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Citation: Izcan, D.; Bektas, E. The
Relationship between ESG Scores and
Firm-Specific Risk of Eurozone Banks.
Sustainability 2022,14, 8619. https://
doi.org/10.3390/su14148619
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sustainability
Article
The Relationship between ESG Scores and Firm-Specific Risk
of Eurozone Banks
Doga Izcan * and Eralp Bektas
Department of Banking and Finance, Faculty of Business Administration, Eastern Mediterranean University,
Famagusta 99628, Turkey; eralp.bektas@emu.edu.tr
*Correspondence: doga_izcan@hotmail.com
Abstract:
This paper investigates the relationship between corporate social responsibility and the
idiosyncratic risk of Eurozone banks. Idiosyncratic risk represents firm-specific risks for banks, and
the Carhart four-factor model is used for 31 Eurozone banks from 2002 to 2019 to determine the
idiosyncratic risk. Thomson Reuters ESG scores are used to determine the ESG scores of these banks
during the same period, and the effects of the environmental, social, and governance dimensions
are investigated separately. The quantile regression method reveals a relationship between ESG
and idiosyncratic risk over different risk levels. A significant negative relationship has been found
between the overall ESG scores and the idiosyncratic risk of banks for medium- to high-risk levels.
The effect becomes stronger as the riskiness of the banks increases. Similar to the overall ESG score,
the governance and environmental dimensions have a negative impact on banks with medium- to
high-risk levels. No significant relationship could be identified between the social dimension and the
idiosyncratic risk of banks.
Keywords: ESG; corporate social responsibility; idiosyncratic risk; banks; sustainable banking
1. Introduction
History repeated itself and the need for safe and sound banks for a functioning econ-
omy was declared during the Global Financial Crisis (GFC) of 2007–2009. Following the
GFC, banks and their stakeholders, including stockholders, depositors, borrowers, and
regulators, started emphasizing the importance of sustainability criteria. Environmental,
social, and governance (ESG) principles are at the center of sustainable banking’s context.
Some international institutions (the European Banking Authority, Bank of England, Euro-
pean Commission) have developed policies and highlighted the role of sustainability, which
is operationalized through ESG strategies. Due to the increasing importance of the ESG
risk concerns through environmental, management, and governance factors, regulatory
authorities, central banks, political institutions, and international financial institutions have
developed and integrated ESG-based tools into the banks’ risk management [
1
–
4
]. Bank
stability is one of the essential prerequisites of efficient and healthy functioning financial
systems. As banking crises present the failure of these institutions creates a damaging effect
on the welfare of the countries, as they are the leading financial intermediaries transferring
funds among the savers and borrowers. The banking literature documents their role in
economic development well [
5
,
6
]. Moreover, bank failures create costs for the taxpayers and
cause moral hazard problems among the public (mainly the depositors) and the risk-averse
banks. As such, it exacerbates intermediation problems by discouraging savings behavior
and encouraging risk-taking by banks. From this aspect, the banks’ safety/soundness/risk
is equivalent to the sustainability concept and is correlated with diverse literature on
the corporate social responsibility (CSR) and ESG [
7
–
11
]. Due to their distinct financial
structure (in which the third parties, mainly depositors, provide 80/90% of the funds) and
opaqueness in financial transactions, the ESG’s role in the banking firm increases. This
study attempts to analyze and diagnose the role of ESG in bank risk management for the
Sustainability 2022,14, 8619. https://doi.org/10.3390/su14148619 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 8619 2 of 21
leading Eurozone countries’ banks by operationalizing the ESG scores and various bank
risk measures from 2002 to 2019.
Though the existing literature documents many studies on environmental, social, and
governance principles, it is still unclear why banks focus extensively on ESG engagement
and invest vast amounts. Traditionally, banks are not accepted as ESG-related companies
compared to industries that directly impact the environment, such as construction, mining,
chemical, and petroleum. However, KPMG’s research showed that banks disclose ESG
information within their annual reports more than any other sector [
12
]. Furthermore,
recent studies argue that environmental and social aspects could be a valuable catalyst
for financial institutions to regain the reputation and trust of stakeholders lost during the
2007–2009 GFC [
13
]. This attribute makes ESG a critical communication tool for banks to
minimize information asymmetries with their stakeholders.
Extensive research has been conducted to understand the effect of ESG on the short-
term financial performance of companies. Nevertheless, contradictory findings could not
reach a definitive conclusion about the impact of the ESG performance on the financial
performance of firms assessed by accounting or market-based measures. A meta-analysis
conducted on 85 studies over 20 years concluded that socially responsible investment
with ethical concerns regarding stock market portfolio management does not create any
advantages or disadvantages according to the stock market’s performance [
14
]. Another
literature analysis of over 167 studies investigating accounting performance measures
found a positive but considerably small relationship between corporate social performance
and financial performance [
15
]. Therefore, further research is needed to understand the
motives behind costly ESG projects financed by the companies. The majority of the studies
perceived ESG to be a financial investment and tried to reveal the short-term financial effects
for the companies. However, ESG is an essential component of stakeholder engagement
which improves the reputation of banks and is used as a risk management tool [
16
]. ESG
assures stakeholder satisfaction and minimizes the firm-specific risk rather than the short-
term profit maximization. Improving relationships with stakeholders over ESG reduces the
risk of conflicts with major stakeholders. Previous literature has argued that the socially
responsible behavior of firms could be seen as a strategic tool and perceived as a “win-win”
scenario that strengthens companies’ market position and reputation while considering
the interest of society [
17
]. Concerning banks, ESG not only protects banks from having
a negative stakeholder crisis, but it may also stimulate a positive impact. ESG is also
an essential communication tool with stakeholders because it legitimates and promotes
socially responsible actions. Therefore, it positively contributes to the bank’s reputation
and the trust of stakeholders. An increased reputation and the trust of stakeholders directly
affect the firm-specific/idiosyncratic risks for banks [18].
Our paper contributes to the banking and CSR/ESG literature from two unique
aspects. First, even though the relationship between ESG and firm-specific risks has been
investigated extensively by the previous literature, only a few research studies focus on
banks [
9
–
11
,
19
,
20
]. Nonetheless, these research studies mostly used accounting-based risk
measures as a proxy for firm-specific risks. However, we argue that market-based risk
measures are more relevant, as they show the effect of ESG scores on the idiosyncratic risk
through stakeholders’ behavior. Therefore, the first contribution of this paper is the use
of a market-based risk measure that is not used in the previous bank CSR/ESG studies,
namely the idiosyncratic risk volatility. Secondly, in contrast to previous studies, which
have used conditional mean methodologies (OLS) and ignored the effect of ESG on different
risk levels/groups, we employ a quantile regression. Notably, we use a newly developed
approach that is useful for panel data with individual effects and endogenous explanatory
variables, as in our data [
21
]. We think this methodological approach will provide more
substantive findings, as the effect of ESG can be different for various risk levels.
Our results suggest that improvements in the ESG scores lower the bank risk signifi-
cantly for medium- to high-risk category banks. They also show that the ESG’s contribution
to bank stability, and therefore sustainability, increases with the bank’s riskiness. Con-
Sustainability 2022,14, 8619 3 of 21
cerning the ESG pillars’ (environmental, social, and governance), results showed that
the governance and environmental dimensions have the highest negative impact on the
idiosyncratic risk for medium- to high-risk level banks. Similar to the overall ESG results,
as the riskiness of a bank increases, the negative effect increases for the governance and
environmental dimensions. No significant relationship could be identified between social
dimension and firm-specific risk. The rest of this paper is organized as follows: The Sec-
tion 2reviews the relevant literature on the theoretical perspective of ESG and idiosyncratic
risk volatility and develops hypotheses. The Section 3explains the methodology, model,
and data used for the study. Finally, the Section 4presents the findings, and the study
concludes with the Section 5.
2. Literature Review and Hypothesis Development
2.1. Theoretical Perspective of ESG
Researchers can use various theories to establish the relationship between the ESG per-
formance and the bank value/risk. For example, “legitimacy theory” states that companies’
activities should be in parallel with society’s beliefs, norms, values, and expectations [
22
].
Moreover, legitimation strategies should be implemented by companies to avoid legit-
imacy crises, such as serious accidents, pollution leaks, or financial scandals [
23
]. As
such, social capital could be an essential tool to legitimate the actions and profits of the
companies [
24
]. Another theory that links ESG strategies to the companies’ performance
is the “Stakeholder theory” [
25
,
26
]. This theory argues that companies should consider
the interest of all stakeholders, rather than stockholders, since this strategy contributes to
long-term value maximization. The stakeholder group includes shareholders, employees,
consumers, public organizations, and the government, representing all social groups within
the community who have a direct or indirect relation with the company [
26
]. According
to the stakeholder theory, considering the interest of all stakeholders ensures long-term
value gain for the company [
25
]. It has been argued that ESG should be viewed with a
multi-theoretical perspective, as ESG is a complex phenomenon and cannot be explained
by a single theory. Actually, the “legitimacy” and “stakeholder” theories are interrelated
by acting complementarily, rather than competing with each other, as the legitimacy of
companies could be ensured by considering the interests of all stakeholders [
27
]. Socially
responsible behavior ensures that companies’ actions align with society’s expectations.
Moreover, ESG contributes to the reputations of companies by creating a moral capital that
generates a flow of resources in many forms, such as financial, human, and technologi-
cal [
28
]. Increasing interest in the concept of banks has perceived ESG as a tool to increase
reputation, trust, and credibility [
29
]. Since banks have many stakeholder groups, such as
depositors, borrowers, stockholders, and the government/public, and are also among the
most heavily regulated firms, these theories provide the theoretical basis for ESG studies in
banking.
2.2. Idiosyncratic Bank Risk and ESG
This study uses idiosyncratic volatility, which represents the gap between a market port-
folio and individual stock fluctuations, to measure the idiosyncratic bank risk. A company’s
stock volatility is determined by the systematic risk and the unsystematic/idiosyncratic/firm-
specific risk. The systematic risk depends on the market portfolio, while the idiosyncratic risk
represents the portion of the market portfolio that cannot be explained by the firm’s actions.
Numerous studies have found that the idiosyncratic risk of the firms represents the majority
of the total stock price variance compared to the systematic risk [
30
,
31
]. Idiosyncratic risk
mainly depends on firm-specific factors. Nevertheless, it is argued that idiosyncratic volatility
is not important, as diversification in efficient markets can eliminate this. However, it is
evident that markets are not perfectly efficient due to transaction costs, agency problems, and
informational problems (asymmetric information). Therefore, market inefficiencies increase
the importance of idiosyncratic risk [32].
Sustainability 2022,14, 8619 4 of 21
An analysis of the ESG scores of firms showed that positive ESG reduces idiosyn-
cratic risks, while negative ESG has a risk-increasing effect [
33
]. Previous research on
controversial industries, including alcohol, tobacco, gambling, and others, found that CSR,
intended as ESG scores, is not a window-dressing activity, as it significantly reduces the
idiosyncratic risk [
34
]. An analysis on the idiosyncratic risk reduction effect of CSR on
different market states concluded that CSR is a significant risk management tool both in
up- and down-trending market states [
35
]. Similarly, another research study show that the
corporate social performance has a negative relationship with the idiosyncratic volatility
for firms with better communication with their stakeholders [
18
]. An increased attention
of the stakeholders in the ESG performances of banks is evident, and the adoption of
ESG practices has been shown to secure the reputation of banks by minimizing the pos-
sibility of sanctions [
36
]. ESG could be a risk-mitigation tool, especially during periods
of financial distress by signaling prudent banking activities, enhancing reputation, and
ensuring good relations with the community [
29
]. These findings suggest that ESG should
be considered an effective risk-reducing tool, as it minimizes the idiosyncratic risk through
communication with the stakeholders.
Concerning the banking industry, previous research has asserted the significance of
the idiosyncratic risk for banks [
37
–
40
]. On the other hand, the risk-related literature
generally undermines the idiosyncratic risk, as it can be eliminated by diversification.
Nonetheless, the failure of one bank can affect the whole banking industry through the
contagion effect [
40
]. Moreover, deposits, insurance, and too-big-to-fail guarantee schemes
encourage banks to increase risk and underestimate risk diversification in a lax regulatory
environment. Therefore, monitoring the idiosyncratic risk is more critical for banks than
other firms. Previous literature has stated that the idiosyncratic risk of banks is related to
the business model, risk culture, and bank-specific factors [
41
]. Furthermore, the above
studies generally report a negative relationship between a bank’s size and its idiosyncratic
risk, as a larger size allows banks to better diversify. Therefore, we think the diversifiable
character of the idiosyncratic risk makes it more critical for the banks than the ESG concern,
since banks can follow the diversification process independently and use ESG principles
(dimensions) as a risk management tool.
This study will be the first study to analyze the idiosyncratic risk of banks and ESG
policies. The significance of the ESG for banks is related to their business structure as it
captures multiple groups of stakeholders. The first two stakeholder groups, depositors
and borrowers, are the products of the financial intermediary role of the banks. The third
one is the regulators. Due to their policy role, deposit insurance, too-big-to-fail guarantee
schemes, and the liability structure, banks are closely regulated by different regulatory
authorities. They also offer investment products to investors who represent the fourth
group. The fifth one is the shareholders, who are the owners. As such, the banks’ ESG
policies should directly affect the groups mentioned above through their ESG dimensions.
Nonetheless, these would have indirect implications for the other public groups as well.
For example, taxpayers who do not have a direct relationship with a bank can be affected by
a bank’s failure, or a villager can be negatively affected by a bank-given loan that destroys
the environment. As such, we believe that ESG policies should be a significant concern of
the banks. The report published by the Canadian Credit Union Association asserts that
the senior managers of eight credit unions perceived socially responsible behavior as an
important risk management tool for their institutions [42].
The role of the ESG arising from the idiosyncratic risk concern is vital for the bank’s
stakeholders described above. As the literature asserts, bank stakeholders, such as de-
positors, borrowers, investors, regulators, and managers, are directly affected by the
idiosyncratic risk; therefore, they care about it [
37
–
40
]. As beneficiary groups, depositors,
borrowers, and investors care about the idiosyncratic risk for sustainable banking services.
In addition, by nature, shareholders are the owners; hence, the idiosyncratic risk is crucial
for them as it affects profitability and the share price. For regulators, the safety and stability
of the banks in the banking system make the idiosyncratic risk a significant risk concern.
Sustainability 2022,14, 8619 5 of 21
These imply that banks need to inform stakeholders about their actions more than other
sectors [43]. As such, ESG could impact key firm-specific risks for banks.
In the light of the legitimacy and stakeholder theories, and the above arguments, we
conclude that ESG is necessary to have a stable relationship with stakeholders and protect
companies from random shocks from the idiosyncratic risk sources. ESG can help banks
minimize their key firm-specific risks related to stakeholders. Increased communication
with key stakeholders through the promotion of socially responsible actions minimizes
the idiosyncratic risks. Nonetheless, ESG is not considered a risk factor by traditional
risk models, such as CAPM or Fama–French, and is inadequately included within firm-
specific risk [
44
]. Therefore, we decided to carry out this research and contribute to the
ESG and banking literature. Accordingly, as stated in Hypothesis 1 (H1), this research
predicts a negative relationship between CSR and idiosyncratic volatility by minimizing
the idiosyncratic risks of banks.
Hypothesis 1 (H1). ESG and idiosyncratic risk have a negative relationship.
2.3. ESG Dimensions and Idiosyncratic Risk
Though the general ESG measure provides guidance, identifying the effect of specific
ESG dimensions on the idiosyncratic risk is more important. Generally, this is done by
adapting the banks’ ESG (environmental, social, and governance) scores to the research
models. Following this idea, we also use ESG scores separately in our analyses. Therefore,
our study will reveal the effectiveness of these dimensions and create better guidance for
banks. Previous researchers found that environmental, social, and governance dimensions
could interact differently with firm-specific risks since stakeholders are not homogenous
and are affected differently by ESG dimensions [8,11,45].
Environmental responsibility leads to energy- and resource-saving as it aims to min-
imize the carbon footprint of banks. Environmental responsibility could increase the
operational efficiency of banks as energy and resource consumption is monitored. Ad-
ditionally, increasing stakeholders’ awareness of environmental manners creates a risk
reduction effect for environmentally responsible companies. Promoting environmentally
friendly actions establishes a communication channel with stakeholders that minimizes the
information asymmetries [
46
]. Moreover, environmental disasters potentially affect bank
risks, such as operational, liquidity, and credit risks. As such, some regulatory bodies and
institutions warn banks to care about the environmental risk [
2
–
4
,
47
]. Due to the increased
awareness of community, the European Central Bank announced that recent bank stress
tests have included environmental risks. The stakeholder theory argues that the actions
of institutions must align with the expectations of the whole society. Research on banks
that has solely focused on the environmental dimension found an inverse relationship
between the environmental performance and firm-specific risk [
9
–
11
]. It has been argued
that the main reasoning behind this is that environmental engagement enhances the repu-
tation of banks and legitimizes the banks’ actions by improving their social images [
19
].
Enhanced reputation and protection from adverse consequences legitimize the actions of
banks and increase the loyalty of stakeholders. As such, H2 predicts a negative relationship
between the environmental dimension and idiosyncratic risk by satisfying the concerns of
environmentally friendly stakeholders.
Hypothesis 2 (H2).
The environmental dimension of ESG and idiosyncratic risk have a negative
relationship.
The social aspect of ESG has a direct impact on reputation, and banks can use it
as a communication tool with various stakeholders. The social element assures product
responsibility, positive community commitment, and good employee relations. Socially
responsible activities could minimize the idiosyncratic risk for banks by considering the
interests of various stakeholders. Higher social performance signals an improved social
Sustainability 2022,14, 8619 6 of 21
capital and increase the reputation for stakeholders [
45
]. Reputation is crucial for healthy
functioning banks due to trust relationships with the depositors, investors, and borrowers.
Employee strikes, boycotts, and lawsuits could damage the reputation of banks within
the community. Therefore, employee relations form an essential part of the social dimen-
sion, and previous research has concluded that good employment practices and policies
minimize firm-specific risks [
48
]. A previous study on international companies found that
the social dimension has a risk-reducing effect on the financial risk of companies [
8
]. In
line with this finding, another research study found a risk-reducing effect of the social
dimension for the banks [
9
]. Nevertheless, some research found an ambiguous influence of
the social dimension [
11
]. Positive social performance will legitimize the actions of banks
and increase stakeholders’ trust and loyalty, which contributes to stakeholder relationship
management. As such, H3 predicts a negative relationship between the social dimension
and idiosyncratic risks.
Hypothesis 3 (H3).
The social dimension of ESG and idiosyncratic risk have a negative relationship.
Governance is another dimension of ESG that is related to idiosyncratic risk. Gover-
nance ensures effective management and accurate decision-making and affects banks from
multidimensional factors. Therefore, good governance directly affects the above-mentioned
stakeholder groups. The stakeholders who transact with the bank need to be assured that
the institution is governed properly. Previous research has shown that the ownership
structure of banks affects the riskiness of banks; a high ownership concentration increases
the incentive for risk-taking, while non-shareholding managers tend to decrease it [
49
].
Good governance ensures risk management and increases the trust that stakeholders feel
towards the bank. As the stakeholder theory predicts, promoting the governance quality
of the bank creates better communication channels with stakeholders and minimizes the
information asymmetries. Therefore, good governance positively affects banks’ reputations,
contributes to bank–stakeholder relationships, and reduces idiosyncratic risk. In line with
the above views, it is been argued that the governance dimension could have a stronger
negative relationship with firm risks, as they are more relevant and visible to the investors
compared to other dimensions [
8
]. Concerning banking, previous research has found a
risk-reducing effect of corporate governance for banks in common law countries [
44
] and
European countries [
9
]. On the other hand, there are some mixed results for a sample of
worldwide banks [
11
]. As we have asserted many positive implications of governance for
idiosyncratic risk, we expect a negative relationship between the governance dimension
and idiosyncratic risk in Hypothesis 4.
Hypothesis 4 (H4).
The governance dimension of ESG and idiosyncratic risk have a negative
relationship.
3. Data and Methodology
3.1. Data
The ESG data is obtained from the Thomson Reuters Eikon ESG Database. Thomson
Reuters is a leading agency that provides financial data and is used intensively by investors.
The database uses algorithmic and human processes together with over 400 ESG metrics
while determining the score of companies. In addition, the database includes negative
media stories, which are captured as ESG controversies and deducted from the overall ESG
scores. This eliminates the bias of relying solely on company-provided sources, which is the
method of some previous studies that used content analysis to determine the ESG scores
of companies. The database uses separate performance indicators and provides scores
for environmental, social, and governance pillars. The main categories of environmental
pillars are resource use, emissions, and innovations. The main categories of social pillars
are workforce, human rights, community, and product responsibility. The governance
categories include management, shareholders, and CSR strategy. Unfortunately, the ESG
Sustainability 2022,14, 8619 7 of 21
scores are the limiting data for the research, and it is available from 2002 to 2019. The
sample includes 31 Eurozone banks from the leading Eurozone countries from 2002 to 2019,
and country distribution is illustrated in Table 1below. In this study, we concentrate on
Eurozone countries that share similar economic and regulatory environments, as well as
common monetary policy under the regulatory council of the European Central Bank. Any
bank with missing data for a particular year has been removed from the analysis, and the
final sample size is 471 firm-year observations.
Table 1. Distribution of banks by country.
Country Number Percentage
Austria 2 6.45
Belgium 1 3.23
France 4 12.90
Germany 1 3.23
Greece 3 9.68
Ireland 3 9.68
Italy 9 29.03
The Netherlands 1 3.23
Portugal 1 3.23
Spain 6 19.35
Total 31 100
Financial and accounting data for banks were obtained from the Thomson Reuters Eikon
database. The data includes daily stock prices, dividend yield, provision for loan loss, operating
profit margin, total loans, return on equity (ROE), capital adequacy, liquidity, and market-to-
book ratio. The Institutional Brokers’ Estimate System (I/B/E/S) estimate is used to determine
12-month forward earnings per share. The country-specific variable, inflation, is obtained from
the World Bank. The detailed variable descriptions are presented in Appendix ATable A1.
Summary statistics for the collected data are presented in Table 2below.
Table 2. Descriptive Statistics.
Variable Mean Median Maximum Minimum Std. Dev.
Dependent Variable
Idiosyncratic Volatility 0.533878 0.363764 4.175236 −1.529179 1.076037
Independent Variable
ESG 0.572691 0.6078 0.9501 0.0791 0.213987
Environment 0.538681 0.63955 0.9744 0 0.336896
Social 0.590842 0.6307 0.9732 0.0657 0.224753
Governance 0.561912 0.58 0.9599 0.0597 0.240326
Dividend Yield 0.033309 0.02605 0.6845 0 0.054551
Provision For Loan Loss 1,633,554 723,870.5 18,549,000 −925,000 2,415,079
Operating Profit Margin 0.046993 0.10085 0.5226 −2.0111 0.250602
Total Loans (ln) 18.57284 18.4779 20.68213 16.04809 1.095184
Return on Equity −0.097159 0.06515 0.9814 −42.9847 2.125264
Inflation 0.015652 0.01539 0.048971 −0.044781 0.013377
IBES 12 Month Forward EPS 2722.307 1.067 323,418.6 −44,314.02 23,956.89
Capital Adequacy 0.135079 0.135 0.2206 −0.061 0.032291
Liquidity 0.970924 0.5906 11.5126 0.0814 1.27492
Market to Book Ratio 1.058286 0.87 5.86 −2.58 0.824165
3.2. Idiosyncratic Risk Measure
The idiosyncratic risk is measured by the standard deviation of residuals from daily
stock returns of the Carhart four-factor model. The Carhart four-factor model, stated below,
includes the momentum factor as an addition to the Fama–French three-factor model [
50
].
This model is used widely by the previous literature to determine the idiosyncratic risk of
companies [18,33,45].
Sustainability 2022,14, 8619 8 of 21
(Rit −Rf t ) = αi+βim RMt −Rf t +βisS MBt+βih H MLt+βiuU MDt+εt(1)
In the model above,
(Rit −Rf t )
represents the excess return for a bank ion a day t. Risk
free rate (
Rf t)
stands for a 1-month T-bill rate and
RMt −Rf t
is the excess return of the
market portfolio for Europe. The other factors of the model are the difference between small
and big stocks
SMBt
; the difference between high and low book-to-market ratio stocks
HMLt
; and the momentum factor
UMDt
. Data for market return and other factors are
obtained from the Kenneth French data library’s European database, and the daily excess
returns for 31 banks are retrieved from the Eikon database. To derive the idiosyncratic risk
volatility of each bank, which is represented by the standard deviation of residuals (
εt
) we
run Equation (1). Following the previous research, logarithmic transformation is applied to
the idiosyncratic volatility to ensure homoscedasticity, as shown in Equation (2) [
18
,
35
,
51
].
The idiosyncratic risk for bank iin year tis represented as IVit in Equation (2).
IVi t =ln(1−R2
i t
R2
i t
)(2)
3.3. Method
The quantile regression method follows in order to investigate the relationship be-
tween the ESG performance and idiosyncratic risk. The quantile regression method is
advantageous compared to conditional mean methods, as it explains the relationship
between different risk levels and ESG within the sample population. The variance infla-
tion factor (VIF) for all variables is tested to assess possible multicollinearity problems.
No multicollinearity is detected, as none of the variables have a VIF higher than 5 (see
Appendix B
). Mean regression methods are sensitive to outliers, non-normal distribution,
and heteroscedasticity of error terms, which could lead to misleading results. The quantile
regression minimizes the sum of the absolute residuals, while mean regression methods
minimize the sum of squared residuals. The quantile regression method has no sample
selection bias when determining the quantiles compared to a piecewise regression [
52
].
The quantile regression divides the sample population into different percentiles with a
quantile-fitting regression. The quantile approach is defined as:
Yit =βθxit +εθit 0<θ<1 (3)
where
Yit
is the dependent variable for bank iat a time t, and
xit
represents the vector of the
explanatory variables at the
θth
percentile for the dependent variable. The model’s error
term, in which the conditional quantile distribution is zero, is
εθit
. In order to investigate
the risk-reducing impact of ESG, the following quantile model is developed:
Qθ(IVit |Xit)=δi+β1CSRit +β2Divit +β3PLLossit +β4OPMit +β5Sizei t
+β6ROEit +β7IBESit +β8MTBit +β9Ca p Adqit
+β10LIQit +β11 I N Fit +εit
(4)
where
Qθ(IVi t |Xi t)
represents the
θth
quantile regression function, and the 0.05, 0.25,
0.5, 0.75, and 0.95 percentiles are assigned to
θ
to investigate the ESG effect of 5 different
percentiles. The dependent variable is the idiosyncratic volatility for bank iat a time
t
(IVi t )
, and
β1CSRit
is assigned for the overall ESG scores of bank iat a time t. The envi-
ronmental, social, and governance scores are also tested separately by replacing
β1CSRit
with
β1ENVit
,
β1SOCit
, and
β1GOVit
, respectively. The above model is estimated by em-
ploying the Machado and Silva (2019) methodology by using the xtqreg command of STATA
software. This method has advantages over other methods as it considers the individual
effects and endogeneity and makes calculations simpler.
Idiosyncratic bank risk (IR) is not independent of the bank-specific variables that
represent bank characteristics. Therefore, following the previous research on idiosyn-
Sustainability 2022,14, 8619 9 of 21
cratic bank risk, we employ the following bank characteristics as control variables in our
model [
37
,
38
,
40
]. Dividend payments (Div) represent the financial health of firms, and
paying out dividends positively signals to the shareholders. A provision for loan loss (PLL)
represents the credit risk associated with the bank and ensures future cash flow; therefore,
it is included as a control variable. The operating profit margin (OPM), which shows a
bank’s financial efficiency and management performance, is related to the idiosyncratic
risk. Size is an important characteristic that could affect the IR of banks: bigger banks could
manage financial risks more efficiently, and total loans are used as a proxy. Instead of total
assets, total loans are used as it is assumed that total loans better represent bank-related
risks. Profitability is another indicator included within the model, and return on equity
(ROE) is used as a proxy. The market-to-book ratio (MTB) shows investment opportunity
and is included as a control variable. The 12-month forward earnings per share rate from
the Institutional Brokers’ Estimate System (IBES) is used to represent the expected future
earnings. Capital adequacy (CA) was incorporated as a proxy to capture the banks’ capital
risks. The liquidity ratio (LIQ) is used to capture bank liquidity risks and is included
as a control variable. Finally, inflation (INF) is used as a control variable to capture the
country-specific risks associated with banks.
4. Empirical Results and Discussion
Table 3, below, presents the results of Equation (4), in which the ESG score is the
main independent variable. The results indicate a negative relationship between ESG and
idiosyncratic risks for the quantiles 0.50, 0.75, and 0.95. These findings suggest that ESG has
a negative relationship with medium/median- and high-risk banks. A closer analysis of
the quantile base also provides some clues regarding the effect of ESG on the different risk
levels. As can be seen from the ESG coefficients, which indicate the economic significance
of the ESG impact on the IR, high-risk banks earn relatively more benefits by increasing
their ESG scores. In other words, better ESG for these banks makes them more stable.
For example, the negative effect of ESG is nearly two times higher when a medium-risk
category bank (quantile 0.50) is compared with the highest risk category bank (quantile
0.95) (i.e., the coefficients are
−
0.879 and
−
1.985, respectively). These results support H1
for medium- and high-risk banks, where a negative relationship is expected between the
ESG scores and idiosyncratic risks of banks. Though ESG is not statistically significant
for the lowest risk group, we think this is acceptable since low-risk banks do not need
ESG promotions. These findings suggest that ESG ensures communication with the key
stakeholders of banks, and, as the risk level of a bank increases, this information flow
becomes more important.
Our results have theoretical and empirical implications as well. The results align with
the stakeholder and legitimacy theories, which argue that ESG strategies ensure a stable
relationship with stakeholders by legitimizing the banks’ actions and providing insurance-
like protection against possible adverse shocks, thereby minimizing the IR. This implies
that ESG could be an important risk management tool and an ESG disclosure become more
important as the risk-mitigation effect increases with the riskiness of the bank. The results
also support the findings of previous researchers who used accounting-based risk measures
(Z-score, non-performing loans) and found a negative relationship between ESG and the
riskiness of banks [
9
–
11
]. Though classical models do not consider CSR as a risk factor,
the findings show that promoting socially responsible actions minimizes the idiosyncratic
risk for medium- to high-risk category banks. Hence, the CSR indicators should not be
undermined by the researchers in their risk models.
Sustainability 2022,14, 8619 10 of 21
Table 3. Quantile regression results with ESG overall.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent Variable
= IV IV IV IV IV IV
ESG 0.00305 −0.492 −0.879 * −1.340 * −1.985 *
(0) (−1.13) (−2.37) (−2.40) (−1.99)
Dividend Yield 1.099 0.211 −0.483 −1.311 −2.467
(−1.03) (−0.33) (−0.88) (−1.60) (−1.68)
Provision For Loan
Loss −5.796 −13.97 −20.37 −27.99 −38.64
(−0.28) (−1.13) (−1.94) (−1.77) (−1.37)
Operating Profit
Margin −0.773 −1.097 −1.351 ** −1.653 * −2.075
(−0.80) (−1.90) (−2.76) (−2.23) (−1.57)
Total Loans −0.343 −0.519 ** −0.657 *** −0.821 *** −1.051 **
(−1.17) (−2.94) (−4.37) (−3.62) (−2.59)
ROE −0.036 −0.0275 −0.0209 −0.013 −0.002
(−1.25) (−1.59) (−1.43) (−0.59) (−0.05)
Inflation −6.749 −8.056 * −9.079 ** −10.3 −12
(−0.99) (−1.97) (−2.61) (−1.96) (−1.28)
IBES 12-Month
Forward EPS −4.18 ×10−6−4.83 ×10−6*** −5.33 ×10−6*** −5.94 ×10−6** −7×10−6
(−1.50) (−2.89) (−3.76) (−2.76) (−1.77)
Capital Adequacy 3.636 2.778 2.107 1.308 0.19
−1.26 −1.61 −1.44 −0.59 −0.05
Liquidity −0.0553 −0.116 * −0.164 *** −0.221 *** −0.301 *
(−0.65) (−2.26) (−3.74) (−3.34) (−2.54)
Market-to-Book 0.128 0.0665 0.0186 −0.0384 −0.118
−0.94 −0.81 −0.27 (−0.36) (−0.63)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Table 4below presents the relationship between the environmental dimension of
ESG and the idiosyncratic volatility of banks. The environmental dimension negatively
correlates with the idiosyncratic risk for low- to high-risk category banks in the 0.25, 0.50,
and 0.75 quantiles. Our findings support the argument that ever since the stakeholders’
attention to environmental issues, such as global warming and the carbon footprints of
institutions, has increased, the pressure on the banking sector has also increased. A signifi-
cant negative relationship is evident between the environmental dimension’s performance
and the idiosyncratic bank risk. Therefore, our second hypothesis, H2, is supported, ex-
cept for the lowest (quantile 0.05) and highest (quantile 0.95) risk category banks. The
economic significance of the negative effect becomes slightly more robust as the banks’ risk
categories increase. Our results show that communicating with stakeholders about the
environmentally friendly actions of banks minimizes the idiosyncratic risk volatility by
legitimizing the banks’ activities. This result aligns with our expectations, as stakeholders,
including the regulatory bodies for European banks, demand increased disclosure over
environmental issues. The results support the argument of the previous literature, which
found a risk-reducing relationship between the environmental performance of banks and
accounting-based risk measures due to improved reputation [11–19].
Results for the effect of the social dimension on banks’ idiosyncratic risks are presented
in Table 5below. H3 could not be supported for all quantile levels as no significant
relationship is detected between the banks’ social dimensions and the idiosyncratic bank
risks. These results show that stakeholders focus more on environmental issues and
good governance, rather than social aspects, such as a positive commitment to society or
good employee relations in banks. However, previous research has found a risk-reducing
relationship for some elements of the social dimension, such as human rights and labor
protections [
11
]. Still, the overall social score has no significant relationship with bank
riskiness.
Sustainability 2022,14, 8619 11 of 21
Table 4. Quantile regression results with environmental score.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent
Variable =
IV
IV IV IV IV IV
Environment
−0.59 −0.708 * −0.786 *** −0.900 * −1.033
(−1.24) (−2.50) (−3.33) (−2.56) (−1.72)
Dividend
Yield 1.163 0.158 −0.507 −1.476 −2.612
−1.07 −0.25 (−0.93) (−1.84) (−1.90)
Provision
For Loan
Loss
−12.86 −15.43 −17.14 −19.63 −22.54
(−0.71) (−1.43) (−1.91) (−1.47) (−0.99)
Operating
Profit
Margin
−1.162 −1.236 * −1.285 ** −1.356 * −1.44
(−1.37) (−2.45) (−3.06) (−2.17) (−1.35)
Total Loans −0.133 −0.355 −0.501 ** −0.715 ** −0.965 *
(−0.40) (−1.81) (−3.05) (−2.93) (−2.32)
ROE −0.0468 −0.0334 * −0.0244 −0.0115 0.00377
(−1.77) (−2.12) (−1.86) (−0.59) −0.11
Inflation −7.818 −8.738 * −9.349 ** −10.24 * −11.28
(−1.20) (−2.26) (−2.90) (−2.13) (−1.38)
IBES
12-Month
Forward EPS
−3.25 ×10−6−3.49 ×10−6*−3.65 ×10−6** −3.88 ×10−6−4.15 ×10−6
(−1.18) (−2.14) (−2.68) (−1.91) (−1.20)
Capital
Adequacy 5.548 4.224 * 3.346 * 2.068 0.569
−1.87 −2.39 −2.27 −0.94 −0.15
Liquidity −0.0519 −0.107 * −0.144 *** −0.198 ** −0.260 *
(−0.62) (−2.15) (−3.44) (−3.18) (−2.46)
Market-to-
Book 0.0773 0.0255 −0.00875 −0.0586 −0.117
−0.56 −0.31 (−0.13) (−0.58) (−0.67)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Table 6below presents the results of Equation (4), in which governance is the main
independent variable. Governance negatively relates to the idiosyncratic risk for medium-
to high-risk category banks. Similar to the overall ESG score, as the riskiness of banks
increases, the negative effect of the governance dimension also increases. The risk-reducing
impact is more than double for the highest risk category (quantile 0.95) compared to
medium-risk category (quantile 0.50) banks, where the coefficients are
−
1.404 and
−
0.638,
accordingly. The signaling effect of governance on stakeholders, regarding management
quality, transparency, and accountability, becomes essential as the risk category of banks
increases. These findings show that the environment and governance dimensions are
similarly crucial and negatively relate to the IR of banks for medium- and high-risk banks.
These results indicate that focusing on the governance and environment dimensions en-
courages a healthy relationship with the stakeholders, which leads to a risk-mitigation
effect of the idiosyncratic risks of banks. In contrast, the social dimension has no significant
effect. However, unlike other dimensions, the governance dimension has a strong negative
relationship with IR for the highest-risk category banks, and this aligns with the expectation
that the governance dimension has a higher negative impact on firm-specific risks, as it
is more relevant and visible to the investors [
8
]. This finding supports the idea that good
governance strengthens the banks’ risk management.
Sustainability 2022,14, 8619 12 of 21
Table 5. Quantile regression results with social score.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent
Variable
= IV
IV IV IV IV IV
Social 0.276 −0.0783 −0.345 −0.693 −1.105
−0.46 (−0.21) (−1.18) (−1.66) (−1.55)
Dividend
Yield 0.996 0.139 −0.508 −1.348 −2.347
−0.9 −0.2 (−0.93) (−1.74) (−1.78)
Provision
For Loan
Loss
−4.344 −12.46 −18.58 −26.54 −36
(−0.20) (−0.95) (−1.79) (−1.79) (−1.42)
Operating
Profit
Margin
−0.724 −1.05 −1.297 ** −1.616 * −1.997
(−0.73) (−1.73) (−2.68) (−2.34) (−1.69)
Total
Loans −0.384 −0.604 *** −0.769 *** −0.985 *** −1.240 ***
(−1.33) (−3.41) (−5.44) (−4.89) (−3.62)
ROE −0.0324 −0.0256 −0.0204 −0.0137 −0.0058
(−1.07) (−1.37) (−1.38) (−0.65) (−0.16)
Inflation −6.96 −8.253 −9.228 ** −10.50 * −12
(−0.99) (−1.93) (−2.72) (−2.16) (−1.45)
IBES 12-
Month
Forward
EPS
−3.8 ×10−6−4.52 ×10−6** −5.05 ×10−6*** −5.75 ×10−6** −6.59 ×10−6*
(−1.40) (−2.71) (−3.83) (−3.04) (−2.04)
Capital
Ade-
quacy
3.475 2.561 1.872 0.976 −0.0889
−1.17 −1.41 −1.3 −0.47 (−0.03)
Liquidity
−0.063 −0.121 * −0.164 *** −0.221 *** −0.288 **
(−0.71) (−2.22) (−3.78) (−3.56) (−2.73)
Market-
to-Book 0.136 0.0789 0.0358 −0.0204 −0.0871
−0.98 −0.93 −0.53 (−0.21) (−0.53)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p<0.001.
Control variables show that size is the most critical factor in determining the IR of
banks. Size has a negative relationship with the IR across all quantile levels, except for the
lowest risk quantile, and the risk-reducing effect of size increases as the riskiness of banks
increases. This result aligns with the previous literature, which argues that size is the most
crucial factor for determining the IR of banks and that, as the size of the banks increases,
the IR tends to decrease [39]. Similar to size, liquidity negatively affects banks’ IR over all
quantile levels, except for the lowest risk quantile, but the magnitude is considerably lower
than the size. As a market estimation figure is anticipated, the IBES 12-month forward EPS
expectations are also significant determinants of the IR of banks for medium- to high risk
level category banks (quantile 0.25, 0.50, and 0.75).
Table 6. Quantile regression results with governance score.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent
Variable
= IV
IV IV IV IV IV
Governance
−0.00772 −0.357 −0.638 * −0.959 ** −1.404 *
(−0.02) (−1.14) (−2.49) (−2.62) (−2.17)
Dividend
Yield 0.868 0.105 −0.51 −1.211 −2.184
−0.81 −0.16 (−0.96) (−1.59) (−1.62)
Sustainability 2022,14, 8619 13 of 21
Table 6. Cont.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent
Variable
= IV
IV IV IV IV IV
Provision
For Loan
Loss
−3.51 −12.25 −19.29 −27.33 −38.47
(−0.17) (−0.96) (−1.87) (−1.84) (−1.47)
Operating
Profit
Margin
−0.61 −1.005 −1.322 ** −1.685 * −2.188
(−0.62) (−1.67) (−2.71) (−2.41) (−1.77)
Total
Loans −0.321 −0.559 *** −0.750 *** −0.969 *** −1.273 ***
(−1.21) (−3.44) (−5.64) (−5.12) (−3.79)
ROE −0.0339 −0.0229 −0.0141 −0.0041 0.00982
(−1.16) (−1.29) (−0.98) (−0.20) −0.27
Inflation −7.745 −8.174 * −8.520 * −8.915 −9.463
(−1.14) (−1.97) (−2.54) (−1.85) (−1.11)
IBES 12-
Month
Forward
EPS
−4.23 ×10−6−5.09 ×10−6** −5.79 ×10−6*** −6.59 ×10−6** −7.69 ×10−6*
(−1.45) (−2.86) (−4.01) (−3.18) (−2.10)
Capital
Ade-
quacy
3.04 2.061 1.273 0.374 −0.873
−1.05 −1.16 −0.89 −0.18 (−0.24)
Liquidity
−0.0526 −0.112 * −0.161 *** −0.216 *** −0.292 **
(−0.59) (−2.07) (−3.63) (−3.41) (−2.60)
Market-
to-Book 0.132 0.0791 0.0369 −0.0113 −0.0781
−0.97 −0.95 −0.55 (−0.12) (−0.45)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
5. Robustness
To confirm the results provided in the previous section, we conducted some robustness
checks. For this purpose, we use accounting-based risk measures to assure the robustness
of our results. The previous literature widely accepts the Z-score and capital adequacy (CA)
as firm-specific risk measures [
9
,
11
,
19
]. The analysis results in replacing the idiosyncratic
volatility with the Z-score as the dependent variable, provided in Appendix C, and the
results for CA, replacing the idiosyncratic volatility, are presented in Appendix D. The
positive coefficients indicate that ESG contributes to bank stability and has an inverse
relationship with bank riskiness. The results align with our findings, in which ESG has
an inverse relationship with the idiosyncratic risk of banks, and the negative relationship
increases as the risk level of banks increases. This shows that results support each other for
a market-based and an accounting-based risk measure. Additionally, these findings are
in line with the previous literature, which found an inverse relationship between the CSR
performance and the riskiness of banks [9–11].
To check the robustness of the ESG dimensions, we regress each dimension to the
Z-score and CA. The Z-score, CA, and environmental dimension analysis results are
presented in Appendices Eand F, respectively. The results presented in Appendices E
and Fshow that the environmental dimension positively correlates with bank stability
for all quantile levels. These findings align with the previous literature and support the
findings of this study. Appendix Gpresents the results of the governance dimension for
the Z-score, and Appendix Hshows the governance dimension for CA. However, we
do not find any significant relationship between the governance dimension and these
accounting-based risk measures. These findings do not support our initial results, which
found a significant inverse relationship between governance and the idiosyncratic volatility
Sustainability 2022,14, 8619 14 of 21
of banks. Finally, Appendix Ishows the relationship between the social dimension and Z-
score, while Appendix Jpresents the relationship between the social dimension and CA. The
results indicate a positive relationship between the social dimension and risk measures of
banks over the 0.25, 0.50, 0.75, and 0.95 quantile levels. These findings contradict the initial
results of this research, which could not identify a significant relationship between the social
dimension and idiosyncratic bank risk. Although the robustness findings of the governance
and social dimensions for accounting-based risk do not align with the idiosyncratic risk,
previous literature has indicated that the governance and social dimensions have different
effects on the various bank risks [
11
]. This shows that the impact of the ESG dimensions
could differ for different risk measures, which could explain the different results for
accounting and market-based risk measures. Our findings also suggest that market-based
risk measures could be more important as these represent the stakeholders’ perceptions
better than the accounting figures of the banks.
6. Conclusions
This research contributed to the literature by analyzing the effect of ESG on idiosyn-
cratic bank risk. For this purpose, first, we use the overall ESG scores, and secondly, the
ESG’s dimensions, environmental, social, and governance, separately. The sample consists
of 31 European banks between 2002 and 2019. This study revealed that ESG has a negative
relationship with the idiosyncratic risk of banks for medium- to high-risk levels. Another
important finding of this research is that the effect of ESG changes according to the risk
levels of banks. As the riskiness of the banks increases, a stronger relationship is detected.
Additionally, this research shows that the findings of a market-based risk measure align
with the findings of accounting-based risk measures for the ESG’s effects on firm-specific
banks’ risks. These findings have both theoretical and practical implications.
From the theoretical perspective, aligned with the stakeholder and legitimacy theories,
this research reveals that ESG has a negative relationship with the idiosyncratic risk of banks
for medium- to high-risk levels. ESG acts as a communication tool with key stakeholders,
minimizes the information asymmetries, and legitimizes the banks’ actions. It is revealed
that ESG has a negative relationship with the idiosyncratic risk over medium- to high-risk
level quantiles. As the riskiness of banks increases, the relationship between ESG and risk
becomes more important. This shows that ESG contributes more to the stability of banks
as the risk level increases. Analyses of the individual ESG dimensions showed that the
governance and environmental dimensions have a strong negative impact. This suggests
that stakeholders focus more on governance quality, which signals the management quality
of banks, and that the environment performance has a significant relationship with the
banks’ reputations due to society’s increased attention to environmental issues. There
was no significant association between the social dimension and idiosyncratic bank risk,
indicating that the stakeholders’ interest in social projects is not as high as in other ESG
dimensions. Alternatively, stakeholders view social projects as window-dressing activities
and do not prioritize them.
Practically, these results reveal some of the reasoning behind banks’ increased commit-
ment to ESG projects. ESG ensures effective communication and good relations with all
stakeholders, including customers, employees, shareholders, government, and regulators.
The results revealed that ESG negatively relates to idiosyncratic risk for medium- to high-
risk banks. Therefore, ESG could be an essential communication tool. With stakeholders
legitimizing the actions of banks, and, as banks’ riskiness increases, this tool becomes more
important. The analyses of the ESG dimensions showed that governance quality and the
environmental dimension have a similar negative relationship with idiosyncratic risk, but
governance quality is more important for the highest risk category. This research showed
that focusing on governance quality and ESG’s environmental dimension could help banks
minimize the idiosyncratic risks. Our results also suggest that regulators and policymakers
can use ESG-type non-financial information disclosure requirements as risk-reducing policy
tools and maintain the financial stability of the system. Nonetheless, these findings suggest
Sustainability 2022,14, 8619 15 of 21
that the environmental and, particularly, the governance dimensions should be empha-
sized more by regulators and policy makers. This may also explain regulators’ increased
disclosure requirements on ESG information.
Lastly, this research has limitations due to the available data on the European market.
Future research could increase the number of banks or focus on other economic regions.
Different market-based risk metrics could be used to isolate the effect of the ESG dimensions
on the riskiness of banks. Additionally, further research could expand this research by
investigating the stakeholders’ perceptions of particular aspects of the environment, social,
and governance dimensions, which could reveal the interests of key stakeholders. This
could provide further guidance for bank management and regulators on socially responsible
and sustainable banking and its relation to firm-specific risks.
Author Contributions:
Supervision, E.B.; Writing—original draft, D.I. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Variable descriptions.
Variable Description Source
ESG Weighted average of environment, social, and
governance score. Represents overall CSR score. Eikon
Environment Environment score measures overall environment
performance of banks. Eikon
Social
Social score measures overall social performance of
banks. Eikon
Governance Corporate governance measures overall
governance performance of banks. Eikon
Dividend yield Dividend yield is the percentage of dividend paid
compared to stock price Eikon
Provision for Loan Loss Shows forecast of future loan losses Eikon
Operating Profit Margin Operating profit margin shows efficiency of banks
by dividing operating income by net sales Eikon
LN(Total Loans) Logarithmic transformation of total loans
representing size of banks Eikon
Return on Equity Return on equity is profitability ratio showing net
income over equity capital Eikon
Inflation Yearly inflation value for the relevant country World Bank
IBES 12-Month Forward EPS Institutional Brokers’ Estimate System forecast for
12-month forward earnings per share of banks Eikon
Capital Adequacy
Capital adequacy ratio shows percentage of capital
to risk-weighted assets Eikon
Liquidity
Liquidity represents ratio of banks’ liquid assets to
obligations of banks Eikon
Market-to-Book Ratio Market-to-book ratio represents market value of
banks over book value Eikon
Sustainability 2022,14, 8619 16 of 21
Appendix B
Table A2. Variance inflation factors.
Variable VIF Variable VIF Variable VIF Variable VIF
ESG 2.188231 E 3.00264 S 2.11221 G 1.42733
DY 1.701535 DY 1.69883 DY 1.69901 DY 1.70765
PLL 2.102082 PLL 2.11047 PLL 2.08823 PLL 2.17415
OPM 1.719296 OPM 1.71379 OPM 1.71122 OPM 1.74815
LNTL 2.606304 LNTL 3.09291 LNTL 2.59405 LNTL 2.00201
ROE 1.339026 ROE 1.33807 ROE 1.3417 ROE 1.33628
INF 4.057103 INF 4.05429 INF 4.04946 INF 4.05695
IBES 1.088169 IBES 1.15034 IBES 1.08901 IBES 1.09471
CA 2.167291 CA 2.16339 CA 2.16309 CA 2.1705
LQ 1.152726 LQ 1.14607 LQ 1.18626 LQ 1.14083
MTB 1.802681 MTB 1.79749 MTB 1.79871 MTB 1.81354
C NA C NA C NA C NA
Appendix C
Table A3. Quantile regression results with overall ESG and Z-score.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent Var.
= Z-Score ZZZZZ
ESG 0.737 1.555 * 2.309 *** 2.949 *** 3.733 ***
(0.58) (1.99) (4.25) (4.34) (3.36)
Dividend Yield −3.314 −3.847 ** −4.339 *** −4.756 *** −5.267 **
(−1.47) (−2.77) (−4.54) (−3.94) (−2.67)
Provision For
Loan Loss −39.33 −38.88 −38.46 ** −38.11 * −37.68
(−1.12) (−1.79) (−2.58) (−2.02) (−1.22)
Operating Profit
Margin −0.900 −1.067 −1.221 −1.351 −1.511
(−0.60) (−1.15) (−1.90) (−1.67) (−1.14)
Total Loans 0.0904 −0.0586 −0.196 −0.313 −0.455
(0.18) (−0.19) (−0.94) (−1.19) (−1.06)
ROE 0.156 * 0.166 *** 0.175 *** 0.183 *** 0.192 **
(2.32) (4.00) (6.13) (5.07) (3.27)
Inflation −40.69 ** −36.18 *** −32.01 *** −28.48 *** −24.15 *
(−3.03) (−4.36) (−5.59) (−3.95) (−2.05)
IBES 12-Month
For EPS 2.94 ×10−61.99 ×10−61.12 ×10−60.38 ×10−60.5 ×10−6
(0.73) (0.81) (0.66) (0.18) (−0.15)
Liquidity 0.00979 0.0635 0.113 0.155 0.207
(0.05) (0.54) (1.41) (1.53) (1.25)
Market-to-Book −0.00397 −0.126 −0.239 * −0.334 * −0.451
(−0.01) (−0.75) (−2.06) (−2.29) (−1.90)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Sustainability 2022,14, 8619 17 of 21
Appendix D
Table A4. Quantile regression results with overall ESG and CA.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent Var. =
Capital Adequacy CA CA CA CA CA
ESG 0.0156 0.0330 * 0.0490 *** 0.0626 *** 0.0792 ***
(0.58) (1.99) (4.25) (4.34) (3.36)
Dividend Yield −0.0703 −0.0816 ** −0.0920 *** −0.101 *** −0.112 **
(−1.47) (−2.77) (−4.54) (−3.94) (−2.67)
Provision For Loan
Loss −0.834 −0.825 −0.816 ** −0.808 * −0.799
(−1.12) (−1.79) (−2.58) (−2.02) (−1.22)
Operating Profit
Margin −0.0191 −0.0226 −0.0259 −0.0287 −0.0321
(−0.60) (−1.15) (−1.90) (−1.67) (−1.14)
Total Loans 0.00192 −0.00124 −0.00416 −0.00663 −0.00966
(0.18) (−0.19) (−0.94) (−1.19) (−1.06)
ROE 0.00330 * 0.00351 *** 0.00371 *** 0.00388 *** 0.00408 **
(2.32) (4.00) (6.13) (5.07) (3.27)
Inflation −0.863 ** −0.767 *** −0.679 *** −0.604 *** −0.512 *
(−3.03) (−4.36) (−5.59) (−3.95) (−2.05)
IBES 12-Month For
EPS 6.23 ×10−84.23 ×10−82.38 ×10−88.14 ×10−9−1.11 ×10−8
(0.73) (0.81) (0.66) (0.18) (−0.15)
Liquidity 0.000208 0.00135 0.00240 0.00329 0.00438
(0.05) (0.54) (1.41) (1.53) (1.25)
Market-to-Book −0.0000842 −0.00268 −0.00506 * −0.00709 * −0.00958
(−0.01) (−0.75) (−2.06) (−2.29) (−1.90)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Appendix E
Table A5. Quantile regression results with environmental score and Z-score.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent
Var. = Z-Score ZZZZZ
Environment 1.941 ** 2.206 *** 2.452 *** 2.634 *** 2.884 ***
(2.67) (5.00) (8.31) (7.21) (4.73)
Dividend Yield
−2.891 −3.445 ** −3.961 *** −4.340 *** −4.863 **
(−1.35) (−2.65) (−4.55) (−4.02) (−2.70)
Provision For
Loan Loss −50.57 −46.65 * −43.00 ** −40.32 * −36.62
(−1.49) (−2.27) (−3.12) (−2.36) (−1.29)
Operating
Profit Margin −1.055 −1.176 −1.290 * −1.374 −1.489
(−0.74) (−1.36) (−2.24) (−1.92) (−1.25)
Total Loans −0.500 −0.650 * −0.791 *** −0.894 *** −1.036 *
(−0.99) (−2.11) (−3.84) (−3.50) (−2.43)
ROE 0.143 * 0.158 *** 0.172 *** 0.182 *** 0.197 ***
(2.19) (3.98) (6.48) (5.55) (3.59)
Inflation −34.13 ** −31.37 *** −28.80 *** −26.91 *** −24.30 *
(−2.61) (−3.95) (−5.41) (−4.08) (−2.21)
IBES 12-Month
Forward EPS −2.19 ×10−6−3.05 ×10−6−3.86 ×10−6*−4.45 ×10−6*−5.26 ×10−6
(−0.51) (−1.16) (−2.20) (−2.04) (−1.45)
Liquidity −0.0298 0.0136 0.0540 0.0837 0.125
(−0.17) (0.13) (0.78) (0.98) (0.87)
Market-to-
Book 0.0791 −0.0233 −0.119 −0.189 −0.286
(0.29) (−0.14) (−1.07) (−1.37) (−1.25)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Sustainability 2022,14, 8619 18 of 21
Appendix F
Table A6. Quantile regression results with environmental score and CA.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent
Var. = Capital
Adequacy
CA CA CA CA CA
Environment 0.0412 ** 0.0468 *** 0.0520 *** 0.0559 *** 0.0612 ***
(2.67) (5.00) (8.31) (7.21) (4.73)
Dividend Yield
−0.0613 −0.0731 ** −0.0840 *** −0.0921 *** −0.103 **
(−1.35) (−2.65) (−4.55) (−4.02) (−2.70)
Provision For
Loan Loss −1.073 −0.990 * −0.912 ** −0.855 * −0.777
(−1.49) (−2.27) (−3.12) (−2.36) (−1.29)
Operating
Profit Margin −0.0224 −0.0250 −0.0274 * −0.0291 −0.0316
(−0.74) (−1.36) (−2.24) (−1.92) (−1.25)
Total Loans −0.0106 −0.0138 * −0.0168 *** −0.0190 *** −0.0220 *
(−0.99) (−2.11) (−3.84) (−3.50) (−2.43)
ROE 0.00303 * 0.00335 *** 0.00365 *** 0.00387 *** 0.00417 ***
(2.19) (3.98) (6.48) (5.55) (3.59)
Inflation −0.724 ** −0.666 *** −0.611 *** −0.571 *** −0.515 *
(−2.61) (−3.95) (−5.41) (−4.08) (−2.21)
IBES 12-Month
Forward EPS −4.65 ×10−8−6.48 ×10−8−8.18 ×10−8*−9.43×10−8*−1.12 ×10−7
(−0.51) (−1.16) (−2.20) (−2.04) (−1.45)
Liquidity −0.000632 0.000288 0.00115 0.00178 0.00265
(−0.17) (0.13) (0.78) (0.98) (0.87)
Market-to-
Book 0.00168 −0.000494 −0.00252 −0.00401 −0.00606
(0.29) (−0.14) (−1.07) (−1.37) (−1.25)
N
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Appendix G
Table A7. Quantile regression results with governance score and Z-score.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent Var. =
Z-Score ZZZZZ
Governance −1.312 −0.712 −0.133 0.388 0.931
(−1.30) (−1.12) (−0.28) (0.62) (0.97)
Dividend Yield −3.186 −3.951 ** −4.689 *** −5.352 *** −6.045 **
(−1.33) (−2.63) (−4.24) (−3.61) (−2.66)
Provision For Loan
Loss −43.13 −46.14 * −49.05 ** −51.66 * −54.39
(−1.16) (−1.98) (−2.86) (−2.24) (−1.54)
Operating Profit
Margin −1.121 −1.280 −1.435 −1.573 −1.718
(−0.71) (−1.28) (−1.95) (−1.59) (−1.14)
Total Loans 0.535 0.412 0.292 0.185 0.0731
(1.13) (1.39) (1.34) (0.63) (0.16)
ROE 0.159 * 0.164 *** 0.168 *** 0.172 *** 0.175 *
(2.17) (3.54) (4.94) (3.76) (2.51)
Inflation −39.19 ** −37.05 *** −34.99 *** −33.14 *** −31.21 *
(−2.71) (−4.07) (−5.23) (−3.68) (−2.26)
IBES 12-Month
Forward EPS 9.78 ×10−75.06 ×10−75.12 ×10−6−3.58 ×10−7−7.85 ×10−7
(0.21) (0.17) (0.02) (−0.12) (−0.18)
Liquidity 0.0425 0.0719 0.100 0.126 0.152
(0.20) (0.55) (1.04) (0.97) (0.77)
Market-to-Book 0.0342 −0.150 −0.329 * −0.489 ** −0.656 *
(0.12) (−0.83) (−2.46) (−2.74) (−2.40)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Sustainability 2022,14, 8619 19 of 21
Appendix H
Table A8. Quantile regression results with governance score and CA.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent Var. =
Capital Adequacy CA CA CA CA CA
Governance −0.0278 −0.0151 −0.00282 0.00822 0.0198
(−1.30) (−1.12) (−0.28) (0.62) (0.97)
Dividend Yield −0.0676 −0.0838 ** −0.0995 *** −0.114 *** −0.128 **
(−1.33) (−2.63) (−4.24) (−3.61) (−2.66)
Provision For Loan
Loss −0.915 −0.979 * −1.041 ** −1.096 * −1.154
(−1.16) (−1.98) (−2.86) (−2.24) (−1.54)
Operating Profit
Margin −0.0238 −0.0272 −0.0304 −0.0334 −0.0364
(−0.71) (−1.28) (−1.95) (−1.59) (−1.14)
Total Loans 0.0114 0.00873 0.00620 0.00393 0.00155
(1.13) (1.39) (1.34) (0.63) (0.16)
ROE 0.00338 * 0.00347 *** 0.00356 *** 0.00364 *** 0.00372 *
(2.17) (3.54) (4.94) (3.76) (2.51)
Inflation −0.831 ** −0.786 *** −0.742 *** −0.703 *** −0.662 *
(−2.71) (−4.07) (−5.23) (−3.68) (−2.26)
IBES 12-Month
Forward EPS 2.07 ×10−81.07 ×10−81.09 ×10−9−7.59 ×10−9−1.67 ×10−8
(0.21) (0.17) (0.02) (−0.12) (−0.18)
Liquidity 0.000902 0.00152 0.00213 0.00267 0.00323
(0.20) (0.55) (1.04) (0.97) (0.77)
Market-to-Book 0.000725 −0.00319 −0.00697 * −0.0104 ** −0.0139 *
(0.12) (−0.83) (−2.46) (−2.74) (−2.40)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Appendix I
Table A9. Quantile regression results with social score and Z-score.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent Var. =
Z-Score Z Z Z Z Z
Social 1.343 1.838 ** 2.208 *** 2.563 *** 2.976 **
(1.22) (2.86) (4.82) (4.46) (3.21)
Dividend Yield −3.551 −3.926 ** −4.207 *** −4.476 *** −4.789 *
(−1.49) (−2.81) (−4.23) (−3.58) (−2.37)
Provision For Loan
Loss −32.13 −34.17 −35.69 * −37.16 −38.86
(−0.88) (−1.60) (−2.34) (−1.94) (−1.26)
Operating Profit
Margin −0.584 −0.922 −1.175 −1.417 −1.699
(−0.38) (−1.02) (−1.81) (−1.74) (−1.29)
Total Loans 0.00257 −0.0769 −0.136 −0.193 −0.260
(0.01) (−0.28) (−0.70) (−0.79) (−0.66)
ROE 0.159 * 0.173 *** 0.184 *** 0.194 *** 0.206 ***
(2.32) (4.34) (6.45) (5.42) (3.56)
Inflation −38.71 ** −33.84 *** −30.20 *** −26.70 *** −22.63
(−2.80) (−4.20) (−5.25) (−3.70) (−1.94)
IBES 12-Month
Forward EPS 2.82 ×10−61.78 ×10−61×10−62.52 ×10−6−6.18 ×10−6
(0.65) (0.70) (0.55) (0.11) (−0.17)
Liquidity 0.0354 0.0950 0.140 0.182 0.232
(0.19) (0.88) (1.81) (1.88) (1.49)
Market-to-Book 0.0297 −0.122 −0.236 * −0.345 * −0.472 *
(0.11) (−0.76) (−2.04) (−2.38) (−2.02)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
Sustainability 2022,14, 8619 20 of 21
Appendix J
Table A10. Quantile regression results with social score and CA.
Q(5) Q(25) Q(50) Q(75) Q(95)
Dependent Var. =
Capital Adequacy CA CA CA CA CA
Social 0.0285 0.0390 ** 0.0468 *** 0.0544 *** 0.0631 **
(1.22) (2.86) (4.82) (4.46) (3.21)
Dividend Yield −0.0753 −0.0833 ** −0.0892 *** −0.0950 *** −0.102 *
(−1.49) (−2.81) (−4.23) (−3.58) (−2.37)
Provision For Loan
Loss −0.682 −0.725 −0.757 * −0.788 −0.824
(−0.88) (−1.60) (−2.34) (−1.94) (−1.26)
Operating Profit
Margin −0.0124 −0.0196 −0.0249 −0.0301 −0.0361
(−0.38) (−1.02) (−1.81) (−1.74) (−1.29)
Total Loans 0.0000546 −0.00163 −0.00289 −0.00410 −0.00551
(0.01) (−0.28) (−0.70) (−0.79) (−0.66)
ROE 0.00338 * 0.00368 *** 0.00390 *** 0.00412 *** 0.00437 ***
(2.32) (4.34) (6.45) (5.42) (3.56)
Inflation −0.821 ** −0.718 *** −0.641 *** −0.566 *** −0.480
(−2.80) (−4.20) (−5.25) (−3.70) (−1.94)
IBES 12-Month
Forward EPS 5.99 ×10−83.78 ×10−82.12 ×10−85.35 ×10−9−1.31 ×10−8
(0.65) (0.70) (0.55) (0.11) (−0.17)
Liquidity 0.000750 0.00202 0.00296 0.00387 0.00493
(0.19) (0.88) (1.81) (1.88) (1.49)
Market-to-Book 0.000630 −0.00260 −0.00501 * −0.00732 * −0.0100 *
(0.11) (−0.76) (−2.04) (−2.38) (−2.02)
N471 471 471 471 471
tstatistics in parentheses * p< 0.05, ** p< 0.01, *** p< 0.001.
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