Transcription
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015 IAUPerformance of Credit Risk Management in IndianCommercial BanksA. SinghMewar University, Chittorgarh, Rajasthan, IndiaReceived 23 March 2014, Accepted 9 August 2014ABSTRACT:For banks and financial institutions, credit risk had been an essential factor that needed to be managed well.Credit risk was the possibility that a borrower of counter party would fail to meet its obligations in accordancewith agreed terms. Credit risk; therefore arise from the bank’s dealings with or lending to corporate, individuals,and other banks or financial institutions.Credit risk had been the oldest and biggest risk that bank, by virtue of its very nature of business,inherited.Currently in India there were many banks in operation. From these some public sector banks arenamely State Bank of India, Punjab National Bank, Oriental Bank of Commerce, Bank of India, Indian Bank,Indian Overseas Bank, Syndicate Bank, Bank of Baroda, Canara Bank, Allahabad Bank, UCO Bank, VijayaBank and private sector banks are Axis Bank, ICICI Bank, IndusInd Bank, ING Vysya Bank, Dhanlaxmi Bank,HDFC Bank, YES Bank, Kotak Mahindra Bank, Karnataka Bank, ABN Amro Bank, Federal Bank, Laxmi VilasBank were selected to examine the impact level of credit risk management towards the profitability of Indiancommercial banks. To examine its impact level the researcher had used multiple regression models by taking 11years return on asset (ROA), non performing asset (NPA) and capital adequacy ratio (CAR) from each bank. Theresearcher had collected data from RBI annual report since 2003 to 2013 for regression purpose.Keywords: Banks, Commercial banks, Private sector banks, Public sector banks, Return on asset,Net performing asset, Capital adequacy ratioINTRODUCTIONEconomic development had been acontinuous process. The success of economicdevelopment depended essentially on the extentof mobilization of resources and investment andon the operational efficiency and economicdiscipline displayed by the various segments ofthe economy. The banking had become thefoundation of modern economic development.Banks played a positive role in the economicdevelopment of a country as they not onlyaccepted and deployed large funds in a fiduciarycapacity but also leveraged such funds throughcredit creation. A commercial bank was afinancial intermediary which accepted depositsof money from the public and lent them with aview to make profits. A post office might acceptdeposits but it could not be called a bankbecause it did not perform the other essentialfunction of a bank, i.e. lending money. Thebanking system formed the core of the financialsector of an economy. The role of nding Author, Email: [email protected], Asha Singh
A. Singhunderdeveloped countries. Through mobilizationof resources and their better allocation,commercial banks played an important role inthe development process of underdevelopedcountries. A commercial bank accepted depositswhich were of various types like current,savings, securing and fixed deposits. It grantedcredit in various forms such as loans andadvances, discounting of bills and investment inopen market securities. It rendered investmentservices such as underwriters and bankers for itsissue of securities to the public.Banks were financial institutions thataccepted deposit and made loans. Commercialbanks in India extended credit (loan) to differenttypes of borrower for many different purposes.For most customers, bank credit was the primarysource of available debt financing and for banks;good loans were the most profitable assets(Mishkin, 2004).Credit risk management determined theeffectiveness of a commercial bank. The mainfunctions of a commercial bank could besegregated into three main areas:(i) Payment System(ii) Financial Intermediation(iii) Financial Services.(i) Payment System: Banks were at the coreof the payments system in an economy. Apayment referred to the means by whichfinancial transactions were settled. Afundamental method by which banks helped insettling the financial transaction process was byissuing and paying cheques issued on behalf ofcustomers. Further, in modern banking, thepayments system also involved electronicbanking, wire transfers, settlement of credit cardtransactions, etc. In all such transactions, banksplayed a critical role.(ii) Financial Intermediation: The secondprincipal function of a bank was to take differenttypes of deposits from customers and then lendthese funds to borrowers, in other words,financial intermediation. In financial terms, bankdeposits represented the banks' liabilities, whileloans disbursed, and investments made by bankswere their assets. Bank deposits serve the usefulpurpose of addressing the needs of depositors,who wanted to ensure liquidity, safety as well asreturns in the form of interest. On the other hand,bank loans and investments made by banksplayed an important function in channellingfunds into profitable as well as sociallyproductive uses.(iii) Financial Services: In addition to actingas financial intermediaries, banks today involvedwith offering customers a wide variety offinancial services including investment banking,insurance-related services, government-relatedbusiness, foreign exchange businesses, wealthmanagement services, etc. Income fromproviding such services improved a bank'sprofitability.As per different researchers and authors,Credit risk was the most significant of all risks interms of size of potential losses. As theextension of credit had always been at the coreof banking operation, the focus of banks’ riskmanagement had been credit risk management.When banks managed their risk better, theywould get advantage to increase theirperformance (return). Better risk managementindicated that banks operated their activities atlower relative risk and at lower conflict ofinterests between parties (Santomero, 1997).The advantages of implementing better riskmanagement led to better banks performance.Better bank performance increases theirreputation and image from public or marketpoint of view. The banks also get moreopportunities to increase the productive assets,leading to higher bank profitability, liquidity,and solvency (Eduardus et al., 2007). Therefore,Effective credit risk management should be acritical component of a bank’s overall riskmanagement strategy and considered essential tothe long-term success of any bankingorganization. It therefore appeared more andmore significant in order to ensure sustainableprofits in banks.Literature ReviewWithin the last few years, a number ofstudies had provided the discipline into thepractice of credit risk management withinbanking sector. An insight of related studiescould be as follows:170
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015Private sector banks were more serious toimplement effective credit risk managementpractice than state owned banks. A studyconducted by Kuo and Enders (2004) of creditrisk management policies for state banks inChina and found that mushrooming of thefinancial market; the state owned commercialbanks in China were faced with theunprecedented challenges and tough for them tocompete with foreign bank unless they couldmake some thoughtful change. In this thoughtfulchange, the reform of credit risk managementwas a major step that determined whether thestate owned commercial banks in China wouldsurvive the challenges or not.Felix and Claudine (2008) investigated therelationship between bank performance andcredit risk management. It could be inferredfrom their findings that return on equity (ROE)and return on assets (ROA) both measuringprofitability were inversely related to the ratio ofnon-performing loan to total loan of financialinstitutions thereby leading to a decline inprofitability.Ahmad and Ariff (2007) examined the keydeterminants of credit risk of commercial bankson emerging economy banking systemscompared with the developed economies. Thestudy found that regulation was important forbanking systems that offered multi-products andservices; management quality is critical in thecases of loan-dominant banks in emergingeconomies. An increase in loan loss provisionwas also considered to be a significantdeterminant of potential credit risk.Ghosh and Das (2005) focused on whether,and to what extent, governments should imposecapital adequacy requirements on banks, oralternately, whether market forces could alsoensure the stability of banking systems. Thestudy contributed to this debate by showing howmarket forces might motivate banks to selecthigh capital adequacy ratios as a means oflowering their borrowing costs. Empirical testsfor the Indian public sector banks during the1990s demonstrate that better capitalized banksexperienced lower borrowing costs. Thesefindings suggested that ongoing reform efforts atthe international level should primarily focus onincreasing transparency and strengtheningcompetition among the banks.Thiagarajan et al. (2011) analyzed the role ofmarket discipline on the behavior of commercialbanks with respect to their capital adequacy. Thestudy showed that the Capital Adequacy Ratio(CAR) in the Indian commercial banking sectorshowed that the commercial banks were wellcapitalized and the ratio was well over theregulatory minimum requirement. The privatesector banks showed a higher percentage of tier-Icapital over the public sector banks. Howeverthe public sector banks showed a higher level oftier-II capital. Although the full implementationof Basel II accord by the regulatory authority(RBI) might have influenced the level of capitaladequacy in the banking sector. The studyindicated that market forces influence the bank’sbehavior to keep their capital adequacy wellabove the regulatory norms. The NonPerforming Assets significantly influenced thecost of deposits for both public and privatesector banks. The return on equity had asignificant positive influence on the cost ofdeposits for private sector banks. The publicsector banks could reduce the cost of deposits byincreasing their tier-I capital.Based upon literature review, this researchpaper analyzed the performance of private sectorand public sector banks undertaken for the study.Statement of the ProblemBanking Industry happened to be thebackbone of an economy, without properbanking channels the total business environmentwould be adversely affected. After liberalizationan extensive banking network had beenestablished and Indian banking system was nolonger confined to urban area: in fact, Indianbanking sector had undergone a tremendouschange in the last few decades. Earlier bankswere only considered as means of depositingmoney but now the total scenario had changed.Today more and more private banks cameforward for providing a number of financial andnon-financial services. The modern banking wasplaced in a very complex and intricateenvironment so its proper functioning was veryessential for the growth of an economy.This study was an attempt to sketch thevarious important aspects of the Private andPublic banking sector. A major part of the workwas to ascertain as to what extent banks couldmanage their credit risks, what tools or171
A. Singhtechniques were at their disposal and to whatextent their performance could be augmented byproper credit risk management policies andstrategies. Also intended to have a comparativestudy of Non Performing Assets (NPAs), CapitalAdequacy Ratio (CAR), Return on Asset (ROA)of Private and Public Sector Banks in India.Objective of the StudyThe main objective of the study was to havebigger picture on credit risk management and itsimpact on their performance and to make thecomparison of the performances of Public SectorBanks (PSB) and Private Sector Banks (PvtSB)in India.Significance of the StudyThe significance of this paper was: To show the relationship between credit riskmanagement and performance. To show relationship between ROA, NPAand CAR.Research HypothesisThe researcher expected with better creditrisk management with high return on asset(ROA) and lower non-performing asset(NPA).With the help of data the study wasestablished and tested the following hypothesis:Hypothesis 1 (H0): credit risk managementhad an effect on the bank performance.Hypothesis 2 (H1): credit risk managementhad no effect on the bank performance.RESEARCH METHODThe researcher used the data from privatesector banks and public sector banks of India foranalysis to examine the relationship betweenreturn on asset (ROA) which was performanceindicators capital adequacy ratio (CAR) andnon-performing assets (NPAs). These two werethe indicators of risk management whichaffected the profitability of banks. NPA, inparticular, indicated how banks managed theircredit risk. The research was quantitativeresearch. Meant for, the researcher usedregression model to analyze the data which wascollected from the public and private sectorbanks of India.RESULTS AND DISCUSSIONAnalysis of DataBefore rushing towards data analysis andpresentation the researcher made a diagnostictest for the data which collected from the annualreport of Reserve Bank of India (RBI).Researcher had collected data of ROA, NetNPAs and CAR of Public and Private Sectorbanks from annual report of RBI since 2003 to2013. The researcher has conducted correlationand linear regression test between ROA & NPAand ROA & CAR of public and private sectorbanks.Comparison between ROA, NPAs and CAR ofPublic Sector Banks (PSB)Table 1 shows the comparison betweenpercentage of ROA, Net NPAs and CAR ofpublic sector banks for 11 years.The result of correlation and linear regressiontest between ROA & NPA was in figure 1.Where Y axis ROA and X axis NPAThe equation of the straight line relatingROA and NPA was estimated as: ROA (0.8409) (0.0503) NPA using the 11observations in this dataset. The y-intercept, theestimated value of ROA when NPA was zero,was 0.8409 with a standard error of 0.0835. Theslope, the estimated change in ROA per unitchange in NPA, was 0.0503 with a standarderror of 0.0400. Table 2 shows the value ofR-Squared, the proportion of the variation inROA that could be accounted for by variation inNPA, was 0.1494. The correlation between ROAand NPA was 0.3865.Table 3 shows, in case of dependent variable,the standard deviation 0.1427, minimum value 0.7800 and maximum value 1.2700 whereasin case of independent variable, the standarddeviation 1.0968, minimum value 0.9400and maximum value 4.5400.172
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015Table 1: Comparison of ROA, Net NPAs & CAR of PSBYearsROA (%)Net NPA (%)CAR ure1: Linear regression between ROA and NPA of PSB173
A. SinghTable 2: Run summary sectionParameterValueParameterValueDependent variableROARows Processed11Independent VariableNPARows used in Estimation11Frequency variableNoneRows with X Missing0Weight VariableNoneRows with Freq. Missing0Intercept0.8409Rows Prediction Only0Slope0.0503Sum of Frequencies11R-Squared0.1494Sum of Weights11.0000Correlation0.3865Coefficient of variation0.1489Mean Square Error0.01923905Square Root of MSE0.1387049Table 3: Descriptive statistics Count1111Mean0.93181.8082Standard 7004.5400Table 4 shows, a significance test that theslope was zero resulted in a t-value of 1.2573.The significance level of this t-test was 0.2403.Since 0.2403 0.0500, the hypothesis that theslope was zero was not rejected.The estimated slope was 0.0503. The lowerlimit of the 95% confidence interval for the slopewas -0.0402 and the upper limit was 0.1408. Theestimated intercept was 0.8409. The lower limitof the 95% confidence interval for the interceptwas 0.6519 and the upper limit was 1.0299.It also shows the least-squares estimates ofthe intercept and slope followed by thecorresponding standard errors, confidenceintervals, and hypothesis tests. These resultswere based on several assumptions.Estimated ModelROA (0.840899782796974) (0.0502816686391794) * (NPA)Table 5 shows the F-Ratio for testingwhether the slope was zero, the degrees offreedom, and the mean square error. The meansquare error, which estimated the variance of theresiduals, was used extensively in the calculationof hypothesis tests and confidence intervals.Table 6 shows that there was no serialcorrelation.174
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015Table 4: Regression estimation sectionParameterIntercept B(0)Slope B(1)Regression Coefficients0.84090.0503Lower 95% Confidence Limit0.6519- 0.0402Upper 95% Confidence Limit1.02990.14080.0400Standard Error0.0835Standardized Coefficient0.00000.3865T Value10.06631.25730.2403Prob Level(T Test)0.0000Reject H0(Alpha 0.0500)YesNoPower (Alpha 0.0500)1.00000.20320.0503Regression of Y on X0.8409Inverse Regression from X on Y0.32330.3366Orthogonal Regression of Y and X0.83960.0510Table 5: Analysis of variance sectionSourceDFSum of SquaresMean 03041218Error90.17315140.01923905Adj. Total100.20356360.02035636Total119.7547F-RatioProb LevelPower (5%)1.58080.24030.2032s Square Root (0.01923905) 0.1387049Table 6: Tests of assumptions sectionAssumption/TestResiduals follow NormalDistribution?Test ValueProb LevelIs the Assumption Reasonable atthe 0.2000 Level of Significance?Shapiro Wilk0.90390.206401YesAnderson Darling0.55830.149388NoD’Agostino Skewness1.49770.134215NoD’Agostino Kurtosis0.50880.610903YesD’Agostino Omnibus2.50190.286229YesConstant Residual Variance?Modified Levene Test0.00110.974035YesRelationship is a Straight Line?Lack of Linear Fit F(0,0) Test0.00000.000000175No
A. SinghResidual Plot SectionFigure 2 shows scattered diagram betweenresiduals of ROA vs NPA.The relationship between ROA vs CAR ofpublic sector banks by using data of table1 wasgiven in figure 3.Figure 2: Residuals of ROA vs NPAFigure 3: Linear regression plot Section between ROA and CAR of PSB176
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015and maximum value 13.2800.Table 9 shows a significance test that theslope was zero resulted in a t-value of 2.8613.The significance level of this t-test was 0.0187.Since 0.0187 0.0500, the hypothesis that theslope was zero was rejected.The estimated slope was 0.2653. The lowerlimit of the 95% confidence interval for theslope was 0.0555 and the upper limit was0.4750. The estimated intercept was -2.4420.The lower limit of the 95% confidence intervalfor the intercept was -5.1104 and the upper limitwas 0.2264.It also shows the least-squares estimates ofthe intercept and slope followed by thecorresponding standard errors, confidenceintervals, and hypothesis tests. These resultswere based on several assumptions.The equation of the straight line relatingROA and CAR was estimated as: ROA (2.4420) (0.2653) CAR using the 11observations in this dataset. The y-intercept, theestimated value of ROA when CAR was zero,was -2.4420 with a standard error of 1.1796. Theslope, the estimated change in ROA per unitchange in CAR, was 0.2653 with a standarderror of 0.0927. Table 7 shows the value of RSquared, the proportion of the variation in ROAthat could be accounted for by variation in CAR,was 0.4763. The correlation between ROA andCAR was 0.6902.Table 8 shows, in case of dependent variable,the standard deviation 0.1427, minimum value 0.7800 and maximum value 1.2700 whereasin case of independent variable, the standarddeviation 0.3712, minimum value 12.2000Table 7: Run summary sectionParameterValueParameterValueDependent variableROARows Processed11Independent VariableCARRows used in Estimation11Frequency variableNoneRows with X Missing0Weight VariableNoneRows with Freq. Missing0Intercept-2.4420Rows Prediction Only0Slope0.2653Sum of Frequencies11R-Squared0.4763Sum of Weights11.0000Correlation0.6902Coefficient of variation0.1168Mean Square Error0.01184408Square Root of MSE0.1088305Table 8: Descriptive statistics Count1111Mean0.931812.7182Standard 270013.2800177
A. SinghEstimated Modelfreedom, and the mean square error. The meansquare error, which estimated the variance of theresiduals, was used extensively in the calculationof hypothesis tests and confidence intervals.Table 11 shows that there was no serialcorrelation.ROA (-2.44197036470223) (0 .265272866416879) * (CAR)Table 10 shows the F-Ratio for testingwhether the slope was zero, the degrees ofTable 9: Regression estimation sectionParameterIntercept B(0)Slope B(1)Regression Coefficients-2.44200.2653Lower 95% Confidence Limit-5.11040.0555Upper 95% Confidence Limit0.22640.4750Standard Error1.17960.0927Standardized Coefficient0.00000.6902T Value-2.07022.8613Prob Level(T Test)0.06830.0187Reject H0(Alpha 0.0500)NoYesPower (Alpha 0.0500)0.45590.7217Regression of Y on X-2.44200.2653Inverse Regression from X on Y-6.15080.5569Orthogonal Regression of Y and X-2.70340.2858Table 10: Analysis of variance sectionSourceDFSum of SquaresMean 09696688Error90.10659680.01184408Adj. Total100.20356360.02035636Total119.7547F-RatioProb LevelPower(5%)8.18690.01870.7217Table 11: Tests of assumptions sectionAssumption/TestResiduals follow NormalDistribution?Test ValueProb LevelIs the Assumption Reasonable atthe 0.2000 Level of Significance?Shapiro Wilk0.90160.193148NoYesAnderson Darling0.50100.207532D’Agostino Skewness1.46260.143640NoD’Agostino Kurtosis0.30480.760556YesD’Agostino Omnibus2.23140.327686YesConstant Residual Variance?Modified Levene Test0.23190.641647YesRelationship is a Straight Line?Lack of Linear Fit F(0,0) Test0.00000.000000178No
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015Residual Plot SectionFigure 4 shows scattered diagram betweenresiduals of ROA vs CAR.Comparison between ROA, NPAs and CAR ofPrivate Sector Banks (PvtSB)Table 12 shows the comparison betweenpercentage of ROA, Net NPAs and CAR ofPrivate sector banks for 11 years. Researcherapplied Correlation and Linear Regression Teston given data in table 12.The result of Correlation and LinearRegression analysis about private sector bankswas as given in figure 5.Figure 4: Residuals of ROA vs CARTable 12: Comparison of ROA, Net NPAs & CAR of private sector banksYearsROA (%)Net NPA (%)CAR 413.72179
A. SinghWhere Y ROA and X NPAThe equation of the straight line relatingROA and NPA was estimated as: ROA (1.2058) (-0.1470) NPA using the 11observations in this dataset. The y-intercept, theestimated value of ROA when NPA was zero,was 1.2058 with a standard error of 0.1514. Theslope, the estimated change in ROA per unitchange in NPA, was -0.1470 with a standarderror of 0.0735. Table 13 shows, the value of RSquared, the proportion of the variation in ROAthat could be accounted for by variation in NPA,was 0.3078. The correlation between ROA andNPA was -0.5548.Figure 5: Linear regression between ROA and NPA of PvtSBTable 13: Run summary sectionParameterValueParameterValueDependent variableROARows Processed11Independent VariableNPARows used in Estimation11Frequency variableNoneRows with X Missing0Weight VariableNoneRows with Freq. Missing0Intercept1.2058Rows Prediction Only0Slope-0.1470Sum of Frequencies11R-Squared0.3078Sum of Weights11.0000Correlation-0.5548Coefficient of variation0.3296Mean Square Error0.1027637Square Root of MSE0.3205678180
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015Table 14 shows, in case of dependentvariable, the standard deviation 0.3655,minimum value 0.1300 and maximum value 1.6300 whereas in case of independent variable,the standard deviation 1.3796, minimum value 0.5300 and maximum value 4.9500.Table 15 shows a significance test that theslope was zero resulted in a t-value of -2.0007.The significance level of this t-test was 0.0765.Since 0.0765 0.0500, the hypothesis that theslope was zero was not rejected.The estimated slope was -0.1470. The lowerlimit of the 95% confidence interval for theslope was -0.3132 and the upper limit were0.0192. The estimated intercept was 1.2058. Thelower limit of the 95% confidence interval forthe intercept was 0.8634 and the upper limit was1.5482.It also shows the least-squares estimates ofthe intercept and slope followed by thecorresponding standard errors, confidenceintervals, and hypothesis tests. These resultswere based on several assumptions.Table 14: Descriptive statistics Count1111Mean0.97271.5855Standard 3004.9500Table 15: Regression estimation sectionParameterIntercept B(0)Regression Coefficients1.2058-0.1470Lower 95% Confidence Limit0.8634-0.3132Upper 95% Confidence Limit1.54820.0192Standard Error0.15140.0735Standardized Coefficient0.0000-0.5548T Value7.9658-2.0007Prob Level (T Test)0.00000.0765Reject H0 (Alpha 0.0500)YesNoPower (Alpha 0.0500)1.00000.4316Regression of Y on X1.2058-0.1470Inverse Regression from X on Y1.7299-0.4776Orthogonal Regression of Y and X1.2174-0.1543181Slope B(1)
A. SinghEstimated ModelTable 17 shows that there was no serialcorrelation.ROA (1.20580591296107) (-0.14701061023921) * (NPA)Residual Plot SectionTable 16 shows the F-Ratio for testingwhether the slope was zero, the degrees offreedom, and the mean square error. The meansquare error, which estimates the variance of theresiduals, was used extensively in the calculationof hypothesis tests and confidence intervals.Figure 6 shows scattered diagram betweenresiduals of ROA vs NPA.The relationships between ROA vs. CAR ofprivate sector banks by using data of table 12was in figure 7.Table 16: Analysis of variance sectionSourceDFSum of SquaresMean 1345Error90.92487310.1027637Adj. Total101.3362180.1336218Total1111.7444F-RatioProb LevelPower(5%)4.00280.07650.4316s Square Root (0.1027637) 0.3205678.Table 17: Tests of assumptions sectionAssumption/TestIs the Assumption Reasonable atTest ValueProb LevelShapiro Wilk0.90400.206587YesAnderson Darling0.64600.091945NoD’Agostino Skewness-0.82860.407346YesD’Agostino Kurtosis1.67980.093005NoD’Agostino Omnibus3.50810.173071NoResiduals follow Normal Distribution?the 0.2000 Level of Significance?Constant Residual Variance?Modified Levene Test0.51770.490065YesRelationship is a Straight Line?Lack of Linear Fit F(0,0) Test0.00000.000000182No
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015Figure 6: Residuals of ROA vs. NPAFigure 7: Linear regression between ROA and CAR of PvtSB183
A. SinghThe equation of the straight line relatingROA and CAR was estimated as: ROA (-0.1455) (0.0818) CAR using the 11observations in this dataset. The y-intercept, theestimated value of ROA when CAR was zero,was -0.1455 with a standard error of 0.8840. Theslope, the estimated change in ROA per unitchange in CAR, was 0.0818 with a standarderror of 0.0642. Table 18 shows, the value of RSquared, the proportion of the variation in ROAthat could be accounted for by variation in CAR,was 0.1529. The correlation between ROA andCAR was 0.3910.Table 19 shows, in case of dependentvariable, the standard deviation 0.3655,minimum value 0.1300 and maximum value 1.6300 whereas in case of independent variable,the standard deviation 1.7468, minimum value 11.7000 and maximum value 16.2900.Table 20 shows, a significance test that theslope was zero resulted in a t-value of 1.2743.The significance level of this t-test was 0.2345.Since 0.2345 0.0500, the hypothesis that theslope was zero was not rejected.The estimated slope was 0.0818. The lowerlimit of the 95% confidence interval for theslope was -0.0634 and the upper limit was0.2270. The estimated intercept was -0.1455.The lower limit of the 95% confidence intervalfor the intercept was -2.1453 and the upper limitwas 1.8543. It also shows the least-squaresestimates of the intercept and slope followed bythe corresponding standard errors, confidenceintervals, and hypothesis tests. These resultswere based on several assumptions.Table 18: Run summary sectionParameterValueParameterValueDependent variableROARows Processed11Independent VariableCARRows used in Estimation11Frequency variableNoneRows with X Missing0Weight VariableNoneRows with Freq. Missing0Intercept-0.1455Rows Prediction Only0Slope0.0818Sum of Frequencies11R-Squared0.1529Sum of Weights11.0000Correlation0.3910Coefficient of variation0.3646Mean Square Error0.1257752Square Root of MSE0.3546481Table 19: Descriptive statistics Count111113.6682Mean0.9727Standard 630016.2900184
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015Estimated Modelfreedom, and the mean square error. The meansquare error, which estimated the variance of theresiduals, was used extensively in the calculationof hypothesis tests and confidence intervals.Table 22 shows that there was no serialcorrelation.ROA (-0.145500032771452) (0.081812440043139) * (CAR)Table 21 shows the F-Ratio for testingwhether the slope was zero, the degrees ofTable 20: Regression estimation sectionParameterIntercept B(0)Slope B(1)Regression Coefficients-0.14550.0818Lower 95% Confidence Limit-2.1453-0.0634Upper 95% Confidence Limit1.85430.2270Standard Error0.88400.0642Standardized Coefficient0.00000.3910T Value-0.16461.2743Prob Level(T Test)0.87290.2345Reject H0(Alpha 0.0500)NoNoPower (Alpha 0.0500)0.05250.2075Regression of Y on X-0.14550.0818Inve
For banks and financial institutions, credit risk had been an essential factor that needed to be managed well. Credit risk was the possibility that a borrower of counter party would fail to meet its obligations in accordance with agreed terms. Credit risk; therefore arise from the bank
