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Abstract—This paper investigates the determinants of
nonperforming loans (NPL) in the Indian banking system with the
help of panel data modelling. Panel dataset of 31 Indian banks with
yearly data that spans the period of 2000 to 2012 totalling 372 firm
years has been analysed. It is found that higher growth rate in savings
and GDP is associated with lower NPLs in Indian banks. Higher
interest and inflation rates contribute positively to rising non
performing loans.
Keywords— Non performing Loans, Panel data, Interest rates,
Inflation rates.
I. INTRODUCTION
ISING Nonperforming loans (NPL) in Indian banking
system in the post financial crisis is catching up attention
of all the cross sections of stakeholders. Regulators, policy
makers and rating agencies started investigating the reasons.
The central bank of the country Reserve bank started advising
banks on proactive steps to manage and arrest the growth.
In general an asset/loan becomes non-performing when it
ceases to generate income for the bank. The economic and
financial costs of NPL’s are significant. Graham and
Humphrey (1978) suggested that, banks with larger amounts of
NPL have greater tendency to incur large amount of future
losses, and hence, NPL should be included as an indicator of
the banking system stability [1]. Fofack (2005) pointed out
that, these loans may negatively affect the level of private
investment, increase deposit liabilities and constrain the scope
of bank credit [2].
The economic development of a nation and stability of
banking system are invariably interrelated. International
experience shows that if NPA is not managed properly, it will
leads to banking failures and nationwide financial fragility.
Regular monitoring of loan quality is thus essential to ensure a
sound finical system and possibly provides an early alarm to
regulatory authorities of banking system.
Given the above discussion, it is necessary to identify the
determinants of NPLs which is the major motivation for this
study. Using panel data modelling, this study empirically
investigates the determinants of nonperforming loans in the
Indian banking system. The purpose of this study is to provide
and insight into the linkages between macro-economic factors
and non-performing loans of banks functioning in India.
Dr.P.Krishna Prasanna, Associate Professor, Indian Institute of
Technology, Madras, India, Email id: [email protected].
II. DETERMINISTIC MACRO ECONOMIC FACTORS
INCLUDED IN ANALYSIS
Recent literature across the countries identified the
following macro economic variables to influence the level of
non-performing loans.
GDP Growth: it is considered as an indicator of a country’s
standard of living. A growing economy is likely to be
associated with rising incomes and reduced financial distress (
Nkusu (2011)[3]. Hence growth in GDP increases the
capability of borrowers to repay their debt and is expected to
contribute to a lower NPL.
Construction Expenditure Growth: Construction sector
expenditure at constant terms is an independent variable that is
expected to influence NPLs.(Vogiazas 2011)[4].
Foreign Reserves Growth: It is a proxy for the growth in the
international trade of the country and hence, the Non
performing loans are expected to decrease with the rise of
foreign reserves.
Stock Market Index Growth: Buoyant stock markets reflects
outlook on firms’ profitability and improved the financial
health of the nation (Bofondi et al .,2011) that is likely to have
impact on the non performing loans[5].
Stock Market Volatility:: Merton’s theory predicts that the
probability of default is positively related to the stock market
volatility (Simons (2009)[6].
Inflation: According to Qu (2008), it is viewed as a hidden
risk pressure which provides an incentive for those with
savings to invest them, rather than have the purchasing power
of those savings erode through inflation[7]. On the other hand,
Nkusu (2011)[3], stated that higher inflation can make debt
servicing easier by reducing the real value of outstanding
loans. There is divided evidence on both the directions in the
literature.
Exchange Rate: According to Nkusu (2011)[3], on the one
hand, increase in exchange rate can reduce the ability of
investor to pay back by weakening their competitiveness of
export. On the other hand, it can improve the debt servicing
capacity of borrowers who borrow in foreign currency. Hence,
an increase in exchange rate can have mixed implications.
Growth Rate in per Capital Income in NNP: It is a
macroeconomic indicator which reflects the strength and
behaviour of per capital income. An increase in GRRT
indicates that the people earns more and hence will have
increased ability to pay back, which results in less NPL.
Determinants of Non-Performing Loans
in Indian Banking System
Dr. P. Krishna Prasanna
R
3rd International Conference on Management, Behavioral Sciences and Economics Issues (ICMBSE'2014) Feb. 11-12, 2014 Singapore
115
Repo/ Reverse Repo Rate: Short term interest rates such as
repo rate and reverse repo rate have played a crucial role in
RBI monetary policy stance, Rajiv&Chandra (2011)[8]. RBI
injects liquidity in the system through Repos and absorbs
liquidity from the system from reverse repos. The objective of
monetary policy has been unidirectional to reduce inflation so
the relationship is expected to be opposite with respect to
inflation variable.
Saving Growth Rate: Swamy (2012)[9] stated that savings
level in an economy explain the economic surplus available, in
general, which is directly proportional to the repayment
capacity of the borrowers of the banking sector. Hence it is
expected to show negative relation with NPL.
Rate of Unemployment: Bofondi et al (2011)[5] mentioned
that increase in the unemployment rate curtail the present and
future purchasing power of household. Hence, it is expected to
have a positive relationship with NPL.
All the above variables are included as exogenous in this
research study. The variables with absolute rupee values were
quantified using natural logarithmic values. In respect of other
variables growth rates were used to proxy the exogenous
variable. In literature, NPL’s are measured on either gross
basis or on net basis. Gross NPLs reflects the quality of loans
portfolio of banks and net NPLs shows the actual load on the
banks. In this study percentage ratio of Gross NPL to total
advances and percentage ratio of Net NPL to total advances
has been considered as dependent variables for the analysis.
III. PANEL DATA ANALYSIS AND INFERENCES
The bivariate regression analysis is conducted to identify the
independent relationship of each macroeconomic variable
upon the gross and net NPL ratio. The data set was a panel
data of 31 banks Indian banks with yearly data from 2000-
2012 totalling 372 firm years. The random effects GLS model
was used to capture and estimate both the cross section as well
as time effects in the variables. Table I presents the impact of
macroeconomic variable up on the non performing loans.
Subsequently the statistically significant variables have been
considered to run multiple regressions. The table II presents
the multiple regression results using Random effects GLS
model.
It is found that Ln GDP at factor cost, GDP Growth rate,
Growth rate in per capital income, foreign trade proxies,
Savings growth rate have significant inverse impact upon non
performing loans. Higher growth rates in savings, per capital
Income and GDP result in lowering non performing loans
significantly. GDP growth rate and savings growth rate had R
square of 44% which explain that they were most influencing
variables in determining level of nonperforming assets
.Exchange rates and stock market volatility also indicated
inverse relationship but were not found statistically significant.
Inflation and Interest rates have significant positive impact
on non performing loans. Higher interest rates results in higher
nonperforming assets. Interest rate had 33% impact on the non
performing assets across the banks and across 12 years study
time. Rate of unemployment was found to have positive impact
but was not significant.
The multiple regression results reconfirm the relationship
observed in bivariate relationship. These macro variables
together contribute 52% of changes in nonperforming loans.
Bank specific policies or customer specific characteristics
explain only the remaining 48% of changes.
IV. CONCLUSION
Impact of macroeconomic variables up on in gross NPL and
net NPL ratio to total advances has been investigated for
Indian banking system in the period of 2000-2012. Our
empirical results indicate that 52% of changes in the non
performing assets are determined by macro economic
variables. Growth rates in GDP, Savings, and per capita
income have significant inverse relationship which inflation
and interest rates have significant positive impact on the level
of nonperforming loans.
REFERENCES
[1] Graham, David R.; David Burras Humphrey., ‘Banks Examination Data
as Predictors of Bank Net Loans Losses’ (1978): Journal of Money,
Credit and Banking, November, Vol 10,No. 4, 491-504.
[2] Fofack, Hippolyte., ‘Nonperforming Loans in Sub-Saharan Africa:
Causal Analysis and Macro-Economic Implications’ (2005): World
Bank Policy Research Working Paper 3769.
[3] Nkusu, Mwanza., ‘Nonperforming Loans and Macrofinancial
Vulnerabilities in Advanced Economies’ (2011): IMF Working Paper,
WP/11/161.
[4] Vogiazas, D. Sofoklis.;Nikolaidou, Eftychia., ‘Investigating the
Determinants of Nonperforming Loans in the Romanian Banking
System: An Empirical Study with Reference to the Greek Crisis’ (2011):
Economics Research International, Vol 2011, Article ID 214689
[5] Bofondi, Marcello.;Ropele, Tiziano., ‘Macroeconomic Determinants of
Bad Loans: Evidence from Italian Banks’ (2011): Occasional Papers.
[6] Simons, Dietske.;Rowles, Ferdinand., ‘Macroeconomic Default
Modelling and Stress Testing’ (2009): International Journal of Central
Banking.
[7] Qu, Yiping., ‘Macro economic Factors and Probability of Default’
(2008): European Journal of economics, Finance and Administrative
Sciences, ISSN 1450-2275, Issue 13.
[8] Ranjan, Rajiv.; Dhal, Sarat Chandra., ‘Non-Performing Loans and Term
of Credit of Public Sector Banks in India: An Empirical Assessment’
(2003): Reserve Bank of India Occasional Papers, Vol. 24, No. 3.
[9] Swamy, Vigneswara., ‘Impact of Macroeconomic and Endogenous
Factors on Non-Performing Banks Assets’ (2012): International Journal
of Banking and Finance, Volume 9 | Issue 1, Article 2.
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TABLE I
IMPACT OF INDEPENDENT MACRO ECONOMIC VARIABLES UPON NON PERFORMING LOANS
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