Journal of Balkumari College (2021), Vol.

10 Issue 1 19
Journal of Balkumari College
ISSN 2467-9321 (Print), ISSN 2738 -957X (Online) Website: http://balkumaricollege.edu.np/journal
Volume : 10 Issue: 1 June 2021, Page No. 19-30

Effect of Credit Risk Management on Financial Performance

Of Nepalese Commercial Banks

Dr. Guna Raj Chhetri

Lecturer, Balkumari College
grchhetri1@gmail.com

ABSTRACT
The main purpose of this study is to investigate the effect of credit risk on the financial performance of
commercial banks in Nepal. The panel data of seventeen commercial banks with 85 observations for the period
of 2015 to 2020 have been analyzed. The regression model revealed that non – performing loan (NPLR) has
negative and statistically significant impact on financial performance (ROA).Capital adequacy ratio (CAR)
and bank size (BS) have negative and statistically no significant impact on financial performance (ROA).
Credit to deposit (CDR) has positive but no significant relationship with the financial performance (ROA)
and the study concluded that the management quality ratio (MQR) has positive and significant relationship
with the financial performance (ROA) of the commercial banks in Nepal. The study recommends that, it
is fundamental for Nepalese commercial banks to practice scientific credit risk management, improve their
efficacy in credit analysis and loan management to secure as much as possible their assets, and minimize the
high incidence of non-performing loans and their negative effects on financial performance.
Keywords: Return on asset, capital adequacy ratio, non-performing loan ratio, management, and quality ratio,
credit to deposit ratio and bank size.
Introduction
Banking sector is an important sector which supports economic development in any country. Financial institution
plays an important role in accelerating development of the country. Bank failure is a problem in different countries.
Risk may be defined as a probability or threat of damage, injury, liability, loss, or any other negative occurrence
that is caused by external or internal vulnerabilities, and that may be avoided through preemptive action. Credit
risks are not only argued to affect financial performance of loans but they also have far reaching implications.
Similarly, credit risk is the king of all risks. Credit risk is one of the most vital risks for banks. Credit risk arises
from non-performance by a borrower. It may arise from either an inability or an unwillingness to perform in the
pre-commitment contracted manner. The banks are inevitably exposed to credit risk because they grant credit
facilities as they accept the deposits (Muriithi et al. 2016). Hence, business without any types of risks is not
a business. Risk is inherent in banking business or any form of business. Banks and financial institutions are
exposed to variety of risks among them credit risk is more severe than other risks.
Credit risk in banks may also arise due to internal weaknesses in any financial institutions such as management
inefficiency. Management deficiency affects liquidity causing an increase in nonperforming loans. In addition,
the non-performing loan (NPL) in the balance sheet of a financial institution represents the ratio of aggregate
non-performing loans and the total gross loan. Banks performance with regards to credit risk depends on various
internal and external factors. Internal factors are bank specific determinants and the external factors are the
determinants related to economic environment. Proper credit management is a precondition for any financial
institutions’ stability and continuing profitability, albeit deteriorating credit quality is the most frequent cause of
poor financial performance of the financial institutions.
The health of the financial sector is a major concern of policy, especially in developing economies where failure
in financial intermediation can disturb the economic growth and retards the developmentprocesses (Das & Ghosh,
2013). Furthermore, it has been proved that the major economic upheavals are the result of banking crisis.The
economic development and financial growth of a country is critically dependent on the financial performance
and strength of its banking sector (Shukla, 2015). The banking sectors serve as the backbone for the economic
development of any country (Ahsan, 2016). The growth and financial stability of the country depends on the
financial soundness of its banking sector. Sound financial health of the banks is the guarantee not only to their
20 Journal of Balkumari College (2021), Vol. 10 Issue 1

depositors but is equally significant for the shareholders, employees, and the economy as a whole (Mohiuddin,
2014).
Risk management issues in the banking sector do not only have greater impact on bank performance but also
on national economic growth and general business development. The bank’s motivation for risk management
comes from those risks which can lead to underperformance. Credit risk management is indeed a very difficult
and complex task in the financial industry because of the unpredictable nature of the macroeconomic factors
coupled with the various microeconomic variables which are peculiar to the banking industry or specific to a
particular bank. Credit risk refers to the risk that a borrower will default on any type of debt by failing to
make required payments. The risk is primarily to the lender and includes lost principal and interest, disruption
to cash flows, and increased collection costs. The loss may be complete or partial and can arise in a number of
circumstances (Muriithi, 2016). Similarly, financial performance of commercial banks is the measureof the level
commercial banks profit or loses within a specified time period. Various measures have been used to measure
the financial performance of commercial banks.
Credit risk management is one of the most essential functions of the bank in the modern banking system. The
risk is inherent in all aspect of banking business operations. Credit business is a one of themajor parts of the bank
(Kattel, 2016).
Credit risk plays a crucial role on the bank’s profitability as the large portion of the bank’s revenue accrues from
loans and advances from which interests is earned (Bhattarai, 2016). For this purpose, the Nepalese commercial
banks will be chosen for the research study to examine the financial performanceof those selected banks by using
CAMEL approach. This is an industry with a long history, giving the possibility to look at changes over the past
decades. There are plenty of actors in the industry which gives us the opportunity to investigate the industry
dynamics.
Statement of the Problem
Banks use the deposits to generate credit for their borrowers, which is the main revenue generating activity for
most banks. With the increase of credit transactions and loan customers in the nation’s economy, credit expansion
is inevitable. The trend in the sector shows growing bank deposit-loan ratio as the economy grows and so does
credit risk. Credit risk impact on banking system is being failure to properly management of balance sheet
which not only contributes to decline in net profit but also enhance liquidity crisis and has negatively effect on
goodwill of the bank as well. Customer’s level of confidence will be decline with the existing situation of financial
performance of banks in future. They might be willing to withdraw their interest towards banking industry.
The impact of credit risk on financial performance has been a topic of interest to many scholars since credit
risk has been identified as one of the major factors known to impact the financial performance of banks. The
overall objective of the study is to investigate the impact of credit risk on the financial performance of seventeen
commercial banks. This study tries to answer the main question i.e.
• What is the effect of credit risk on the profitability of selected bank?
• Is there any relationship between profitability and credit risk?
Objectives of the Study
The main objective of this study is to examine the impact of credit risk on the financial performance of Nepalese
Commercial Banks. The specific objectives are
• To study the effect of credit risk on the profitability of the selected commercial bank.
• To examine the relationship among profitability and credit risk, (capital adequacy, credit to deposit,
management quality and bank size)
Significance of the Study
The result of this research will have implications and importance:
• To regulator and policymakers, the research will provide the basis for the regulatory policy framework to
mitigate the financial system from the financial crisis and to better appreciate and quantify those credit risks
exposures.
Journal of Balkumari College (2021), Vol. 10 Issue 1 21

• To investors, this study will help them to understand the factors that influence the returns on their investments.
• To commercial banks, this study will provide an insight into the credit risk attributes which may need to be
incorporated in their investment decision processes. The study will improve not only researcher’s scope of
understanding risk management but also entire public hence gain exposure to the banking industry. These
findings will be used as reference material by future researchers interested in further research on credit risk
management and its effects on financial performance of Nepalese commercial banks.
Limitations of the Study
Due to various constraints and unfavorable situations during the entire research period, there has been following
limitations in the study:
• This study is based on secondary data and covers the 5 years period i.e. 2015 to 2020.
• Seventeen commercial banks of Nepal has been taken as a sample so that the research might not generalized
all commercial banks.
• The research was conducted by taking major six variables i.e ROA, NPLR, CAR. CDR, MQR and BS of the
bank which may not provide satisfactory result because it has not considered other variables that affects the
financial performance of the commercial banks.
• Primary data is not in used in this research so that the qualitative aspects cannot be explores from this study.
Literature Review
Commercial banks is not influenced by the amount of credit and nonperforming loans suggesting that other
variables other than credit and non- performing loans impact on profits. Commercial banks that are keen on
making high profits should concentrate on other factors other than focusing more on amount of credit and non-
performing loans.
Kurawa and Garba (2014) have assessed the effect of credit risk management (CRM) on the profitability of
Nigerian banks with a view to discovering the extent to which default rate (DR), cost per assets (CLA), and
capital adequacy ratio (CAR) influence banks profitability (ROA). The secondary data from the annual reports
and accounts of quoted banks during the period of 2002 to 2011 were used for analysis. The author concluded
that credit risk management components have a significant positive effect on the profitability of Nigerian banks.
Abiola and Olausi (2014) have analyzed the impact of credit risk management on the commercial banksperformance
in Nigeria. The panel regression model was employed for the estimation of the model. In this model, Return on
Equity (ROE) and Return on Asset (ROA) were used as the performance indicators whereas Non-Performing
Loans (NPL) and Capital Adequacy Ratio (CAR) as credit risk management indicators of the commercial banks.
The findings have revealed that credit risk management has a significant impact on the performance of the banks
in Nigeria. Furthermore, the results have shown that the sampled have poor credit risk management practices;
hence the high levels of the non-performing loans in their loans portfolios. Despite the high levels of the NPLs,
their profit levels keep rising as an indication of the transfer of the loan losses to other customers in the form of
large interest margins.
Ugoani (2015) has examined the relationship of poor credit risk management and bank failure in Nigeria
using survey research design. The results from CHI- square statistics revealed that weak corporate governance
accelerates bank failures and the credit risk management functions is to the greatest extent the most diverse
and complex activity in the banking business. The author, at last, concludes that poor credit risk management
influences bank failures.
Bhattarai (2016) has conducted research and examined the effect of credit risk on performance of Nepalese
commercial banks. The results revealed that non-performing loan ratio has negative effect on profitability of the
commercial banks while cost per loan assets has positive effect on profitability. In addition to credit risk indicators,
bank size has positive effect on profitability. Capital adequacy ratio and cash reserve are not considered as the
influencing variables on profitability of the banks. The study has concluded that there is significant relationship
between profitability and credit risk indicators of the selected commercial banks in Nepal. Nepalese commercial
banks have poor credit risk management and hence the banks need to follow prudent credit risk management and
safeguardingthe assets of the banks and protect the interests of the stakeholders.
 5
22 Journal of Balkumari College (2021), Vol. 10 Issue 1

universal banks positively.

Gadzo, Kportorgbi andResearcher
Gatsi (2019) recommend that banks
have assessed be encouraged
the effect of credit and to operational
cut-down their lending
risk on rates in
the financial
other to decrease credit risk and subsequently boost profitability.
performance of universal banks in the context of the structural equation model (SEM). The results showed Regarding operational risk,
banks should
that credit risk influences reduceperformance
financial leverage andnegatively
have theircontrary
portfolio tomore concentrated
the empirical studyonbut liquid investment
in line with the
income so
information asymmetry as toofboost
tenant profitability.
the lemon theory. It was also found that operational risk influences the financial
performance of the universal banks in Ghana negatively. Furthermore, the study indicated that bank specific
Theoretical
variables measured by (assetFramework
quality, bank leverage, cost to income ratio and liquidity) significantly influence
CAMELS
credit risk, operational risk aswell asisthe a financial
widely used approach
performance to universal
of the analyze banks
a bank. CAMELS has a six
positively.
components which include Capital Adequacy, Asset quality, Management capabilities,
Researcher recommend
Earningsthat banks be encouraged
sufficiency, to cut-down
Liquidity position, their lending
and Sensitivity to rates
market in risk.
other to decrease credit risk
and subsequently boostBaralprofitability. Regarding operational risk, banks should
(2005) has examined that the financial health of the joint reduce leverage
ventureand haveintheir
banks the
portfolio more concentrated on liquid investment income so as to boost profitability.
CAMEL framework and found the health of joint venture banks is betterthan that of the other
commercial banks. In addition to this, the perusal of indicators of different components of
Theoretical Framework
CAMEL have indicated that the financial health of joint venture banks is not so strong to
CAMELS is a widely
manageusedthe approach
possible large to analyze a bank. to
scale shocks CAMELS has asheet
their balance six components whichisinclude
and their health fair. Capital
Adequacy, Asset quality, Fredrick (2012) has concluded that credit risk management by use Sensitivity
Management capabilities, Earnings sufficiency, Liquidity position, and of CAMEL to
market risk. indicators has a strong impact on the financial performance of the commercial banks in
Baral (2005) hasKenya. Thethat
examined study reveals health
the financial that capital adequacy,
ofthe joint ventureasset
banksquality, management
in the CAMEL frameworkefficiency and
and found
liquidity have weak relationship while earnings have strong relationship
the health of joint venture banks is betterthan that of the other commercial banks. In addition to this, the perusal with the financial
of indicators of performance of the banks.
different components of CAMEL This have
studyindicated
has concluded that CAMEL
that the financial healthmodel
of jointcan be used
venture as isa
banks
proxy for
not so strongto manage thecredit
possible risklarge
management
scale shocks oftothe commercial
their balance sheet banks
and in Kenya.
their health Poudel
is fair. (2012) has
shown that default rate, cost per loan assets and capital adequacy ratio have an inverse impact
Fredrick (2012) has concluded that credit risk management by use of CAMEL indicators has a strong impact
of the bank's financial performance whereas default rate is the most predictor of the bank's
on the financial performance of the commercial banks in Kenya. The study reveals that capital adequacy, asset
financial performance.
quality, management efficiency
Iheanyi and and liquidity
Sotonyehave weak
(2017) haverelationship
accessed while earnings have
the performance of strong
banks relationship with
in Nigeria using
the financial performance
CAMEL ratingof the banks.
approach. ThisThestudy has concluded
regression modelthat CAMEL
results havemodel
showncanthat
be used as aadequacy,
capital proxy for
credit risk management of the commercial banks in Kenya. Poudel (2012) has shown that
management efficiency, Earning and liquidity have no significant impact on the profitability default rate, cost per
loan assets and capital
of the banks. However, assets quality has a negative impact on that profitability default
adequacy ratio have an inverse impact of the bank’s financial performance whereas of the
rate is the most Nigerian
predictor banks.
of the bank’s financial performance.
Conceptual Framework
Conceptual Framework
Independent Variables Dependent Variable

Capital Adequacy Ratio (CAR)

Non-performing Loan Ratio

(NPLR)
Dependent Variable
Credit to Deposit Ratio
(CDR) Return on Assets (ROA)

Management Quality Ratio

(MQR)

Bank Size (BS)

Fig.1. Conceptual Framework for the Study.
Fig.1. Conceptual Framework for the Study.
Research DesignsResearch Designs and Methods.
and Methods.
This study examines the effect of credit risk on the performance of commercial banks
This study examines the effect
in Nepal over of
thecredit riskofon5the
period performance
years of commercial
(2015-2020). banks
The reason in Nepal
behind over the
choosing period
of the of
latest
5 years (2015-2020). The reason behind choosing of the latestsix year period is to include the afresh data in the
analysis and as the data are from pooling of cross-sectional and time series, thus it seems sufficient to generate
data for the analysis. This study has adopted descriptive and causal comparative research design.
Journal of Balkumari College (2021), Vol. 10 Issue 1 23

The convenience sampling method was used in choosing the banks for the study. Moreover, in selecting the 17
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: NMB Bank
Ltd, Century Bank Ltd, Prime Commercial Bank Ltd, Standard Chartered Bank Ltd, Nepal Bangladesh Bank Ltd,
Mega Bank Ltd, Kumari Bank Ltd, Siddhartha Bank Ltd, Nabil Bank Ltd, Civil Bank Ltd, NIC Asia Bank Ltd,
Everest Bank Ltd., Citizen Bank International Ltd, Global IME Bank Ltd, Machhapuchchhre Bank Ltd., Nepal
SBI Bank Ltd. And Agriculture Development Bank Ltd. The selected commercial banks appear fairly represent
the study population. The population of this study constitutes the “A” class commercial banks in Nepal which are
listed in the Nepalese Stock Exchange.
Data were sourced from the annual reports of the banks in the sample. The data include time-series and cross-
sectional data, i.e. pooled data set and estimated the effect of credit risk on the performance of commercial banks
using pooled data regression. Data analysis was done using the Stata software.
Study variable and hypothesis
The dependent variables and independent variables used in this study are as follows:
Dependent variable. The measures of bank performance may be varied and the choice of the specific
performance measure depends on the objective of the study. Thus, this study has used ROA as dependent
variables to represent bank performance.
Return on Assets. (ROA)Return on Assets is the ratio of net income and total assets of any institutions. It
measures the efficiency of the banks management in generating profits out of its scarce resources. The
more the amount of ROA the better the efficiency of the bank management, (Gizaw, et al, 2015). Return on
assets ratio is important profitability ratio because it measures the efficiency with which the company is
managing its investment in asset and using them to generate profit (Harelimana, 2017).A basic measure
of bank profitability that corrects the size of the bank is the return on assets (ROA), which divides the net
income of the bank by the amount of its assets. ROA is a useful measure of how well a bank manager is doing
on the job because it indicates how well a bank’s assets are being used to generate profits (Chowdhury,
2013). Furthermore, return on total assets measures the profitability of the total assets available to the
business. It measures earnings in all investments provided by owners and creditors.
Independent Variables.
Capital adequacy ratio. This is an independent variable for the determination of the performance and is considered
as the core measure of a bank’s financial strength from a regulator’s point of view.
Capital requirement (capital adequacy) is the amount of capital a bank or other financial institution has to
hold as required by its financial regulator. It is a measure of the amount of bank’s capital expressed as a percentage
of its risk weighted exposure. It consists of the types of financial capital considered the most reliable, primarily
shareholders’ equity. Theoretically, banks with good capital adequacy ratio have a good profitability. A bank with
a strong capital adequacy is also able to absorb possible loan losses and thus avoids bank „run, insolvency and
failure.
Bank capital increases the capacity to raise non-insured debt and thus banks ability to limit the effect of a drop
in deposits on lending. Since higher capital reduces bank risk and creates a buffer against losses, it makes
funding with non-insured debt less information sensitive. Thus, capital adequacy can enhance bank performance.
However, empirical studies on the relationship between firms performance and capital adequacy ratio have shown
mixed results Jha and Hui (2012) have found negative association between capital adequacy ratio and ROA
and the coefficient was statistically significant (p< 0.05). Ezike and Oke (2013) mentioned that holding capital
beyond the optimal level would inversely affect the efficiency and profitability of commercial banks.
24 Journal of Balkumari College (2021), Vol. 10 Issue 1

H1: Capital adequacy ratio has a significant and positive effect on bank performance.
Asset Quality. The asset quality indicators highlight the use of non-performing loans ratios (NPLs) which
are the proxy of asset quality, and the allowance or provision to loan losses reserve.Non-performing loans
ratio (NPLR) reflects the bank’s credit quality and is considered as an indicator of credit risk management.
NPLR, in particular, indicates how banks manage their credit risk because it defines the proportion of loan
losses amount in relation to total loan amount. NPLR has been used as the default rate on total loan and
advances. However, empirical studies produce mixed results. Alshatti (2015) found the positive effect of
non-performing/ gross loans ratio on the financial performance of banks. Contrary to these findings, Jha
and Hui (2012) found negative association between NPL ratio and ROA but the coefficient is statistically
insignificant. Although there are conflicting evidences on this issue, in view of the theory and majority of
the empirical literature, a negative relationship is expected between non- performing loan and bank‟s
performance (β2< 0).
H2: Non-performing loan ratio has a significant and negative effect on bank performance.
Liquidity. The credit to deposit ratio (CDR) is a major tool to examine the liquidity of a bank and measures
the ratio of fund that a bank has utilized in credit out of the deposit total collected. Higher the CDR more
the effectiveness of the bank to utilize the fund it collected (Jha & Hui, 2012). This ratio measures theability
of the management to use the assets in offering loans which ultimately creates high profitability(Ibrahim,
2014). This ratio helps us showing the relationship between loans and advances which are granted and the
total deposited collected by the bank. A high ratio indicates better mobilization of collected deposit and
vice-versa. It should be noted that too high ratio may not be better from liquiditypoint of view. This ratio is
calculated dividing loan and advances by total deposits.
H3: Credit Deposit Ratio (CDR) has negative and significant related to bank performance.
Management Quality Ratio. Management soundness is a qualitative variable that expresses the control of
board of directors over the resources of the bank to protect shareholders interest. It is measured by the
ratio of total operatingincome to total assets.
H4: Total Operating income to Total Assets as a measure of Management Quality Ratio (MQR) has positive and
significant related to bank performance.
Bank size. Bank size as measured by total assets is one of the control variables used in analyzing performance of
the bank system (Smirlock, 1985). Bank size is generally used to capture potential economies or diseconomies of
scale in the banking sector. This variable controlsfor cost differences in product and risk diversification according
to the size of the financial institution. This is included to control for the possibility that large banks are likely
to have greater product and loan diversification. In most finance literature, natural logarithm of total assets of the
banks is used as a proxy for bank size. The effect of bank size on profitability is generally expected to be positive
(Smirlock, 1985).
H5: Bank size has a significant and positive effect on bank performance.
The model
Pooled data regression model has been used in the analysis which was taken from (Bhattarai,2016) article.. The
technique of pooled data estimation takes care of the problem of heterogeneity in the 17 banks selected for the
study. The econometric model employed in the study is given as:
Y = β0 + β Xit +εit
Where: Y is the dependent variable; β0 is constant; β is the coefficient of explanatory variables; Xit is the vector
of explanatory variables; and εit is the error term (assumed to have zero mean and independent across the time
period). By adopting the prescribed econometric model, particularly to this study, the impact of credit risk
(controlling the effect of cash reserve requirement and bank size) on the performance of the commercial banks
has been estimated with the following regression equation:
ROAit = β0 + β1 CARit + β2 NPLRit + β3 CDRit + β5 BSit + eit
Where:
Journal of Balkumari College (2021), Vol. 10 Issue 1 25

ROAit = Return on assets (ratio of earnings after taxes to total assets) of bank in year t
CARit = Capital adequacy ratio.
NPLRit = Non-performing loan ratio of i year t
CDRit = Credit to deposit ratio of ith bank in year t
BSit = Bank size (natural logarithm of total assets) of ith bank in year t
Β0 = The intercept (constant)
β1, β2, β3, β4, β5 = The slope which represents the degree with which bank performancechanges as the independent
variable changes by one unit variable.
eit = error component
The selected study variables, their definition, basis of measurement and priori expectedsign have been depicted
in Table.
Table 1: Variables definition, measurement and expected sign
Expected sign is a statistical technique which shows the relationship between two variables. The positive expected
sign means that one variable increase, the other variable will also increase while negative expected sign means
that when one variable increase, the other variable will be decrease.
No. Abbreviation Description Measurement Expected
variables sign
1 ROA Return on Assets ROA is the ratio between net profits NA
to Total
Assets of the bank.
2 NPLR Non-performing loan ratio Non-performing loan/Gross loans and –
advances
3 CAR Capital adequacy ratio Capital/ Risk weighted Assets. +

4 CDR Credit to Deposit ratio Credit to Deposit Ratio is a ratio between _

total loans and total deposits. This ratio
measures the ability of the management
to use the assets in offering loans
which ultimately creates high
profitability.
5 MQR Management Quality Ratio Total Operating Income to Total +
Assets as a measure of management
quality.
6 BS Bank size Natural logarithm of total assets +
Result and Discussion
Descriptive Statistics
The summary of the descriptive statistics for all variables used in the study is presented in Table 1. Thetable reports
single bank financial performance indicator is return on assets (ROA) and five credit risk indicators which are
the capital adequacy ratio (CAR), non-performing loan ratio (NPLR), management quality as measured by total
operating income to total assets (MQR), credit to deposit ratio (CDR) and bank size(BS).. The result shows that
the average value of the bank performance
ROA is 1.66 percent indicating that during the period 2015 to 2020, on average, the total assets of sample
commercial banks in Nepal generate 1.66 percent return. The standard deviation of the ROA is 0.497 percent,
which shows the substantial variation in returns of the banks. The minimum capital adequacy ratio is 10.96
percent
 The summary of the descriptive statistics for all variables used in the study is presented
in Table 1. The table reports single bank financial performance indicator is return on assets
(ROA) and five credit risk indicators which are the capital adequacy ratio (CAR), non-
performing loan ratio (NPLR), management quality as measured by total operating income to
total assets (MQR), credit to deposit ratio (CDR) and bank size(BS).. The result shows that
the average value of the bank performance
26 ROA is 1.66 percent indicating that during the period 2015 to
Journal of 2020, on average,
Balkumari the(2021), Vol. 10 Issue 1
College
total assets of sample commercial banks in Nepal generate 1.66 percent return. The standard
deviation of the ROA is 0.497 percent, which shows the substantial variation in returns of the
Table 2: Summary Statistics
banks. The minimum capital adequacy ratio is 10.96 percent
Table 2: Summary Statistics

Sources: Annual Report of Sample Banks and Results are drawn from Stata.
Table 2 shows that the number
Sources: Annualof Report
observations per each variable
of Sample Banksisandequal. This may
Results be drawn from Stata.
are
explained by the balanced nature of the panel data used in the analysis. Table 2 additionally
Table 2 shows that theshows
number that on
of average the overallper
observations mean
eachreturn on assets,isnon-
variable performing
equal. This mayloan ratio, credit to
be explained by the balanced
deposit ratio, capital adequacy ratio, management quality ratio and bank size were 1.666,
nature of the panel data used72.720,14.560
1.205, in the analysis.
and 4.129Table 2 additionally
percent shows that
respectively. Therefore, over on
the average
period the the
banks overall mean return
on assets, non- performing loan ratio,
were positively creditadequately
profitable, to deposit ratio, capital
capitalized adequacy
and experienced someratio, management
relatively high levels quality ratio and
bank size were 1.666, of deterioration
1.205, in asset quality
72.720,14.560 during
and the study
4.129 period.respectively. Therefore, over the period the banks
percent
were positively profitable, adequately
Correlation Analysiscapitalized and experienced some relatively high levels of deterioration in
asset quality during the study period.
The correlation matrix of the variables presented Table 3 reveals that all correlations
coefficients among the independent variables are less than 0.7, implying the absence of
Correlation Analysis
10
The correlation matrix of the variables presented Table 3 reveals that all correlations coefficients among the
independent variables are less than 0.7,
multicollinearity. implying
Thus, there isthe
noabsence
evidenceofofmulticollinearity. Thus, there isamong
presence of multicollinearity no evidence
the of
presence of multicollinearity among
independent the independent variables.
variables.
Table 3: Correlation Coefficient Matrix
Table 3: Correlation Coefficient Matrix

Source: Result drawn from Stata.

Source: Result drawn from Stata.
Regression Analysis
Regression Analysis Table 4 indicates that the value of R-square was 0.4976, which means that 49.76
percent
Table 4 indicates that of theoftotal
the value variation
R-square wasin 0.4976,
the valuewhich
of ROA was that
means due to the effect
49.76 of the
percent independent
of the total variation
variables. The adjusted R-square was 0.4658 which shows that on an adjusted
in the value of ROA was due to the effect of the independent variables. The adjusted R-square was 0.4658 basis, the which
independent variables were collectively 46.58 percent related to the dependent variable ROA.
shows that on an adjusted basis, the independent variables were collectively 46.58 percent related to the dependent
Table 4: Regression Result of Credit Risk Management on Performance.
variable ROA.
 Source: Result drawn from Stata.

Regression Analysis
Table 4 indicates that the value of R-square was 0.4976, which means that 49.76
percent of the
Journal of Balkumari College totalVol.
(2021), variation
10 Issuein1 the value of ROA was due to the effect of the independent 27
variables. The adjusted R-square was 0.4658 which shows that on an adjusted basis, the
independent variables were collectively 46.58 percent related to the dependent variable ROA.
Table 4: Regression
Table 4:Result of Credit
Regression ResultRisk Management
of Credit on Performance.
Risk Management on Performance.

Source:
Source: Annual report of sample bankAnnual report
and results areofdrawn
sample bank
from and results are drawn from Strata.
Strata.
As expected, there is a strong negative association
As expected, there is a strong negative association between non-performing between non-performing
loans and financial
loans and financial performance of commercial banks but, it has significant relationship with
performance of commercial banks but, it has significant relationship with ROA. The result is contrary to
ROA. The result is similar to the findings of Kargi (2011); Kodithuwakku (2015); and
the findings of Alshatti(2016)
Bhattarai (2015) whothey
where found the
found positive
negative effect ofbetween
association non-performing /gross
non-performing loans
loans and ratio on the
financial performance of banks. The result reveals that as CDR commercial bank increases, the performance
of the bank will also increase. However, there is positive and no significant correlation between return
on assets. The result indicates that capital adequacy ratio is negative and insignificant. The sign of the
coefficient is as unusual because theoretically capital adequacy ratio was expected to have a positive
relationship with a performance of the commercial banks. However, the finding of this study supports the
hypothesis that capital adequacy ratio has a significant effect on financial performance of the commercial
banks in Nepal. The result is contrary to the findings of Bhattarai (2016).
The result indicates that, management quality ratio has positive and statistically significant. TheFinancial
performance of the banks (ROA) is significantly positively correlated with management quality ratio which
implies that as the value of total operating income increases, the performance of banks will also increase.
Finding of this study has supported the hypothesis that management efficiency ratio has a significant
effect on financial performance of the commercial banks in Nepal. There is negative and no significant
relationship between bank size an financial performance (ROA) which indicates that the relationship is
weak between bank size and financial performance of the commercial banks. The result is contrary to the
findings of Bhattarai (2016)

Hence, Regression analysis is valid as it has satisfied all assumptions. all the assumptions are tested to check the
validation of the regression analysis which is shown in figure below.
Linear regression assumes that there is liitle or no multicollinearity in the data. Multicollineraity
occurs when the independent variables are too highly correlated with each other. Researcher has test
multicollinearity by Variance Inflation Factor (VIF) The variance inflation factor (VIF) value describe
the multicollinearity of the statistics; and a VIF of 10 or more show the problem of multicollinearity The
value of VIF value shown in table below indicate that there isno problem of multicollinearity in the statistics
of the data under study.
 figure below.
Linear regression assumes that there is liitle or no multicollinearity in the data.
Multicollineraity occurs when the independent variables are too highly correlated with each
other. Researcher has test multicollinearity by Variance Inflation Factor (VIF) The variance
28 inflation factor (VIF) value describe the multicollinearity of the
Journal statistics;College
of Balkumari and a(2021),
VIF of 1010 Issue 1
Vol.
or more show the problem of multicollinearity The value of VIF value shown in table below
indicate that
Table: 5 Test For there is no problem of multicollinearity in the statistics of the data under study.
Multicollinearity
Table: 5 Test For Multicollinearity

In statistics, the Breusch–Pagan test, developed in 1979 by Trevor

In statistics, the Breusch–Pagan test, developed in 1979 by Trevor Breusch and Adrian
Breusch and Adrian Pagan, is used to test for heteroskedasticity in a linear regression model. Pagan, is used to test
for heteroskedasticity
It was independently suggested with some extension by R. Dennis Cook and Sanford by R.
in a linear regression model. It was independently suggested with some extension
Dennis Weisberg
Cook andinSanford Weisberg in 1983
1983 (Cook–Weisberg (Cook–Weisberg
test). Derived from the test). Derivedmultiplier
Lagrange from thetest
Lagrange multiplier
principle,
test principle,
it tests itwhether
tests whether the variance
the variance of thefrom
of the errors errorsa from a regression
regression is dependent
is dependent on the of
on the values values
the of the
independent variables.
independent Hence, There
variables. Hence, exist no problem
There exist no of heteroskedasticity.
problem of heteroskedasticity.
Table 6: Test for Heteroskedasticity
Table 6: Test for Heteroskedasticity 12

Multiple regression needs the relationship between the independent and dependent
Multiple regression needs the relationship between the independent and dependent variables to be linear. It is also
variables to be linear. It is also important to check for outliers .It is important to check for
important to check for outliers
outliers since .It is important
multiple linearto check for outliers
regression since multiple
is sensitive linear regression
to outlier effects. Theis sensitive to
linearity
outlier effects. The linearity assumption can best be tested sensitive to outlier’s effects. The linearity
assumption can best be tested sensitive to outlier’s effects. The linearity assumption can best assumption
can best be tested with scatter
be tested plots. plots.
with scatter
Linear regression analysis
Linear regression analysis requires all variables to requires all variables
be multivariate normal. to Normality
be multivariate
test arenormal.
used toNormality
determine
if a data set is test are used to
well-modeled by determine if a data set
a normal distribution andistowell-modeled
compute howby a normal
likely it is fordistribution and to
a random variable
compute how likely it is for a random variable underlying the data set
underlying the data set to be normally distributed. For the normality test, Kernel density test is done to check to be normally
whether data are distributed.
normal or notForand
theitnormality
shows almosttest,allKernel
data aredensity
normallytestdistributed.
is done to check whether data are
normal or not and it shows almost all data are normally distributed.
Linear regression analysis requires
Linear that there
regression is little
analysis or nothat
requires autocorrelation
there is littleinorthe
nodata. Autocorrelation
autocorrelation in the occurs
data.
when the residuals are not independent
Autocorrelation occurs whenfromtheeach other. areStudy
residuals shows therefrom
not independent is noeach
autocorrelation
other. Studywhile,showsa
there
scatterplot allows is no autocorrelation
to check while, a scatterplot allows to check for auto correlated.
for auto correlated.
Conclusion. Conclusion.
Credit risk managementCredit
shouldrisk
be atmanagement shouldoperations
the center of banks be at thein center
order toofmaintain
banks financial
operations in order
stability. to
Credit
risk management maintain
includesfinancial stability.
the system Credit
process risk management
and control includes
which a company hastheinsystem
place toprocess
ensure and control
the efficient
which apayment
collection of customer company andhastheinrisk
place to ensure theToefficient
of no-payment. achieve collection
the goal ofof customer
owners’ payment
wealth and the
maximization,
risk of their
banks should manage no-payment. To achieve
assets, liabilities the goal
and capital of owners'
efficiently. wealth
In doing maximization,
this, credit banks
policy should set should
out the
manage their
bank’s lending philosophy, assets,procedures
specific liabilities and
andmeans
capitalofefficiently.
monitoringInthedoing this,activity.
lending credit policy should set out
the bank's lending philosophy, specific procedures and means of monitoring the lending
activity (Shakya, 2017).
The main purpose of this study is to investigate the impact of credit risk on the
financial performance of commercial banks in Nepal. The financial performance in terms of
return on assets selected as dependent variables. The capital adequacy ratio, non-performing
Journal of Balkumari College (2021), Vol. 10 Issue 1 29

The main purpose of this study is to investigate the impact of credit risk on the financial performance of commercial
banks in Nepal. The financial performance in terms of return on assets selected as dependent variables. The
capital adequacy ratio, non-performing loan asset, management efficiency, liquidity and bank size are taken as
independent variables. The balance panel data of seventeen commercial banks with 85 observations for the period
of 2015 to 2020 have been used for the analysis. The regression results indicate the existence of the relationship
between the dependent and independent variables hence has the ability to predict the influence of credit risks
on the profitability of the commercial banks in Nepal. The model is well fitted with 49.76 percent ability to
influence the financial performance of the commercial banks in Nepal. The regression model revealed that NPLR
has negative and statistically significant impact on financial performance of the commercial banks in Nepal. 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 performance
(profitability) of commercial banks in Nepal.
Capital adequacy ratio and bank size have negative and statistically no significant impact on the financial
performance of the commercial banks in Nepal. Credit to deposit ratio has positive but no significant relationship
with the financial performance (ROA). The study concluded that the MQR has positive and significant relationship
with the financial performance (ROA) of the commercial banks in Nepal. The study also suggests that the further
study can be done on the impact of credit risk management by the use of CAMELS indicators on the financial
performance of other bank and financial institutions like micro finance institutions, development banks, finance
companies etc. The study recommends that it is fundamental for Nepalese commercial banks to practice scientific
credit risk management, Nepal should enhance their capacity in credit analysis and loan administration while the
regulatory authority should pay more attention to banks compliance to relevant directives and prevailing rules
and regulations.
Banks need to place and devise strategies that will not only limit the banks exposition to credit risk but will develop
performance and competitiveness of the banks, and banks should establish a proper credit risk management
strategies by conducting sound credit evaluation before granting loans to customers, improve their efficacy in
credit analysis and loan management to secure as much as possible their assets, and minimize the high incidence
of non-performing loans and their negative effects on financial performance.
It is recommended that bank’s credit- granting activities conform to the established strategy that written procedures
should be developed and implemented, and that loan approval and review responsibilities are clearly and properly
assigned. Senior management must also ensure that there is a periodic independent internal assessment of the
bank credit-granting and management functions.

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