Determinants Of Non-performing Loans in Pakistan

Many countries have been facing the problem of bank insolvency across the globe. Asset deterioration is one of the main reasons for insolvency of banks. The objective of the paper is to ascertain the determinants of nonperforming loans (NPLs) in the banking sector of Pakistan for the period 2006-16. Other than the bank specific and macro variables proposed by the literature, the roles of weighted maturity and output gap are for the first time examined. We find significant impact of output gap on NPLs however weighted maturity has insignificant role in shaping the future NPLs. Bank specific drivers of NPLs include bank size and capital adequacy ratio.


Introduction
Banks play a vital role for the economy and act as intermediary between depositors and borrowers.
The expansion of the financial sector is a step closer to the development of an economy hence making it a key factor in growth. The financial crisis of 2007-08 was a major setback for the global economy and put them at a financial risk upsetting the credit quality of loan portfolios across the globe (Dimitrios, Helen, & Mike, 2016). Pakistan's banking sector is facing the problem of nonperforming loans for the last three to four decades. Pakistan's banks had crossed the 14 percent limit of NPLs to gross advances in 2010-11, which however declined to 13 percent in the coming two years, that was an encouragement for the banking sector. The growth of an economy cannot be assumed without the presence of a sound financial sector, and one of its many uses include getting an idea of the economy's performance. The major focus of this paper is to view the determinants of these non-performing loans: loans that are more than 90 days past due (NPLs), in Pakistan's banking sector and investigate the bank specific and macroeconomic variables.
The banking sector can deteriorate due to managerial issues, low capital adequacy ratios and poor asset quality. Non-performing assets is also the single largest cause of irritation of the banking sectors (Tiwari, Sontakke, & Wardha, 2013). The assets deterioration is a serious problem unless the bank is efficient enough to recognize the problem before hand and handle it right away. This is one of the common factors which leads to bank failure. Poor asset quality leads to nonperforming loans that can seriously damage a banks' financial position having an adverse effect on bank's operations (Epure, & Lafuente, 2015). Since its one of the major issues, its origin and cause must be taken into consideration. Different economies have their own reason for bank failure and a rise in non-performing loans. The reason may be situational factors such as the economic conditions or the bank level factors.
NPLs affect the liquidity and the profitability of the bank which is one of the major components of the overall bank performance. The rise in NPLs leads to the diminishing income in the economy.
Non-performing loans should be given thorough consideration due its effect on the sustenance of the bank and survival of the economy. Mismatch of maturities between asset and liability create liquidity risk for the banks that deteriorate bank`s overall credit rating including its image (Badar & Javed, 2013).
2 NPL's have led to many problems in the financial sectors of the developing countries including Pakistan. Along with economic growth, increasing NPL's also effect the banking sectors. Kaminsky, & Reinhart (1999) state that the rising trend of non-performing loans in any economy indicates financial crisis. Therefore, the main objective is to examine the bank specific and macroeconomic factors of non-performing loans for Pakistan's banking sector. We aim to explore the relationship of two new variables namely output gap and weighted maturity with the nonperforming loans.

Literature review
A plethora of studies are conducted on non-performing loans due to its importance on the banking sector and financial sector. NPLs are determined by three different categories namely bank specific factors, macroeconomic factors, and the regulatory framework. Bank specific factors include lagged NPLs, Loans to total assets ratio, weighted average lending rate and credit policy, credit deposit ratio, total assets, return on equity, return on assets are important bank specific factors and macroeconomic factors are real GDP per capita, interest rate, inflation, unemployment rate, real GDP growth, nominal exchange rate (NEER), harmonized index of consumer prices, share prices index and the 3-month money market interest rate. The relationship between these factors is discussed in the context of the available literature as follows.
Bad luck hypothesis by Berger and DeYoung (1997) states that events outside the control of banks lead to an increase in problem causing loan default. Loans that default cause banks to get involved in additional managerial effort to deal with the problem loans which lead to extra operating costs.
Thus, under bad luck hypothesis, decreased measure cost efficiency leads to increases in nonperforming loans. Bad management hypothesis by Berger and DeYoung (1997) states that poor management at the senior level leads to low measured cost efficiency. Low cost efficiency is reflected in inefficient monitoring and poor control of operating expenses. It is not common for managers in these banks to practice adequate loan underwriting and therefore come under bad The study also establishes that African economies are exposed to unprecedented and external

Data & Methodology
This study utilizes various data sources including Economic Survey of Pakistan, State Bank of Pakistan's database, annual financial reports of individual banks to collect the data on nonperforming loans, capital adequacy ratio, total assets, interest rate, output gap, weighted maturity and market capitalization for 21 banks in Pakistan for 2006-16 period. The output gap is computed from GDP data and weighted maturity from individual bank specific statistics.
We choose panel data modelling for empirical estimation to cater for individual heterogeneity, collinearity, and to reduce the biases resulting from aggregation over firms (Baltagi, 2008).
where & denote banks and time respectively, is a scalar and is a 1 vector of coefficients and is a matrix of bank specific & macroeconomic covariates for NPLs. The unobservable individual-specific effects are denoted by and is the disturbance term.
Panel models that do not cater for cross-sectional dependency may lead to misleading inferences (Baltagi, 2008). To test the null hypothesis of no cross-sectional dependence, we call in the crosssectional dependence (CD) test proposed by Pesaran.
The null hypothesis of no cross-sectional dependence can be stated as correlation among the innovations of different banks.
where is the error term from equation (1) for bank i and is the disturbance term of bank j.
Pesaran suggests a simple test of error cross-section dependence in panel with short T and large N as follows: under the null hypothesis of no cross-sectional dependence, CD test follows the standard normal distribution i.e. CD→N(0,1).
Based on the outcome of cross-sectional dependence test, ADF-Fisher test is used to explore the stochastic properties of our data. The Hausman test is applied to choose between the random vs. The Wu-Husaman statistic is given below: under the null hypothesis, this statistics follows the Chi-square distribution asymptotically with degrees of freedom equal to the rank of the matrix: ( (( 1 ) − ( 0 ).

Results & Discussion
First step in panel data modeling is testing the cross sectional dependence. For short T (time) and large N (cross sections), Pesaran's cross-sectional dependence (CD) test is employed and there is    This study introduces output gap to capture the business cycle and potential growth of an economy.
Output gap posits the away movements from potential growth of an economy which explains the positive and significant relationship with NPLs. Inefficient recovery during the recessionary period increases the nonperforming loans. In other words, an economy in the contractionary phase of the business cycle is observed to have more loan default than in expansionary phase of the business cycle.
The interest rate which is also the lending rate has significant impact on NPLs, which means lending rate does affect the ability of borrowers in repaying the loans. In case of commercial banks, interest rate has a significant positive effect on non-performing loans as well. Increase in interest rates makes savings attractive and lowers income resulting in possible loan defaults and therefore a positive association is observed. Manz (2019) asserts that interest rate increases the cost of borrowing and borrower chooses to increase the loan period and sometimes it becomes so long that the borrower becomes incapable of repaying and hence defaults.
Capital adequacy ratio (CAR) is used to promote stability in the financial system and to protect the depositors. A bank with a high proportion of funding to resources, all else equal to, is more ready to withstand a sudden misfortune than a bank with a low capital-asset ratio. Thus, such a bank is more averse to be tossed into indebtedness or subject to a run. This result is consistent with This study establishes a negative relation between market capitalization (CAP) and nonperforming loans which is consistent with the moral hazard hypothesis which argues that low capitalization increases the riskiness of banks (Berger & De Young, 1997). Market capitalization represents the market activity and higher the market capitalization higher will be the market activity resulting in more business possibilities. Thus, reduces the possibility of default.
Weighted maturity is treated as a dummy and it takes the value 1 if the loan maturity period is 0-2 years else zero value is assigned. Short run loans fall in the category of 0-2 years period while the long run comprises of all loans greater than 2 years. Results are indicative of the fact that NPLs in Pakistan do not depend their maturity.

Conclusion
Nonperforming loans (NPLs) have led to many problems in the financial sectors of the developing countries including Pakistan. The financial system of an economy is assumed to function smoothly.
Most of the macroeconomic theories therefore presume its smooth functioning to the extent of The study ascertains the determinants of nonperforming loans (NPLs) in Pakistan banking sector using a panel data of 21 banks for the period 2006-16. On balance, our findings corroborate with the previous literature on determinants of NPLs. We find, in particular for commercial banks, macro variables such as output gap and interest rate exert a strong influence. Bank specific variables like bank size, market capitalization, and capital adequacy ratio turned out to be the main drivers of NPLs. This is first empirical study that explores the role of weighted maturity (WM) in determining NPLs along with output gap as potential explanatory variables for banking sector in Pakistan. We could not establish any association between WM and NPLs however, output gap significantly impacts the NPLs.