Members X2= Board meeting frequency X3= Audit committee

Members

X2= Board meeting frequency

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X3= Audit committee meeting frequency

X4= CEOs’ compensation

Moreover ? is the constant or intercept. ? represents
coefficient or slope.  ?1, ?2,
?3 and ?4 are slope or regression coefficients and e is error
term for the model.

4.0 Results and Discussions

Analysis
and interpretation of collected data are discussed in this section of this
research paper. This section discusses the inferential
statistics of the data.

4.1 Results
of selected state owned commercial banks

Following results are getting from ordinary least
square (0LS) multiple regression analysis based on investigated state owned
commercial banks.

Table 2: Model summary and ANOVA analysis for
investigated State owned commercial banks

Details

ROA

ROE

Sample size

12

12

Multiple correlation coefficient (R)

0.822

0.689

Co-efficient of determination (R2)

0.864

0.593

Adjusted R2

0.580

0.019

Standard error of the estimate

0.86960

7.22236

F value

4.827

2.535

Source:
Appendix table 3 to 8

4.1.1
Influential CG factor analysis

Model 1: ROA= -7.79+ 0.30(x1) + 0.187(x2)
+ (-1.19) (x3) + 0.112(x4) + e

From above model -7.39 is the constant value that
indicates if the independent variables has zero impact on ROA then the companies
will face loss about -7.39 million BDT. Most influencing variable for ROA is
board meeting frequency then CEO’s compensation and most uninfluential variable
is audit meeting frequency.

Model 2: ROE= -33.186+ 1.826 (x1) + 1.646(x2)
+ (- 7.82) (x3) + 0.293(x4) + e

For model-2 the constant value is -33.186 that
indicates if the independent variables has zero impact on ROE then the companies
will face loss about -33.186 million BDT . Most influencing variable are bored
size and board meeting frequency and most uninfluential variable is audit committee
meeting frequency on ROE.

4.1.2 Co-efficient
of correlation (R):

Co-efficient of correlation measures the strength of
the linear relationship between dependent variable and independent variables.
Multiple co-efficient of correlation for ROA is 82.2% which means dependent
variable and independent variables are very strongly positively correlated. On
the other hand, co-efficient of correlation for ROE is 68.9% which just exceeds
the moderate level but positively correlated.

4.1.3 Co-efficient
of determination (R2):

Co-efficient of determination measures the percentage
or proportion of total variation in dependent variable explained by the
independent variables. R2 is 86.4% which is very high for ROA. It
means independent variables can explain perfectly 86.4 % variation of ROA. The co-efficient
of determination is 59.3% for ROE.

4.1.4 Adjusted
R2

Adjusted r2 measure whether the model is
fit or not that is adjusted for the number of explanatory variables in the
model. If more useful explanatory variable will add in the model, the more
adjusted r2 will increase. For ROA adjusted r2 is 58%. On
the other hand, for ROE it is 1.9%. There may be other variables which affect
more on the banks financial performance.

4.1.5 Standard
error of estimate:

A
standard error is the standard
deviation of the sampling
distribution of a statistic. In this analysis standard errors are 0.86960 and 7.22236
respectively for ROA and ROE.

4.1.6 F-test:

Here level of significance is 5%. Degrees of freedom
for the Numerator and denominator are respectively 4 and 7. 

Condition
rule:

If f-calculated> f-tab then the overall model is
significant

If f-calculated< f-tab then the overall mode is insignificant Therefore f table value found is 4.12 from f distribution table. For ROA f-calculated value is greater than the f-tab value. So the null hypothesis is rejected and alternate hypothesis is accepted that means there has impact of corporate governance on ROA and the model is significant. On the other hand for ROE, f-tab value is greater than the f-calculated value so we cannot reject the null hypothesis. Also it is clear that the overall model is insignificant and there has no impact of corporate governance on ROE. 4.2 Results of selected private commercial banks Following results are getting from the ordinary least square (OLS) multiple regression analysis based on selected private commercial banks. Table 3: Model summary and ANOVA analysis for selected private commercial banks. Details ROA   Sample size 12 12 Multiple correlation coefficient (R) 0.792 0.692 Co-efficient of determination (R2) 0.609 0.568 Adjusted R2 0.302 0.223 Standard error of the estimate .38539 3.6342 F value 1.376 1.494 Source: Appendix table 9 to 14 4.2.1 Influential CG factor analysis Model 1: ROA: 1.475+ 0.063(x1) + 0.082(x2) + -7.56(x3) + -0.043(x4) +e From above model-1 the constant value is 1.475 that indicates if the independent variables has zero impact on ROA then the companies will generate about 1.475 million BDT. Most influencing variable for ROA is board meeting frequency then board size and most uninfluential variable is Audit committee meeting frequency. Model 2: ROE: 10.4534+ 0.682(x1) + 0.069(x2) + -6.30 (x3) + -0.147(x4) +e From above model-2 the constant value is 10.4534 that indicates if the independent variables has zero impact on ROE then the companies will generate about BDT 10.4534 million. Also in this case most influencing variable for ROE is board size then board meeting frequency and most uninfluential variable is Audit committee meeting frequency. 4.2.2 Co-efficient of correlation: For selected private banks multiple correlation of coefficient is 79.2% and 69.9% respectively for ROA and ROE which are defined as perfectly positively correlated between independent variables and dependent variable. 4.2.3 Co-efficient of determination (R2): Co-efficient of determination is approximately 60% for ROA which means independent variables can express almost 60% variation in ROA. On the other hand for ROE is 56.8%. 4.2.4 Adjusted R2: Adjusted R2 is 30.2% and 22.3% respectively for ROA and ROE. There must be other variables which have more influence on selected private banks' financial performance than those are taken. 4.1.5 Standard error of estimate: A standard error is the standard deviation of the sampling distribution of a statistic. In the analysis standard errors are 0.38539 and 3.6342respectively for ROA and ROE. 4.2.5 F test: Here level of significance is 5%. Degrees of freedom for the Numerator and denominator are respectively 4 and 7.  As the calculated f val