ACTA VŠFS, 2/2016, vol. 10142
­Determinants of the Profitability
in the Czech Banking Industry
Determinanty rentability
v českém bankovním sektoru
IVETA PALEČKOVÁ
Abstract
The aim of this paper is to estimate the banking profitability determinants of the Czech
commercial banks during the period 2004-2014. For estimation of banking profitability
we used three common measures, namely the Return on Assets, Return on Equity and Net
Interest Margin. We estimated twelve determinants of banking performance. The effect of
the determinants of banking profitability is estimated using panel data analysis. The data
set is consists of seventeen commercial banks in the Czech Republic. The results show that
the profitability was positively influenced by the bank’s size, capitalization, credit risk, level
of concentration, ownership structure and bank’s market share. Number of branches of
the bank had the negative impact on ROA and ROE. on the other hand, the variables Gross
Domestic Product, interest rate, liquidity risk, riskiness of bank’s portfolio and affiliation
with financial conglomerate have not got the significant influence on profitability of the
Czech commercial banks.
Keywords
performance, return on assets, return on equity, panel data analysis, bank-specific factors,
market-specific factors, banking sector, Czech Republic
Abstrakt
Cílem příspěvku je odhadnout determinanty rentability českých komerčních bank v období
2004-2014. Pro odhad rentability bank jsou použity tři ukazatele rentability, kterými
jsou návratnost aktiv, návratnost kapitálu a čistá úroková marže. V příspěvku je odhadován
vliv dvanácti faktorů ovlivňujících rentabilitu rozděleny do tří základních skupin. K odhadu
determinantů rentability je využita analýza panelových dat. Datový soubor se skládá ze
17 českých komerčních bank. Výsledky odhadu ukazují, efektivnost je pozitivně ovlivněna
velikostí banky, její kapitalizací, úvěrovým rizikem, stupněm bankovní koncentrace, vlastnickou
strukturou banky a podílem banky na trhu. Naopak počet poboček banky má
negativní vliv na ROA a ROE. U faktorů hrubý domácí produkt, riziko likvidity, rizikovost
bankovního portfolia a příslušnost k finanční skupině nebyl zjištěn statisticky významný
vliv na rentabilitu českých bank.
Klíčová slova
rentabilita, ROA, ROE, analýza panelových dat, bankovní specifické faktory, tržní specifické
faktory, bankovní sektor, Česká republika
JEL Codes
G21
ACTA VŠFS, 2/2016, vol. 10 B143
Introduction
The aim of this paper is to estimate the banking profitability determinants of the Czech
commercial banks during the period 2004-2014. First we estimate the performance of the
Czech commercial banks. Performance refers to how adequately a financial firm meets
the needs of its stockholders, employees, depositors and other creditors, and borrowing
customers (Rose and Hudgins, 2013). We calculate three profitability indicators, especially
Return on Assets, Return on Equity and Net Interest Margin. For estimation of
profitability determinants we use panel data analysis. The data sets consist of 17 Czech
commercial banks. We simultaneously estimated the influence of twelve bank-specific,
industry-specific and macroeconomic factors.
The structure of the paper is following. First section presents the empirical literature regarding
the profitability determinants in banking sector. Second section describes using
methodology and data. The profitability ratios are presented and brief information about
panel data analysis is described. Next part of this section presented selection of variables.
Third chapter shows the empirical analysis and results and discussion of findings. Last
part of the paper concludes results.
1	 Literature Review
Several empirical literatures estimated the determinants of banking profitability in selected
countries. The previous studies divided the factors influencing the profitability of banks
into internal and external factors or bank-specific, industry-specific and macroeconomic
determinants. We can mention e.g. Kosmidou et al. (2008) who considered five bankspecific
measures and four measures representing the influence of market structure and
macroeconomic conditions. the measures used as internal determinants of performance
are: cost to income ratio as an indicator of efficiency in expenses management; ratio of
liquid assets to customer and short term funding to represent liquidity; ratio of loan loss
reserves to gross loans as an indicator of banks’asset quality; ratio of equity to total assets
representing capital strength; and the total assets of a bank representing its size. As external
determinants they considered two which represent the influence of macroeconomic
conditions (the rate of GDP growth and inflation) and the other two of financial market
structure (concentration in the banking industry and stock market capitalization).
Naceur and Goaied (2008) used five banks’ characteristics indicators as internal determinants
of performance: the ratio of overhead to total assets, the ratio of equity capital to
total assets, the ratio of banks’loans to total assets, the ratio of noninterest bearing assets
to total assets and the log of bank assets. And also they used two macro-economic variables:
inflation and GDP per capita growth. Kosmidou et al. (2008) summarized that the
main conclusion emerging from previous empirical studies is that internal factors explain
a large proportion of banks profitability; nevertheless external factors have also had an
impact on their performance. Some recent studies also focus on the impact of regulations
on banks performance and profitability (e.g. Barth et al., 2003, 2004), and report only weak
evidence to support that bank supervisory structure and regulations affect bank profits.
ACTA VŠFS, 2/2016, vol. 10144
Černohorská (2015) used the two most common profitability ratios, i.e., return on equity
and return on assets as endogenous variables in regression analysis for estimation of factors
influencing bank profitability of the Czech banks and their international parent companies.
As exogenous variables she selected bank size, the bank's capital adequacy, the
ratio of high-risk loans to assets, interest margin, the cost/income ratio, market concentration,
inflation, gross domestic product per capita, taxation rate, and the central bank's
interest rate. The studied factors' influence on bank profitability was demonstrated only
for ČSOB and Société Générale using regression analysis. For ČSOB, it was demonstrated
that inflation level and the amount of the central bank's interest rate influenced the return
on assets ratio and that capital adequacy and market concentration influenced the return
on equity ratio for Société Générale.
Fišerová et al. (2015) analysed the role of the economic fundamentals on the foreignowned
banks. They concluded that the economic fundamentals affect the performance
of foreign-owned banks and cannot reject that economic fundamentals of the host country
influence the performance of a foreign-owned bank operating in that country. Their
analysis hinted that in explaining the determinants of the banks’performance the macroeconomic
indicators were not sufficient. They also found evidence of the fact that more
capitalized and operationally efficient banks outperform their peers. Furthermore, a low
non-performing loans (cost of risk) ratio was another key factor of foreign-owned banks’
performance.
Kosmidou et al. (2008) investigated the impact determinants on UK owned commercial
banks’ profits, measured by return on average assets (ROAA) and net interest margins
(NIM). They found that capital strength, represented by the equity to assets ratio, is the
main determinant of UK banks’ profits providing support to the argument that well capitalized
banks face lower costs of external financing, which reduces their costs and enhances
profits. They found that the efficiency was negative and significant. That suggested that
efficiency in expenses management is a robust determinant of UK bank profits. Kosmidou
(2008) and Pasiouras et al. (2006) also confirm this inverse relationship for Malaysia, Greece
and Australia.
The findings of Sufian and Habibullah (2009) suggested that bank specific characteristics,
in particular loans intensity, credit risk, and cost have positive and significant impacts on
bank performance in Bangladesh, while non-interest income exhibits negative relationship
with bank profitability in this country. The results suggested that size has negative
impact on return on average equity, while the opposite is true for return on average assets
and net interest margin. As for the impact of macroeconomic indicators, they found that
the variables have no significant impact on bank profitability, except for inflation, which
had negative relationship with banks profitability in Bangladesh.
Shehzad et al. (2013) used a dynamic panel model for more than 15 000 banks from 148
countries from 1988 to 2010 and examined the relationship between size, growth and
profitability of banks. They did not reject the hypotheses that the variability of bank profitability
and the level and variability of bank growth are independent of bank size. They also
found that bank growth and bank profitability were independent of each other. DemirgüçKunt
and Huizinga (1999) used bank-level data for 80 countries in the years 1988-1995 and
ACTA VŠFS, 2/2016, vol. 10 B145
confirmed some findings in earlier research, for instance a positive relationship between
capitalization and profitability, and a negative relationship between reserves and profitability.
The findings of the paper stated that foreign ownership is associated with higher
interest margins and bank profitability, especially in developing countries. They also found
that a larger ratio of bank assets to gross domestic product and a lower market concentration
ratio lead to lower margins and profits, controlling for differences in bank activity,
leverage, and the macroeconomic environment.
Ben Naceur and Goaied (2008) estimated the impact of bank characteristics, financial
structure, and macroeconomic conditions on performance of Tunisian banks’. They suggested
that banks that hold a relatively high amount of capital and higher overhead expenses
tent to exhibit higher net-interest margin and profitability levels, while size was
negatively related to bank profitability. They also suggested that private banks were relatively
more profitable than their state owned counterparts. Macroeconomic conditions
had not impact on Tunisian banks’ profitability.
Ben Naceur and Omran (2008) examined the impact of bank regulations, concentration, financial
and institutional development on Middle East and North Africa countries commercial
banks margin and profitability. They found that bank specific characteristics, namely
bank capitalization and credit risk, had positive impact on banks’NIM, cost efficiency, and
profitability. on the other hand, macroeconomic and financial development indicators had
not impact on bank performance. Sufian and Chong (2008) estimated the determinants
of banking profitability in Philippine during the period 1990-2005. They found that the
bank-specific determinant had a statistically significantly impact on bank profitability.
Size, credit risk, and expense preference behaviour were negatively related to banks' profitability,
while non-interest income and capitalisation had a positive impact. Their results
suggested that inflation has a negative impact on bank profitability of the Philippines
banks. Pasiouras and Kosmidou (2007) investigated the performance of domestic and foreign
commercial banks in 15 EU countries during the period 1995-2001. They found that
profitability of both domestic and foreign banks was affected by bank specific characteristics,
financial market structure and macroeconomic conditions. Their results showed that
all variables had significant relationship with bank profitability, although their impacts
and relation is not always uniform for domestic and foreign banks.
Smirlock (1985) examined the link between profitability and a bank's economic cycle and
also the relationship between size and bank profitability, which relates to a bank's capital
adequacy. He found a positive and significant relationship between size and bank profitability,
large banks have a tendency to grow foreign capital and, therefore, seem to be
more profitable. Kosmidou et al. (2008) measured bank’s size by its total assets and argued
that large bank size might result in scale economies with reduced costs, or scope
economies that result in loan and product diversification, thus providing access to markets
that a small bank cannot entry. Short (1979) stated that size is closely related to the
capital adequacy of a bank since relatively large banks tend to raise less expensive capital
and appear more profitable. Also Stavárek and Polouček (2004) confirmed the positive
relationship between bank size and profitability. However, other study suggested that
little cost saving can be achieved by increasing the size of a banking firm (Berger et al.,
1987). Eichengreen and Gibson (2001) suggest that the effect of a growing bank's size on
ACTA VŠFS, 2/2016, vol. 10146
its profitability may be positive up to a certain limit. Beyond this point, the impact of its
size could be negative due to bureaucratic and other factors. Hence, the size-profitability
relationship may be expected to be non-linear.
Kosmidou et al. (2008) found that coefficient of the cost to income ratio was negative and
significant, suggesting that efficiency in expenses management is a robust determinant of
UK bank profits. Guru et al. (1999), Kosmidou (2008) and Pasiouras et al. (2006) also confirm
this inverse relationship for Malaysia, Greece and Australia respectively.
The results concerning liquidity are mixed. Kosmidou et al. (2008) confirmed that the liquidity
ratio had a positive effect on ROAA. on the contrary, Molyneux and Thorton (1992)
and Guru et al. (1999) reveal a negative effect of liquidity on bank profits. Kosmidou (2008)
and Pasiouras et al. (2006) also confirm this negative effect of liquidity ratio on net interest
margin. In contrast, Bourke (1989) estimated an opposite result, while the effect of credit
risk on profitability appears clearly negative (Miller and Noulas, 1997). Athanasoglou et
al. (2008) explained this result by taking into account the fact that the more financial institutions
are exposed to high-risk loans, the higher is the accumulation of unpaid loans,
implying that these loan losses have produced lower returns to many commercial banks.
Kosmidou et al. (2008) found that the impact of loan loss reserves was positive and significant
on NIM, suggesting that higher risks result in higher margins for UK banks. Berger
(1995) concluded that the relationship between bank concentration and performance
in the US depend critically on what other factors are held constant. Bourke (1989) and
Molyneux and Thornton (1992) found that ownership status is irrelevant for explaining
profitability.
Hoffmann (2011) found a negative link between the capital ratio and the profitability in
US banking sector. Other results point to a non-monotonic relationship between the capital
ratio and profitability, supporting the efficiency-risk and franchise-value hypotheses.
Also Kosmidou (2008) found that profitability is positively associated with well capitalized
banks and lower cost to income ratios.
The last group of profitability determinants deals with macroeconomic control variables.
Bikker and Hu (2002) suggested that such correlation exists, although the variables used
were not direct measures of the business cycle. Sufian (2011) and Davydenko (2011) examined
the negative impact of GDP on ROA. But Kosmidou (2008) argued that the growth
of gross domestic product (GDP) is positively related to bank profitability, while inflation
rate is negatively related to bank profitability. Kanwal and Nadeem (2013) found that GDP
had an insignificant positive effect on ROA, but an insignificant negative impact on ROE
and equity multiplier.
2	Methodology
In this section we describe the banking profitability, namely Return on Assets, Return
on Equity and Net Interest Margin. Next, we describe brief information about panel data
analysis and panel unit root test which is used for empirical analysis of profitability deter-
ACTA VŠFS, 2/2016, vol. 10 B147
minants in the Czech banking sector. Last part of this section presents data and selection
of variables.
2.1	 Banking Profitability
Most of empirical studies measure profitability by two measures which are Return on Assets
and Return on Equity. Several studies (e.g. Fišerová et al., 2015; Sufian and Habibullah,
2009; or Alkassim, 2005, among others) add other profitability variable, namely Net
Interest Income.
As Palečková (2016) described the profitability is the indicator of management‘s success
or failure in its strategic and leadership activities. Return on Assets (ROA) measures the
bank‘s ability to efficiently employ its assets. As such, it is considered by many analysts
to be one of the best single ratios for evaluating the performance of management. ROA
equals net income divided by total assets and thus measures net income per currency unit
of average assets owned during the period. Table 1 presents the relationship between the
value of ROA and Return on Assets.
	
In this section we describe the banking profitability, namely Return on Assets, Return on Equ
and Net Interest Margin. Next, we describe brief information about panel data analy
and panel unit root test which is used for empirical analysis of profitability determina
in the Czech banking sector. Last part of this section presents data and selection of variable
2.1 Banking Profitability
Most of empirical studies measure profitability by two measures which are Return on Ass
and Return on Equity. Several studies (e.g. Fišerová et al., 2015; Sufian and Habibullah, 20
or Alkassim, 2005, among others) add other profitability variable, namely Net Interest Inco
As Palečková (2016) described the profitability is the indicator of management‘s success
failure in its strategic and leadership activities. Return on Assets (ROA) measures the ban
ability to efficiently employ its assets. As such, it is considered by many analysts to be
of the best single ratios for evaluating the performance of management. ROAequals net inco
divided by total assets and thus measures net income per currency unit of average assets ow
during the period. Table 1 presents the relationship between the value of ROA and Ret
on Assets.
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 =
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑎𝑎𝑎𝑎
(1
Return on Equity (ROE) measures the percentage return on each currency unit of sharehold
equity. It is the aggregate return to shareholders before dividends. The higher the ret
the better, as banks can add more to retained earnings and pay more in cash dividends w
profits are higher (Rose and Hudgins, 2013).
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 =
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛
𝑛𝑛𝑛𝑛𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑒𝑒𝑒𝑒
(2)
Each of the ratios looks as a slightly aspect of profitability. Return on Assets indicates h
capable management has been in converting assets into net earnings. Return on Equity
a measure of the rate of return flowing to shareholders. It approximates the net benefit
the stockholders have received from investing their capital in the financial firm (R
and Hudgins, 2013).
Net interest income (NII) is the difference between interest income and interest expense
represents the amount by which the interest received from the loan portfolio exce
the interest paid on deposits or borrowed funds. in interest rate term, it represents the inte
spread differential. The net interest margin (NIM) provides a measure of asset productiv
NIM should be higher that 3%.
	
(1)
Return on Equity (ROE) measures the percentage return on each currency unit of shareholders’
equity. It is the aggregate return to shareholders before dividends. The higher
the return the better, as banks can add more to retained earnings and pay more in cash
dividends when profits are higher (Rose and Hudgins, 2013).
	
In this section we describe the banking profitability, namely Return on Assets, Return on Equ
and Net Interest Margin. Next, we describe brief information about panel data analy
and panel unit root test which is used for empirical analysis of profitability determina
in the Czech banking sector. Last part of this section presents data and selection of variable
2.1 Banking Profitability
Most of empirical studies measure profitability by two measures which are Return on Ass
and Return on Equity. Several studies (e.g. Fišerová et al., 2015; Sufian and Habibullah, 20
or Alkassim, 2005, among others) add other profitability variable, namely Net Interest Inco
As Palečková (2016) described the profitability is the indicator of management‘s success
failure in its strategic and leadership activities. Return on Assets (ROA) measures the ban
ability to efficiently employ its assets. As such, it is considered by many analysts to be
of the best single ratios for evaluating the performance of management. ROAequals net inco
divided by total assets and thus measures net income per currency unit of average assets ow
during the period. Table 1 presents the relationship between the value of ROA and Ret
on Assets.
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 =
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑎𝑎𝑎𝑎
(1
Return on Equity (ROE) measures the percentage return on each currency unit of sharehold
equity. It is the aggregate return to shareholders before dividends. The higher the ret
the better, as banks can add more to retained earnings and pay more in cash dividends w
profits are higher (Rose and Hudgins, 2013).
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 =
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛
𝑛𝑛𝑛𝑛𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑒𝑒𝑒𝑒
(2)
Each of the ratios looks as a slightly aspect of profitability. Return on Assets indicates h
capable management has been in converting assets into net earnings. Return on Equity
a measure of the rate of return flowing to shareholders. It approximates the net benefit
the stockholders have received from investing their capital in the financial firm (R
and Hudgins, 2013).
Net interest income (NII) is the difference between interest income and interest expense
represents the amount by which the interest received from the loan portfolio exce
the interest paid on deposits or borrowed funds. in interest rate term, it represents the inte
spread differential. The net interest margin (NIM) provides a measure of asset productiv
NIM should be higher that 3%.
	
(2)
Each of the ratios looks as a slightly aspect of profitability. Return on Assets indicates how
capable management has been in converting assets into net earnings. Return on Equity
is a measure of the rate of return flowing to shareholders. It approximates the net benefit
that the stockholders have received from investing their capital in the financial firm (Rose
and Hudgins, 2013).
Net interest income (NII) is the difference between interest income and interest expense. It
represents the amount by which the interest received from the loan portfolio exceeds the
interest paid on deposits or borrowed funds. in interest rate term, it represents the interest
spread differential. The net interest margin (NIM) provides a measure of asset productivity.
NIM should be higher that 3 %.
	
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 =
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑎𝑎𝑎𝑎 (3)
Agood NIM is indicative of good yields on loans, lower cost rates, and effective use of earnin
assets and sensible mix of interest-bearing liabilities. Weakness of this indicator is the facts t
as banks move toward more fee-generating activities, the NII margin will decline in importan
	
(3)
ACTA VŠFS, 2/2016, vol. 10148
A good NIM is indicative of good yields on loans, lower cost rates, and effective use of
earnings assets and sensible mix of interest-bearing liabilities. Weakness of this indicator
is the facts that as banks move toward more fee-generating activities, the NII margin will
decline in importance as a measure of asset profitability (Grier, 2012).
2.2	 Panel Data Analysis
A panel data set is formulated from a sample that contains N cross-sectional units (in this
paper commercial banks) that are observed at different T time periods (Asteriou and Hall,
2011). A simple linear model with one explanatory variable, as given by:
	
Agood NIM is indicative of good yields on loans, lower cost rates, and effective use of earnings
assets and sensible mix of interest-bearing liabilities. Weakness of this indicator is the facts that
as banks move toward more fee-generating activities, the NII margin will decline in importance
as a measure of asset profitability (Grier, 2012).
2.2 Panel Data Analysis
A panel data set is formulated form a sample that contains N cross-sectional units (in this paper
commercial banks) that are observed at different T time periods (Asteriou and Hall, 2011).
A simple linear model with one explanatory variable, as given by:
𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼𝛼𝛼𝑖𝑖𝑖𝑖 + 𝛽𝛽𝛽𝛽𝑋𝑋𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖, (4)
where the variables Y and X have both i and t subscripts for i = 1, 2, …, N sections and t = 1,
2, …, T time periods. The coefficient 𝛼𝛼𝛼𝛼𝑖𝑖𝑖𝑖 can differ for each bank in the sample.
In the fixed effects method the constant is treated as group (section)-specific. This means that
the model allows for different constants for each group (section). The fixed effects estimator is
also known as the least squares dummy variable estimator because, to allow for different
constant for each group, it includes a dummy variable for each group (Asteriou and Hall, 2011).
One of the assumptions of panel data analysis is a stationarity of data. For testing stationary
of data we use unit root test. One of the first panel unit-root tests was that developed by Levin
and Lin (1992) and their work was finally published in Levin et al. (2002). More detailed
information about panel unit root test is described in Asteriou and Hall (2011).
2.3 Data and Selection of Variables
The data set used in this paper was obtained from the annual reports of the Czech commercial
banks during the period 2004-2014. All the data is reported on an unconsolidated basis. We
analyse only commercial banks that are operating as independent legal entities. We use
unbalanced panel data from 17 Czech commercial banks (with regard to mergers
and acquisitions of banks). Due to some missing observations we have an unbalanced panel
of 137 bank-year observations.
We measure the profitability of banks using three common measures of profitability: Return
on Assets, Return on Equity and Net Interest Margin. Following Fišerová et al. (2015) or
Alkassim (2005) we used Return on Assets, Return on Equity and Net Interest Margin
individually as the dependent variable. Thus, we constructed three models with these three
dependent variables (ROA, ROE and NIM). As independent variables we selected several
factors which can influence the profitability of the Czech banking sector. We distinguish
between bank-specific, industry-specific and macroeconomic factors. As bank-specific factors
we included bank size, market share, level of capitalization, efficiency, credit risk and liquidity
risk, riskiness of the bank’s overall portfolio, number of branches of individual bank, bank
ownership structure, interest rate and affiliation with financial conglomerate. An industryspecific
factor included market concentration and as a macroeconomic factor we chose Gross
Domestic Product (GDP).
	
(4)
where the variables Y and X have both i and t subscripts for i = 1, 2, …, N sections and
t = 1, 2, …, T time periods. The coefficient αi
can differ for each bank in the sample.
In the fixed effects method the constant is treated as group (section)-specific. This means
that the model allows for different constants for each group (section). The fixed effects
estimator is also known as the least squares dummy variable estimator because, to allow
for different constant for each group, it includes a dummy variable for each group (Asteriou
and Hall, 2011).
2.3	 Data and Selection of Variables
The data set used in this paper was obtained from the annual reports of the Czech commercial
banks during the period 2004-2014. All the data is reported on an unconsolidated
basis. We analyse only commercial banks that are operating as independent legal entities.
We use unbalanced panel data from 17 Czech commercial banks (with regard to mergers
and acquisitions of banks). Due to some missing observations we have an unbalanced
panel of 137 bank-year observations.
We measure the profitability of banks using three common measures of profitability: Return
on Assets, Return on Equity and Net Interest Margin. Following Fišerová et al. (2015)
or Alkassim (2005) we used Return on Assets, Return on Equity and Net Interest Margin individually
as the dependent variable. Thus, we constructed three models with these three
dependent variables (ROA, ROE and NIM). As independent variables we selected several
factors which can influence the profitability of the Czech banking sector. We distinguish
between bank-specific, industry-specific and macroeconomic factors. As bank-specific factors
we included bank size, market share, level of capitalization, efficiency, credit risk and
liquidity risk, riskiness of the bank’s overall portfolio, number of branches of individual
bank, bank ownership structure, interest rate and affiliation with financial conglomerate.
An industry-specific factor included market concentration and as a macroeconomic factor
we chose Gross Domestic Product (GDP).
Bank size is represented by the amount of total assets. The market share is a percent of
total assets of individual banks to total assets of the Czech banking sector. The level of
capitalization is the ratio of equity to total assets. Efficiency is estimated using the Data
ACTA VŠFS, 2/2016, vol. 10 B149
Envelopment Analysis with variable return to scale. More information about the Data Envelopment
Analysis and estimation of banking efficiency is described in Stavárek and
Řepková (2012). The ratio of total loans to total assets was used as a proxy for credit risk.
Liquidity risk is represented by the ratio of total loans to total deposits. Interest rate is
measure as a ratio of interest income to total loans. Riskiness of the bank’s overall portfolio
is computed as a ratio of loans loss provision to total assets. Branches of individual
banks is number of total branches of each bank. For measure the market concentration is
used the Herfindahl-Hirschmann index. More information about calculation of HerfindahlHirschmann
index and concentration is presented in Řepková (2013). GDP presents the
gross domestic product that is an aggregate measure of production equal to the sum of
the gross values added of all resident institutional units engaged in production per capita
in each year. Bank ownership structure is proxy by the market share of foreign-owned
banks (% of total assets). An affiliation with financial conglomerate as a dummy variable
represents whether the bank belongs to the financial conglomerate. Descriptive statistics
of variables is presented in Table 1 and definition of individual variables is described in
Table 2.
Table 1: Descriptive Statistics of Variables
Variable Mean Median Maximum Minimum St. dev.
ROA 0.69954 0.768 4.1853 -8.4129 1.6406
ROE 7.83059 9.3888 24.935 -34.257 11.115
NIM 0.02517 0.0228 0.0705 0.0006 0.0133
BS 216026 70313 920524 696.4 274892
BR 149.862 53.5 667 1 193.4
CAP 0.12584 0.0915 0.8234 0.02454 0.1148
CR 0.54326 0.5444 0.8819 0.00005 0.1882
EFF 89.0541 100 100 31.5346 17.35
GDP 352706 360444 404843 286979 33742
HHI 1014 989 1112 947 63.722
IR 0.08402 0.0746 0.8853 0.0001 0.0858
LR 0.75576 0.7736 2.1351 0.00045 0.2859
OWNSHIP 88.9209 87 97.1 82 5.5455
MS 0.05323 0.0202 0.2125 0.00017 0.0664
RISKASS 0.01384 0.0045 0.1419 4.1E-06 0.0239
Source: author’s compilation
ACTA VŠFS, 2/2016, vol. 10150
Table 2: Definition of Independent Variables
Variable Definition of variable Expected effect
BR Number of branches of individual bank +
BS Bank size +
CAP Level of capitalization +
CR Credit risk EFF
Efficiency +
GDP Gross Domestic Product +
HHI Market concentration +
IR Interest rate on loans +
LR Liquidity risk OWNSHIP
Bank ownership structure +
MS Market share +
RISKASS Riskiness of the bank’s overall portfolio FC
Affiliation with financial conglomerate +
MS 0.05323 0.0202
RISKASS 0.01384 0.0045
Source: author’s compilation
3	 Empirical Analysis and Results
First, we calculated the profitability of the Czech commercial banks. We measured simultaneously
the Return on Assets and Return on Equity.
Figure 1: Average Value of ROA of the Czech Banking Sector (in %)
Source: author’s calculations
ACTA VŠFS, 2/2016, vol. 10 B151
Figure 1 presents results of average value of Return on Assets of the Czech commercial
banks within the period 2004-2014. The average ROA reached the value between -0.33
to 1.17 %. The average ROA in the Czech banking sector is weak. Return on Assets should
be higher than 1.75 %.
Figure 2: The Average Value of ROE of the Czech Banking Sector (in %)
Source: author’s calculations
Figure 2 presents the average value of Return on Equity of the Czech commercial banks
during the period 2004-2014. The ROE should be in range between 15 to 20 %. The average
values of ROE were in the range of 3.91 to 13.07 % in the Czech banking sector. The ROE
was very low in the Czech banking sector.
Figure 3: The Average Value of NIM of the Czech Banking Sector (in %)
Source: author’s calculations
Figure 3 represents the average value of Net interest income of the Czech commercial
banks during the period 2004-2014. The average value of NIM reached 2.19-2.9 %. It also
shows very low profitability of the Czech commercial banks.
Next, we estimated the determinants influencing the profitability of the Czech banking
sector. For estimation of the determinants of banking profitability we used the panel data
ACTA VŠFS, 2/2016, vol. 10152
analysis. We employed econometrics software EViews 9. Before estimating the model it is
necessary to test the time series for the stationarity. We used Levin, Lin and Chu test to test
the individual variables for the existence of the unit roots. The result of the test indicates
that the variables are stationary on the values. So that the null hypothesis of a unit root
can be rejected for any of the time series. All times series are stationary and can be used
in panel regression analysis. We estimate Equation (4) using Ordinary Least Squares (OLS)
method. OLS method has several prerequisites. First, for correction of heteroscedasticity
is used White (1980) test. Using this test the heteroscedasticity was rejected and the error
term is homoscedastic. For detecting multicollinearity we used correlation coefficient.
From the correlation matrix (in Appendix) it is obvious that any variables are not correlated
together. We also found normality of the error term, thus the prerequisite that the residual
must have normal probability distribution. The absence of autocorrelation of the error
term is determined by the Durbin-Watson test. The Durbin-Watson statistic (DW) is used
for testing autocorrelation in the residuals and takes the following form:
	
multicollinearity we used correlation coefficient. From the correlation matrix (in Appendix) it
is obvious that any variables are not correlated together. We also found normality of the error
term, thus the prerequisite that the residual must have normal probability distribution
The absence of autocorrelation of the error term is determined by the Durbin-Watson test
The Durbin-Watson statistic (DW) is used for testing autocorrelation in the residuals and takes
the following form:
𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 =
∑ (𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖 − 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖−1)2𝑛𝑛𝑛𝑛
𝑖𝑖𝑖𝑖=2
∑ (𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖)2𝑛𝑛𝑛𝑛
𝑖𝑖𝑖𝑖=1
. (6)
To allow for heterogeneity across the banks, we use an error-component model, with the bank
and market-specific error components estimated as fixed effects. The regression results
of Equation (4) where we chose as a depend variable the ROA, then ROE and finally NIM are
presented in Table 3 and Table 4. We presented only final estimations with statistical significant
variables in individual results. These final models were selected according to the Akaike
information criterion. in the final models are not included variables that are not statistically
significant impact on dependent variables. For this paper it is not necessary to find the impact
of all variables but we examined which variables have statistically significant impact
on profitability. These final models were also tested for assumption of OLS methods (residuals
were tested for normality distribution, heteroscedasticity and autocorrelation).
	
(6)
To allow for heterogeneity across the banks, we use an error-component model, with the
bank and market-specific error components estimated as fixed effects. The regression results
of Equation (4) where we chose as a depend variable the ROA, then ROE and finally
NIM are presented in Table 3 and Table 4. We presented only final estimations with statistical
significant variables in individual results. These final models were selected according
to the Akaike information criterion. in the final models are not included variables that are
not statistically significant impact on dependent variables. For this paper it is not necessary
to find the impact of all variables but we examined which variables have statistically
significant impact on profitability. These final models were also tested for assumption
of OLS methods (residuals were tested for normality distribution, heteroscedasticity and
autocorrelation).
Table 3 presents the results of empirical analysis of profitability determinants in the Czech
banking sector during the period 2004-2014. From this table is clearly visible that the
effect is very similar on each dependent variable. The empirical analysis shows that the
effect of number of bank’s branches is not uniform. Number of bank’s branches has negative
impact on ROA and ROE, but this variable has positive impact on Net Interest Margin.
The bank size shows the positive impact on profitability. It means that large banks are
more profitable than small banks. Capitalization has a positive impact on NIM. Next we
found positive impact of credit risk on ROA, we remind that we calculate credit risk as ratio
of total loans to total assets. Efficiency positive influences the ROE and NIM in the Czech
Republic. Effective banks are more profitable. The level of concentration has also positive
impact on ROA. Ownership structure positively influences ROE and market share of bank
has a positive impact on ROA and ROE. Banks with higher market share and foreign owner
banks are more profitable than others in the Czech banking sector. Impact of other variables
on profitability was not statistical significant, thus we are not able to confirm the
impact of other variables on banking profitability in the Czech Republic.
ACTA VŠFS, 2/2016, vol. 10 B153
Table 3: Estimation Results
Dependent variable
ROA ROE NIM
Coefficient t-statistics Coefficient t-statistics Coefficient t-statistics
Constant -6.571034a -2.930444 -42.122300a -2.787086 0.000419 0.103545
BR -0.012328b -2.463202 -0.114976a -3.771149 0.000032c 1.759930
BS 0.000004b 2.279409 0.000032a 2.660728 0.000000a -2.915147
CAP 0.028852a 5.365552
CR 3.353560 a 3.402296 0.015657a 3.301611
EFF 0.184369a 3.441205 0.000144a 3.664508
GDP
HHI 0.004352b 2.437246
IR
LR
OWNSHIP 0.296679a 2.076558
MS 33.660010b 2.552457 304.257800b 3.804407
RISKASS
FC
Estimation diagnostics
Number
of obser-
vation
137 137 137
Adjusted
R-squared
0.648661 0.704041 0.698759
F-statistic 12.95667 16.40586 58.91445
Prob (F-
statistic)
0 0 0
DW
statistics
1.853602 1.995868 1.936947
Note: a denotes significance at 1 % level, b denotes significance at 5 % level, c denotes significance at 10 % level
Source: author’s calculation 
Table 4 summarizes the effect of individual variable on the banking profitability. We can
assume that negative impact of number of bank’s branches on ROE and ROE is caused the
fact that the operations of branches decreased revenue of banks. Banks size is positive
related to profitability. This result is in line with the conclusion of e.g. Smirlock (1985),
Kosmidou et al. (2008), Stavárek and Polouček (2004), Eichengreen and Gibson (2001).
Also Sufian and Habibullah (2009) found that size has positive impact on ROA and NIM.
ACTA VŠFS, 2/2016, vol. 10154
Table 4: Effect of Individual Determinant on Dependent Variables
Variable ROA ROE NIM
BR - - +
BS + + +
CAP 0 0 +
CR + 0 +
EFF 0 + +
GDP 0 0 0
HHI + 0 0
IR 0 0 0
LR 0 0 0
OWNSHIP 0 + 0
MS + + 0
RISKASS 0 0 0
FC 0 0 0
MS 0.05323 0.0202 0.2125
RISKASS 0.01384 0.0045 0.1419
FC 0 0 0
Source: author’s calculation
The findings that capitalization has a positive impact on NIM is confirmed in the studies
of Ben Naceur and Omran (2008), Sufian and Chong (2008) or Kosmidou (2008). Our result
that credit risk has positive impact on ROA and NIM confirm the findings of Sufian and
Habibullah (2009) Ben Naceur and Omran (2008) who found that credit risk has positive
impact on bank performance. We found the positive impact of efficiency on ROE and NIM.
We estimated the positive impact of efficiency on profitability. But on the other hand,
Kosmidou et al. (2008), Kosmidou (2008) and Pasiouras et al. (2006) found the efficiency
negative influence profitability. the results of panel data analysis show that the impact of
GDP on profitability was not confirm but in empirical literature most of studies e.g. Ben
Naceur and Goaied (2008), Ben Naceur and Omran (2008) Shehzad et al. (2013), Sufian
and Habibullah (2009) or Kanwal and Nadeem (2013) estimated that GDP had not impact
on banks’ profitability, on contrary, Sufian (2011) and Davydenko (2011) examined the
negative impact of GDP on ROA. on the other hand, Kosmidou (2008) found the positive
impact of GDP on bank profitability.
This paper do not confirm the result of empirical studies that found a positive (e.g. Kosmidou
et al., 2008 or Bourke, 1989) or negative (Kosmidou, 2008; Pasiouras et al., 2006 or
Guru et al., 1999) because we do not confirm the impact of liquidity risk on performance
of banks. We found that ownership structure has a positive impact on ROE, but we do not
found the impact of this factor on ROA and NIM. Also Bourke (1989) and Molyneux and
Thornton (1992) stated that ownership is irrelevant determinant of profitability. The riskiness
of overbank’s portfolio and affiliation with financial conglomerate were not statisti-
ACTA VŠFS, 2/2016, vol. 10 B155
cal significant variables influencing the banking profitability in the Czech banking sector
during analysed period.
Conclusions
The aim of the paper was to estimate the banking profitability determinants of the Czech
commercial banks during the period 2004-2014. First we calculate the profitability ratios,
namely Return on Assets, Return on Equity and Net Interest Margin. We found that average
values of ROA were in range -0.33 to 1.17 %. The average ROE were reached the value
of 3.91-13.07 % and the average net interest income was 2.19-2.9 %. The level of average
profitability of the Czech banking sector was very low. The highest level of profitability
reached the largest banks.
We estimated the impact of bank size, market share, level of capitalization, efficiency,
credit risk and liquidity risk, interest rate, riskiness of the bank’s overall portfolio, number
of branches of individual bank, market concentration, bank ownership structure, Gross
Domestic Product and affiliation with financial conglomerate on banking profitability.
We estimated three models where we chose Return on Assets, Return on Equity and Net
Interest Income as dependent variables.
When we summarize the results of analysis, profitability was positively influenced by the
bank’s size. Number of branches of the bank had the negative impact on ROA and ROE.
Number of bank’s branches and capitalization has a positive impact on NIM. The results
show the positive impact of credit risk and level of concentration on ROA. Then we found
that efficiency positive influenced ROE and NIM. Ownership structure positive influences
ROE and market share of bank has a positive impact on ROA and ROE. Concentration of
banking sector had a positive impact on ROA. We found that GDP, interest rate, liquidity
risk, riskiness of bank’s portfolio have not the statistically significant influence on profitability
of the Czech commercial banks. Also affiliation bank with the financial conglomerate
was not statistical significant impact on banking profitability in the Czech Republic.
We can conclude that large, well-capitalized and foreign owners’ banks are more profitable.
Also efficient banks with higher market share are more profitable than other banks
in the Czech banking industry. The highest impact on profitability (namely on ROA and
ROE) had market share. It means that if market share of bank increase, the profitability of
bank increase too. But only marginal change in market share significantly influenced the
banking profitability. It was also found that especially bank-specific factors determined
the profitability of the Czech commercial banks.
Acknowledgement
Research behind this paper was supported by the Czech Science Foundation within the
project GAČR 16-17796S ‘Affiliation with financial conglomerate as a determinant of performance
and risk of banks’.
ACTA VŠFS, 2/2016, vol. 10156
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Ing. Iveta Palečková, Ph.D.
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Faculty of Business Administration in Karviná / Obchodně podnikatelská fakulta v Karviné
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(paleckova@opf.slu.cz)
Appendix
Correlation matrix of independent variabless
BR BS CAP CR EFF GDP HHI IR LR OWN MS RISK
BR 1 0.78 -0.10 -0.14 0.20 0.09 -0.10 0.18 -0.11 -0.09 0.80 0.07
BS 0.78 1 -0.20 -0.30 0.10 0.21 -0.19 0.15 -0.23 -0.20 0.80 -0.03
CAP -0.10 -0.20 1 0.03 0.23 -0.08 0.07 0.17 0.29 0.08 -0.23 0.27
CR -0.14 -0.30 0.03 1 0.39 0.21 -0.19 -0.15 0.78 -0.15 -0.36 0.21
EFF 0.20 0.10 0.23 0.39 1 0.20 -0.18 0.08 0.46 -0.16 0.08 0.16
GDP 0.09 0.21 -0.08 0.21 0.20 1 -0.75 0.10 0.24 -0.76 0.04 0.21
HHI -0.10 -0.19 0.07 -0.19 -0.18 -0.75 1 -0.03 -0.22 0.77 -0.05 -0.31
IR 0.18 0.15 0.17 -0.15 0.08 0.10 -0.03 1 -0.10 -0.04 0.14 0.04
LR -0.11 -0.23 0.29 0.78 0.46 0.24 -0.22 -0.10 1 -0.19 -0.28 0.25
OWN -0.09 -0.20 0.08 -0.15 -0.16 -0.76 0.77 -0.04 -0.19 1 -0.04 -0.38
MS 0.80 0.80 -0.23 -0.36 0.08 0.04 -0.05 0.14 -0.28 -0.04 1 -0.09
RISK 0.07 -0.03 0.27 0.21 0.16 0.21 -0.31 0.04 0.25 -0.38 -0.09 1
Source: author’s calculation