Bank-specific determinants of nonperforming assets of Indian banks

Abstract

The paper examines the role of bank-specific variables in explaining the dynamics of non-performing assets (NPAs) of Indian banks in a panel data framework over the post liberalisation period, 1995-2011. The results have been derived after controlling for macroeconomic factors like real GDP, inflation, exchange rate etc. Applying several variants of Generalized Method of Moments (GMM) technique in dynamic models, we find that that there is significant time persistence of NPAs in Indian banking system. We also find that larger banks are more prone to default than smaller banks. We find support for the 'bad management hypothesis' as we observe that an increase in profit level of the banks reduces NPAs in the next period. Lagged capital adequacy ratio as an important prudential indicator also significantly reduces current NPAs of banks. The paper also draws some important policy implications about NPA management.

Industry-Specific and Macroeconomic Determinants of Non-Performing Loans: A Comparative Analysis of ARDL and VECM

“…Inflation and unemployment were found to be positively and significantly associated with NPLs. Bardhan and Mukherjee [25] found results supporting the 'bad management hypothesis' predicting negative future relationships with NPAs (non-performing assets). They used performance as a proxy for managerial efficiency.…”

“…Rajan, Bardhan, and Mukherjee [25,26] identified that future NPLs are related to past earnings and increases in profit can reduce NPLs. They also explained that managers could manipulate their power to alter credit policies to inflate current earnings, change the terms of the loan, and relax the conditions, which may lead to bad loans.…”

Determinants of non-performing loans of deposit banks in Turkey

“…Potential increases in the amount of bad loans in the loan portfolio may cause banks to have difficulty in their financial intermediation process and this severely influences their liquidity and profitability. If the increases in loan defaults are not controlled, banking failures are inevitable (Bardhan and Mukherjee, 2016;Ghosh, 2015;Kasman and Kasman, 2015;Nkusu, 2011). Likewise, banks' asset quality deterioration could decelerate economic growth by weakening the stability of banking system (Ghosh, 2015;Zeng, 2012).…”

“…So, macroeconomic environment can be seen as one of the most important determinants of banking credit risk (Demirguc-Kunt and Detragiache, 1998;Castro, 2013;Messai and Jouini, 2013;Skarica, 2014;among others). As well as macroeconomic conditions, bank specific indicators are also used in many previous banking studies to explore the determinants of NPLs (Us, 2016;Abdioglu and Aytekin, 2016; Bardhan and Mukherjee, 2016;Dimitrios et al, 2016;Ghosh, 2015;Berger and DeYoung, 1997;among others).…”

Does Banking Management Affect Credit Risk? Evidence from the Indian Banking System

“…Similarly, IV estimators require strong external IVs, which are very hard to find in practice (Himmelberg et al 1999). Therefore, the study employed the generalized method of moments (GMM) estimation developed by Arellano and Bond (1991) and generalized by Arellano and Bover (1995) and Blundell and Bond (1998), which resolves the problems of endogeneity and dynamic panel bias (Baltagi 2008). Specifically, we used the two-step system GMM estimator (Windmeijer 2005).…”