Impact of macroeconomic and banking factors on the level of non-performing loans in the banking sector in Kosovo Текст научной статьи по специальности «Экономика и бизнес»

https://doi.org/10.29013/EJEMS-19-4-19-25

Dermaku Burim, University of Pristina, Faculty of Economics E-mail: burimdermaku2@gmail.com

IMPACT OF MACROECONOMIC AND BANKING FACTORS ON THE LEVEL OF NON-PERFORMING LOANS IN THE BANKING SECTOR IN KOSOVO

Abstract. According to studies on non-performing loans, various analysts have attempted to directly link the level of non-performing loans with two categories of factors: (1) macroeconomic factors and (2) bank-specific or bank-specific factors. The research will address the problem of NPLs through the econometric model, where the macroeconomic factors that are addressed in the research are: GDP, inflation, unemployment rate and interest rate, while the banking factors are: ROEA, ROAA and CAR. The research covers the period 2007-2017. The results of the research showed that inflation and interest rate have an impact on NPLs in the Kosovo banking sector.

Keywords: NPL, Inflation, Interest Rate, Macroeconomic Factors, Banking Factors.

Indroduction ing mitigation criteria [16, 13, 12]. led to a significant

The consequences for the banking industry as increase in NPLs, affecting banks' liquidity and prof-a result of nonperforming loans can be severe if itability, and thus the financial stability of the bank-no precautionary steps are taken. Non-performing ing system and macroeconomic stability in general. loans adversely affect the performance and stability of the banking industry, increasing provisioning, never lending, and in more severe cases, can bring a financial institution into insolvency. The banking industry in Kosovo has also been very cautious in terms of credit portfolio quality management, making the rates of these loans very low, which has made confidence in this sector even higher.

It is common for financial institutions to play a vital role in the economy by allocating capital from surplus agents to deficit agents in various economic sectors [20]. This means that a sound banking sector is needed for economic growth because it provides macroeconomic stability and develops sound financial institutions [14]. However, over the past two decades, the liberalization process has strengthened competition among banks. Competition increased banks' credit risk, affecting their loan portfolios with regard to bad credit review procedures and borrow-

Many indicators are used to measure banks' lending activity, but the most commonly used indicators to identify credit risk are non-performing loans to total loans (NPLs) and loan loss provisions to total loans (LLPs) [19]. over the last decade, the loan portfolio loan quality remained relatively stable. Subsequently, the quality of banks' lending activity deteriorated significantly. The deterioration in the quality of banks' loan portfolios caused concerns in the banking sector in developed and emerging economies. The problem of increasing the NPL ratio is evident in the banking sector in many countries. Saba [10] points out that since 2008 the level of NPLs has increased significantly and the link between NPLs and the decline in bank credibility is considered a major factor in the failure of credit policy. It is well known that the stability of the financial sector and its likelihood of anxiety depend heavily on the share of NPLs; so NPLs serve as a common indicator in the

financial sector. A number of studies have shown that excessive credit growth often precedes the financial crisis [18].

Literature review

In recent years, more precisely, since the end of the global financial crisis, academic circles have increased their interest in the NPL, and the overview of empirical bibliography provides valuable information on the factors that influence them. However, research results need to be taken care of, and they are difficult to compare, as a definition of NPL used in all, or at least, in most countries does not exist. Although there is no internationally accepted definition, the most commonly used definitions are those given by the International Monetary Fund (IMF) and the Basel Committee on Banking Supervision (BCBS) [15].

As defined by the IMF, the NPL is a loan where the debtor is late for at least three months (90 days) with the payment of principal and / or interest in respect of the term specified in the loan contract; and a loan where the interest amount of three months (90 days) or more was capitalized (reinvested on the principal amount), refinanced, or its late payment agreed [6, 3]. According to the definition given by the BCBS, it is also recommended that the "90 days" rule is adhered to, ie it is considered to be a failure to meet the obligation if the debtor is late with the obligations to the bank for more than 90 days [1, 17].

The criteria most often used to differentiate NPL national definitions are to delay the number of days in which the bank owes its obligations, but it is not the only criterion. In addition, the debtor's financial eligibility criteria and whether a litigation against the debtor has been initiated, whether the NPL has been presented in Gross or Net Amount, and often the collateral and collateral criteria are also used. However, most of the research conducted is related to the factors that influence the NPL, while only a few studies have addressed the definition itself [4].

One of the earliest studies on the determinants of NPLs is the work of Keeton and Morris [9], who

investigated the underlying drivers of loan losses for a sample of approximately 2,500 U.S. commercial banks for the period 1979-1985. Using simple linear regressions, they found that local economic conditions coupled with the poor performance of certain sectors explain the changes in credit losses recorded by banks. The study also reported that commercial banks with higher risk appetite tend to record higher losses [2].

A simple definition of omission is a loan that is not earning full payment of principal and interest is no longer anticipated or a loan that is not earning income and principal or interest is 90 days or more overdue or a loan that is not income earning and the due date has expired and payment in full has not been made. There is no global standard for determining non-performing loans at the practical level. Variations exist in terms of the system of classification, scope, and content [7]. This problem potentially adds to the disorder and uncertainty in NPL issues. For example, as described by Se-Hark Park [8], during the 1990s, there were three different methods for determining non-performing loans in Japan: the 1993 method based on banking laws; "Bank Self-Esteem" in March 1996; and "Revaluation of Debts Based on Financial Recovery Laws" in 1999. These measurements have gradually expanded the scope and degrees of the risk management method. Similar to the trend in Japan, more countries, regulators and banks are moving towards adopting and adapting best practice and consensus. In the US, for example, federally regulated banks are required to use the five-level BIS non-performing loan rating system: Pass, Special Mention, Substandard, Doubtful and Hoss. Currently, the five-tier system is the most popular method of risk classification, or, in some cases, a dual-tier reporting system according to their internal policy guidelines, as well as the five-tier system [5].

Methodology of research

Descriptive data analysis was used in this study, where central variable statistical analysis used highly variable regression. Descriptive research involves col-

lecting data, describing the phenomenon, and then organizing, collecting, describing the data, in the form of graphs and tables, in order to help the reader understand the distribution of data. In the literature, two logical ways of developing a study structure can be used, namely inductive approach and deductive approach [11]. Inductive approximation is based on the assumption that theory is developed by empirical event research. This means that from individual research to build a general model. Deductive approach is realized by identifying the ideas set by the theories and then testing the theory. This method consists of the general in a given situation and is the opposite of the inductive approach.

In this paper deductive approach is more appropriate because of the theories given in the revised literature on non-performing loans, so it is first necessary to analyze the literature on existing theories that explain the phenomenon of NPLs.

The paper addresses the macroeconomic and banking factors of nonperforming loans in the banking sector in Kosovo. The data collection was done through secondary data from the reports and bulletins of the Central Bank of Kosovo (CBK), for the period 2007-2017.

Among the factors that will be studied in this research are:

Macroeconomic Factors:

1. Economic Growth (GDP);

2. inflation;

3. Unemployment rate;

4. Interest rate.

Banking factors:

1. Average Return on Assets (ROAA)

2. Average Return on Equity (ROEA)

3. CAR - Capital adequacy ratio

To build the econometric model of research many different researches have been used in the field of NPLs. This study is also supported and inspired by previous studies conducted by various researchers such as: Khemraj and Pasha (2005), Hess, Grimes

and J. Holmes (2008), Kumar Dash and Gaurav Kobra (2010), Kalluci and Kodra (2010), Bofondi and Ropele (2011).

The econometric model of research is: Yt = C + ßlt + ß2t + ß3t + ß4t + ß5t + ß6t + ß7t + e

Where,

NPLt = C + GDP1t + INF2t + UR3t + NI4t + + ROEA5t + ROAA6t + CAR7t + e

Description of variables:

NPL - nonperforming loans, expressed in%;

GDP - Economic Growth, expressed in%;

INF - Inflation, expressed in%;

UR - Unemployment rate, expressed in%;

NI - Interest rate, expressed in%;

ROEA - Average rate of return on equity, expressed in%;

ROAA - Average rate of return on assets, expressed in%;

CAR - Capital adequacy ratio, expressed in%

C - Constant for variables;

E - random error for period t;

T - 2007 to 2017.

Table 1. - Source of data set in the model

Variable The Source of data

NPL The Central Bank of Kosovo

GDP The Central Bank of Kosovo and World Bank

INF The Central Bank of Kosovo

NP Kosovo Agency of Statistics

NI The Central Bank of Kosovo

ROEA The Central Bank of Kosovo

ROAA The Central Bank of Kosovo

CAR The Central Bank of Kosovo

Statistical analysis

Data analysis will include the following statistical analyzes: descriptive analysis, correlation analysis, exclusion of extreme variables from the regression model, and regression analysis. Within the correlation analysis we will address the positive and negative relationship between the variables placed in the econometric model.

Table 2. - Correlation analysis

Corre ations

GDP Inflation UR IR ROAA CAR ROAE NPL

GDP Pearson Correlation 1 .891 .520 .224 .147 .155 .066 -.570

Sig. (2-tailed) .000 .101 .508 .666 .650 .848 .067

N 11 11 11 11 11 11 11 11

Inflation Pearson Correlation .891 1 .372 .602 -.084 -.137 -.067 -.391

Sig. (2-tailed) .000 .259 .050 .807 .688 .846 .234

N 11 11 11 11 11 11 11 11

UR Pearson Correlation .520 .372 1 -.187 -.210 .439 -.247 -.033

Sig. (2-tailed) .101 .259 .583 .535 .176 .464 .923

N 11 11 11 11 11 11 11 11

IR Pearson Correlation .224 .602 -.187 1 -.408 -.617 -.250 .044

Sig. (2-tailed) .508 .050 .583 .213 .043 .458 .898

N 11 11 11 11 11 11 11 11

ROAA Pearson Correlation .147 -.084 -.210 -.408 1 .586 .949 -.605

Sig. (2-tailed) .666 .807 .535 .213 .058 .000 .048

N 11 11 11 11 11 11 11 11

CAR Pearson Correlation .155 -.137 .439 -.617 .586 1 .589 -.265

Sig. (2-tailed) .650 .688 .176 .043 .058 .057 .431

N 11 11 11 11 11 11 11 11

ROAE Pearson Correlation .066 -.067 -.247 -.250 .949 .589 1 -.488

Sig. (2-tailed) .848 .846 .464 .458 .000 .057 .128

N 11 11 11 11 11 11 11 11

NPL Pearson Correlation -.570 -.391 -.033 .044 -.605 -.265 -.488 1

Sig. (2-tailed) .067 .234 .923 .898 .048 .431 .128

N 11 11 11 11 11 11 11 11

The correlational correlations between the variables set in the econometric model show that the two correlational correlations are positive and negative. The highest positive correlation is between ROEA and ROAA, where we have R = 0.949, so the increase of one factor influences the growth of the other factor. The weakest positive relationship is between the NPL and the interest rate, so we have R = 0.044, which is interpreted as the increase in the interest rate will have an increase in nonperforming loans. The strongest negative relationship is between the interest rate and the CAR, so we have R = -0.617, which indicates that one variable will increase while the other will decrease.

For the estimation of the econometric model, the analysis of the exclusion of extreme variables from

the regression line of the econometric model will be used. SPSS software has the "Boxplot" modeling technique for eliminating extreme variables. Table 3. - Boxplot Summary

Case Processing Summary

Valid Missing Total

N Percent N Percent N Percent

GDP 11 100.0% 0 0.0% 11 100.0%

Inflation 11 100.0% 0 0.0% 11 100.0%

UR 11 100.0% 0 0.0% 11 100.0%

NPL 11 100.0% 0 0.0% 11 100.0%

ROAE 11 100.0% 0 0.0% 11 100.0%

CAR 11 100.0% 0 0.0% 11 100.0%

ROAA 11 100.0% 0 0.0% 11 100.0%

IR 11 100.0% 0 0.0% 11 100.0%

Table 3 shows that none of the variables has any sion condition for the regression to have a linear "outliers" value, thus meeting the primary regres- line.

Table 4. - Summary of the econometric model

Model Summaryb

Ad- Change Statistics

justed R Std. Error of R Square F Sig. F Durbin-

Model R R Square Square the Estimate Change Change df1 df2 Change Watson

1 .812a .660 -.532 2.37365% .660 .554 7 2 .767 2.081

a. Predictors: (Constant), ROAE, IR, GDP, CAR, UR, ROAA, Inflation

b. Dependent Variable: NPL

The results of the econometric model summarization show that the coefficient of determination is 66%, which is a fairly safe rate indicating that the independent variables in the model are reasonable and explain the dependent variable quite well. The Durbin Watson coefficient, which measures the

Table 5. - Regression Summary

presence of the series correlation, takes the values of 1 to 4, while the values of 1.5 to 2.5 indicate that the series correlation has nothing to do with the econometric model used, so the value of 2.081 indicates the robustness of the econometric model used in the paper.

Coefficients3

Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations

B Std. Error Beta Zero-order Partial Part

1 (Constant) 26.581 37.861 .702 .555

GDP -1.702 5.541 -.754 -.307 .788 -.518 -.212 -.127

UR .041 .399 .095 .102 .928 .084 .072 .042

Inflation .300 1.578 .514 2.190 .007 -.385 .133 .078

IR -.433 .868 -.619 2.499 .012 -.077 -.333 -.206

ROAA -4.308 6.898 -1.531 -.625 .596 -.542 -.404 -.258

CAR -.592 2.084 -.240 -.284 .803 -.001 -.197 -.117

ROAE .338 .752 1.061 .450 .697 -.394 .303 .186

a. Dependent Variable: NPL

Based on the regression results we see that 2 factors affect nonperforming loans in Kosovo, from macroeconomic factors is inflation with a significant level of 0.7% and from bank factors is the interest rate, with a signiicant level of 1.2%.

NPLt = 26.581-1.702 GDP1t + 0.300INF2t +0.041 UR3t - 0.433NI4t - 4.308R0EA5t -0.592R0AA6t + 0.338CAR7t + e

Conclusions

The consequences for the banking industry as a result of nonperforming loans can be severe if no precautionary steps are taken. Non-performing loans adversely affect the performance and stability of the banking industry, increasing provisioning, never lending, and in more severe cases, can bring a financial institution into insolvency. The banking industry in Kosovo has also been very cautious in

terms of credit portfolio quality management, making the rates of these loans very low, which has made confidence in this sector even higher.

Kosovo has lower rates of non-performing loans compared to the countries of the region, including Albania, Macedonia, Montenegro, Serbia etc. According to the World Bank data, at the end of 2015, Kosovo recorded a percentage of non-performing loans of 7.1% in relation to the total loans the banking industry has issued to its clients. This lower level compared to all other countries presented for comparison shows the high quality of credit portfolio that the banking industry in Kosovo has to their clients.

The results of the econometric model are treated on the basis ofhighly variable regression, which contains the two elements of the topic, macroeconomic factors and banking factors. Testing of this model fulfills the parameters and conditions foreseen for testing of econometric models.

The overall conclusion of the research is that within the macroeconomic and banking factors that affect nonperforming loans are the inflation rate and the interest rate on household loans.

Recommendation

The paper has addressed the two main elements affecting NPLs, macroeconomic and banking factors, so the recommendations will be general and in the context of the problem addressed:

More care should be taken for the sectoral diversification of credit. Today there is a large concentration of loans in some specific sectors such as: manufacturing, construction and trade, vehicle and household repair;

Banks should make up for lost time in executing collateral of their clients. In fact, this procedure is greatly hampered by the current judicial system, and it should be noted that despite interventions by the Central Bank of Kosovo in unlocking the difficult situation, much remains to be done;

Banks should lower the interest rates applied on loans to businesses and individuals. It is noted that the interest rates on loans in the Kosovo banking system are among the highest in the region, and this has a direct impact on the increase of non-performing loans;

Banks should review lending policy strategies. There is an inverse relationship between the level of credit and non-performing loans. Therefore banks should increase the level of credit, as this would bring down the level of non-performing loans;

The reconfirmed correct relation of the unemployment rate to the level of non-performing loans is presented as a further argument for the usefulness and need of government bodies to intensify policies that reduce the unemployment rate and subsequently affect the reduction of the unemployment rate. non-performing loans in the banking system of Kosovo.

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