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Asian Finance & Banking Review; Vol. 3, No. 1; 2019 

ISSN 2576-1161   E-ISSN 2576-1188 

Research Article               Published by Centre for Research on Islamic Banking & Finance and Business, USA 

 

     1 
 

Collision of NPLs on the Financial Performance of Commercial 

Banks: A Case Study of Ethiopia 
 

 

Hailu Megersa Tola 

Dean 

College of Business and Economics 

Ambo University 
Ethiopia 

 

D. Guna Sankar 

Department of Accounting and Finance 

Ambo University 

Ethiopia 

 
 

Abstract 

Credit risk in banking relates to the possibility that loans will not be paid or that investments will   deteriorate in 

quality or go in to default with resultant loss to the bank. This is the most obvious and most important risk to the 

banking industry in terms of potential losses. Credit risk is not confined to the risk that borrowers are unable to pay; 
it also includes the risk of payments being delayed, which can also cause problems for the bank. In order to protect 

their own interest and the wealth of bank depositors, banks need to investigate and monitor the activities of the will 

be and existing borrowers. Adequately managing of those risks related with credit is critical for the survival and 

growth of any financial institution. The present case study projects the effects of Non-Performing Assets on the 

Financial Performance of Commercial Banks in Ethiopia. 

 

Keywords: Commercial Banks, Effects, Ethiopia, Financial Performance, NPLs. 

 

1. Introduction  

A non-performing loan is a loan that is in default or close to being in default. Banks face different elements of risk 

that require to be identified measured and managed. Managing these risks is a process by which one identifies the 
risk, measures and quantifies the risk and develops strategies to manage the risk. The banking industry is facing 

different types of risks associated with its functions. But according to Van Gestel & Baesens (2009), credit risk has 

been the most principal and perhaps the most important risk type that has been present in finance, commerce and 

banks too. Credit risk has been defined from different perspectives by different researchers and organizations. Most 

researchers agreed with the definition given by Basel (1999) who defines it as the potential that debtor or 

counterparty default in satisfying contractually predetermined obligation according to the agreed up on terms. 

Because failure of trading partner to repay its debts in full can seriously damage the affair of the other partner, credit 

risk always has been the vicinity of career throughout the world (Achoo & Tenguh, 2008). 

According to Zewude (2011), for banks, the issue of credit risk is of even of greater concern because of the higher 

level of perceived risk resulting from the loan book which is the largest asset for any commercial bank. Even though 

credit creation is the main income generating activity for commercial banks, it involves a huge risk to both the banks 

and the borrowers.  

2. Statement of the Problem 

Banks are exposed to risks like credit, market, operational, interest rate and liquidity risk. The appropriate 

management of these risks is a key issue to reduce the earnings risk of the bank, and to reduce the risk that the bank 

becomes insolvent and depositors cannot be refunded. Banks use deposits of their customers to generate credit for 

their borrowers, which in fact is a revenue generating activity for the banks themselves. This credit creation process 

exposes the banks to a high default risk which might lead to financial distress including bankruptcy. The banks can 

either choose from the proposed options or employ their own as long as it gives sound and fair results.  

The importance of the credit risk management and its impact on performance has motivated researcher to pursue this 

study. The research assumes that if the credit risk management is sound, the performance (profit level) was 

satisfactory. The other way around, if the credit risk management is poor, the performance (profit level) was 



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relatively lower. The central question is how significant is the impact of credit risk management on performance 

(profitability). 

3. Objectives of the Research 

 The general objective of this study was to assess the impact of NPLs on the performance of selected 

commercial banks in Ethiopia. 

 To analyze the impact of credit risk management on the performance of the bank. 
 To determine the relationship between credit risk management and performance in terms of profit for 

the commercial banks in Ethiopia. 

4. Hypotheses of the Research 

Throughout the research, the following two hypotheses were tested. 

 H1: Non-performing Loan Ratio (NPLR) has significant impact on the performance of commercial Banks 

in Ethiopia.  

 H2: Capital Adequacy Ratio (CAR) has significant impact on the performance of commercial Banks in 

Ethiopia. 

5. Significance of the Research 

It was show the impact of NPLs on bank performance; it was give a motivation to other researchers to conduct a 

research about the NPL‟s practices in the commercial banks and it was useful for financial institutions by providing 

information about NPLs. 

6. Scope of the Research 

In Ethiopia, there were banks which give service in number twenty one. This study was limited to a manageable of 

five banks in commercial banks of Ethiopia and even if there were different problems which need investigation, the 

aim of the study was to to assess the impact of NPLs on the performance of selected commercial banks in Ethiopia. 

These researches were limited on the measure of the performance of commercial banks in terms of credit risk 

management under the selected sample.  The study was employ non-performing loan ratio/NPLR/ and capital 

adequacy ratio/CAR/ as the measuring instruments of credit risk management and return on asset /ROA/ as indicator 

of performance in terms of profit. 

7. Limitations of the Research 

The researcher limited this study to only five commercial banks; the study was limited to 10 years of bank data and 

the study was based on secondary data only. 
The researcher decided to limit this study to Commercial Banks of Ethiopia namely, Awash international bank, 

Bank of Abyssinia, Nib International Bank, Dashen Bank and Commercial Bank of Ethiopia. These banks have been 

selected with criteria taken as the five banks from other banks expected to have more than ten years of experience on 

the lending activities. 

8. Sampling Design 

The researchers selected five major commercial banks in Ethiopia and collected the necessary data from each bank. 

Those data was collected from National Bank of Ethiopia annual report from 2007 to 2016, and used for regression 

purpose. The reason why the researcher purposively selects five banks is, to have more observations. For those 

banks with 10-year life span is selected. Therefore, there are 50 observations in the regression analysis. 

9. Source of Data and Data Collection Instrument 
The main source of data for the study was found from the audited balance sheet and income statement of five 

purposively selected banks. From those banks, 10 consecutive years of balance sheet and income statement report 
were used for the study. In our country it‟s a must for banks to submit its annual report to the NBE not only that they 

are supposed to submit their off balance sheet too. So the researcher„s easily gets annual reports of all selected banks 

from the NBE.  

10. Data Analyzing Instrument 

The data collected from the annual reports of the banks were analyzed using multiple regression analysis: the 

relation of one dependent variable to multiple independent variables. The regression output was obtained using 

Statistical Package for Social Sciences (SPSS 20 version). 

11. Model Specification 

In this study, multiple regression models with two independent variables were used. To measure for financial 

performance i.e. ROA (Net Income/Total asset): for credit risk management are NPLR (Non-performing 

Loans/Total Loans) and CAR] respectively. 

12. Inferential Analysis of Commercial Banks in Ethiopia 

12.1 Diagnostic Tests 

Here the researcher used regression command for handling the regression. This is followed by the output of these 

SPSS commands. 



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Table 1 

Variables Entered/Removedb 

Model Variables Entered Variables Removed Method 

1 CAR, NPLRa . Enter 

a. All requested variables entered.  

b. Dependent Variable: ROA  

Source: SPSS regression out put 

 

Table one shows the variables entered or variables removed from the study at any point of time from the beginning 

till the end of the work. As it is explained the variables entered in column   two are independent variables of the 

study i.e., capital adequacy ratio and non-performing loan ratio. Since there was no variable removed from the 

study, variable removed column is free. The last column shows the method that was used by the researcher, enter 

method was used to remove or enter the variables. All variables are entered on the above table. The dependent 

variable which is return on asset explained in the bottom of the table. 

 

Table 2: Linearity of the Variables  

Model Summaryb 

Model R R Square Adjusted 

R Square 

Std. Error of 

the Estimate 

Change Statistics 

R Square 

Change 

F Change df

1 

df

2 

Sig. F 

Change 

1 .422a .178 .143 .00571 .178 5.080 2 47 .010 

a. Predictors: (Constant), CAR, NPLR       

b. Dependent Variable: ROA       

Source: SPSS regression out put 

 

Table 2; demonstrates about large R, which shows the multiple correlation coefficients and the correlation between 

the observed and predicted values of the dependent variables. And the value of R for models produced by the 

regression procedure range from 0 to 1. The larger the value of R display that there is strong relationship among 

observed and predicted value. In our case R is 0.422.  

R square tells us, how much of variance in the dependent variable is explained by our independent variable. So, in 

our case we were known how much NPLR and CAR explained by ROA.   As of R and the value of R square ranges 
between 0 and 1, beside to that small value indicates that the model does not fit the data well. As the table indicates, 

the independent variable explained the dependent variable by 17.8%. This means that our model using two predicted 

variables (NPLR & CAR) explain about 17.8% variance of our dependent variable (ROA). Right next to R square 

we get adjusted R square. If we had small sample size the R square value in the sample tend to be a little over 

estimated and little optimistic over estimation of what probably really happening in the population. So, Adjusted R 

square corrects this value to provide a better estimation of what actually happening in the population. In our case 

Adjusted R square is .143. Standard error of the estimate this is basically gives an idea of how much our prediction 

might be off. If the number is large the more variability it indicates from the table, we have Standard error of the 

estimate (0.006) which is very small and good. 

R square is significant at 5 % level of significance as the SE <.05. 

Table 3: ANOVA Table  

ANOVAb 

Model Sum of Squares df Mean Square F Sig. 

1 Regression .000 2 .000 5.080 .010a 

Residual .002 47 .000   

Total .002 49    

a. Predictors: (Constant), CAR, NPLR    

b. Dependent Variable: ROA     

Source: SPSS Regression Out put 

ANOVA, table summarizes the output of the analysis of variance. In regression row, the output for regression 

displays information about the variation accounted for by the existing model. Residual displays information about 

the variation that is not accounted for by the model. And total in the table shows the sum of regression and residual. 

Mean square is the sum of squares divided by the degrees of freedom. And F statistics is the regression mean square 



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divided by the residual mean square. If the significance value of the F statistics is small, then the independent 

variable does a good job in explaining the variation in the dependent variables. 

 

Table 4:  Collinearity Diagnostics Test Table  

Collinearity Diagnosticsa 

Mode

l 

Dimension Eigenvalue Condition Index Variance Proportions 

(Constant) NPLR CAR 

1 1 2.561 1.000 .01 .06 .01 

2 .387 2.574 .03 .92 .05 

3 .052 7.005 .96 .02 .94 

a. Dependent Variable: ROA    

Source: SPSS Regression Out put 

 

Table 5 is a table which displays statistics that help to determine whether there are any problems with collinearity or 

not. Collinearity (multicollinearity) is the undesirable situation where the correlations among the independent 

variables are strong.  

Eigenvalues proved an indication of how many different dimensions are there among the independent variables. 

When several Eigen values are close to zero, the variables are highly interring correlated and small changes in the 

data values may lead to large changes in the estimates of the coefficients.  

Condition index are the square roots of the ratios of the largest eigenvalue to each successive eigenvalue. A 

condition index greater than 15 indicates a possible problem and an index greater than 30 suggests a serious problem 

with collinearity (SPSS output).  

Even if eigenvalues are used for checking the existence of collinearity, the best way is conditional index. So in this 

research case, since conditional index value scored around 1, 2 and 7, from this ground the researcher can say that 

there is no multicollinearity among independent variables. 

 

Table 5 Residuals Statistics 

Residuals Statisticsa 

 Minimum Maximum Mean Std. Deviation N 

Predicted Value .0186 .0331 .0280 .00260 50 

Residual -.01952 .01035 .00000 .00559 50 

Std. Predicted Value -3.609 1.973 .000 1.000 50 

Std. Residual -3.416 1.812 .000 .979 50 

a. Dependent Variable: ROA    

Source: SPSS Residual Out put 

 

Table 6, tells about the residual and predicted value. For each case, the predicted value is the value predicted by the 

regression model and for each case; the residual is the difference between the observed value of the dependent 

variable and the value predicted by the model. Residuals are estimate of the true errors in the model, if the model is 

appropriate for the data, the residuals should follow a normal distribution. Standardized predicted values are 

predicted values standardize to have mean 0 and standard deviation of 1. In short standardize residuals are ordinary 

residuals divided by the sample standard deviation of the residual and have mean of 0 and standard deviation of 1. 

12.2 Test of Normality of Residuals 

One of the assumptions of linear regression analysis is that the residual is normally distributed, at the mean of zero 

and standard deviation of one. All of the results from the examiner command suggest that the residual or the error 

terms are normally distributed. The skewness and kurtosis are near to 0. As one can observe from the histogram and 

p-p plot it looks normal. Based on these results, the residuals from this regression appear to conform to the 

assumption of being normally distributed. 

 

 

 



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Figure: 1. Histogram (Test of Normality) 

 
Source: SPSS regression out put 

Figure: 2 Normal p-p plot of RegressiS on tandardized Residual 

 
Source: SPSS Regression Out put 

The above Figures show whether the data are normally distributed or not. The error term should be normally 

distributed at the mean of 0 and standard devotion 1, here in this model the mean is approximately 0 and the 

standard devotion is 0.979 approximately 1, so the model is normally distributed. The researcher watched from the 

histogram and from the p- p plot too. 

 

 



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12.3 Relationships between ROA and NPLR 

Figure 3: Scatter Diagram for ROA vs. NPLR 

 
Source:  SPSS Regression Out put 

The scatter diagram above shows the negative relationship between the two variables hence a negative gradient. This 

confirms the coefficient of the NPLR of the variable in the regression equation and hence non-performing loans is 

good indicator of return on asset from the above results. 

The points are closely clustered at one point. This may indicate the nature of performance of the bank and the level 

of shareholding and if the institution is public listed or private companies. The nature of relationship does not give a 

positive relationship to the whole banking sector in this research analysis. 

 

12.4 Relationship between ROA and CAR 

 
 

Figure 4: Scatter Diagram for ROA vs. CAR 

 

From the scatter diagram above the points along the line of the best fit are observed to have a big dispersion in 

regard to the line. The Positive points make line look like horizontal reducing the gradient/slope between the two 

variables. 

 
 

 



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12.5 Descriptive Analysis of Commercial Banks in Ethiopia 

The Relationship between Credit Risk Management and Profitability at CB of Ethiopia 

Graph 5 

 
Scatter Diagram for ROA, CAR and NPLR of Commercial Bank of Ethiopia 

 

From the above graph it‟s that observed the relations between ROA, NPLR & CAR of commercial bank of Ethiopia. 

 

When NPLR reaches its maximum at (15), ROA reaches its minimum at (2). This means that 15% from the total 

loan are non-performing or default to be paid by the bank customer. So an increase trend of ROA indicates that the 

profitability of the company is improving. Conversely, a decreasing trend means that profitability is deteriorating. 

So, 2% indicates a deteriorating profitability of CB of Ethiopia. 

 During (2007-2008) CBE have low performance regarding credit risk management in terms of (NPLR) and from 

(2009-2016) dramatically decrease and ROA is above NPLR this shows that CB of Ethiopia manages its default loan 

properly and the profit of the bank also increase dramatically. 

CAR of commercial bank of Ethiopia indicate normal trend throughout the years. 
 

 12.6 The Relationship between Credit Risk Management and Profitability at BOA 

 

Graph 6The Relationship between Credit Risk Management and Profitability at BOA 

 
Scatter Diagram for ROA, CAR & NPLR OF Bank of Abyssinia 

 

From the above graph were observed the trend of ROA, NPLR & CAR for Bank of Abyssinia. 

 

During 2008 NPLR reaches its maximum with 9.8 % this shows that from the total loan 9.8 % default to be paid by 
the customer. 



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NPLR shows a zigzag trend from 2008-2013 and it has decrease initially. Finally from 2014-2016 it decreases at an 

increasing rate. In recent year the trend shows BOA has managed its NPLR. 

From this trend were observed that when the number of NPLR reaches its maximum ROA reaches its minimum 

from our sample of 10 years of data. 

ROA is greater than NPLR from year 2012 this shows BOA was managed its default loans properly.  

CAR shows high trend which means that BOA kept high capital for risk weighted sum for bank assets. 
The Relationship between Credit Risk Management and Profitability at AIB 

Graph 7 

 
 

Scatter Diagram for ROA, CAR &NPLR of Awash International Bank 

From the above graph were observed the relations between ROA, NPLR & CAR of Awash International Bank 

Like other banks, AIB have bad NPLR from (2007-2010) years but it decreases continuously from (2011-2016) as 

the above figure indicated. 

ROA is below NPLR From (2007-2010) and above NPLR from (2011-2016) this indicates that Awash International 

Bank have managed its NPLR properly during years. 

CAR of Awash International Bank is little to high relative from the above mentioned banks.  

 
 The Relationship between Credit Risk Management and Profitability at NIB 

Graph 8 

 
 

Scatter Diagram for ROA, CAR &NPLR of Nib International Bank 

The above graph shows the relation between ROA, CAR & NPLR of Nib International Bank. 

 

NIB has relatively lower NPLR when compared to that of the other banks. 

From (2007-2010), NPLR is above ROA and from (2011-2016) ROA is greater than NPLR. NIB has managed its 

default loan properly.  

 



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CAR of NIB are very high like other private banks 

The Relationship between Credit Risk Management and Profitability at DB 

Graph 9 

 
 

Scatter Diagram for ROA, CAR &NPLR of Dashen Bank 

 

The above graph shows the relation between ROA, CAR & NPLR of Dashen bank  

Dashen bank NPLR is low from year (2007-2016). This shows that the bank managed loan properly. It should be a 
good example for other banks regarding Loan management. 

CAR of Dashen is high like other private banks. 

13. Findings of the Research 

 This study shows that there is a significant relationship between performance (in terms of profitability) and 

credit risk management (in terms of loan performance and Capital adequacy). The results of the analysis 

states that NPLR have negative and significant effect whereas CAR have positive and relatively significant 

effect on ROA, with NPLR having higher significant effect on ROA in comparison to CAR. The regression 

as a whole has significant result; this means that NPLR is reliably predicted of ROA but CAR has relative 

low significant prediction.  

 From the data analyzed above, the relationship of the three variables i.e. ROA, CAR, and NPLR well 

explains the credit risk management on the performance of selected institutions. Since banks take deposits 

and use the same to advance loans the costs associated with these loans e.g. insurance costs reduce the 
profitability margins of the bank.  

 Return on asset (ROA) measures the ability of the bank management to generate income by utilizing 

company assets at their disposal. Therefore, increased portfolio at risk will reduce the revenue aspect and 

increase the cost associated as indicated by the analysis of nonperforming loans.  

 The output from the regression analysis. First of all, let„s looks the p value of the F test to see if the overall 

model is significant or not (from MODEL SUMMERY table 2). With the p value of 0 to the four decimal 

places, the model is statistically significant. The R square is 0.178 meaning that approximately 18% of the 

variability of ROA is accounted for by the variables in the model. The coefficient for each of the variables 

indicates the amount of change one could expect in ROA given a one unit change in the value of that 

variable, given that all other variables in the model are held constant. For example, let„s consider the 

variable NPLR (Coefficients table 4); the researcher would expect a decrease of -.075 in the ROA score for 
every one-unit increase in NPLR, by assuming that all other variables in the model are held constant. 

 Here the researcher answer about the two predictors, whether they are statistically significant and if so the 

direction of the relationship. (From table4) The effect of NPLR (non-performing loan ratio) which is (Beta 

= -.075, P 0.012) is significant and its coefficient is negative indicating that the greater the non-performing 

loan ratio the lower the profitability of commercial banks in Ethiopia. If the NPLR is high lower 

profitability of banks. This result also makes sense, because both the theoretical and empirical evidences 

„support that too. The effect of capital adequacy ratio is also (CAR, Beta = 0.042) significant (p, 0.061) and 

as watched it is positive and p value greater than 0.05 which indicates that CAR have relatively low or in-

significant effect on ROA. 



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14. Conclusion and Recommendation 

The purpose of this chapter is to review the whole thesis and highlight future researcher directions. The next section 

displays recommendation made by the researcher for all concerning issues. 

Conclusion 

 The main objective of this study was to assess the impact of credit risk management on the performance of 
selected commercial banks in Ethiopia, for the sample period of ten years i.e. 2007 to 2016.  

 The study concluded that NPLR had a statistically significant effect on the level of ROA. However, the 

results of this multiple regression model revealed that CAR has no significant effect of on the level of 

ROA.  

 There was no correlation among independent variables (NPLR and CAR) which means each of the 

independent variables explained the dependent variable separately. 

 There was no collinearity (multicollinearity) among independent variables. 

 Non-performing loan ratio have negative impact, on the other hand capital adequacy ratio have positive 

impact on profitability of selected commercial banks in Ethiopia.  

 Finally, from the data analyses, non-performing loans and capital adequacy ratio have shown that there 

exists a relationship between credit risk management and the performance of selected banks. But CAR has 

relatively lower effect on measuring the performance of selected banks. An increase in NPLR increases the 
credit risk of banks. 

Recommendation 

Based on the findings and conclusions of the study the following recommendations are given.  

 

 The bank management needs to be cautious in setting up a credit policy that will not negatively affects 

profitability and also they need to know how credit policy affects the operation of their banks to ensure 

judicious utilization of deposits and maximization of profit.  

 Banks should establish credit policies and standards that confirm to regulatory requirements and the bank's 

overall objectives to further reduce the level of their credit risk exposure (unprotected).  

 Banks are advised to provide adequate training in terms of their credit policies and standards to their 

employees who are working in Loan disperse department. 
 This study could be further developed by including more independent variables to the regression model and 

increasing the sample size. 

 It is better if this study is supplemented with qualitative study of credit risk management so that the 

findings would be more objective and informative. 

 Performance indicator could be developed by adding other relevant dependent variable to grasp 

(understand) the whole variations in performance. 

 Finally, the study suggests that a further study should be done on the impact of credit risk management on 

different aspects of banks' activity. 

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open-access article distributed under the terms and conditions of the Creative Commons Attribution license 

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