Yuga Raj Bhattarai, Ph.D.
Faculty
of Management, Patan Multiple Campus, Tribhuvan University, Nepal.
yugarajbhattarai@gmail.com
Abstract
This
study has investigated the effect of credit risk on the profitability of
commercial banks in Nepal over the period of 8 years (2009 to 2016). Panel data
of six commercial banks were analyzed using pooled OLS model, fixed effects
model and random effect model. The results from the estimated regression models
show that default risk is significantly positively associated with banks’
profitability. However, capital adequacy ratio is
found significantly negatively associated to profitability. The effect
of cost per loan assets seems minimal in explaining the variation of commercial
banks’ profitability. Thus, this study concludes that credit risk indicators
like: default risk and capital adequacy ratio have
significant impact on the profitability of commercial banks in Nepal.
Keywords: Commercial Banks, Credit Risk,
Nepal, Panel Data Regression, Profitability.
JEL Classification: C23, C33, G21, G32, N25, O16
I.
Introduction
A key function of banks is to channel
savers’ deposits to people that wish to borrow. However, lending is an
inherently risky business. Bad lending is the root of many banking crises and
presence of huge amount of nonperforming loans. At the most general level, a
nonperforming loan is a loan where a borrower is not making repayment in
accordance with contractual obligations. In many jurisdictions and many firms,
an NPL is defined as a sum of borrowed money upon which the debtors has not
made his or her scheduled payments for at least 90 days (Bholat, Lastra,
Markose, Miglionico & Sen, 2016). The increasing level of nonperforming
loan rates in banks books, poor loan processing, undue interference in the loan
granting process, inadequate or absence of loan collaterals among other things
are linked with poor and ineffective credit risk management that negatively
impact on banks performance (Muriithi, Waweru & Muturi, 2016). Thus, credit
risk may have a vital effect on the profitability of banks since it gives rise
to nonperforming loans.
Credit risk management is crucial to
banks because it is an integral part of the loan process. A bad credit policy can
lead to inappropriate allocation of credit which may result in bad debts and,
hence, lost of income in the form of interest and banks’ asset on the principal
loaned out. Moreover, a bad credit policy can move up default rate and hence,
could reduce the profitability of commercial banks. Thus, sound credit risk
management is necessary as it can enhance sustainable financial performance (profitability)
of commercial banks.
Commercial banks may have a keen
awareness of the need to identify, measure, monitor and control credit risk as
well as to determine that they hold adequate capital against these risks and
that they are adequately compensated for risks incurred. Moreover, Nepalese
commercial banks have faced difficulties over the years for a multitude of
reasons, the major cause of serious banking problems continues to be directly
related to the relaxed credit standards for borrowers and counterparties, poor
portfolio risk management whereby they fail to determine the best asset
combination to invest in, which should have a negative correlation or lack of
attention to changes in economic or other circumstances that can lead to a
deterioration in the credit standing of a bank's counterparties thus, making
them default in honoring their obligations as regards repayment of the loans. However,
in recent years, some policies have been reformed to improve banks performance
and some measures have been taken to minimize on the negative effects of lending.
Moreover, policy makers have focused on mergers of commercial banks to increase
capital requirements and lessened the competition.
Despite the some policies measures
undertaken to reduce credit risk in the banking sector in Nepal, there is still
increasing trend of loan defaults and nonperforming loans of Nepalese
commercial banks. Thus there is the need of such study that can uncover the credit
risk measures and their impact on bank profitability in Nepalese context. This
study, therefore, seeks to investigate the impact of credit risk indicators on the
profitability of commercial banks in Nepal. Thus, this study aims to analyze
the effect of credit risk on profitability of commercial banks listed in the
Nepalese Stock Exchange. The findings of this study would serve as the basis to
provide policy measures useful to the various authorities on how to tackle the
effect of credit risk in order to enhance the quality of banks’ risky assets. This
study also provides the empirical evidence in confirming the validity of the
theories to assist the bank’s management in determining the best credit risk
strategies that enhance banks’ profitability.
The remainder of the study is outlined
as follows section two reviews related literature, section three discusses the
research methodology, section four focuses on results and discussion and section
five presents the conclusion.
II.
Literature Review
Some of the recent studies related to
the credit risk and commercial banks’ profitability have been summarized as
follows:
Fredrick (2012) has examined the impact
of credit risk management on the financial performance of commercial banks in
Kenya. The secondary data were collected from the CBK publications and
financial statements of respective banks in sample for the period of 20062010.
The results of the study indicate that earnings have a strong relationship with
financial performance. The author concludes that CAMEL components have strong
impact on the financial performance of commercial banks.
Funso, Kolade and Ojo (2012) have
investigated the effect of credit risk on the performance of commercial banks
in Nigeria over the period of 11 years (20002010). Panel data from five
commercial banks were analyzed using constant effect model (pooled OLS model)
and fixed effects model. Based on the results from these models, the authors
conclude that nonperforming loan and loan loss provision have significant
negative effect on profitability; however, loan and advances ratio (LA) has
significant positive effect on profitability across the banking firms.
Kaaya and Pastory (2013) have examined
the relationship between the credit risk and bank performance as measured by
return on asset. Regression model was used to in order to analyze the data. The
results show that credit risk indicators have produced negative correlation
with bank performance.
Kurawa and Garba (2014) have assessed
the effect of credit risk management on the profitability of Nigerian banks.
The study covered the period from 2002 to 2011. Secondary data of 6 commercial
banks were used for the study. The findings of the study reveal that default
risk and cost per loan assets have significant positive relationship with
profitability (ROA).
Tehulu and Olana (2014) have examined
the bankspecific determinants of credit risk in Ethiopian commercial banks
using a balanced panel data of 10 commercial banks both stateowned and private
owned for the period of five years (2007  2011). Data were analyzed using
random effects GLS regression. The authors concluded that credit growth and
bank size have negative and statistically significant impact on credit risk;
however, operating inefficiency and ownership have positive and statistically
significant impact on credit risk.
Alshatti (2015) has examined the effect
of credit risk management on financial performance of the Jordanian commercial
banks during the period 20052013. A sample of thirteen commercial banks has
been chosen to represent the whole Jordanian commercial banks. The author
concludes that the credit risk management indicators have a significant effect
on financial performance of the Jordanian commercial banks.
Djan, Stephen, Bawuah, Halidu and Kuutol (2015) have examined the impact of
credit risk on performance of banks in Ghana. The study has selected banks
listed on the Ghana Stock Exchange (GSE) as sample for a 10 year period
(20052014). The statistical tools like descriptive, correlation and regression
model were used to analyze the data. The study revealed that all coefficients
of the parameters are negative implying an inverse relationship between the
dependent variable (Return on Asset) and the independent variables (Default
Rate, Capital Adequacy Ratio and Cost per loan Asset). The authors conclude
that these parameters have an inverse impact on banks’ performance; however,
the default rate is the most predictor of bank financial performance.
Kodithuwakku (2015) has examined the
impact of credit risk management on the performance of the commercial banks in
Sri Lanka using both primary and secondary data. The return on assets (ROA) was
used as performance indicator and loan provision to total loan (LP/TL), loan
provision to nonperforming loans (LP/NPL), loan provision to total assets
(LP/TA) and nonperforming loans/ total loans (NPL/TL) were used as indicators
of credit risk. The result concludes that nonperforming loans and provisions
have an adverse impact on the profitability.
Lalon (2015) has analyzed the impact of
credit risk management on financial performance of bank using the secondary
data relating to the financial status of Basic Bank Ltd. The author finds
significant negative association between nonperforming loan ratio and banks
profitability. The author concludes that an attempt to decrease on
nonperforming loan ratio can positively contribute on banks financial
performance.
Olamide, Uwalomwa and Ranti (2015) have
investigated the impact of effective risk management on bank’s financial
performance using the data of 14 banks listed on the floor of the Nigerian
Stock Exchange over a period of 2006 to 2012.The results of their study show that there exist a negative
nonsignificant relationship between risk management proxies and bank’s
performance as captured with return on equity. The authors conclude that the
increased drive for the management of risk poses a limit on the earning
capacity of Nigerian banks.
Abubakar, Shaba, Ezeji and Ahmad 2016)
have examined the effect of credit risk management on bank performance in
Nigeria over the period 2000 through 2013 using a sample of 14 Deposit Money
Banks quoted on the Nigerian Stock Exchange. The study has adopted panel
regression estimation technique to analyze the data. The findings from the
regression model show that credit risk management indicators impact
significantly on bank performance in Nigeria. The authors conclude that the
increase in loans and advances, equity capital and bank size positively
contributes on the performance of Deposit Money Banks in Nigeria.
Otieno, Nyagol and Onditi (2016) have investigated
the relationship between credit risk management and financial performance of
microfinance banks in Kenya using a sample of 6 microfinance banks. The study has
utilized panel data covering the period from 2011 to 2015. The findings of the
study reveal that portfolio at risk (default risk) and loan loss provision
coverage ratio had a strong negative relationship with both return on average
assets and return on average equity performance measures. The authors conclude
that credit risk management impacts performance of microfinance banks in Kenya.
The
past empirical evidences elsewhere suggest that credit risk management is a
predictor of banks’ profitability. Specifically, most of the past related
studies advocate that credit risk indicators like: default risk, capital
adequacy ratio and cost per loan assets may have significant effect on banks’
profitability.
III. Research Methodology
The
sample
This study has examined the credit risk and
its impact on the financial performance of commercial banks in Nepal over the
period of 8 years (2009 to 2016). This study has adopted descriptive and causal
comparative research design. The convenience sampling method was used in
choosing the banks for the study. Moreover, in selecting the 6 banks for the
study, due care is given to include banks such as: joint venture, domestic,
best performer, average performer and comparatively week performer in the
sample. The banks selected
for the study are: Everest Bank Ltd., Kumari Bank Ltd., Nabil Bank
Ltd., Siddhartha Bank Ltd., Nepal Bangladesh Bank Ltd. and Sunrise Bank Ltd.. The
population of this study is the “A class” commercial banks listed
in the NEPSE. This study assumes that the selected samples fairly represent the
study population.
The
data
Data were collected from the annual
reports of the banks in the sample. The data include crosssectional and
timeseries data, i.e. panel data set. According to Greene (2007), the models
for panel data can be arranged as: pooled regression, fixed effects, random
effects and random parameters. In practice, panel data models are estimated
using pooled OLS, fixed effects or random effects techniques (Mujeri &
Younus, 2009). In view of theoretical perspective, this study has employed pooled
OLS model, fixed effects model and random effects model in the data analysis
procedures. Moreover, data analysis was done using the Gretl Version 1.1.
The
model
This study has used three econometric
models in analyzing the data. Initially pooled OLS model has been estimated in the
current study. The simplest estimation for panel data is pooled OLS (Cottrell &
Lucchetti, 2017). The pooled OLS model can be written as:
Y_{it} = α + β X_{it}
+ε_{it}
Where: Y is
the dependent variable; α is
constant; β is the coefficient
of explanatory variables; X_{it }is
the vector of explanatory variables; and ε_{it }is the error term (assumed to have zero mean and
independent across the time period). Based on the prescribed econometric model,
impact of credit risk on the financial performance of commercial banks has been estimated with the following
regression equation:
PROF_{it}
= β_{0 }+ β_{1} DR_{it }+ β_{2} CAR_{it}
+ β_{3} CLA_{it} +e_{it}
Where:
PROF_{it} = Profitability, which is calculated as
net income divided by total assets of i^{th}
bank in year t
DR_{it} = Default risk, which is calculated as nonperforming
loans to total loans of i^{th}
bank in year t
CAR_{it} _{ }= Capital adequacy ratio of i^{th} bank in year t
CLA_{it }=
Cost per loan assets ratio of i^{th}
bank in year t
β_{0 } = The intercept of the regression line
β_{1,}
β_{2,} β_{3}_{ }=
The slope which represents the degree with which lending interest rates changes
as the independent variable changes by one unit variable. The priori
expectation is that the coefficients β_{1,} β_{2, }and β_{3 }<
0.
e_{it} = error component.
In addition to the pooled OLS model, this
study has employed other panel models like: fixed effects model and random
effect model. Fixed effects estimation
allows for the unobservable bank heterogeneity. Specifically, the model assumes
that intercepts for each bank are allowed to vary, but the slopes for each bank
are equal. In this instance, Greene (2007) has suggested following fixed effect
model:
Y_{it}
= X_{it} β + α_{i }+ ε_{it}
Where, α_{i = }z_{i }α, embodies all the observable
effects and specifies an estimable conditional mean. Greene (2007) assets that
fixed effect approach takes α_{i }to
be a groupspecific constant term in the regression model. The author indicates
that each α_{i }is treated as an unknown parameter to be estimated.
In some cases, fixed effects estimations
become less efficient than random effects estimations. Random effects
estimations take into consideration the unobservable bank heterogeneity
effects, but incorporate these effects into the error terms, which are assumed
to be uncorrelated with the explanatory variables. Likely, Greene (2007) has
asserted that if the unobserved individual heterogeneity, however, formulated,
can be assumed to be uncorrelated with the included variables, then the model
may be formulated in random effect form. The random effect model suggested by
Greene (2007) can be written as:
Y_{it}
= X_{it} β + α_{ }+ u_{i }+ ε_{it}
According to
Greene (2007) this random effects approach specifies that u_{i } is a group specific random element,
similar to ε_{it }except that for each group, there
is but a single draw that enters the regression identically in each period.
However, the ε_{it }represents within entity error.
In order to
capture the deferring attributes of panel data, the current study has employed
these three regression models to estimate the association between credit risk
and commercial banks’ profitability in Nepal.
Variable and hypothesis
In
this study, the choice of variables was mostly affected by the approach in the
past empirical studies.
Dependent variable
Profitability (PROF)
In this study, profitability is computed as net income divided by
total assets. Return on assets is generally considered as a good indicator to
evaluate the profitability of the assets of a bank in comparison to other banks
in the banking industry. It is hypothesized that profitability of commercial
bank is influenced by the bank specific credit risk variables like: default
risk, capital adequacy ratio and cost per loan advanced.
Independent variables
Default risk
(DR)
Default risk is a ratio that measures
the proportion of nonperforming loans as against the total loans for a period.
It gives an assessment of the total borrowers default on the conditions of
loans and advances for a given period. It simply measures the efficiency of the
loan portfolio management for a given bank within a given period (Appa, 1996;
Ahmed et al., 1998; Kolapo et al., 2012). In this respect, Kurawa and Garba
(2014), Alshatti (2015) have found significant positive relationship between default
risk and profitability. However, Poudel (2012), Kaaya and Pastory (2013) and
Djan, Stephen, Bawuah, Halidu and Kuutol (2015) found significant negative
association between nonperforming loan (default risk) and profitability of
commercial banks. Likely, Kodithuwakku (2015) has also asserted that nonperforming
loans and provisions have an adverse impact on the profitability. In line with
majority of past empirical evidences, a negative relationship is expected
between default risk and bank profitability (β_{1}< 0).
H_{1}: Default
risk has a significant and negative effect on bank
profitability.
Capital
adequacy ratio (CAR)
Capital adequacy ratio is calculated
dividing capital fund by risk weighted assets. Capital adequacy increases the
strength of the bank which improves the solvency of the bank and capacity to
absorb the loan loss and protect bank from bankruptcy. Alshatti (2015) has
asserted that capital adequacy ratio don’t affect the profitability of
Jordanian commercial Banks. However, Poudel (2012) found significant negative
association between capital adequacy ratio and bank performance in Nepalese
context. Likely, Djan, Stephen, Bawuah, Halidu and Kuutol (2015) also found
that capital adequacy ratio have an inverse impact on banks’ performance. In
this scenario, a negative relationship is expected between capital adequacy
ratio and bank profitability (β_{2}< 0).
H_{2}:
Capital adequacy ratio has a significant and negative effect on bank profitability.
Cost
per loan assets (CLA)
Cost per loan
assets is calculated dividing total operating costs by total amount of loans
advanced to customers. Cost per loan assets points out efficiency in
distributing loans to customers (Appa, 1996; Ahmed et
al., 1998; & Kolapo et al., 2012). Banks that are efficient in managing
their expenses (costs), holding other factors constant, earn high profits.
Therefore, it is expected that cost per loan assets and bank performance to be
negatively associated. This may not always be true because in cases where there
are high expenditures due to a lot of businesses done, the bank can still
increase the returns. However, the empirical studies show the mixed results on
this issue. In Nepalese context, Paudel (2012) has found negative but
statistically insignificant association between cost per loan assets (CLA) and
bank performance (ROA). Kurawa and Garba (2014) have found significant positive
association between cost per loan
advanced and profitability. However, Djan, Stephen, Bawuah, Halidu and
Kuutol (2015) also found that Cost per loan advanced has an inverse impact on
banks’ performance. In view of theoretical perspective and empirical evidences,
a negative relationship is expected between cost per loan assets and bank profitability
(β_{3}< 0).
H_{3}: Cost per loan assets has a significant and negative effect on bank profitability.
IV. Results and Discussion
Descriptive
statistics
The descriptive statistics of the
variables used in the study have been presented in Table 1. The result shows
that the minimum and maximum profitability (ROA) of Nepalese commercial banks
during the sample period are o% and 18.04% respectively. The average profitability
(ROA) is 2.21%,
which indicates the weak performance of Nepalese commercial banks.
Table1
Descriptive statistics of variables (n=48)
Variables

Scale

Mean

Std.
Deviation

Minimum

Maximum

CV

Skewness

Ex. kurtosis

ROA

%

2.2092

2.6366

0.0000

18.0400

1.1935

4.8568

25.8440

DR

%

2.4785

3.7198

0.1500

19.8000

1.5008

3.7104

13.8530

CAR

%

11.3860

1.1622

5.5500

13.7600

0.1021

2.3492

11.7070

CLA

%

0.0975

0.0298

0.0400

0.1700

0.3055

0.2795

0.5966

Source:
Annual report of sample banks and results are drawn from Gretl 1.1 Version
The average default risk (DR) is 2.48%, which shows that default
risk is not so severe in Nepalese commercial banks. Capital adequacy ratio is
found less volatile during sample period. The key
indicator of efficiency in loan management is the ratio of operating costs to
loan and advances. The results of operating costs to loan and advances ranged
from 0.04% in the most efficient to 0.17% at the other
extreme. The average
operating cost to loan and advances is 0.10% which shows that cost
per loan advanced is not so high in Nepalese context.
Cost per loan assets is found less volatile as compared to the other study
variables used in the current study which is evident from low standard deviation of
the cost
per loan advanced variable, which is 0.03%.
Correlation analysis
The
correlation coefficients among study variables are shown in Table 2. The
results of the correlation coefficients of variables indicate that profitability
is positively associated with default risk and cost per loan assets. This
implies that the bank profitability (ROA) tends to move in the same direction as
with default risk and cost per loan advanced.
Moreover, the relationship between default risk and profitability is found
strong but the liaison between cost per loan assets and
profitability seems weak.
Table2
Pearson correlations (n = 48)
ROA

DR

CAR

CLA

Variable

1.0000

0.5952

0.6380

0.1066

ROA


1.0000

0.5563

0.4727

DR



1.0000

0.0641

CAR




1.0000

CLA

Correlation coefficients are drawn from Gretl1.1 Version
However,
capital adequacy ratio is negatively associated to profitability. The relationship
between capital adequacy ratio and profitability is found strong. Moreover, the
correlation matrix of the variables presented Table 2 reveal that all
correlations coefficients among the independent variables are less than 0.60,
implying the absence of multicollinearity. Thus, there is no evidence of
presence of multicollinearity among the independent variables.
Regression
results
The
Table3 presents the results of three regression models for measuring the
effect of credit risk on the profitability of Nepalese commercial banks.
Initially, the model diagnostic test has been conducted to choose appropriate
model in the current study. The diagnostic test starts with the use of ‘Joint
significance of differing group means’ test to compare pooled OLS model with fixed
effect model. The results of F statistics F(5,39) = 0.6490
with pvalue = 0.6639 accept the null hypothesis that the pooled OLS model
is adequate. The general rule is that a low pvalue counts against the null
hypothesis that the pooled OLS model is adequate, in favor of the fixed effect
alternative. Since, in this study, the pvalue is more than 0.05, pooled OLS
model seems adequate as compared to the fixed effect model.
Likely, in order to compare the pooled
OLS model with random effect model, BreuschPagan test statistic has been used.
The test result LM = 0.5750 with pvalue =
prob (Chisquare (1) >0.5750 = 0.4483, proves
that pooled OLS model is adequate because pvalue of chisquare is higher than
0.05.The results of BreuschPagan test statistic recommends pooled OLS model
instead of random effect model for the current study.
Moreover, Hausman test was used to
compare between fixed effect model and random effect model in the current study.
The Hausman test statistic: Chisquare (3) = 0.8681 with its pvalue =0.8331,
indicate that the nullhypothesis is accepted, supporting random effect model instead
of fixed effect model. Although, the model diagnostic test statistics have suggested
polled OLS model and random effect model
to be used in the current study, however, the results of three models (pooled
OLS model, fixed effect model and random effect model) have been presented and
discussed in the current study for better estimation.
The variance inflation factor (VIF)
shows a value less than 2.10 for each variable. The larger the value of VIF,
the more troublesme or collinear the variables and as rule of thumb a VIF
greater than 10 is unacceptable (Gujarati, 2004). However, multicollinearity
problem is not a concern in the current study.
Table3
Regression coefficients (n = 48)
Variables

Pooled OLS Model

Fixed Effect Model

Random Effect Model


Coefficients

t

Sig.

VIF

Coefficients

t

Sig.

Coefficients

t

Sig.


Constant

13.0859

3.792

0.0005


11.8523

3.145

0.0032

12.9129

3.724

0.0006

DR

0.3006

2.813

0.0073

2.005

0.3339

2.877

0.0065

0.3053

2.847

0.0067

CAR

0.9296

3.078

0.0036

1.563

0.8151

2.451

0.0188

0.9136

3.008

0.0043

CLA

10.6393

0.957

0.3436

1.390

12.2041

1.063

0.2944

10.8577

0.981

0.3319


R^{2}=0.5011, Adj.R^{2 } =0.4671,
F = 14.7299, Pvalue(F)
= 0.0000
DurbinWatson statistic = 1.9884,
Pvalue
(DW) = 0.7781
Joint significance of differing group means: F(5, 39) = 0.6490,
pvalue =
0.6639

R^{2 }=0.5394, Adj.R^{2} = 0.4449,
F = 5.7090,
Pvalue(F)=
0.0001
DurbinWatson = 2.1325
Pvalue(DW) = 0.6955

S.
E. of regression = 1.9033
BreuschPagan
test:
Chisquare(1) = 0.5750,
Pvalue
= 0.4483,
Hausman test:
Chisquare(3) = 0.8681
Pvalue = 0.8331

***Significant at the 0.01 level (2tailed) ,**
Significant at the 0.05 level (2tailed), * Significant at the 0.1 level (2
tailed). Results are drawn from GretlStatistical
Software
The results of the three regression
models employed in the study are presented in Table3. The results from the
pooled OLS model indicate that the value of R^{2
}and adjusted R^{2 }are 0.5011 and 0.4671
respectively. The overall explanatory power of the regression model looks good
with R^{2 }of 0.5011. The
result implies that about 50.11% change in profitability (ROA) is explained
by the variations in explanatory variables, denoting that the regression has
good fit and is reliable. Likely, the overall
explanatory power of the fixed effect regression model also looks good with R^{2
}of 0.5394. Moreover, the pooled OLS model and
fixed effect model are found fairly fitted well statistically because in
both models (Pooled OLS and fixed effect model), the pvalue
of Fstatistics are significant at the 1% level of significance.
In order to test the autocorrelation,
DurbinWatson test has been used. In pooled OLS model, DurbinWatson statistic = 1.9884 with its pvalue =
0.7781, indicate the nonpresence of autocorrelation problem. Likely, DurbinWatson
statistic = 2.1325 with its pvalue = 0.6955 in fixed effect model also confirm the nonpresence
of autocorrelation in the regression model.
The
empirical findings from three models (pooled OLS
model, fixed effects model and random effects model) employed show that
default risk is significantly positively associated with banks’ profitability. The result is contrary to priori expectation. This result also rejects the
methodological juxtaposition of Kolapo et al (2012) where they opined that an
increase in nonperforming loan (default risk) would eventually lead to a decrease in
profitability. However, the result is consistent
with
Kurawa and Garba (2014), Alshatti (2015), where they have found significant
positive relationship between default risk and profitability.
Capital adequacy ratio is
found significantly negatively associated to profitability in three models
employed. The result is as per priori expectation and is similar to the
findings of Poudel (2012), where the author found significant
negative association between profitability and capital
adequacy ratio. Cost per loan assets is found insignificant in
explaining the profitability of commercial banks.
V. Conclusion
This
study has examined the effect of credit risk on the profitability of commercial
banks in Nepal. The descriptive and causal comparative research designs have
been adopted for the study. The panel data of 6 commercial banks over the
period of 8 years (2009 to 2016) have been collected from the annual reports of
the banks in the sample. Panel regression models (pooled OLS model, fixed
effect model and random effect model) have been used to assess the impact of
credit risk on the profitability of commercial banks.
The estimated regression models reveal
that default risk is significantly positively associated with banks’
profitability. However, capital adequacy ratio is
found significantly negatively associated to profitability in three models
employed. The cost per loan assets seems weak in explaining the
variation of commercial banks’ profitability. Eventually, this study concludes
that the commercial banks’ profitability in Nepal is mainly influenced by
credit risk indicators like: default risk and capital
adequacy ratio. The result in this study therefore, suggested the need
for strong credit risk and loan service process management must be adopted to
keep the level of NPL as low as possible which will enable to maintain the high
profitability of commercial banks in Nepal.
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