Non-performing Assets Ratio Research Articles

Analyzing the Influence of Non-Performing Assets: A Comparative Study between SBI and HDFC

Non-performing assets (NPAs) pose an enduring and intricate challenge within the banking sector, carrying far-reaching consequences for both financial stability and broader economic growth. This paper provides an in-depth analysis of NPAs and their impact on the financial performance of HDFC and SBI. Based on the analysis of the NPA data of the two banks, it is found that the NPA ratio for State Bank of India is in the range of 1.02%-5.73%, which is on the higher side in comparison to the NPA of HDFC, which lies between 0.20%-0.64% for the period 2007-8 to 2021-2022. In both cases, NPA has a negative impact on profitability, but it is much higher in SBI. These findings may help SBI reinvent its credit policy and the credited process of its credit business to different customer segments.

Assessing Non-Performing Assets and Their Impact on Bank Profitability: A Comparative Analysis of HDFC Bank and ICICI Bank

Non-Performing Assets (NPAs) significantly affect the stability and profitability of banks by reducing income and increasing provisioning requirements. This study investigates the impact of NPAs on bank performance, with a specific focus on HDFC Bank and ICICI Bank. Utilizing secondary data from 2019 to 2023, the research analyzes key metrics including Gross NPA Ratio, Net NPA Ratio, Provision Ratio, and Problem Asset Ratio. Findings reveal that HDFC Bank maintains superior performance with lower NPAs and more conservative provisioning compared to ICICI Bank. The study highlights the broader economic implications of NPAs and offers recommendations for improving NPA management. This research contributes valuable insights into effective strategies for managing NPAs and enhancing bank profitability.

EFFECT OF DEPOSITS AND NON-PERFORMING LOANS ON BANK PROFITABILITY – A CASE STUDY ON CONVENTIONAL COOPERATIVE BANK OF BARODA REGION

The banking sector plays a vital role in providing essential financial services, facilitating the allocation of funds to productive ventures, and aiding in the implementation of government fiscal and monetary policies. Historically, Non-Performing Assets (NPAs) have posed significant challenges for banks, but reducing NPAs has now become a top priority. The strength of a nation's economy relies heavily on the health of its cooperative banking sector. This study analyzes the non-performing assets (NPAs) of The Pragati Co-operative Bank Ltd. over a six-year period from 2017-18 to 2022-23. The research examines various aspects related to NPAs, including gross NPAs, net NPAs, provisions made towards NPAs, loans and advances, deposits, and relevant ratios such as net NPA ratio, provision coverage ratio, and credit-to-deposit ratio. The data analysis reveals that the bank maintained a zero net NPA throughout the period, indicating effective provisioning strategies. While gross NPAs fluctuated, the provision coverage ratio remained strong, suggesting the bank's ability to withstand asset quality pressures. The study also investigates the relationship between gross NPAs and net profits using correlation and regression analysis, finding a positive correlation but minimal significant impact of gross NPAs on profitability. Overall, the research highlights The Pragati Co-operative Bank Ltd.'s proactive approach to NPA management, financial stability, and adaptability to changing market conditions.

Credit Growth and Risk Management in Indian Banks: Analysis Through Sectoral Deployment

This research paper explores the dynamics of sectoral credit deployment and its impact on risk management within the Indian banking sector over three fiscal years (2021-2023). Utilizing data from the Reserve Bank of India's annual reports, the study delves into the growth patterns of credit allocation to agriculture, industry, services, and personal loans, and assesses corresponding risk metrics such as the Gross Non-Performing Assets (NPA) ratio, Provision Coverage Ratio (PCR), and Capital Adequacy Ratio (CRAR). Descriptive statistics, correlation analysis, and regression modelling were employed to identify relationships and impacts. The findings reveal substantial credit growth in the services and personal loan sectors, accompanied by significant improvements in risk management indicators, including a marked reduction in the Gross NPA ratio and enhanced Provision Coverage Ratios. These trends highlight the sector’s increasing resilience and risk preparedness, reflecting broader shifts towards a consumer-driven financial landscape. The paper discusses the implications of these findings for banking stability and economic policy, emphasizing the need for nuanced regulatory frameworks to manage sector-specific risks. Future research could extend this analysis to predict long-term impacts of credit expansion on financial stability using advanced econometric models.

A STUDY ON NON-PERFORMING ASSETS MANAGEMENT AT STATE BANK OF INDIA, ANANTHAPURA GATE, BANGALORE KARNATAKA

Non-performing assets (NPAs) are advances that are not repaid or serviced by the borrowers, posing a risk to the financial stability and profitability of banks. This research aims to inspect the management of NPAs at ‘State Bank of India’ (SBI), one of the largest public segment banks in India. The study has four objectives: to study the gross and net NPAs position at SBI, to analyse the sector-wise and segment- wise NPAs position at SBI, to ascertain the extent of provisions made for NPAs, and to determine the impact of NPAs and provisions on the profitability of SBI. The learning uses secondary data from the annual reports of SBI for the period from 2019 to 2023. The study employs descriptive statistics, correlation analysis, and hypothesis testing to analyse the data. The study discoveries that SBI has improved its asset quality and credit risk management over the five-year period, as evidenced by the declining trend of gross and net NPAs, the of NPAs in high-risk sectors, and the increase in provisions and profitability. The study also finds that there is a significant relationship between gross and net NPAs, and that there is a difference between provisions and net profit. The study suggeststhat SBI should focus on reducing the NPA ratio of the industries with high exposure and default rates, diversifying its loan portfolio, and strengthening its credit assessment, monitoring, and recovery systems. The study concludes that SBI has successfully managed its NPAs and enhanced its financial performance, which is beneficial for the country’s growth andeconomic growth. Key words: Non-performing assets, Gross NPA, Net NPA, Sector wise Performance, Provisions, Profitability

Major Determinants of Bank Profitability in India: A Machine Learning Approach

From reforms and fin-tech revolutions to macro-economic shocks, the Indian banking sector has witnessed rapid changes over the last two decades, which has significant implications for banks’ profitability. Viewing bank profitability from three different dimensions, Net Interest Margins (NIM), Return on Assets (RoA) and Return on Equity (RoE), this study has explored the key determinants with the help of machine learning algorithms. It has used a pooled data set of domestic and commercial banks covering 2005–2021. As a dependent variable, profitability by each measure (NIM, RoA and RoE) is reclassified into three categories, above average, average and below average, based on their quartiles. Twenty-one explanatory variables comprising bank-specific, macroeconomic and policy variables are chosen after due validation using feature selection methodology and multicollinearity check. The random forest (RF) classification algorithm is executed using the CARET package in R. The results obtained from feature selection are corroborated with the RF classification findings. The results are robust and give clear-cut visibility of unique and common factors influencing three profitability measures at varying levels. The classification estimates suggest that the bank-specific variables are major determinants of NIM, while macroeconomic and policy variables are the key determinants of RoA and RoE. Further, the results also suggest that the ratio of non-performing assets to total assets and business per employee are two such bank-specific determinants that play an important role in all three dimensions of profitability. Thus, recapitalization and automation will play an important role in bank profitability.

Current Situation and Challenges in Indian Banking Sector

Abstract: The Indian banking sector is facing several challenges due to the rapidly changing financial environment. The key issues include the high level of Non-Performing Assets (NPAs), increasing competition from fintech companies and digital payment platforms, difficulty in meeting capital adequacy requirements, concerns about governance and risk management, and challenges in Asset Liability Management (ALM). Addressing these challenges is critical for the Indian banking sector to remain competitive and continue playing a crucial role in the country’s economy. One of the largest in the world and essential to the nation's economy is the Indian banking sector. However, it is currently facing several challenges due to the rapidly changing financial environment. Here are some of the key issues and challenges faced by the Indian banking sector: Non-Performing Assets (NPAs): The high level of NPAs, or bad loans, is one of the main problems Indian banks face. The gross NPA ratio of Indian banks, according to the Reserve Bank of India (RBI), was 7.5% in March 2021. The profitability and capital sufficiency of banks have been under intense strain as a result of this is digital disruption.The traditional banking sector has been impacted by the growth of fintech businesses and digital payment platforms. These new competitors, who can provide customers with speedier, more convenient, and less expensive services, are putting pressure on Indian banks.

Intellectual capital and non-performing assets: the role of knowledge assets in improving credit quality of Indian banking sector

Purpose Intellectual capital (IC) is beneficial to the improved performance of businesses, irrespective of their industry. The present study proposes to check if the use of IC can also help in improving the asset quality of banks. Thus, this study aims to examine the impact of IC and its components on non-performing assets (NPAs). Design/methodology/approach The study has been conducted with a sample of 30 Indian commercial banks and analysed over a time frame of 15 years (2004–2005 to 2018–2019). The modified value-added intellectual coefficient model has been used to measure the independent variables, IC, and its components. The dependent variable, NPA, has been represented by the net NPA ratio. Two-step system generalized methods of moments (SGMMs) have been applied for the regression analysis. Along with the short-term estimates provided by the SGMM approach, the long-term impact of explanatory variables on the dependent variables has also been seen. Findings The results of the study show that IC and its components are indeed helpful for the management of NPA, as they impact the problem loans negatively. Furthermore, the long-term benefits of IC in enhancing bank credit quality are more substantial. Practical implications The results from the present study can be used by bank management. The bank managers can draw inferences that the efficient application of IC can help them reduce their loan losses. Developing skills and knowledge of employees, maintaining close relations with stakeholders, significantly the customers, and putting more sophisticated processes and infrastructure to use can help banks to control their loan losses. Originality/value A major proportion of studies examining the role of intangible assets in various aspects of the banking sector focuses on the association between IC and the financial performance of banking entities. However, for banking institutions, apart from financial performance, improving credit quality is also imperative for staying afloat. Thus, to the best of the authors’ knowledge, the present study is one of the first to examine the relationship between knowledge-based assets (i.e. IC) and bank credit quality.

Impact of Corporate Governance Factors on the Profitability of Banks: A study on Indian public sector banks

The paper aims to look at the impact of corporate governance parameters on the financial performance of the banks. The variables considered for the quantitative analysis are the female directors, the chairman changed, the total remuneration, the total committees, the cost to income ratio, the non-performing assets ratio, and the provision coverage ratio. The performance of the banks is explained by the return on equity (ROE) and the return on assets (ROA). The descriptive statistics and fixed effects estimation model are considered for the quantitative analysis of the panel data. The sample consists of data from eighteen public sector banks during five years ranging from 2014-2015 to 2018-2019 when the banks were in the limelight for various discomforting reasons. The results of the study indicate that the variable the total remuneration showed a significantly positive impact on the performance of the banks. In contrast, the chairman changed, the total committees, the cost to income ratio, the gross non-performing assets ratio, and the provision coverage ratio displayed a significantly negative impact on the performance of the banks. The study is a preliminary attempt and can be considered as a basis for future research.

Identification of Weak Banks Using Machine Learning Techniques: Evidence from the Indian Banking Sector

Early identification of ‘weak’ banks is essential for safety and soundness of the banking sector. Longer the delay in such identification, heavier the cost on an economy. In this article, we apply multiclass classification to classify banks operating in the Indian banking sector as ‘strong’ and ‘weak’ banks. Such classification is expected to provide direction to bankers for bank management. Using average return on equity (ROE) as basis for classification, we apply five machine learning models to the bank data set, namely Naïve Bayes, support vector machine, k-nearest neighbours, random forest and average neural networks. We find that all five models are able to predict the bank classes with a very high degree of accuracy. Ratio of non-performing assets to net advances turned out to be the most important variable in classifying banks as ‘weak’, followed by inflation and real exchange rate. The study is the first of its kind that successfully applies machine learning models in the Indian banking sector.

COVID-19: boon/disguise for Indian banks?

Non-performing assets (NPAs) have long been one of the most visible and frightening issues that have shaken the whole banking industry around the world. As a result, the study's primary goal is to elucidate the facts surrounding NPAs, which have harmed the Indian banking industry's soundness, profitability, and performance between 1998–1999 and 2019–2020. The future trends NPAs across ownership types and sectoral groups were also explored. The estimations of NPAs drivers are examined in the second stage regression analysis, which takes into account factors such as innovative COVID-19 and policy initiatives. Later, a clear ranking for 52 samples in Indian banks was utilised to verify stability using the ratio of net NPAs to net advances. During the pre-crisis and crisis periods, gross NPAs and net NPAs as a percentage of gross advances both fell dramatically, according to the trend analysis. During turbulent years, however, they significantly rose. Finally, we noticed that the asset quality of scheduled commercial banks and public sector banks is improving following a seven-year gap during the COVID-19 years. In a Phase II regression analysis, this scenario was thoroughly explored for Indian banks and statistically proved that severe policy actions and the negligible influence of the unique COVID-19 crisis contributed to a decrease in their NPAs ratio during 1999–2020. Credit expansion, recognition and resolution of non-performing assets via the Insolvency and Bankruptcy Code Act, recapitalization of PSBs, and ongoing reforms in the COVID-19 period are all major contributors to sporadic credit default in SCBs and PSBs' balance sheets—which began in 2017–2018 and continued in 2019–2020.

Investor Perception, Market Reaction, and Post-Issue Performance in Bank Seasoned Equity Offerings

Using a large sample of bank seasoned equity offerings (SEO) from 2002 to 2017, we first documented detailed descriptive statistics, and showed that nonperforming assets ratio, our primary measure of bank asset quality, reached the highest value immediately after the 2008 economic crisis, which also corresponds to a higher number of SEOs around these years because banks needed to recapitalize. The capital ratio, which is required to be at least at a minimum level (relative to risk-weighted assets) for banks by regulation, also increased after the economic crisis, which may be due to a higher requirement for banks as well as banks’ desire to hold more capital. The SEO announcement period abnormal stock-returns reached the lowest number around the economic crisis, as did the longer-run 6-month, post-SEO cumulative abnormal returns and buy-and-hold abnormal returns. Examining the differences between banks, we found, in both univariate and multivariate regression results controlling for other variables, that the bank capital ratio as at the time of the SEO announcement is significantly and positively associated with announcement period abnormal returns, while nonperforming assets ratio of the bank as at the time of SEO announcements is not. However, the nonperforming assets ratio as at the time of the SEO announcements had a significantly negative association with post-SEO, 6-month longer-run abnormal stock returns, while the capital ratio did not have any significant association. The nonperforming assets ratio as at the time of SEO announcement was also significantly and negatively related to bank return on assets 6 months and 12 months after SEO, while the capital ratio was not. Thus, investors appear to perceive a well-ingrained, well-publicized regulatory norm—the capital ratio—as indicative of value creation as at the time of bank SEO announcements, while loan asset quality, which may be relatively more opaque, may determine post-SEO performance.

Loan Repayment Dynamics of Self-help Groups in India

The overall ratio of non-performing assets (NPAs) to total advances (the NPA ratio) extended to self-help groups (SHGs) has historically remained below 8 per cent in India. However, in the central, northern, and north-eastern regions of India, this ratio is relatively high and has remained above 15 per cent since 2015–2016. Using state-level data, the study identifies SHG-specific and state-specific factors that might be responsible for higher NPAs ratios. It also examines whether higher NPAs constrain access to future credit. Spatial analysis confirms the existence of geographical clustering of NPAs in the northern and central regions of the country. SHGs with lower outstanding loans and lower savings are more likely to default, the relationship being stronger in states with higher NPAs. The article finds that states with higher SHGs densities, per capita incomes, and road and railway connectivity, and lower infant mortality rates have lower NPAs in their SHGs. SHGs with lower savings and higher NPAs operating in states with lower per capita incomes and banking penetration face difficulties accessing fresh credits. JEL Codes: G21, O18, R51

The nexus of economic growth, priority sector lending and non-performing assets: case of Indian banking sector

PurposeIn recent years, the Indian banking sector is facing a major cause of concern in the form of Nonperforming Assets (NPA), and the priority sector lending (PSL) is generally recognized as the major factor contributing to it. Thus, the present study has been carried out with the objective of examining the relationship between priority sector lending and GDP growth. Thereafter, the role of PSL and certain other bank-specific, industry-specific and macroeconomic variables in determining NPA has been studied.Design/methodology/approachTaking a sample of 45 scheduled commercial banks, the study has been carried out for 14 years (2004–2018). Granger causality between PSL and GDP has been examined by applying the Dumitrescu-Hurlin test. For the purpose of investigating the impact of PSL and other determinants on NPA, both static and dynamic panel regression have been performed. Under the dynamic panel, system generalized methods of moments (S-GMM) approach has been followed.FindingsThe findings show that there exists a positive correlation and bidirectional causal relationship between PSL and GDP, which implies that PSL brings additional growth for the whole economy. In addition to it, PSL is found to be insignificant for the NPA ratio, and thus, it can be inferred that credit extended to government-specified sectors does not bring any major increase in the bad loan portfolio of banks.Practical implicationsThe policymakers and bank management can take a cue from the findings of this study to decrease the exposure to loan nonrepayment issue. The priority sectors are in need of formal credit for their growth, and since the rising population of the country can find employment in these sectors, banks should meet their credit needs while securing their position with regard to the NPA problem.Originality/valueThe issue of NPA determinants, and in particular, the contribution of priority sector lending in it has not been much explored for Indian banking sector. Also, the present study adds to the literature by using the causality approach for examining the importance of directed credit schemes for economic growth.

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

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.

Corporate Governance Practices and Bank Performance: Evidence from Indian Banks

The study examines the corporate governance practices and analyzes the role of the board characteristics (size of the board, the composition of the board, and functioning of the board) on the performance and asset quality of banks. We use a sample of 34 commercial banks consisting of 19 public sector banks and 15 private sector banks from 2009 to 2018 accounting for 93 percent of the total banking industry in India. The study finds that busy directors and the number of meetings have a positive significance on bank performance. The percentage of independent directors and the percentage of busy directors influence a significant negative relationship on the net non-performing assets ratio. The board size and number of meetings are associated negatively with Tobin's Q significantly and the percentage of busy directors is a significantly positive impact on Tobin's Q. The board size has a significantly negative impact on bank performance. The research findings provide some insights into corporate governance to the RBI for considering appropriate policy guidelines on corporate governance in the banking industry in India. The paper adds to the existing literature on corporate governance mechanisms and banking industry performance.

Non-performing assets in India: A critical analysis of public and private sector banks

The paper identifies and analyzes the causes that affect non performing assets (NPAs), hinder its effective observance, and recommends appropriate measures to ensure their effective monitoring and control. The banks selected for this research work are having higher NPAs and are top banks in their sector. As per the Global Financial Stability Report of International Monetary Fund (IMF, 2009), identifying and dealing with distressed assets, and recapitalizing weak but viable institutions and resolving failed institutions are stated as the two of the three important priorities which directly relate to NPAs. This research work finds the reasons for non-performing loans by considering a set of 50 variables and provides the necessary measures. Statistical tool SPSS was used to run the factor analysis test. Sectoral disparities in the NPA ratio to advances in public and private sector banks were the main source of motivation to analyze and compare factors affecting non-performing assets (NPAs) of public and private sector banks in India. Some of the reasons for NPA are lack of frequent interaction or follow-up with borrowers, manipulation of income or financial statement by borrowers, industrial problem and death of earning member of the family.

Do Accounting Numbers Have Any Relation with Stock Prices? A Case of Public and Private Sector Banks of India

Capital market research examines the ability of accounting numbers to influence the stock prices thus stock returns. This paper attempts to study the association between the accounting variables and stock price for banking sector in India and also to explore whether same variables influence the stock price of private and public sector banks in India. Nine accounting variables for the period of ten years have been studied for BSE listed banks. The study used panel least regression analysis by incorporating Fixed effect model (FEM) and Random effect model (REM), where nine set of accounting variables regressed against market share price of private and public sector banks for a period of 10 years from 2005 to 2014. The empirical findings revealed that, out of nine independent variables used; the earnings per share, book value per share, assets turnover and current ratio were significant in private sector banks and earnings per share, book value per share, return on equity and net non-performing assets ratio were significant in public sector banks. Overall empirical findings reported that the fundamental accounting variables in public sector banks are more relevant than that of private sector banks and this supports the hypothesis of the study. This is against the notion raised in 90s that accounting information has become less value relevant.

Trend of NPAs in public sector Banks: A study of pre and post crisis period

An effort has been made to evaluate the impact of crisis on non-performing assets of Public Sector Banks. Secondary data were used in the present study. The secondary data to non-performing assets from 2001–02 to 2011–12 were collected from various issues of RBI Bulletins. The results mainly indicate that Public Sector Banks have high ratio of nonperforming assets in agriculture Sector due to their primary obligation to lend this sector. The impact of crisis was seen in all Public Sector Banks where the non-performing assets were decreased significantly in the pre-crisis period but was increased at higher rate in Allahabad Bank. The government has realized the importance of management and control of NPAs and in that light introduced legislation through the Securitization and Reconstruction of Financial Assets and Enforcement of Security Interest Act, 2002 (the SARFAESI Act), to institutionalize the process of creating value for investors in the Non-Performing Asset Portfolios of banks and financial institutions. During the pre-crisis period level of NPAs were low but during post crisis period level of NPAs increase in all public Sector Banks. But the impact of crisis was seen in SBI, SBOP, Central Bank of India, Indian Overseas Bank, Vijaya Bank and PNB. Information collected through field survey and secondary data was also used and analysed with different statistical tools simple regression and step wise multiple regression.

Balanced Scorecard Evaluation of the Performance of Indian Public Sector Banks

This study used the balanced scorecard (BSC) concept developed by Kaplan and Norton (1992). The empirical study evaluated the performance of top three public sector banks in India namely, State Bank of India, Bank of Baroda, and Punjab National Bank using the balanced scorecard framework developed for these banks. Further, profitability of these banks during the period from 2006 - 2015 was measured in terms of return on assets (RoA) and return on equity (RoE). In addition, this paper also examined the relationship between profitability and variables in the BSC framework using correlation and multiple regressions. The results revealed variance in the performance of the three banks in balanced scorecard perspectives. The outcomes also disclosed statistically significant relationship of the variables - capital adequacy ratio, net non-performing assets ratio, number of ATMs, and number of skilled employees with RoA. The variables - capital adequacy ratio, net non-performing assets ratio, number of ATMs, number of skilled employees, and ratio of wage bills to total income disclosed statistically significant relationship with RoE. The results of regression analysis revealed the variable - net non-performing assets ratio had a significant direct linear relationship with RoA. On the other hand, the variable - capital adequacy ratio was found to have a direct relationship and net non-performing assets ratio had an inverse relationship with RoE.