RIFT VALLEY UNIVERSITY

SCHOOL OF BUSINESS AND ECONOMICS

DEPARTMENTMASTER OF BUSINESS
ADMINISTRATION (MBA) PROGRAM

The Impact of Credit Risk Management on

Profitability of Commercial Banks in Ethiopia
By: ARGA SHURE
Advisor: ODA YADATA (PhD)
July, 2024
Shashemene, Ethiopia
 DEFENCE CONTENT
Background of the Study………………….…..……………...
Statement of the Problem……………..……….………...........
Objectives of the study……………………………….………
General objective……………………………………………..
Specific objectives……………...............................................
Research Approach/Method………….....…………...............
Sample Design …………………………………..………….
Types of Data and Collection Instruments …………………..
Data Quality Assurance …………………………………….
Method of Data Analysis and Interpretation ………………
Reliability and Validity of the Study ……………………….
Result And Discussion…………………...............................
Conclusion……………… ………………..………………..
Recommendation…………… ………………………...........
 Chapter 1: Introduction
Background of the Study
• Banks are financial institutions that accept deposit, make loans and
extend credit (loan) to different types of borrower and investors for
varies different purposes.
• Banking industries are viewed as an essential element of a healthy and
vibrant economic development to a given country. They are seen as a
vital role in promotion of banks investments and development of
financial system within economy (Kechema T.et.al, 2019). For most
customers, bank credit is the primary source of available debt financing
and for banks good loans are the most profitable assets.
• Credit in bank is a contractual agreement in which a borrower receives
something of value now and agrees to repay the lender at some later
date, however, credit risk is defined as the probability that some of a
bank‘s assets, especially its loans, will decline in value and possibly it
arises from non-performance by a borrower, either an inability or an
unwillingness to perform in the pre-committed contracted manner or it is
the potential that a bank borrower/counter party fails to meet the
obligations on agreed terms (R.S. Raghavan, 2013).
 Cont.…
• Bank credit is the primary source of available debt financing and for
banks; good loans are the most profitable investment asset and relevant
to economic development through the financial services they provide
for most customers.
• An efficient and effective performance of the banking investment over
time is an index of financial stability in any nation and extent to which a
bank extends credit to the public for productive activities accelerates the
pace of a nation’s economic growth and its long-term sustainability on
the banking investments activities (The Basel Committee on Banking
Supervision, 2021). Even if, credit creation is the main income
generating activity for banks, it also involves huge risks to both the
lender and the borrower.
• The risk of a trading partner not fulfilling his/her obligation as per the
contract on due date or anytime thereafter can greatly jeopardize the
smooth functioning of a bank‘s business. On the other hand, a bank
with high credit risk has high bankruptcy risk that puts the depositors in
jeopardy (danger) that can easily and most likely prompts bank failure
(Frederic S. Mishkin, 2014, pp 8-9).
 Cont.…
According to David H. Pyle, 1997, stated that credit risk in a bank‘s loan portfolio
consists of three components;
Transaction Risk: Transaction risk focuses on the volatility in credit
quality and earnings resulting from how the bank underwrites
individual loan transactions. Transaction risk has three dimensions:
selection, underwriting and operations
Intrinsic Risk: It focuses on the risk inherent in certain lines of
business and loans to certain industries. Commercial real estate
construction loans are inherently more risky than consumer loans.
Intrinsic risk addresses the susceptibility to historic, predictive, and
lending risk factors that characterize an industry or line of business.
Historic elements address prior performance and stability of the
industry or line of business. Predictive elements focus on
characteristics that are subject to change and could positively or
negatively affect future performance. Lending elements focus on how
the collateral and terms offered in the industry or line of business
affect the intrinsic risk.
 Cont.…
Concentration Risk: Concentration risk is the aggregation of
transaction and intrinsic risk within the portfolio and may result from
loans to one borrower or one industry, geographic area, or lines of
business. Bank must define acceptable portfolio concentrations for
each of these aggregations. Portfolio diversify achieves an important
objective. It allows a bank to avoid disaster. Concentrations within a
portfolio will determine the magnitude of problems a bank will
experience under adverse conditions.
In case of banks, the issue of credit risk is of even of greater concern
because of the higher level of perceived risk resulting from some of the
characteristics of clients and business conditions that they find
themselves (Achou and Tenguh, 2018) .
 Cont.…
According to Developmental Bank of Ethiopia; Strategic plan, 2020,
empirical results indicate that higher capital adequacy ratio and
prudent provision policy seem to reduce the level of problem loans. We
also report a desirable impact of private ownership, foreign
participation and bank concentration. Findings do not support the view
that market discipline leads to better economic outcomes and to reduce
the level of problem loans. In contrast, all regulatory devices either
exert a counterproductive impact on bad loans or do not significantly
enhance credit risk exposure for countries with weak institutions,
corrupt business environment and little democracy.
The results are interesting for regulators, bankers and investors to
reduce credit risk exposure, the effective way to do it through
enhancing the legal system, strengthening institutions and increasing
transparency and democracy, rather than focusing only on regulatory
and supervisory issues (National Bank of Ethiopia; Strategic plan
2020).
 Cont.…
Commercial banks make profit from the spread between the interest
rate they charge to borrowers and the interest rate they pay to
depositors. Lending has always been the primary functions of banks,
and accurately assessing a borrower‘s credit worthiness has always
been the only method of lending successfully (Andrew Fight, 2004).
To insure reasonable profit, banks attempt to make loans that will be
fully repaid with interest on due date. Therefore, banks are directly
concerned about borrowers repaying their loans on a timely basis so
that the value of the banks can be maximized. If banks don‘t manage
credit risks effectively, they won‘t be profitable and won‘t be in
business very long.
Credit risk management needs to be a robust process that enables the
banks to proactively manage the loan portfolios to minimize the losses
and earn an acceptable level of return to its shareholders. The
importance of the credit risk management is recognized by banks for it
can establish the standards of process, segregation of duties and
responsibilities such in policies and procedures endorsed by the banks
 Cont.…
Generally, Credit risk management is a structured approach to mange
uncertainties arising from the probability that the borrower will default
to pay the money that he/she takes as a loan (either the principal or
interest or both). Effectiveness in this area has an impact on the
profitability, liquidity, solvency, loan portfolio and financial leverage
of commercial banks in every country. In this problem area, i.e.
impacts of credit risk management on banks profitability there are
some studies conducted in different countries such as: Ara Hosna,
Bakaeva Manzura and sun Juanjuan in 2019 studied, Credit Risk
Management and Profitability of Commercial Banks in Sweden. They
took four banks to study this area and used multiple regression models
to analyze their findings.
The researcher study will be important to the audience by providing a
literature review on the determinants of credit risk management
mechanisms like screening and monitoring, long term customer
relationship, collaterals and credit rationing on the profitability of
commercial banks in Ethiopia.
 Statement of the problem
This study examines and focuses mostly on the relationship
between effective credit risk and should be a critical component of a
bank‘s overall risk management strategy and essential to the long-
term profitability of commercial banks in Ethiopia.
In order to protect their own interest and the wealth of bank
shareholders/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, better bank performance increases their reputation,
image from public or market point of view and it also get more
opportunities to increase the productive assets, leading to higher
bank profitability, liquidity, and solvency. (Tandelilin, Kaaro,
Mahadwartha, Supriyatna, 2017).
Therefore, purpose of this study is will be to measuring the
determinants level of credit risk management on profitability‘s of
 Research question
In order to achieve the objective of this investigation, the following
specific research question has been developed:
What are impacts of credit risk management on the growth and
profitability of the commercial banks in Ethiopia?
Is that nonperforming loan has an impact on profitability‘s of
commercial banks in Ethiopia?
Does loan growth rate affect banks’ profitability of the commercial
banks in Ethiopia?
What are most important credit risk management mechanisms to
reduce credit risk of commercial banks in Ethiopia?
 Objectives of the study
General objective
Based on the forgoing information, the general objective of this study
is assessing an impact of credit risk management on profitability of
commercial banks in Ethiopia.
Specific objectives
More specifically, the study would attempt to achieve the following
objectives:
To analyze an impacts of credit risk management on the growth and
profitability of the commercial banks in Ethiopia?
To identify that nonperforming loan has an impact on profitability‘s of
commercial banks in Ethiopia?
To describe the factors of loan growth rate affect banks’ profitability of
the commercial banks in Ethiopia?
To point out the most important credit risk management mechanisms
to reduce credit risk of commercial banks in Ethiopia?
 Chapter 2
2. Review of Related Literature
2.1 Theoretical Reviews
2.2 Empirical Reviews
 Chapter 3
3.Research Methodology and Design
To achieve the research objective, the researcher uses quantitative
research approach or method to analyze the data that collected from the
National Bank of Ethiopia (NBE) and from seven commercial banks of
the country. Those are Awash Bank, Oromia Bank, Abyssinia Banks,
Wegagen Bank, Commercial Bank of Ethiopia, United Bank and
Dashen Banks. Depending on the result of regression output and
feedback from research question, then analysis were conducted.
The researcher selects seven commercial banks of the country who
submit their annual report to NBE starting from 2013 to 2024 years that
reason, the researcher does have 96 observations in the regression
analysis. Theoretically, the number of observations should be 20:1 (20
observations per one independent variable) in the regression analysis
and as low as 5:19. (as sited as, Princeton University.), but in this study
the researcher used more than double from what actually expected for
regression.
 Sample Design
The researcher selects seven major commercial banks in Ethiopia as a
target population and collects the necessary data from each bank and
from national bank of Ethiopia too, for sake of comparison. Those
data are collected from 2013 to 2023 and used for regression purpose.
The reason why the researcher purposively selects seven banks is,
to have more observation. Therefore, there are 72 observations in
the regression analysis.
Theoretically the number of observation should be 20:1 (20
observation per one independent variable) in the regression
analysis and as low as 5:19 in our case, the researches added 96
observation and seven independent variables
 Types of Data and Collection Instruments
The main sources of data for the study are both primary and secondary
data found from seven purposively selected commercial banks. For
primary sources questionnaires to be distributed to Risk and Compliance
Management Officer of the head office, Risk Management Department
Officers, loan officers and selected staffs of the head office.
For secondary sources twelve years 2013 to 2023 annual reports of
the bank were important data for this study. In addition, data from
different documents of the bank officials (like Risk Management
reports), Banking proclamations of National Bank of Ethiopia
(different years), manuals, articles, journals, magazines, books,
previous research and various internet sites will be used for the proper
accomplishment of this study.
The researcher have used questioner, both open ended and close
ended. The questioner is prepared with respect to the research
objective and research question, and mainly it was designed to make
the supporter of results which came from regression output.
 Cont….
Data from off balance sheet report is highly essential for this
research to run the model. There are also questionnaire which is
distributed to banks, Risk and Compliance Management Officer of
the head office, Risk Management Department Officers, loan
officers and selected staffs of the head office. Then the results of the
regression output are compared and contrasted with the questioner
results, which is received from the credit risk managers of each
bank to strengthen the analysis part of the research.
Description of Variables /Measures/
This study undertakes the issues of identifying key variables that
influence the impact of credit risk and profitability of commercial
banks in Ethiopia. Most of the variables identified in the investigation
have been taken from the existing literature on level of credit risk
management on profitability‘s of seven commercial banks in Ethiopia.
The study takes into account of all the variables discussed below
S/N VARIABLE ABRREVIATION TYPE MEASUREMENT
1 Return on Equity ROE Dependent Profit after tax
Shareholders’ Fund
2 Capital Adequacy Ratio CAR Independent Shareholders’ Fund Risk
Weighted Assets
3 Loan to Deposit Ratio LDR Independent Total Loans & Advances
Total Deposits
4 Loan Loss Provision LLP Independent Loan Loss Provision
Non-Performing Loans
5 Liquidity Ratio LQR Independent Cash & Cash Equivalents
Total Assets
6 Non-Performing Loan Ratio NPLR Independent Non-Performing Loans
Total Loans & Advances
7 Risk Asset Ratio RAR Independent Total Loans & Advance
Total Assets
8 Size of Banks SOZ Independent Log of Total Assets

Data Quality Assurance

So, data quality assured in proper designing, pre-testing, coding,
categorizing of questionnaire and interviews. Then each completed
questionnaire checked to ascertain all questions were properly fill and
corrected by investigator.
 Method of Data Analysis and Interpretation
• In processing the data, filled and completed questionnaires were carefully
check to assure that the data is accurate and uniformly entered and arrange
to facilitate percentages and tabulation systems. According to Punch
(2005) data analysis means categorizing, ordering, editing and
summarizing data in order to obtain answer to research problems. Once
the necessary data gathered it would been organized, checked and coded
the computer SPSS (statistical product and services solution) software
version 20.0 for windows and use of E-View 8.0.
• To analyze the data descriptive statistics used to, however, descriptions
made based on the results of the tables and figures using mean value,
percentage, rank order and standard deviation.
• The data collected through interviews analyzed qualitatively by
descriptive statements. Then, the results obtained from the above-
mentioned data gathering instrument separately presented and jointly
analyzed using narration and quotation. Finally, the result of descriptive
statistic presented in appropriate figures and tables.
 Reliability and Validity of the Study
After clearly identifying the dependent and the independent variables, the
researcher use multiple regression model to show the relationship between the
dependent and independent variables. Then the outputs of the SPSS were
interpreted through charts, tabular and graphics. Validity means the accuracy of
measurement of which the data is intended to be measured and how truthful the
results of the research Arell, Patti and Ariccia, (2004).
For the reliability & validity of this study, the researcher follows procedures
starting from the data collation up analysis. The researcher first collects the
data from audited annual reports of banks by the authorized body and
published reports. Then those data is compared from the annual report which
is found in the National Bank of Ethiopia/NBE, 2010-222/.
The survey questionnaires would be pretest by different target customers. In
addition scientific articles, journals and books will be used to guarantee the
reliability and validity of the data. The largest part is, statically analysis tools
like Regression model, SPSS 20.0 computer program and E-view 8
application will be used to analysis obtained data in order to increase the
validity. That long list of care reduces the possibility of getting wrong
answers.
 Chapter 4
4 Result and Discussion
In order to address the research problems and achieve research
objective, the results of different data collection methods jointly
analyzed and interpreted in this chapter. The chapter discusses the
result of the empirical data collected.
It would also define the conclusions and also the
recommendations, proffered for improvement of the credit risk
management activity of the CBEs.
The analysis part is done with two main parts in relation to source,
primary and secondary, used for the study. The primary data are
analyzed using frequency distribution tables and the secondary
data are also descriptively analyzed using different ratios.
The questionnaires were distributed to represents the key credit
risk managing organs. As per the Bank’s organizational structure,
the credit processing activity of the Bank is organized with the new
concept, liability processing center, under the credit division
 Credit Risk Assessment
Level of credit risk on various transactions
No risk Very low risk High risk Very High risk

Direct lending 10% 90%

Guarantees or letter of credit 87% 13%

Source: own survey 2024

The researcher asked the above question in order to know the
understanding of the respondents about the type of credit product and
the associated degree of risk that the Bank will highly exposed to. Such
question will also help to evaluate the understanding and the associated
measures of the respondent in relation to the risk level. Accordingly, the
respondents indicates that direct lending is a high risk category product
with which 90% of them attested on, while the risk associated to
guarantees or letter of credit risk is categorized as very low risk
category. Hence, direct lending product is highly vulnerable to credit
risk to the Bank.
 The technique / instrument used for credit risk management
Credit approval authority 2
Prudential limit 3
Risk rating 5
Portfolio management 6
Loan review policy 4
Collateral 1
Diversification 72
Other please specify-----------------
Source-own survey 2024
On an effort to know more about a question associated on identifying the most important
instrument/techniques which were used by the Bank for credit risk management, is forwarded
to the respondents. As per the summarized result presented on the above table, number one
instrument which is used as a key instrument or technique for managing the risk associated to
credit is collateral, which is followed by credit approval, prudential limit. On summarizing the
above table the respondent is choosing more than one instrument/ techniques for managing
credit risk, which they utilized on their day to day activities. Diversification, prudential limit
and portfolio management are ranked low by majority of the respondents. Considering this fact,
the Bank rely on collateral as a key instrument to manage the risk associated to borrower
default​.
 Off- Balance Sheet Commitment
• One of the questions associated to credit risk management is the
question associated to off- balance commitment, which some time
neglected by due to understanding of the nature of standard accounting
practice. Which mainly focused on the items associated and
categorized as , asset , liability and capital and not critically examining
the impact of such exposures associated to other balance sheet items.
This exposure is one of the major sources of credit risk, it is important
to access whether the Banks has defined any exposure to manage off
balance-sheet exposure in its book. Hence, the question, does the Bank
have defined exposures for managing off- balance sheet exposures? Is
forwarded to the respondent. Accordingly, 75% of them attested that
the existence of exposure limit, while the remaining 25% of them
didn’t responded to the questions, this may occur due to the
unfamiliarity and lack of due concern about the office balance sheet
commitment. As per the respondent, the Bank has defined off-balance
sheet commitment exposure limit associated to its capital
 Pricing Credit Risk
Factors affects considered for pricing credit risk in CBEs
Highly considered considered Somehow considered Not considered
Portfolio quality 80% 14% 4% 2%
Value of collateral 86% 14% 1%
Future business potential 27% 17% 40% 16%
Portfolio industry exposure 20% 12% 31% 37%
Tenure period of credit 25% 20% 32% 23%
Any other please specify - - - -

Source-own survey 2024

The responses obtained in this regard are presented in the above table. According to the
amassed data presented on the above table, value of collateral and portfolio quality are
the most important and the second most important factors that are considered for
pricing credit risk by the majority of the respondent. Tenure period of credit and future
business potential have claimed third and fourth position, respectively. The last rated
parameter is the portfolio industry exposure.
The pricing of credit risk should depend on how much the riskier of the loan product
and viability of the customers’ business than concentrating on value of collateral, which
is the second way out in the event of default. While the practice of the Banks associated
to pricing credit risk is mainly rely on collateral value.
 Descriptive statistics of the variables indicating profitability
performance and credit risk management
To address research questions the study used on survey self administered questionnaire
Table 8 ANOVA
Model Sum of Squares df Mean Squares F Sig.

1 Regression 3042.482 2 1521.241 10.026 .000a

Residual 10165.628 67 151.726
Total 13208.111 69

a). Predictors: (Constant), CAR, NPLR

b). dependent variable: Return on Equity
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 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 P value is 0.05
then it‘s better to compare with significance level which is 0.000 and the p value is greater than
that of sig value).
 Correlation test
Correlation Coefficients
Model Capital adequacy ratio Non-performing loan ratio

 Correlation: Capital adequacy ratio 1.000 .150

Non-performing loan ratio .150 1.000
Covariance: Capital adequacy ratio .071 .006
Non-performing loan ratio .006 .021

a
. Dependent Variable: Return on Equity
Source: SPSS regression output
Table above displays the correlation and covariance matrices of the independent
variables included in the model at each step. In the correlation matrices, the values
of the correlation coefficients range from -1 to 1.correlation coefficient describes
about two variables , to check whether they are related each other or not . When the
correlation coefficient is -1, its displays that there is perfectly negatively
correlation , when the correlation coefficient is +1 its indication is perfectly
positively correlated , When it became in between 0.3 and 1it shows that there is
positive correlation among variables, and when it lies in between -0.3 and -1 it
display that negative correlation among variables. But when the variable is in
between -0.3 and +0.3, it shows that there is no correlation among variables. As we
can from the table 4 the correlation coefficient is 0.150 it shows that there is no
correlation among independent variables.
 Collinearity (Multicolinearity) test result
Un standardized Standardized Collinearity Statistics
Model Coefficients Coefficients t Sig.
B Std. Error Beta Tolerance VIF

(Constant) 0.860 4.826 8.466 .000

NPLR -.543 .144 -.409 3.777 .000 .978 1.023
CAR -.783 .266 -.319 2.943 .000 .978 1.023
a). Dependent Variable: Return on Equity
Source: SPSS regression output
Table above concentrated on un standardized and standardizes coefficients. Un
standardized coefficients are the coefficients of the estimated regression model.
Whereas standardize coefficients are or beta are an attempt to make the regression
coefficients more comparable. The t-statistics can help us to determine the relative
importance of each variable in the model. As a guide regarding useful predictors, look
for t values well below -2 or above +2. Therefore, it is beneficial to examine
associations/correlation between explanatory variables and exclude one of a pair of
highly correlated variables before conducting multivariable analysis. Let‘s first look at
the regression we did from the last section, the regression model predicting ROE from
NPLR and CAR using SPSS. As we can see form the table above the tolerance and
VIF are all quite acceptable
 Display Statistics Collinearity Diagnostics
Collinearity Diagnostics
Variance Proportions
Model Dimension Condition (Constant) Non-performing loan Capital adequacy ratio
Eigenvalue Index ratio

1. 1 2.592 1.000 .01 .05 .02

2 .354 2.707 .02 .79 .08
3 .054 6.898 .97 .16 .90
a). Dependent Variable: ROE
Source: SPSS regression output
Table above is a table which display statistics that help for determine whether there are any
problems with collinearity or not. Collinearity (multicollinearity) is the undesirable situation
where the correlations among the independent variables are string. Eigenvalues proved an
indication of how many districts dimensions are there among the independent variables. When
several eigenvalues are close to zero, the variables are highly inter correlated and small
changes in the data values may lead to large changes in the estimates of the coeffients.
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. Even if eigenvalus 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, 3 and 7, from this ground the researcher
can say that there is no multicollinearity among independent variables.
 Residual Statistics a
Minimum Maximum Mean Std. Deviation N

Predicted value .8996 30.8130 22.4780 6.64033 72

Residual 48.23534 47.71675 .00000 12.13787 72

Standardized Predicated Values -3.250 1.255 .000 1.000 72

Standardized Residual -3.916 3.874 .000 .985 72

a
. Dependent Variable: Return on Equity
Source: SPSS regression output
The above table 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 predicated values are predicated
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.
As one can see from the model NPLR has inverse relation with that of ROE, Whereas CAR has
direct relationships with dependent variable.
Source variable = NPLR W= Weight
Power value = 1.000 Dependent variable = ROE
 Return on Equity (ROE) of each Banks
Log likelihood Function = 259.465917 Power value = 0.500

Log likelihood Function = 252.526389 Power value = 1.000

Log likelihood Function = 252.735645 Power value = 1.500

Multiple R = .54190 Adjusted R Square = .27257

R Square = .29366 Standard Error = 2.98531

 Analysis of Variance
ANOV

Model DF Sum of Squares Mean Square F Sig

Regression 2 248.24593 124.12297 13.92750 .0000

Residuals 67 597.10915 8.91208

Total

Source: SPSS regression output

Let‘s examine the output from the regression analysis. First of all let‘s look the p
value of the F test to see if the overall model is significant or not. With the p value
of 0 to the four decimal places, the model is statistically significant. The R-square is
0.29366, meaning that approximately 30% of the variability of ROE is accounted
for by the variables in the model .The coefficient for each of the variables indicates
the amount the amount of change one could expect in ROE 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 CAR from the next table; the
researcher would expect a decrease of 0.831316 in the ROE score for every one
unit increase in CAR, by assuming that all other variables in the model are held
constant.
 Variables in the Equation
Un standardized Standardized

coefficient coefficient

Variable B SE B BETA T Sig T

(Constant) 42.201476 3.527050 11.965 .0000

NPLR -.594077 .163470 -.377922 -3.634 .0005

CAR -.831316 .190887 -.452886 -4.355 .0000

 Cont…
As we can see from table 3 both the constant, NPLR and CAR are
significant.
First the researchers answer about the two predictors, whether they are
statistically significant and if so the direction of the relationship. The
effect of NPLR (non-performing loan ratio) which is (Beta =.-
0.594076) significant (P, 0.05) is and its coefficient is negative
indicating that the greater the nonperforming loan ratio the lower the
profitability of commercial banks in Ethiopia. The NPLR is highly
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 = - 0.836180)
significant (p, 0.05) and as watched it is negative which indicates that
the one unit increase in capital adequacy ratio leads in- 0.836180
decrease in profitability of the banks of the country.
 Each Banks Regression Results
If there is an individual who need to know the beta value of both
nonperforming ratio and capital adequacy ratio of each bank, the
table below answer this question. In addition to that one can get the
adjusted R square and significance level of each bank separately.

AWB ABB CBE DB OB UB WB

β of NPLR -1.081 -.530 -.938 -1.811 .259 -1.360 -2.175

β of CAR -1.283 -1.488 -1.951 -.926 -.370 -.334 -.674

Adj.R2 0.778 .371. -.022 .474 -.150 - .556 .469

Sig. 0.02 .082 0.447 .044 .677 0.25 .045

 Cont…
Source: SPSS regression output of each bank
Based on the date tabulated above, first the beta value of Awash bank on nonperforming
ratio is -1.081, capital adequacy ratio is -1.283, adjusted R square is 0.778 and significance
level of 0.02. Second, the beta value of Abyssinia bank on nonperforming ratio is -.530,
capital adequacy ratio is -1.488; adjusted R square is .371 and significance level of .082.
Third, the beta value of Commercial Bank of Ethiopia on nonperforming ratio is -.938,
capital adequacy ratio is -1.951, adjusted R square is -.022 and significance level of 0.447.
Four, the beta value of Dashen bank on nonperforming ratio is -1.811, capital adequacy ratio
is -.926, adjusted R square is .474 and significance level of 0.44. Five, the beta value of
Oromia Bank on nonperforming ratio is .259, capital adequacy ratio is -.370, adjusted R
square is -.150 and significance level of 677. Six, the beta value of United Bank on
nonperforming ratio is -1.360, capital adequacy ratio is -.334, adjusted R square is 556 and
significance level of 0.25. Lastly, the beta value of Wegagen Bank on nonperforming ratio is
-2.175, capital adequacy ratio is -.674, adjusted R square is .469 and significance level of
0.45. Finally, an impact level of nonperforming loan ratio is negative which means, a single
unit increase in nonperforming loan ratio leads in (-0.594076) decrease of profitability of
commercial banks in Ethiopia. Non-performing ratio have inversely related with
profitability whereas capital adequacy ratio has a direct relation with profitability of banks.
The impact level of capital adequacy ratio had also been negative; it indicates that a unit
increase of capital adequacy ratio leads 0.836180 decreases in profitability of commercial
banks in Ethiopia.
 Chapter 5: Conclusion & Recommendation
5.1 Conclusion

Based on the analysis made in chapter four the following conclusions made
regarding,
There was no correlation among independent variables (NPLR and CAR)
which means each of the independent variables explained the dependent
variable separately.
The model in the study is 23% good before remedial action was made.
But it increased in to 29% after remedial action were taken by the
researcher. Or after the tail of the error term is captured by the researcher.
Which means the independent vaiable explained dependent variable
around 29%.
There was no collinearity (multicollinearity) among independent
variables. And the error term are normally distributed at the mean of zero
and standard deviation of 0.987 which is around 1.
There was a problem of hetroscedacity and to address the problem the
researcher uses weighted list square (WLS) than Ordinary list square
(OLS)
 Cont…
Both nonperforming loan ratio and capital adequacy ratio has a
negative impact on profitability‘s of commercial banks in Ethiopia.
The impact level of nonperforming loan ratio is negative which
means, a single unit increase in nonperforming loan ratio leads in
(.594077) decrease of profitability of commercial banks of Ethiopia.
Credit risk, credit risk management, internal control ,banks
profitability, the ways how risks are managed , impacts of supervision
of banks to nonperforming loan , corporate governance and banks
profitability , and many others which has linkage in banks
profitability and credit risk management separately.
Measuring the impact level of credit risk management is needed, to
make countries credit risk management department well aware about
the impacts level of credit risk management towards profitability of
their business and very much important for policy makers.
 Cont…
Banks board of directors are responsible for each and every activities
of the bank, so they need to conduct continues training for their
employees particularly for credit risk management department
managers and employees as well. Policy maker of banks (in our
country NBE ) need to set policy , and guidelines which force banks
to think over their credit policy, risk management policy, and other
related things.
Since there was no formal research conducted on such area other
researcher of the country needs to conduct different and important so
that contribute their responsibility and should have to made changes
on the attitude of the community and the responsible bodies as well.
 RECOMMANDATION
Banks needs to hire the one who has high experience and qualification
on credit risk management and the one who aware about its significant
impact to banks profitability.
It‘s better if university colleges of the country added credit risk
management course on their curriculum, not only for Msc program
students but also for BA and Diploma program student‘s too.
Banks board of directors are responsible for each and every activities
of the bank, so they need to conduct continues training for their
employees particularly for credit risk management department
managers and employees as well.
Policy maker of banks (in our country NBE ) need to set policy , and
guidelines which force banks to think over their credit policy ,risk
management policy , and other related things . Since there was no
formal research conducted on such area other researcher of the
country needs to conduct different and important so that contribute
their responsibility and should have to made changes on the attitude
of the community and the responsible bodies as well.
 Cont…
Finally, Commercial banks in Ethiopia are profitable for the time
being, however to sustain their profit in the future and even to make
them more profitability than before, to be competent with foreign
commercial banks, the impact level of credit risk management must
be identified and corrective action must be taken in advance.
 Thanks‘
For your
attention