How Macroeconomic Factors Impact Non-Performing Loans in the Middle East and Northern Banking - Research Proposal Example

INTRODUCTION

With the recent disturbance in banking sectors and the rise in non-performing loans (NPLs) in MENA (The Middle East and North Africa) region, there is revived interest in the impact of external and internal factors on NPLs of banks. There is much concern directed towards the impact of economics on the level of nonperforming loans to financial institutions. The study examines this issue by exploring the relationship between macroeconomic factors and non-performing loans.

Background

This study will investigate the effect of macroeconomic factors on the performance of non-performing loans in MENA (The Middle East and North Africa) region over the period 2002-2011. The research is motivated by the economic trend in MENA (The Middle East and North Africa) region which has been varying consistently while the rate of non-performing loans has been decreasing. Therefore, the analysis will be made on the regression model where the relation between a set of non-performing loans and fundamental macroeconomic indicators, specifically, consumer price index (CPI), the real GDP growth rate, capital inflow (FDI) and money supply (M2) will be investigated to explore the relationship between the two aspects. The objectives of the study are to ascertain the impact of gross domestic product (GDP) on non-performing loans in MENA (The Middle East and North Africa) region, to establish the influence of capital inflows on nonperforming loans in Kenya, to assess the impact of money supply on nonperforming loans in MENA (The Middle East and North Africa) region and to determine how changes in consumer price index results on nonperforming loans in MENA (The Middle East and North Africa) region. The study will use econometrics to model the relationship between the various selected variables and test their explanatory power on changes in nonperforming loans. It will look at the univariate analysis of each variable to test for normality, bivariate analysis to examine the correlation and covariance, and multiple regression analysis to explore the relationship between the dependent and independent variables.

Risks are one of the factors that portray the surrounding environment that every individual and institution run in. The type, level and magnitude of risks that are encountered depend on the activities type that components of an environment engage. The banking industry is a Financial institution that has faced a range of risks such as, market risk, operational credit risk, and liquidity risk among others. One of the identified major oldest risk factors includes Credit risk that banks and other monetary institutions have been standing from point to point. Credit risk pertains to the possibility that a borrower is incapable repay money furnished to them by a commercial lending institution. According to (Karumba & Wafula, 2012) default risk results due to the probability that borrowers are unable or fail to repay the loans hence affecting the institution performance.

Under IMF guidelines (2006) on Accounting framework and sectoral financial statements, If a borrower is unable to repay a debt or no interest is received from a loan asset for a period more than three months or 90 days, an institution is required to classify this loans as non-performing loans. Non-performing loans are well known to deaden performance of systems and also drive to financial crises .for example, the banking crisis and credit crunch in United States of America (Inoguchi, 2012) and MENA during 1980’s and 90’s. Non-performing loans are expenses or costs to banks and affect the performance and operation of a bank negatively.

Empirical and theoretical literature specifies various factors to be the cause of trend and emergence of nonperforming loans in monetary institutions. It includes macroeconomic factors, bank-specific factors and financial factors (Louzis, Vouldis & Metaxas, 2010; Ng’etich & Wanjau 2011). Macroeconomic factors deal both at the national level regional degree in the entire economy. It affects the whole population. Such factors include interest rates, exchange rate, monetary policy rates, inflation, investments, savings, GDP, unemployment, CPI, money supply among others. Government businesses and consumers regularly control them. Studies have revealed that utmost of these macroeconomic factors tend to hold a significant differing effect on the non-performing loans level in banks.

Karuba observed that MENA banks undergo the same problems and risks emanating from such risks faced by other institutions. The nonperforming loans are as a result default loans, where they are of no interest to an institution. Nonperforming loans results from loan defaults. in other words they are of no benefit to the organization since there is no cash flow is generated from them. The aspect of nonperforming loans in the market is receiving a lot of attention because of the impact brought by a large number of non performing failure or banks collapse. Sattar, Chaudhry & Khalil, 2012) in their research found that the non performing loans have played part in financial crisis occurrence experienced in many parts of the world mostly in middle east and northern region.

Hardy, 1998 reveals the banking history crisis commencing with the great depression of the united states, different period marked by failure of banking system is during the 1980’s and 90’s where many African countries had to restructure their banking system after a crisis caused by loans to parastatals. Hardy observed that occurrences of some irregular activities in an economy are likely to have banking system impact. Failure of banking system in Middle East and northern region was caused by a lot of factors as observed by Hardy (1980). Government businesses and consumers regularly control them. Studies have revealed that utmost of these macroeconomic factors tend to hold a significant differing effect on the non-performing loans level in banks. Nge’tich & Wanjau, (2011) state that the major supporting factor of banking and financial crises adopted mostly in the Middle East and Sub-Saharan Africa nations is high levels of nonperforming assets. Eichengreen & Rose, (1998) affirmed that the banking system in the 1990’s was the financial crises causes in emerging economies..

Purpose of the Study

Macroeconomic variables deal with the whole economy both at the national and regional and influence the entire group. This research proposal aims to examine the influence of various macroeconomic factors on the level of non-performing loans within Middle East and northern Africa region banking period within the years of 2002 to 2011. This season was marked by a decreasing trend of non-performing loans and, therefore, the research seeks to build the level of importance of various macroeconomic variables that also underwent considerable variability through that period, on neglected loans.

General Objective

To study the macroeconomic factors impacts on the non-performing loans in the Middle East and northern banking industry during the time of (2002- 2011)

Specific Objective

1. To determine the impact of (GDP) a gross domestic product on non-performing loans in the Middle East and northern region.

2. To ascertain the influence of capital inflows on non-performing loans in the Middle East and northern region.

3. To assess the impact of money supply on nonperforming loans in the Middle East and north of the country.

4. To evaluate how changes in inflation impact on nonperforming loans in the Middle East and northern region.

1.6 Research Questions

1. What is the influence of (GDP) gross domestic product growth rate on the Middle East and northern region non-performing loans?

2. Do Foreign direct investments (capital inflows) have a meaningful influence on non-performing loans in the middle east and northern region?

3. What is the result of money supply on the Middle East and northern region nonperforming loans?

4. What is the significance of inflation in the Middle East and northern region nonperforming loans?

Significance of the Study

The study points to enlighten on the macroeconomic variables of loan default. Consequently, the banks can recognize the factors contributing to the creation of non-performing loans and tighten their lending strategy as well. The makers of the policy can have a common view regarding the effects of their previously taken decision. They also can make a prudent judgment in making further non-performing loans alleviating policy. It includes macroeconomic factors, bank-specific factors and financial factors (Louzis, Vouldis & Metaxas, 2010; Ng’etich & Wanjau 2011). Macroeconomic factors deal both at the national level regional degree in the entire economy. It affects the whole population. Such factors include interest rates, exchange rate, monetary policy rates, inflation, investments, savings, GDP, unemployment, CPI, money supply among others. Government businesses and consumers regularly control them. Studies have revealed that utmost of these macroeconomic factors tend to hold a significant differing effect on the non-performing loans level in banks.

Justification of the Study

It’s pretty typical for commercial banks in the Middle East and northern region to experience bad loans as a standard part of their business. Therefore; we would not anticipate observing effects on commercial bank behavior at normal levels. However, once non-performing loans rise above this standard operational level, it suits a primary concern for the entire economy. Scores of commercial banks have sunk, essentially because of non-performing loans. It has led majorly in bank crises/failures and financial crises in most sectors of the world. Poor quality of loan has its roots in both internal and external factors characterizing the context of a financial institution. As studies have shown, macroeconomic factors play a significant role in the evolution of non-performing loans. Therefore, the study will estimate the significance and explanatory of these factors in influencing of non-performing loans and make a distinctive contribution to the existing knowledge.

Scope of the Study

The inquiry will focus on the macroeconomic variables impacts on non-performing loans within the Middle East and northern region banking industry. The time range under attention on the trend of non-performing loans of banks will take place between the period 2002 and 2011. On the other side, the geographical scope will incorporate the whole banking industry in the Middle East and northern region.

LITERATURE REVIEW

INTRODUCTION

This research studied at literature did on macroeconomic factors and its impacts on non-performing loans by several scholars/researchers from several sources; journals, articles, books, websites, and others. The review of the research mostly stared at the works and studies done in several nations and observed the level of significance of influencing the level of nonperforming loans by the macroeconomic variables.

Theoretical Review and models

Credit portfolio management models

Kuo (2008) Observed that for a bank to broaden the risk associated with funds lending to borrowers, it has to have a portfolio of loans that comprises different types of loans with varying levels of risk. Four credit portfolio models have been introduced to measure portfolio credit risk; J.P Morgan credit metric 1997, KMV Portfolio Manager introduced in 1993, Credit Suisse First Boston launched Credit Risk+ 1997 and McKinsey portfolio-view in 1997.

McKinsey portfolio-view in 1997

Kuo (2008) Credit portfolio management state that the first macro-factor model introduced by McKinsey is MPV that involves a view of the condition of the economy. Credit Portfolio View model suggests a methodology which links macroeconomics factors to migration probabilities and default.

It is an approach which is conditional that carries the risk of the economy which has a mostly predictable impact on default probabilities and credit migration. The historical default rate for country/industry combinations is defined as function of macroeconomic variables stipulated by the user. The model catches the key aspect that economy-wide defaults fall and rise with macroeconomic conditions.

(Probability of Default) = f (GDP growth, Capital inflow, CPI, Money supply…)

According to Karunakar, Vasuki & Saravanan, (2008), all nonperforming assets impacts the entire economy regarding growth. But the greatest impact is frequently felt by commercial institutions since they own large portfolios, a broad customer base, assets, owners/ shareholders, bank and deposit holders of the bank. About the fact that no interest or income is obtained from the nonperforming loans, a commercial bank provides provisions to shield themselves. Therefore, nonperforming loans are costs to a commercial institution. Due to this effect have the following impact; the cost of capital will go up, bank profitability is affected, interest income will fall, return on investment is reduced, and asset liability mismatch will stretch.

Karunakar et al., (2008) in their study states that the business of a bank involves the collection of funds from those in low demands (depositors) and make it available to those in high demand (borrowers) at interest. This business of extending credit involves some level of risks depending on characteristics of the borrower, and when the loans become nonperforming where no interest is obtained and the principle amount they become assets to the banks. The following section looks at hypothesis and models used to study obstacle loans.

Moral hazard hypothesis

Khatib (2010) talks about the moral hazard hypothesis which characterizes the behavior of excessive risk taking in a case where a party takes on the part of the risk that cannot be charged for or prevented. Brown bridge (1998) Looks at moral hazard as the behavior of bank owners to act in a manner not acceptable to the banks customers mostly depositors or the government that jeopardizes the interests of this parties by undertaking risky activities such as lending to high-risk borrowers which in turn if futile would put the institution in an unfortunate position. This behavior of bank owners most likely would lead to the emergence of problematic loans in case the borrowers default and the probability is usually very high.

Information Asymmetry

Castell & Tanchuco (2003) observed information asymmetry as a situation where a bank lacks all the necessary information on borrowers regarding their creditworthiness. This puts a bank at a position where it’s not able to differentiate high-risk borrowers from low-risk borrowers. They also note that there some rules/norms/boundaries/laws that protect the privacy of loan applicants. A bank is usually exposed to higher level of risks as a result of all these limitations. Khatib, (2010) observes that due to information asymmetry banks usually end up giving loans that would not gain interest to the institution. Brownbrigde (1998) also states that IA can affect the performance of a bank through an adverse selection of its borrowers.

Hypothesis of Disaster Myopia

Guttentag & Herring (1984 as quoted in Khatib, 2010) looks at disaster myopia as the difficulty of not being able to compute the probability of default of debt. They assume that this may occur as a result of dynamic in the economy, regulatory frameworks or disasters which in turn renders an institution incapable of measuring future adverse events and be prone to credit expansions blindly. Not knowing the probability of default of loans may put an organization at the risk of emergence of non-performing loans.

Research design

The research approach adopted in this study was a simple time series analysis model that assisted in providing a reliable assessment of the determinants of nonperforming loans in MENA (The Middle East and North Africa) region. The research used a regression model for the intended study as it designed to explore the significance level of macroeconomic factors in determining nonperforming loans. Ahmed (2006) in his study on the relationship between NPAs and financial factors and macroeconomic factors in Bangladesh uses a similar approach. The approach will focus on the entire population of commercial banks in MENA (The Middle East and North Africa) region.

Target Population

The target population consisted of all the MENA commercial banks within MENA (The Middle East and North Africa) region banking industry from where data on nonperforming loans was derived. In the current situation in the middle east and northern region, statistics by the CBK show that the population of financial institutions comprises of 43 licensed commercial banks and one mortgage finance company, 27 of the commercial banks are locally owned and 13 are foreign owned.

Sampling Design

The study will use high-frequency quarterly data (monthly) for all period of 6 years from 2006 to 2011 to achieve the stated objectives. The high frequency quarterly data will allow the researcher to get the correct results. Sampling is one of the best designs used in this study.

Data collection

The study will consider secondary data sources to acquire data. The primary data sources for this study will include central bank of MENA annual reports, surveys and publications on commercial banks performance, MENA National Bureau of Statistics and World Bank database.

Methods used in analysis

Econometrics models will be employed in the study to analyze the data. Data analysis was done using multiple linear regression, univariate analysis and descriptive statistics, charts and graphs. (Eviews 5) was used to aid in the analysis of the study.

Univariate, descriptive, and bivariate analysis

To analyze the relationship between the nonperforming and the macroeconomic factors separately and observe the trends first, we presented the data using graphs. To check for normality in variables descriptive statistics was used to analyze the mean, variance, skewness and kurtosis. Further, the study looked at the covariance (relationship either positive or negative) between variables and the correlation coefficient (strength and magnitude of the linear relation).

Regression Analysis Model

Univariate analysis will be used to determine the importance of each of the macroeconomic variable in affecting non performing loans. A multivariate regression model will be adopted to show the regression analysis of non-performing loans as the dependent variable and GDP, Money supply, consumer price index, and FDI as the independent variables is given below similar to Ahmed, (2006); regression was developed by Sir Francis Galton 1885.

NPLt = f (GDPt, M2t, CPIt, CFt)………………………………..General specification

The general idea of a multiple linear regression models is that the response variable Y is a straight-line function of a given number of explanatory variables. Where the unknown parameters are denoted as α, the independent variables as X and the dependent variable as Y.

• Research Proposal budget in dollars

Item

No.

Unit Cost (USD)

Days

Total (USD)

Stationery including internet bundles

Ls

All

All

3,000

Development of research proposal

2 copies

250

28

500

Final copies of research proposal

1 copies

250

2

250

TOTAL

3,750

Research project timetable (Gantt chart)

ACTIVITY

March-15

April-15

May-15

June-15

Duration

Wk

1

Wk

2

Wk

3

Wk

4

Wk

1

Wk

2

Wk

3

Wk

4

Wk

1

Wk

2

Wk

3

Wk

4

Wk

1

Wk

2

Wk

3

Wk

4

Research proposal

Research proposal defense

Amending the proposal

Pretesting questionnaires

Data Collection

Data analysis

Report production and defense

Amendments and final report