How Bank Managers Anticipate Non-Performing Loans:  Evidence from Islamic Banks of Bangladesh

Non-performing loan (NPL) is that part of the loan that is already defaulted or close to default. If a bank does not collect interest payments or the principal amount of a loan, that loan shall be considered NPL(M. M. H. Chowdhury, 2018). Most of the loans become non-performing after 90 days of default, but this may depend on contract terms(Sarker, 2017). Also, the IMF, defines NPLs as ―A loan is non-performing when payments of interest and principal are past due by 90 days or more, or at least 90 days of interest payments have been capitalized, refinanced or delayed by agreement, or payments are less than 90 days overdue, but there are other good reasons to doubt that payments will be made in full. Recently this has become the burning issue of our country as it is affecting the gross economy of the country. Caused by the economic framework of the OIC, Islamic banking was introduced in Bangladesh in 1983. The Islamic Bank Bangladesh Ltd. (IBBL) was the first Islamic bank to be found in Bangladesh according to Islamic principles.

How Bank Managers Anticipate Non-Performing Loans: Evidence from Islamic Banks of Bangladesh

Literature review

Review of the existing literature:

The literature on bank-specific variables is not that widespread because very few researches have been conducted on NPL associated with both macroeconomic and bank-specific factors.

Chapter -03: Methodology of the study

3.1. Data and sample:

We have conducted this study with the secondary data. This study considered balanced panel data of 7 Islamic Banks of Bangladesh of the period 2013-2017 which are listed in DSE. At present we know that in Bangladesh there are eight full-fledged Islamic banks with 1068 branches(Islam, 2017).I have taken data of seven banks due to non-availability of NPL data of Union Bank, and I have not considered that bank. Since we are conducting this study on NPL, the non-availability of this data can make a biased result.

3.2. Variable Description:

Variables used in this study have been collected from previous works by different scholars. Also, table 1 shows the summary of these variables.

3.2.1. Dependent Variable:

Non-performing loans for Islamic banks (NPL):

NPLs is one of the most current issues of the economic stagnation. Many authors relate the NPL to macroeconomic and bank-specific variables. Keeton and Morris mention that banks can have high losses because of pure chance, a delicate process of credit management, specialization and economic conditions(Keeton & Morris, 1987). Robert T. Clair analyzes the relation between credit growth and loan quality. To measure the loan quality author uses two standards: the credit loss to total loans ratio and the NPL to total loans ratio(Clair & Tucker).

In this study, NPL has been considered the dependent variable while all other variables are

3.2.2. Independent Variable:

Bank Specific Variables:

  1. Growth in the gross loan (LOAN):(Cucinelli, 2015) showed in his study about the relationship between NPL and the growth in the gross loan. He evidenced Italian banks to show the relation.
  2. Loan to asset ratio (LOTA): This is the ratio of total loan total asset. A significant positive sign of LOTA indicates that risky banks tend to have more NPL. So, on the other hand, a negative sign is remarked on the LOTA variable, it would clearly show that the loan assets of risky banks seem to be well regulated, hence low NPL.
  3. Net interest margin (NIM): Net interest margin represents a substantial part of their operating income for most banks. (Candidate & Shingjergji, 2013) showed in their paper the positive relation between NIM and NPL.
  4. Capital Adequacy ratio (CAR): The relationship of CAR with the bank is significant because it helps the bank to stabilize and recover from uncertain shocks. The amount of capital adequacy or solvency ratio between a bank and the NPL is related. Capital is a buffer for absorbing losses resulting from different risks.

Macro-Economic Variables:

  • GDP growth (GDP): On the basis of theoretical literature of life cycle consumption models (Miller & Modigliani, 1967) and the business cycle theory(Hayek, 1940),(Salas & Saurina, 2002),(Bangia, Diebold, Kronimus, Schagen, & Schuermann, 2002),(Carey, 2002), mention that GDP growth has a significant negative effect on NPLs for the macroeconomic developments make economic agents more able to pay back their debt. (Makri, Tsagkanos, & Bellas, 2014)also showed in their research the inverse relation of NPL and GDP growth.
  • Inflation rate (INFLA): NPL has a very positive relationship with inflation.(Fofack, 2005) considers inflation as one of the eminent causes for the rapid loss of commercial banks’ funds, which brings about higher credit risk.
  • Methodology of the study:

The analysis is done in three ways: descriptive analysis, correlation analysis, and empirical analysis.

  1. Hypothesis development and Model specification:

Hypothesis Development

A study by Brown Bridge (1998) indicates, most of the bank failures were caused by nonperforming loans. The provisions are not adequate protection if the level of non-performing loan is higher.

In this study, I have developed a hypothesis, which aim is to test the causality between the variables.

  • H1: Increase in Capital Adequacy Ratio (CAR) decreases the NPL’s ratio
  • H2: Increase in a loan to asset ratio (LOTA) increases NPL’s ratio
  • H3: Growth in the gross loan (LOAN) has a positive relationship with NPL’s ratio
  • H4: Increase in net interest margin (NIM) increases the NPL’s ratio
  • H5: GDP growth has a negative relationship with NPL’s ratio
  • H6: Increase in inflation (INFLA) increases NPL’s ratio

Chapter-04: Data analysis

  1. Result Analysis:

Data analysis are divided into descriptive statistics, correlation analysis, and regression analysis.

Descriptive statistics will describe the basic features of the collected data. It will provide summaries about the sample and measures. Correlation analysis will help to evaluate the strength of the relationship between two numerically measured variables. And the regression analysis will indicate the statistical relationship between the independent and dependent variables.

  1. Descriptive Statistics:

The descriptive statistics for the period 2013-2017 for our sample banks are represented by the Table2. In this section, we also analyzed the normality of data. The mean-median ratio is approximately 1, which indicates the normality of data. Regarding the credit quality of the banks in our sample, non- performing loans (NPL) represents an average of 17% of the gross loan.

  1. Regression Analysis:

Simple linear regression (Hypothesis test):

After testing the significance of the model, the influence of each independent variable on the dependent variable is obtained. Also, the comparison of the results of the regression carried out with the original hypothesis was formulated to interpret the actual research conditions subsequently.

A distinction of the results of the regression with the earlier hypothesis is summarized in Table 4 below:

Multiple linear regression (Test the model):

We have also done the multiple regression analysis to determine the relationship between NPL along with the other independent variables. As we know, in multiple regression analysis, it is used to elaborate the relationship between one continuous dependent variable and two more independent variables.

The stata result of the multiple regression analysis is detailed in table 5:

Chapter-05: Findings and recommendations

  1. Findings:

The main findings from analyzing the data can be described as:

  • Macroeconomic variables are not significantly related to NPL but can influence the NPL.
  • Increase in growth to gross loan can increase the level of NPL.
  • Increase in Loan to asset ratio also increase NPL level
  • Increase in capital adequacy ratio reduces the NPL growth.
  • Net interest margin increases the NPL level.
  1. Limitations of the study:

This study has several limitations.

First, the sample used in this study is limited to the period 2013 to 2017.

Second, the factors used in this study are limited to six variables: Loan, Lota, NIM, Car as internal bank-specific factors and GDP and Inflation as external factors macroeconomic factors.

Third, All the Islamic banks could not be included here as Union bank’s NPL data was not available.

  1. Recommendations:

Authorities should insist on management performance in order to mitigate possible increases in

  • The Capital Adequacy Ratio which is also known as Risk Asset Ratio should be kept high. Higher CAR is expected to result in greater safety and lower NPL.
  • Change in gross loan are, particularly, sensitive to credit risk considerations and for that banks should decrease growth in loan when they expect low NPL.
  • The loan to asset ratio which is negatively related to the NPLs ratio indicating that an increase of loans to asset ratio can determine a reduction of the NPLs ratio. So, if the managers can keep the ratio of loan to asset in high level, NPLs ratio will fall.

  • Conclusion:

The study evaluated the present conditions of NPLs in Bangladesh from the Islamic banking sector, determinants of non-performing loans, the actions that have been taken by the banks to try and solve this problem and the perceived level of success of such actions. The result indicates that the macroeconomic variables GDP and inflation do not significantly relate to NPL but can influence NPL. Bank specific factors affecting non-performing loans include the growth in gross loan, capital adequacy ratio, net interest margin, loan to asset ratio. Therefore, our findings are mainly relevant to the definition of regulatory measures.

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Categories: Dissertation