Determinants of Non Performing Loans: Evidence from Sri Lanka

In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings KeywordsNonperforming Loans; Macroeconomic Determinants


INTRODUCTION
Deterioration in the quality of the loan books of the Banks has been a main cause of financial instability in the banks which would ultimately impact the entire financial system of the country. Past studies (Bonilla, 2012) [4], (Khemraj & Pasha, 2009) [17] and the incidents reported around the world shows that accumulated bad loans play a major role in Bank failures. The scope of this research is limited to explore the determinants of macroeconomic variables. As discussed number of studies were found in the literature where the impact of macroeconomic variables has been studied. Non availability of publicly accessible data prevents a comprehensive study on bank specific variables. In studying the impact of types of the borrowers and their behaviour, factors which are specifically affecting different sectors (Keeton & Morris, 1987) [16] that lead to varying level of nonperforming loans in different sectors, data at more granular level are required. Non accessibility to these data is a limitation in studying the factors that may have a notable impact on the aggregate nonperforming loans. In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households' and firms' defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The  It can clearly be identified that the nonperforming loans have increased over the period in the financial sector in Sri Lanka. There is a greater impact from nonperforming loans to the profitability of the Banking industry. Banks are supposed to set aside considerable amount of money for loan loss provisions. It could be clearly identify the impact the nonperforming loans have on the profitability of the Banks. Regular monitoring of loans and advances, possibly with an early warning system capable of alerting regulatory authorities of potential bank stress, is thus essential to ensure a sound financial system and prevent systemic crises. Currently no such tools or methods are being used by the local regulators to monitor the quality of the loan books of the bank. Events took place during the past decade, that shook the financial system of the country necessitate and highlighted the importance of careful management of the assets and liabilities of the financial industry. The banks play a key role in the financial system in any country across the globe. The link between banks around the world made the situation even more sensitive when it comes to the stability of the world economy. The impact of the bad loans and effects to the stability of the financial system of the county has a clear relationship. What is important to understand is what causes the loans to go bad. Essentially lot of factors influence a loan to become a nonperforming loan (NPL) and may consist of microeconomic, macroeconomic, socio-economic, geographical, behavioral and many other valid factors. However scope of this paper is limited to the study of key macroeconomic factors that causes a loan to become a nonperforming loan. As depicted in  This paper attempts to study the macroeconomic determinants of banks' loan quality in Sri Lanka by analyzing secondary data over the period 1998-2014. Macroeconomic developments may have a different impact on loan quality depending on the type of the borrower. The nonperforming loans have been identified as one of the main causes of Bank failures. A failed financial institution brings many negative effects to the entire economy. Therefore managing nonperforming loans and maintaining it at an acceptable level is an important task for the stability of Banks as well as the financial system of a country. In order to manage the nonperforming loans, it is important to understand the determinants of the nonperforming loans and their relationship which is the main problem that would be addressed throughout the research. Thus, Identify the macroeconomic determinants of Nonperforming loans of the Sri Lankan Banking sector and their explanatory power is the main objective and sub objectives are to study the short term relationship of explanatory variables with nonperforming loans.

SIGNIFICANCE OF THE STUDY
Banking and Finance sector has faced difficulties over the years for different reasons. The main causes of serious banking problems continue to be lack of credit standards for borrowers and counterparties, poor portfolio risk management, and a lack of attention to changes in economic or other circumstances that can lead to deterioration in the credit standing of a bank's counterparties.
The impact of Nonperforming loans (NPLs) is two folds for the capital adequacy ratio and it erodes the profitability of a bank as a result of provisions for nonperforming loans. The increase in nonperforming loans would increase the risk weighted assets and it further reduce the retain earnings which will be one of the components considered for capital of the Bank. Internationally, banks have been moving towards the use of sophisticated models for measuring and managing risks in an integrated manner with a view to ensuring a comprehensive Internal Capital Adequacy Assessment Process (ICAAP) under Pillar 2 of the Basel II framework. However in the local context Banks are using mostly scenario based approaches for stress testing activities.
The significance of this study is that, it attempts to develop a relationship between the NPL and the macroeconomic variables which will help all the Banks in the country in devising a similar model to have a forwardlooking stress testing mechanism to forecast the level of nonperforming loans in response to the changes in the macroeconomic environment. The model could also be used by the Banks to forecast the nonperforming loans and have a set of action plans ready to face adverse situations.

A BRIEF LITERATURE REVEIW
The literature review supports to identify the research phenomenon, the variables and the hypotheses. One of the widely discussed relationships in literature is that the stability and the vulnerability of the banking sector in the boom and depression of business cycles. Quagliariello (2007) [22] analyses more than 200 Italian Banks over a period of almost two decades to understand the effect of evolution of the business cycle on Bank losses due to credit risk. The outcome of the study confirms that banks' loan loss provisions and new bad debts are affected by the evolution of the business cycle. It is observed from the empirical studies that, during economic boom, growth in bank loans are accelerated and a notable decline in credit growth in the depression business cycles ( Marcucci & Quagliariello (2008)). In an economic depression situation the opposite of what was discussed under the boom can be seen. The banks are reluctant to extend credit for investments which would reduce the growth in overall lending in the economy. As identified by Marcucci & Quagliariello (2008) [20] this may have a feedback effect on the economy as well. One important finding of their study is that when capital surplus over the regulatory minimum are low, banks may reduce lending, which in turn negatively affect the output of the economy. As discussed the literature on the NPLs observed this cyclic effect where NPLs are low during boom due to high revenue of borrowers provide them with stable cash flows to meet their loan obligations (Quagliariello, 2007) [22]. The main reason for the growth in the NPLs has been identified by these studies as the fall in the value of collaterals during the depression, consequently not covering the outstanding balance in case of a default.  [17] to study the determinants NPLs in the Guyana banking sector. Although a positive relationship is observed, the study found to be insignificant. The literature review revealed that previous studies conducted on NPLs focused mainly on two factors. The empirical evidence identified that NPLs could be explained by bank specific factors and macroeconomic factors. The literature review revealed that numbers of studies have been conducted in different parts of the world that found evidence to support the both relationships.

METHODOLOGY AND THE CONCEPTUAL FRAMEWORK OF THE STUDY
Following the review of literature this chapter presents an overview of the conceptual framework, macroeconomic model, econometric tests conducted, sources and the type of data used in the analysis. The empirical evidence identified that NPLs could be explained by bank specific factors and macroeconomic factors. Louzis, Vouldis and Metaxas (2011) [19] explained that determinant of NPLs should not be sought exclusively among macroeconomic variables. While recognizing the fact that bank specific factors may have an impact on the aggregate level of NPL's the scope of this study is limited to studying the impact of macroeconomic factors. The conceptual framework graphically illustrated in Based on the above conceptual framework and the scope of the study regression model has been developed taking only the macro economic variables into consideration. The current study is a correlational study which attempts to identify the relationship between the macroeconomic determinants of nonperforming loans in Sri Lanka and the explanatory power of such variables. The analysis process is econometrical in nature which uses secondary time series data to analyse the relationship using the ordinary least square method of regression (OLS).

Macroeconomic Factors
Bank

STATISTICAL MODEL SPECIFICATION
Multiple regression analysis has been used as the initial method in studying the relationship between macroeconomic variables and the NPls is Sri Lankan banking sector. The regression analysis helps identifying the determinants and their explanatory power. Review of the empirical studies revealed that, regression analysis has been used in majority of the studies ( 4 ) In order to capture the relationship of the growth rates, all the variables were converted to the log first difference using E views statistical software package. The log converted variables represent the growth rates which will be used in the regression model to estimate the relationship between nonperforming loans and the macroeconomic variables. It is required for the time series to be stationary for drawing useful inferences. A data series is said to be stationary if the mean and the variance of the time series are constant over time and the value of the covariance between two time periods depends only on the distance or lag between the two time periods and not on the actual time at which the covariance is computed. The correlation between a series and its lagged values are assumed to depend only on the length of the lag and not when the series started. This property is known as stationarity and any series obeying this is called a stationary time series. (Ahmed, 2008) [2]. Use of non stationary data for estimation can lead to make inaccurate regression results. Since the current study uses time series data it is required to test the data for stationarity prior to regression of the variables. Accordingly the time series has been tested for stationarity using Augmented Dickey Fuller (Dickey and Fuller, 1981) unit root tests in E-views. The unit root tests were conducted by including an intercept and a trend in the test equation as appropriate. The unit root tests for stationarity were carried out in level and first difference.

ANALYSIS OF THE DATA AND THE ESTIMATED RESULTS
Initially the data has been analysed to recognize the basic properties of normality, asymmetric and stationary aspect of the time series. The Table 8.1 illustrates a detailed statistical summary of the variables considered in the model.

Stationarity Test Analysis
A data series is said to be stationary if the mean and the variance of the time series are constant over time and the value of the covariance between two time periods depends only on the distance or lag between the two time periods and not on the actual time at which the covariance is computed.
In order to ensure the stationarity of the data Augmented Dicky Fuller tests were conducted on the variables which were used in the model. Based on the following hypothesis, the Error! Reference source not found.
shows the summary of ADF unit root tests results. The test statistic of both the variables is less than the critical value in absolute term at 5% level and therefore the null hypothesis of presence of unit root is accepted. All the other variables are stationary at level according to the ADF test results which is tabulated in Error! Reference source not found.. INTR and GDP are stationary at first difference According to the unit root test outcome of ADF test it is identified that some variables are non stationary at level and those variables are stationary at fist difference. To bring all the variables to a common platform in order to apply OLS regression, the variables have been converted to first difference (I 1 ) to make the time series data stationary. Implicit assumption that is made when applying the OLS estimation is that explanatory variables are not correlated with each other. The high correlation among explanatory variables is referred to as multi collinearity problem. Multi collinearity makes it tedious to assess the relative importance of the independent variables in explaining the variation caused by the dependent variable. (Lani, 2015)  According to the correlation matrix presented in Error! Reference source not found. no strong correlations were observed among the explanatory variables. However a notable correlation is observed among the Inflation and the GDP growth rate which is slightly over 0.5. Correlation among GDP and the ASPI is also found to be somewhat higher though it is not considered significant in affecting the OLS estimation.