CHAPTER 1: INTRODUCTION
Over the last decade, the world banking industry suffered due to economic downturn. It is widely accepted that the percentage of nonperforming loans (NPLs) is often associated with bank failures and financial crises in both developed and developing countries. In other words, the aggregate rate of nonperforming loans (NPLs) is commonly used as soundness indicators.
Nonperforming loan can be explained in term of default payment made by borrower for over 90days or the bank not received any payment until the maturity date depends on contract terms.
In view of this reality, its can analyzed the sensitivity of nonperforming loan to several key of macroeconomic variables. This is because problem loans are often used as an exogenous variable to explain other banking outcomes such as bank performance, failures, and bank crisis.
1.1 Background of Study
Nowadays, business in financial institution becomes more complex to deal with economics conditions. So, financial institutions need to work harder to maintain their performance and also profitability of business. In the study conducted, Public Bank has been chosen to take a look the reflection of the result. Established in 1966 by its Founder and Chairman, Tan Sri Dato’ Sri Dr. Teh Hong Piow, Public Bank is a leading financial services provider serving over five million customers in Malaysia.
The Public Bank Group is highly reputed for its prudent management, strong balance sheet, superior asset quality, superior customer service, strong corporate governance and effective corporate culture. Public Bank Brand is the best brand in financial services in Malaysia.
Study with attempt to focusing on how non performing loan react to those macroeconomic indicator will give an expectations of result to those independent variables. If NPL’s are kept active and constantly, thus it will break the economic growth and impairing the banking sector performance. In previous study conducted by Dimitrios, Angelos and Vasilios (2010), shows that the real gross domestic product growth rate and interest rate is negatively related to changes in the NPL, while the unemployment rate, interest rate and the inflation rates have a positive impact on the dependent variable In hope, the expectations result shows that the macroeconomics indicator has same significant influence on nonperforming loan of the Public Bank.
1.2 Problem Statement
NPLs create problems for banking sector’s balance sheet on the asset side. They also create a negative impact on the income statement as a result of provisioning for the loan losses.
There have three types of loan which is mortgage loan, business loan and consumer loan. From previous study, it’s show that the NPLs of consumer loans are the most sensitive towards a change in both gross domestic product growth rate, interest rates and inflation rates. As compared to the NPLs of business loans, there are most sensitive towards a change in the unemployment rate while NPLs of mortgage loans is the least sensitive to changes of macroeconomic factors (Dimitrios, Angelos and Vasilios 2005). These results are indicative of the differences in the driving forces behind NPLs for various types of portfolio loans.
Hence, the purpose of this study is to analyze and evaluate the performance of Public Bank in overall types of default risk toward macroeconomic variables. This can be as a reference and for general knowledge to individual and banking institution in handling lending activities and cash flow.
1.3 Research Question
In this study, there are several research questions that has been developed regarding the problem statement occurred. This main research questions is:
1. Does macroeconomics variable giving the serious impact on Nonperforming loan of Public Bank?
2. Does macroeconomic variable such as Gross Domestic Product (GDP), unemployment rate, inflation rate, and interest rates on base lending rate that are giving the most positive feedback of bank performance on the supply of credit?
This research questions will be useful for the guidelines to answering the problem statement clearly. Based on the research question result, hopefully that it will answering which macroeconomic variable giving the impact on non performing loan of Public Bank.
1.4 Objective of Study
This investigation of study is focused on the effect of nonperforming loan of Public Bank toward macroeconomic variable. Viewing quarterly in 2005 to 2009 as a reference of investigation, it can be used for bankers as a benchmark for their future planning.
The objective of the study are;-
1. To determine the relationship between Nonperforming Loan and macroeconomics variables.
2. To indicate that macroeconomic variables (GDP, unemployment rates, interest rates on BLR and inflation rates) will give the significance result of the study.
1.5 Significance of Study
There are several benefits that can be taken in this study. The information gathered will give advantages to few parties including;-
1.5.1 Investors or potential borrower
The investors or potential borrower can have a bird’s eye view regarding the results of their decision previously taken. They also can make prudent judgment in preparing further NPLs alleviating policy.
1.5.2 Banking Institution
The banks can identify the factors contributing to the creation of NPLs and adjust their lending strategy.
As for investor, it’s also will giving the overview to the industries about the fluctuated of performance of the bank due to economic condition. This is beneficial knowledge to make good investment for industries.
1.6 Scope of Study
The study will focus on the non performing loan of Public Bank before, during and after the economic crisis which struck worldwide late in 2007. But researcher will focus more on the performance during the economic crisis with return between year 2005 until 2009 and the measures using multiple linear regression method as a benchmark to determine the performance of the bank. Researcher will take few samples of macroeconomic factors as to compare of their performance.
1.7 Limitation of Study
In the way to successfully complete this project paper, there are some constraints that have to be faced by the researcher. They are;-
1.7.1. Time Constraint
The study worked on with the data of 5 years. In case of macroeconomic study, it is expected that the better results can be found if more than 10 years are taken.
1.7.2. Data Availability
There are hardly to find the non performing loan in quarterly of the bank. The effect of the study conduct for bank performance is limited of data available for the selected independent variable which is only 5 year. This may indirectly affect the accuracy of the result.
1.8 Definition of Terms
1.8.1 Non performing loan
Nonperforming loan is repayment of loan. When a borrower cannot repay the interest and principal of the loan until the maturity date, then it is qualified as nonperforming loans. Usually length of overdue is around 90days or 3month but it’s depending on term of contract.
1.8.2 Macroeconomic Variables
Macroeconomic variable is branches of economic that explain the behaviours of an economy as a whole. This study used four economic variables included gross domestic product, interest rate, inflation rate and unemployment rate.
1.8.3 Gross domestic product
Gross domestic product is the amount of goods and services produced in a year, in a country. It is the market value of all final goods and services made within the borders of a country in a year.
GDP = private consumption + gross investment + government spending + (exports − imports)
In the expenditure-method equation given above, the exports-minus-imports term is necessary in order to null out expenditures on things not produced in the country (imports) and add in things produced but not sold in the country (exports).
1.8.4 Unemployment rates
Unemployment rate is the percentage of the total labour force that is unemployed but actively seeking employment and willing to work (investopedia). Unemployment can be classified into three; cyclical, structural and frictional. Cyclical unemployment is trends in growth and production that occur within the business cycle. Another example is structural unemployment. This happen generally when technological revolution and lastly, frictional unemployment which is happen when people tend to not to take other work for holding out for better paying jobs.
1.8.5 Inflation rate
In economics, inflation is a rise in the general level of prices of goods and services in an economy over a period of time (Wikipedia, 2011). Basically it’s give the big impact on purchasing power of money because of there is no value of money in medium of exchange. This happen because of when the prices of goods and services increase, people tend to cut the cost of household expenses.
Under banking perspectives, inflation rate may affects economic growth by reducing the overall amount of credit that available to lending activities. In common, higher inflation rate may decrease the return on assets rate but in other hand as result to increasing the debt burden and should lead to higher number of NPL’s.
1.8.6 Interest rates on base lending rate
According to InvestorWords.com, Interest rates can be defined as annual percentage of the principal that being charged by lenders to the borrower in order for borrower to obtain a loan. While, base lending rate announced by financial institutions. It is seldom reflected in business done with borrowers; loans are usually written at a margin above the published base rate, with the margin varying according to the borrower’s credit ranking.
Increasing the rates of interest will leads to default payment or causes to greater number of NPLs where is interest that a borrower would pay will not fulfill the loan covenants. The default interest is usually much higher than the original interest since it is reflecting the stress in the financial risk of the borrower.
This study shows some important macroeconomic factors. These macroeconomic factors are Gross Domestic Product, unemployment rate, inflation rate, and interest rates on BLR. Analysis is applied in order understand the relationship between macroeconomic variables and non performing loan of Public Bank. This study is also to seek whether the expectation result will be positive or negatives significant between this dependent and independent variables.
CHAPTER 2: LITERATURE REVIEW
Economic condition plays the important role toward the performance of the bank. During the past two decades, many countries have experienced significance increase in banking system credit. Episodes of profound banking system distress have occurred not only in emerging and transition countries, but also in development and strong economy country, such as the United States, and more recently Japan. In all cases, banking institution have recorded as the large losses of wealth and led to disturbances in the credit supply to the economy. Resolving the crises has frequently imposed a large burden of debt to the public.
History has shown that, the relationship between macroeconomic variables and non performing loan has been a subject of interest among academics and practitioners. A significant research has been done to investigate the relationship between non performing loan and a range of macroeconomic variables. This study conducted the linkages between NPLs and macroeconomic variables can provide investor more information on the performance of the bank by the changes in these variables. Future, the banking institutions also can play a more active role to stabilize fluctuations in economic condition.
2.1 Previous Study
From the previous study, its show many articles published about the relationship between macroeconomic determinants of nonperforming loan in banking and financial sector. Overall, the literature on the major economies has confirmed that macroeconomic conditions matter for credit risk. Previous research done by others researcher has found a linkage between credit risk increase and adverse macroeconomics condition (Mueller, 2000; Anderson and Sundaresan, 2000; Collin- Dufresne and Goldstein, 2001). Based on studies by Rajan, and Dhal (2003), the observed analysis showed macroeconomic shock do not has any significance influences on nonperforming loan.
2.1.1 Gross Domestic Product and NPLs
The studies conducted by M. Arpa, G. Irene, I Andreas and P. Franz (2001) are seek to determine the macroeconomics indicators of potential instability in the bank system. The main finding of their study showed that the bank will increase the risk provisions in time of falling real GDP growth rates of rising bank operating income.
The econometric cases study of Guyana conducted by Tarron and Sukrishnalall (2005) showed the evidence that real income change are due by growth in real GDP that led to the significant negative effect on NPLs. The empirical results reveal that the GDP growth rates are not an important determinant of NPLs in the Guyanese banking systems.
Another study conducted by Luc and Giovanni (2002) on Loan loss provisioning and economic slowdowns: Too much, too late?; showed the significant explanatory variables is given by bank earnings before taxes and provision. The empirical result showed the coefficient on GDP growth is negative and suggesting a anti-business cyclical performance of bank‘s loan loss provisioning.
According the issues, the GDP expected to slow down to 5.8% as export growth moderate, causing industrial production to slow further. “With growth momentum expected to taper off slightly in 2011 and our forecast for Bank Negara to hold rates steady in the first half 2011, we might see some gradual unwinding in the foreign holdings of Malaysian assets and that could weaken the ringgit,” OSK DMG (Malaysian Insider, Jan 12).
Moreover, others researcher like Salas and Suarina (2002); Rajan and Dhal (2003); Fofack (2005); and Jimenez and Saurina (2005) also give the significant empirical evidence of negative relationship between GDP growth rates and NPLs. The explanation provided by the literature for this relationship is that strong positive growth in real GDP usually translates into more income which improves the debt servicing capacity of borrower which in turn contributes to lower non-performing loans. Conversely, when there is a slowdown in the economy (low or negative GDP growth) the level of NPLs should increase.
2.1.2 Unemployment rates and NPLs
Unemployment rates also reflect to NPLs. This is happen when the borrowers are unable to pay back the loan due to increasing number of retrenchment during economic downturn. It’s show the negative significance in term of increasing in debt burden and also household income.
According to world economic situation and prospects update as mid of 2009, weakening economy in global demand and rapid growth of the labour force support rising unemployment rates throughout the region, putting extra strain on fiscal budgets through increased spending on social benefits and measures aimed at employment conception.
Beside that, the study conducted by Dimitrios, Angelos and Vasilios (2010) showed that unemployment rates may provide additional information regarding the impact on macroeconomics conditions on household and firms. Specifically, an increasing in unemployment rate gives the negative feedback on cash flow of households and decreases the debt burden. So it will affect households’ ability to service their debt with three-month time or 90 days delays. On the other view, an increasing in unemployment rate may signal to a decrease in productions as well as demand will declining. It’s seems that firms cut their labours cost before they face credit repayment problems.
One fundamental assumption for the classic theory of political business cycle is the being the negative relationship between inflation and unemployment. Phillips (1958) documents an inverse relationship between inflation and unemployment levels in the British economy from 1861 through 1957; a higher level of unemployment is associated with a lower degree of inflation. If this relationship holds, then the government can manipulate the expansion of the economy to gather political support at the cost of inflation, which will only rear its head later.
2.1.3 Inflation rate and NPLs
The study conducted by Tarron and Sukrishnalall (2005) provides evidence of a positive relationship between the inflation rate and nonperforming loans. Their results suggest a mixed relationship between inflation and non performing loans which is between current time and previous time. This means that the declining inflation in the current period should see the rising in the level of loan loss provisioning in the banking sectors. However, decreasing inflation from the previous period causes commercial banks to incur lower loan loss provision. The study concluded that a part from the mixed effects, the inflation appear to exert on NPL, the coefficient of inflation variables are not significant in the regression methods.
Fofack (2005), also provide the evidence of a positive relationship between inflation rate and non performing loans. Fofack (2005), for instance, shows that inflationary pressures contribute to the high level of impaired loans in a number of Sub-Saharan African countries with flexible exchange rate regimes. The study shows that inflation is responsible for the rapid attrition of commercial banks’ equity and consequently higher credit risk in the banking sectors of these African countries.
The inflation rate affects economic growth by reducing the overall amount of credit that available to lending activities. In common, higher inflation rate may decrease the return on assets rate but in other hand as result to increasing the debt burden and should lead to higher number of NPL’s.
2.1.4 Interest Rates and NPLs
During economic downturn, interest rates is the main indicators of uncertainty for the value of the banking or financial institutions. This is because when the interest rates increase, borrowers are unable to made repayment of the percentage based of principal of the loan. This may lead to interest rates risk. Generally, interest rates are very sensitive toward banking business.
Generally, increasing interest rates are typical for strong economic growth,
and declining interest rates are typical for the early stages of an economic trough and during recession ( Markus, Irene and Andreas, 2001). The study conducted by Syeda Zabeen Ahmed (2006) was showed that lending rate as independent variable is not significantly related with non performing loans in commercial bank in Bangladesh.
According to the analysis made by Raphael and Ananthakrishnan (2010); Nonperforming loans in the GCC banking system and their macroeconomic effects shows that the results of the Panel VAR determine that the higher interest rates increase NPLs (although the effect is not significant) and higher credit reduces the NPL ratio. By that, it’s giving the feedback effect of higher NPLs suffered by the banking sector.
From the previous study reported that high interest rate is positively related to the depend variable which is nonperforming loans (Tarron and Sukrishnalall (2005)). In a higher number of NPL, banking sector tend to improve their asset rather than giving the credit to the borrowers. Other than that, rising NPLs requires bank to raise provision for loan loss that decreases the bank bank’s profitability and minimize the funds for lending activities.
The study conducted by Yixin Hou (2005) clarify that here is slight proof that the growth of non-performing loans will cause banks in Japan to reduce their lending in either cases as neither of the coefficients related with NPL growth is statistically significant, which is to the different to the hypothesis that non-performing loans have negative effect on bank’s lending. In fact, the results show that non-performing loan growth rate does not significantly affect the lending growth rate.
The choices of GDP, unemployment rate, inflation rate and interest rate are relevance for this research. The study based on previous research focusing on macroeconomic factors such as GDP, unemployment rate, inflation rate and interest rate that influence the non-performing loans. The findings will enhance the researcher and the bank understanding.
CHAPTER 3: METHODOLOGY
This chapter will explain the method and procedure use in this study. The data collection and method analyzing data are discussed in order to understand the relationship between variables used. The particular chapter focuses on the research methodology that had been used for this research including data collection, sampling frame, theoretical framework and so on .The objective of this study was to determine the factors that affect default risk of Public Bank. In order to analyze the significant of variables the hypothesis will be tested to prove the relation. To achieve the above objectives, this study used the data of NPLs based on past financial quarterly report of Public Bank and also GDPs rates, unemployment rates, interest rates on BLR and inflation rates.
3.1 Data Collections
This study is specifically based on the secondary data that are previously collecting for the purpose that made by the other researchers. The qualitative and quantitative data for this study will be based on the secondary data. This is because secondary data are much easier and faster than using primary data method.
3.1.1 Secondary Data
Secondary data is the data that have been published before other parties, bodies or person and collected from other sources. It is easier and less time consuming to collect as compared to the primary data. It’s including internal and external sources. In this research, the secondary data have been gathered mainly from:
- Bank Negara Malaysia
- Departments of Statistic Malaysia
- Past Financial Quarterly Report of Public Bank.
3.2 Sampling Frame
This study used past financial quarterly report of Public Bank from year 2005 to 2009 for analyzing the trend of banking institutions performance due to economic conditions. The independent variables taken like GDPs, unemployment rates, interest rates and also inflation rates are to represent the major factors that most effect to default risk faced by banking institutions. The macroeconomics variables will be indicates based on quarterly scope of data.
3.3 Sources of Data
Data concerning on a secondary date which is collected from past financial quarterly report of Public Bank. For the independent variables, selected macroeconomic variables like GDP, unemployment rate, inflation rate, and interest rates on BLR are used to measure the significance result of engaged relationship. The length is from 2005 until 2009.
3.4 Variables and Measurement
The variables used in this study can be categorized into two main types which are the dependent and the independent variables.
3.4.1 Dependent Variables
The dependent variable for this study is nonperforming ratio based on past financial quarterly report of Public Bank from the year 2005 to 2009.
3.4.2 Independent Variables
For this study, there are four relevant independent variables that will be measured. There are the Gross Domestic Product (GDP), unemployment rate, inflation rate, interest rates on BLR from the year 2005 until 2009 on quarterly basis.
3.5 Research Design
This research is designed to explore the relationship between dependent and independent variables. In this study, it engages in hypotheses testing that will explain the certain significant correlations between NPLs and macroeconomic variables.
3.5.1 Purpose of the study
In order to understand more about default risk faced by the banking institutions due to economic conditions, this study wants to determine the relationships between macroeconomic variables such as GDPs, unemployment rates, inflation rates and interest rates on BLR towards non performing loans.
3.5.2 Types of investigation
This study involved the correlation types of investigation in order to understanding the relationship between dependent and independent variables. In hopes, the study conducted is to identify the significant influences between independent and dependent variables.
3.5.3 Unit of analysis
In this study involve GDPs, unemployment rates, inflation rates and also interest rates on BLR and which play the role as independent variables for macroeconomic factors and Nonperforming loan act as dependent variables to examine the reflections toward macroeconomics indicators.
3.6 Theoretical Frameworks
This framework is design for the purposed to make it clear about the research ongoing control or the guidelines. This is to see the potential feedback of each variable.
Dependent variable: Nonperforming loan
Independent variables: Gross Domestic Product (GDP), unemployment rate, inflation Rate, and interest rates on BLR.
Gross Domestic Product (GDP)
Non-performing loan of Public Bank
Interest Rate on Base Lending Rate
Figure 1: Schematic Diagram (Relationship Diagram)
3.6.1 Data Analysis and Treatment
The statistical tools use in the study is Multiple Linear Regression Model. The general purpose of multiple regressions is to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable. This model of analysis is done to examine the simultaneous effects of several independent variables on a dependent variable that is interval scaled. In other used since is can explain the correlation between variable and also response variable by fitting liner equation to observe data.
Multiple Linear Regression Model Equation:
Y = Dependent variable which represent NPL
= The constant number of equation
= Coefficient Beta value
= Independent variable which represent GDP
= Independent variable which represent unemployment rates
= Independent variable which represent inflation rates
= Independent variable which represent interest rates on BLR
126.96.36.199 Correlation Coefficient (R)
The correlation coefficient is used to measure of how well trends in the predicted values follow trends in past actual values. It is to measure the strength of association between 2 variables. In this study, the coefficient of correlation can be useful to test relationship between NPLs and four independent variables gross domestic product, unemployment rate, inflation rate, and also interest rates on BLR.
188.8.131.52 Coefficient of Determination (R2)
The R-square value is an indicator of how well the model fits the data and the coefficient of the determination is better measure than (R) because the value of R-Squared can be interpreted precisely. In this study movements of the variables that can be explained by movements in a benchmark index R- Squared when BNM statistical explained by the four independent variables including gross domestic product, unemployment rate, inflation rate, and also interest rates on BLR.
H0: There is no relationship between macroeconomic factors and non-performing loans.
H1: There is a relationship between macroeconomic factors and non-performing loans.
H0: There is no relationship between GDP and non-performing loans.
H1: There is a relationship between GDP and non-performing loans.
H0: There is no relationship between unemployment rate and non-performing loans.
H1: There is a relationship between unemployment rate and non-performing loans.
H0: There is no relationship between inflation rate and non-performing loans.
H1: There is a relationship between inflation rate and non-performing loans.
H0: There is no relationship between BLR and non-performing loans.
H1: There is a relationship between BLR and non-performing loans.
According to this study, it was being discussed about the potential feedback of NPL of Public Bank toward macroeconomic variable. Multiple linear regressions method is used to prove the result of reflection between dependent variable and independent variable. The expectation results are informative and could be used by individual or borrower and also the banker or financial institution to handling the cash flow activities.
CHAPTER 4: DATA ANALYSIS AND RESULTS
This chapter will cover and explain the analysis of the data collected for the macroeconomics regression model. The results from the analysis will be interpreted to see the significance of each variable towards the dependent variable which is the Non Performing Loan.
4.1 Multiple Regression Output
Table 4.1.1: Model Summary
Adjusted R Square
Std. Error of the Estimate
a. Predictor: (constant), BLR, INFLATION, GDP, UNEMPLOYMENT
b. Dependent Variable: NPL’s
Table 4.1.1 result shows the values or R, R², and adjusted R². R is the square root of R-squared and is the correlation between the observed and predicted values of dependent variables. The multiple correlation coefficient R= 0.679 indicates that there is a strong correlation between the observed non performing loans and those predicted by the regression model. R² is measure the proportion of variations that explained by independent variables in the regression model. It shows that 46.1 % of changes in nonperforming loan can be explained by the changes of the four independent variables. It’s also proven that the four independent variables were contributed to bank performance. The adjusted R² is an attempt at improved estimation of R² in the population. Its show that 0.318 or 31.8% of macroeconomic variables are accounted to the non performing loans in Public Bank.
Table 4.1.2 Coefficientsª
a. Dependent Variable: NPL
Y = 13.173 + 0.016GDP – 0.317UNMP – 0.053 INF + 0.666BLR
Table 4.1.2 shows the coefficient above, its show the result of beta which is among the four independent variables influences most the variances in Non Performing Loan (NPLs). Coefficient is to determine the degree each predictor affect the outcome is the effect of other predictors are held constant. When the β value is positive, it’s will show that the relationship between independent variables and dependent variables is positive and vice versa.
From the equation above,
When all independent variables are constant, the nonperforming loans will increase by 13.173 units.
The result showed that GDP was positive correlated with NPL’s. When GDP changed by 1%, NPL’s will increase by 0.016 units. Furthermore, its show that GDP is not significant toward NPL’s of Public Bank. This is because when P-value 0.300 >0.05, so the null hypothesis had been failed to rejected at 5% level of significant. The results are consistent with previous study of Guyana conducted by Tarron and Sukrishnalall (2005). The observed results reveal that the GDP are not an important determinant of NPL’s.
The unemployment rate and NPL’s were negatively correlated. Its show that, when unemployment rate changed by 1%, NPL’s will decrease by 0.317 units. The result also stated not significant because P-value was 0.304>0.05. So the null hypothesis had been failed to reject at 5% level of significant.
Inflation showed a significant contribution p-value 0.064<0.05 thus the null hypothesis had been rejected at 5% level of significant and it was negatively related. When inflation changed by 1%, NPL’s will decrease by 0.053units. The results also consistent with previous study conducted by Tarron and Sukrishnallal (2005) which is proved that the coefficients of inflation variable is not significant in the regression methods.
BLR show the significant contribution to the NPL’s of Public Bank; P-value 0.042<0.05. Meaning that, the null hypothesis had been rejected at 5% level of significant. It was positive correlated to NPL’s of Public Bank. When the BLR changed by 1%, NPL’s of Public Bank will increase by 0.666 units. The study conducted by previous researcher; Raphael and Ananthakrishnan (2010), also giving consistent result which is strongly agreed that interest rate are giving feedbacks toward nonperforming loans of the bank.
4.2 Hypothesis Selections
For hypothesis testing of this study,
H0: There is no relationship between macroeconomic factors and non-performing loans.
H0: There is no relationship between GDP and non-performing loans. It can be explained according to P-Value, which is 0.300>0.05.
H0: There is no relationship between unemployment rate and non-performing loans. It can be explained according to P-value was 0.304>0.05.
H0: There is no relationship between inflation rate and non-performing loans It can be explained according to P-value was 0.064>0.05.
H1: There is a relationship between BLR and non-performing loans. It can be explained according to P-value was 0.003<0.05
Based on the result, this study choose hypothesis of H1 which have been support by base lending rate. This is because significant results for macroeconomic variables show below than 0.05. Its mean that the hypothesis that has been accepted based on the result is the macroeconomic variables have influence on nonperforming loan of Public Bank.
As a conclusion for chapter 4 of finding and analysis, its show that only one out of four of the macroeconomic variables which is base lending rate has an influence on the nonperforming loan of Public Bank.
CHAPTER 5: CONCLUSION AND RECOMMENDATION
This chapter summarizes the outcome of the study including concluding remarks and proposing suitable and relevant recommendations in order to identify the factors affecting the outcome of nonperforming loans.
This study is conducted to identify the factor that can affect the nonperforming loan of Public Bank, which includes the relationship between nonperforming loans and the macroeconomics independent variables. Gross domestic product, inflation, BLR, and unemployment rate are those four main independent variables which had been tested individually by using the multiple regression analysis. Its analysis had been used in order to identify the correlations, coefficient and significant relationship between each independent variable with nonperforming loans of Public Bank. From the result of coefficient correlations, it’s having proven that 67.9% macroeconomics variables have strong relationships toward nonperforming loans of Public Bank.
From the finding, out of four independent variables, only base lending rate is significant and has relationship toward nonperforming loans. The result on the significant of base lending rate, the result reliable with the previous study done by Raphael and Ananthakrishnan (2010), Tarron and Sukrishnalal (2005), which come with conclusion that interest had a greater effect on NPL’s and the result of analysis tested is significant.
This study had been conducted in quarterly for 5years from 2005 until 2009. The researcher had concluded that, from the finding and result of the analysis, some recommendation is needed to improve this research in the near future.
A more precise result of regression can be achieved by collecting more data for each variable. A range of longer time period can be used such as 15years to perform the reliable and more accurate result.
The model of the regression is dynamic; a new study can be done by adding more variable which theoretically relevant factors such as exchange rate, money multiplier, and others relevant variables that listed as macroeconomic explanatory.
Besides multiple linear regression method, others methods such as unit root test and Granger causality also can be used to examine the linked between variables. Unit root test is a statistical test for the proposition that in an autoregressive statistical model of a time series, the autoregressive parameter is one. In Granger Causality for determining whether one time series is useful in forecasting another. So the results of the data are more accurate and no bias of the data occurred.
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