DOI: 10.5817/FAI2017-1-1 No. 1/2017
5
The Efficiency of Selected Banking Sectors
in the European Union
Liběna Černohorská
University of Pardubice
Faculty of Economics and Administration, Institute of Economic Sciences
Studentská 95, 53210 Pardubice, Czech Republic
E-mail: libena.cernohorska@upce.cz
Abstract: When measuring bank efficiency, there is no generally accepted
concept of efficiency nor is there a uniform system of indicators for its
measurement. It is, however, possible to use the method of financial analysis
to measure bank efficiency. In this paper, the following ratios are used for
measuring bank efficiency: ROA, ROE, total assets, nonperforming loans/total
loans, quick liquid assets/total assets, quick liquid assets/short-term liabilities,
loans/deposits, and capital adequacy. The goal of this paper is to assess the
efficiency of Czech banks using cluster analysis on the basis of selected ratios
and to conduct a comparison with bank efficiency in Poland, Austria, Greece,
Portugal, France, and Slovakia. The collective ratios for the entire banking
sector will be compared for the selected countries for the years 2010–2013.
Cluster analysis demonstrates that the Czech banking sector is the most
similar to the Slovakian sector. According to a combination of selected ratios,
it is possible to designate the cluster composed of the Czech and Slovak
banking sectors as being the cluster with the highest banking sector efficiency.
It differs extensively from the cluster of Greece and Portugal.
Keywords: bank, banking sector, banks efficiency, cluster analysis, principal
component analysis
JEL codes: G14, G21, C38
Introduction
A functional banking market is an integral and vital part of every market
economy. Like other industries, the banking industry has its own unique
characteristics and specifics that adapt to internal and external influences of
the economic sector. Each state requires a reliable and stable banking system
for the economy to function properly, because problems in the banking sector
may have an impact on the entire financial sector. Each country’s banking
system has its own specifics that influence universal globalization. The system
is based on banking systems around the world. Each country accepts various
elements in different ways. Certain states retain more of their traditional
banking features, which arose during the system’s development; others
however, adopt various elements of the globalized economy (Černohorský and
Prokop, 2016).
Banks are an inseparable part of life for all economic subjects (Hedvičáková
and Svobodová, 2015). Bank stability and efficiency is an important
prerequisite for the financial markets’ operation (Serdarevic and Teplý, 2011;
Černohorský, 2014). For qualified analysis, it is necessary to work with a time
6
series of ratios and monitor the trends of their development over previous
periods of time (Tokarčíková et. al., 2014; Svobodová, 2013). The aim of this
article is to conduct a cluster analysis for the efficiency of selected banking
sectors in the following European Union countries: Slovakia, Poland, Austria,
the Czech Republic, France, Portugal, and Greece. The selected countries are
European Union countries that have financial markets and banking sectors at
different levels of development. Economic transformation has deeply affected
the structure, nature, and role of the banking sector across the entire national
economy. Currently, the banking sectors in the Czech Republic, Poland, and
Slovakia have a stable ownership structure. The majority share of the
domestic banking sector's overall registered capital consists of long-term
foreign capital with a direct share. Czech, Polish, and Slovak banks are
characterized by unprecedented stability and have shown many billions in
profit even during the period of the global financial and economic crisis
(Vodová, 2013). Cluster analysis results in creating clusters of individual
banking sectors exhibiting similar values for the selected criteria.
The banking system in the European Union is unique in that there is a
European Central Bank plus national central banks and commercial banks in
individual countries. There are two types of banking systems in individual
European Union (EU) countries, universal or two-tiered. Harmonized legal
standards governing banking apply to them, and the same conditions exist for
providing commercial banks with banking licenses. The Eurosystem is
comprised of the ECB as well as all the national central banks that have
accepted the euro. These countries have conceded jurisdiction on national
monetary policy, which they have handed over to the ECB, which implements
the given monetary policy in the given economic space. This grouping of
central banks shapes and implements the Eurozone’s monetary policy through
their representatives. The countries that are part of the EU but have not
accepted the euro implement their own monetary policy. Together with the
ECB, their central banks comprise the European central banking system. The
Czech Republic and Poland are examples of such countries.
The creators of the European Monetary Union believe that member economies
will begin to converge after the introduction of a common currency. However,
the Eurozone is quite heterogeneous and diverse as a monetary union because
of the dissimilarity of the individual member countries. A single monetary
policy for all countries will not satisfy all the member countries. Moreover, a
uniform interest rate encourages peripheral economies to overheat and slower
growth for more advanced countries. In addition to the global financial crisis,
it is also possible to record the impact of a so-called debt crisis. This is the
situation where countries begin to fall into bankruptcy in the wake of their
inability to pay the debt they have gradually accumulated. The term PIIGS has
been widely used for these countries – it is an acronym of the first letters of
the English names off the indebted countries (Portugal, Italy, Ireland, Greece,
and Spain).
The goal of this paper is to assess the efficiency of Czech banks using cluster
analysis on the basis of selected ratios and to conduct a comparison with bank
efficiency in Poland, Austria, Greece, Portugal, France, and Slovakia. There will
7
be a comparison of the stable banking sectors (the Czech, Polish, Austrian,
and French banking sectors) and the banking sectors that were affected by
financial crises (the Portuguese and Greek banking sectors).
The paper continues as follows: in Section 1, the author presents a review of
the literature on bank efficiency. In Section 2, cluster analysis is conducted. In
Section 3, an evaluation of banking sector performance is conducted for the
chosen countries using the selected ratios. In Section 4, data visualization is
presented using factor analysis and cluster analysis. In last section, the author
presents conclusions and final remarks.
1 Literature Review
Based on current research in the literature on bank efficiency, it is evident that
there is a wide range of views on evaluating bank efficiency and that
measuring efficiency is therefore very difficult. There are numerous methods
for measuring efficiency; the fundamental question is which indicators to use
to measure this efficiency.
Efficiency is often understood in the same sense as performance and
profitability (as in Atemnkeng and Nzongang, 2006 or Molyneux and Thornton,
1992). When banks are run efficiently, the operational costs are reduced,
leading to an increase in the profits realized by the banks. The authors
Richard, Devinney, et al. (2009) conducted an analysis of more than 213
articles in leading international journals that use particular indicators based on
accounting data to measure efficiency; these indicators mainly include cash
flow, financial results, revenues and their growth, and asset profitability
indicators.
Profitability was used to measure bank efficiency by Altunbas (1998); Bonin et
al. (2005); Abbasoglu, Aysan, and Günes (2007); and Berger et al. (1993), for
example. These authors evaluate bank profitability using return on assets
(ROA) or return on equity (ROE). Bonin et al. (2005) also monitored the
amount of total deposits, total assets, loans, and liquid assets. The size of a
bank is judged by its total assets (Dabla-Norris & Floerkemeier 2007; Fuentes
& Vergara, 2003). Berger et al. (1993) also use the indicators of total assets,
loans, and total loans/total deposits to assess bank efficiency in addition to
ROE and ROA. Groenveled and de Vries (2009) use the capital ratio for
measuring bank efficiency. Bank efficiency is frequently evaluated using bank
ownership structure (Fuentes & Vergara, 2003; Bonin et al., 2005; Mester,
1993). Some authors take into account the cost of labor when measuring bank
efficiency (Stavárek & Řepková 2013; Tulekns, 2006; Berger et al., 1993) and
the cost of capital (Berger et al., 1993). Another factor influencing bank
efficiency is interest margin (Stavárek & Řepková, 2013; or Dabla-Norris &
Floerkemeier, 2007). These last authors also use the indicator of rapidly
nonperforming loans/total loans, liquid assets/total assets, and quick liquid
assets/short-term liabilities.
2 Methodology and Data
Evaluating bank efficiency is a relatively complicated analytical problem. There
is no generally accepted concept of efficiency nor is there a uniform system of
indicators for measuring bank efficiency. It is, however, possible to use the
method of financial analysis to measure bank efficiency. The goal of financial
8
analysis is to evaluate the financial ratios for efficiency and competitiveness
that were achieved in prior periods of time. In this paper, the following ratios
are used for measuring bank efficiency: ROA, ROE, total assets, nonperforming
loans/total loans, quick liquid assets/total assets, quick liquid assets/shortterm
liabilities, loans/deposits, and capital adequacy. The collective ratios for
the entire banking sector are compared for the selected countries for the years
2010–2014. The necessary data were obtained from the Bankscope database
and were chosen with regard to the specifics of the selected banking sectors,
international accounting standards, and banks’ information requirements.
A comparison was made of the average values of the selected indicators in
individual banking sectors. In further scientific study, it would be possible to
use a longer time series for the selected indicators of these banking sectors for
a more detailed analysis. It would be possible to monitor factors that affect the
efficiency of banking sectors (such as the period before the financial crisis and
the impact of the financial crisis on selected criteria) and to subsequently track
the clusters created, etc.
Peer analysis makes it possible to conduct a comparison of the financial
variables using tables and graphs. For this peer analysis, the traditional
methods of multiple statistical analysis – especially cluster analysis and
principal components analysis – were used. The method of cluster analysis
was used to compare the efficiency of the Czech banking sector with the
banking sectors of the other selected European countries. Cluster analysis
divides the selected countries into clusters according to similarity. Using the
method of principal component analysis, it was determined that there are two
main components that jointly explain nearly three-quarters of the variability.
2.1 Cluster Analysis
The primary method for determining the similarity of quantitative variables is
factor analysis. This is based on principal component analysis, which is used to
reduce the size of the task (instead of determining many variables for further
calculations, only a small number of principal components are determined –
these can be expressed as linear combinations of the original variables).
Principal component analysis is computed by the singular value decomposition
of X. (Friedman et al., 2013). The general formula (1) is:
T
UDWX  , (1)
where D is a diagonal matrix consisting of the set of all eigenvalues of C along
its principal diagonal and 0 for all other elements; U is an n-by-n matrix, the
columns of which are orthogonal unit vectors of the length n, called the left
singular vectors of X; W is a p-by-p matrix whose columns are orthogonal unit
vectors of the length p, called the right singular vectors of X.
In principal component analysis (PCA), the data are summarized as a linear
combination of an orthonormal set of vectors. The first principal component
accounts for as much of the variability in the data as possible, and each
successive component represents as much of the remaining variability as
possible (Zou et al., 2006). Components accounting for maximal variance are
9
retained while other components accounting for a trivial amount of variance
are not retained. These techniques are typically used to analyze groups of
correlated variables representing one or more common domains. The result of
PCA enters into the factor analysis. Its aim is to assess the structure and
relationships of the selected indicators to see if they can be divided into groups
in which the indicators from the same groups are more closely related than
correlated variables from different groups.
Cluster analysis is a collective term covering a wide variety of techniques for
delineating natural groups or clusters in data sets. This paper uses hierarchical
agglomerative clustering.
Hierarchical agglomerative clustering starts at the bottom and recursively
merges a selected pair of clusters into single clusters at each level. This
produces a grouping at the next higher level with one less cluster. The
algorithm of hierarchical agglomerative clustering begins with every
observation representing a singleton cluster. At each of the N-1 steps, the
closest two (least dissimilar) clusters are merged into a single cluster,
producing one less cluster at the next higher level (Friedman et al., 2001).
In the first phase, clustering calculates the relative distances of objects and
transcribes them into a matrix. This leads to a square symmetric matrix
 ),( SRdD
, which has zeros on the main diagonal. The Euclidean method
is the metric distance matrix that is normally used for calculation. It is based
on the geometric model (Klímek, 2005). The objects characterized by p
characters are assigned to the points in p-dimensional Euclidean space Ep,
i.e., two dots ( , ). This is defined by Euclidean distance as described by the
general formula (2):


p
i
siri xxSRd
1
2
)(),( (2)
On the basis of the distance matrix, the second phase calculations (also
clustering) can be initiated. The farthest neighbor clustering method was used
(also called complete-linkage clustering). Complete-linkage agglomerative
clustering takes the intergroup dissimilarity to be that of the farthest (most
dissimilar) pair according to formula (3):
 ),(max),( ji
SO
RO
OOdSRd


 for SR  , (3)
where , represent two such groups; , represents the dissimilarity
between R and S as computed from the set of pairwise observation
dissimilarities , , where one member of the pair, , is in R, and the
other, , is in S.
10
The clustering methods were selected based on the degree of credibility and
the cophenetic correlation coefficient, "CC". The higher the value of the
correlation coefficient cophenetic (a value close to 1), the greater the
credibility and the better the choice for a suitable model cluster. (Friedman et
al., 2001; Romesburg, 2004).
The result is a diagram called a dendrogram, which provides a complete
description of the hierarchical agglomerative clustering that is easy to
interpret.
3 Comparison of Banking Sector Performance Using the
Selected Ratios
According to Polouček (2006), evaluating bank performance is important for
various interest groups. For shareholders, it influences not only the value of
their assets via a specified price for bank shares but also the dividends paid
out. Manager evaluations are often derived from economic results. Economic
results are also an important issue that is monitored by the central bank as a
regulatory and supervisory body in order to preserve the stability of the
banking sector. Not least, a bank’s performance is also important for clients,
whom it can assist when selecting the most suitable institution for their
intended financial activities. It is evident that the impact of bank performance
is far-reaching and discernible not only for individuals and the banking sector
but also consequently for the business sector and the national economy as a
whole. This is also why bank management must be constantly and very
carefully monitored and evaluated. Next, performance analysis primarily
serves as a foundation for managerial decision making and, at the same time,
it is frequently used by banking’s regulatory and supervisory body for adopting
adequate measures that attempt to preserve the stability of the banking
sector. There are numerous ratios that express a great deal about bank
performance itself; these are calculated and evaluated for the internal needs
of banks and the needs of bank regulation and supervision. In this section,
evaluation of banking sector performance has been conducted for the chosen
countries using the selected ratios for 2010-2013.
As to the percentage of nonperforming loans, it is clear from Fig. 1 that the
greatest percentage of loans to assets is held by Greece, whose banking sector
performance is strongly disrupted by this high percentage of nonperforming
loans. Portugal follows after Greece, although there is a distinct lag between
them. Austria’s results for this ratio are the best; during the period being
examined, they show a value of around 3%. The other countries fall in a
similar range, and their share of nonperforming loans is stable, around 5%.
The percentage of easily liquid assets to total assets is depicted in Fig. 2. The
best results of the selected countries for this ratio were shown by France,
Slovakia, and the Czech Republic, with values fluctuating around 35%. Even
Greece showed surprisingly good results. The worst results for this ratio were
recorded by Portugal. In 2013, Portugal possessed too many easily liquidated
instruments in its asset structure at the expense of less liquid ones, which
tend to be more profitable.
11
Figure 1 Nonperforming loans in the selected countries for 2010-2013 (in %)
Source: Bankscope
Figure 2 Liquid assets to total assets in the selected countries for 2010-2013
(in %)
Source: Bankscope
For capital adequacy, all the countries examined exceed the set minimum limit
of 8%, which is clear from Fig. 3. The highest values were shown for this ratio
over the long term by Slovakia, the Czech Republic, and Austria. The lowest
values were shown by Portugal and Greece, which dipped just beyond the
minimum set limit in 2012. In the last year examined, however, both
countries also achieved the 8% set limit for capital adequacy.
0
5
10
15
20
25
30
35
40
45
2010 2011 2012 2013
Slovensko
Francie
Rakousko
Polsko
Česko
Portugalsko
Řecko
12
Figure 3 Capital adequacy in the selected countries for 2010-2013
Source: Bankscope
Although Greece showed good return on assets in 2013, it is clear from Fig. 4
that it had significant problems with this ratio in the immediately preceding
years, because its value ranged deep into the negative numbers. Negative
results were also recorded for the other problem country, Portugal. Austria
also had a very low return on assets, and not even France reached the
optimum ROA value of 1%. It can be said that the remaining countries
showed standard values for their return on assets.
Figure 4 Return on assets in the selected countries for 2010-2013 (in %)
Source: Bankscope
The highest values for return on equity were achieved by the Czech Republic
and Poland. On the other hand, it is clear at first glance from Fig. 5 that
Greece showed distinctly negative values exceeding 100%. Portugal also
13
ranged in the negative values for return on equity. Again, Austria also
recorded low values.
Figure 5 Return on equity in the selected countries for 2010-2013 (in %)
Source: Bankscope
On the basis of the above, it is clear that the Czech and Slovak banking
sectors have achieved excellent performance in comparison with other
European countries. On the other hand, the banking sectors of Greece and
Portugal have been faring the worst. Cluster analysis is conducted next to
determine similarities in banking sector performance.
4 Results and Discussion
The basic prerequisite for performing cluster analysis is to prove that the data
are not affected by multicollinearity. Multicollinearity could very significantly
affect the final quality of the clustering and the classification of the individual
elements in the resulting clusters. It is necessary to establish the correlation
matrix. Next, caused to that show must be eliminated afor is kept, itis
necessary to provide a justification for its further presence in the cluster
analysis. For more information, see Friedman et al. (2001).
Based on the results of the correlation matrix, the ratio of nonperforming
loans/total loans was removed from the analysis. This indicator showed very
high levels of correlation with ROE as well as with the proportion of quick liquid
assets/short-term liabilities, which is highly correlated with ROA.
To obtain information on the impact of these indicators, the principal
component method was applied, followed by factor analysis. Both methods are
used for data visualization and obtaining input information.
4.1 Data Visualization Using Factor Analysis
The principal component method determined that there are two main
components which together explain nearly three-quarters of the variability
(Table 1).
14
The first principal component depletes approximately 47.96% of the total
variability in the data, the second approximately 25.81%. The results of factor
analysis are shown in Table 1 and Fig. 1. Table 1 shows which criteria are
important for further exploration in terms of classification into certain objects,
respectively clusters (boldface type).
Table 1 The result of the factor analysis
First Principal
Component 1
Second Principal
Component 2
Total assets 0.39 0.80
Liquid assets/Total assets 0.77 0.26
Loans/Deposits 0.60 0.04
Capital adequacy 0.62 0.64
ROA 0.91 -0.12
ROE -0.19 0.97
Source: Author’s own work
Graphic representation of the data visualization from the factor analysis
assumes the possible creation of approximately four relevant clusters (Figure
6).
Figure 6 Factor analysis – the number of clusters
Source: Author’s own work
4.2 The Results of Cluster Analysis
Cluster analysis was used to compare the selected banking sectors. This
analysis divides the selected countries into clusters according to their
similarities. To perform cluster analysis, we have assumed agglomerative
hierarchical clustering. For more information, see Romesburg (2004). Next
followed the selection of clustering procedures, namely a clustering method
15
(the farthest neighbor method, i.e., complete-linkage clustering, using
statistical software) and the distance calculation method (Euclidean distance).
The clustering method was selected based on the degree of credibility, namely
the correlation coefficient. The degree of credibility, or degree of closeness,
has been verified by the correlation coefficient. The higher the value (i.e.,
approaching 1), the greater the credibility and the better the choice for a
suitable cluster model. The correlation coefficient was chosen on the basis of
achieving a value approaching 1 via the farthest neighbor method. A
prerequisite to performing the cluster analysis is that the data cannot be
affected by multicollinearity.
Determining the relevant number of clusters began by using the clustering
schedule, which determined a degree of distance of approximately 60%. The
relevant number of clusters was determined from below this level (Figure 7).
The division of the countries into four clusters with the values of the individual
indicators can be seen in Table 2.
Figure 7 Dendrogram
Source: Author’s own work
Table 2 Average values of the selected indicators (in %)
Cluster Country
Change
in total
assets
Liquid
ass./Total
assets
Loans/
Deposits
Capit.
adeq.
ROA ROE
1st
Slovakia 1.4 36.2 110 17.93 1.3 9.1
Czech R. 8.9 33.8 132 17.08 1.27 16.2
2nd
France 9.6 39.1 81 15.03 0.5 8.4
Austria -5.8 24.5 87 15.83 0.1 5.5
Poland 4.0 21.4 90 14.91 1.1 14
3rd Portugal -7.7 16.9 117 13.20 -0.7 -11
4th Greece -10.8 29.9 89 13.50 1.4 -169
Average
total
-0.06 26.44 88.14 15.35 0.71 -18.1
Source: Author’s own work acc. to Bankscope
16
Conslusions
Four clusters were created using cluster analysis. Considering the efficiency of
the banking sectors using the selected indicators with the first principal
component, which explains almost 48% of the variability of the investigated
group, the greatest correlations were the ratio of liquid assets to total assets
and ROA. In the ratio of liquid assets to total assets, the best values were
achieved by France, the Czech Republic, and Slovakia. The Czech Republic and
Slovakia exceeded the average ROA limit without fail, but France did not. The
average ROA value was also exceeded by Poland; however, it does not record
results comparable to these countries in in its ratio of liquid assets/total
assets. The first cluster is formed by the Czech Republic and Slovakia. Because
France and Poland lag behind in at least one indicator, they are grouped into
another cluster together with Austria. The third cluster consists of only one
country, Portugal. Portugal achieved the worst results in both of the indicators
listed above. The Greek banking sector achieved better results than Portugal,
but because it achieved very low levels in the indicator corresponding to the
second part of the component, it forms a separate cluster. It achieved high
negative values especially in terms of ROE, which prevents it from being
compared to other countries; therefore, Greece forms the fourth separate
cluster.
Depending on the combination of the selected indicators, the cluster composed
of the Czech Republic and Slovakia can be qualified as the cluster with the
highest efficiency in the banking sector. This first cluster achieves significantly
better values than the other banking sectors for the indicators examined here.
These two banking sectors were not impacted by the global financial crisis
(compared to Greece and Portugal and, to some extent, France). The average
values of the indicators for the first cluster are significantly above the average
for all markers in the selected banking sectors.
Acknowledgments
The paper has been created with the financial support of the Czech Science
Foundation (project GACR No. 17-02509S, “Emerging financial risks during a
global low interest rate environment”).
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