Data ?? ?????? ???????? ???????????????, ?? ?????? ??? ????? ????????, ????? ??????. ??????????. ????????? Thepurpose of the study is to investigate the factors affecting the NPL in the forbank based in the European Union of the total 28 Countries. Eurozone.Literature review provides evidence that both aggregate and disaggregate(individualbank) data are used for similar investigations. Nevertheless, according toBoudriga,Taktak, and Jellouli (2009b), aggregate data for the whole banking systemof eachcountry (in contrast to the examination of individual data for each bank) areconsideredpreferable as the risk of non-representativeness of the sample is reduced.Moreover,aggregate data were used by Rinaldi and Sanchis-Arellano (2006) in orderto overcomethe obstacle of disaggregate data unavailability in the euro area.

Forthesereasons, we chose to examine exclusively aggregate data in our research.Weextracted our data from the databases of the International Monetary Fund(IMF),the World Bank and the Eurostat. Our main objective was to collect data fromall 17countries of the Eurozone, for the longest possible period.

However, the natureof theresearch and the multitude of the examined variables created difficulties inobtainingthe required data for all countries. The main target of our study was toinvestigatethedeterminants of NPL ratio exclusively on the pre-crisis period. In thiscontext,the final sample consisted of an unbalanced panel of 14 countries with 120observationsfor the period 2000-2008. According to Rinaldi and Sanchis-Arellano(2006),unbalanced panel data include more observations and their results are lessdependenton a particular period. The distribution of observations is presented in Table1.

2.2 Methodology As mentionedabove, this study identifies the factors that affect positively or negativelythe NPLrate in 14 of the 17 Eurozone countries. Based on the merits of studiesthatinvestigate NPLs, we use a set of explanatory variables that are commonlyexaminedin suchmodels. However, one of our novelties is the inclusion of publicfinancevariables. Additionally, contrary to Boudriga, Taktak, and Jellouli (2009a),TarronKhemraj and Sukrishnalall Pasha (2009), Cotugno, Stefanelli, and Torluccio(2010),we used a dynamic panel regression method for our analysis. Specifically, inorderto provide consistent and unbiased results, we implemented the differenceGeneralizedMethodof the Moments (GMM difference) estimation, which is based onfirstdifferences and was introduced by Manuel Arellano and Stephen Bond (1991).Thechoice of this estimation is also in line with the empirical investigations ofGabrielJimenezand Jesus Saurina (2006), Louzis, Vouldis, and Metaxas (2010) and DeBockand Demyanets (2012).

However, we investigate the effect of banking andmacroeconomicfactorson NPLs for two separate periods, t and t-1. Our fisteconometricmodelis expressed as follows:NPLit = a0 + aiXi,t + aiMi,t + ?i,t (1)where NPL is the aggregatenon-performing loans to total gross loans, X denotes thebankspecific variables and M the macroeconomic factors, as presented on Table 2.Notethat i corresponds to theexamined country of the sample and t to the year.Furthermore,with the purpose of extending our investigation we use one lagforboth bank-specific and macroeconomic regressors, targeting to capture thedynamicsofexplanatory variables over the previous year. Generally, the inclusion oftimelags is commonly used in the literature e.

g. Jimenez and Saurina (2006),Cotugno,Stefanelli,and Torluccio (2010), Louzis, Vouldis, and Metaxas (2010). Therefore,oursecond econometric model is expressed as follows:NPLit =a1+aiXi,t-1 +aiMi,t-1+ ?i,t-1.

(2)Inorder to obtain deeper insight into the relevance of explanatory variables, weestimateEquation(1) and (2) in three different versions; we begin by examining onlymicrovariables as regressors, secondly only macro variables and finally both microandmacro.For theGMM estimation, we employed first and second period lagged variablesasinstruments for the explanatory variables, which are in line with the resultsofSargan test. In order to check whether our series are autoregressive, weimplementedKaopanel cointegration test. The results indicated that the null hypothesis(H0 = no cointegration) is not rejected (p-value = 0.

2547).One ofthe examined bank-specific factors is the capital adequacy ratio (CAP).CAPmeasures the risk that a bank can undertake. Generally, regarding capitaladequacyratios,although they are widely used in similar studies, the results are not clearwhetherthey affect positively or negatively the NPL index (Sinkey and Greenawlat1991; Bertrand Rime2001).

Research Design and Approach Research design is a master plan specifyingthe methods and procedures for collecting and analyzing the required data. Thechoice of research design depends on objectives that the researchers want toachieve (John, 2007). Since this study was designed to examine therelationships between NPLs and its determinants, a logical reasoning eitherdeductive or inductive is required. Deductive reasoning starts from laws orprinciples and generalizes to particular instance whereas inductive reasoningstarts from observed data and develops a generalization from facts to theory.Besides, deductive reasoning is applicable for quantitative research whereasinductive reasoning is for qualitative research. Thus, due to quantitativenature of data, the researcher used deductive reasoning to examine the causeand effect relationships between NPLs and its determinants in this study.The objectiveto be achieved in the study is a base for determining the research approach forthe study.

In case, if the problem identified is factors affecting the outcomehaving numeric value, it is quantitative approach (Creswell, 2003). Therefore,the researcher employed quantitative 31 research approach to see the regressionresult analysis with respective empirical literatures on the determinants ofNonperforming loans. Thus, the researcher was used a panel data from 2002 to2013 period. Dependent variableNonperforming LoanA performing loan is the one that generateprofitability for the bank and make it able to extend new loans. When borrowersare not able to meet theirs’ payment obligations for 90 days or more, the bankmust set aside capital equal to the reaming amount of the loan, both inprincipal and interest, under the assumption of that the loan will not be paidback. That loan from now on is characterized a non-performing loan (NPL). Non-performingloans as commonly used as a measure in order to assess the quality of the loanportfolio of a financial institution.

Deterioration of the quality of a bank’s assets is essential issue as,beside everything else, is a common cause of bank failure. NPLs can seriouslydamage a bank’s financial position having on banks operation. As EuropeanCentral Bank mention “To be successful inthe long run, banks needs to keep the level of bad loans at a minimum so theycan still earn a profit from extending new loans to customers” (ECB, 2016). Inour study will use the following independent variables in order to examine ifthey have impact to the amount of NPL, in the form of logarithm. Independent Variables Independent variables are explanatoryvariables that explain the dependent variable of NPLs. In our study, we bothinclude bank specific and macroeconomic independent variable. Bank specificvariables are the indictors of bank profitability (ROA and ROE), totalliabilities to assets ratio, capital adequacy ratio (CAR) and the logarithm of totalAssets (Size).

The macroeconomic independent variables are the Gross DomesticProduct (GDP), inflation rate (INF), unemployment rate (UNEMP) and interest rate(INT). The majority of these variables adopted from previously empirical studies,based on the extent of their effect on nonperforming loan. whereas one of these variable, that iseffective tax rate is added from the researcher’s own perception. Return on Asset (ROA): ROA isqualitative ratio that represents the efficiency of assets to generate netincome. It provides a measurement of the ability of bank management to exploit theirassets to generate profits. Since assets are the “investments” of the bank inorder to produce revenue, this ratio helps bank management and investors tomonitor how well the bank convert assets into profits.

Thus, the higher the ROAratio the better performance of the management to utilize assets to aprofitable way. Bank’s profitability in terms of Return On Assets is might aresult from high interest rates, commission and fees of services that provideto the bank growth in size andprofitability. Researchers likeMessai (2014), Kjosevski (2016) and Khan(2016) proved that the ROA has negative relationship with NPL and as Godlewski(2004) mentioned “The lower the return onasset the higher will be the NPLs and vice versa”. Return on Equity (ROE): ROE is aqualitative ratio that represents the ability of equity to generate net income.

Is one of the most important profitability metrics as it reveals the after-taxincome in comparison to the total shareholder equity. Profitability in terms ofReturn On Equity is a result of the amount of money shareholders have investedto the bank. TheCalculation of ROE comes for the Equity=Assets-Liabilities and Net EQUATIONIn manyrelative studies, the results concerning NPLs and bank profitability measure, interms of ROE, are as expected. For instance: Kjosevski (2016) and Makri etal.(2014)found negative relationships between ROE and NPLs. Total Liabilities to Total Assets RatioThe total liabilities to Total Assets Ratio oras it commonly used as debt ratio. Debt is the part of the balance sheet thatshows the obligation of the company that been monitored, debt ratio is interpretedas the leverage that a company has due to it obligations. In case of commercialbanks’ balance sheet though, liabilities (obligation to depositors or debt) isconsisted mostly by the deposits of the clientele of the bank.

Generally totaldebt to total assets ratios gives a comparison measure that shows the bank’sassets that are financed by deposits (or bank’s loans), rather than equity. Louzis et al (2010), in their study concerningdeterminants of NPLs for the Greek banking sector, they did not find the expected signs of neither the variable was statisticallysignificant (for all types of loans of the study). In our research we want to examine if thelevel of leverage in terms of assets, for our segment, is significant and ableto determine the level of NPLs of a bank’s loan portfolio. Capital Adequacy Ratio (CAR) CapitalAdequacy Ratio is also known as Capital to Risk (Weighted) Assets Ratio (CRAR),is a way of measure bank`s financial strength since it shows the ability aboutthe toleration of operational and abnormal losses. As noted by Makri et al.(2014), CAR determinesrisk behavior of banks. It is a measure of banks capital and it isexpressed as percentage in respect of risk weighted credit exposure, as it isshows the bank’s solvency and ability to absorb risk. Thus, this percentage ofcapital is the amount that used to protect depositors, promote efficiency and stabilityof financial system.

Accordingto Makri et al.(2014), there is negative relationship with NPLs indicating arisky loan portfolio is marked by a high NPL (equivalent to high credit risk).However, Djiogapand Ngomsi (2012) found positive association between NPLs and capital adequacyratio. It is 37 measured by total Equity to total asset ratio.

However, it is expected to have negative association with NPLs in thisstudy. This implies that well capitalized banks are less incentive to takerisk. Size Inflation Rate Inflation rate is interpreted as the rate inwhich the purchasing power is decreased or increased, in terms of the currency,and consequently the overall level of prices rising or falling respectively. Wecan say that is the situation in which the economies overall price level isrising. So, if the inflation is high and unexpected, it can be very costly forthe country. At the same time, inflation generally shifts cost from borrowersto lenders and savers, since borrowers can repay their loans with less worthyamount of money.

Thus, in theory inflation reduce the value of dept. as itreduce the real value of a currency hence make lending easier. However, in caseof high inflation rates the nominal lending interest rates may increase inorder to maintain the debt in its actual value.

Additionally, due to theimpacts of high inflation rates, as the reduce of purchasing power, individualhold less cash and try to counteract through interest rates of time deposits.Finally, inflation can also determine as the general consumer price index (CPI)as they are highly correlated. High changes in CPI requires monetary regulatorsto use necessary measures by for example, increasing the interest rate in orderto control inflation which later increase the cost of borrowing and ultimatelycause NPLs.

Based on this, the relationship between NPLs and inflation isexpected to be negative for this study. According to Jordan et al. (2013) ……………. theyfound a positive relationship between NPLs and Inflation rate. The expected results of the increase of NPLs whileInflation rate rises, is confirmed also by the literature regarding the relationship between NPLs andinflation rate. According to Farhan et al.(2012), Skarica(2013), Klein(2013)and Tomak(2013) found as there is a positive relationship between NPLs andInflation rate. UnemploymentRate Unemployment rate is the percentage of theworking force that stay unemployed.

Individuals who would like to work but theyare not able due to disability for example, are not considered as unemployed.In periods of economic crisis and recession, a high unemployment rate is almostinevitable. The positive impact of unemployment, to the increase ofnon-performing loans is relatively expected, as the increase of unemploymentrate lead to the general decline of households’ income and finally causeindividuals not able to pay their loan’s obligations. Theexpected positive impact of unemployment rate to the increase of NPLs, is alsoconfirmed between other, by the Jordan et al.

(2013), Messai 2013 and Makri2014. GrossDomestic Product (GDP) Several empirical studies have found a negativeassociation between NPL and real GDP growth (Salas and Saurina 2002; Fofack,2005; Jimenez and Saurina, 2006; Khemraj and Pasha, 2009; Dash and Kabra,2010). Anotherindependent variable we consider is the Gross Domestic Product of the countrythat each bank is based. GDP is the best measure to measure a country’s economy(Amadeo, 2017), as it summarizes everything that being produced by allindividuals and companies between the boundaries of the country, despite theorigin of the producer. GDP rate is the percentage increase or decrease of theGDP among the years and provide us the growth rate of each country. An economyin growth is favorable to a decrease of financial distress and to an increaseof revenues.

A high positive GDP rate habitually entails a higher level ofhousehold income and subsequently a better capacity of the borrower to meet hisobligations and pay his debts. As a result, a negative impact of GPD to NPLs isexpected. Severalempirical studies have found the negative association between GDP and NPLs, asexpected (Clichici, ????, Messai 2013 and Makri 2014) Interest RatesInterest rate is the price a borrower pays for the use of money they borrowfrom a lender/financial institutions or fee paid on borrowed assets (Crowley,2007). It is “rent of money” fundamental to a’capitalist society’ and normally expressed as a percentage rate over the period ofone.

Interest rate as a price of money reflects market information regardingexpected change in the purchasing power of money or future inflation (Ngugi,2001). Fluctuationsof market interest rates exert significant influence on the activities ofcommercial banks. Banks determine interest rates offered to consumers, themortgage production line ends in the form of purchased by an investor.

The freemarket determines the market clearingprices investors will pay for mortgage-backed securities. These prices feedbackthrough the mortgage industry to determine the interest rates offered toconsumers http://erepository.uonbi.ac.ke/bitstream/handle/11295/95462/Mwangi_Effect%20of%20interest%20rates%20on%20non-performing%20loans%20in%20Commercial%20Banks.pdf?sequence=5 The aim of this study is to examine the determinants ofNPLs of commercial banks in Ethiopia.

Similar to the most noticeable previousresearch works conducted on the nonperforming loans of financial sectors, thisstudy used nonperforming loans ratio as dependent variables whereas Loan todeposit ratio, capital adequacy ratio, return on asset, return on equity,Average lending rate, inflation rate and effective tax rate as explanatoryvariables. These variables were chosen since they are widely existent for thecommercial bank in Ethiopia. Accordingly, this study examined the determinantsof NPLs of commercial banks in Ethiopia by adopting a model that is existed inmost literature.

The regression model which is existed in most literature hasthe following general form; Yit= ?o + ?Xit + ?it Where: – Yit is the dependentvariable for firm ‘i’ in year ‘t’, ?0 is the constant term, ? is thecoefficient of the independent variables of the study, X it is the independentvariable for firm ‘i’ in year ‘t’ and ?it the normal error term. Thus, thisstudy is based on the conceptual model adopted from Fawad and Taqadus (2013).Accordingly, the estimated models used in this study are modified and presentedas follow; NPLit= ?0 +?1(LTD)it + ?2(CAR)it+ ?3(ROA)it+?4(ROE)t+ ?5(ALR)it+ ?6(INFR)it+ ?7(ETR)it+?it Where; § ?0 is an intercept § ?1, ?2, ?3, ?4, ?5, ?6, and ?7represent estimated coefficient forspecific bank i at time t , § LTD,CAR, ROA, ROE, ALR, INF and ETRrepresent Loan to deposit ratio, capital adequacy/Solvency ratio, return onasset, return on equity, Average lending rate, inflation rate and effective taxrate respectively § ?it represents error terms forintentionally/unintentionally omitted or added variables. It has zero mean,constant variance and non- auto correlated. The coefficients of explanatoryvariable were estimated by the use of ordinary least square (OLS) technique. amadeohttps://www.thebalance.com/what-is-gdp-definition-of-gross-domestic-product-3306038ECBhttps://www.ecb.europa.eu/explainers/tell-me/html/npl.en.html