Does the Credit Risk Management Affect the Financial Performance of Banks? Evidence from Jordan

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http://myjms.mohe.gov.my/index.php/ijbtm

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Does the Credit Risk Management Affect the Financial Performance of Banks? Evidence from Jordan

Areen Zuhair Alta’ani^1*, Nuradli Ridzwan Shah Mohd Dali¹

1 Faculty of Economics and Muamalat, Universiti Sains Islam Malaysia, Nilai, Malaysia

*Corresponding Author: [email protected]

Accepted: 15 October 2020 | Published: 31 October 2020

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Abstract: The purpose of this study is to examine the relationship between credit risk management indicators and the financial performance of listed banks in Jordan, and compare the efficiency of credit risk management between Islamic and conventional banks.

The study used the annual report for listed Jordanian banks during the period of 2013 to 2017. The researchers found capital adequacy ratio has a positive and significant relationship with ROA and ROE, and cost per loan ratio has a negative and significant relationship with ROA, ROE, and TQ. Loan loss reserve ratio has a negative and significant relationship with TQ, while bank size has positive and significant impacts on the financial performance of banks measured by TQ. Credit risk management in Islamic banks is better than conventional banks and Islamic banks have higher profitability. The paper investigates only the banking sector in Amman stock exchange, future research may look into the whole sectors in Amman stock exchange.

Keywords: bank performance, credit risk management, Jordan, Amman stock exchange, Islamic banks, conventional banks, and Islamic finance

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1. Introduction

Banking sector is vital to economic growth and active player in the financial markets. The overall financial system of most economies in the world is influenced by the banking system (Ali et al., 2011). Banking sector provides a wide variety of financial services such as, wealth management services, insurance related services, investment banking, government related business, and foreign exchange business for enhancing the profitability of banks (Singh, 2015). Banks can be defined as financial intermediary that channels funds from depositors (surplus units) to borrowers (deficit units), and the process profit from the spread of the interest charged (Heffernan, 1996) as cited in Rajha (2017).

The poor performance of banks is not only hamper it’s depositors, shareholders, and structure, it’s also influences other banks, and business markets (Rajha, 2017). The banking sector around the world has witnessed several significant development and risks over the last decades, there is a highly expanding in the financial services provided by this sector related to the new technology products. In addition, the globalization and liberalization of the financial practices have increased a competition and necessitate a need for effective risk management in the banks (Al-jarrah, 2012). The primary hurdle in this sector is the credit risk because credit provision is a core business of banks and credit quality is considered a main indicator of bank’s financial health and soundness. Therefore, poor loan or credit quality contributes enormously to bank’s failures. Furthermore, poor loan portfolio risk management and lenient

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credit standards for counterparties and borrowers is the major cause of serious problems among banks (Boahene et al., 2012).

As per various authors and researchers, credit risk was the most significant risk in terms of potential losses, in addition, risk management in any bank always focuses on credit risk management area (Singh, 2015). Better risk management indicates that banks operate their activities at lower conflict of interest between parties and lower relative risk, leading to increase their performance (profit) (Santomero, 1997), in consequence thus will enhance their image and reputation from market and public point of view, and will get more opportunities to increase the productive assets which lead to higher quality in their solvency, liquidity, and profitability (Eduardus et al., 2007) as cited in Singh (2015).

Therefore, effective credit risk management is essential to the long term success for any banking organization and should be a critical component of bank’s overall risk management strategy. In addition, determining the factors that affect the performance of banks and risk management is significant not only for bank’s management but for several stakeholders, such as governments, central banks, bankers associations, customers, suppliers, and academicians (Sufian et al., 2012).

In the present study aims to explore the most important factors for credit risk management that have an impact on the performance of listed banks in Jordan. In particular, it aims to answer the following questions:

1) What is the relationship between credit risk management and bank performance in Jordanian listed banks?

2) Do the Islamic listed banks differ from the conventional listed banks in credit risk management in Jordan?

2. Literature Review

There are many professionals using credit risk management indicators in their study such as Alshatti (2015), who use non-performing loans ratio and loan loss provision ratio to evaluate the financial performance of Jordanian listed banks. He found Jordanian banks should develop their strategies to enhance the competitiveness and the performance of banks not only limit the banks exposition to credit risk, in addition maintaining sound credit granting processes and suitable credit risk environment, because there is a significant relationship between credit risk management indicators and bank performance.

Singh (2015) mentioned that credit risk is significant factor that should be managed well, it’s the biggest and oldest risk in banks that raising from the bank’s lending to or dealing with individuals, corporate, and other financial institutions or banks. In addition, there is a significant and inverse relationship between non-performing assets ratio and the performance of banks measured by ROA.

Abiola & Olausi (2014) mentioned that credit risk management in banks has become a crucial notion which set the growth, survival, and the profitability of banks. According to this research paper the Nigerian banks have poor credit risk management practices that need to develop their loan policies to become more clear and precious.

In the review of Afriyie & Akotey (2013) state that the credit risk management has a dynamic and significant role in the performance of rural banks in Ghana, there are unusual strong

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70 positive relationship between non-performing loan ratio with the profitability, which means these banks don’t have effective institutional measurement to treat with credit risk management, they just increase the interest rate on loans to shift the cost on loan default to other consumers.

In a study of the Kenyan banks Musyoki & Kadubo (2012) examined empirically whether credit risk management indicators effect on the performance of banks, by analyzing the financial reports of 10 banks from 2000 to 2006. They found that credit risk management contributed up to 35.6% of the performance of banks among the risk management indicators and has a significant association with the profitability of banks, in addition non-performing loan ratio is the main component for risk management and major predictor for the financial performance of banks.

Research made in determining the characteristics of external and internal factors that affect on the profitability of banks by Ramadan et al. (2011), they found that there is a significant and negative relationship between credit risk and the profitability. Almumani (2013) found that there is an insignificant relationship between credit risk and the profitability of Jordanian banks.

Chowdhury & Rasid (2015) investigated the factors that affect on the profitability of Islamic banks by using ordinary least square method for 44 Islamic banks in 2013 from African and Asian region. The researcher found the macroeconomic factors such as GDP growth has insignificant influence on the Islamic banks profitability, in addition there is insignificant relationship between the performance of Islamic banks with liquidity and credit risk factors.

The researcher recommend the Islamic banks should decrease their cost and improve their portfolio for equity financing rather than debt financing, because there is a significant and positive association between equity financing, capital adequacy, and operational efficiency with the performance of banks.

A study conducted in Pakistan by Khan et al (2016) to analyze the financial ratios that related to performance of banks, and evaluate the efficiency degree of conventional and Islamic banks during the period of 2007 to 2014. The researchers found Islamic banks are less risk than conventional banks, higher profitability, but conventional banks have a better asset quality than Islamic banks because their diverse product range and have a long history in the performance of banks.

Another research developed by Chazi (2010) to compare between Islamic and conventional banks in terms of inherent risk during the period of 2005 to 2008. The results show the Islamic banks have better gross revenue ratio, leverage ratio, and capital adequacy ratio.

Therefore, Islamic banks less risk than conventional banks.

3. Research Methodology

The objective of this paper is to determine the relationship between credit risk management factors and the performance of Jordanian banks listed in Amman stock exchange (ASE) and explore the difference in credit risk management factors between Islamic and conventional banks using annual report for extracting data from 2013 to 2017. For the purpose of this study descriptive analysis (includes, the mean, minimum, median, and standard deviation) and random effect regression analysis.

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Table 1: Jordanian listed banks Banks

1 JOR ISLAMIC BANK 8 ARAB BANKING CO.

2 JOR KUWAIT BANK 9 CAPITAL BANK

3 JCBANK 10 SOCGEN BK - JORDANIE

4 HOUSING BK TRD FIN 11 CAIRO AMMAN BANK

5 ARAB JOR/INV/BANK 12 BANK OF JORDAN

6 SAFWA ISLAMIC BANK 13 JORDAN AHLI BANK

7 BANK AL ETIHAD 14 ARAB BANK

15 INVESTBANK

3.1 Research variables

The following equation are used for the objective of this paper and estimated based on random effect regression method.

ROA= β0+ β1NPL+ β2CAR+ β3CLR+ β4LLR+ β5CGR+ β6Bsize+ β7Btype+ ε ROE= β0+ β1NPL+ β2CAR+ β3CLR+ β4LLR+ β5CGR+ β6Bsize+ β7Btype+ ε TQ= β0+ β1NPL+ β2CAR+ β3CLR+ β4LLR+ β5CGR+ β6Bsize+ β7Btype+ ε

Table 2: Study Variables

Dependent variables

ROA Return on assets Control variables

ROE Return on Equity Bsize Bank size

TQ Tobin’s Q Btype Bank type

Independent variables

NPL Non-performing loans ratio

CAR Capital adequacy ratio

CPL Cost per loan ratio

LLR Loan loss reserve ratio

CGR Credit growth ratio

Return on assets (ROA)

ROA represents accounting based measure that preserves a straight relationship with the performance of banks (Wulf, 2007) and reflects management ability to utilize the bank’s real investment and financial resources to generate profit (Bennaceur & Goaied, 2008). ROA is calculated as net earning divided by the book value of assets (Al-rdaydeh et al., 2017;

Alshatti, 2015; Wasiuzzaman & Gunasegavan, 2013; Abiola & Olausi, 2014).

Return on equity (ROE)

ROE is an indicator of how much profit the firm generates concerning the amount of the invested money by the investor. It’s calculated by dividing the firm’s net earnings by its total equity. This variable has been used frequently as a dependent variable to evaluate bank performance (Al-rdaydeh et al, 2017; Alshatti, 2015; Wasiuzzaman & Gunasegavan, 2013;

Abiola & Olausi, 2014; and Kertapati & Dali, 2004).

Tobin’s Q (TQ)

TQ is market based measure demonstrates the relationship between the current cost of replacement assets to the market value of the firm’s assets containing shares and stocks (Tobin, 1969), it reflects the market anticipation for future profit (Short & Keasey, 1999).

Tobin's Q equals the ratio of firm market value to replacement value; market value of common equity plus the book value of debt divided by the book value of total assets (Pan &

Tian, 2015; Battaglia & Gallo, 2015; and Liang et al; 2013).

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72 Non-performing loans ratio (NPL)

The loans are considered NPL if a borrower has failed or disregarded to pay the principle value or their interest for 90 days or more depending on the case of the loan (Guleria &

Laveena, 2016). This ratio can be calculated as non-performing loan/ Total loans (Alshatti, 2015; Abiola & Olausi, 2014).

Capital adequacy ratio (CAR)

CAR shows the capacity of banks to absorb any possible losses, evaluates the capital strength of banks, and indicates how the bank’s equity affects its profitability. This ratio can be calculated as shareholder’s fund/ Total assets (Alswalmeh & Dali, 2020; Wasiuzzaman &

Gunasegavan, 2013; and Afriyie & Akotey, 2013).

Cost per loan ratio (CPL)

CPL demonstrates the bank’s efficiency in allocating credit to borrowers. This ratio can be calculated as total operating costs/ Total amount of loans (Poudel, 2012; and Danson Musyoki, 2012).

Loan loss reserve ratio (LLR)

LLR is a reserve for any predicted losses from loans, the lower this ratio suggests that the quality of the loan portfolio is very high and the less problematic the loans and vice versa (Al-rdaydeh et al., 2017). This ratio can be calculated as loan loss reserve/ Total amount of loans (Al-rdaydeh et al., 2017; and Abdel Megeid, 2017).

Credit growth ratio (CGR)

CGR demonstrates the credit expansion in the banks. Sharp declining in the bank’s capital is similar to exaggerated rapid credit growth; it’s an indicator for the deterioration in the financial system of banks and could be used as an early warning sign for future problems in the loans (Das & Ghosh, 2007) as cited in Tehulu & Olana (2014). This ratio can be calculated as (current year loans minus previous year loans)/ previous year loans (Tehulu &

Olana, 2014; and Foos et al., 2010).

Bank size (Bsize)

This factor appears as a control variable in the measurement of bank performance in many previous studies (Chowdhury, 2015; Noman et al., 2015)

Bank type (Btype)

Various previous literatures have used dummy variable to distinguish between Islamic and conventional banks, Islamic banks are assigned the value of one (1) while conventional banks the value of zero (0), (Warrad, 2017; Chowdhury, 2015; Hanif, 2012).

4. Empirical Findings

This section provides a detailed analysis of the relationship between credit risk management and bank performance. The descriptive statistics for the independent, dependent, and control variable

Table 3: Summary statistic of dependent and explanatory variables

Mean Minimum Maximum Std.dev Skewness Kurtosis Observ

NPL 5.41 1.45 11.6 2.42 .58 2.89 75

CAR 13.72 7.81 24.47 3.07 .29 3.69 75

CPL 5.07 2.06 7.77 1.33 -.046 2.43 75

LLR 4.54 1.24 8.92 1.83 .33 2.82 75

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CGR 10.35 -10.13 85.01 14.3 2.46 12.10 75

Bsize 9.35 8 10.69 .48 .90 5.60 75

ROA 1.17 .05 2.05 .49 -.47 2.51 75

ROE 8.97 1.14 17.66 .0378 -.1347 2.97 75

TQ 1.10 .54 2.47 .45 1.51 4.67 75

Bank type proportion skewness Kurtosis

0 .8666 2.15 5.65

1 .1333

Table 3 displays descriptive statistics for bank performance, credit risk management, and control variables used in the previous model. In average, the mean of TQ was 1.10, Tobin’s Q ratio is more than “1” indicates that the bank has performed well with its investment opportunities, conversely if TQ is lower than “1”, this shows that the value of assets is higher than the market value. ROA 1.17, ROE 8.97 and the mean of all other variables are positive.

The mean of capital adequacy ratio is the largest (13.72) and modifies extremely a cross banks (min= 7.81, max=24.47).The mean of credit growth ratio was 10.35, as of non- performing loans, cost per loan ratio, and loan loss reserve ratio shows a mean of 5.41%, 5.07%, and 4.54% respectively.

In term of standard deviation, the highest value is the CGR 14.3, while the lowest value for CPL 1.33 followed by LLR, NPL, and CAR 1.83, 2.42, and 3.07 respectively.

As for the control variables over the period, the average logarithm of bank size 9.35, and the standard deviation was .48. In terms of bank type is a dummy variable 0 for conventional banks and 1 for Islamic banks, the percentage .866 and .133 respectively. Table 3 also presents the results of the skewness and kurtosis test. It can be observed the skewness results are in the acceptable extent of (± 1.96), except in the case of CGR and bank type that exceeds the range (± 1.96) which is 2.46, 2.15 respectively. The kurtosis results are in the acceptable range of (± 7), except again for CGR which is (12.10) and thus increases the range of (±7).

Therefore, under the central limit theorem, the study takes sufficiently large sample 75 observation, then the distribution of the sample will be approximately normally distributed because the sample size more than 30 (Bárány & Vu, 2007).

Table 4: The relationship between credit risk management and ROA

Variables Predict sign Beta t-statistic Sig

Constant -.0057 -.21 .833

NPL + .0321 .60 .549

CAR + .2339 2.63 .009***

CPL - -.3327 -2.71 .007***

LLR + .0292 .35 .726

CGR - -.0089 -1.53 .125

Bsize - -.0022 -1.06 .290

Btype .0218 1.51 .131

Within R squared .5207

Wald Chi² Prob.

55.81 0.0000

Table 5: The relationship between credit risk management and ROE

Variables Predict sign Beta t-statistic Sig

Constant -.1565 -.88 .379

NPL + .1682 .48 .633

CAR + 1.6783 2.87 .004***

CPL - -2.1841 -2.71 .007***

LLR + .3320 .61 .545

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CGR - -.0511 -1.34 .181

Bsize - -.0155 -1.10 .272

Btype .2511 2.64 .008

Within R squared .6817

Wald Chi² Prob.

68.14 0.0000

*, **, *** indicates significance at the 10%, 5%, 1%, levels

As shown in Table 4, the regression analysis evidence that the R² (within) for the model is 52.07%. This shows that the explanatory variables explain 52.07% of the variance of Return on Assets. Moreover, the model is significant (Wald Chi2 = 55.81, p = 0.0000), indicating that the model significantly explains the changes in Return on Assets.

While, the regression analysis evidence in table 5 that the R² (within) for the model is 68.17%. This shows that the explanatory variables explain 68.17% of the variance of Return on Equity. Moreover, the model is significant (Wald Chi2 = 68.14, p = 0.0000), indicating that the model significantly explains the changes in Return on Equity.

The results of Table 4 and Table 5 present that CAR and CPL ratio are significantly related to ROA at the 1% significance level, respectively and the coefficient signs of the mentioned variables are consistent with study expectations. CAR positively related with ROA and ROE, that mean higher CAR leads to higher ROA and ROE in Jordanian banks ,this finding is consistent with previous studies (Hussain et al., 2016; Ramadan et al., 2011; Almazari, 2014).

While CPL are negatively associated with ROA and ROE, that mean the profitability of Jordanian banks will increase, if the management reduces the operating expenses. This finding is consistent with previous studies (Staikouras & Wood, 1998; Iannotta et al., 2007;

Rosly & Abu Bakar, 2013). Nevertheless, Non-Performing Loan (NPL), Loan-Loss Reserve ratio (LLR), Credit Growth ratio (CGR) are non-significant related with the ROA and ROE.

These variables are inconsistent with study expectations.

As for the control variables the results of the regression analysis in Table 4 and table 5 indicate that there is no association between the bank size (SIZE) and ROA, ROE. On the other hand, there is no association between the bank type (TYPE) and ROE where (t= 2.64, Sig. = .008), that mean credit risk management in Islamic banks is better than conventional banks and Islamic banks have higher profitability.

Table 6: The relationship between credit risk management and TQ

Variables Predict sign Coefficients t-statistic P-value

Constant -1.517 -2.87 .004

NPL + .1253 .12 .905

CAR + 2.607 1.50 .133

CPL - -9.888 -4.13 .000***

LLR - -3.299 -2.02 .043**

CGR - -.1834 -1.62 .106

Bsize + .1341 .001 .001***

Btype -.2369 .401 .401

Adjusted R2 Wald Chi²

Prob.

.9463 207.83 0.0000

*, **, *** indicates significance at the 10%, 5%, 1%, levels

As shown in Table 6, the regression analysis evidence that the R² (within) for the model is 94.63%. This shows that the explanatory variables explain 94.63% of the variance of Tobin's

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Q. Moreover, the model is significant (Wald Chi2 = 207.83, p = 0.0000), indicating that the model significantly explains the changes in Tobin's Q.

Table 6 shows the regression results for Tobin’s Q as a measurement for bank performance. It can be observed LLR is significantly different from the results depends on ROA measurement. However, LLR has a negative coefficient sign and significantly related to TQ at 5% significance level, which means a substantial adverse effect on the profitability of Jordanian banks, higher LLR leads to higher credit risk which indicate poor quality of loans that increase the provisioning costs for banks that could reduce the return of banks. This finding is consistent with previous studies (Al-rdaydeh et al., 2017; Wasiuzzaman &

Gunasegavan, 2013; Noman et al., 2015). In addition, CPL ratio has a significant relationship and negative sign at 1% significance level, which are supported by the ROA and ROE measurement. The results shown the coefficient of CGR is negative and non-significant with TQ, is consistent with the expectation.

The results also reached to there are insignificant and negative relationship for NPL, positive and non-significant for CAR.

As for the control variables the results of the regression analysis in Table 2 indicate that there is a significant positive association between the bank size (BSIZE), and the Tobin's Q where (t= 3.18, Sig. = .001). Large banks have higher resource mobilization and aggressive strategy in collecting deposits with lower equity to asset ratio, which lead to higher profitability (Terraza, 2015). Also, large banks reduce their risk by diversifying the operations across product sectors, regions, and lines (Mester, 2010), on the other hand, there is no significant association between the bank type (TYPE) and the Tobin's Q where (t= -0.84, Sig. = .401).

5. Conclusion

The main aim of this paper is to investigate the impact of credit risk management on the financial performance of listed banks in Jordan, and identifying if the credit risk management in conventional banks is better than Islamic banks, by using the financial indicators of credit risk management and financial performance.

The Jordanian banks should maintain the required capital adequacy ratio to keep stable and efficient profitability and be able for absorbing loan losses. Furthermore, cost per loan ratio that was significant with all dependent variables, so Jordanian banks need to greater efficiency in their operating expenses. Amazingly, non-performing loans ratio that was in contrast to what is expected, this finding show regardless of a huge amount of unpaid loans have a positive and insignificant impact on the profitability of banks. In addition, on the basis of result the researchers conclude that in terms of financial performance and credit risk management Islamic banking is performing better than conventional banks.

6. Recommendations

Depends on the results from the empirical analysis, the paper provides these recommendations to enhance the efficiency of credit risk management and to have an effective function in achieving profitability. Jordanian listed banks should take into consideration the indicators of capital adequacy ratio, cost per loan ratio, loan loss reserve, bank type, and bank size that were found significant in determining credit risk management.

The researchers should expand their studies by taking other variables that may effect on

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76 credit risk management, regardless of NPL ratio and CGR because that has insignificant relationship with bank performance. Furthermore, the researchers recommend the banking managers should improve their equity financing rather than debt financing and reduce their operating costs.

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