A thesis submitted in partial fulfillment of the requirements for the degree Master of Science in Finance. Tahar Tayachi for his constant guidance and support during this research journey, for his great effort to enable me to complete this thesis. Finally, I would deeply thank my family for being by my side at every stage and step, encouraging me during this journey to get high grades.
I, (Ahmed Al-Minawi), declare that all material presented in this article is my own work, or fully and specifically acknowledged, wherever adapted from other sources. I agree that if at any time it is found that I have materially misrepresented material presented to the Effat College of Business at Effat University, any degree or credits awarded to me based on this material may be revoked . Banking is one of the highly sensitive industries as most of the income comes from loans.
The purpose of the current study is to investigate the impact of interest rates, capital adequacy, asset quality and liquidity on lending by leading Gulf Cooperation Council (GCC) commercial banks. The results of the conducted research show that there is a strong influence of various factors of GCC commercial banks on lending behavior such as interest rates, capital adequacy, asset quality and liquidity. The current research study also found that GCC banks need to remain more careful in using multiple factors while following loan trends in order to increase their revenue and profits.
INTRODUCTION
- Background of the study
- Lending behavior
- Determinants of lending behavior
- Problem Statement
- Research Aims
- Research Rationale
6Felicia Omowunmi Olokoyo, "Determinants of Commercial Banks Lending Behavior in Nigeria," International Journal of Financial Research 2, no. Determinants of bank lending behavior among commercial banks relate to the elements that influence commercial banks' loan expansion. 7Mitku Malede, "Determinants of Commercial Banks' Lending Behavior: Evidence from Turkey," Asian Journal of Empirical Research 3, no.
As a result, it is the most important source of risk to commercial banks for their safety and soundness. To ascertain the impact of interest rates on lending behavior in commercial banks in the GCC. To investigate the impact of asset quality on lending behavior in commercial banks in the GCC.
The purpose of this research was to determine the lending habits of commercial banks in GCC.
LITERATURE REVIEW
- Theoretical Framework
- Portfolio theory to credit risk management
- Information Asymmetry Theory
- Theory of delegated monitoring of borrowers
- Loan Pricing Theory
- Empirical Framework
- Conceptual Framework
- Research Gap
As a result of these two emerging concerns, the advancement in credit risk management at the portfolio level has increased tremendously. The asset-by-asset method is based on a thorough credit examination as well as the bank's own credit risk assessment system. The fundamental shortcoming of the asset-by-asset approach is its failure to provide a holistic perspective of the portfolio's credit risk, where risk refers to the possibility that actual losses exceed anticipated losses.
The inability to detect and assess concentration risk is the main drawback of the asset-by-asset method. As a result, commercial banks complement this technique with a quantitative assessment of their loan portfolio using multiple credit models. This concept is based on the notion that a borrower may know a great deal of information about the risk involved with the project they have asked the bank to finance that the lender may not have.
13M Ivanović, "Determinants of credit growth: the case of Montenegro", Journal of Central Banking Theory and Practice 5, no. 23 Most of the recent work has focused on general credit expansion in both emerging and developed countries. Some studies focus on the variables that affect the provision of bank credit to aspects of the economy, while others have examined the effects of credit on economic progress.
As a result, it is essential to evaluate and consider some of the elements suggested by other researchers in their attempt to learn the determinants of commercial bank credit establishment. This study uses the following conceptual framework to address the variables that have been identified as influencing lending behavior in commercial banks. As a result, it is possible to conclude that there is a lack of research on the variables that drive the credit origination behavior of commercial banks, with a special emphasis on risk and relationship aspects.
Most of the previous research provides insufficient empirical data as the authors concentrated on the effect of such actions on bank borrowers instead of what the results showed for the bank and banking sector. Furthermore, the few studies that have been conducted have focused on organizations registered with the stock exchange stock market, and as a result there have been few attempts to support research on commercial banks.
DATA & METHODOLOGY
- Research Design
- Target Population
- Data Collection
- Data Analysis and Presentation
- Data Analysis
- Model for Data Analysis
- Data Sources and Description
- Multiple Linear Regression
- OLS Regression
- Procedure for OLS Regression
- Assumptions for OLS Regression
- Data Presentation
Also, the information available on the banks' websites is used in the investigation. Descriptive statistical techniques are used by the researcher to help describe the data and determine the degree of difficulty. 28 During the research, descriptive and inferential statistics will be used in the analysis.
The trend analysis method was developed to identify the behavior of the variables over a period of seventeen years. A t-test with a 95 percent confidence interval will be used to calculate the means of the variables and to identify relationships between them. Once the degree of correlation between the variables is determined, the research will use an econometric approach using the multiple regression analysis of the Ordinary Least Squares (OLS) technique, which will be carried out using E view & Excel.
This is done in the course of the research on each bank, and the results will be combined to determine the overall industry position. For the purpose of determining the significance of the regression constants 𝛽0,𝛽1, 𝛽2 ,𝛽3 and 𝛽4, the t-statistic with a 95 percent confidence level is used. The F-test will be used to determine the significance of the entire regression at a 95 percent confidence level.
However, even though the regression plane does not touch every point in the data cloud, it can model the partial relationships between each slope (ie, each regression coefficient) and the outcome variable while simultaneously controlling for the effects of other variables in the model . As a result, in OLS, the regression coefficients are determined by minimizing the sum of the squares of the differences between the values fitted in the regression plot and the values observed in the data. Since the conditional mean of the dependent variable is affected by the skewed distribution when predictors are considered, this is the case.
At the end of the day, multimodal error distributions can cause data to be dichotomized into groups, resulting in non-normality in the error distribution. To test this assumption, a scatter plot of the studentized residuals against the non-standardized projected values should be used.
Results & Discussions
To get an idea of any kind of stationarity in the time series data, the Stationarity test is performed. Only one variable called "RR" is significant and rest of all variables are insignificant. To remove the effect of stationarity, the First Difference technique is applied to the data set.
The 1st difference technique is not as effective as expected because 6 variables are still insignificant. The value of R-square explains that the 99% of the data set shows variation and the adjusted regression model is a good fitting model. Data set is collected for the year 2005 to 2021, which means the collected data set is time series data set.
A stationarity test is used to check the effect of stationarity in a time series data set. After the 1st difference, the data set is again checked using the stationarity test and the results are given in Table # 7. 40 Regression analysis is performed on the data set which is first transformed using log transformation.
The value of R-squared explains that 99% of the data set explains variation and the fitted model fits well. To remove the effect of stationarity, the 1st Difference technique is applied to the data set as shown in Table 11. The value of R-squared explains that 99% of the data set shows variation and the fitted regression model is a good fitted model.
To check the stationarity in the data set the stationarity test is used and the results are mentioned in table # 14. After the 1st difference the data set is again checked using the stationarity test and the results are mentioned in the table. The p-value is compared with a 5% level of significance to test the significance and non-significance of the variables.
Log transformation is applied to the entire data set and after this transformation, regression analysis is performed.
Conclusion and Recommendations
First, provision of currency in a systematic and efficient manner to commercial banks such as KSA and UAE Banks, etc. Sometimes commercial banks keep ATMs full of cash that is not a function of the Federal Reserve Bank. Keeping cash at ATM is the special task of commercial banks and they have to use it for lending and borrowing.
In some cases, cryptocurrency is used for lending and borrowing that does not exist in physical form and is not issued by a central authority, so its use should be avoided. Third, the central bank can increase the reserve requirements for commercial banks - so banks would have to keep more money with the central bank, which will reduce their ability to lend. In addition, the central bank can increase the share of cash reserves as a monetary tool - for the same scenario.
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Determinants of Credit Growth: The Case of Montenegro." Journal of Central Banking Theory and Practice 5, no. Determinants of Commercial Banks' Lending Behavior: Evidence from Turkey.” Asian Journal of Empirical Research 3, No. Identification of the Macroeconomic Factors Influencing Credit Card Usage in Turkey Using the MARS Method.” China-US Business Review, 15, no.
