Research Article: 2022 Vol: 26 Issue: 6
Bouhrine Fatiha, University of Constantine
Citation Information: Fatiha, B. (2022). Effect of governance on the financial performance of banks in Qatar. Academy of Accounting and Financial Studies Journal, 26(5), 1-10.
This study attempts to measure the impact of Governance mechanisms on the financial performance of QATAR banks in period for 2011-2020. This is realized using a Panel regression model to determine the effect of the change on the independent variable, i.e. firm’s corporate governance, which has been measured by the board size, Independence of the Board of Directors & The number of committees that make up the council , with controlled variable which has been measured by age of bank and total assets at the end of year on the dependent variable that is the financial performance, which is measured by the return on assets & return on equity. The study finds a significant strong, negative significance impact of governance variables on the performance of Qatar banks under study during the period specified.
Governance Mechanisms, Financial Performance, Banks, Qatar.
G21, G32, G34.
The concept of governance is new in terms of use and old in terms of theory. By the way the emergence of the term corporate governance theoretically dates back to researchers Mains* and Wallace† in 1932, when they dealt with the theory of the separation of management from ownership. Then, in 1937, Ronald Coast‡ discussed the method of reconciling the owners and managers of the company. In 1976, the agency theory appeared by economics researchers Jensen§ and Meckling**.
We have many definitions of governance: e.g.: the commotion of Cadbury defined it as the system by which companies are directed and controlled according to (Cadbury & Cadbury, 2002). This definition is specific because it speaks about processing of governance in companies. Bath shleifer and vishy defined corporate governance as the way in which suppliers of finance assure themselves of getting a return on their investment (Thonsen & Conjon, 2012) this definition complex (Hatchea, 2013).
In general we can define governance, as a complex process meat to ensure accountability, transparency, rule of law….ex. Corporate Governance is a very general concept because it’s used in all sciences and we have many types of it: e.g.: banking governance, local governance, global governance (Al-Manaseer et al., 2012).
The corporate governance is very importance for banking system because it is the basis of the economy of any country, by the way (Malik, 2005). The Qatari banking sector is the third largest banking sector in the Middle East, and so the interest about the corporate governance in Qatar started early. Evidence for this is the set of laws and regulations issued by the Central Bank of Qatar and the Securities Authority in the field of governance, among which we mention:
1. The Central Bank of Qatar issued a Governance Guidebook for Financial Institutions in 2008
2. Governance Code for Companies & Legal Entities Listed on the Main Market: The QFMA’s Board Decision No. (5) Of 2016.
Therefore, the aim of this study is empirically analyze the impact of governance on the bank's performance, (Amba, 2014) Qatari banks case study’s carry out this research, we used a data set with for commercial banks or Qatar and the analysis period covers from 2011 to 2020. More specifically, we propose to analyze the following research questions (RQs):
1. RQs1: What is the effect of applying corporate governance on the return on assets (ROA ( for the Qatari banks under study?
2. RQs2: What is the effect of applying corporate governance on the return on equity (ROE( for the Qatari banks under study?
There is a great theoretical debate among researchers regarding the impact of governance on the performance of banks. There are some studies that have proven the existence of a relationship between the mechanisms of corporate governance and the financial performance of banks represented in the return on assets and the return on equity. Thus, many researchers have examined this theme. For example Jamal Hayek (2016) examined the relationship between corporate governance and financial performance of managerial entities in the Middle East region, particularly in Lebanon.
Study of Shawky Ashour Bourqaba, Abdel Halim Ammar Gharbi this study aimed to study the relationship between corporate governance variables and the financial performance of Islamic banks. The study reached several results, including:
1. There is a positive relationship between the financial performance of Islamic banks (return on assets) and the governance variable, the composition of the board of directors (number of independent members).
2. There is a positive relationship between the financial performance of Islamic banks (return on assets) and the governance variable: the size of the board of directors (the number of managers).
Emesuanwu catherine, Gadi dung paut. The study aimed to analyze the impact of corporate governance on the financial performance of microfinance banks in Nigeria. The study reached several results, the most important of which is the lack of a significant relationship between the composition of the board of directors and board committees and the return on shares.
Types of Research
In this study, the data used is secondary data, which is the financial report of the commercial banks in Qatar during 2011-2020.
Variables Operationalization
The following Table 1 operationalization variables in the study:
| Table 1 Operationalization of Variable | ||
| Variables | Measurement | Scale |
| return on assets (Y1) | NET INCOM / TOTAL ASSeTS | Ratio |
| return on equity (Y2) | (NET Earning/ shareholder’s equity)*100 | Ratio |
| FSIZE(x1) | The natural logarithm of total assets at the end of the year | NUMBER |
| Age (x2) | Logarithm of age | NUMBER |
| NBM (x3) | The number of board members | NUMBER |
| NC (x4) | The number of committees that make up the council | NUMBER |
| InBD (x5) | Number of independent board members | NUMBER |
Source: Author’s computation.
Dependent Variable
Financial performance: ROA and ROE measures have been used by several authors to denote performance corporate finance.
Independent Variable
Governance: We used three variables, all of which are the governance variable they are:
1. The number of board members
2. The number of committees that make up the council
3. Number of independent board members
The control variable: These are the variables that control the model, which are the size and age of the bank.
Where many researchers used the logarithm for the totality of assets, such as (Almoneef & Samontaray, 2019; Al-Ghamdi & Rhodes, 2015; Brown & Caylor, 2006).
FSIZE(x1)= log (total of assets)
However they are many researchers used logarithm for the age, such as: (Almoneef & Samontaray, 2019; Al-Ghamdi & Rhodes, 2015; Brown & Caylor, 2006).
DMA (x2)= log (number of age)
Population Data Sample
The study population consisted of the total number of Qatari banks, which were 18 banks by the end of the year 2020. The study sample is made up of banks whose lists and annual reports are regularly available, with disclosures about governance during the study period set at 10 years, i.e. during 2011-2020, the banks list is given below:
1. Commercial bank
2. Q.S.C Doha Bank
3. Qatar Islamic bank QIB
4. Qatar National bank
Data Analysis Methods: Data analysis techniques in the study
1. The statistical analysis program Eviews was used to perform the statistical tests.
2. Descriptive statistical methods were used
3. The multiple regression models were relied upon to test the relationship between the independent variables and the dependent variable.
Descriptive statistics: Table 2 presents summary statistics of the variables we use in our model.
| Table 2 Description Statistical | |||||||
| Y1_? | Y2_? | X1_? | X2_? | X3_? | X4_? | X5_? | |
| Mean | 0.017173 | 0.143470 | 2.66E+08 | 41.00000 | 8.800000 | 3.650000 | 3.975000 |
| Median | 0.017100 | 0.156000 | 1.33E+08 | 39.50000 | 9.000000 | 4.000000 | 4.500000 |
| Maximum | 0.028000 | 0.220000 | 1.07E+09 | 56.00000 | 10.00000 | 6.000000 | 6.000000 |
| Minimum | 0.004000 | 0.030000 | 52420292 | 29.00000 | 7.000000 | 1.000000 | 0.000000 |
| Std. Dev. | 0.005619 | 0.049202 | 2.95E+08 | 7.372854 | 0.911465 | 1.051251 | 1.731866 |
| Skewness | -0.469074 | -0.498337 | 1.574453 | 0.449014 | -0.831276 | -0.872394 | -1.280896 |
| Kurtosis | 3.039299 | 2.354011 | 4.049246 | 2.208366 | 3.001372 | 3.813055 | 3.633487 |
| Sum Sq. Dev. | 0.001231 | 0.094411 | 3.39E+18 | 2120.000 | 32.40000 | 43.10000 | 116.9750 |
| Observations | 40 | 40 | 40 | 40 | 40 | 40 | 40 |
| Cross sections | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
Source: Author’s computation
This Table 2 presents summary statistics about four banks in Qatar for the period 2011-2020.
The Table 2 shows that the y1 (roa) ranges from a minimum of 0.4% to a maximum of 2.80% with an average of 1.71% for the overall sample. The y2 (roe) ranges from a minimum of3% and maximum of 22% with average of 14.34% for the combined sample.
The x1(fsize) ranges from a minimum of 52420292 million Q.D to a maximum of 1.07E+09 million Q.D with an average of 2.66E+08for the overall sample.
The x2(age) ranges from a minimum of 29 year to a maximum of 56 year with an average of 41 year for the overall sample.
The x3(nbm) ranges from a minimum of 7number of board members to a maximum of 10 number of board members with an average of 8number of board members for the overall sample.
The x4(nc) ranges from a minimum of 1 number of committees that make up the council to a maximum of 6 number of committees that make up the council with an average of 3 The number of committees that make up the council for the overall sample.
The x5(InBD) ranges from a minimum of 0 Number of independent board members to a maximum of 6 Number of independent board members with an average of 4 Number of independent board members that make up the council for the overall sample.
Stability studies: the stability of time series is important because the relationship between unstable cross- sectional time series gives misleading results. There are several tests to determine the stability of time series, including the unit root, and the following Table 3 summarizes for us the degree of stability of series related to the study.
| Table 3 Stability Studies | |
| Variable of degree 2 | Variable of degree 1 |
| X3, x4, x5 | y1, y2, x1, x2, |
Source: Author’s computation.
Test the appropriate panel model: we have three models in Panel data analyses they are:
1. Pooled Regression Model
2. Fixed Effect Model
3. The fixed random model
The standard models used in the study depend on panel data according to the balanced panel and long panel data. For the purpose of testing the impact of corporate governance on both the return on assets and the return on equity of the Algerian banks under study, and for the purpose of testing the hypotheses of the study, the method of least squares (OLS) Ordinary Least Squares was used, where the standard study models were formulated according to the order of the main hypotheses in the study on as follows:
ROA= β0+ β1 X1+ β2X2+ β3X3 +B4X4+ B5X5
ROE= β0+ β1 X1+ β2X2+ β3X3 +B4X4+ B5X5
1- relation between y1 and x1,x2, x3,x4, x5: In order to measure the effect of corporate governance on the return on assets, we used the following models related to the following cross-sectional data:
Pooled Regression Model: This model is considered one of the simple models that is based on neglecting the effect of the time dimension, and the following Table 4 shows us the results obtained from the eviews9 program.
| Table 4 Pooled Regression Model | ||||
| Dependent Variable: Y1_? | ||||
| Method: Pooled Least Squares | ||||
| Sample: 2011 2020 | ||||
| Included observations: 10 | ||||
| Cross-sections inclue: 4 | ||||
| Total pool (balanced) observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob |
| X1_? | -3.78E-12 | 4.42E-12 | -0.85632 | 0.3976 |
| X2_? | -0.000107 | 0.000208 | -0.51092 | 0.6126 |
| X3_? | 0.002131 | 0.001115 | 1.910265 | 0.0643 |
| X4_? | -0.000414 | 0.001236 | -0.335138 | 0.7395 |
| X5_? | 0.001281 | 0.000627 | 2.044461 | 0.0485 |
| R-squared | -0.091435 | Mean dependent var | 0.017173 | |
| Adjusted R-squared | -0.21617 | S.D. dependent var | 0.005619 | |
| S.E. of regression | 0.006197 | Akaike info criterion | -7.213097 | |
| Sum squared resid | 0.001344 | Schwarz criterion | -7.001987 | |
| Log likelihood | 149.2619 | Hannan-Quinn criter. | -7.136766 | |
| Durbin-Watson stat | 0.462788 | |||
Source: Author’s computation.
Fixed Effect Model: Aims to show each section separately and the following Table 5 shows us the results reached.
| Table 5 Fixed Effect Model | ||||
| Dependent Variable: Y1_? | ||||
| Method: Pooled Least Squares | ||||
| Included observations: 10 | ||||
| Cross-sections inclue: 4 | ||||
| Total pool (balanced) observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 0.07174 | 0.009742 | 7.364173 | 0 |
| X1_? | -1.41E-12 | 3.74E-12 | -0.377418 | 0.7084 |
| X2_? | -0.001129 | 0.000288 | -3.92106 | 0.0005 |
| X3_? | -0.000582 | 0.001096 | -0.53041 | 0.5996 |
| X4_? | -0.002028 | 0.001579 | -1.284186 | 0.2086 |
| X5_? | 0.001162 | 0.000402 | 2.888211 | 0.007 |
| Fixed Effects (Cross) | ||||
| 01--C | 0.000137 | |||
| 02--C | 0.013928 | |||
| 03--C | -0.009686 | |||
| 04--C | -0.004378 | |||
| Effects Specification | ||||
| Cross-section fixed (dummy variables) | ||||
| R-squared | 0.675456 | Mean dependent var | 0.017173 | |
| Adjusted R-squared | 0.591703 | S.D. dependent var | 0.005619 | |
| S.E. of regression | 0.003591 | Akaike info criterion | -8.225924 | |
| Sum squared resid | 0.0004 | Schwarz criterion | -7.845926 | |
| Log likelihood | 173.5185 | Hannan-Quinn criter. | -8.088529 | |
| F-statistic | 8.064829 | Durbin-Watson stat | 1.333357 | |
| Prob (F-statistic) | 0.000008 | |||
Source: Author’s computation
Comparison between the two models: We compare the two models by conducting the Redundant Fixed Tests and the following Table 6 shows us the test results.
| Table 6 Comparison Between the two Models | ||||
| Redundant Fixed Effects Tests | ||||
| Effects Test | Statistic | d.f. | Prob. | |
| Cross-section F | 15.036684 | (3,31) | 0 | |
| Cross-section Chi-square | 35.927726 | 3 | 0 | |
| Cross-section fixed effects test equation: | ||||
| Dependent Variable: Y1_? | ||||
| Method: Panel Least Squares | ||||
| Sample: 2011 2020 | ||||
| Included observations: 10 | ||||
| Cross-sections inclue: 4 | ||||
| Total pool (balanced) observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 0.042188 | 0.011899 | 3.545669 | 0.0012 |
| X1_? | 7.27E-12 | 4.94E-12 | 1.471998 | 0.1502 |
| X2_? | -0.000342 | 0.000193 | -1.775541 | 0.0848 |
| X3_? | -0.002217 | 0.001562 | -1.419449 | 0.1649 |
| X4_? | 0.001006 | 0.001144 | 0.879142 | 0.3855 |
| X5_? | 0.00073 | 0.000565 | 1.291435 | 0.2053 |
| R-squared | 0.203192 | Mean dependent var | 0.017173 | |
| Adjusted R-squared | 0.086014 | S.D. dependent var | 0.005619 | |
| S.E. of regression | 0.005372 | Akaike info criterion | -7.477731 | |
| Sum squared resid | 0.000981 | Schwarz criterion | -7.224399 | |
| Log likelihood | 155.5546 | Hannan-Quinn criter. | -7.386134 | |
| F-statistic | 1.734046 | Durbin-Watson stat | 0.745225 | |
| Prob(F-statistic) | 0.153422 | |||
Source: Author’s computation
From the previous Table 6, we conclude that the fixed effects model is better than the pooled regression model. The model can be projected into the following equations:
(Commercial bank)Y1_01 = 0.000136571306949 + 0.0717397860679 - 1.41012605741e-12*X1_01 - 0.00112913260085*X2_01 - 0.0005815723009*X3_01 - 0.00202755618919*X4_01 + 0.00116238833916*X5_01
(Qatar National Bank)Y1_02 = 0.013927819815 + 0.0717397860679 - 1.41012605741e-12*X1_02 - 0.00112913260085*X2_02 - 0.0005815723009*X3_02 - 0.00202755618919*X4_02 + 0.00116238833916*X5_02
(DOHA BANK)Y1_03 = -0.00968637549276 + 0.0717397860679 - 1.41012605741e-12*X1_03 - 0.00112913260085*X2_03 - 0.0005815723009*X3_03 - 0.00202755618919*X4_03 + 0.00116238833916*X5_03
(Qatar Islamic bank)Y1_04 = -0.00437801562916 + 0.0717397860679 - 1.41012605741e-12*X1_04 - 0.00112913260085*X2_04 - 0.0005815723009*X3_04 - 0.00202755618919*X4_04 + 0.00116238833916*X5_04
2- Relation between y2 and x1, x2, x3, x4, x5; we followed the same method as before:
Pooled Regression Model: The following Table 7 shows us the results obtained from the eviews9 program.
| Table 7 Pooled Regression Model | ||||
| Dependent Variable: Y2_? | ||||
| Method: Pooled Least Squares | ||||
| Sample: 2011 2020 | ||||
| Included observations: 10 | ||||
| Cross-sections inclue: 4 | ||||
| Total pool (balanced) observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| X1_? | 2.68E-11 | 3.63E-11 | 0.737174 | 0.4659 |
| X2_? | 5.32E-05 | 0.001713 | 0.031067 | 0.9754 |
| X3_? | 0.014181 | 0.009166 | 1.547095 | 0.1308 |
| X4_? | -0.008908 | 0.01016 | -0.876818 | 0.3866 |
| X5_? | 0.010057 | 0.005148 | 1.953456 | 0.0588 |
| R-squared | 0.038767 | Mean dependent var | 0.14347 | |
| Adjusted R-squared | -0.071088 | S.D. dependent var | 0.049202 | |
| S.E. of regression | 0.05092 | Akaike info criterion | -3.000636 | |
| Sum squared resid | 0.090751 | Schwarz criterion | -2.789526 | |
| Log likelihood | 65.01273 | Hannan-Quinn criter. | -2.924306 | |
| Durbin-Watson stat | 0.454629 | |||
Source: Author’s computation
Fixed Effect Model: Aims to show each section separately and the following Table 8 shows us the results reached.
| Table 8 Fixed Effect Model | ||||
| Dependent Variable: Y2_? | ||||
| Method: Pooled Least Squares | ||||
| Sample: 2011 2020 | ||||
| Included observations: 10 | ||||
| Cross-sections inclue: 4 | ||||
| Total pool (balanced) observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 0.405148 | 0.07754 | 5.225031 | 0 |
| X1_? | 3.51E-11 | 2.97E-11 | 1.180755 | 0.2467 |
| X2_? | -0.000527 | 0.002292 | -0.229891 | 0.8197 |
| X3_? | -0.01927 | 0.008727 | -2.207976 | 0.0348 |
| X4_? | -0.031949 | 0.012567 | -2.542268 | 0.0162 |
| X5_? | 0.009252 | 0.003203 | 2.888181 | 0.007 |
| Fixed Effects (Cross) | ||||
| 01--C | -0.028938 | |||
| 02--C | 0.047125 | |||
| 03--C | -0.069147 | |||
| 04--C | 0.05096 | |||
| Effects Specification | ||||
| Cross-section fixed (dummy variables) | ||||
| R-squared | 0.731816 | Mean dependent var | 0.14347 | |
| Adjusted R-squared | 0.662607 | S.D. dependent var | 0.049202 | |
| S.E. of regression | 0.028579 | Akaike info criterion | -4.07718 | |
| Sum squared resid | 0.02532 | Schwarz criterion | -3.697182 | |
| Log likelihood | 90.5436 | Hannan-Quinn criter. | -3.939785 | |
| F-statistic | 10.57404 | Durbin-Watson stat | 1.503311 | |
| Prob(F-statistic) | 0.000001 | |||
Source: Author’s computation
Comparison between the two models: We compare the two models by conducting the Redundant Fixed Tests and the following Table 9 shows us the test results.
| Table 9 Comparison between the two Models | ||||
| Redundant Fixed Effects Tests | ||||
| Test cross-section fixed effects | ||||
| Effects Test | Statistic | d.f. | Prob. | |
| Cross-section F | 14.843526 | (3,31) | 0 | |
| Cross-section Chi-square | 35.622014 | 3 | 0 | |
| Cross-section fixed effects test equation | ||||
| Dependent Variable: Y2_? | ||||
| Method: Panel Least Squares | ||||
| Sample: 2011 2020 | ||||
| Inclue observations: 10 | ||||
| Cross-sections inclue: 4 | ||||
| Total pool (balanced) observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 0.377577 | 0.094345 | 4.002067 | 0.0003 |
| X1_? | 1.26E-10 | 3.92E-11 | 3.209701 | 0.0029 |
| X2_? | -0.002053 | 0.001527 | -1.344782 | 0.1876 |
| X3_? | -0.024727 | 0.012382 | -1.997088 | 0.0539 |
| X4_? | 0.003803 | 0.009073 | 0.419203 | 0.6777 |
| X5_? | 0.005123 | 0.00448 | 1.143594 | 0.2608 |
| R-squared | 0.346578 | Mean dependent var | 0.14347 | |
| Adjusted R-squared | 0.250486 | S.D. dependent var | 0.049202 | |
| S.E. of regression | 0.042596 | Akaike info criterion | -3.33663 | |
| Sum squared resid | 0.06169 | Schwarz criterion | -3.083298 | |
| Log likelihood | 72.73259 | Hannan-Quinn criter. | -3.245033 | |
| F-statistic | 3.606747 | Durbin-Watson stat | 0.864922 | |
| Prob(F-statistic) | 0.010054 | |||
Source: Author’s computation
From the previous table, we conclude that the fixed effects model is better than the pooled regression model. The model can be projected into the following equations:
(Commercial bank)Y2_01= 0.028937978003 + 0.4051476209 + 3.511422711e-11*X1_01 - 0.000526927566634*X2_01 - 0.0192696991971*X3_01 - 0.0319488001256*X4_01 + 0.00925198782319*X5_01
(Qatar National Bank)Y1_02= 0.0471250820834 + 0.4051476209 + 3.511422711e-11*X1_02 - 0.000526927566634*X2_02 - 0.0192696991971*X3_02 - 0.0319488001256*X4_02 + 0.00925198782319*X5_02
(DOHA BANK)Y1_03= -0.0691471772036 + 0.4051476209 + 3.511422711e-11*X1_03 - 0.000526927566634*X2_03 - 0.0192696991971*X3_03 - 0.0319488001256*X4_03 + 0.00925198782319*X5_03
(Qatar Islamic bank)Y1_04= 0.0509600731231 + 0.4051476209 + 3.511422711e-11*X1_04 - 0.000526927566634*X2_04 - 0.0192696991971*X3_04 - 0.0319488001256*X4_04 + 0.00925198782319*X5_04
The State of Qatar has paid great attention to the issue of governance, as corporate governance has become the locomotive of economic progress. Evidence for this interest is the legislation and regulations issued by the Central Bank of Qatar and the Qatar Stock Exchange.
The study examined the impact of the corporate governance on ROA and ROE of banks in Qatar during 2011-2020. According to the panel data analysis, a number of results were reached, which are:
They aren't differences between banks in this study about effect governance at ROA in general bath
1. They is a strong and negative relationship between ROA x3 and x4. These are the same results obtained by the study of both Sekhar Muni Amba, Sondos Shayeb Ainu.
2. They is a strong and negative relationship between ROA and x1, x2, .These are the same results obtained by the study of both Hussein and Darrar, Shawky Ashour Bourqaba & Abed el-Halim Ammar Gharbi.
3. They are a strong and positive relationship between ROA and x5. These are the same results obtained by the study of both Almanaseer.
They aren't differences between a banks in this study about effect governance at ROE in general bath:
1. They are a strong and positive relationship between ROE and x1 & x5.
2. They are a strong and negative relationship between ROE and x2, x3 & x4.
*Means Russell: he’s American economist, he borne in 10/11/1939 and died in 22/10/2012.
†Adolph Berle: he’s American economist, he borne in 29/01/1895 and died in 1971.
‡Ronald Coast: he’s britches economist , he borne in 29/12/1910 and he Recipient of the Nobel Prize in 1991
§Michael Cole Jensen: he’s American economist, he borne in 30/11/1939 and he Recipient of the Nobel Prize in1990
**William H. Meckling: he borne in 1922 and he died in 15/5/1998.
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Received: 01-Jul-2022, Manuscript No. AAFSJ-22-12286; Editor assigned: 04-Jul-2022, PreQC No. AAFSJ-22-12286(PQ); Reviewed: 18-Jul-2022, QC No. AAFSJ-22-12286; Revised: 28-Jul-2022, Manuscript No. AAFSJ-22-12286(R); Published: 04-Aug-2022