.012 
0.056 * 
0.073 ** 
Developed 
0.004 *** 
0.005 *** 
0.005 *** 
Large caps 
‒ 0.003 *** 
‒ 0.001 
‒ 0.001 
Value 
‒ 0.016 *** ‒ 0.017 *** 
‒ 0.017 *** 
Utilities 
‒ 0.002 
‒ 0.003 
‒ 0.001 
Materials 
0.009 *** 
0.008 *** 
0.008 *** 
Communication 
0.009 *** 
0.011 *** 
0.010 *** 
Consumer 
cyclical 
0.010 *** 
0.011 *** 
0.010 *** 
Consumer 
defensive 
‒ 0.006 *** ‒ 0.008 *** 
‒ 0.008 *** 
Energy 
0.017 *** 
0.016 *** 
0.015 *** 
Financials 
0.014 *** 
0.014 *** 
0.014 *** 
Healthcare 
0.006 *** 
0.005 *** 
0.005 *** 
Industrials 
0.009 *** 
0.007 *** 
0.008 *** 
Technology 
0.017 *** 
0.014 *** 
0.014 *** 
Age 
‒ 0.002 ** 
‒ 0.001 
‒ 0.001 
Size 
0.017 *** 
0.012 ** 
0.013 ** 
Tracking error 
 
0.129 *** 
0.167 *** 
Tracking error * 
ESG 
 
 
‒ 0.195 *** 
Intercept 
2.958 *** 
2.878 *** 
2.803 *** 
Observations  
124 250 
83 185 
83 185 
R2 
0.699 
0.707 
0.707 
Note: ‘ESG’ is a dummy taking the value of 1 if a fund is an ESG fund 
at a given date. ‘Institutional’ is a dummy taking the value of 1 if the 
fund targets institutional clients. ‘Passive’ is a dummy taking the value 
of 1 if the fund is passively managed. ‘FoF’ is a dummy taking the 
value of 1 if a fund is a fund of fund or a feeder fund. ‘Developed’ 
indicates the share of the portfolio invested in developed economies. 
‘Large caps’ indicates the share of the portfolio invested in large caps. 
‘Value stocks’ indicates the share of the portfolio invested in value 
stocks. ‘Utilities’, ‘Materials’, ‘Communication’, ‘Consumer cyclical, 
‘Consumer defensive’, ‘Energy’, ‘Financials’, ‘Healthcare’, ‘Industrials’ 
and ‘Technology’ indicate the share of the portfolio invested in each 
sector. ‘Age’ measures the age of the fund from its inception date 
expressed in years. ‘Size’ represents fund size in terms of net assets 
in logarithmic terms. ‘Tracking error’ is the yearly tracking error. The 
date, domicile and Asset Management Company variables are hidden 
from the results. Significance levels are reported as follows: (0.01 
(***), 0.05 (**), 0.1 (*)). 
Sources: Morningstar, ESMA. 
 
This means that sectoral exposures cannot 
entirely explain the outperformance of ESG 
funds. The technology and industrials sectors are 
the two prominent sectoral exposures for ESG 
funds in September 2021, and these two sectors 
had strong performances between April 2019 and 
September 202138 (Table 8). However, ESG 
funds significantly reduced their exposure to the 
financial sector, which was the best performing 
sector between April 2019 and September 2021. 
37  These results were expected as the MSCI Europe Small 
cap Index outperformed the MSCI Europe Large cap 
Index between April 2019 and September 2021. 
Likewise, the MSCI Europe Growth Index outperformed 
the MSCI Europe Value Index during the same period. 
38  Based on the MSCI Europe sectoral indexes.

ESMA TRV Risk Analysis 
23 May 2022 
12 
 
Model (2) of Table 7 shows that a higher tracking 
error 
is 
associated 
to 
a 
higher 
gross 
performance. In this model, the coefficient 
attached 
to 
the 
ESG 
variable 
loses 
its 
significance. Model (3) of Table 7 interacts the 
tracking error with the ESG variable and shows 
that the effect of tracking error on the gross 
performance is different for ESG and non-ESG 
funds. For non-ESG funds, the tracking error is 
positively correlated with the performance, 
whereas for ESG funds, the tracking error has no 
impact on the performance.39 
The regressions with the detailed ESG variable 
(Table 9) show that funds identified as ESG and 
created before 2019 (‘Older ESG’) outperformed 
non-ESG funds. The performance of the funds 
created after 2018 (‘Created ESG’ or ‘Converted 
ESG’) is not significantly different from the 
performance of non-ESG funds. In addition, 
Model (2) shows that even if impact funds seem 
to outperform non-ESG funds, the significance 
level is low and the difference of performance 
between impact funds and funds employing other 
ESG strategies is not significant. 
 
39  The impact of tracking error on performance for non-
ESG funds is given by the coefficient attached to the 
tracking error variable (0.167 ***). For ESG funds, the 
impact of tracking error on performance is obtained by 
summing the coefficient of the tracking error variable 
Table 9 
Regression analysis of EQ fund performance 
ESGs created before 2019 perform better 
Pooled OLS  
Dependent variable: Monthly gross performance 
 
Model with the 
different ESG 
funds 
(1) 
Model with the 
different ESG 
strategies 
(2) 
Created ESG 
‒ 0.018 
 
Older ESG 
0.075 *** 
 
Converted ESG 
‒ 0.078 
 
Impact funds 
 
0.047 * 
Other ESG 
 
0.064 *** 
Institutional  
‒ 0.023 
‒ 0.018 
Passive  
‒ 0.047 * 
‒ 0.045 
FoF  
‒ 0.012 
‒ 0.010 
Developed 
0.004 *** 
0.004 *** 
Large caps 
‒ 0.003 *** 
‒ 0.003 *** 
Value 
‒ 0.016 *** 
‒ 0.016 *** 
Utilities 
‒ 0.002 
‒ 0.002 
Materials 
0.009 *** 
0.009 *** 
Comm 
0.008 *** 
0.009 *** 
Consumer cyclical 
0.010 *** 
0.010 *** 
Consumer 
defensive 
‒ 0.006 *** 
‒ 0.007 *** 
Energy 
0.017 *** 
0.017 *** 
Financials 
0.014 *** 
0.014 *** 
Healthcare 
0.006 *** 
0.006 *** 
Industrials 
0.009 *** 
0.009 *** 
Technology 
0.017 *** 
0.017 *** 
Age 
‒ 0.002 *** 
‒ 0.002 ** 
Size 
0.016 *** 
0.017 *** 
Intercept 
2.977 *** 
2.957 *** 
Observations  
124 250 
123 544 
R2 
0.699 
0.699 
Note: ‘Created ESG’ is a dummy taking the value of 1 if the fund 
was created as an ESG fund. ‘Older ESG’ is a dummy taking the 
value of 1 if the fund is identified as ESG but created before 2019. 
‘Converted ESG’ is a dummy taking the value of 1 if the fund was 
repurposed as an ESG fund. ‘Impact fund’ is a dummy taking the 
value of 1 if the fund is identified as an impact fund. ‘Other ESG’ is 
a dummy taking the value of 1 if the fund follows other ESG 
strategies. For all the ESG variables, the relation is with respect to 
non-ESG funds. ‘Institutional’ is a dummy taking the value of 1 if 
the fund targets institutional clients. ‘Passive’ is a dummy taking the 
value of 1 if the fund is passively managed. ‘FoF’ is a dummy taking 
the value of 1 if a fund is a fund of fund or a feeder fund. ‘Developed’ 
indicates the share of the portfolio invested in developed 
economies. ‘Large caps’ indicates the share of the portfolio 
invested in large caps. ‘Value stocks’ indicates the share of the 
portfolio 
invested 
in 
value 
stocks. 
‘Utilities’, 
‘Materials’, 
‘Communication’, ‘Consumer cyclical’, ‘Consumer defensive’, 
‘Energy’, ‘Financials’, ‘Healthcare’, ‘Industrials’ and ‘Technology’ 
indicate the share of the portfolio invested in each sector. ‘Age’ 
measures the age of the fund from its inception date expressed in 
years. ‘Size’ represents fund size in terms of net assets in 
logarithmic terms. The date, domicile and Asset Management 
Company variables are hidden from the results. Significance levels 
are reported as follows: (0.01 (***), 0.05 (**), 0.1 (*)). 
Sources: Morningstar, ESMA. 
 
Even if funds created as ESG and impact funds 
have the lowest fees, they do not outperform non-
ESG funds in net terms. The regressions (not 
reported here) with the monthly net performance 
and the coefficient of the interaction term (0.167 + 
(- 0.195) = - 0.028). This summed coefficient is not 
significant. 
Table 8 
Equity funds’ change in sectoral exposures 
ESG funds reduced their exposure to financials 
Change in mean 
exposure 
Return 
ESG 
Non-ESG 
 
Basic materials 
– 0.55 
– 0.16 
24 % 
Communication 
3.52 
4.92 
– 8 % 
Consumer 
cyclical 
– 0.63 
– 0.65 
36 % 
Consumer 
defensive 
– 2.29 
– 0.88 
5 % 
Energy 
– 1.62 
– 1.26 
– 25 % 
Financials 
– 3.02 
– 1.51 
52 % 
Healthcare 
2.26 
1.27 
25 % 
Industrials 
1.34 
– 0.04 
40 % 
Real Estate 
– 0.30 
– 1.42 
– 6 % 
Technology 
1.58 
– 0.24 
14 % 
Utilities 
– 0.31 
– 0.02 
15 % 
Note: The two left columns represent the change (in percentage 
points) in sectoral exposure between April 2019 and September 2021 
for ESG and non-ESG EU equity UCITS. The right column represents 
the return of the MSCI Europe sectoral indexes (in percent) between 
April 2019 and September 2021. The figures in green represent the 
two highest returns or changes whereas the red and orange figures 
represent the two lowest returns or changes. 
Sources: Morningstar, ESMA.

ESMA TRV Risk Analysis 
23 May 2022 
13 
 
show that the coefficient attached to the variable 
‘created ESG’ is not significant and the coefficient 
attached to the ‘impact funds’ variable is only 
significant at the 10 % confidence level. 
Replacing the ESG dummy variable with different 
metrics of carbon exposure40 shows that a higher 
exposure to carbon is associated with a greater 
performance. The coefficients attached to the 
measure of the portfolio environmental risk 
score41 or to the carbon risk score42 are both 
positive and statistically significant, at least at the 
5 % confidence level. In consequence, the higher 
the exposure to environmental risks, the higher 
the performance (see Model (1) of Table 10).  
This result could indicate that the outperformance 
of ESG funds might not hold for funds focusing on 
the E pillar. To test this hypothesis, we assign to 
each ESG fund a specific focus (E, S or G). To do 
so, we compare the portfolio environmental risk 
score, the portfolio social risk score and the 
portfolio governance risk score for every ESG 
fund. The lowest risk score determines the 
specific focus for each ESG fund (i.e. if for a given 
fund the governance risk score is lower than the 
environmental risk score and the social risk 
score, then we consider the fund as oriented 
towards the G pillar). 
Model (2) of Table 10 shows that funds focusing 
on the S or G pillars strongly outperform non-ESG 
funds, whereas the performance of E funds is not 
statistically different from the performance of non-
ESG funds. S funds appear to be the best 
performing category, all else being equal a S fund 
outperformed 
a 
non-ESG 
fund 
by 
0.257 
percentage points. The difference of performance 
between S or G funds on the one hand and E 
funds on the other hand is statistically significant 
at the 5 % confidence level. 
 
 
 
40  However, including these variables reduces the 
sample’s size. Indeed, to calculate such metrics, 
Morningstar 
first 
needs 
to 
know 
the 
portfolio 
composition. Then, the companies composing the 
portfolio should disclose enough information about their 
environmental exposures and transition in order to be 
assigned an environmental or carbon risk score. 
41  According to Morningstar, the portfolio environmental 
risk score is ‘The asset-weighted average of the 
Company Environmental Risk scores […]. Company 
Environmental 
Risk 
Scores 
from 
Sustainalytics 
measure the degree to which a company’s economic 
Table 10 
Regression analysis of EQ fund performance 
Social funds outperform environmental funds 
Pooled OLS  
Dependent variable: Monthly gross performance 
 
Model with the 
env. score 
(1) 
Model with the ESG 
focus 
(2) 
Env. risk score 
0.030 *** 
 
Environmental 
 
‒ 0.0003 
Social  
 
0.257 ** 
Governance 
 
0.141 *** 
Undefined 
 
‒ 1.061 ** 
Institutional  
‒ 0.006 
‒ 0.011 
Passive  
‒ 0.050 * 
‒ 0.041 
FoF  
0.007 
‒ 0.003 
Developed 
0.005 *** 
0.004 *** 
Large caps 
‒ 0.001 *** 
‒ 0.002 *** 
Value 
‒ 0.016 *** 
‒ 0.016 *** 
Utilities 
‒ 0.006 *** 
‒ 0.006 *** 
Materials 
0.008 *** 
0.009 *** 
Comm 
0.011 *** 
0.010 *** 
Consumer 
cyclical 
0.010 *** 
0.010 *** 
Consumer 
defensive 
‒ 0.008 *** 
‒ 0.006 *** 
Energy 
0.011 *** 
0.016 *** 
Financials 
0.016 *** 
0.015 *** 
Healthcare 
0.005 *** 
0.007 *** 
Industrials 
0.009 *** 
0.008 *** 
Technology 
0.014 *** 
0.016 *** 
Age 
‒ 0.001 * 
‒ 0.002 ** 
Size 
0.010 ** 
0.013 *** 
Intercept 
1.459 *** 
2.982 *** 
Observations  
111 395 
122 484 
R2 
0.701 
0.701 
Note: ‘Env. risk score’ measures ‘the degree to which a company’s 
economic value may be at risk driven by environmental factors’ 
(Morningstar), ‘Environmental’ is a dummy taking the value of 1 if the 
fund is identified as ESG and focuses on the E aspect. ‘Social’ is a 
dummy taking the value of 1 if the fund is identified as ESG and focuses 
on the S aspect. ‘Governance’ is a dummy taking the value of 1 if the 
fund is identified as ESG and focuses on the G aspect. ‘Undefined’ is 
a dummy taking the value of 1 if it is not possible to assess the focus 
of an ESG fund (in most cases this is due to equal risk scores). For the 
E, S, G and undefined variables, the relation is with respect to the non-
ESG funds. ‘Institutional’ is a dummy taking the value of 1 if the fund 
targets institutional clients. ‘Passive’ is a dummy taking the value of 1 
if the fund is passively managed. ‘FoF’ is a dummy taking the value of 
1 if a fund is a fund of fund or a feeder fund. ‘Developed’ indicates the 
share of the portfolio invested in developed economies. ‘Large caps’ 
indicates the share of the portfolio invested in large caps. ‘Value stocks’ 
indicates the share of the portfolio invested in value stocks. ‘Utilities’, 
‘Materials’, 
‘Communication’, 
‘Consumer 
cyclical’, 
‘Consumer 
defensive’, ‘Energy’, ‘Financials’, ‘Healthcare’, ‘Industrials’ and 
‘Technology’ indicate the share of the portfolio invested in each sector. 
‘Age’ measures the age of the fund from its inception date expressed 
in years. ‘Size’ represents fund size in terms of net assets in logarithmic 
terms. The date, domicile and Asset Management Company variables 
are hidden from the results. Significance levels are reported as follows: 
(0.01 (***), 0.05 (**), 0.1 (*)). 
Sources: Morningstar, ESMA. 
 
value may be at risk driven by environmental factors. 
The environmental risk represents the unmanaged 
environmental risk exposure after taking into account a 
company’s management of such risks.’ 
42  According to Morningstar, the carbon risk score is ‘The 
asset-weighted carbon-risk score of the […] holdings in 
a fund […]. To calculate the portfolio carbon-risk scores, 
Morningstar uses Sustainalytics’ company carbon-risk 
ratings, which indicate the risk that companies face from 
the transition to a low-carbon economy.’ The regression 
with this variable is however not reported here.

ESMA TRV Risk Analysis 
23 May 2022 
14 
 
Conclusion 
This analysis has first shown that ESG funds are 
on aggregate more exposed to large caps and 
more oriented towards developed economies and 
the econometric analyses have demonstrated 
that these exposures are correlated with lower 
ongoing costs. The analyses also confirmed 
previous findings: funds targeting institutional 
clients, passive funds and more recent funds are 
associated with lower costs.  
However, ESG funds remained cheaper even 
after controlling for these various factors. All else 
being equal, an ESG fund appears less 
expensive than a non-ESG fund by 0.080 
percentage points over the period April 2019 – 
September 2021. Besides, among ESG funds, 
the study showed that funds created as ESG 
funds present on average lower fees than funds 
that were launched as conventional funds and 
later converted to ESG funds. Similarly, impact 
funds are cheaper than ESG funds employing 
other ESG strategies. 
In addition, the analysis highlights some 
differences in sectoral allocation between ESG 
and non-ESG funds. ESG funds are for instance 
more exposed to the healthcare and technology 
sectors, but these differences are not the only 
driver of the ESG funds’ outperformance. Funds 
created as ESG and impact funds that were 
among the cheapest funds are not, however, the 
best performing funds (both in gross and net 
terms). 
Finally, the analysis demonstrated that higher 
environmental risk is associated with higher 
performance. This apparently counterintuitive 
result can be explained by the outperformance of 
funds focusing on the S pillar or on the G pillar 
compared to funds focusing on the E pillar 
between April 2019 and September 2021. 
In a context of strong interest for costs and 
performance of investment funds and exponential 
growth of the ESG market, the specific issue of 
costs and performance of ESG funds is of primary 
interest from an investor protection angle. This 
study provides additional information on this field.  
As part of ESMA’s risk analysis work, the 
monitoring of costs will continue given its 
relevance 
from 
an 
investor 
protection 
perspective. Monitoring the evolution of costs will 
be all the more important as on the ESG side, the 
market is still developing and might quickly 
evolve. Besides, assessing to what extent the 
relative cheapness of ESG funds will impact the 
costs of non-ESG funds would also be 
interesting. 
In the meantime, further research is needed to 
fully understand the reasons behind the relative 
cheapness of ESG funds, as it might provide 
some useful insights. One hypothesis to test 
would be whether ESG funds remain cheaper 
when total costs are considered and not only 
ongoing charges. However, the possibility to 
conduct in-depth analyses on costs will be limited 
by the scarcity of data in this area.  
Besides, analysing the environmental/social 
performance alongside the financial performance 
would be of primary interest. Finally, it would be 
interesting to study the risk-adjusted performance 
to assess whether the higher performance of 
ESG funds still holds with this metric. 
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Practices, Progress and Challenges, OECD 
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EFAMA (2021), ‘ESG investing in the UCITS 
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ESMA (2021a), Annual statistical report on 
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ESMA (2021b), Report on Trends, Risks and 
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ESMA (2022a), Annual statistical report on 
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ESMA TRV Risk Analysis 
23 May 2022 
15 
 
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