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Purpose: Worldwide economies have been shattered by the alarming increase in Non-Performing Assets (NPAs) in Banking Sector. In India, the rise in NPA levels gives a clear insight into the health of industry and state. This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: Punjab National Bank (PNB), Bank of India (BOI), UCO Bank, Punjab and Sind Bank (PSB), HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Design/methodology/approach: The study utilised IBM SPSS version 20 application to carry out our statistical analysis of measures of central location (mean and median), measures of dispersion (standard deviation), to carry out the Kolmogorov–Smirnov test to check the normality of data, the Mann–Whitney U test (for two groups) for median comparison between private and public sector banks and the Kruskal–Wallis test (for more than two groups) for median comparison for more than two banks. p ≤0.01 and p ≤0.05 were the two-tailed significance level used for determining the significance of all statistical tests.

Findings: Trend analysis and statistical tests show that the trend in public sector banks to have NPAs is higher compared to private sector banks, and losses arising from NPA impact the banks’ profitability.

Practical implications: It is apparent that NPAs are a large threat to banks in India as it reflects the state of the Indian economy. The growth of the economic cycle is predominantly dependent on the smooth and profitable functioning of private and public sector banks. This current study focusses on and compares the impact of NPAs on the profitability of public and private sector banks. NPAs have grown exponentially more in the case of public sector banks than private sector banks, which has affected the former banks’ financial health and performance. Increases in the level of NPAs adversely affect the working style and long-term stability of public and private sector banks in the economy.

Social Implications: NPAs have a negative influence on the profitability of the banks as well as on the economic growth of the country too. However, it is recommended that management in the banking sector, particularly the public banks, should use various preventive and recovery strategies to reduce the risk of failure and to keep track of NPAs to stay safe.

Originality/value: This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: PNB, BOI, UCO Bank, PSB, HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

The banking industry faces increasing scrutiny from stakeholders regarding its environmental and social impacts, given its crucial role in fostering economic growth. Banks have been encouraged to adopt environmental, social and governance (ESG) practices to mitigate risks and safeguard their reputation. However, the effectiveness of ESG sensitivity within the banking industry is contingent upon ownership and structural factors. The extent to which banks can integrate ESG considerations into their operations and decision-making processes may vary based on their ownership structures. Therefore, this study aims to examine if the impact of ESG on the performance of Indian banks varies between private and public sector banks.

The study employs six years of panel data from two separate samples of 12 private sector banks and 10 public sector banks in India. It utilises fixed and random effect estimation techniques with robust standard errors to derive accurate and reliable econometric results.

The main findings of this study reveal intriguing insights into the relationship between ESG factors and bank performance, considering the influence of ownership structure. For private sector banks, the ESG composite score, particularly the social dimension, negatively impacts financial performance. However, there is a contrasting positive effect on efficiency. In contrast, public sector banks demonstrate a positive and significant association between the environmental score and return on equity and non-performing assets.

The findings highlight the need for tailored strategies that align with ownership structure to achieve sustainable financial and societal outcomes in the banking industry. Furthermore, it emphasises the need for private-sector banks to streamline their ESG initiatives, especially in the social dimension, to mitigate negative impacts on their financial performance.

This study introduces a novel dimension by addressing the “one size fits all” bias in prior research that overlooked bank ownership differences when examining the impact of ESG factors on bank performance.

The study examines whether the Basel-III regulations impact the financial performance, operational efficiency and resilience of Indian banks. Further, the study tests whether there is a variance in the impact between private- and public-sector banks.

The study uses panel data regression on data from 16 private- and 12 public-sector banks from the years 2016–2022. Random-effect estimation is used, and robust standard errors are calculated.

The main findings indicate that the Basel-III regulations related to capital and leverage boost public-sector banks' financial performance and resilience. However, a similar impact is not detected in the case of private-sector banks.

The findings signify that the Basel-III framework does not address the differences between public and private-sector banks. Therefore, the policy implications are of practical importance and indicate that Basel-III regulations should not be considered a one-size-fits-all type of bank. Instead, policymakers should consider the structural differences between private and public-sector banks concerning Basel-III regulations.

The study addresses a significant limitation of the Basel-III regulations, which, in their current state, somehow fail to account for the differences between the public- and private-sector banks.

The study aims to evaluate the financial distress position of selected sample banks in India. The top 10 banks with the highest levels of gross non-performing assets (NPA) under both public and private sector ownerships have been chosen for the study. Application of the Altman Z-score model has been used to compare both ownership banks’ financial distress for five years from 2017 to 2021. Based on the study’s findings, it was found that private sector banks demonstrated better financial stability than their public sector counterparts. Specifically, the average Z-score of the selected sample banks was higher than the safe zone threshold of 2.9 during the study period.

In the backdrop of an increase in market-based banking activities, this paper aims to study operational efficiency of Indian banking sector during 2009–2010 through 2017–2018 considering Capital Gain and Gain from Forex Market (as desirable outputs) and Slippage (as undesirable byproducts) simultaneously, along with Advances – a desirable output considered in the traditional banking performance assessment literature. This enables to have an assessment of performance (as captured by the measured efficiency scores) of Indian Banks following an alternative viewpoint about the banking activities. The authors also explain such efficiency scores in terms of bank-specific factors, banking industry competition scenario and interest rate channel.

Using data envelopment analysis (DEA) method, the authors estimate six alternatives but interlinked operational efficiency scores (TES) of the Indian domestic commercial banks. In the second stage, they explain such TES in terms of bank-specific factors, banking industry competition scenario and interest rate channel.

The authors observe that the private sector banks as a group outperform those under public ownership. Moreover, although the private sector banks could maintain somewhat consistency in their operational efficiency performance over the sample period, public sector banks clearly show a declining tendency. The second stage econometric estimation results show that the priority sector lending has a negative effect on efficiency. Interestingly, the authors get varying results for the relationship between maturity and efficiency score depending on banks’ strategies on stressed assets management. Furthermore, the analyses result that banks are not so efficient in managing relatively larger-volume loans. It is also observed that banks’ efficiency positively depends on the Credit-to-Deposit (CD) ratio. It is found that the overall operational efficiency of the banks to manage their credit risk portfolio improves with a reduction in the lending rate (LR). However, the interaction of lending activities and capital market shows that with the increase in LR, corporate borrowers may switch to capital market to explore for desired funds, which may induce the banking sector to investment in capital markets and create a positive market sentiment.

Literature, although scanty, is there dealing stressed assets of a bank as some undesirable byproducts of its operational and business activities. However, such literature mostly done within the traditional framework of banking business activities and modern market-based business activities are almost absent in the literature. The authors have done it in the present study.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

The purpose of the study is to investigate the correlation between credit supply to government and credit supply to the private sector to determine whether there is a crowding-out or crowding-in effect of credit supply to government on credit supply to the private sector.

The study used data from 43 countries during the 1980–2019 period. The study employed the Pearson correlation methodology to analyze the data.

There is a significant positive correlation between credit supply to government and credit supply to the private sector. There is also a significant positive relationship between credit supply to government and credit supply to the private sector, implying a crowding-in effect of government borrowing on private sector borrowing. The positive correlation between credit supply to government and credit supply to the private sector by banks is stronger and highly significant in the period before the Great Recession, while the positive correlation is weaker and less significant during the Great Recession, and the correlation further weakens after the Great Recession. The regional analyses show that the positive correlation between credit supply to government and credit supply to the private sector by banks is stronger and highly significant in the African region than in the Asian region and the region of the Americas.

There is no evidence on the correlation between credit supply to government and credit supply to the private sector during the Great Recession.

Purpose: The purpose of the study is to determine the significant difference between the performance of the Indian banks in pre coronavirus disease (COVID 19) and post COVID 19 periods. Further, it explores the impact of COVID 19 on the profitability of the Indian banks by investigating variation between the non-performing assets (NPAs) and the net profit of the banks during pre and post COVID 19 periods.

Need of the study: The COVID 19 outbreak has affected various industries including Indian banks which reported an increase in NPAs, and demand for credit which in turn impacted profitability. This study was carried out to examine the impact of COVID 19 outbreak on Indian banking sector.

Methodology: This study uses different banks’ NPA and net profits performance to examine the effect of COVID 19 on banks’ overall performance. The data have been collected from secondary sources, commercial websites, and websites of Indian banks (private and public sectors). t-Test was used to analyse the data.

Findings: Among public sector banks, Canara Bank was found to have a significant difference in net profit in the pre and post COVID 19 periods. In private sector banks, HDFC Bank showed a significant difference in the net profit in pre and post COVID 19 periods. For NPAs, all private banks showed no significant difference in pre and post COVID 19 period results.

Implications: The study revealed that both private and public sector banks in India were mildly affected by pandemic and most of them are significantly reporting no difference in net profit and NPAs during pre and post COVID 19 periods.

The authors aim to look at the conditions under which central bank easing – and specifically the purchase of private sector securities – can/should contribute to growth, rather than fiscal policy and also ask when can the central bank’s purchase of private sector securities help supplant traditional monetary policy.

After surveying the existing literature, the authors use simple correlation of central bank private asset (stocks, bonds, etc.), interest rates, gross domestic product (GDP) growth, inflation and the extent of the functioning of domestic banking sectors (among others) to classify countries in which these purchases promote pro-growth investment.

Under the typical conditions for unconventional monetary policy, central bank purchases of private companies’ securities can promote growth in some places (like Bulgaria, the Ukraine, Georgia and Greece at the time of our writing) while hurting it in others (like in parts of Latin America and Africa in the mid-2010s). The authors find how such purchases effectively “bypass” dysfunctional banking systems and central/government local bodies fiscal spending, making the central bank the “funder of last resort” in many middle and lower income countries.

This paper tells specific central banks to ramp up – or reduce – their purchases of private sector securities. The authors identify exact jurisdictions where such “helicopter money” has led to investment in the past economic growth.

All previous work on conventional and unconventional monetary policy has looked at the way monetary policy affects investment and growth through the banking system and holding companies’ reactions constant. The authors do the opposite – looking at how companies respond, holding banking system responses constant. No one has ever looked at these private purchases (taken from a special IMF database), much less tried to argue that central banks can sometimes target investment growth much better than fiscal policy. No one has ever considered them as a funder (rather than lender) of last resort.

The present study aims to comprehensively examine the impact of the Union Bank of Switzerland (UBS) takeover of Credit Suisse on the banking and financial services sector in the Indian stock market. To fully comprehend the impact of the event, the study separately investigates the response of private sector banks, public sector banks, overall banking companies and financial services companies to the takeover of the second-largest financial institution in Switzerland.

The study employs event study methodology, using the market model, to analyze the event's impact on Indian banking and financial services sector stocks. The data consists of daily closing prices of companies included in the Nifty Private Bank Index, Nifty PSU Bank Index, Nifty Bank Index and Nifty Financial Services Index from the National Stock Exchange (NSE). Furthermore, cross-sectional regression analysis has been conducted to explore the factors that drive abnormal returns.

The empirical findings of the study suggest the event had a heterogeneous impact on the stock prices of Indian banks and financial services companies. While public sector banks experienced a significant negative impact on select days within the event window, the overall Indian banking sector and financial services companies also witnessed notable declines. In contrast, Indian private sector banks were relatively resilient, exhibiting minimal effects. However, the cumulative effect is found to be insignificant for all four categories across different event windows. The study also observed that the cumulative abnormal returns (CARs) were significantly influenced by certain variables during different event windows.

To the best of the authors' knowledge, the present study is the earliest attempt that investigates the impact of the UBS takeover of Credit Suisse on the Indian banking and financial services sector using event study methodology and cross-sectional regression model.