Credit Risk and Growth of Banking System

8/21/2019 Credit Risk and Growth of Banking System

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Universal Journal of Management and Social Sciences Vol. 4, No. 11; November 2014

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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

*[email protected]

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

mailto:[email protected]:[email protected]


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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


0.0002 -0.0072 0.02 0.8837

Intercept 0.2530 7.52 0.000

GDPG 0.0112 0.15 0.884


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.


0.0066 -0.0007 0.91 0.3424

Intercept 0.2770 6.58 0.000

LER -0.0031 -0.95 0.342


0.0002 -0.0072 0.02 0.8837

Intercept 0.2530 7.52 0.000

GDPG 0.0112 0.15 0.884


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



0.023 0.0158 3.2 0.0756

Intercept 0.3025 6.99 0.000

CS -2.4222 -1.79 0.076


0.0002 -0.0072 0.02 0.8837

Intercept 0.2530 7.52 0.000

GDPG 0.0112 0.15 0.884


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



0.0243 0.0171 3.38 0.0681

Intercept 0.4139 4.42 0.000

CM -

0.1564

-1.84 0.068


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



0.1772 0.1711 29.29 0.000

Intercept 0.2340 7.64 0.000

CCR 0.0047 5.41 0.000


0.0002 -0.0072 0.02 0.8837

Intercept 0.2530 7.52 0.000GDPG 0.0111 0.15 0.884


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



0.0259 0.0188 3.62 0.0592

Intercept 0.2772 7.80 0.000

COR -4.4160 -1.90 0.059


0.0002 -0.0072 0.02 0.8837

Intercept 0.2530 7.52 0.000

GDPG 0.0111 0.15 0.884


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



0.0235 0.0163 3.28 0.0725

Intercept 0.2742 7.79 0.000

NPLC -0.1271 -1.81 0.073


0.0002 -0.0072 0.02 0.8837

Intercept 0.2530 7.52 0.000

GDPG 0.0111 0.15 0.884


0.0896 0.0829 13.29 0.0004

Intercept 0.2237 7.58 0.000

NPLC*GDPG 1.0182 3.65 0.000

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Credit Risk and Growth of Banking System

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