CAPITAL STRUCTURE AND BANK PERFORMANCE OF ISLAMIC AND COMMERCIAL IN YEMENSamer Ahmed Ali Assirri ^{1},^{ }Dr. C.K. Hebbar ^{2}^{} ^{1 }Research Scholar, Department of Studies and Research in Commerce, Mangalore University, India.^{2 }Associate Professor
and Research Guide, Department of Commerce, Mangalore University, India. 




Received 17 November2021 Accepted 5 December2021 Published 31 December2021 Corresponding Author Samer
Ahmed Ali Assirri, malshawesh37m@gmail.com DOI 10.29121/granthaalayah.v9.i12.2021.4411 Funding:
This
research received no specific grant from any funding agency in the public,
commercial, or notforprofit sectors. Copyright:
© 2021
The Author(s). This is an open access article distributed under the terms of
the Creative Commons Attribution License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original author and source are
credited. 
ABSTRACT 


This study aims to examine the impact of capital
structure on bank performance. This research verified the existence of
several relationships between capital structure as measured by LAR, EAR, and
Total Debt ratio on bank’s performance as measured by ROA and ROE, EPS, and
NPM. Using the panel data of bank from 2010 to 2019, In Islamic banks, the
results of the present study revealed that the contributions of the capital
structure to ROA were significant. This result was in line with the findings
of the past studies. For instance, ElChaarani and ElAbiad (2019) found that positive and significant impacts of
shortterm debt and total debt on the return on equity of the banking sector
in Middle East region, a negative and significant impacts of shortterm debt
and total debt on the return on assets, and a positive impact of longterm
debt on the return on assets ratio. In commercial banks sector the regression
analysis revealed that the contributions of the three independent variables
to the EPS were nonsignificant. Also, the contributions of the total debt
and LAR to the independent variables ROE were significant. In contrast, the
contribution of the EAR to the independent variable ROE was nonsignificant.
Moreover, the contribution of the LAR to NPM was significant. Also, the
contributions of the EAR and the total debt to NPM were nonsignificant.
Furthermore, the contributions of the LAR and EAR to ROA were significant. In
contrast, the contribution of the total debt to ROA was nonsignificant. In
general, the contributions of the LAR and EAR to ROA were significant. 


Keywords: Capital
Structure, Performance, Total Debt, LAR, EAR, ROE, ROA, And EPS. 1. INTRODUCTION Banks are essential in
economic life because they are the foundation of the modern financial system,
and international trade cannot be imagined without them. Banking systems are
regarded as one of the most important inventions of modern society due to the
primary role in bridging the gap between depositors, who form the money
supply side of the banking system, and borrowers, who include the demand side
for these funds. The banking system is a critical mechanism for collecting
savings, converting them into investments, attracting foreign investments,
and directing them to the most efficient, productive, and profitable
projects. The banking system's
role in economic development is emphasized by providing credit to finance
capital formation, and this will only occur as banks' ability to create
credit that exceeds their cash reserves and paidup capital, as well as
various deposits resulting from current cash savings, or savings from
previous earnings, improve. 


Financial performance is one of the most basic and necessary guarantees for the growth and continuity of businesses in all sectors, whether productive or serviceoriented, such as traditional and Islamic banking institutions. The liabilities side of the balance sheet is represented by the financing structure in the financial statements, which includes equity (internal financing), longterm liabilities (external funding), and current liabilities. Companies seek to reduce costs and increase profits by managing the financing structure, as corporate finance necessitates a balance of internal and external financing.
The measurement of the bank's financial performance serves as the foundation for investment and financing decisions. Investors are interested in evaluating the bank's performance to determine the management's success in applying their capital, which represents equity. On the other hand, Debtors evaluate the performance of the interest rate report, which serves as their fixed return. Longterm financing options include equity and share issues, longterm loans, and debentures. As a result, the bank must comprehend the capital structure. The capital structure refers to the method, and sources of financing, Total debt, Total Liabilities to Total Assets Ratio (LAR), and Equity to Total Assets Ratio (ETAR) will be used to calculate capital structure (EAR).
2. OBJECTIVE OF STUDY
1) To identify the relationship between capital structure and bank’s performance of of Islamic and commercial Bank in Yemen.
2) To identify the moderation effect of bank’s size, growth opportunities and bank’s age on the relation between capital structure and performance of Yemen Bank for Reconstruction and Development.
3. RESEARCH HYPOTHESES
(Ho1): Capital structure does not have statistically significant effects of Islamic bank performance in Yemen.
(Ho1): Capital structure does not have statistically significant effects of commercial bank performance in Yemen.
(H02): Banks’ size, Growth opportunities and Banks’ age does not have a statistically significant moderation role in the relation between capital structure and bank’s performance of Islamic and commercial Bank in Yemen.
4. RESEARCH METHODOLOGY
The current study makes use of secondary data spanning the years 20102019. An examination of the correlation coefficient, the regression coefficient, the variance, and the standard deviation for the research variables Pearson The correlation coefficient will be calculated to determine the magnitudes and directions of the correlations between the independent and dependent variables. The regression analysis will be used to assess the impact of capital structure on bank performance. The Islamic and commercial banks in Yemen will be the dependent variable in the regression equation.
Y_1=Constant+β_1 X_1(it) +β_2 X_2(it) +β_3 X_3(it) +β_4 X_4(it) +Error
5. LITERATURE REVIEW
Ali (2018) determined the factor affecting the profitability in a sample of seventeen conventional and five Islamic banks in Pakistan during the period 2008 – 2012. Return on Equity and Return on Asset were selected to represent the profitability (dependent variables) of Islamic and conventional banks in Pakistan. Whereas Credit Risk, Efficiency, Capitalization, Liquidity, Bank Size, Economic Performance, Inflation and a dummy variable were selected as independent variables. Panel Data analysis revealed: (1) Liquidity does not significantly related to profitability for both types of banks; (2) all the independent variables do not significantly relate to dependent variables for all the type of banks, (3) the impact of credit risk, capitalization, and the efficiency on profitability are more significant for conventional banks than Islamic banks
Meero (2015) identified the similarity of capital structure between Conventional and Islamic banks; and to investigate the relationship between capital structure and performance of Islamic and Conventional Banks in Gulf Countries (GC). The sample consisted of sixteen GC Banks (8 Islamic Banks and 8 Conventional Banks) over the period 20052014. Return on equity and return of asset have been used as performance indicators (dependent variables). Equity to total assets, total debt to total assets, debt to equity ratios and size of the bank have been used as capital structure indicators (independent variables). Data analysis (ttest, descriptive and regression analysis) revealed: (1) a similarity of capital structure of Islamic banks and Conventional banks in Gulf Countries; (2) return of asset significantly and negatively related to financial leverage and a positively related to equity to assets ratio in Islamic and Conventional banks; and (3) bank size has a positive relationship with return on equity and return of asset as performance measures in Islamic and Conventional banks
Kalash (2019) conducted his study to deal with a comparison of the capital structure of Islamic and traditional banks in Turkey in addition to investigate the effect of the debt ratio on the performance of these banks, as a comparative study was conducted between 5 Islamic banks and 11 traditional banks for a period of time extending from 2008 to 2017. The results revealed that the debt ratio has a positive and significant effect on the performance of Islamic banks, as the rate of return on total assets increases, and the rate of return on property rights as a result of the increase in the debt ratio. On the other hand, it was found that the debt ratio negatively affects the performance of traditional banks, as the rate of return on total assets decreases as a result of the high debt ratio of conventional banks. The results of the research are generally consistent with the theory of balance between the benefits and costs of debt, where the costs of debt (agency costs associated with debt and bankruptcy costs) are relatively low for Islamic banks within the framework of the balance theory, and therefore the high debt ratio of these banks will contribute to enhancing their performance as a result of the decline relative to costs.
Sheikh and Qureshi (2017) explored the determinants of capital structure of Islamic and conventional commercial banks from Pakistan. This study aimed to answer the following question: How conventional commercial banks and Islamic commercial banks choose their own capital structures. As well as what are the most important factors affecting the choice of capital structure? Financial reports and secondary data were used for banks listed on the Karachi Stock Exchange. Panel data were used to measure the regression and the relationship between the variables for the banks under the head from 2004 to 2014. The results of the study showed that traditional commercial banks are more used than Islamic commercial banks, as they are more profitable and have a fairly secure income. Islamic commercial banks, however, have relatively stable operating assets and growth in total assets compared to conventional commercial banks. The results also indicated that the profitability, growth and tangible capacity variables have a negative impact on the capital structure while the size of the bank and the fluctuations in profits has positive effects on capital structure. The study also pointed out that conventional banks and Islamic banks have their own ways of choosing capital.
6. EMPIRICAL RESULT
The Pearson product moment correlation coefficients were used to represent the relationship between the main independent variables (I.e., Total Debt, LAR, and EAR) and the dependent variables (ROA, ROE, NPM, EPS). Table 1 shows the correlation matrix that illustrates the interrelationships among the research variables.
Table 1 Summary Results of Pearson Product Moment Correlation Coefficients 

ROE 
ROA 
NPM 
EPS 
Total
debt 
LAR 
Kurtosis 
Skewness 

ROE 
1 
1.197 
0.605 

ROA 
.447^{**} 
1 
0.564 
0.566 

NPM 
.929^{**} 
.653^{**} 
1 
0.249 
0.735 

EPS 
0.062 
.539^{**} 
0.191 
1 
1.054 
0.03 

Total
debt 
.292^{*} 
.437^{**} 
.476^{**} 
.456^{**} 
1 
1.388 
0.52 

LAR 
.337^{*} 
.940^{**} 
.560^{**} 
.511^{**} 
.325^{*} 
1 
0.148 
0.115 
EAR 
.438^{**} 
.983^{**} 
.659^{**} 
.540^{**} 
.601^{**} 
.791^{**} 
0.231 
0.983 
It was observed that the total debt negatively and significantly related to ROE (r.292; p<.05), NPM (r=.476; p<.01), and ROA (r=.437; p<.01). In contrast, the total debt positively and significantly related to EPS (R=.456; p<.01). LAR positively and significantly related to ROA (r=.940; p<.01), NPM (r=.560; p<.01), and ROE (r=.337, p<.05). In contrast, LAR negatively and significantly related to EPS (r=.511; p<.01). EAR positively and significantly related to ROE (r=.438; p<.01), ROA (r=.983; p<.01), and NPM (r=.659; p<.01). In contrast, EAR negatively and significantly related to EPS (r=.540; p<.01).
Multiple linear regression analysis was conducted to investigate the relative contribution of the predictors (independent variables) to the dependent variable, EPS. The model summary revealed that the model of the three independent variables explained 48% of the variance in EPS (R2 =.48). Results of ANOVA revealed that the model of total debt, LAR, and EAR was significantly predictor of EPS (Table 2).
Table 2 Summary Results of ANOVA 

Model 
Sum
of Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
142.071 
3 
47.357 
9.238 
.000 
Residual 
235.803 
46 
5.126 

Total 
377.874 
49 
As we can see in Table 4, the contributions of the independent variables to the EPS were nonsignificant.
Table 3 Multiple Regression Analysis Summary for Total debt, LAR, and EAR Predicting EPS 

Model 
Unstandardized
Coefficients 

Standardized
Coefficients 
t 
Sig. 
Collinearity
Statistics 



B 
Std.
Error 
Beta 


Tolerance 
VIF 
1 
(Constant) 
0.314 
0.76 

0.413 




Total
debt 
0.108 
0.056 
0.294 
1.921 
0.859 
0.577 
1.732 

LAR 
2.269 
1.146 
0.397 
1.981 
0.515 
0.338 
2.96 

EAR 
0.126 
0.552 
0.054 
0.227 
0.475 
0.241 
2.147 
Further, Multiple linear regression analysis was conducted to investigate the relative contribution of the predictors (independent variables) to the dependent variable, ROE. The model summary revealed that the model of the three independent variables explained 69% of the variance in ROE (R2 =.69). Results of ANOVA revealed that the model of total debt, LAR, and EAR was significantly predictor of ROE (Table 4).
Table 4 Summary Results of ANOVA 

Model 
Sum
of Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
50.874 
3 
16.958 
5.692 
0.002 
Residual 
137.036 
46 
2.979 

Total 
187.91 
49 
As we can see in Table 5, the contribution of EAR to the independent variables ROE was positive and significant (Beta=.691; p<.01). In contrast, the contribution of the total debt and LAR were nonsignificant.
Table 5 Multiple Regression Analysis Summary for Total debt, LAR, and EAR Predicting ROE 

Model 
Unstandardized
Coefficients 
Standardized
Coefficients 
t 
Sig. 
Collinearity
Statistics 

B 
Std.
Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
1.966 
0.579 
3.395 
0.001 

Total
debt 
0.017 
0.043 
0.065 
0.393 
0.696 
0.577 
1.732 

LAR 
0.725 
0.873 
0.18 
0.831 
0.411 
0.338 
2.96 

EAR 
1.134 
0.421 
0.691 
2.695 
0.01 
0.241 
2.147 

Multiple linear regression analysis was conducted to investigate the relative contribution of the predictors (independent variables) to the dependent variable, NPM. The model summary revealed that the model of the three independent variables explained 79% of the variance in NPM (R2 =.79). Results of ANOVA revealed that the model of total debt, LAR, and EAR was significantly predictor of NPM (Table 6).
Table 6 Summary Results of ANOVA 

Model 
Sum
of Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
244.116 
3 
81.372 
22.792 
0 
Residual 
164.23 
46 
3.57 

Total 
408.346 
49 
As we can see in Table 7, the contribution of EAR to NPM was positive and significant (Beta=.982; p<.01). In contrast, the contribution of the total debt and LAR were nonsignificant.
Table 7 Multiple Regression Analysis Summary for Total debt, LAR, and EAR Predicting NPM 

Model 
Unstandardized
Coefficients 

Standardized
Coefficients 
t 
Sig. 
Collinearity
Statistics 

B 
Std.
Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
1.941 
0.634 
3.061 
0.004 

Total
debt 
0.011 
0.047 
0.03 
0.243 
0.809 
0.577 
1.732 

LAR 
1.553 
0.956 
0.261 
1.624 
0.111 
0.338 
2.96 

EAR 
2.377 
0.461 
0.982 
5.16 
0 
0.241 
4.147 

Multiple linear regression analysis was conducted to investigate the relative contribution of the predictors (independent variables) to the dependent variable, ROA. The model summary revealed that the model of the three independent variables explained 89% of the variance in ROA (R2 =.89). Results of ANOVA revealed that the model of total debt, LAR, and EAR was significantly predictor of ROA (Table 8).
Table 8 Summary Results of ANOVA 


Model 
Sum
of Squares 
df 
Mean
Square 
F 
Sig. 
1 
Regression 
0.572 
3 
0.191 
356.775 
0 
Residual 
0.025 
46 
0.001 

Total 
0.597 
49 
As we can see in Table 9, the contribution of the total debt to ROA was positive and significant (Beta=.089; p<.05), the contribution of LAR to ROA was positive and significant (Beta=.439; p<.01), the contribution of the EAR to ROA was positive and significant (Beta=.639; p<.01). In general, the contributions of the capital structure to ROA were positive and significant.
Table 9 Multiple Regression Analysis Summary for Total debt, LAR, and EAR Predicting ROA 

Model 
Unstandardized
Coefficients 

Standardized
Coefficients 
t 
Sig. 
Collinearity
Statistics 

B 
Std.
Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
0.144 
0.008 
18.554 
0 

Total
debt 
0.001 
0.001 
0.089 
2.267 
0.028 
0.577 
1.732 

LAR 
0.1 
0.012 
0.439 
8.519 
0 
0.338 
2.96 

EAR 
0.059 
0.006 
0.639 
10.488 
0 
0.241 
4.147 
Third: The contributions of the independent variables on capital structure for the commercial banks sector
The Pearson product moment correlation coefficients were used to represent the relationship between the main independent variables (I.e., Total Debt, LAR, and EAR) and the dependent variables (ROA, ROE, NPM, EPS). Table 10 shows the correlation matrix that illustrates the interrelationships among the research variables.
Table 10 Summary Results of Pearson Product Moment Correlation Coefficients 

ROE 
ROA 
NPM 
EPS 
Total
debt 
LAR 
Kurtosis 
Skewness 

ROE 
1 
1.187 
0.605 

ROA 
.449^{**} 
1 
0.664 
0.966 

NPM 
.829^{**} 
.562^{**} 
1 
0.249 
0.735 

EPS 
0.061 
.203^{*} 
0.168 
1 
1.197 
0.605 

Total
debt 
0.109 
.367^{**} 
.207^{*} 
.191^{*} 
1 
0.566 
0.589 

LAR 
0.147 
.374^{**} 
.427^{**} 
.213^{*} 
.505^{**} 
1 
0.249 
0.735 
EAR 
0.011 
.487^{**} 
.189^{*} 
.216^{*} 
.608^{**} 
.320^{**} 
1.054 
0.03 
It was observed that the total debt negatively and significantly related to ROA (r=.367; p<.01), NPM (r=.207; p<.05). In contrast, the total debt positively and significantly related EPS (r=.191; p<.05). In contrast, the total debt does significantly relate to ROE.
LAR negatively and significantly related to ROA (r=.374; p<.01), NPM (r=.427; p<.01). Furthermore, LAR positively and significantly related to EPS (r=.213; p<.05). In contrast, LAR does significantly relate to ROE.
Further, EAR positively and significantly related to ROA (r=.487; p<.01), NPM (r=.189; p<.05). EAR negatively and significantly related to EPS (r=.216; p<.05). In contrast, EAR does significantly relate to ROE.
Multiple linear regression analysis was conducted to investigate the relative contribution of the predictors (independent variables) to the dependent variable, EPS. The model summary revealed that the model of the three independent variables explained 26% of the variance in EPS (R2 =.26). Results of ANOVA revealed that the model of total debt, LAR, and EAR was significantly predictor of EPS (Table 11).
Table 11 Summary Results of ANOVA 

Model 
Sum
of Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
77.412 
3 
25.804 
2.908 
0.038 
Residual 
1029.366 
116 
8.874 

Total 
1106.778 
119 
As we can see in Table 12, the contributions of the three independent variables to the EPS were nonsignificant.
Table 12 Multiple Regression Analysis Summary for Total debt, LAR, and EAR Predicting EPS 

Model 
Unstandardized
Coefficients 
Standardized
Coefficients 
t 
Sig. 

B 
Std.
Error 
Beta 

1 
(Constant) 
1.641 
0.83 
1.978 
0.05 

Total
debt 
0.006 
0.037 
0.02 
0.16 
0.873 

LAR 
1.191 
0.805 
0.153 
1.479 
0.142 

EAR 
0.627 
0.456 
0.155 
1.374 
0.172 
Multiple linear regression analysis was conducted to investigate the relative contribution of the predictors (independent variables) to the dependent variable, ROE. The model summary revealed that the model of the three independent variables explained 27% of the variance in ROE (R2 =.27). Results of ANOVA revealed that the model of total debt, LAR, and EAR was significantly predictor of ROE (Table 13).
Table 13 Summary Results of ANOVA 

Model 
Sum
of Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
7.726 
3 
2.575 
2.938 
0.036 
Residual 
101.696 
116 
0.877 

Total 
109.423 
119 
As we can see in Table 14, the contribution of total debt to the independent variables ROE was positive and significant (Beta=.295; p<.05) and the contribution of the LAR to the independent variables ROE was positive and significant (Beta=.209; p<.05). In contrast, the contribution of the EAR to the independent variables ROE was nonsignificant.
Table 14 Multiple Regression Analysis Summary for Total debt, LAR, and EAR Predicting ROE 

Model 
Unstandardized
Coefficients 

Standardized
Coefficients 
t 
Sig. 

B 
Std.
Error 
Beta 

1 
(Constant) 
2.58 
0.261 
9.891 
0 

Total
debt 
0.028 
0.012 
0.295 
2.382 
0.019 

LAR 
0.656 
0.253 
0.269 
2.593 
0.011 

EAR 
0.105 
0.143 
0.083 
0.734 
0.464 
Multiple linear regression analysis was conducted to investigate the relative contribution of the predictors (independent variables) to the dependent variable, NPM. The model summary revealed that the model of the three independent variables explained 43% of the variance in NPM (R2 =.43). Results of ANOVA revealed that the model of total debt, LAR, and EAR was significantly predictor of NPM (Table 15).
Table 15 Summary Results of ANOVA 

Model 
Sum
of Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
24.689 
3 
8.23 
8.941 
0 
Residual 
106.765 
116 
0.92 

Total 
131.454 
119 
As we can see in Table 16, the contribution of the LAR to NPM was positive and significant (Beta=.432; p<.01). In contrast the contributions of the EAR and the total debt to NPM were nonsignificant.
Table 16 Multiple Regression Analysis Summary for Total debt, LAR, and EAR Predicting NPM 

Model 
Unstandardized
Coefficients 

Standardized
Coefficients 
t 
Sig. 



B 
Std.
Error 
Beta 


1 
(Constant) 
1.464 
0.267 

5.479 
0 

Total
debt 
0.007 
0.012 
0.067 
0.581 
0.562 

LAR 
1.155 
0.259 
0.432 
4.452 
0 

EAR 
0.128 
0.147 
0.092 
0.87 
0.386 
Multiple linear regression analysis was conducted to investigate the relative contribution of the predictors (independent variables) to the dependent variable, ROA. The model summary revealed that the model of the three independent variables explained 54% of the variance in ROA (R2 =.54). Results of ANOVA revealed that the model of total debt, LAR, and EAR was significantly predictor of ROA (Table 17).
Table 17 Summary Results of ANOVA 

Model 
Sum
of Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
0.024 
3 
0.008 
15.796 
0 
Residual 
0.059 
116 
0.001 

Total 
0.083 
119 
As we can see in Table 18, the contribution of the LAR to ROA was positive and significant (Beta=.246; p<.05), the contribution of EAR to ROA was positive and significant (Beta=.413; p<.01), the contribution of the total debt to ROA was nonsignificant). In general, the contributions of the LAR and EAR to ROA were positive and significant.
Table 18 Multiple Regression Analysis Summary for Total debt, LAR, and EAR Predicting ROA 

Model 
Unstandardized
Coefficients 
Standardized
Coefficients 
t 
Sig. 

B 
Std.
Error 
Beta 

1 
(Constant) 
0.048 
0.006 
7.729 
0 

Total
debt 
0.001 
0 
0.008 
0.076 
0.94 

LAR 
0.017 
0.006 
246 
2.711 
0.008 

EAR 
0.014 
0.003 
0.413 
4.194 
0 
7. CONCLUSION
7.1 For the Islamic banks sector
It was observed that the total debt negatively and significantly related to ROE, NPM, and ROA. This means that ROE, NPM, and ROA increase as the total debt decreases. In contrast, total debt positively and significantly related to EPS. As such, EPS increases as the total debt increases. LAR positively and significantly related to ROA, NPM, and ROE. This means that ROE, NPM, and ROA increase as the LAR increase. In contrast, LAR negatively and significantly related to EPS. This means that EPS increases as LAR decreases. EAR positively and significantly related to ROE, ROA, and NPN. This means that ROE, NPM, and ROA increase as the EAR increase in contrast, EAR negatively and significantly related to EPS. This means that EPS increases as EAR decreases. Regression analysis revealed that EAR was a significant predictor of ROE and NPM. Furthermore, the contributions of the capital structure to ROA were significant. In this study, the total debt negatively and significantly related to ROE, NPM, and ROA. Similarly, LAR and EAR are negatively and significantly related to EPS. Further, the results of the present study revealed that the contributions of the capital structure to ROA were significant.
7.2 For the commercial banks sector
It was observed that the total debt negatively and significantly related to ROA and NPM. This means that ROA and NPM increase as the total debt decreases. Further, the total debt positively and significantly related to EPS. This means EPS increases as the total debt increases. In contrast, the total debt does not significantly relate to ROE. LAR negatively and significantly related to ROA, NPM. This means that ROA and NPM increase as LAR decrease. Furthermore, LAR positively and significantly related to EPS. This means that EPS increases as LAR increases. In contrast, LAR does not significantly relate to ROE. Further, EAR positively and significantly related to ROA and NPM. This means that ROA increases as EAR increases. Further, EAR negatively and significantly related to EPS. This means that EPS increases as EAR decreases in contrast, EAR does not significantly relate to ROE. Further, the regression analysis revealed that the contributions of the three independent variables to the EPS were nonsignificant. Also, the contributions of the total debt and LAR to the independent variables ROE were significant. In contrast, the contribution of the EAR to the independent variable ROE was nonsignificant. Moreover, the contribution of the LAR to NPM was significant. Also, the contributions of the EAR and the total debt to NPM were nonsignificant. Furthermore, the contributions of the LAR and EAR to ROA were significant. In contrast, the contribution of the total debt to ROA was nonsignificant. In general, the contributions of the LAR and EAR to ROA were significant.
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