Financing Structures, Bank Specific Variables and Credit Risk ...

Financing Structures, Bank Specific Variables and CreditRisk: Malaysian Islamic BankFaridah Najuna Misman 1aAbstractEmpirical literature on credit risk has mostly focus on conventional banks. As Islamicbanks become one of the fastest growing industries globally, understanding about creditrisk is important. This study will identify the relationship between types of financingstructure, bank specific variables and credit risk in Malaysian Islamic banks. This studyuses bank level data for a period of 1995 to 2010. The result finds financing structuresand several bank specific variables have a significant relationship with credit risk. Thisfinding will contribute a new literature in Islamic banking studies.Fields of Research: Islamic Banks, Credit Risk________________________1 School of Economics and Finance, Faculty of Law and Management,La Trobe University, Kingsbury Drive, Bundoora, Vic 3083, Australia. Phone: +613 9479 5318.Fax: +613 9479 1654. E-mail: fnmisman@students.latrobe.edu.aua Faculty of Business and Management, Universiti Teknologi MARA, Malaysia1

1.0 IntroductionIslamic Banking is one of the fasters growing industry in Islamic financial market.After three decade, Islamic banking is not a new phenomenon. In every corner ofworld, Islamic banking been discusses and introduced by not only Muslim countrybut non-Muslim country such as United Kingdom, France, Hong Kong andSingapore. The acceptance of customers towards Islamic banking system areproven through the amount of Islamic assets where as it reach up to USD 1 trillion in2009. According to Islamic Financial Service Board (IFSB), the amount of assets willreach USD1.6 trillion by 2012. Looking to the current situation of Islamic bankingindustry, understanding about risks involved in Islamic banks operation is vital.Objective of bank is to maximise profit and increase shareholder wealth. In order toachieve this objective it requires a proper assets portfolio management. Accuraterisk pricing is important as it will affect profit level of banks(Bonfim, 2009)Loans hold the biggest proportion in the bank assets. Due to this situation, loandefault will give huge impact to profit level of banks. Clear understanding about creditrisk divers will help banks in managing their assets portfolio. Bonfim (2009) discusesthere are three groups of model related to credit risk management; (i) models thatuses accounting variables; (ii) models rely on market information; (iii) models thatuses macroeconomics variables. The objective of this paper is to identify thedeterminants of credit risk in Islamic banks by applying the first group of model whichused bank specific variables.Using the data from Malaysian Islamic banks for the period of 1995 to 2010, thispaper will estimate the relationship between financing structures, bank specificvariables (BSV) and credit risk. Malaysian has been choose as a sample countrybecause of it position in the Islamic banking industry.2.0 Literature ReviewMost of the empirical studies in credit risk are focussing on conventional banks(Berger and DeYoung, 1997, Akhter and Daly, 2009, Cebenoyan and Strahan, 2004,Chen, 2007, Dangl and Zechner, 2004). Several issues of credit risk in conventionalbanks such as the causes of credit risk, capital structure and many more beenstudied. Even though, Islamic banks has comes into existence more than threedecade, a studies about credit risk is still limited. Among the early researchersstudies about credit risk in Islamic banks are Ahmad and Ahmad (2004), How, Karimand Verhoeven (2005), Khan (2003) and Rahman and Shahimi (2010) . Earlystudies in Islamic banks credit risk focus more on theoretical basis.Credit risk is a risk that the value of their portfolio will change due to the unexpectedchanges in the credit quality of issuers or trading partners (McNeil et al., 2005). Inthe case of Islamic banks, the trading partner can be classified as a borrower orcounterparty or investor. The changes in credit quality such as downgrading ofborrowers in an internal or external rating system can cause losses to the banks dueto the defaults by the borrowers. In traditional banking system, lending activities isconsidered as a credit risk business. However, in Islamic banking system, lendingoperations have been replaced with investment and partnership contracts, thus2

credit risk management becomes more important and crucial matter to looks anddiscusses. Basically there are three types of financing structure in Islamic banksnamely asset based, debt based or supporting types of financing. All types offinancing are binding by the Shariah law which free from riba, gharar and maysir.Ariffin et al (2009) explained that Islamic financing structures promotes a principal ofrisks sharing which is this principal is not available in conventional banking practices.In Islamic banks, each type of contract will bring different credit risk exposure to thebanks‟ profit. Among many types of contracts in Islamic banks, credit risk is expectedto be higher in asset based financing under Mudharabah and Musharakah contracts.This happen due to asymmetric information problem where as the entrepreneur maydo not provide sufficient information to the bank (Khan and Ahmed, 2001).Previous empirical studies on credit risk suggest there are two main determinant ofcredit risk in banks. First determinant is bank specific variables (BSV). BSV has asignificant relationship to credit risk exposure of commercial banks (Ahmad andAhmad, 2004, Berger and DeYoung, 1997, Angbazo, 1997, Ahmad and Ariff, 2007,Jiménez and Saurina, 2004, Cebenoyan and Strahan, 2004). Second determinant ofcredit risk is macroeconomic variables such as growth domestic product (GDP),money supply, interest rate and inflation. Ali and Daly (2010), Bonfim (2009) andHackbarth et al. (2006) find that macroeconomic does affect credit risk level in bank.This study will focus on BSV as a determinant of credit risk in Malaysian Islamicbanks.2.1 Financing Structures and BSVAs explain in previous section, there are three types of financing in Islamic banks(Figure 1). Assets based financing which follows profit and loss sharing principlecarry higher credit risk compared to the other two types of financing (Khan andAhmed, 2001).According to Angbazo (1997) asset quality has been identified as one of the factorsinfluenced on credit risk in commercial banks. As the proportion of loan in bankassets is big, assets quality normally being measured by using a ratio of loan lossprovision to total assets (LLP). Previous studies find that loan quality has asignificant and positive relationship with credit risk (Eng and Nabar, 2007, Ahmed etal., 1999). When banks make a higher provision for loss, this indicates that the loansor financings are low quality. Thus it will increase the credit risk level. Anotherimportant variable related to asset is loan expansion (TL). TL is a ratio of total loan tototal asset. The higher proportion of total loan to total asset will potentially increasenon-performing loan and credit risk.The other important BSV in determinant of credit risk is capital. Higher capital willreduces the risk of insolvency. Capital consists of equity or debt capital or both.Increased in equity capital will lower cost of borrowing, however it will also increasedthe average cost of capital. It is because equity capital is more expensive sources ofcapital compared to debts. Therefore the higher the ratio of total equity to total asset(TE) will potentially increase the credit risk level, because higher net interest margincould be required (Angbazo, 1997). Thus, TE is expected to have negativerelationship with credit risk level.3

Capital adequacy requirement introduced by Basel committee with intention toreduces or control risk taking by the banks. Commercial banks including Islamicbanks are required to follow the capital adequacy ratio which is consisting of TIER 1and TIER 2 capital. All commercial banks must maintain a minimum total capital of8% from risk weighted assets (RWA) of the bank (Basel, 2001). Under thisframework, TIER 1 must exceed at least 4% of the risk weighted assets and 3% oftotal assets. In TIER 2, the amount must not exceed the amount of TIER 1. Thissystem therefore requires at least 50% of the amount of total capital to be suppliedby TIER 1 capital. There are mixed result regarding relationship between capitalratios (CAPR) with credit risk. Berger and DeYoung (1997) suggest that capital ratioswill have negative relationship with credit risk. While Ahmad and Ariff (2007) finds apositive relationship between regulatory capital and credit risk in Japan, Malaysiaand Mexico.Figure 1: Three Types of Financing in Islamic BanksSize of the bank is expected to have either positive or negative relationship withcredit risk. Previous study done by Rahman and Shahimi (2010), Al-Smadi andAhmad (2009), Ahmad and Ariff (2007), Ahmad and Ahmad (2004) and Konishi and4

Yasuda (2004) reports mixed results. Size (TA) being measured by taking a naturallog of total assets of the banks. Theoretically, larger bank will be able to absorb morerisk taking activities compared to the smaller banks. Thus size of the banks mayincrease credit risk as larger banks are able to offer more loan compared to smallbanks.3.0 Data and methodology3.1 DataThe data for this paper is gathered from the financial statements and annual reportsof each Islamic bank in Malaysia. The data were gathered from Bankscopedatabases and website of each bank. As at June 2011, there are 17 full-fledgeIslamic banks in Malaysian banking system and 4 international Islamic banks. In thispaper, the observation focuses on the 17 full-fledge Islamic banks as data forinternational Islamic banks are not available in the Bankscope databases. Table 1below provide a list of Malaysian Islamic banks and year of establishment.Table 1: List of Full-fledge Islamic Banks in MalaysiaYear ofNoNameEstablishment1 Affin Islamic Bank Berhad (L) 20062 Al Rajhi Banking & Investment Corporation (Malaysia) Berhad (F) 20063 Alliance Islamic Bank Berhad (L) 20084 AmIslamic Bank Berhad (L) 20065 Asian Finance Bank Berhad (F) 20056 Bank Islam Malaysia Berhad (L) 19837 Bank Muamalat Malaysia Berhad (L) 19998 CIMB Islamic Bank Berhad (L) 20069 EONCAP Islamic Bank Berhad (L) 200610 Hong Leong Islamic Bank Berhad (L) 200511 HSBC Amanah Malaysia Berhad (F) 200412 Kuwait Finance House (Malaysia) Berhad (F) 200513 Maybank Islamic Berhad (L) 200814 OCBC Al-Amin Bank Berhad (F) 200815 Public Islamic Bank Berhad (L) 200816 RHB Islamic Bank Berhad (L) 200517 Standard Chartered Saadiq Berhad (F) 2008Source: Bank Negara Malaysia as at June 2011.Note: L = Local owned bankF = Foreign owned bankThis study employed unbalanced sample from the year 1995 to 2010 due to thedifferent in term of year establishment of each banks. Total number of observation is74.5

3.2 VariablesThe objective of this study is to determine factors influence in credit risk level ofMalaysian Islamic banks. Credit risk in this study has been defined as a ratio of nonperformingfinancing to total financing. The definition of non-performing financing isvaries among countries. In Malaysia non-performing financing is defined as;“(i) where the principal or interest/profit or both is past due for more than 90 days or3 months. In the case of revolving facilities (e.g. overdraft facilities), the facility shallbe classified as impaired where the outstanding amount has remained in excess ofthe approved limit for a period of more than 90 days or 3 months; or(ii) where the amount is past due or the outstanding amount has been in excess ofthe approved limit for 90 days or 3 months or less, the loan/financing exhibitsweaknesses8 that render a classification appropriate according to the bankinginstitution‟s credit risk grading framework.”Sources: Central Bank of Malaysia (2010).This study only used bank specific variables (BSV) as the independent variables.Five independent variables and three dummies are being regress against thedependent variable to determine the relationship between BSV and credit risk levelfor Malaysian Islamic banks. The independent variables are constructed using theinformation from balance sheet and income statement of each Islamic bank. Table 2explained the detail of the variables definition used in this study.6

Table 2: Independent and Dependent variablesVariables Definition Expected SignCredit Risk (CR)FinancingExpansion (TL)Financing Quality(LLP)Non-performing financing to totalfinancing outstandingTotal financing to total assets +Loan loss provisions to total assets +Capital Buffer(TE) Total equity to total assets -Capital Ratio(CAPR)Total capital (TIER 1 and TIER 2capital) to total assets ratioSize (TA) Natural logarithm of total assets +/-DABDDBDSBDummy variables; „1‟ for offering assetbased financing and „0‟ for not offeringasset based financingDummy variables; „1‟ for offering debtbased financing and „0‟ for not offeringdebt based financingDummy variables; „1‟ for offeringsupporting based financing and „0‟ fornot offering supporting basedfinancing+/-+++/-3.3 Descriptive StatisticsTable 3 provide a summary of descriptive statistic for the variables used in this study.In average, credit risk level of Malaysian Islamic banks is about 4.35 percent (from1995 to 2010). This figure can be considered low as compared to the amount offinancing. As we can see, TL (total financing to total assets) is 50 percent. It is meanthat 50 percent of the assets in Malaysian Islamic banks is contributed by financingactivities.Looking to the provision for loss, Malaysian Islamic banks make quite a smallamount of provision. In average Islamic banks in Malaysia only make a provisionabout 0.7 percent and the highest is 9 percent. This amount of provision is quitelower compared as compared to the ratio of financing to total assets (TL).Size is one of the important BSV that will determine credit risk in bank. In this study,size is measured by taking a value of total assets (TA). Total assets value forMalaysian Islamic banks range from RM291 million to RM44157 million. The range isbig may due to the different in age of the sample banks.7

Table 3: Descriptive StatisticsVariables N Mean Std. Dev Minimum MaximumCR (%) 74 4.35 3.39 1.31 19.16TL (%) 74 50.03 17.98 0.83 79.64LLP (%) 74 0.70 1.14 -0.08 8.99TE (%) 74 10.21 9.65 -1.90 77.18CAPR (%) 74 23.05 32.74 -2.80 211.90FCOST (%) 74 1.87 0.76 0.01 3.89MGT (%) 74 73.84 17.92 17.84 99.34TA (RM) 74 10473.05 8400.90 291.38 44157.503.4 MethodologyPanel data set has been used to identify the relationship between BSV, financingstructure and credit risks level in Malaysian Islamic banks. Panel data set allow thestudy to observed on cross-section Islamic banks, over a several times series.The regression is estimates using generalized least square (GLS) method.Below is the general form of panel data estimation equation:Y it = α + X΄ it β + ε it Equation (1)Baltagi (2001) states that a one-way error component model is mostly used in paneldata techniques. The error term ε it therefore, can be written as:ε it = ε i + v it Equation (2)The error term represents the unobservable individual firm-specific effect.Estimation equation used:CR = ƒ (TL, LLP, TE, CAPR, FCDAB, DDB, DSB) Equation (3)CR it = β 0 + β 1 TL it + β 2 LLP it + β 3 TE it + β 4 CAPR it + β 5 Log(TA) it+ β 6 DAB it + β 7 DDB it + β 8 DSB it + εi t Equation (4)8

4.0 Analysis of Finding4.1 CorrelationMulticollinearity problem have been tested by preparing the correlation matrix usingPearson correlation test. The correlation result indicates there is no seriousmulticollinearity problem. Thus all the variables can be used to test the model. Table4 provide the value of correlation for the variables used in the study. From the result,there are mixed relationship between banks specific variables and credit risk level.Out of five variables, three variables have negative relationship with credit risk. TL,TE and CAPR have a negative correlation with CR. LLP and TA has a positivecorrelation with CR.Table 4: Correlation MatrixCR TL LLP TE CAPR TACR 1.0000TL -0.0743 1.0000(-0.6362)LLP 0.5553 0.1897 1.0000(-5.7044)*** -1.6619TE -0.2207 -0.2645 -0.1735 1.0000(-1.9337)* (-2.3595)** (-1.5158)CAPR -0.1781 -0.5183 -0.1729 0.5121 1.0000(-1.5361) (-5.1783)*** (-1.4996) (5.1291)***TA 0.3345 0.3033 0.1033 -0.2874 -0.2965 1.0000(3.0324)*** (2.7382)*** (-0.8935) (-2.6160)** (-2.6711)******, ** and * denotes significance at 1%, 5% and 10% confidence level, respectively.4.2 Regression ResultFive independence variables and three dummy variables have been regress againstcredit risk (CR). The estimation result shows that four BSV has a significantrelationship with credit risk. TL and TE have a negative significant relationship withCR. Negative relationship between TE and CR is expected as lower equity capitalindicates that higher debt capital. Higher debt capital will increased net interestmargin ratio. TL has contradicted result; TL is expected to have positive relationshipwith CR, however this study finds that TL has negative relationship with CR. Thisresult is same with Rahman and Shahimi (2010).LLP has positive significant relationship with CR as expected. Higher amount ofprovisioning indicates that a bank have a problem with loan quality. Lower quality ofloan will potentially increase loan default.9

Size as measured by taking a log of TA, has positive relationship with CR asexpected. However, TA does not significantly influence CR in this study.Dummy variable has used to identify whether types of financing have significantinfluenced on credit risk or not. From the regression, this study finds that assets anddebt based financing (DAB and DDB) have positive significant relationship with creditrisk level in Islamic banks. Debt based financing has more impact to credit risk as thecoefficient value is larger than asset based financing coefficient. This situation maydue to higher amount of financing using debt based compare to asset based inMalaysian Islamic banks. Supporting based financing does not have significantinfluence on credit risk.Table 5: Estimation ResultExpected Model 1Coeff.GLSSign Coefficient T-stat Prob.C -4.0708 -1.1607 0.2508TL + -0.0268* -1.9199 0.0601LLP + 1.5625*** 4.4761 0.0000TE - -0.0600* -1.8075 0.0761CAPR +/- 0.0227* 1.8259 0.0733Log(TA) +/- 0.5570 1.6570 0.1032DAB + 1.4337*** 2.7780 0.0075DDB + 2.9192* 1.9472 0.0566DSB +/- -0.4079 -1.1269 0.2647R-Sq 0.6604Adj. R-Sq 0.6110DW 1.0473***, ** and * denotes significance at 1%, 5% and 10% confidence level, respectively.5.0 ConclusionThis paper aims to identify relationship between BSV, financing structures and creditrisk of Malaysian Islamic banks. Five BSV and three dummy have been regressagainst credit risk. The estimation result shows that four of BSV; TL, LLP, TE andCAPR have a significant relationship with credit risk in Malaysian Islamic banks.Financing structures also influence the level of credit risk. This study suggests thatasset and debt based financing will have an impact towards credit risk in MalaysianIslamic banks. This finding gives an indication to the Islamic banks to balance thefinancing structures used by them in providing financing to the customers.10

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