The Impact of Non-Performing Loans Ratio on Banking Profitability in
the Albanian Banking System
ZAMIRA VEIZI, ROBERT ÇELO
Department of Accounting and Finance,
“Eqrem Çabej” University,
Rruga “Studenti”, Lagjia “18 Shtatori”, Gjirokastra,
ALBANIA
Abstract: - Most of the studies have determined credit risk as the biggest risk among all other risks with a direct
impact on the financial performance of a bank. The main objective of any bank, like any business unit, is to
maximize shareholder value, so we considered it important to treat the impact of one of the most important
bank’s risks on its profitability. Bank profitability and the non - performing loans ratio (NPLR) are the two
main concepts in our study. This study aims to investigate the relationship and the stability over time of this
relationship between the non - performing loans as the main indicator of credit risk management and
profitability of commercial banks in Albania. The linear regression model is used to test the hypothesis raised
in the study which tests the relationship that exists between the credit risk management indicator and bank
profitability indicators. NPLR is taken as a representative indicator of credit risk management and ROE (return
on equity) and ROA (return on assets) are taken as representative indicators of bank profitability. The statistical
analysis in the study showed a negative impact of the NPLR indicator on the two indicators of bank
profitability in our country. The findings of our study constitute a valuable contribution to banks and policy
makers because a strong banking system motivates the financial stability of a country and increases the
elasticity of economies to face economic crises. To conduct the study, we faced difficulties and challenges in
finding a long time series of data for all the indicators taken in the study for each of the commercial banks
operating in Albania. Although we were not able to provide the data for each commercial bank, we overcame
this difficulty by taking the total data from the official website of the Bank of Albania. Taking into
consideration that the banking history in Albania is relatively short we managed to find data for an 8-year study
period which we used in our study.
Key-Words: - Banking Risks, Credit Risk Management, Non-performing Loans Ratio, Profitability,
Commercial Banks, Profitability indicators.
Received: July 24, 2023. Revised: November 15, 2023. Accepted: December 19, 2023. Available online: December 29, 2023.
1 Introduction
The problem that this paper undertakes to study is
very tangible nowadays and of vital importance.
When we talk about the good financial performance
of the Albanian Banking system, we mean
indirectly the trend of the future development of
our country’s economy. Today commercial banks
are the largest financial institutions in our country
and their role of mediation could catalyze the
growth of the Albanian Economy. They use their
customer’s deposits to provide loans to borrowers,
emphasizing the fact that in general, the interest
earned on loans constitutes the main source of
income. The lending process exposes commercial
banks to high risk. For this reason, we found it
interesting to conduct a study to identify the impact
of one of several indicators of credit risk
management which is NPLR on the profitability
indicators of banking institutions represented by
ROA and ROE. The financial stability of a country
depends on how integrated its banking system is. In
a country where lending is the main activity
through which our economy is financed,
phenomena such as credit risk, efficient
management, and profitability will be at the center
of attention for all commercial banks. As a result,
this phenomenon is particularly important to study
for our country because it serves interested parties
such as commercial banks or economic and
financial policy makers. This study aims to
investigate the relationship and the stability over
time of this relationship between the non -
performing loans as the main indicator of credit
risk management and profitability of commercial
banks in Albania. To conduct the study, we faced
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Zamira Veizi, Robert Çelo
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difficulties and challenges in finding a long time
series of data for all the indicators taken in the
study for each of the commercial banks operating
in Albania. Although we were not able to provide
the data for each commercial bank, we overcame
this difficulty by taking the total data from the
official website of the Bank of Albania. Taking into
consideration that the banking history in Albania is
relatively short we managed to find data for an 8-
year study period which we used in our study.
2 Literature Review
The numerous theoretical and empirical studies that
we will discuss in this section will help us
determine the final research design of this study
with which we will analyze the data and draw the
results related to the phenomenon we are studying.
Although credit risk management practices and
techniques are generally well known because the
banking sector has long experience in this field,
credit quality problems in commercial banks are
one of the major causes affecting their bankruptcy.
For this reason, all bankers should know the main
factors that affect the quality of a loan portfolio and
the methods of managing it. The quality of a bank’s
loan portfolio can be affected by changes in credit
risk that affect the bank’s overall performance, [1].
This argument was further supported as mentioned
by, [2], that a big change in bank profitability can
be attributed to changes in credit risk management.
Banks that are more exposed to credit risk result in
lower profitability compared to those with lower
credit risk. If banks are exposed to risky loans, non-
performing loans tend to increase, which ultimately
reduces the bank’s profitability, [3]. Risk
Management in banking is the most important
factor for financial stability and economic growth
in developed economies. This risk occurs when the
parties in credit transactions and derivative
transactions may not fulfill their obligations,
meaning that the parties are unable to repay the
principal and interest on the due day, [4].
According to, [5], also cited by, [6], an appropriate
process for risk management is to identify the risk,
measure the level of risk, and then develop
strategies to manage it. According to, [7], risk
management issues in the banking sector not only
have a significant impact on the bank’s
performance but also on the national economic
development and the development of a favorable
business climate in general. The serious problems
the banks have suffered are directly related to the
poor quality of lending. A poor portfolio and the
lack of attention to changes in economic
circumstances are common in developing
economies, [8]. According to, [9], credit risk is the
biggest risk for banks and their success depends on
the accurate measurement and effective
management of this risk more than any other risk.
The authors, [10], showed that credit risk is the rate
of fluctuations in the values of debt instruments or
their derivatives due to changes in the credit quality
of the borrower and their related parties. According
to, [11], credit risk is the biggest risk for banks due
to its connection with potential losses. They have
argued that commercial banks recognize the credit
risk associated with bank loans; therefore, credit
risk management depends on general analysis and
market research. Author, [12], defined profitability
ratios as financial metrics that assess the ability of a
business to generate income against business
expenses and costs over a given period. These
ratios are considered the basic financial ratios of
banking institutions. According to, [13], the
improvement of financial performance requires
improvement in the functions and activities of
commercial banks. According to, [14], when a bank
aims to increase and maximize profits, it should
also increase risk or decrease operating costs. He
also addressed a similar issue for conventional
Indonesian banks, using ROE and ROA as proxies
of financial performance. The co-authors, [15],
showed that profit maximization is the goal of
commercial banks. All the strategies designed, and
activities carried out aim to realize this major
objective. They also concluded that among all
measures of bank profitability, ROA and ROE are
the most important. The authors, [16], show the
advantages of using ratios as indicators of
profitability. They mention that researchers prefer
to use ratios as measures of profitability since they
are indexed to inflation. According to, [17],
previous studies have used indicators such as ROE
(return on equity) and ROA (return on assets) as
indicators of profitability. The researchers, [18],
[19], [20], [21], [22], [23], [24], used the return on
assets ratio (ROA) as an indicator of a bank’s
profitability, to test the impact of credit risk
management on the banks’ tax returns. The
researchers, [25], to test the effect of credit risk
management on the profitability of their banking
systems, used the ROE ratio as an indicator of bank
profitability. In their study, [26], they examined the
effect of financial risk on the profitability of
Montenegrin commercial banks using ROA and
ROE as profitability indicators.
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3 The Purpose and Study Objective
The purpose of this study is to provide interested
parties with accurate information about the
relationship between credit risk management
represented by the indicator of non-performing
loans and bank profitability for commercial banks
operating in Albania. The main objective of this
study is to determine if there is a stable relationship
in the time between the non - non-performing loans
ratio (NPLR) and profitability indicators (ROE and
ROA) of commercial banks in Albania.
4 Methodology & Study Hypotheses
The methodology to be used will be in line with the
stated objective and hypothesis to be tested in
relation to this study. Since the main objective of
this study is to determine whether there is a stable
relation over time between the percentage of non-
performing loans and profitability ratios of banks,
we analyzed these indicators:
1. NPLR as a representative indicator of
credit risk management
2. ROE and ROA as representative indicators
of bank profitability
All these variables are calculated in the form of
ratios. The use of ratios in measuring credit risk
management and profitability is common in the
theoretical and empirical literature. The biggest
advantage of using ratios to measure bank
performance is that they eliminate banking
inequality created by bank size (Samad, 2004).
ROE and ROA are the two variables that we will
use as proxies for bank profitability and all other
variables will be used as proxies for credit risk
management.
ROE shows the level of net profit generated by
the capital invested in the bank. A high value of
ROE usually informs us about a more stable and
safer situation for the bank. ROA shows how well
the bank's assets are being used in terms of
generating profit. A high value of this ratio
confirms that the bank has properly built its asset
portfolio, contributing to ensuring higher profits.
NPLR measures the percentage of gross non-
performing loans in a bank's loan portfolio. A low
ratio is an indicator of good asset quality and an
indicator of a low level of problem loans and
therefore lower credit risk.
The study was conducted for a total of 16
commercial banks currently operating in Albania
over a period of 8 years. The literature and data
sources form the basis of a serious paper. We
collected the data used to test the case from data
published (quarterly) in the statistics of the Bank of
Albania on its official website. The research
consists of combining quantitative methods with
qualitative methods. Quantitative methods will aim
to determine the quantitative relationship between
the credit risk management ratio and banks'
profitability ratios, assessing the temporal stability
of this relationship in the future. While qualitative
methods were used to prove the hypothesis raised
in the study, the interpretation of the quantitative
results and the drawing of conclusions and
recommendations for the future development of the
Albanian banking system. What should be
emphasized is the fact that these methods do not
give high efficiency when used individually but
must be used in combination with each other to
achieve optimal benefits.
Starting from the main objective of this study
which is to determine whether there is a stable
relationship over time between bank profitability
and the rate of non-performing loans for
commercial banks operating in Albania, the entire
theoretical and empirical literature helped us to
understand the most used credit risk management
ratios and bank profitability ratios. From what we
studied, ROE and ROA are the most used
indicators as representatives of bank profitability.
We believe that these are the most important
indicators for measuring profitability even for
banks in Albania which are directly related to the
net result of the banks by putting it in relation to the
total assets and the bank's share capital. Therefore,
in raising the hypotheses of our study, we will take
both indicators as representatives of banks’
profitability. On the other hand, the “empirical
analysis” helped us to become familiar with the
most used indicators that represent credit risk
management where we identified that in all studies
NPLR is a key indicator.
The hypothesis we will test in our study is as
follows:
Null hypothesis (Ho): No stable relationship over
time exists between profitability (ROA, ROE) and
non-performing loan rate (NPLR) for commercial
banks in Albania.
Alternative hypothesis (Ha): There is a stable
relationship over time between profitability (ROA,
ROE) and non-performing loan rate (NPLR) for
commercial banks in Albania.
H0: 𝛽1 = 0
Ha: H0 it’s not true
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We will form the hypothesis of this study to
test the stability of the relationship between NPLR
(NPL rate) and bank profitability represented by the
variables (ROA and ROE) over time, since NPLR
as we mentioned above, is defined as the most
important indicator of credit risk management, and
the large increase noted by this indicator during the
period of our study, has been seen as a threatening
to our banking system. Considering the hypothesis
derived by this study since ROE and ROA are the
variables that describe the size of the profitability
of the lending banking system, they will be the
dependent variables in the hypothesis testing
process. The NPLR indicator, which is calculated
as the ratio of bank non-performing loans to total
loans, will represent the independent variable. In
the study, we also included a control variable
(PERIOD), which determines the time chain of the
data study. This variable will serve in the study as a
control variable. To test the hypothesis, we need to
construct the regression to measure the relationship
of the dependent and independent variables.
"Regression analysis examines the statistical
robustness of our model", [27]. The technique we
decided to use to build the model is "Ordinary
Least Squares" (OLS).
5 The Model of Study
The model of this study is deductive. “Deductive
model” starts from some existing theories and then
these theories are tested to extract conclusions. The
data collected for the NPL ratio and bank
profitability ratios are analyzed with statistical
methods (tests) using a linear regression model.
The research model for quantitative correlation
investigation between the non-performing
loans ratio and the two profitability indicators of
commercial banks in our country will be:
where:
is the dependent variable (ROA or ROE)
is the model constant
are the model coefficients for the independent
variables that are included in the model
εt is the model error
A multivariate regression coefficient indicates
the expected change in the value of the dependent
variable when the value of an independent variable
increases by one unit holding the other independent
constant, [28].
6 Hypothesis Testing and Linear
Regression Results
6.1 Descriptive Statistics
For the statistical processing of the data collected in
our study, we have used the SPSS v. 21 program.
From the results of descriptive statistics by the
processing of this data, identified that these results
do not provide conclusions about the relationship
that exists between variables, as result they do not
provide information for hypothesis testing, but they
help us to conclude about the descriptive behavior
of the model’s dependent variables. In this way, it
is observed that the dependent variable ROA has an
observed average of =0.6738, and with a
standard deviation of , while
the other dependent variable ROE has an observed
average =7.7988, with a very high standard
deviation The high standard
deviation of ROE compared to the other dependent
variable ROA, indicates a high fluctuation
distribution of ROE observations by the average of
this variable, different by the dependent variable
ROA which shows more stable trends of values
observed from the average of this variable. Figure 1
expresses this phenomenon more clearly. High
dispersion (standard deviation) does not cause
problems in the construction of statistical models of
the relationship between the dependent variable and
the responding (independent) variables when these
models do not carry the risk of multicollinear
phenomena and unacceptable levels of statistical
significance. In many studies, bank profitability is
naturally represented by both variables. For this
reason, in our study for the verification of the
hypotheses we built linear regression models
treating both dependent variables ROA and ROE.
The relationship between ROE and ROA, despite
the results of correlative relationships which will be
analyzed in the following, shows almost linear
trends as shown in Figure 2. What is noticed is that
in the third quarter of the period we studied, the
linearity between the data for the two dependent
variables visibly breaks down. During this period,
the growth of the shareholder capital has been
fluctuating from one quarter to another different
from previous periods in which its growth has been
uniform and almost distributed equally.
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Fig. 1: Distribution of observed values of ROA
and ROE
This came because of the greater willingness of
shareholders to support banking activity with the
necessary source of capital during this period since
the component with the greatest material impact on
the increase of shareholder capital was paid capital.
We think this is the reason for the breakdown of
linearity between the data for the two dependent
variables.
Fig. 2: Relationship between ROA and ROE
6.2 Statistical Relationship of Variables and
Multicollinearity
Below we have built the correlation matrix,
interpretation of their results, and controlling for
multicollinear effects.
Table 1. Correlation matrix for hypothesis
Correlations
ROA
ROE
NPLR
ROA
Pearson Correlation
1
ROE
Pearson Correlation
.995**
1
NPLR
Pearson Correlation
-.068
-.104
1
Results presented in Table 1 represent
statistical relationships through coefficients PCC
between variables we have used for testing of
hypothesis. For both dependent variables, ROA and
ROE, correlation relationships with the
independent variable NPLR, are far from the limits
of the risk of multicollinearity [PCC (ROA, NPLR)
= - 0.068 the PCC (ROE, NPLR) = - 0.104]. It is
also noted that these relationships are expressed
with a negative coefficient, which shows that the
collected data for couples (dependent variable,
independent variable) present development trends
in opposite directions, even though both
coefficients do not ensure the level of statistical
significance below the minimum limit (p <
0.05).
6.3 Linear Regression Results and
Hypothesis Testing
To verify the hypothesis, as the dependent variable
we have taken ROA or ROE, while NPLR is taken
as the independent variable. To verify the
significance and strength of the regression
relationship between the profitability measured by
ROA as a dependent variable and the non-
performing loans ratio measured by NPLR as an
independent variable, we processed the data of each
quarter with the statistical program SPSS v 21.
In Table 2, it is noted that R2 =0.005, a value
that is too small to claim that the built model with
the data processed by the program SPSS v 21, has a
good predictive ability of the model with reality.
So, the mathematical model which will be built
through results presented in Table 2, will not be a
good predictive model of the relationship between
ROA and NPLR. Similarly, Table 3 presents model
results for the hypothesis (dependent variable
ROA).
The mathematical equation that expresses the
linear mathematical relationship between ROA and
NPLR has the form:
ROA = 0.751 - 0.005 (NPLR) + ε (1)
In this equation, it is noted that the regression
coefficient for the independent variable NPLR is
different from zero, but this is not enough to
conclude the sustainability of the hypothesis by the
dependent variable ROA, because this coefficient
does not provide sufficient statistical significance
(p= 0.713>0.05) for its correct evaluation.
Consequently, we cannot say that the hypothesis is
stable according to the model. To make a final
statistical decision about the sustainability of the
hypothesis we built the linear regression model
with dependent variable ROE and independent
variable NPLR.
The results presented in Table 4, show that the
dependent variable ROE, in linear regression with
independent variable NPLR, has a coefficient of
adaptability to reality (R2=0.011), insufficient to
give the mathematical equation predictive quality,
but it is comparatively better (doubly) than the
same coefficient for the dependent variable ROA
(0.011>0.005). However, NPLR, as an
independent variable, does not provide a predictive
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quality for profitability, whether this is represented
by the dependent variable ROA as well as the
dependent variable ROE. Lastly, the model results
for the hypothesis (dependent variable ROE) in this
case are presented in Table 5.
Table 2. Model summary for hypothesis (dependent variable ROA)
Model Summary a
Model
R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F
Change
df1
df2
Sig. F
Change
1
.068a
.005
-.029
.48571
.005
.137
1
30
.713
a. Predictors: (Constant), NPLR
Table 3. Model results for hypothesis (dependent variable ROA)
Coefficients a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
95.0% Confidence
Interval for B
Collinearity
Statistics
B
Std. Error
Beta
Lower
Bound
Upper
Bound
Tolerance
VIF
1
(Constant)
.751
.226
3.320
.002
.289
1.214
NPLR
-.005
.013
-.068
-.371
.713
-.030
.021
1.000
1.000
a. Dependent Variable: ROA
Table 4. Model summary for hypothesis (dependent variable ROE)
Model Summary a
Model
R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F
Change
df1
df2
Sig. F
Change
1
.104a
.011
-.022
5.75544
.011
.325
1
30
.573
a. Predictors: (Constant), NPLR
Table 5. Model results for hypothesis (dependent variable ROE)
Coefficients a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
95.0% Confidence Interval
for B
Collinearity
Statistics
B
Std. Error
Beta
Lower
Bound
Upper
Bound
Tolerance
VIF
1
(Constant)
9.213
2.682
3.436
.002
3.736
14.690
NPLR
-.085
.149
-.104
-.570
.573
-.389
.219
1.000
1.000
a. Dependent Variable: ROE
The mathematical equation of linear regression
between ROE and NPLR has the form:
ROE = 9.213 - 0.085 (NPLR) + ε (2)
In this equation, it is noted that the regression
coefficient for the independent variable NPLR is
different from zero, but this is not enough to
conclude the sustainability of the hypothesis by
dependent variable ROE, since this coefficient
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does not provide sufficient statistical significance
(p= 0.573>0.05) for its significant evaluation.
Consequently, we cannot say that the hypothesis
is stable according to the model. Finally, we can
say that in both cases (ROA, ROE) regression
coefficients are different from zero and they
indicate a negative impact on profitability
measured by them, the lack of sufficient level of
significance does not allow us to say that the
model provides the stability of the hypothesis.
7 Conclusions about the Model
During this work, we studied the relationship
between the credit risk management indicator
(NPLR) and bank profitability indicators (ROA
and ROE), taking into consideration the 16
commercial banks operating in Albania for a
period of 8 years. This is of huge practical
importance for the banks in our country as it
follows from this empirical analysis, we can
support the arguments of how banks should act to
manage credit risk as well as possible, which will
lead to improved indicators of profitability. The
linear regression model analysis that we studied
above aimed to find out what relationship exists
between the credit risk management ratio (NPLR)
and the profitability of commercial banks
measured by ROA and ROE. The whole analysis
was based on secondary data published in 3-
month statistics for an 8-year study period, official
sources, and the Bank of Albania.
The results from testing the hypothesis showed
that even though in both cases (ROA, ROE) the
regression coefficients are differently measured
by them, the lack of sufficient level significance
does not allow us to say that the model ensures the
stability of this hypothesis. This finding is the
same as the literature suggests. Compared to the
other similar works that we saw, almost all of
them found a negative relationship between
NPLR, ROA, and ROE indicators (despite the
level of significance), except for a few studies that
surprisingly found a positive relationship between
them.
8 Conclusions & Recommendations
In our study, we performed statistical tests to
investigate the impact of the credit risk
management indicator (NPLR), once on the
profitability indicator ROA and the other on the
ROE indicator. We also performed statistical tests
to investigate if the relationship between them is
stable over time or fluctuating.
Below we list some general conclusions about the
study:
Taking into consideration the fact that lending
is the main activity through which the
Albanian economy is financed; credit risk and
its management techniques remain the most
important issues for commercial banks
operating in our country.
From the empirical studies we cited above, we
verified that most researchers have defined
credit risk as the main risk that affects bank
profitability indicators, and so we found it
valuable to verify this for Albania as well.
The review of the theoretical and empirical
literature in the identification of foreign
lending issues in commercial banks is one of
the most important resources left in their
account. That’s why, all bankers, not only
those who work in the credit risk departments,
should be aware of the main factors that affect
the quality of a loan portfolio and the methods
for its efficient management. It is important to
highlight that the commercial banks in our
country, to increase their profitability, must
make continuous efforts to have an effective
structure for credit risk management and to
necessarily analyze the NPLR indicator.
We also identified that Banks that are mainly
exposed to credit risk result in a reduction in
their profitability. Profitability indicators
(ROE and ROA) are two indicators of the
effectiveness of management in generating
income, both from the money invested by
shareholders and from the total investments
made in the form of assets. The reason why
these two indicators complement each other is
that while ROE does not indicate anything
about the debt financing situation, ROA does,
so both are taken in our study as proxies of
bank profitability.
In an overview of the progress of lending in
the Albanian banking system, we noticed that
throughout the years taken in the study, the
rate of credit growth has been at very low
levels. The low growth rate of lending activity
in recent years is due to both the decrease in
demand for financing from the private sector
and tight lending conditions from banks.
During this period of study, lending has had a
great orientation towards the business, loans
which are expressed as a cargo in portfolio,
have been gaining ground compared to
individual loans. It can be concluded that the
high weight of loans for the business has
exposed banks to a higher credit risk.
Doing an analysis of loan risks according to
sectors, we conclude that the high percentage
of the loans in four sectors during this period
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has exposed the banking sector to a high level
of credit risk.
Although throughout the period has been
recorded an increase in the share of the loans
in local currency and a decrease in the share
of loans in foreign currency had the largest
share in the portfolio compared to the loan in
Lek. The high weight in currency had exposed
banks to a high risk because of currency
fluctuation.
Throughout the period the long-term loan has
the main weight in the total loan portfolio,
followed by the short and medium loan terms.
This also proves that the bank sector is
exposed to a high relative risk due to the
greater difficulty in predicting the ability to
pay borrowers for long periods of time
compared to medium and short-term ones.
The trend of two indicators ROA and ROE
through these years is the same as the trend of
the net result of the banking system. During
our period of study, these indicators have had
a lot of fluctuation. In recent years emphasis
on these two indicators of profitability ROA
and ROE have experienced growth year after
year, and the net income has increased
significantly because of the decrease in
expenses for provisions.
Statistical testing of the hypothesis for
evidence of the stability over time of the
relationship between NPLP and two indicators
of bank profitability obtained in the study
showed that in two cases (ROA, ROE) the
regression coefficient is different from zero
and they indicate a negative impact of NPLR
on the profitability measured by them. We
have expressed that the lack of a sufficient
level of significance does not let us conclude
that the model ensures the stability of this
hypothesis. However, we must emphasize that
the lack of a sufficient level of significance is
the result of the limited number of data, which
is a consequence of a short relative history of
private loan banking in Albania.
Regarding the analysis of the lending in
system banking in Albania, to reduce the level
of exposure to the loan risk, we recommend to
the banks that new loans must have a large
sectoral diversity, a better balance between
loans ratio individuals and businesses, for the
loan ratio in Lek-loan in foreign currency long
term, medium and short ratio.
Based on the result of the hypothesis
verification, we recommend to the
commercial bank in our country to do
maximum efforts to reduce the problematic
loan level which will bring an increase in
banking profitability.
The contribution of our study beyond
alternative studies is an added value for all
interested parties to manage properly risk
management loans which will affect directly
in financial performance of our banking
system. It is worth emphasizing that this work
makes a valuable contribution to our banking
system taking into consideration the fact that
the relevant studies for this phenomenon are
very few for our country.
9 Limitations of the Study
This paper has its limitations in the collection of
secondary data from official sources for a long
time series so the statistical analysis could be
more complete. It’s necessary to emphasize that
our paper is original as it is part of a doctoral
study that has never been published before.
1. In the conduction of this study, we
encountered difficulties in finding data for a long
time series for 16 commercial banks. For this
reason, the data used is taken in total for all banks
operating in Albania from the official source
which is the Bank of Albania. For this reason, we
can’t give conclusions and recommendations for
each bank, but they are in general. We think that
in the future we will carry out such a study.
2. The time series of data used in this study
is for 8 years, with a 3-month basis because we
can’t provide official data for a longer period. In
case we would provide data for a longer time
series, the statistical analysis would be more
complete to determine the connection and stability
over time of the variables taken in the study.
10 Practical Application of the Study
1. The results of our study can be widely used by
commercial banks that operate in our country
to control the indicator of the non-performing
loans ratio, which directly affects the
indicators of bank profitability.
2. The results can serve the supervisory
authority of the commercial banks in our
country, which is the Bank of Albania, in
drafting its policies for the control
mechanisms of commercial banks.
3. The results of our study can be useful to
different foreign and Albanian researchers,
who are interested in continuing and
expanding their research in this field of study.
They can further complete the research in this
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.38
Zamira Veizi, Robert Çelo
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Volume 21, 2024
field by completing the limitations of our
work.
References:
[1] Sufian F., (2009). Determinants of bank
efficiency during unstable macroeconomic
environment: Empirical evidence from
Malaysia. Research in International
Business and Finance, vol.23(1), pp.54-77.
[2] Owojori A. A., Akintoye I. R. & Adidu F.
A., (2011). The challenge of Risk
Management in Nigerian Banks in the post
Consolidation era, Journal of Accounting
and Taxation, vol.3(2), pp.23-3
[3] Miller S. M. and A. G. Noulas, 1997.
Portfolio mix and large-bank profitability
in the USA. Applied Economics, vol.29(4),
pp.505-512.
[4] Koch T. W. and Macdonald S. S., (2003).
Bank Management. 5th Edition. Ohio:
Thomson South -Western.
[5] Gestel T. V. and Baesens B, (2008). Credit
risk management. [E-book] Available
through: Oxford Scholarship Online.
[6] Fan Li and Yijun Zou, (2014). The impact
of credit risk management on Profitability
of commercial banks: A study of Europe,
Corpus ID: 28566351, Semantic Scholar.
[7] Adeusi S. O., Akeke N. I., Adebisi O. S.
and Oladunjoye O. (2013). Risk
Management and Financial Performance of
Banks in Nigeria, IOSR Journal of
Business and Management, vol.14 (6),
pp.52-56, [Online].
https://www.iosrjournals.org/iosr-
jbm/papers/Vol14-issue6/H01465256.pdf
(Accessed Date: December 8, 2023).
[8] Lopez, J. A. (1999), Using CAMELS
ratings to monitor bank conditions. FRBSF
Economic Letter, 6, [Online].
https://www.frbsf.org/economic-
research/publications/economic-
letter/1999/june/using-camels-ratings-to-
monitor-bank-conditions/ (Accessed Date:
December 8, 2023).
[9] Gieseche K. (2004), Credit Risk Modelling
and Valuation: An Introduction Credit
Risk: Models and Management,Vol.2,
Cornell University, London
[10] Chen K. and Pan C. (2012). An Empirical
Study of Credit Risk Efficiency of Banking
Industry in Taiwan, Web Journal of
Chinese Management Review, vol.15(1),
pp.1-16, [Online].
http://cmr.ba.ouhk.edu.hk/cmr/webjournal/
v15n1/CMR326E11.pdf (Accessed Date:
December 8, 2023).
[11] Hosna A., Manzura B., & Juanjuan S.
(2009). Credit Risk Management and
Profitability in Commercial Banks in
Sweden. Master of Science in Accounting,
Gothenburg University Publications
Electronic Archive.
[12] Farisai Immaculate Chin'Anga, (2015).
The effect of credit risk management on
the profitability of the four major south
African banks, MCom, University of
Johannesburg.
[13] Nimalathasan B. (2008). A comparative
study of financial performance of banking
sector in Bangladesh-An application of
CAMELS rating system. Annals of the
University of Bucarest, the Economic &
Administrative Series, No.2, pp.141-152.
[14] Ruziqa A. (2013). The impact of credit and
liquidity risk on bank financial
performance: the case of Indonesian
Conventional Bank with total asset above
10 trillion Rupiah, International Journal of
Economic Policy in Emerging Economies,
vol.6(2), pp.93-106.
[15] Ongore V. O. and Kusa G. B., (2013).
Determinants of Financial Performance of
Commercial Banks in Kenya. International
Journal of Economics & Financial Issues
(IJEFI), vol.3(1), pp.237-252.
[16] Guru B. K., Staunton J., Balashanmugam
B., (1999). Determinants of commercial
bank profitability in Malaysia.
Proceedings of the 12th Annual Australian
Finance and Banking Conference, Sydney,
Australia, December 16-17, 1999.
[17] Chirwa E. W., (2003). Determinants of
commercial banks' profitability in Malawi:
a cointegration approach. Applied
Financial Economics, Vol.13(8), p.565-
571.
[18] Noman A., Pervin S., Chowdhury M.,
Banna H. (2015). The effect of credit risk
on the banking profitability: A case in
Bangladesh, Global Journal of
Management and Business Research,
vol.15(3), p.41-48.
[19] Kodithuwakku, Sujeewa 2015. Impact of
credit risk management on the performance
of commercial banks in Sri Lanka.
Proceedings of the 1st International
Conference in Accounting Researchers and
Educators (ICARE 2015), October 09,
2015. Department of Accountancy,
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.38
Zamira Veizi, Robert Çelo
E-ISSN: 2224-2899
456
Volume 21, 2024
University of Kelaniya, Sri Lanka. pp.136-
143.
[20] Funso K., Kolade A., Ojo O., (2012).
Credit risk and commercial banks'
performance in Nigeria: A panel model
approach, Australian Journal of Business
and Management Research, Vol.2, No.02
pp.31-38.
[21] Gizaw M., Kebede M. Sujata, (2015). The
impact of credit risk on profitability
performance of commercial banks in
Ethiopia, African Journal of Business
Management, vol.9(2), pp.59-66.
[22] Abiola I., & Olausi A. S., (2014). The
Impact of Credit Risk Management on the
Commercial Banks Performance in
Nigeria, International Journal of
Management and Sustainability, vol.3(5),
295, 306.
[23] Ali Sulieman Alshatti, 2015. The effect of
credit risk management on financial
performance of the Jordanian commercial
banks, Investment Management and
Financial Innovations, vol.12(1-2),
pp.338-345.
[24] Kithinji A. M., (2010). Credit risk
management and profitability of
commercial banks in Kenya, Working
paper, School of Business University of
Nairobi, Kenya, [Online].
http://erepository.uonbi.ac.ke (Accessed
Date: December 8, 2023).
[25] Aduda, J. A., (2011). The Relationship
between Credit Risk Management and
Profitability among Commercial Banks in
Kenya, Journal of Modern Accounting and
Auditing, vol.7, pp.934-946.
[26] Tafri F. H., Hamid Z., Meera A. and Omar
M. A., (2009). The Impact of Financial
Risks on Profitability of Malaysian
Commercial Banks: 1996-2005, World
Academy of Science, Engineering and
Technology International Journal of
Economics and Management Engineering,
Vol.3, No.6, 2009.
[27] Parramore K., & Watsham T., (1997).
Quantitative Methods in Finance. 1st
edition. Oxford: Thomson.
[28] Studenmund A. H., (2011), Using
econometrics: A Practical Guide. 6th
Edition, Boston, Mass.: Pearson.
Contribution of Individual Authors to the
creation of a scientific article (Ghostwriting
policy)
- Zamira Veizi was responsible for the literature
review, methodology, study model, and
supervision.
- Robert Çelo was responsible for gathering the
data from office sources, processing them with
the statistical software SPSS v. 21 program
and editing the paper.
Sources of Funding for Research Presented in
a Scientific Article or Scientific Article Itself
No funding was received for conducting this
study.
Conflict of Interest
The authors have no conflicts of interest to
declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.
en_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.38
Zamira Veizi, Robert Çelo
E-ISSN: 2224-2899
457
Volume 21, 2024
