Journal of Financial Reporting and Accounting

The determinants of bank profitability: empirical evidence from European

banking sector
Elisa Menicucci, Guido Paolucci,
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JFRA
14,1
The determinants of bank
profitability: empirical evidence
from European banking sector
86 Elisa Menicucci and Guido Paolucci
Department of Management, Polytechnic University of Marche,
Received 29 May 2015
Revised 29 May 2015
Ancona, Italy
Accepted 4 August 2015
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Abstract
Purpose – The purpose of this paper is to investigate the relationship between bank-specific
characteristics and profitability in European banking sector to find the role of internal factors in
achieving high profitability.
Design/methodology/approach – A regression analysis is built on an unbalanced panel data set
comprising 175 observations of 35 top European banks over the period 2009-2013. To this end, the empirical
data are collected from Bankscope and a comprehensive set of internal characteristics is examined.
Findings – All the determinant variables included in the model have statistically significant impacts
on European banks’ profitability. However, the effects are not uniform across profitability measures.
Regression findings reveal that size and capital ratio are significant company-level determinants of
bank profitability in Europe, while higher loan loss provisions result in lower profitability levels.
Findings also suggest that banks with higher deposits and loans ratio tend to be more profitable but the
effects on profitability are statistically insignificant in some cases.
Practical implications – This study has considerable policy implications, as the performance of the
European banking sector depends on its efficiency, profitability and competitiveness. In view of these
findings, some suggestions may be functional for bank regulatory authorities to intensify and sustain
robustness and stability of the banking sector.
Originality/value – The results provide interesting insights into the characteristics and practices of
profitable banks in Europe. Few econometric studies have empirically explored the determinants of
bank profitability in Europe so far, even though similar studies have been conducted in several
developed countries. Therefore, this paper tries to close an important gap in the existing literature
improving the understanding of bank profitability in Europe.
Keywords Performance, Determinants, Banks, Profitability, European banking sector
Paper type Research paper

1. Introduction
Banks play a central role in the operation of economy, and it is generally agreed that
sound banking is a requirement for sustainable economic development. The banking
sector also fulfils an important economic function in providing financial intermediation
and economic acceleration by converting deposits into productive investments. In this
respect, banks are important providers of funds, and their stability is relevant and
Journal of Financial Reporting and
Accounting critical for the financial system. Consecutively, if a financial system is efficient, then it
Vol. 14 No. 1, 2016
pp. 86-115
should record profitability improvements, increasing volume of funds flowing from
© Emerald Group Publishing Limited
1985-2517
savers to borrowers, and better-quality services for customers. The importance of bank
DOI 10.1108/JFRA-05-2015-0060 profitability in the economy can be assessed at the micro and macro levels.
 At the micro level, profit is the essential prerequisite of a competitive banking Determinants
institution. It is not just a result but also a requirement for successful business in a of bank
period of growing competition on financial markets. Hence, the basic aim of a bank’s
management is to realize profits, as the critical condition for conducting any business.
profitability
The existence, growth and survival of a business organization mostly depend upon the
profit which it is able to earn. At the macro level, a profitable banking sector is better
able to endure negative shocks and contribute to the stability of the financial system. 87
Given the relation between the soundness of the banking sector and the growth of the
economy (Rajan and Zingales, 1995), the study of banking sector performance is of great
prominence in developed economies. As such, an understanding of determinants of
bank profitability is essential and pivotal to the stability of the economy because the
well-being of the banking sector is very critical to the welfare of the economy at large.
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During the past two decades, the banking sector has experienced global major
transformations in its operating context. Both internal and external factors have
affected its structure and performance. Recent trends in financial deregulation,
technological and financial innovation and globalization are surely posing new
challenges for market participants in the financial sector and have made the concept of
efficiency more important for financial institutions and banks (Altunbas et al., 2001). All
these developments will certainly have implications on the costs and revenues and
hence on the profitability of banks.
The term “profitability” refers to the ability of the business organization to maintain
its profit year after year. The profitability performance of the banks indicates the
success of the management and it is one of the most important performance indicators
for the investors. Changes in profitability contribute to economic progress, as profits
influence the investment and savings decisions of companies. This is because a rise in
profits improves the cash flow position of companies and offers greater flexibility (i.e.
through retained earnings) in the source of finance for corporate investments. Easier
access to finance facilitates greater investments which improve productivity,
competitiveness and employment.
Many researchers in industrial economics, strategic management, marketing and
accounting and finance have attempted to identify the sources of variation of bank-level
profitability. Several studies have been conducted in some countries, and they investigated
the determinants of bank profitability. The identification of such determinants is critical for
the success of bank management – even in the time of crises – and for existing and potential
national and international investors. It is well known that the growth of profitability is
probably the most important factor of the increase in shareholders’ value.
The main conclusion emerging from most of the studies is that internal factors can
largely influence the bank performance. While there has been extensive literature examining
the profitability of financial sector in developed countries, empirical studies on factors
influencing the performance of financial institutions in European economy are quite few.
The aim of this study is to investigate the possible internal determinants of bank
profitability in Europe, especially after the peak period of the 2008 financial crisis.
This paper is organized as follows. After the introduction which is provided in
Section 1, Section 2 presents the European banking sector. Section 3 reviews the existing
relevant literature on the determinants of bank profitability. Moreover, research
hypotheses based on existing theories are developed here. Section 4 outlines our
research methodology and data sample. This section explains the econometric model
 JFRA applied and describes the dependent and independent variables used in the regression
14,1 analysis. Empirical findings of the study are presented and investigated in Section 5.
Section 6 concludes the study and offers some suggestions for future researches.

2. The European banking sector

The financial system of European countries is characterized by the central role of banks.
88 The banking sector is the backbone of the European economy and fulfils an important
financial intermediary function. European banking is one of the few industries in which
companies operate together in a competitive market. The European Central Bank (ECB)
has constantly pursued stable measures that would enhance profitability and stability
of banks operating in the European banking industry. In the past, the European banking
sector has undergone considerable expansion and change, as witnessed by the rapid
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growth of its total assets and by significant strengthening of competition in previous

decades (De Bandt and Davis, 1999, 2000; Mamatzakis et al., 2005). Over the past few
years, European banks have been experiencing most important challenges in the
dynamic environment. Especially in recent years, the European economic situation is
becoming less than favorable due to a continued general trend of decline and
disintegration of the European banking sector. The euro area banking sector is facing
the strongest difficulties and different nations within the European Union (EU) are
taking different approaches to dealing with their challenges, whereas banking presence
has reduced in terms of numbers and financial activities.
The European banking sector provides an interesting context for studying bank
profitability. The globalization of commerce and financial markets and deregulation
and technological developments have impacted on banks’ operations and consequently
have affected their profitability. Furthermore, technological changes have distorted the
potentials of economies of scale and scope. As part of the globalization phenomenon,
international bank participation has intensified in local banking markets, increasing
competition and reducing profit margins. To face up to negative shocks and preserve
financial stability, banks have increased competitive pressure by proposing a wider variety
of products, services, leading their businesses out of the core ones. Diversification has
allowed many banks to enlarge size considerably, to decrease average costs and to
differentiate their business to preserve competitiveness on a Europe-wide scale. For instance,
Hughes et al. (1999) found that development through product and geographic diversification
reduces bank risk and tends to improve efficiency.
It is reasonable to undertake that all the changes significantly challenged the
European banks as the context in which they operated rapidly transformed. All these
developments certainly affect the performance of the European banking industry and
consequently have implications on the determinants of its profitability.
It is important to identify the factors that mostly impact on the overall performance
of banks in Europe and in this regard some prior studies contributed to find out the
determinants of profitability for banking sector, exploring, for example, the size of the
bank, the extent to which the bank is diversified, the attitude of the bank’s owners and
managers toward risk, the bank’s ownership characteristics and the level of external
competition a bank encounters (Goddard et al., 2001).
In fact, the earnings performance differs widely from one bank to another and a number
of causes could be assumed to contribute to the variability of bank profits, such as
 differences in bank’s size, structure and location, as well as differences in quality of bank Determinants
management, asset portfolios and liability composition. of bank
The importance of bank profitability at both the micro and macro levels led researchers,
academics, bank managers and bank regulatory authorities to develop extensive interests
profitability
on the determinants of banks’ profitability (Athanasoglou et al., 2008). Some banks gain
relatively high rates of return, while others earn lower ones. How much variation in these
banks’ profitability come from differences in endogenous factors under the control of bank 89
management? Answer to this question is relevant for the development of operative
strategies aimed to maintain stability of banks operating in the European sector. In the
literature, the performance of the banking system has been widely discussed. In particular,
many researchers have tried to identify the sources of variation of bank-level profitability,
but there are few studies which examine the determinants of profitability in European
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banking sector.
Especially with regard to the effects of internal factors on banks’ profitability, a limited
number of theoretical studies have been carried out for the European region, while several
others have investigated the matter related to specific countries. Likewise, limited
econometric studies have inspected the determinants of profitability for the European
banking system. For example, Abreu and Mendes (2002) investigated the causes of bank’s
interest margins and profitability for some European countries in the previous decade. They
found that well-capitalized banks face lower estimated bankruptcy costs, and this
circumstance results in greater profitability. Additionally, prior studies on European banks
were focused on other aspects of bank performance. For instance, Claeys and Vander Vennet
(2008) examined the determinants of bank interest margins in the Central and Eastern
European countries (CEEC), and they evaluated to what extent the low bank margins in
CEEC can be accredited to limited efficiency and non-competitive market conditions of the
macroeconomic environment. Beccalli (2007) instead inspected whether investments in
information technology (IT) – hardware, software and other IT services – affect the
performance of banks. Using a sample of 737 European banks over the period 1995-2000
Vander Vennet (2002) analyzed the cost and profit efficiency of European and universal
banks. Altunbas and Marques (2008) examined the impact of European Union banks’
strategic similarities on post-merger performance, and they discovered that, on average,
bank mergers lead to improved performance. Thus, a specific, more recent, analysis of the
determinants of bank profitability in Europe is indeed missing, as only few authors
(Molyneux and Thorton, 1992; Abreu and Mendes, 2002; Pasiouras and Kosmidou, 2007)
focused on an explicit analysis of the profitability determinants of European banks.
The objective of this paper is to examine bank profitability in the context of top 35
European banks, by using cross-national time series data. We follow an extensive
literature that focuses on specific determinants of bank profitability. Consequently, on
the basis of the prior studies that highlighted the impact of internal factors on bank
profitability, we have included in our regression model a set of internal variables to
capture their effects on European banks’ performance.
Hence, this study analyzes only the internal determinants of bank’s profitability of
European banking industry over the period 2009-2013 which has witnessed
considerable challenges following the global financial crisis.
The slowdown in economic activities can be connected with the instability and the
downward trend that begun in capital markets of the USA toward the end of 2007. Then,
the descending move assumed a global feature in 2008 by having negative effects on the
 JFRA world economy and particularly on countries related with the USA, i.e. EU and other
14,1 developed and developing countries. Even in 2009, the global economy has endured
severe pressure, as the crisis has increased in both developed and developing economies,
and this period has been considered as a global crisis by many economists. The global
crisis has also affected the financial sector considerably in Europe even if the European
banking sector has remained safe and sound, and it has continued to support the
90 financing of economic activities due to measures taken by relevant authorities and the
effective public supervision.

3. Literature review and “hypotheses’” development

This section provides an overview of the studies related to the determinants of bank
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profitability. This issue has received considerable attention in academic literature and
has been widely investigated theoretically and empirically. There have been several
studies about the effects of firm characteristics on profitability, and following early
works edited by Short (1979) and Bourke (1989), a number of more recent studies have
attempted to identify some of the major determinants of bank profitability in many
countries. Some studies are country-specific, while few of them consider panel of
countries.
For example, the studies by Berger et al. (1987), Goddard et al. (2004a), Neely and
Wheelock (1997), Athanasoglou et al. (2008), Ben Naceur and Goaied (2001, 2008) and
Garcia-Herrero et al. (2009) dedicated their analysis on a specific country. In particular,
some empirical studies on bank profitability were focused on countries including Greece
(Mamatzakis and Remoundos, 2003), UK (Saeed, 2014; Kosmidou et al., 2004a, 2004b,
2006), Australia (Williams, 2003), Tunisia (Ben Naceur and Goaied, 2001; Ghazouani
Ben Ameur and Moussa Mhiri, 2013), Pakistan (Gul et al., 2011; Ali et al., 2011), Kenya
(Tarus et al., 2012), China (Sufian and Habibullah, 2009), the Philippines (Sufian and
Chong, 2008), Turkey (Alp et al., 1997) and Switzerland (Dietrich and Wanzenried, 2009).
The second group of studies, that analyzed a panel of countries, includes: Haslem
(1968), Short (1979), Bourke (1989), Demirguc-Kunt and Huizinga (1999), Angbazo
(1997), Abreu and Mendes (2002), Staikouras and Wood (2004), Pasiouras and Kosmidou
(2007). Molyneux and Thorton (1992). These studies explored the determinants of bank
profitability in a multi-country setting in Europe, and they found a significant positive
association between return on equity and interest rate, inflation rate, bank concentration
and government ownership in each European country. Hassan and Bashir (2005)
inspected profitability of a sample of Islamic banks based in different 21 countries;
Demirguc-Kunt and Huizinga (1999) considered a wide range of bank-specific
characteristics, as well as macroeconomic conditions, taxation, regulations, financial
structure and legal indicators, finalized to the examination of the determinants of bank
profitability. Using bank-level data of 80 countries in the 1988-1995 period,
Demirguc-Kunt and Huizinga (1999) explored how bank and overall macroeconomic
characteristics affect both interest rate margins and bank returns. The empirical results
of these above-mentioned studies diverge considerably because of the differences in
data sets, time periods and investigated countries. Apart from a single country or a
panel of countries-based study, a look at previous literature on banking profitability
reveals numerous factors which affect it. In fact, we found some common elements that
are used to classify further the determinants of bank profitability, and, in all of the above
studies, these factors are classified in two main categories, namely, those that are
 controlled by the management (internal factors) and those that are beyond the control of Determinants
management (external factors). For this reason, it may be more appropriate to classify of bank
the related literature according to internal and external determinants of bank profitability
profitability investigated in the previous studies rather than according to investigation
based on a particular country or on a set of countries. In this regard, more recent studies
have distinguished managerial factors from environmental ones, i.e. a number of
internal and external factors that affect bank profitability. For example, the study by 91
Abreu and Mendes (2002) inspected the impact of bank-specific variables along with
other variables on profitability of commercial banks from four different EU countries for
the period 1986-1999. Another study by Athanasoglou et al. (2006) on determinants of
bank profitability in the southeastern European region found that all bank-specific
determinants (the internal factors) have significant effects on bank profitability.
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According to the nature and the purpose of each study of the literature review, a
number of explanatory variables have been proposed for both categories mentioned
above.
The internal determinants of bank profitability are generally influenced by bank
management strategies and decisions. These determinants could also be termed micro
or bank-specific factors that basically reveal the differences with regard to sources and
uses of funds management, capital, liquidity and expenses management, i.e. the level of
liquidity, provisioning policy, operational efficiency, capital adequacy, expenses
management and bank size. For example, in most prior studies, internal determinants
focused on bank-specific variables such as bank size, risk, capital ratio, loans and
deposits.
On the other hand, the external determinants are variables that are not related to
bank management and generally they reflect the economic and legal environment (both
industry-related and macroeconomic) that affects the operation and the performance of
financial institutions, i.e. economic growth, inflation and market capitalization. Some
recent studies concerning this second group of determinants are focused also on the
impact of regulations on bank performance (Barth et al., 2003, 2004), but only weak
evidence has been reported to support that bank supervisory structure and regulations
affect bank profits.
In the literature, bank profitability is usually expressed as a function of internal and
external determinants, but especially bank-specific factors have been shown to be just
important in determining the profitability of banks. The internal determinants of
profitability are empirically well explored and most of the previous studies have stated
that size (Berger et al., 1987; Bikker and Hu, 2002), capital ratio (Molyneux and Thorton,
1992), liquidity ratio (Bourke, 1989; Molyneux and Thorton, 1992), asset quality and
operational efficiency of the banks are important factors in achieving high profitability.
The mixed results reached in prior literature caused a vague understanding of the effect
of internal factors on bank profitability and then an increase in the interest toward this
subject.
The aim of this study is to investigate the relationship between internal factors and
profitability in top 35 European banks and to contribute to the literature in this way.
Based on the nature and the purpose of each study mentioned in the literature review, a
number of explanatory variables have been proposed for internal determinants of bank
profitability. In particular, the management-controllable (internal) determinants
 JFRA considered in this study are: size, capital ratio, loan ratio (liquidity ratio), deposits and
14,1 loan loss provisions (asset quality).
According to the prior literature, the present study seeks to test the following hypotheses.

3.1 Size
92 One of the most important questions in the literature is whether bank size maximizes
bank profitability. The association between size and profitability has been investigated
in several previous studies and many evidences in empirical research confirmed the role
of size as a determinant of bank profitability. Following the review of the studies
concerning the relation between bank size and profitability, different results have been
found.
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In prior studies by Alp et al. (2010), Smirlock (1985), Boyd and Runkle (1993), Bikker
and Hu (2002) and Dogan (2013), a significant positive relationship between size and
profitability has been observed. Also Camilleri (2005), Athanasoglou et al. (2008),
Pasiouras and Kosmidou (2007), Gul et al. (2011), Saeed (2014) found that size positively
affects the profitability of the banks they inspected. Mainly, the previous studies on the
effect of size on bank profitability joined with the idea that large banks can benefit from
economies of scale, enabling cost reduction (Bourke, 1989; Molyneux and Thorton, 1992;
Bikker and Hu, 2002; Goddard et al., 2004a, 2004b) and they are expected to have a
higher amount of production than smaller banks. At least up to a certain level, if the
relative size of a firm enlarges, its market powers, reduced risk and economies of scale
lead to the increase of operational efficiency. On the basis of this relative efficiency
hypothesis (Clarke et al., 1984), larger banks are more efficient on average (Berger and
Humphrey, 1997) and more profitable than smaller ones, as a result of their superior
efficiency. Large banks might also benefit from scope economies with reduced risks and
with loan and product diversification, thus providing access to markets in which small
banks cannot enter. As a result, size variable is included in the regression model to catch
the possible cost advantages associated with size (economies of scale) and the higher
capability of larger bank in the differentiation of their products and services.
Literature review underlines that size may have a positive effect on bank profitability
if there are significant economies of scale, while product and risk diversification (scope
economies) may lead to a negative relationship between size and bank profitability
because the increase of diversification could determine higher risks. However, the
evidence of such economies is not univocal because the findings do not reveal that an
increase in size always amplifies the profitability level. Some studies have found
economies of scale for large banks (Berger and Humphrey, 1997; Altunbas et al., 2001),
while others have found diseconomies for them or economies of scale for small ones. In
particular, Vander Vennet (2002) observed economies of scale only for the smallest
banks in Europe and diseconomies of scale for the largest ones. Some researchers
supposed that banks could reduce costs by increasing their size, but, on the other hand,
they might incur in scale of inefficiencies (Berger and Humphrey, 1997); for this reason,
smaller banks could be more profitable than their larger counterparts. Hence, empirical
findings from previous studies are mixed. For example, Scholtens (2000) verified that
small European banks’ profits increased faster than those of the larger banks and
Williams (2003) suggested the opposite for foreign banks operating in Australia.
In this regard, some authors demonstrated that very large banks often face scales of
inefficiencies because only little cost saving can be achieved by increasing the size of a
 banking firm (Berger et al., 1987; Boyd and Runkle, 1993). According to these studies, Determinants
banks that have become extremely large might show a negative relationship between of bank
size and profitability, caused by costs related to the management of extremely large
firms, overheads of bureaucratic processes and agency costs (Stiroh and Rumble, 2006;
profitability
Pasiouras and Kosmidou, 2007; Athanasoglou et al., 2008).
Also other researchers found a negative relation between profitability and bank size,
implying that larger banks attain a lower level of profits compared to smaller ones. 93
These results are suggested by Sufian and Chong (2008) in Asia, Miller and Noulas
(1997) in the USA, Jiang et al. (2003) in Hong Kong and Bashir (2003) for Middle Eastern
Islamic banks. Ben Naucer (2003) especially claimed that the size has negative and
significant influence mostly on net interest margins. This inverse relationship was also
found by Spathis et al. (2002), Kosmidou et al. (2008) and Sufian and Habibullah (2009)
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for conventional banks. Finally, Dietrich and Wanzenried (2011), in their banking
performance study, concluded that a negative relationship observed in large banks
depends on huge losses caused by several irrecoverable loans.
The mentioned previous findings produce a vague understanding of the effect of size
on profitability in the banking sector and also of the upsurge in the interest in this topic.
As in the literature, bank size is included in this study as an independent variable and it
is measured by total assets. Based on main literature review, a bank’s profitability has
been stated to be positively associated with size and we hypothesize that:
H1. There is a positive relationship between size and bank profitability.

3.2 Capital ratio

Capital ratio is included in the regression model to examine the relationship between
profitability and bank capitalization. The capital ratio is an appreciated tool for
assessing capital adequacy, and it captures the general soundness of banks, as it
represents how well the bank is capitalized. Thus, the equity to total assets ratio (i.e.
capital ratio) is considered as one of the basic measures of capital strength (Golin, 2001),
and bank’s capital is also widely used to analyze the status of a bank’s financial power.
Especially in developing countries, a solid capital structure is crucial for a financial
institution, as it provides additional power to face financial crises and to consolidate
security for depositors during unstable macroeconomic conditions. Banks with a weak
capital structure could hardly withstand dangerous situations; it is essential for
financial institutions to preserve a higher strength of capital structure to bear losses and
to dismiss the risk of insolvency during difficult times.
It is generally assumed that well-capitalized banks challenge lower probable costs of
financial distress, and such advantage will then be turned into high profitability (Abreu
and Mendes, 2002; Ben Naceur, 2003). In particular, Abreu and Mendes found that in
some European countries, well-capitalized banks face low predicted bankruptcy and
funding costs and higher interest margins on profitable assets, thus demonstrating a
positive relationship between capital and bank profitability. Then, higher levels of
equity will reduce the cost of capital, causing a positive impact on profitability.
Moreover, it is expected that banks with higher equity-to-assets ratio have a reduced
need for external funding. Therefore, they achieve greater profitability because lower
risk increases banks’ creditworthiness and reduces the cost of funding. On the contrary,
lower capital ratio in banking entails higher leverage risk which conducts to higher
borrowing costs.
 JFRA Researchers extensively theorize that banks with higher capital are more protected
14,1 from insolvency. For example, some empirical evidence by Bourke (1989),
Demirguc-Kunt and Huizinga (1999), Ben Naceur and Goaied (2008), Pasiouras and
Kosmidou (2007), Garcia-Herrero et al. (2009), Kosmidou et al. (2006), Kosmidou (2008),
Obamuyi (2013), Dietrich and Wanzenried (2009) revealed that the best-performing
banks are those who preserve a high level of equity relative to their assets.
94 Other numerous theoretical explanations are proposed in the literature. Accordingly
with these findings, Bourke (1989) stated a significant positive relation between capital
adequacy and profitability in his study on the determinants of banks’ performance for
12 countries selected from Europe, North America and Australia. Similarly, the studies
of Berger (1995b) and Angbazo (1997) concluded that the US banks, those which are
well-capitalized, are more profitable than the others. In another study of banking
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profitability across 18 European countries for the period 1986-1989, Molyneux and
Thorton (1992) also argued that the capital ratio impacts banks’ performance positively,
in relation to state-owned banks. Athanasoglou et al. (2008) investigated the effect of
bank-specific, industry-specific and macroeconomic determinants on the profitability of
Greek banks, and their empirical study showed that increased exposure to credit risk
drops profits. Indeed, most other studies that use capital ratio as an explanatory variable
of bank profitability stated a positive relationship between capital and profitability.
Such positive correlation has been demonstrated, for example, by Sufian and Chong
(2008), Hassan and Bashir (2005) and Vong and Chan (2009).
Even though overall capitalization has been verified to play an essential role in the
performance of financial institutions, empirical evidence on the relation between capital
ratio and profitability is not always certain. Anticipating the net impact of changes in
capital ratio could be difficult. Some authors mentioned above consider banks with
higher capital ratio less risky compared to others with lower capital ratio. In line with the
conventional risk-return hypothesis, it should be expected that banks with lower capital
ratio have higher profits compared to well-capitalized financial institutions (Saona,
2011; Ali et al., 2011; Staikouras and Wood, 2004). This risk-return assumption would
therefore imply a negative relationship between capital ratio and bank profitability. To
this extent, high capital ratio can be considered an indicator of low leverage and
therefore low risk. Thus, well-capitalized banks are estimated to be less risky and profits
are likely to be lower as far, as these banks are supposed to be relatively safer in the
event of loss or liquidation. In this context, the profitability of a bank would be related to
the management’s approach toward risk, and in this regard, the risk’s attitude can be
studied by inspecting the level of capital and reserves held by the bank, as well as its
liquidity management policies.
On the other hand, highly capitalized banks endure to be profitable even during
economically challenging times. Furthermore, lower risk increases a bank’s soundness
and decreases its funding cost. In addition, banks with higher equity-to-assets ratios are
usually less dependent on external funding, with a positive impact on their profits.
Hence, with respect to the majority of prior literature cited above, capital ratio is
expected to have a positive relation with profitability because well-capitalized banks are
estimated to be more profitable.
The results of some of the previous studies lead us to the following hypothesis:
H2. There is a positive relationship between capital ratio and bank profitability.
 3.3 Loan ratio Determinants
In addition to the capital ratio, many researchers consider the asset and liability of bank
composition ratios as internal determinants of bank performance. In this regard, the
volume of loans and deposits detained is used to measure the efficiency of asset and
profitability
liability portfolio management, respectively. In accordance with prior literature, total
loans-to-total assets ratio (i.e. loan ratio) is considered as an indicator of liquidity, and
much literature found a positive relationship between liquidity and profitability (Bashir, 95
2003; Sufian and Habibullah, 2009). A bank holding a reasonably high proportion of
liquid assets is unlikely to gather high profits, but it is also less exposed to risk and
therefore shareholders should be disposed to receive a lower return on equity. Liquidity
is very important in explaining bank profitability, and loans are the main source of
income and are estimated to have a positive impact on bank performance.
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Although bank loans are the main source of returns and are expected to affect profits
positively, evidences from various studies revealed a negative correlation between bank
loans and profits. For these reasons, empirical results of studies concerning the
relationship between the level of liquidity and profitability in banks are diversified.
When banks increase their loans portfolio, it could be assumed that they have to pay
upper costs for their funding provisions. In this case, a very elevated loan ratio could
imply that banks have rapidly grown their loans portfolio, paying a higher cost for their
funding requirements, and this circumstance could lead to a negative effect on
profitability.
From a theoretical perspective, the impact of the amount of total loans on bank
performance is very difficult to predict. For example, a bank with a higher growth rate
of its loan volume, apparently, would be more profitable in consequence of the added
business created. However, a high growth of the loan volume might also lead to a drop
of credit quality and thus to a reduced profitability. Furthermore, if the bank increased
loan volume through lower margins, it could be presumed a negative effect on
profitability. Because the impacts of loan ratio on profitability move towards opposite
directions, the general result on bank profitability cannot be predicted theoretically.
To this extent, while the study by Abreu and Mendes (2002) – who scrutinized banks
in Portugal, Spain, France and Germany – revealed a positive relationship between loan
ratio and profitability, those by Hassan and Bashir (2005) and Staikouras and Wood
(2004) documented that a higher loan ratio really influences profitability negatively. In
fact, the profits of a bank depend on either the amount or the composition of its credit
portfolio. Normally, loans produce interest revenue and, in this way, a large credit
portfolio should imply improved bank profits (Rhoades and Rutz, 1982). However, a
large credit portfolio could also lead to reduced bank profits if it mostly includes
high-risk loans which could cause lower returns and financial losses. In this regard,
Duca and McLaughlin (1990), among others, concluded that variations in bank
profitability largely depend on changes in credit risk and also Miller and Noulas (1997)
revealed a negative relationship between credit risk and profitability, whereas
variations in credit risk may reflect changes in the credit quality of a bank’s loan
portfolio (Cooper et al., 2003).
Hence, it is possible to conclude that the size of a bank’s credit portfolio affects its
profitability either positively or negatively, depending on its composition in terms of
credit quality. However, with respect to the majority of the studies mentioned above, the
following hypothesis is suggested:
 JFRA H3. There is a positive relationship between loan ratio and bank profitability.
14,1
3.4 Deposits
Banks rely significantly on customer deposits to allocate credits to other customers.
Thus, more deposits a bank will get, more loan opportunities it will be able to provide to
customers and then it will be able to generate further profits. This argument is
96 underlined by Lee and Hsieh (2013) by concluding that additional deposits can
advantage banks in producing more profits, while low deposits may impact negatively
on their profitability. Therefore, customer deposits are positively related with bank
profitability; but, on the other hand, banks’ incapacity in not releasing money through
loans may reduce its profitability level because of the interests paid to depositors.
However, if there is insufficient loan demand, more deposits may dampen earnings, as
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this kind of funding is costly.

Being the main source of funding for banks, it is generally supposed that customer
deposits affect banking performance positively if there is a satisfactory demand for
loans in the market. It could be expected that a higher growing deposits would be able to
expand the business of the bank and consequently generate more profits. Nevertheless,
the impact on profitability that originates from a growth in deposits depends on several
factors. First, the impact is influenced by the bank’s ability to transform deposit
liabilities into income-earning assets, which also reveals banks’ operating efficiency.
The effect also depends on the credit quality of these assets because a positive effect on
bank profitability is regularly achieved by investing in assets of higher credit quality.
Empirical evidence from Ben Naceur and Goaied (2001) showed that the
best-performing banks are those that have preserved high levels of deposit accounts
related to their assets. Increasing deposits (i.e. the ratio of total deposits to total assets)
implies growing the funds available to different profitable uses (e.g. lending activities
and investments), which should upsurge the bank’s return on assets when other factors
are constant (Allen and Rai, 1996).
The effect of fund source on profitability is captured by deposits over total assets
ratio and is based on some of the previous studies. It can be hypothesized that:
H4. There is a positive relationship between deposits and bank profitability.

3.5 Loan loss provisions

Asset quality refers mainly to the quality of the bank’s earning assets which include its
loan portfolio. The ratio of loan loss provisions over total loans is now analyzed to
measure the effect of a bank’s asset quality on profitability. The ratio of loan loss
provisions to total gross loans (i.e. asset quality ratio) is incorporated as an independent
variable in the regression analysis because a high ratio could indicate poor quality of
loans and therefore a higher risk of the loan portfolio.
Furthermore, the level of loan loss provisions is an indicator of a bank’s asset quality,
and it points out changes in the future performance. A higher ratio indicates a reduced
credit quality and, therefore, a lower profitability. Therefore, the coefficient is estimated
to be negative because bad loans are expected to reduce profitability. A negative impact
of loan loss reserves on bank profitability would suggest a reduced quality of loans that
upsurges the provisioning costs and declines interest revenue. Thus, the loan loss
provisions to total loans ratio is expected to have a negative relationship with bank
profitability. If banks operate in more risky and uncertain environment and they find it
 difficult to control their lending operations, it will probably lead to a higher loan loss Determinants
provision ratio. For example, Miller and Noulas (1997) believe that the reduction of loan of bank
loss provisions is the primary cause for the rise of profit margins in many cases. In their
study, they advised that as the exposure of the financial institutions to high-risk loans
profitability
increase the growth of unpaid loans will enlarge and profitability will decrease. In this
direction, Miller and Noulas found a negative relationship between credit risk and
profitability, and they argued that such a correlation signifies a greater risk linked with 97
loans. Then a higher level of loan loss supplies could create concerns regarding the
profit-maximizing strength of a bank. Changes in credit risk may reflect changes in the
health of a bank’s loan portfolio (Cooper et al., 2003), which may affect the performance
of the institution. In this regard, Duca and McLaughlin (1990), among others, claimed
that modifications in bank profitability are mainly due to variations in credit risk, as
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amplified exposure to credit risk is generally related to reduced firm profitability.

The challenge for bank management is to reduce the risk of loan default and to value
loans so that returns could be considered more appropriate to cover loan losses (Golin,
2001). This started a discussion concerning not the volume of loans but the importance
of their quality. In fact, with a sound quality of loans, a high ratio could suggest a
positive relationship between risks and profits, according to the risk-return hypothesis.
In this regard, Kosmidou et al. (2008), Athanasoglou et al. (2008) and Vong and Chan
(2009) found a positive relationship between the ratio of loan loss provisions over total
loans (asset quality) and profitability.
According to Fu and Heffernan (2010), the estimated relationship of this ratio with
profitability can be positive or negative due to the greater provision indications which
assess a possible loan loss in the future or it could also show a timely recognition of bad
banks’ loans. Hence, it is difficult to anticipate the sign of the relationship between asset
quality ratio and profitability, but the results of the majority of previous studies lead us
to the following hypothesis:
H5. There is a negative relationship between asset quality and bank profitability.

4. Data source and research methodology

To investigate the determinants of European banks’ profitability, the cross-section and
time series data have been examined by applying a panel data multiple regression. As in
many prior studies, we adopted both a descriptive analysis and a multivariate one to
explore the combined effect of bank’s characteristics on the level of profitability of
selected banks. Three models are tested in the analysis, and each one includes a different
measure of profitability (dependent variable). Specifically, the regression analysis
includes alternatively three different measures of profitability as dependent variables
[return on equity (ROE), return on assets (ROA) and net interest margin (NIM)] and five
determinants of profitability as independent variables (Size, Capital ratio, Loan ratio,
Deposits and Loan loss provisions).
In this paragraph, first, information regarding data set, sample selection, dependent
and independent variables is given. Then, the model of the study including the
mentioned variables is presented.

4.1 Data set and sample selection

Our core data source for the bank-specific characteristics is the Bankscope database,
which supplies annual financial information for banks in 180 countries all over the
 JFRA world and thus it is considered the most comprehensive database for research in
14,1 banking. To use the accounts data downloaded from Bankscope for our statistical
analysis, we had to oversee them according to the following sample selection criteria.
Our sample is an unbalanced panel data set of 35 top European commercial banks,
based on 175 observation over a five-year period from 2009 to 2013. Within sample
selection, any active bank which operated in a European country and had complete,
98 consistent and accessible data for each of the years of the time period chosen for the
analysis was selected. Banks had to meet the following conditions to be included in the
sample, given the period of investigation. First, they had to be European-owned
commercial banks among the financial institutions operating within the European
Union banking sector, according to the nationality analysis of the ECB as at December
31, 2013. Second, they had to have available data obtained from the annual balance
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sheets and income statements collected from the Bankscope database for all the years
between 2009 and 2013. Regarding the time period, the panel data are collected from
2009 to 2013 to study the period after the beginning of the financial crisis. The
investigation of banks’ profitability is particularly interesting in this period, as the
financial system and banks have been exposed to several financial shocks and
challenges in many countries.
As our study regards commercial banks in Europe, we excluded non-banking credit
institutions, securities houses, investment banks and ECB. In the next step of the sample
selection, the top 35 European banks have been selected for data collection in our study,
as these banks are the largest ones in Europe and they cover almost 75 per cent of the
total asset base of total banks in the European banking sector. In practice, they had to be
scheduled as “largest bank” by Bankscope according to the amount of total assets
reported in the last available year. Then, we removed duplicate information, i.e. we
focused attention on consolidated data if Bankscope reported both unconsolidated and
consolidated statements.

4.2 Dependent and independent variables

In this section, we also describe both the dependent and independent variables that we
selected for our analysis on bank profitability. Although the definition of profitability
varies among banking studies, in line with previous literature that inspected the
determinants of banks’ profitability, we rely on three most commonly used measures of
bank profitability. The mentioned dependent variables are considered as alternative
measures of profitability in this study. In the literature, there are several ratios used to
measure the profitability of banks (Sufian and Habibullah, 2009; Ben Naceur and
Omran, 2011). Our research does not comprise all dimensions of the profitability and all
possible internal determinants but it is limited to the following variables. This study
postulates ROE, ROA and NIM as the dependent variables, and it considers a number of
internal factors as the independent variables.
In line with prior studies on bank profitability, ratios will be used as indicators for
profitability, as they are inflation-invariant (i.e. they are not affected by changes in
general price level). As the numerator and the denominator in the profitability ratios are
measured in monetary terms based on period price levels, in a time series analysis such
as this, the real value of profits may be influenced by the time-changing inflation rates.
Hence, deflating for prices using some base period price index would leave the ratios
unvaried.
 The first measure is the ROE which measures the return to shareholders on their Determinants
equity, and it is calculated as the ratio of net profits to total equity. ROE indicates the of bank
returns to shareholders on the book value of their investments (equity) and reveals how
well a bank management is in using shareholders’ funds. In other words, ROE measures
profitability
a firm’s efficiency to generate profits from every unit of shareholders’ equity and it
shows how successfully a company uses investment funds to cause earnings growth.
Although the financial literature usually uses ROE to measure profitability, it is not 99
the unique indicator in assessing the profitability, as ROA can be considered another
significant measure to compare the operating performance of banks, for example, for
banks which usually report a lower ROE due to a lower leverage ratio (higher equity).
Thus, in our analysis, we also examine the ROA, the ratio of net profits to total assets, as
an alternative indicator of profitability, and we use it as a second dependent variable in
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the regression model, similar to that used in the studies of Abbasoglu et al. (2007), Ben
Naceur and Goaied (2008) and Kosmidou (2008). In fact, as Golin (2001) points out, ROA
has appeared as the key functional indicator of bank profitability and has become the
most common measure of bank profitability in the literature.
ROA reflects the ability of a bank’s management to generate profits from the assets,
and it indicates how effectively the bank’s resources are managed to produce profits
(Golin, 2001; Hassan and Bashir, 2005). In principle, ROA measures the profit earned per
euro of assets and it reflects a bank’s management ability and efficiency in using the
bank’s financial and investment resources to generate revenues. For example, according
to Rivard and Thomas (1997), ROA is a basic indicator of a bank manager’s capability to
make profit from bank’s financial and real assets, as it is not influenced by high equity
and it assesses the return-generating capacity of entire assets of a bank. Different banks
in the banking industry are compared with each other on the basis of ROA, as ROE
neglects financial leverage, while ROA represents a better measure of the ability of a
firm to generate returns on its portfolio of assets. Following Ben Naceur and Goaied
(2008), Kosmidou (2008), and among others, we consider ROA as a dependent variable in
this study.
Finally, the NIM attends as the third profitability measure. The NIM variable is
defined as the net interest income divided by total assets. NIM is expressed by the
difference between the interest income generated by banks (i.e. income from loans and
securities) and the amount of interest the bank must pay to its depositors and creditors
from whom it has borrowed funds, divided by the average amount of their
interest-earning assets (i.e. the sum of all bank’s assets that earn interests, including
loans and investments in fixed-income securities). As a measure of the ROA, the NIM
has been used in many studies of bank performance because it quantifies the
profitability of the bank’s interest-earning business. While the ROA focuses on profit
earned per euro on total assets and reflects how well bank management uses the bank’s
actual investment resources, the NIM measures the profit earned on interest activities
(Berger, 1995b; Ben Naceur and Goaied, 2001). The ratio of NIM represents bank’s
efficiency and how successful the investment made by banks is.
As potential determinants of European banks’ profitability, we consider only five
bank-specific independent variables. Precisely, the internal factors used as internal
determinants of performance are: total assets of a bank representing bank’s size (SIZE),
ratio of equity to total assets representing capital strength (CAP), loans to total assets
(LOAN), total deposits to total assets (DEP) and asset quality expressed as the ratio of
 JFRA loan loss provisions over total loans (LLP). Internal determinants of bank performance
14,1 can be recognized as factors that are largely determined by a bank’s management
decisions and objectives which are able to definitely influence the operating results of a
bank. As the management’s effects on profitability can be inspected by examining the
balance sheet and the profit and loss accounts of these institutions, the internal
determinants directly come from bank management’s policies and decisions are
100 generally based on financial information collected from banks’ balance sheets and
income statements.
The bank-specific variables being examined in this study come from both the income
statements and the balance sheets of the top 35 European banks included in the sample.
The definitions, formulas and sources of these variables are described below, while their
theoretical assumptions are explained in the above literature review section. Previous
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studies provide some information in defining the meaning of independent variables so

that we use the variables described in the literature review as a measure for banks’
internal factors.
One of the most important issues concerning bank strategy is which size improves
bank profitability. In most of the prior literature, the total assets of banks are used as an
indicator of bank size. Hence, in this study, the size of the bank (SIZE) is included in the
regression model as an independent variable and is measured by total assets. Usually,
the effect of increasing size on profitability has been verified to be positive to a certain
extent. In fact, for banks that come to be excessively large, the impact of size could be
negative, especially due to bureaucratic reasons.
Capital ratio is included as an independent variable to survey banking profitability.
As an indicator for a bank’s capital, we use the capital ratio measured by equity over
total assets (CAP). The ratio of equity to total assets represents bank capitalization and
identifies the ability of a bank to manage losses and risk exposures. The ratio should be
an important variable to assess bank’s profitability, as it indicates capital adequacy and
reflects the overall well-being and soundness of the bank. A higher capital level raises
profitability, as a bank can easily be compliant with regulatory capital standards by
having more capital and consequently by using the excess capital as loans. Capital ratio
is expected to have a positive relationship with profitability because well-capitalized
banks are less risky and more profitable (Bourke, 1989; Hassan and Bashir, 2005; Berger,
1995b and Demirguc-Kunt and Huizinga, 1999).
The ratio of net loans to total assets (LOAN) is considered in this study as an
independent variable to determine the impact of loans on banks’ profitability. Loans are
the main part of interest-bearing assets and are estimated to have a positive relationship
with bank profitability. Other things being constant, the more the deposits are converted
into loans, the higher the level of profitability should probably be. Nevertheless, it could
be possible that banks that are fast growing their loans have to pay a higher cost for
their funding requirements, and this could lead to a negative impact on profitability.
Deposits are the main source of bank funding, and their impact on profitability is
indicated by the deposits-to-total assets ratio (DEP). The ratio of deposits to total assets
is considered as an independent variable in this study, and it is expected to have a
positive impact on banks’ profits. Though, the effect on profitability originating from a
growth in deposits is influenced by several factors. For example, it depends on a bank’s
operating efficiency (i.e. the bank’s capability to transform deposit liabilities into
income-earning assets) and on the credit quality of interest-earning assets.
 The ratio of loan loss provisions over total gross loans (LLP) is used as a measure of Determinants
a bank’s asset quality. This ratio is incorporated as an independent variable in the of bank
regression analysis, as it is the determinant of how asset quality could influence the
profits of European banks. The ratio of loan loss provisions to total loans is also an
profitability
indicator of credit risk because it is based on the total loan portfolio that has been
reserved for bad loans. If banks act in risky contexts and fail to supervise their lending
operations, a higher loan loss provision ratio will possibly arise. A higher ratio shows 101
lower credit quality and, thus, a lower profitability. Hence, the ratio is expected to have
a negative relationship with profitability.
The explanations of dependent and independent variables considered in the analysis
are presented in Table I. Table I lists all the variables used to assess profitability and its
internal determinants, including description, measure and the expected effects of the
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determinants on profitability according to the major literature.

4.3 The regression model

To test the relationship between bank profitability and bank-specific determinants
described before, we use a linear regression model. The feedback from the literature on
bank profitability reveals that the functional linear form of analysis is the proper one. In
this regard, most of the studies on bank profitability, such as Short (1979), Bourke (1989),
Molyneux and Thorton (1992), Demirguc-Kunt and Huizinga (1999), Athanasoglou et al.
(2006), Garcia-Herrero et al. (2009) and Goddard et al. (2004a), used linear models to
assess the impact of different factors that may be significant in explaining profitability.
As with previous studies, a linear model is used to analyze the cross-section time series
data and a simple linear equation is estimated using a pooled sample of European banks
in the period 2009-2013. Hence, the basic estimation strategy is to pool the observations
across banks and apply the regression analysis on the pooled sample.
A pooling analysis allows to obtain more consistent estimates of the parameters
within the model and it is a useable method, whereas the association between the
variables is stable through cross-section units. Our data set proves that European banks
present similar reply to cyclical movements and this is why we decided to apply pooled
least squares (OLS) method, as it is the most consistent regression estimation because of
its general quality of minimized bias and variance (Koutsoyiannis, 2003 and Greene,
2004). Therefore, a multivariate analysis is carried out using a OLS-regression model to

Expected effect
Variable Description Measure on profitability

Dependent variables
ROE Return on equity Net income/Average total equity (%) NA
ROA Return on assets Net income/Average total assets (%) NA
NIM Net interest margin Net interest income/Average earning assets (%) NA
Independent variables
SIZE Bank size Total assets (mil EUR) ⫹
CAP Capital ratio Equity/Total assets ⫹ Table I.
LOAN Loan ratio Net loans/Total assets ⫹ Explanation of
DEP Deposits Total deposits/Total assets ⫹ variables used in the
LLP Loan loss provisions Loan loss reserve/Total gross loans ⫺ regression model
 JFRA verify the hypotheses of this study and panel regression techniques are used to
14,1 investigate the internal determinants. We select panel data (or cross-sectional time
series data) because they allow to measure respectively individual variability and
dynamic change of the cross-section units over time.
To examine the profits’ determinants of European banks, we estimate a linear
regression model of the following form:
102
yjt ⫽ ␦t ⫹ ␣it= Xijt ⫹ ␧jt (1)

where j refers to an individual bank; t refers to year; yit refers to the profitability of bank
i at time t and it is the observation of bank j in a particular year t; Xi represents the
internal factors (determinants) of a bank’s profitability; ␧jt is a normally distributed
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random variable disturbance term (error term).

Extending equation (1) to reflect the variables considered in the study, as described in
Table I, the regression model is formulated as follows:

yjt ⫽ ␦0 ⫹ ␣1SIZEjt ⫹ ␣2CAPjt ⫹ ␣3LOANjt ⫹ ␣4DEPjt ⫹ ␣5LLPjt ⫹ ␧jt (2)

where yit is the profitability of bank i at time t. Three indicators, namely, ROE, ROA and
NIM, represent three alternative performance measures for the bank j during the period
t. Hence, three models are alternatively tested in the analysis, and each one includes a
different measure of profitability (dependent variable).
Equation (2) is estimated through a fixed effects regression analysis, taking each
measure of bank’s profitability as the dependent variable. Hence, we use the least square
method to a fixed effects model. We apply White’s (1980) transformation to control for
cross-section heteroskedasticity of the variables, and the standard errors reported for all
coefficients are therefore based on White’s adjustment.
The option of a fixed effects model rather than a random effects one has been verified
with Hausman test (Baltagi, 2001). We also used the Breusch–Pagan test to check for
residual heteroskedasticity.

5. Results and discussion

This section inspects the empirical methods used to verify the research hypotheses and
illustrates the econometric results of the study, including descriptive statistics,
regression analysis and the test for multicollinearity. The empirical evidence on the
determinants of banks’ profitability is based on panel data, and all the variables are
observed for each cross-section and time period. Descriptive statistics, correlation
matrix and multivariate regression results are calculated and presented in Tables II-IV,
respectively.

5.1 Descriptive analysis

Table II shows the summary statistics of the dependent and independent variables used
in the empirical model. The table reports the results of descriptive statistics for all the
variables in the sample data set.
A wide variety of profitability information is found. Particularly the value of ROE
has significant dispersion in the scores, as revealed by the minimum, maximum and
standard deviation values. On average, the European banks included in our sample
exhibit a ROE of 0.303074 over the entire period from 2009 to 2013. The amount of ROE
 ranges from ⫺94.5780 to 21.9700, and the highest standard deviation for ROE is 15.5895. Determinants
The difference between mean and standard deviation points out great differences of bank
among the profitability of banks.
However, the banks in our sample display a ROA of 0.0791600 on average and a
profitability
standard deviation of 0.807409, which indicates that observations in the data set are
more close to the mean. Same thing happens for our third profitability measure, NIM,
which amounts to 1.35266 on average. The highest standard deviation of 0.776909 103
shows that there are only few variations because interest rates on all kinds of finances
are quite consistent among almost all the banks.
On the contrary, a wide range of variation is evident with regard to some of the
independent variables as represented by their minimum and maximum values.
Especially, there is a large variation in the data set of SIZE and LOAN because the
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sample includes banks with very different sizes and loans. Some of the banks have a
large size, and they use higher capital and equity because they are well established since
a long period, while the other ones have small size and thus minor capital and equity
which downturn bank’s ROE and ROA. Thus, the huge difference among banks in our
sample regards size. The standard deviation for this variable amounts to 535,142, while
all the other dependent variables show lower standard deviation values which indicate
much more consistency of the data set. For example, the value of capital ratio varies
among banks (as well as the other internal determinants), but the standard deviation is
quite low (2.64964), showing a slight variation in the values. The best-capitalized bank
in our sample has a capital ratio of 16.7850, whereas for the least-capitalized bank, the
ratio of equity over total assets amounts to ⫺0.0780000.

5.2 Regression analysis

Before carrying out the regression analysis, the existence of an econometric problem of
data set applied in the model is tested by using the correlation matrix. We have checked
the independence of variables to be secured the absence of multicollinearity problems
that may prejudice our results. The correlations among the research variables described
in the model can be found in Table III.
Table III presents the correlation coefficients for the variables included in the regression
analysis. The results confirm that no collinearity problem occurs between the independent
variables, as multicollinearity can be considered a problem when the correlation is above
0.80 (Kennedy, 2008). In this regard, the correlation between each of the variables is not
elevated and the highest degree of correlation found is very satisfactory. The matrix shows
that multicollinearity problems do not exist, confirming that the model used is valid and
reliable. As a result, the coefficients indicate that a multivariate analysis can be performed by
examining individual correlations between independent and dependent variables.
The empirical analysis shows that all the independent variables have a statistically
significant relationship with profitability measures included in the model. Overall, we
observe some relevant differences between the estimation findings of the time periods,
with respect to both the significance and the size of the coefficients. The regression
results on the relationship between bank profitability and the explanatory variables are
displayed in Table IV.
Table IV reports the regression models for ROE, ROA and NIM. In particular, the R2
value indicates how internal factors are related to the bank profitability indicators and
the adjusted R2 value refers to the rigorousness of additional predictor variables with
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14,1

104
JFRA

Table II.
Summary statistics
Variables Mean Median Minimum Maximum SD 5 (%) 95 (%)

Dependent variables
ROE 0.303074 4.33500 ⫺94.5780 21.9700 15.5895 ⫺26.3502 14.2552
ROA 0.0791600 0.186000 ⫺6.83400 1.62400 0.807409 ⫺1.25740 0.895600
NIM 1.35266 1.13500 ⫺0.256000 4.45400 0.776909 0.473800 2.84580
Independent variables
SIZE 707,512 581,709 16,589.0 2.51572e⫹006 535,142 44,207.2 1.85501e⫹006
CAP 4.77901 4.38000 ⫺0.0780000 16.7850 2.64964 1.40300 10.4994
LOAN 46.7139 48.2780 8.99200 75.2040 14.6267 19.8116 71.2518
DEP 2.56561 2.35700 0.220000 8.50000 1.54503 0.534600 5.63060
LLP 0.556714 0.560000 0.00000 0.855000 0.131195 0.343600 0.807600
 statistical shrinkage. The models perform reasonably well, with most variables Determinants
remaining stable across the various regressions tested. The difference between R2 and of bank
adjusted R2 (i.e. shrinkage level) values is low in each model, showing an acceptable level profitability
of correlation between dependent and independent variables. The values of F-statistic
are significant, endorsing the validity and the stability of the model used in our study.
The explanatory power of the models is reasonably high, as the adjusted R2 value ranges
from 0.181059 to 0.676821. The highest value for the adjusted R2 (0.676821) results in 105
Model 3 which evidences that about 67 per cent of the variation of the dependent
variable NIM is explained by the independent variables included in the model. Hence,
the 33 per cent variation in the dependent variable remains unexplained by the
independent variables.
The results of the diagnostics show that SIZE has a positive impact on profitability.
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The positive coefficient, significant in all cases, indicates that larger banks succeed
better than smaller ones in achieving a higher ROE, ROA and NIM. These results are
consistent with prior evidence (Pasiouras and Kosmidou, 2007; Staikouras et al., 2008;
Molyneux and Thorton, 1992; Bikker and Hu, 2002; Goddard et al., 2004a; Gul et al.,
2011). In particular, many researchers found that little cost savings can be achieved by
increasing the size of banking assets (Berger et al., 1987), while others depicted
significant economies of scale for banks which asset size ranges into more than €1bn
values (Shaffer, 1985).
Because a bank expands its operations, there are more opportunities of growth in
bank’s profitability. The first explanation for the positive relationship between size and
profitability is related with economies of scale (Hauner, 2005; Pasiouras and Kosmidou,
2007; Staikouras et al., 2008). In this regard, a potential cause regards market power
because banks having huge amounts of assets generally control a larger portion of the
market, improving profits through the allocation of fixed costs over a larger volume of
services (Hauner, 2005). This position should enable such banks to pay less for their
inputs and to acquire less expensive capital. It also reveals that larger banks are able to
benefit from higher product and loan diversification opportunities (Smirlock, 1985;
Bikker and Hu, 2002). For these reasons, as the unit costs of large-scale banks are likely
to be low than those of smaller banks, their profitability ratios are expected to be higher.
The regression analysis displays a positive and significant (at the level of 1 per cent)
impact especially on the dependent variable NIM, meaning that larger banks in Europe
experience higher net interest margins than smaller banks mainly as a consequence of
economies of scale in transactions and reputational advantages.

Variables ROE ROA NIM SIZE CAP LOAN DEP LLP

ROE 1.0000 0.7088 0.3513 0.0199 0.3427 0.0976 0.1014 ⫺0.2730

ROA 1.0000 0.4478 ⫺0.0287 0.4616 0.1382 0.2582 ⫺0.3083
NIM 1.0000 ⫺0.1610 0.6687 0.5261 0.2440 ⫺0.5524
SIZE 1.0000 ⫺0.1691 ⫺0.4284 ⫺0.0123 ⫺0.4410
CAP 1.0000 0.2574 0.1478 0.3642
LOAN 1.0000 0.2320 0.4364
DEP 1.0000 ⫺0.0784 Table III.
LLP 1.0000 Correlation matrix
 JFRA Variables Coefficient SE t-ratio p-value
14,1
Model 1 – Dependent variable: ROE
const ⫺23.5004 6.91259 ⫺3.3997 0.00085***
SIZE 5.95525e-06 2.37307e-06 2.5095 0.01305**
CAP 1.96368 0.463393 4.2376 0.00004***
106 LOAN 0.0327959 0.0924595 0.3547 0.72326
DEP 2.02138 0.81607 ⫺2.4770 0.01426**
LLP ⫺24.8956 10.3264 2.4109 0.01701**
R2 0.223418 Log-likelihood ⫺706.3423
F-statistic 5.274403 Schwarz criterion 1464.333
S.E. of regression 14.10774 Akaike criterion 1432.685
Adjusted R2 0.181059 Hannan-Quinn 1445.522
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p-value(F) 2.57e-06 Durbin-Watson 1.865130

Model2 – Dependent variable: ROA
Const ⫺1.03482 0.319631 ⫺3.2376 0.00146***
SIZE 2.5783e-07 1.09728e-07 2.3497 0.01997**
CAP 0.143701 0.0214269 6.7066 ⬍0.00001***
LOAN 0.00587298 0.00427524 1.3737 0.17139
DEP 0.211474 0.0377343 ⫺5.6043 ⬍0.00001***
LLP ⫺0.921535 0.477484 1.9300 0.05532*
R2 0.381015 Log-likelihood ⫺168.4045
F-statistic 11.28502 Schwarz criterion 388.4568
S.E. of regression 0.652328 Akaike criterion 356.8089
Adjusted R2 0.347252 Hannan-Quinn 369.6462
P-value(F) 1.15e-13 Durbin-Watson 1.944568
Model 3 – Dependent variable: NIM
const ⫺1.59913 0.216409 ⫺7.3894 ⬍0.00001***
SIZE 3.25158e-07 7.42923e-08 4.3767 0.00002***
CAP 0.145557 0.0145072 10.0334 ⬍0.00001***
LOAN 0.0165286 0.00289458 5.7102 ⬍0.00001***
DEP 0.0390608 0.0255483 1.5289 0.12820
LLP ⫺2.0725 0.323284 6.4108 ⬍0.00001***
R2 0.693538 Log-likelihood ⫺100.1545
F-statistic 41.48911 Schwarz criterion 251.9568
S.E. of regression 0.441663 Akaike criterion 220.3089
Adjusted R2 0.676821 Hannan-Quinn 233.1462
P-value(F) 5.76e-38 Durbin-Watson 2.059420

Notes: *** , ** and * indicate two-tail significance at the levels of 0.01, 0.05 and 0.10, respectively,
Table IV. using White’s (1980) heteroskedasticity-consistent standard error; this table reports coefficients from
Regression analysis annual cross-sectional regressions of profitability on the variables listed

For hypotheses testing, results document that capital ratio (CAP) is positively related
with profitability in all the models, meaning that well-capitalized banks experience
higher returns, thus reducing their cost of funding and facing lower risks of going
bankrupt. On the contrary, lower capital ratios in banking imply greater leverage and
risk, and then higher borrowing costs. If an increase in the amount of equity may allow
banks to reduce their levels of debt, we expect the funding costs of these banks to be
 lower. Therefore, it is reasonable that the profitability level should be higher for the Determinants
better capitalized banks. In fact, the regression coefficients of the capital ratio are of bank
positive and statistically significant (at the level of 0.01), reflecting the positive impact of
capital strength on profitability in European banking sector. The regression analysis
profitability
also reveals that the capital has the highest positive effect on ROE (the value of the
coefficient is 1.96368). These empirical results are consistent with previous studies of
Kosmidou et al. (2006), Berger (1995a,1995b), Demirguc-Kunt and Huizinga (1999), 107
Staikouras and Wood (2004), Goddard et al. (2004a), Pasiouras and Kosmidou (2007),
Sufian and Chong (2008), Saeed (2014).
It can be concluded that banks financed by high amounts of equity (i.e. banks with
low leverage ratios) are able to be more profitable. A strong capital structure is crucial
for financial institutions in pursuing business opportunities more successfully and in
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withstanding unexpected losses, thus achieving more profitability.

Our results about loan ratio (LOAN) indicate that its effect on European bank profits
is not clear and varies according to the measure of profitability used. The variable
LOAN shows a positive but insignificant relationship with ROE and ROA. This
indicates that more loans increase the chances of achieving higher profitability, but as
the relationship is insignificant, the effect is not conclusive. This result does not confirm
those obtained from other similar studies; for example, the study by Kosmidou (2008)
has found that the ratio of net loans to total assets of European banks has a negative
impact on profitability. On the contrary, the positive relationship is confirmed whether
we use interest net margin (NIM) as a dependent variable. Loans to total assets (LOAN)
have a positive and statistically highly significant (at the level of 0.01) effect on NIM as
found by Ben Naceur (2003). This result is consistent with the concern that European
banks play the intermediary role between lenders and borrowers, whereas more
deposits are transformed into loans. In this case, higher lending generates higher
income.
Turning to the other explanatory variable, the amount of deposits to total assets
(DEPOSIT) does not have a significant impact on bank profitability as measured by
NIM. However, it has a positive and significant effect (at the level of 5 per cent) on ROE
and ROA as dependent variables (period 2009-2013). This result is in line with similar
studies that have focused on banks’ profitability such as Al-Jarrah et al. (2010), Gul et al.
(2011) and Saeed (2014). The results concerning the variable DEPOSIT sustain the view
that banks depending on deposits to obtain their funding can achieve better ROA. More
deposits improve the lending capacity and determine higher profits. After the crisis
period, top banks in Europe were able to collect additional saving deposits and to
transform the growing amount of deposit liabilities into considerably greater income
earnings. As the demand for lending increased, even profitability enlarged because
banks had been able to find attractive investment opportunities lending their additional
deposits. As expected, the positive coefficient for both ROE and ROA indicates that
European banks with a higher lending growth rates are more profitable than slowly
lending banks.
Therefore, regarding LOAN and DEP, in some cases, our hypotheses are not
supported by the findings. The analysis suggests that LOAN has an insignificant
influence on the level of ROE and ROA, while DEP has no statistically significant
relationship with NIM. The regression results disprove a correlation between these
independent variables and the mentioned measure of profitability used as dependent
 JFRA variables. This is in contrast with H3 and H4 which respectively state that loan ratio
14,1 and deposits to total assets are positively related to profitability.
Theory suggests that increased exposure to credit risk is normally associated with
decreased firm profitability and, hence, we expect a negative relationship between
profitability and the ratio of provisions to total gross loans ratio (LLP). In our analysis,
LLP is found to have a significant negative impact on banks’ profitability. As expected,
108 the regression coefficient is negative and significant for ROE, ROA and NIM (at the level
of 0.05, 0.10 and 0.01, respectively) equations, implying increased exposure to credit risk
is associated with the decrease in profitability. The sign of this ratio is consistent with
the results of similar studies carried out in the most developed countries as mentioned in
the literature review. The empirical results show that European banks with higher
credit risk tend to exhibit lower profitability levels. In this regard, European banks
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should dedicate more on credit risk management, which has been confirmed by the
failure of financial institutions to recognize impaired assets and to create reserves for
their write off. Efforts to reduce these problems would be reinforced by refining the
transparency of the financial system, which would support financial institutions to
assess efficaciously credit risk. The findings advise that European banks would
improve profitability by screening and monitoring more efficiently credit risk and thus
by improving the forecasting of future levels of risk.

6. Concluding remarks
A healthy and financially solid banking system is one of the basics of sustainable
economic growth. In this regard, the European banking sector shows a successful
performance despite the last global financial crisis that affected the worldwide
economies and also banking system since 2008. It is reasonable to assume that these
developments posed great challenges to European banks as the context in which they
operated changed rapidly. So, it has become interesting to identify the determinants of
profitability of European banks to guarantee the sustainability of the financial stability
and then to challenge the negative consequences of the crisis.
This subject shapes the purpose of our study, and, in this scope, the determinants of
bank profitability have been analyzed in a multiple regression model by using a sample
consisting in a number of top banks operating in Europe in the period 2009-2013. In
particular, this study investigates the impact of bank-specific characteristics (internal
factor) on European banks’ profitability. Panel data estimation has been applied to 35
top European banks, analyzing the cross-section and time series data for the mentioned
period. An unbalanced panel data set of 175 observations has provided the basis for the
econometric analysis. The empirical findings of the analysis suggest that all the
bank-specific variables have a statistically significant effect on profitability, measured
by ROE, ROA and NIM. However, the impacts are not uniform across bank measures.
Some consistent conclusions can be drawn from the research. Regression results
clearly show that there are large differences in profitability among the banks included in
the sample and a significant amount of this variation can be explained by the
independent variables included in our analyses. The results indicate that size,
represented by total assets, is the main determinant of European banks’ profits,
supporting the argument that large banks have took advantage of economies of scale.
The findings also show that capital strength, measured by equity to total assets, is a
significant determinant of bank profitability in Europe. Well-capitalized banks face
 lower costs of external financing and such an advantage can be translated into higher Determinants
profitability. On the other hand, regression analysis reveals that a higher ratio of net of bank
loans to total assets (LOAN) may not certainly lead to a higher level of profits. Based on profitability
the empirical results, it is difficult to find a conclusive relationship between loan ratio
(LOAN) and profitability in all cases. The loan ratio is statistically significant only when
NIM is used in the regression model, otherwise it is insignificant. Hence, loan ratio is not
able to explain the variability of European bank’s profitability measured by ROE and 109
ROA. On the contrary, the impact of deposits (DEP) on ROE and ROA is positive and
significant but insignificant on NIM. Finally, empirical results reveal that provisions to
total gross loans ratio (LLP) is another internal determinant of bank profitability in
Europe; however, the relationship is negative. The impact of LLP on bank performance
is always statistically significant, but the statistical relevance varies according to the
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measure of profitability used.

The findings provide interesting insights into the characteristics and practices of
successful commercial European banks in terms of profitability. In view of these
findings, some suggestions may be useful for banks’ management, policymakers and
shareholders. To increase their profitability, European banks should attempt to
reinforce their capital structures and the growth of their assets. Because these efforts
will strengthen the reliance of current and potential investors, banks will have the
opportunity to collect less expensive capital. In relation to asset portfolios, the results
indicate that the commercial banks should concentrate on deposits and loans rather than
investment in securities and investment in subsidiaries to improve their performance.
Finally, the findings of this study have considerable policy relevance. It could be
argued that the ability to maximize risk-adjusted returns on investment and to sustain
stable and competitive advantages is a crucial element to safeguard the competitiveness
of the European banking sector. The success of the European banking sector depends on
its efficiency, profitability and competitiveness. Thus, from a regulatory perspective,
the performance of a financial sector is based on its efficiency and profitability. To attain
this objective, it would be helpful to identify the profitability determinants of successful
banks to formulate policies for intensifying and maintaining the strength and the
stability of the banking sector in Europe.
Given the relation between the well-being of the banking sector and the growth of the
economy (Rajan and Zingales, 1995), knowledge of the underlying factors that influence
the financial sector’s profitability is therefore essential for the managers of the banks
and for stakeholders (i.e. the central banks, banker associations, governments, other
financial authorities). Awareness of these factors would be useful in helping the
regulatory authorities and bank managers to formulate future policies aimed to provide
the profitability to the Europe banking sector.
The paper concludes with some remarks on the practicality of the results. The
findings of this study have important implications and are relevant for several reasons.
First, the results provide comprehensive new insights into the reasons that determine
the profitability of commercial banks in Europe. Individual bank’s characteristics
explain a substantial part of the within-country variation in European bank
profitability, suggesting that much more attention should be dedicated on bank’s
specifics to increase the profitability. Second, the study could be a support for European
banks, government, policymakers, stakeholders, investors in their decision-making
 JFRA process and especially could be important for the global institutional investors looking
14,1 for profitable investment opportunities in European banking sector.
The study extends the existing literature in several ways. To date, very few
econometric studies have empirically explored the determinants of profitability of the
European banking sector (Goddard et al., 2004a; Athanasoglou et al., 2006), even though
similar studies have been conducted in some developed countries. Therefore, the present
110 paper will fill an important gap in the existing literature and improve the understanding
of bank profitability in Europe. It is hoped that this study will be useful to expand the
existing literature on banking and finance in Europe, particularly in the context of bank
profitability.
Hence, our study extends the knowledge on bank profitability in Europe with respect
to several important dimensions. Based on this study, many others could be conducted
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by inspecting other internal and/or external variables that could affect the bank
profitability. Moreover, future research may be done by increasing the number of
European banks analyzed or by including further variables to improve the reliability of
findings presented in this study. In particular, further research can be conducted
comprising some other internal factors such as doubtful loans, general bank charges or
reserves ratios. For instance, the inclusion of additional aspects in our study – i.e. the
effect of mergers – would support us to better appreciate the determinants of bank
profitability. Furthermore, it could be successful to address in the analysis other
information on employees, management and board members (e.g. number, education,
skill level, experience), all of which are increasingly important factors in understanding
bank profitability. For example, future research could include more variables such as
taxation, regulation indicators and exchange rates, as well as indicators of the quality of
the offered services. Another possible extension could be the examination of differences
in the determinants of profitability between small and large banks or high and low
profitable banks. Because there are a limited number of top banks in Europe, medium
banks could be included in the analysis to increase the sample and longer time period
could be considered to obtain more accurate results.

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Minh To, H. and Tripe, D. (2002), “Factors influencing the performance of foreign-owned banks in 115
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About the authors

Elisa Menicucci, Research Fellow, PhD has a high school degree in “maturità scientifica”. In 2002,
she got a degree in Economics and Business Administration, from the Faculty of Economics,
Polytechnic University of Marche, 110/110 cum laude; in 2004, a degree in Economics and
Management, from the Faculty of Economics, Polytechnic University of Marche, 110/110 cum
laude; PhD in Business Administration at Polytechnic University of Marche. She was a temporary
Professor in Business Administration at Polytechnic University of Marche; a Teaching Expert in
Business Valuation at International Uninettuno University; a Research Fellow at IRDCEC. She
participated in numerous research projects of national and international significance and in
International Commission of OIC (Organismo Italiano di Contabilità). Elisa Menicucci is a
Chartered Accountant and Tax Auditor since 2008. Her research and teaching activity is in
Business Administration and Business Valuation at the Department of Management, Polytechnic
University of Marche, since 2005. She is a Teacher of Business Administration and Accountability
at Master in Governance and Auditing, Roma Tre University. Her research activity includes
business administration and accountability, accounting in banking sector, business valuation,
international accounting standards IAS/IFRS and auditing. Elisa Menicucci is the corresponding
author and can be contacted at: e.menicucci@univpm.it
Guido Paolucci is a Full Professor. In 1992, he achieved a degree in Business Administration,
from the Faculty of Economics, University of Rome, 110/110 cum laude. Guido Paolucci is a
Chartered Accountant since 1993 and a Registered Auditor since 1995. He got a PhD in Business
Administration in 1998, and is an Associate Professor in Business Administration since 1998;
Extraordinary Professor in Business Administration since 2004; and a Full Professor in Business
Administration since 2008. He is a Teacher in Risk Accounting at Roma Tre University and
Teacher in Business Valuation at Polytechnic University of Marche and at International
Uninettuno University. He was the Director of the degree course in Economics and Management
since 2006 until 2010 at the Polytechnic University of Marche. He is an ordinary member of the
AIDEA and SIDREA. His research and teaching activity include Business Administration and
Business Valuation at the Department of Management, Polytechnic University of Marche, since
1998. Guido Paolucci is a Teacher of Business Administration and Accountability at Master in
Governance and Auditing, Roma Tre University. His research activity includes business
administration and accountability, accounting in banking sector, business valuation,
international accounting standards IAS/IFRS and auditing.

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