4.3 Summary statistics
4.3.2 Secondary Data Aanalysis
The study used descriptive statistics for analysis of the dependent and independent variables. The purpose of descriptive statistics is to enable the researcher to meaningfully describe a distribution of scores or measurements using indices or statistics. The type of statistics or indices used depends on the type of variables in the study and the scale of measurement. Measures of central tendency are used to determine the typical or expected score or measure from a sample of measurements or a group of scores in a study. Measures of central tendency are used to give expected summary statistics of variables being studied. The commonly used measures of central tendency are mode, mean and median. This study particularly used mean/average, median, range, percentages and standard deviation to analyze the objectives which were to establish how Corporate Governance, Capital Requirement, Credit Risk Management and Liquidity Management affects the performance of commercial banks’ in Kenya as shown in table 4.10.
Descriptive Statistics of Independent Variables
Table 4.10: Independent Variables one-Sample Statistics
One-Sample Statistics
Variables N Minimum Maximum Mean Std.
Deviation
Std. Error Mean
Corporate Governance 215 40.1 56.5 50.6000 5.29685 .36124
Capital requirement 215 20.5 23 21.6005 .70820 .04830
Credit risk Management 215 4.4 8 5.7200 1.10005 .07502
liquidity Management 215 37 44 40.6005 2.02218 .13791
Table 4.11: Independent Variables One-Sample Test
One-Sample Test Test Value = 0 t df Sig. (2- tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper Corporate governance 140.072 214 .000 50.6000 49.888 51.312 Capital requirement 447.228 214 .000 21.6004 21.505 21.695 Credit risk Management 76.243 214 .000 5.7200 5.572 5.8679 Liquidity Management 294.395 214 .000 40.6004 40.329 40.872
Corporate Governance
Corporate Governance (CG) had a mean value of 50.600 with minimum and maximum values of 40.1 and 56.5 respectively. The Corporate Governance (CG) had standard deviations of 29.7% which shows little dispersion of operating income to total income ratio from its mean for the commercial banks in Kenya. The Corporate Governance (CG) which is expressed by average operating income to total income ratio was 50.6. This was lower than that of Ongore and Kusa (2013) who found that management efficiency/ Corporate Governance), proxies by operating income to total income were 72.23 on average. The study shows that in Kenya more than 51% of commercial banks income is derived from the conventional intermediation (operating) function. These results are similar to Ogilo (2012) who evaluated the impact of credit risk management on financial performance of commercial banks in Kenya. Ongore and Kusa (2013) also reported the
4.3.1.3 Financial Performance of Commercial Banks in Kenya-(Dependent variable)
Table 4.12: Financial Performance of Commercial Banks in Kenya
Statements Mean SD
The bank had good improvement on return on equity in last three years 4.28 0.773
The bank had good improvement on return on asset in last three years 4.13 0.785
The bank has better return on equity than industry average ( Benchmark)
3.74 0.633
The bank has better return on asset than industry average ( Benchmark) 3.91 0.633
Average 4.015 .706
In this study bank performance represents the financial performance improvement. Bank performance also can be seen in comparison with the related industry as a benchmark. Table 4.12 shows four item questions that represent bank performance. The responses were tabulated in table 4.12 and analyzed using mean and standard deviation on a likert scale ranging from 1-5. In the likert scale where 5 represented strongly agree and 1 represented strongly disagree (Likert, 1932). The questions concern managers’ judgment on return on equity and its benchmarks and return on assets and its benchmarks. It can be revealed that 60% of the respondents agreed that the bank had good improvement of return on equity in the last three years. Similarly, 70% noted that the bank had good improvement of return on assets in the last three years. As concerns the industry, 57.5% of the respondents indicated that the bank had better return on equity than industry average while 67.5% agreed that the bank had better return on assets than industry average. Hence, the researcher deduced that the banks had better performance on both return on equity and assets in the industry irrespective of the type of ownership. The financial performance of commercial banks in Kenya was expressed by proxy indicators: ROA and ROE as shown by table 4.12.
Capital Requirement
The study found that the mean value of the Capital Requirement (CR) was 21.6005, with minimum and maximum values of 20.5 and 23 respectively. In terms of standard deviations the capital requirement had standard deviations of .70820 which shows a high dispersion of Capital Adequacy ratio (Equity/Total Asset) from its mean for the commercial banks in Kenya. Looking at the minimum, mean and maximum values,
generally, the statistics indicate a slight variation in the capital requirement determinants of profitability of banks in Kenya. The average Capital Ratio (CR) of Commercial Banks in Kenya was 21.60. This average is way above the statutory minimum of 12.0 percent set by CBK (Olweny & Shipho, 2011).
This shows that the Kenyan commercial banks hold more capital than required. This could imply that banks could prefer less risky investment, which results in lower profit. This gives banks adequate buffer to absorb unforeseen shocks. The banking sector is expected to maintain its growth momentum supported by the rollout of full file credit information sharing, regional integration initiatives, advances in information and communications technology and the introduction of the devolved governance system in Kenya. As a result, if equity requirements are conventionally viewed as a function of the balance sheet's debt/equity ratio, then no equity or only a fraction (related to the recourse provided) of that required by conventional debt financing is required to fund assets through a securitization
Credit Risk Management
The mean value of Credit risk management (CRM) was 5.72 with minimum and maximum values of 4.4 and 8.0 respectively. The Credit risk management (CRM) had also standard deviations of 10% which shows little dispersion of Asset Quality (Non- performing loans to total loans) ratio from its mean for the commercial banks in Kenya. The Credit risk management (CRM) which is expressed by average asset quality of the commercial banking sector in the stated period was as high as 5.72, this was lower than that of Ongore and Kusa (2013) who found that average asset quality ratio ( Credit risk management) was 15.52. This shows that there is low exposure of banks to credit risk.
Liquidity Management
The mean value of Liquidity Management (LM) was 40.6005 with maximum and minimum values of 44.0 0 and 37.0 respectively. The Liquidity Management had also standard deviations of 2% which shows little dispersion of liquid assets to total assets ratio from its mean for the commercial banks in Kenya. The Table 4.10 also shows that
Kusa (2013) whose study found that the average total loans to total deposits were 77.50%. From the study we can conclude that the customer’s deposit is one of the cheapest sources of fund due to the high margin between deposit and lending rate that banks utilize to generate income.
Inferential Analysis
Inferential statistics analysis was conducted through the use of correlation analysis and regression analysis to determine the relationship between the independent and the dependent variables.
Diagnostic Test
Diagnostic testing has become an integral part of model specification in econometrics. There have been several important advances over the past 20 years. Various diagnostic tests were conducted to ensure that the coefficients of the estimates were consistent and could be relied upon in making economic inferences. As argued by Greene (2002) regression can only be accurately estimated if the basic assumptions of multiple linear regressions are observed.
Normality test
A normal distribution is not skewed and is defined to have a coefficient of kurtosis. Jarque-Bera formalizes this by testing the residuals for normality and testing whether the coefficient of skewedness and kurtosis are zero and three respectively (Brooks 2008). The study used Jarque-Berra’s statistic to determine whether the sample data have the skewedness and kurtosis matching a normal distribution. It is a test based on residuals of the least squares regression model. For normal distribution JB statistics is expected to be zero (Guajarati, 2007). In this study JB statistics values were: Corporate Governance (skewedness 0.196, kurtosis 0.623); Capital requirement (skewedness 0.196, kurtosis 0.623),Credit Risk Management (skewedness 0.196, kurtosis 0.623) and Liquidity Management(skewedness 0.196, kurtosis 0.623). This result was consistent with Ongore and Kusa (2013) in their study even though their JB statistics result was 0.09 with skewedness of 0.14 and kurtosis of 3.38. Thus, the JB is very close to zero and that the variables are very close to normal distribution. This implies that the research variables are normally distributed.
Table 4.13 Results of Normality Diagnostic Test Variable Descriptive Statistical Statistical Values Std. Error Comment
Corporate Governance Skewedness .196, .36124 Normally distributed
Kurtosis .623 Normally
distributed Capital requirement Skewedness .196 .04830 Normally distributed
Kurtosis .623 Normally distributed
Credit risk
Management
Skewedness .196 .07502 Normally distributed
Kurtosis .623 Normally distributed
Liquidity Management Skewedness .196 .13791 Normally distributed
Kurtosis .623 Normally distributed
Multi-collinearity Test
Multi-collinearity is a problem in multiple regressions that develops when one or more of the independent variables are highly correlated with one or more of the other independent variables. If an independent variable is an exact linear combination of the other independent variables, then we say the model suffers from perfect collinearity, and it cannot be estimated by OLS (Brooks 2008). Failure to account for perfect multicollinearity results into determining regression coefficients and infinite standard errors while existence of imperfect multi-collinearity results into large standard errors. Large standard errors affect the precision and accuracy of rejection or failure to reject the null hypothesis. During estimation, the problem is not lack of multi-collinearity but rather its severity. According to Gujarati (2004), the standard statistical method for testing data for multi-collinearity is analyzing the explanatory variables correlation coefficients (CC); condition index (CI) and variance inflation factor (VIF). Therefore in this study, to determine multi-collinearity variance inflation factors (VIF) and tolerance were used. For tolerance, values of less than 0.1 suggest multi-collinearity while for values of VIF that exceed 10 are often regarded as indicating multi-collinearity. The average data for 43 commercial banks in the last 5 year period (2009-2013) was used.
Table 4.14: Multicollinearity Test
Variables Collinearity Statistics
Tolerance VIF
Corporate Governance 0.940 1.064
Capital Requirement 0.974 1.027
Credit risk Management 0.992 1.008
Liquidity Management 0.951 1.051
The results was that VIF for Corporate Governance had VIF of 0.940 and tolerance of 1.064 ; Capital Requirement had VIF of 0.974 and tolerance of 1.027 ; Credit Risk Management had tolerance of 0.992 and tolerance of 1.008,While Liquidity Management had VIF of 0.951 and tolerance of 1.051 . The mean VIF for all variables is 1.037 and tolerance of 0.964. This shows that the variables had a VIF that is less than 10 and tolerance value of more than 0.1 ruling out the possibility of multi-colliearity (Field, 2009). Therefore, the results imply that there was no multi-collinearity problem among independent variables.
Autocorrelation test
This study used the Wooldridge test for serial correlation to test for the presence of autocorrelation in the linear panel data. Serial autocorrelation is a common problem experienced in panel data analysis and .has to be accounted for in order to achieve the correct model specification. According to Wooldridge (2002), failure to identify and account for serial correlation in the idiosyncratic error term in a panel model would result into biased standard errors and inefficient parameter estimates. The null hypothesis of this test was that the data had no serial autocorrelation. If serial autocorrelation was detected in the study data, then the feasible generalized least square (FGLS) estimation procedure would be adopted. The test for autocorrelation was made by using Durbin and Watson (1951). Durbin--Watson (DW) is a test for first order autocorrelation that is it tests only for a relationship between an error and its immediately previous value. This study used Durbin Watson (DW) test to check that the residuals of the models were not auto correlated since independence of the residuals is one of the basic hypotheses of regression analysis. The results in the table 4.11 and 4.12 show that there was no DW statistics that were close to the prescribed value of 2.0 for residual independence; this implied that the data had no autocorrelation.
Table 4.15 Autocorrelation test with ROE Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson ROE .091a .008 -.006 1.25746 1.603 Corporate governance .073a .005 .001 1.25341 1.621 Capital requirement .004a .000 -.005 1.25674 1.601
Credit risk management .013a .000 -.005 1.25664 1.602
Liquidity management .067a .004 .000 1.25393 1.583
Table 4.16 Autocorrelation test with ROA
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson ROA .132a .018 .004 .42646 2.165 Corporate Governance .086a .007 .003 .42666 2.208 Capital requirement .011a .000 -.005 .42821 2.162 Credit Risk Management .015 a .000 -.004 .42818 2.157 Liquidity Management .113a .013 .008 .42546 1.902
Inferential Analysis of Independent variables
The analysis of variance (ANOVA) on the effects of Central bank regulatory requirements on bank performance was done to test statistically if the means were significantly different between these groups.
Table 4.17: ANOVA – Corporate Governance and ROA
ANOVA
Return on Asset for Commercial banks Sum of
Squares
df Mean Square F Sig.
Between Groups 7.644 31 .247 1.436 .076
Within Groups 31.416 183 .172
ANOVA
Return on Equity for Commercial banks Sum of
Squares
df Mean Square F Sig.
Between Groups 75.256 31 2.428 1.701 .017
Within Groups 261.160 183 1.427
Total 336.416 214
Result from table 4.18 revealed that corporate governance with ROE has F statistic of 1.701 and the P-value is 0.017. This P-value is less than 0.05 implying that the mean difference of corporate governance is no statistically significant with bank performance (ROE) at a level of significance of 0.05. From Table 4.19 the corporate governance has the F statistic of 1.436 and the P-value is 0.076 with ROA. The P-value is greater than 0.05 results indicate that there is significant mean difference of corporate governance with ROA.
Table 4.19: ANOVA – Capital requirement and ROA
ANOVA
Return on Asset for Commercial banks Sum of
Squares
df Mean Square F Sig.
Between Groups 2.735 13 .210 1.164 .308
Within Groups 36.325 201 .181
Total 39.060 214
Table 4.20 : ANOVA – Capital requirement and ROE
ANOVA
Return on Equity for Commercial banks Sum of
Squares
df Mean Square F Sig.
Between Groups 50.572 13 3.890 2.735 .001
Within Groups 285.844 201 1.422
Total 336.416 214
According to table 4.19 capital requirement with ROA has F statistic of 1.164 and the P-value is 0.0308 which is greater than 0.05 implying that the mean difference of capital requirement with bank performance (ROA) is statistically significant at a level of significance of 0.05. According to table 4.20 result revealed that capital requirement
with ROE had the F statistic of 2.735 and the P-value is 0.01 which is less than 0.05 results indicate that there is significant mean difference of capital requirement with ROE.
Table 4.21: ANOVA – Credit risk transfer Management and ROA
ANOVA
Return on Equity for Commercial banks Sum of
Squares
df Mean Square F Sig.
Between Groups 23.381 19 1.231 .767 .745
Within Groups 313.035 195 1.605
Total 336.416 214
Table 4.22: ANOVA – Credit risk Management and ROE
ANOVA
Return on Asset for Commercial banks Sum of
Squares
df Mean Square F Sig.
Between Groups 5.191 19 .273 1.573 .066
Within Groups 33.869 195 .174
Total 39.060 214
According to table 4.21 credit risk management with ROA has the F statistic of 0.767 and the P-value is 0.745 which is greater than 0.05 results indicate that there is significant mean difference of credit risk management is statistically significant with bank performance (ROA) at a level of significance of 0.05. According to table 4.22 result revealed that credit risk management with ROE has have the F statistic of 1.573 and the P-value is 0.066 which is greater than 0.05 results indicate that there is no significant mean difference of credit risk management with ROE.
Table 4.23: ANOVA – Liquidity Management and ROE (Secondary Data)
ANOVA
Return on Asset for Commercial banks Sum of
Squares
df Mean Square F Sig.
Between Groups 3.103 22 .141 .753 .779
Within Groups 35.957 192 .187
Table 4.24: ANOVA – Liquidity Management and ROE (Secondary Data)
ANOVA
Return on Equity for Commercial banks Sum of
Squares
df Mean Square F Sig.
Between Groups 41.488 22 1.886 1.228 .228
Within Groups 294.928 192 1.536
Total 336.416 214
According to table 4.24 Liquidity management with ROA has have the F statistic of 0.753 and the P-value is 0.779 which is greater than 0.05 implying that the mean difference of liquidity management was statistically significant with bank performance (ROA) at a level of significance of 0.05. According to table 4.24 result revealed that Liquidity management with ROE has have the F statistic of 1.228 and the P-value is 0.228 which is greater than 0.05 results indicate that there is no significant mean difference of Liquidity management with ROE.
Dependent Variable -Financial Performance of Commercial Banks in Kenya. Descriptive Statistics
Table 4.25: Five-Year’ Performance of Commercial Banks in Kenya.
Descriptive Statistics
Variables N Minimum Maximum Mean Std. Deviation
Return on Asset (ROA) 215 2.60 4.70 4.0000 .42723 Return on Equity(ROE) 215 25.00 30.90 28.6600 0.25381
Table 4.25 presents the average financial performance of commercial banks as expressed by ROA and ROE for the year 2009 to 2013. The study found that the mean value of the average ROA was 4.0 with minimum and maximum values of 2.6 and 4.7 respectively. In term of standard deviations the ROA had 42.7% which shows high dispersion of ROA from its mean for the commercial banks in Kenya. This result was higher than the result of Ongore and Kusa (2013) study which was 1.95 for the year 2001 to 2010. These findings were consistent with the findings of Flamini et al. (2009). It is important to note that the study results revealed that ROA was twice the average ROA in Sub-Saharan Africa,(SSA) which was about 2%, Ongore and Kusa (2013). Thus, it can be concluded that the average ROA of Kenyan banks is above average of the SSA.
The results revealed that the mean value of ROE was 28.66 with minimum and maximum values of 25 and 30.9 respectively. In terms of standard deviations the ROE had 25% which shows high dispersion of ROE from its mean for the commercial banks in Kenya. The study result was almost twice that of Ongore and Kusa (2013) study that found14.8 for the year 2001 to 2010. From the results above it can be concluded that on average the financial performance of commercial banks in Kenya has continued to improve compared to the financial performance of banks in developing countries, the overall financial performance of commercial banks in the country is good (Flamini et al., 2009). Compared to other countries bank performances as expressed by the above ratios, the Kenyan banks' performance is average. This is consistent with the findings of Flamini et al., (2009). According to the above author the average ROA in Sub-Saharan Africa, (SSA) was about 2%. Thus, the average ROA of Kenyan banks is double average of the SSA. This could have resulted in improved bank financial performance which was observed by the average ROA and ROE for the sector as a whole as 4.0 and 28.66 respectively in the year 2009 to 2013 from the one reported by Ongore and Kusa (2013) study results that had revealed that ROA, and ROE was 1.95 and 14.8 respectively for the year 2001 to 2010. This was supported by Sarkisyan (2011) who argued securitization reduces cost of funds; achieves reliable and constant funding source, credit exposure, enhance liquidity, diversifies and brings about favorable regulatory/accounting treatment which lead to increased profit.
Statistical Tests of Significance for Dependent variable
Correlation Analysis between Variables and performance of commercial banks. It gives the Pearson’s coefficient value (correlation test) and the significance value (measuring significance of the association). In this study, the Pearson r statistic is used to calculate bivariate correlations Values between 0 and 0.3 (0 and -0.3) indicate no correlation (variables not associated), 0.3 and 0.5 (-0.3 and -0.5) a weak positive (negative) linear association, Values between 0.5 and 0.7 (-0.5 and -0.7) indicate a moderate positive (negative) linear association and Values between 0.7 and 1.0 (-0.7 and-1.0) indicate a strong positive (negative) linear association. The significance of the relationship is tested at 95% level with a 2-tailed test where a statistically significant
out of 100, so the result indicates the presence of an association. Correlation analysis results for the association between effects of CBK regulatory requirement and the banks’ performance of commercial banks is presented in table 4.12 below
Table 4.26:Banks’ Performance and Effects of CBK regulatory requirement
ROE ROA CR LM CRM CG Spearman 's rho ROE Correlation Coefficient 1.000 .011 .008 .070 -.003 -.054 Sig. (2-tailed) . .871 .905 .309 .960 .435
ROA Correlation Coefficient 1.000 .023 .048 .035 -.091
Sig. (2-tailed) . .736 .487 .605 .184 CR Correlation Coefficient 1.000 .068 .032 -.192** Sig. (2-tailed) .322 .639 .005 LM Correlation Coefficient 1.000 -.100 -.203** Sig. (2-tailed) . .144 .003 CRM Correlation Coefficient 1.000 -.054 Sig. (2-tailed) .435 CG Correlation Coefficient 1.000 Sig. (2-tailed) . N 215 215 215 215 215 215
**. Correlation is significant at the 0.01 level (2-tailed).
Return on Equity (ROE), Return on asset (ROA), Capital Requirement(CR) , Liquidity Management(LM) , Credit risk management and Corporate governance (CG