credit risk and growth of banking system
TRANSCRIPT
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Credit Risk and Growth of Banking System
*Bilal Aslam, Saima Batool, Bilal Wasim, Ahmed Arif
Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Pakistan
Drawing on the financial data 0f 21 banks for the period 2004-2011, the current study investigates
the role of credit risk in the growth of the banking system. A conceptual model has been developed
based on the seven antecedents of credit risk which include credit risk exposure, a loan to equity
ratio, non-performing loans to capital, credit monitoring, credit screening, a credit collection ratio
and a charge-offs ratio. . Panel data analysis has been performed to analyse the relationship
between credit risk and growth of banking system. . The results show that credit risk plays a
significant role in the growth of the banking system under favourable economic conditions. The study
contains important policy implications for growth of banking system in Pakistan.
Keywords: Credit Risk, Growth of Banking, Economic Growth
1.
Introduction
Banking system is an important segment of the economy of a country and its sound financial health
is a prerequisite to ensure economic stability. Banks facilitate the establishment new industries that
result in raising the employment level and economic growth. However, most of the banking business
activities entail significant financial risks in one form or other. This risky nature of the banking
business stipulates that banks must follow a prudent approach while performing their risk weighted
operations in order to sustain competitiveness and survival.
Now a day’s volatile economic environment exposes the banks to different kinds of risk. These risksinclude market risk, interest rate risk, liquidity risk, operational risk and credit risk (Sensarma &
Jayadev, 2009). The events leading to different risks may range from environmental hazards to
economic downturns which affect businesses (Weber, 2011). The most crucial among all is credit risk
in banking system (Fatemi & Fooladi, 2006). Although the banks are involved in a varied nature of
business now, however, their major focus still remains on credit operations. This diversified nature
of banking operations makes credit risk the most dominant which may negatively affect the banks’
profitability (Fatemi & Fooladi, 2010).
Credit risk arises from various activities including lending operations, forward contracts, foreign
exchange dealings, the letter of credits and the letter of guarantees etc. The intensity of losses
resulting from credit risk threatens the bank’s growth. Thus, credit risk becomes a burning issue herethat seeks the immediate attention of practitioners, academicians and regulatory bodies.
The banks can mitigate the credit risk partially through different risk mitigation techniques.
However the banks have to accept some part of the risk which is essential to continue business
operations. This situation stipulates that the banks must specialize in managing credit risk (Fatemi &
Fooladi, 2006). The risk management has become an integral part of the commercial banks. A risk
management department in banks performs risk management activities. This department hires the
risk managers who monitor business operations to manage the risk as such risk tends to threaten
the stability of the banking business.
Banking system has been facing multiple issues has both in the developing as well as a developed
economies. The shortcomings of credit risk management have intensified these problems.The
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intensity of credit risk subsides when economic conditions are favourable. However the economic
conditions are not favourable all the time. Therefore the banks should follow a proactive approach
while managing the credit risk (Arif et al., 2012). The credit risk management system helps to
stabilize the bank. It helps in increasing profitability and accelerating the growth. The credit risk
management needs to be strengthened to ensure the rapid growth of the banking system in a
favourable economic situation.
Credit risk management has been an area of interest for many researchers. They have focused on
credit risk management (Nandi & Choudhary, 2011) and have also investigated the impact of credit
risk on banking productivity (Mukherjee et al., 2001), profitability (Sufian, 2009), efficiency (Sun &
Chang, 2011) and shareholder value (Arif et al,. 2012). A few researches have discussed the issue of
growth (Cyree et al., 2000; Noulas, 1997). However, there is a lack of studies that evaluate the direct
relationship of credit to the growth of the banking system. It has created new paradigms and
opportunities of research for the present and upcoming risk management scholars.
Risk management is an important function in all business organizations regardless of the nature of
business. Carey (2001) identifies that risk management is more important in the financial sector ascompared to any other segment of the economy. The present study focuses on the credit risk
because this is the most significant risk in the banking system due to large scale credit operations of
the banking system. A number of banks all over the world have suffered huge losses in their credit
operations. This situation resulted in large scale failures of the banking system. This crisis has given
rise to concentration on the stability of the financial system and the requirement for closer
supervision of credit operations (Boudriga et al., 2009). Loans which represent a significant
percentage of the bank's assets are the source of income, but they also entail the brunt of credit risk.
Therefore, these credit operations put a detrimental effect on the stability of a bank, if the involved
risk is not managed properly. The present study is an endeavour to evaluate the impact of credit risk
on growth of the banking system. This evaluation has been performed while incorporating the
impact of the economy as well.
The aim of this study is to develop a thoughtful understanding about credit risk and highlighting its
significance and contribution to the growth of the banking system. Following are the major objective
of the study:
• To investigate the role of credit risk that causes fluctuation in banking growth.
• To analyse the role of economy (as being moderator) in banking growth.
In any business, earnings can only be realized constantly if right risk management prevents gigantic
capital losses. Banks always aim at increasing their credit portfolio while being on the safe side to
avoid the threats of survival. Developing and managing such a mix is not easy.
The significance of the study can only be understood when we realize the severity of business losses
that the banking industry around the globe suffered from in the recent financial crisis. This research
identifies the key areas which may expose the banks to the threatening level where the collapse may
become disastrous. The study also takes into account the role of economic conditions that mediate
the impact of credit risk on banking growth. This study develops a linkage between two existing
concepts to drive the literature in a different paradigm.
1.2 Credit Risk
Credit risk refers to the uncertainty linked with the repayment of loans from the customers of the
bank. It is also termed as the risk of loss, which occurs when an obligor fails to fulfil the terms of afinancial contract or an obligation is left unfulfilled as promised (Eccles et al., 2001). Credit risk
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affects the business in different ways. If it is managed properly, it helps to increase the profitability.
However, it has destructive effects if proposed risk management system is not well in place which
may lead to the failure of the bank under extreme conditions. Salas & Saurina (2002) find that GDP
growth rate, family's indebtedness, past credit growth, the branch network, inefficiency, portfolio
mix, size, the net interest margin, the capital ratio and market power are indicators of credit risk.
A successful credit risk management (CRM) requires designing a suitable credit risk atmosphere,
making credit process effective, maintaining a proper credit management involving monitoring and
control overcredit risk. It needs the higher management commitment to ensure that effective and
clear guiding principles are in place for credit risk management. The factors of the CRM system for a
commercial bank operating in a developing country are different as compared to those in a
developed country. The economy in which the bank functions is a thoughtful concern in designing a
credit risk management system. Developing economies face the problem of credit risk due to
inadequate information about clients.
Business practices may vary among different banks depending upon the nature of their credit
activities. CRM for a bank includes a credit assessment and credit monitoring at the individual andportfolio level. The internal credit risk assessment model is developed to evaluate organizational
financial health before granting loans (Boguslauskas et al., 2011). Careful monitoring of debtors
needs to keep contact with them, create an image that the bank stands to solve problems and
extend advices, show them that they are recognized and can call in difficulties, keeping an eye on
the flow of the borrower’s business from a bank account, regularly examine the borrowers’ reports,
pay on-site random visits and reviewing the borrowers ranking/rating periodically (Mwisho, 2001).
Capital requirements to absorb risk are lower in booms due to lower risk and increase during
recessions when risk exposure tends to rise (Wahlen, 1994).
1.3 Banking System Growth
Growth in any sector of the economy is possible only when the performance, profitability,
development and efficiency stand out showing continuous improvement. Commercial banks
perform a fundamental role in the economy. Measuring their business performance and financial
position is important to regulators, savers, owners, investors, employees and other stakeholders.
Bank size and portfolio composition are the major determinants of the domestic banks’
performance. However, labour productivity, economic conditions, capitalization and liquidity do not
seem to affect the bank performance to a greater extent. Whereas in case of foreign banks’
performance, factors like leverage, economic conditions, capitalization and capital productivity are
found to be most significant. While the less important factors for foreign banks’ performance is
banks’ portfolio composition, liquidity, concentration and costs (Al-Tamimi & Al-Mazrooei, 2007).
The type of bank, sufficient capital and higher efficiency are the important determinants of the
bank’s profitability (Schiniotakis, 2012). Growth of the banking system is dependent on the level of
profitability achieved by banks and the macroeconomic conditions. Higher profitability and
favourable economic conditions stimulate the banking system’s growth (Goddard et al., 2004).
Growth of a bank is also the function of its policy that whether it goes for a centralized strategy or a
dispersed strategy. But the impact of these policy issues can only be observed in the presence of
favourable economic conditions (Marquis & Huang, 2009).
A larger asset size and specialization of product mix brings in higher productivity growth while higher
equity to assets hinders growth (Mukherjee et al., 2001). The ownership type is key determinant in
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productivity growth. This is the reason why private banks (joint-stock) perform better as compared
public banks with respect to productivity growth and efficiency (Du & Girma, 2011).
The credit risk is no doubt the most significant risk to any bank. Banks are unable to forecast the
losses in worse economic conditions. This situation leads to huge losses in the shape of bad debts.
These bad debts have an adverse effect on the bank’s capital. This means the economy tends to
enhance or suppress the level of the bank’s credit risk.
2. Research Methodology
The modern banking system focuses on lending activity for maximization of shareholders’ profit and
banking growth. This study evaluates the credit risk of the banking system in the context of
management, assessment, credit exposure and its relation to banking growth. Researcher has
developed the model given in figure A1. The model focuses on the main theme of study and
addresses all the research objectives. This study follows the holistic approach and provides new
knowledge in existing literature.
(InsertFig. A1)
The hypotheses formulated on the basis of the framework are given below:
H1: Higher the credit risk exposure (CRE) boosts the growth of a bank.
H2: Increase in total loans to total equity (LER) causes the growth of a bank.
H3: Successful credit screening (CS) causes an increase in the growth of a bank.
H4: Successful credit monitoring (CM) causes an increase the growth of a bank.
H5: A high credit collection ratio (CCR) causes an increase in the growth of a bank.
H6: Increase in charge-offs ratio (COR) decreases the growth of a bank.
H7: Increase in the NPLs to total capital (NPLC) causes a decrease in the growth of the bank.
H8: Economy moderates the relationship between CRE and AGR.
H9: Economy moderates the relationship between LER and AGR.
H10: Economy moderates the relationship between CS and AGR.
H11: Economy moderates the relationship between CM and AGR.
H12: Economy moderates the relationship between CCR and AGR.
H13: Economy moderates the relationship between COR and AGR.
H14: Economy moderates the relationship between NPLC and AGR.
2.1 Description Of Variables
The description and the measurement of variables are provided as follows:
2.1.1 Independent Variable
The credit risk exposure, management and measurement tools are taken as the independent
variables in this study. These variables are explicated as below:
2.1.1.1 Credit Risk Exposure
This ratio measures the credit risk and efficiency of banks. It illustrates the percentage of financial
institution assets are tied in loan activities (Sufian, 2012). (Insert Eq. (A.1))
2.1.1.2 Loans to Equity Ratio
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Loan to equity ratio (LER) indicates the capability of banks’ capital to absorb the financial losses (Veli,
2007).
(Insert Eq. (A.2))
2.1.1.3 Credit Screening
This ratio is an indicator of credit risk and shows the loan loss provision of the bank in a year
compared to its total loans (Sufian, 2012). The small value of CS indicates minimum loan losses and
effective monitoring process.
(Insert Eq. (A.3))
2.1.1.4 Credit Monitoring
Credit monitoring (CM) measures effective process of data collection, analysis, and communication
with the borrower (Sackett & Shaffer, 2006).(Insert Eq. (A.4))
2.1.1.5 Credit Collection Ratio
Credit Collection Ratio (CCR) measures the effectiveness of collection process, and shows recoveries
of defaulted loans from the amount of gross charge off (Sackett & Shaffer, 2006). The high value of
CCR is an indicative of a better credit risk management system.
(Insert Eq. (A.5))
2.1.1.6 Charge off Ratio
This ratio measures the gross credit loss of loans in a certain period as a subject to total loans
(Macerinskiene & Ivaskeviciute, 2008). The ratio has been denoted by COR.
(Insert Eq. (A.6))
2.1.1.7 NPLs to Capital Ratio
This ratio determines the proportion of capital that entails non-performing loans (Macerinskiene &
Ivaskeviciute, 2008). It has been denoted by NPLC hereafter.
(Insert Eq. (A.7))
2.1.2 Moderating Variable
The economy has been taken as the moderating variable in the present study. The Economy has
been proxied by the nominal GDP growth here (Pastor, 2002).
2.1.3 Dependent Variable
The present study takes the growth of the banking system as a dependent variable (Froot et al.,
1993). It has been denoted by AGR in the study. It has been measured as follows:
(Insert Eq. (A.8))
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2.2 Data And Analysis
The data used in this study is panel data in nature. This data has been collected from the annual
reports of 21 banks over the period 2004 to 2011. This data has been analysed through a series of
statistical tests explained in section 3.
3.
Results
Varied statistical techniques have been used for data analysis. Correlation and regression analysis
have been applied to examine the research model. Breusch-Pagan / Cook-Weisberg test was run for
heteroskedasticity and Wooldridge test was run to see the autocorrelation in panel data. Baron &
kenny (1986) were used to test the moderation. Many of the previous researchers have also used
this method for moderation testing (Elliott & Beverly, 2011; Flatt & Kowalczyk, 2006; Kramer &
Weber, 2011). Moderation is tested by taking the product of moderator and independent variable. If
this product shows a significant relationship with dependent variable, it means that moderation
exists in the model.
3.1 Descriptive Analysis
Table A.1 shows the descriptive statistics. It includes the values of mean, standard deviation,
minimum and maximum. Mean values show the average trend of different variables. The mean
value of CRE shows that banks have 53.77% of assets in the form of loans. This shows a high credit
risk exposure. Most of the assets of the banking system are comprised of advances and lending to
financial institutions. The minimum value of CRE is 0.036 and maximum value is 0.8456. The
maximum values signify that the banking system is still focusing on the traditional source of interest
income earned through loans.
LER is quite high; loan amount is six times higher as compared to the equity amount. The banking
system does not have the capacity to cover the financial loss from the equity amount. The maximum
value demonstrates that the loan amount is extremely high as compared to equity. The variation in
values illustrate that some of the banks are adopting the conservative approach in lending activity.
The credit screening (CS) ratio is 1.90%, it illustrates the proportion of non-performing loans as
compared to total loans is quite low. The low mean value signifies that most of the bankshave a well
organized screening process. The variation in high and low values represents that some of the banks
are not managing CS proficiently.
CM mean value is 1.040 which signifies that most of the banks have an efficient process of data
collection, analysis, and communication with the borrower.The minimum value of CM is 0.1257 and
maximum value is 3.809.The values’ variation demonstrates that certain banks are not giving
attention on CM.
The CCR mean value 1.030. It explains that banks are successfully recovering the loan amount of
defaulted customers. The minimum value of CCR is 0.001 and maximum value is 13.30. These values
illustrate that various banks have successfully recovered the previous year default amount, but some
banks are not.
Charge-off ratio (COR) has a mean value of 0.67%, indicates the small percentage of loan write-offs.
The bank faces the smaller amount of financial loss resulting from credit operations which
represents banks have efficient screening, monitoring and collecting process. The small variation
denotes a vigilant credit risk management prevailing in the banking system.
The mean value of NPLC is 15.43%, revealing a satisfactory situation. The minimum value is 0.0012and the maximum value is 5.039. The difference in the values exists due to excess lending as
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compared to the capital. GDPG and AGR findthe mean values of 0.0477 and 0.2545 respectively.
These values represent the satisfactory condition. The minimum value shows the negative growth of
AGR and GDPG. The maximum value signifies the higher growth.
The mean values of most of the variables indicate that the banking system manages the credit risk
effectively and efficiently. Standard deviation shows the spread of observation from the mean. The
values are lying 0.0150 to 4.848. The gap exists between the minimum and the maximum value of all
variable. This situation leads to the conclusion that credit risk fluctuates from bank to bank
dramatically. This gap can be ascribed to a difference in sizes of banks.
(Insert Table A.1)
3.2 Correlation Matrix
The correlation matrix is given in table A.2. This table shows that LER, CS, CM, COR, NPLC, GDPG are
negatively correlated whereas CCR and CRE are positively correlated with AGR. The values of r for
LER, CS, CM, COR, NPLC, and GDPG with AGR are -0. 0548, -0. 1176, -0.0988, -0.1832, -0.1396 and -
0.0003 respectively. This shows the presence of weak negative correlations of IVs with AGR (DV).The values of r for CRE and CCR are 0.1324, 0.0215 respectively. This indicates a weak positive
correlation of the stated variables with AGR. The table exhibits weak correlations among the IVs
which negate the presence of multicollinearity.
(Insert Table A.2)
3.3 Panel Data Analysis
Breusch-Pagan/Cook-Weisberg and Wooldridge tests revealed that heteroskedasticity and
autocorrelation does not exist in panel data.Panel data analysis is performed to test the developed
model. For penal data analysis, a common effect, fixed effect and random effect models are applied.
The Hausman test is used to find out the best model. However, Hausman tests show the fixed effects
model is more reliable and efficient than the other two models.
Table 3 showsthe results of the fixed effect model. The value of R2 is .2149. This value demonstrates
that there is a 21.49% variation in AGR by IVs. The value of F statistics = 4.30 ( p< 0.01) authenticates
the model fitness.
CRE has a value of 0.0181 that revealed the positive relation of CRE with AGR. This relation is
insignificant as shown by the value of t statistics i.e. 0.19 ( p > 0.05). CRE defines the amount of assets
of the financial institution is tied up in credit activities. Thus, H1 not accepted.
H1: Higher the credit risk exposure (CRE) boosts the growth of a bank. (Not accepted)
LER has the β value of -0.0015 that shows the negative weak relation of LER with AGR. Any increase
in LER causes a decline in the growth of a bank. However, the relation of LER with AGR proves
insignificant as revealed by t statistics -0.30 ( p> 0.05). Thus, H2 is not accepted.
H2: Increase in total loans to total equity (LER) causes the growth of a bank. (Not accepted)
The value of -2.2234 of CS shows a negative relation of CS with AGR, but the relationship is still
insignificant as the value of t-statistics i.e. -1.00 ( p> 0.05) shown. Thus, H3 is not accepted.
H3: Successful credit screening (CS) causes an increase in the growth of a bank. (Not accepted)
CM has the value -0.0081 that shows the weak negative relation of CM with AGR. The value of t
statistics = -0.10 ( p> 0.05) exhibit the insignificant relationship between CM and AGR. CM is the ratio
of gross charge-offs to NPLs. The finding elaborate that CM does not have an effective role in
banking growth. Thus, H4 is not accepted.
H4: Successful credit monitoring (CM) causes an increase the growth of a bank. (Not accepted)
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CCR has the value of 0.0033 that identifies the presence of the positive relation among CCR and
AGR. Though the value of -coefficient is trivial here, but the relationship is still significant as
revealed by the value of t statistics i.e. 3.45 ( p < 0.01). Hence, the significant relation of CCR with
AGR has been proved here. CCR is the ratio of recovery to gross charge-offs. The increase in the
amount of recovery results an increase in AGR. These results lead to the acceptance of H5.
H5: A high credit collection ratio (CCR) causes an increase in the growth of a bank. (Accepted)
COR has the β value of -5.7948 that authenticates a strong negative relation between COR and AGR.
The value of t -statistics i.e. -2.15 ( p < 0.01) revealed that there is a significant relation. COR shows
the loans’ write-offsin proportion to total loans. The beta value means that there would a 5.7948
unit negative change in AGR because of one unit change in COR. These results lead to acceptance of
H6.
H6: Increase in charge-offs ratio (COR) decreases the growth of a bank. (Accepted)
NPLC has the value of -0.0674 that symbolize the negative week relationship among NPLC and
AGR. The decline in NPLC will boost the growth of a bank. The value of t statistics = -0.60 ( p >0.05)
reveals that the relationship is still insignificant between NPLC and AGR. Thus, H7 is not accepted.H7: Increase in the NPLs to total capital (NPLC) causes a decrease in the growth of the bank. (Not
accepted)
(Insert Table A.3)
3.4 Moderation Testing
The economy has been taken as a moderating variable in the research model. This study assumes
that the economy (GDP) moderates the relationship between credit risk and banking growth.Tables
A.4 to A.10 show the results of moderation.
The first measure of credit risk is CRE. Table A.4 shows the value of R2= 0.0001, F statistics (0.01),
and p> 0.05, β-coefficient = 0.0068. This was a path “a” in which relationship of CRE with AGR is
insignificant. Path “b” also shows an insignificant relationship between GDPG and AGR where R2=
0.0002, F statistics (0.02) and p> 0.05. However, the first two paths are not related to the concept of
moderation as testing as per Baron & Kenny Method (1986). The product of AGR and GDPG is taken
in the third step and the regression test is applied to confirm the moderation. Path “c” shows the
significant relationship with R2= 0.7516, F statistics (411.61), and p< 0.01. R
2shows the variation in
AGR, that is 75.16%. -coefficient is 1.4177 with t -statistics = 20.29 ( p< 0.01). These results prove
that economy (GDPG) moderates the relationship between CRE and AGR and modify the weak
relation into strong.
H8: Economy moderates the relationship between CRE and AGR. (Accepted)The second measure of credit risk is LER. Table A.5 shows the value of R
2=0. 0066, F statistics (0.91),
and p > 0.05, β-coefficient = -0.0031. In the path “a”, the relationship of LER with AGR is found to be
insignificant and model fitness is not proved. Path “b” also shows an insignificant relationship
between GDPG and AGR with R2= 0.0002, the F statistics (0.02) and p> 0.05. The product of LER and
GDPG is taken and regression is applied to test its relationship with AGR. Path “c” shows a significant
relationship with R2 = 0.1908, the F statistics = 32.07(p< 0.01). R
2shows that LER combined with
GDPG brings about 19% variations in AGR. β-coefficient is .0574449 witht -statistics = 5.66, ( p< 0.01)
prove that GDPG positively moderates (strengthens) the relationship between LER and AGR. After
the introduction of moderator (GDPG) weak negative relation transforms into the strong positive
relation between LER and AGR and model fitness is also proved.
H9: Economy moderates the relationship between LER and AGR. (Accepted)
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The 3rd measure of credit risk is CS. Table A.6 shows the value of R2= 0.023, F statistics = 3.2 ( p>
0.05), β-coefficient = -2.4222. From path “a”, it is concluded that the relationship of CS with AGR is
insignificant and model fitness is not proved. Path “b” also shows an insignificant relationship
between GDPG and AGR with R2
= 0.0002, andF statistics =0.02 ( p> 0.05). The combine effect of CS
and GDPG on AGR is tested through path “c” that shows a significant relationship. The value of R2 =
0.48 and F statistics = 124.61 ( p< 0.01). R2shows the variation in AGR, that is 48%. The β-coefficient is
21.4987, with t -statistics = 11.16, ( p 0.05), β-coefficient = -0.1564. Thus, the relationship of CM with AGR comes to be
insignificant and model fitness is not proved in the path “a”. Path “b” finds an insignificant
relationship between GDPG and AGR with R2of 0. 0002, and F-statistics 0.02 ( p> 0.05). The product
of CM and GDPG is taken andregression is applied to test its relationship with AGR. In the path “c”,CM and GDPG are taken together to test the moderating role of GDPG in either enhancing or
suppressing the relationship of CS with AGR. A significant relationship is found with R2 = 0.9428 and
F statistics is 2207.12 ( p< 0.01). R2value explains that a 94.28% change in AGR is being brought in by
CM and GDPG together. The positive β-coefficient is 0.9733494, witht -statistics = 46.98, ( p< 0.01)
proving that the GDPG transform the negative relation into positive.
H11: Economy moderates the relationship between CM and AGR. (Accepted)
The fifth measure of credit risk being taken in this model is CCR. Table A.8 shows the value of R2=
0.1772 and F statistics = 29.29 ( p < 0.01) with a β-coefficient = 0.0047. In path “a” the relationship of
CCR with AGR is found to be a significant and model fitness is proved. Path “b”indicates an
insignificant relationship between GDPG and AGR with R2= 0.0002, andF -statistics 0.02 ( p> 0.05). The
product of CCR and GDPG is taken and regression is applied to test its relationship with AGR. Path
“c” shows a significant relationship with R2 = 0.2004, F statistics (32.08), and P< 0.01. R
2shows the
variation in AGR due to CCR and GDPG i.e. 20%. The value β -coefficient is 0.2279, with t -statistics =
5.66, ( p< 0.01) prove that GDPG positively moderates (strengthens) the relationship between CCR
and AGR and model fitness is also proved here.
H12: Economy moderates the relationship between CCR and AGR. (Accepted)
The sixth measure of credit risk is COR. Table A.9 shows the value of R2= 0.0259 and F statistics is
3.62 ( p> 0.05), and β-coefficient = -4.4160. The relationship of COR with AGR is insignificant and
model fitness is not proved in the path “a”. Path “b” also shows an insignificant relationship
between GDPG and AGR with R2= 0. 0002, F statistics = 0.02 ( p> 0.05). Then the product of COR and
GDPG is taken and regression is applied to test the relationship with AGR. Path “c” finds a significant
relationship with R2 = 0.2419 and F statistics =38.6 ( p< 0.01). R
2represents the variation in AGR due
to predictor variables i.e. 24.19%. β-coefficient is 88.82105, with t -statistics = 6.21, ( p < 0.01) prove
that GDPG modify the negative relation into positive relation and model fitness is proved here.
H13: Economy moderates the relationship between COR and AGR. (Accepted)
The 7th measure of credit risk is NPLC. Table A.10 shows the value of R2= 0.0235, F statistics (3.28),
and p> 0.05, β-coefficient = -0.1271. This was a path “a” in which relationship of NPLC with AGR is
insignificant and model fitness is not proved. Path “b” illustrations an insignificant relationship
between GDPG and AGR with R2
= 0.0002 and F statistics = 0.02 ( p> 0.05). The product of NPLC andGDPG is taken and regression is applied to test its relationship with AGR. Path “c” proves the
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significant relationship with R2 = 0.0896 and F statistics 13.29 ( p< 0.01). R
2 demonstrate the 8.9%
variation that NPLC along with GDPG is bringing in AGR. β-coefficient is 1.018261, with t -statistics =
3.65, ( p< 0.01) prove that there is a significant positive impact of GDPG on the relationship between
NPLC and AGR.
H14: Economy moderates the relationship between NPLC and AGR. (Accepted)
(Insert table 4-10)
4. Discussion
Researchers believe that credit risk management plays an important role in profitability, which in
turn affects the growth of the banking system (Goddard et al., 2004). This study tested the role of
the economy as a moderator. The moderating effect was proved which is concurs with other studies
(e.g. Pastor, 2002; Tan & Floros, 2012). Therefore, the economy plays an important moderating role
in the relationship of credit risk and banking system growth.
The present study measures the credit risk with seven variables CRE, LER, CS, CM, CCR, COR and
NPLC. Financial institutions can earn higher profits by increasing the credit risk exposure. The resultsrevealed that CRE has an insignificant positive relationship with AGR. The results of this study are
contradicted with the existing findings (Schiniotakis, 2012). It shows that CRE can only have a
positive effect on the growth of the banking system when the economy is strong, which reduces the
default ratio, NPLs and write-offs. As a result, the credit risk reduces and growth speeds up.
LER shows the ability of a bank’s capital to absorb loan losses. If banks keep the percentage of loans,
it would signify intentional forego of productive opportunities. If banks keep higher LER, this signifies
a poor capital buffer and higher capital risk (Salas & Saurina, 2002). This relationship is found to be in
contradiction to existing literature. When the economic conditions are favourable, higher loans bring
increased earnings and growth. In such situation a significant impact of LER on growth can be
observed.
The third measure of credit risk taken in the present study was CS. Successful credit screening boosts
banking system growth by decreasing the NPLs. The higher value of NPLs to total loans shows the
poor credit screening. This relation was also found to be insignificant (Cyree et al., 2000). CS
positively affects growth when the economic conditions are favourable, non-performing loans
improve and credit risk decreases (Saba, Kouser, & Azeem, 2012).
CM ratio shows the effectiveness of the credit monitoring process. A higher ratio indicates the
bank’s inefficiency which distorts profitability. If the write-offs remain low despite the increasing size
of the delinquencies, it signifiesstrong credit controls. The stated relationship is found to be
statistically insignificant. During healthy economic conditions, the relationship can stand true due to
reduced charge-offs and higher loan performance.
The credit collecting ratio (CCR) represents the recovered amounts from defaulted loans in prior
years. The financial institutions can avoid financial distress and increase their growth by recovering
the default loans. Better recovery leads to reduced NPLs and loans write-offs. This increases ROE for
a particular period. The rise in earnings represents the growth in the bank’s assets. The CCR has a
significant positive relation with AGR, found in line with existing studies (Noulas, 1997). However,
the recovery rate improves when the economy is flourishing.
COR was negatively correlated with the AGR. This relation was found to be significant. When banks
periodically write-off the loans, they have to bear severe credit risk. An effective system of loan
recovery reduces loan losses. This decrease in percentage of write-offs increases the asset portfolio
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size, which in turn brings higher earnings and growth. The higher profitability and favourable
economic conditions lift up the growth in a banking system.
NPLC indicates the bank’s capacity to bear the shocks without going into bankruptcy. When the
economy is favourable, banks need to maintain a small capital buffer. They also observe a reduction
in NPLs which in turn reduces provisioning and increases ROE. This relationship was found to be
insignificant in contradiction to existing evidences (Boudriga et al., 2009). NPLs fall down in
favourable economic conditions and banks need to maintain small capital buffer. This phenomenon
affects the earnings and growth positively. The banking system experiences improvements in credit
activities, loan performance and loans’ recovery rate under favourable economic conditions. This
situation accelerates the growth of the banking system.
Some of the relations of credit risk with the banking system growth were found to be significant in
present study. The present study also proves the moderating role of the economy in the growth of
the banking system. The results are in line with the view of a number of past researchers (Sunde,
2009).
5. Conclusion
This study showed that credit risk measurement and management has a mixed impact on the
banking system growth. These findings suggest that managers should analyze the level of credit risk
in banks and the optimal level should be identified. The consumption of extra resources by credit
risk management practices should be restricted. The results of this study also give insight about the
importance of credit risk regulations, realistic credit risk appetites and favourable economic
conditions for achieving higher growth.
Based on the findings of this study, credit risk managers should focus on setting up the standards to
achieve an optimal level of credit risk in the banking system. This approach should be based on
efficiency, effectiveness and growth maximization. The bank managers are besieged with the credit
risk management by actively devising and implementing different tools for the measurement and
management of credit risk.
Managers need to use numbers and intuition together to look beyond the current scenarios. This is
highly required in today’s uncertain business environment where the future is difficult to be
forecasted. Thus education and experience which determine the quality of decisions matters a lot.
5.1 Implications
Many studies have published up till now on credit risk, but most of the studies are conceptual in
nature. Although some of the studies provide empirical evidence, but still some deficiencies are
prevailing in the existing literature. This research is distinctive from other studies and has the
following managerial and academic implications:
5.1.1 Managerial Implications
The alignment of credit risk management with growth is a very important issue, which demands the
attention of the managers. The model developed in the current study comprises of seven major
credit risk factors. Using this model, the relationship of credit risk with banking system growth is
analyzed. The growth defines the potential of business and it serves as the criteria for assessmentfor investors. The larger organizations are more successful in attracting more investors.
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Risk management, including credit risk management, is about ensuring the stability of earnings.
Therefore, those credit risk management activities which can help stabilize the earnings of a bank
need special attention. The banks also need to develop such control systems which should assess the
usefulness of different credit risk management techniques. This system should identify these
loopholes and guide management regarding the optimal and better use of these techniques.
The credit risk appetite needs to be ascertained with due diligence. Management should ensure t
that credit risk tolerance is not violated.
The managers are also required to be educated about the credit risk appetite anddifferent credit risk
management practices.Managers and employees of the banks are responsible for mitigation and
management of the credit risk. Therefore, the growth of the banking system can be stimulated by
increasing the understanding of bank management about credit risk management and mitigation.
5.1.2 Academic Implications
This study adopted a novel approach to determine the role of credit risk in banking growth. Past
researchers and practitioners have not given due research attention to banking system growth.Researcher believes that this study is a valuable addition to the literature on risk management and
banking growth. It helps in understanding the various factors of credit risk and analyzing their role in
the growth of the banking system.
This study tested the moderating effect of an economy which has not been tested before within the
stated context.
The recent financial crisis has increased the risk factor in organizations for both public and private
organizations. The findings emphasize that credit risk management guidelines and techniques should
be reviewed and revised.
5.2 Limitations And Future Recommendations
The time period in this study is 2004-2011. The authors have tried to cover the most relevant credit
risk measures within the context of banking system growth. However, exclusion of the market
factors and unavailability of data are the major limitations, of the present study.
Researchers have ignored the mediating role of profitability, whose addition within the existing
model can help explore new findings. This study focuses only on the asset side growth of the banks.
However, growth can also be assessed in other perspectives which include productivity growth,
network expansion etc.
This study is confined to the commercial banking sector excluding the foreign banks, thrifts and
micro-finance banks. Future efforts may incorporate them. Further research may also conduct the
comparative study of the credit risk management system for Islamic banks and conventional banks
and its role in banking system growth.
Finally, future researchers may conduct this study in other economies. The results may vary because
of the economic and country specific practices.
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APPENDICES
Fig. A. 1
Research Framework
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Eq. (A.1)
Eq. (A.2)
Eq. (A.3)
Eq. (A.4)
Eq. (A.5)
Eq. (A.6)
Eq. (A.7)
TL/TAit =Total Loansit
Total Assetsit
TL/TEit=Total Loansit
Total Equityit
Npls/TLit=Non Performing Loans it
Total Loansit
GCO/NPLit=Gross Charge-offs
it
Non Performing Loansit
R/GCOit=
Recoveriesit
Gross charge-offsit
LWo/TLit=
Loans Write-Off it
Total Loansit
NPLs/TCit=Non Performing Loansit
Total Capitalit
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Eq. (A.8)
Growth it =
T.A. it – T.A. it-1
T.A. it-1
Where T. A. = Total Asset
Table A.1
Descriptive Statistics
Variable Mean Std. Dev. Min Max
CRE 0.5377 0.1070 0.036 0.8465
LER 6.885 4.848 0.036 35.36
CS 0.0191 0.0236 0.0001 0.1425
CM 1.040 0.3691 0.1257 3.809CCR 1.030 1.761 0.0001 13.30
COR 0.0067 0.0150 0.0001 0.1347
NPLC 0.1543 0.4487 0.0012 5.039
GDPG 0.0477 0.4515 -0.5326 0.7849
AGR 0.2545 0.3935 -0.2103 2.805
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Table A.2
Correlation Matrix
CRE LER CS CM CCR COR NPLC GDPG AGR
CRE 1.0000
LER 0.4067 1.0000
CS -0.0969 -0.0838 1.0000
CM 0.0226 0.0538 -0.0048 1.0000
CCR 0.0468 -0.0217 0.0187 -0.0708 1.0000
COR -0.1100 -0.2249 0.4169 -0.0004 0.0326 1.0000
NPLC 0.0283 0.3964 0.7502 0.0044 0.0355 0.1027 1.0000
GDPG -0.0713 0.0648 -0.0742 -0.0951 0.0426 -0.0451 -0.0012 1.0000
AGR 0.1324 -0.0548 -0.1176 -0.0988 0.0215 -0.1832 -0.1396 -0.0003 1.0000
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Table A.3
Fixed-Effects (within) Regression
R-square: Number of Obs. 138
Within 0.2149 Number of groups 21
Between 0.1634 Obs. per group:
Overall 0.1837 Min. 5
F (7, 110) 4.30 Avg. 6.6
Prob >F 0.0003 Max 7
AGR Coef. Std. Err. T P >t [95% Conf. Interval]
CRE 0.0181 0.0932 0.19 0.846 -0.1666 0.2028
LER -0.0015 0.0050 -0.30 0.761 -0.0114 0.0084
CS -2.2234 2.2201 -1.00 0.319 -6.623 2.1763
CM -0.0081 0.0836 -0.10 0.923 -0.1738 0.1577
CCR 0.0033 0.0009 3.45 0.001 0.0014 0.0053COR -5.7948 2.6920 -2.15 0.034 -11.12 -0.4597
NPLC -0.0674 0.1115 -0.60 0.546 -0.2883 0.1535
Cons 0.3388 0.1042 3.25 0.002 0.1324 0.5453
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Table A.4
Regression Table
Description R2
Adjusted R2
F -stats Sig. Beta t -Statistics Sig.
Dependent Variable: AGR
0.0001 -0.0073 0.01 0.9281
Intercept 0.2488 4.56 0.000
CRE 0.0068 0.09 0.928
Dependent Variable: AGR
0.0002 -0.0072 0.02 0.8837
Intercept 0.2530 7.52 0.000
GDPG 0.0112 0.15 0.884
Dependent Variable: AGR
0.7516 0.7498 411.61 0.0000
Intercept .0461 2.35 0.02
CRE*GDPG 1.4178 20.29 0.000
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Table A.5
Regression Table
Description R2
Adjusted R2
F -Stats Sig. Beta t -Statistics Sig.
Dependent Variable: AGR
0.0066 -0.0007 0.91 0.3424
Intercept 0.2770 6.58 0.000
LER -0.0031 -0.95 0.342
Dependent Variable: AGR
0.0002 -0.0072 0.02 0.8837
Intercept 0.2530 7.52 0.000
GDPG 0.0112 0.15 0.884
Dependent Variable: AGR
0.1908 0.1848 32.07 0.000
Intercept 0.1571 4.54 0.000
LER*GDPG 0.0574 5.66 0.000
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Table A.6
Regression Table
Description R2
Adjusted R2
F -Stats Sig. Beta t -Statistics Sig.
Dependent Variable: AGR
0.023 0.0158 3.2 0.0756
Intercept 0.3025 6.99 0.000
CS -2.4222 -1.79 0.076
Dependent Variable: AGR
0.0002 -0.0072 0.02 0.8837
Intercept 0.2530 7.52 0.000
GDPG 0.0112 0.15 0.884
Dependent Variable: AGR
0.48 0.4761 124.61 0.000
Intercept 0.1576 6.77 0.000
CS*GDPG 21.4987 11.16 0.000
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Table A.7
Regression Table
Description R2
Adjusted R2
F -Stats Sig. Beta t -Statistics Sig.
Dependent Variable: AGR
0.0243 0.0171 3.38 0.0681
Intercept 0.4139 4.42 0.000
CM -
0.1564
-1.84 0.068
Dependent Variable: AGR
0.0002 -0.0072 0.02 0.8837
Intercept 0.2530 7.52 0.000
GDPG 0.0111 0.15 0.884Dependent Variable: AGR
0.9428 0.9423 2207.12 0.000
Intercept 0.0038 0.43 0.665
CM*GDPG 0.9733 46.98 0.000
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Table A.8
Regression Table
Description R2
Adjusted R2
F -Stats Sig. Beta t -Statistics Sig.
Dependent Variable: AGR
0.1772 0.1711 29.29 0.000
Intercept 0.2340 7.64 0.000
CCR 0.0047 5.41 0.000
Dependent Variable: AGR
0.0002 -0.0072 0.02 0.8837
Intercept 0.2530 7.52 0.000GDPG 0.0111 0.15 0.884
Dependent Variable: AGR
0.2004 0.1942 32.08 0.000
Intercept 0.0022 7.48 0.000
CCR*GDPG 0.2279 5.66 0.000
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Table A.9
Regression Table
Description R2
Adjusted R2
F -Stats Sig. Beta t -Statistics Sig.
Dependent Variable: AGR
0.0259 0.0188 3.62 0.0592
Intercept 0.2772 7.80 0.000
COR -4.4160 -1.90 0.059
Dependent Variable: AGR
0.0002 -0.0072 0.02 0.8837
Intercept 0.2530 7.52 0.000
GDPG 0.0111 0.15 0.884
Dependent Variable: AGR
0.2419 0.2356 38.6 0.000
Intercept 0.1970 6.21 0.000
COR*GDPG 88.8210 6.21 0.000
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Table A.1
Regression Table
Description R2
Adjusted R2
F -Stats Sig. Beta t -Statistics Sig.
Dependent Variable: AGR
0.0235 0.0163 3.28 0.0725
Intercept 0.2742 7.79 0.000
NPLC -0.1271 -1.81 0.073
Dependent Variable: AGR
0.0002 -0.0072 0.02 0.8837
Intercept 0.2530 7.52 0.000
GDPG 0.0111 0.15 0.884
Dependent Variable: AGR
0.0896 0.0829 13.29 0.0004
Intercept 0.2237 7.58 0.000
NPLC*GDPG 1.0182 3.65 0.000