Threshold Effect of Bank-specific Determinants of Non-performing Assets: An Application in Indian Banking

Bardhan, Samaresh; Sharma, Rajesh; Mukherjee, Vivekananda

doi:10.1177/0972652719831546

Cited by 10 publications

(10 citation statements)

References 38 publications

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“…More recent studies which employ threshold panel data regression include Piatti and Cincinelli (2019) and Bardhan et al (2019). Piatti and Cincinelli (2019) employ panel threshold model to a data set of 298 Italian banks for the period 2006–2014 to examine if banks’ credit processing quality results in non-performing loans reaching a certain threshold level and report that if non-performing loans remain below a preset value, then an increase in the quality of credit and loan monitoring reduces the bad loans ratio; however, if the bad loans ratio surpasses a preset threshold, this relationship reverses, and now an increase in monitoring of loans results in an increase in bad loans.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Piatti and Cincinelli (2019) employ panel threshold model to a data set of 298 Italian banks for the period 2006–2014 to examine if banks’ credit processing quality results in non-performing loans reaching a certain threshold level and report that if non-performing loans remain below a preset value, then an increase in the quality of credit and loan monitoring reduces the bad loans ratio; however, if the bad loans ratio surpasses a preset threshold, this relationship reverses, and now an increase in monitoring of loans results in an increase in bad loans. Bardhan et al (2019) employ threshold regression model of Hansen (1999) on an unbalanced panel data set of 82 Indian banks for the period from 1996 to 2011 to examine the role of bank-specific endogenous factors on non-performing loans and report that above a preset value, both—capital adequacy ratio and loan growth rate—wield negative impact on bad loans.…”

Section: Review Of Literaturementioning

confidence: 99%

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Non-performing Loans and Moral Hazard in the Indian Banking Sector: A Threshold Panel Regression Approach

Thomas

Thakur

2020

Global Business Review

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This article attempts to examine the effect of non-performing assets (NPA) on behaviour of banks in India. The objectives of this article is to test if lending choices of Indian Banks demonstrate moral hazard and to test whether an increase in NPA ratio of banks raises riskier bank lending. We employ a threshold panel data regression model on a data set retrieved from the Reserve bank of India, which covered 45 commercial banks during the period 2009–2015, to test if lending choices of Indian banks demonstrate moral hazard. The results establish that the moral hazard hypothesis does not hold true for the given sample of India Banks, suggesting that an increase in the NPA ratio does not potentially increase riskier lending in sample banks. We find empirical evidence for the notion that ‘too-big-to-fail’ banks possibly have certain incentives to take higher risks and thus have higher NPA ratios. Graphical approach to NPA threshold explanation reveals presence of threshold; however, it could not be statistically established. Future implications of findings are evaluated. The study seminally adds to the empirical literature on use of fixed effects threshold panel data regression model in the context of Indian banks.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Non-performing Loans and Moral Hazard in the Indian Banking Sector: A Threshold Panel Regression Approach

Thomas

Thakur

2020

Global Business Review

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“…This typically happens in case of PSBs because market discipline is poorly exercised in these banks and there is an expectation that government will bail out in case of bank." (Bardhan et al, 2019)…”

Section: Major Causes Of Npamentioning

confidence: 99%

An Effectiveness Assessment of Preventive Management Strategies in order to Manage Non Performing Assets in Indian banks: A Case Study

Meher

Puntambekar

Hawaldar

et al. 2020

SAEB

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There are two kinds of strategies to control Non Performing Assets i.e. curative and preventive. The paper is an attempt to focus on the effectiveness of various preventive strategies in controlling NPA in future.For this study primary data have been collected from 82 branches out of 138 branches of Sagar District in Madhya Pradesh of India. The respondents are the branch managers or recovery officer of each branch.The primary data are related to the causes of NPA, actual usage of preventive measures and effectiveness of each preventive strategy. It highlights few new causes which are barely covered by the earlier researches. The study also represents the actual usage of various preventive measures along with the effectiveness of preventive measures in averting NPA to be occurred in future. The outcome of this study could provide a valuable insight about which strategy is more effective to prevent these stressed assets. Besides that, it could aware the banking authorities regarding the problems faced by the managers in using the preventive measures.

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“…It is believed that issues of risk management in banks and issues of NPA in the bank can best be handled through disclosure, which is the primary motivation for this study (Goyal, 2010; Bardhan and Mukherjee, 2016). This study can be a game-changer in executing the T&D regulation in the banks in India.…”

Section: Introductionmentioning

confidence: 99%

Ownership concentration and its influence on transparency and disclosures of banks in India

Bhimavarapu

Rastogi

Kanoujiya

2022

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Purpose The disclosures in banks have become a matter of grave concern, especially post 2008 world financial crisis. The issue further gets exacerbated because disclosers in banks are part of the III pillar of BASEL-II floated in 1999, and despite that, banks face challenges in this regard. Ownership concentration (OC) is a point of discussion because it may affect banks’ corporate governance and transparency and disclosures (T&D) issues. This study aims to determine how OC affects the transparency in the banks. Design/methodology/approach A T&D index is built into the study covering all the relevant contemporary issues regarding disclosures in banks. The panel data specification is used to find out the association of components of the OC on the T&D practices in the banks. Bank data of 34 banks are gathered for four years for the study. Findings It is found that except for retail investors, other classes of OC are not concerned with the disclosures in the banks even though substantial financial and non-financial interests are at stake concerning them. The study’s findings suggest framing policies and regulations considering the accountability of promoters and institutional investors for ensuring disclosures in banks. Research limitations/implications A few proxies to measure T&D found in the literature have not been used in the study. Similarly, the definition of promoter’s class of investors can be improved. Originality/value To the best of the authors’ knowledge, no other study builds T&D for banks and examines their impact because of the ownership classes (as used by the current study). This study is unique in this aspect.

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Threshold Effect of Bank-specific Determinants of Non-performing Assets: An Application in Indian Banking

Cited by 10 publications

References 38 publications

Non-performing Loans and Moral Hazard in the Indian Banking Sector: A Threshold Panel Regression Approach

Non-performing Loans and Moral Hazard in the Indian Banking Sector: A Threshold Panel Regression Approach

An Effectiveness Assessment of Preventive Management Strategies in order to Manage Non Performing Assets in Indian banks: A Case Study

Ownership concentration and its influence on transparency and disclosures of banks in India

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