A well-developed and efficient banking sector is the fundamental requirement for smooth functioning of any economy. The present study is an attempt to examine the technical, pure technical and scale efficiencies of the Indian banks across different ownership categories for the period 2009–2012. About 7 out of the 44 banks selected lie on the efficiency frontier and form the reference set for their peers. Further, it is observed that efficiency scores do not vary much across the public sector, private sector and foreign banks. Performance of the public sector and private sector banks is almost at par with respect to technical efficiency whereas in the case of foreign banks, there lays scope for improving scale efficiency. A second stage regression analysis is carried out using Tobit regression to examine the determinants of efficiency. Non-interest income emerges the most important determinant of efficiency of banks in India.

This study evaluates the impact of self-help group (SHG) intervention and entrepreneurial traits of SHG members on the economic and socio-cultural empowerment of rural women. A purposive sampling method has been used to collect a sum of 344 responses from the SHG of women who are engaged in entrepreneurial income-generating activities in their village. A linear regression model has been used to analyse the data. The study found and argues that SHG interventions have a positive association with the economic and socio-cultural empowerment of rural women. The parameters identified and analysed in the study are helpful to the stakeholders to make policy decisions to strengthen all the factors, which are responsible for the success and growth of SHG in general and the micro-enterprise of SHG women entrepreneurs, in particular.

Financial distress is a socially and economically significant issue that affects almost every firm across the world. Predicting financial distress in the banking industry can substantially aid in the reduction of losses and can help avoid misallocation of banks’ financial resources. Models for financial distress prediction of banks are being increasingly employed as important tools to identify early warning signals for the whole banking system. This study attempts to forecast the financial distress of commercial banks by developing a bankruptcy prediction model for banks. The sample size for the study is 75 Indian banks. Logistic, linear discriminant analysis (LDA) and artificial neural network (ANN) models have been applied on the last 5 years’ (2015–2019) data of these banks. Data analysis results reveal the logistic and LDA models exhibiting similar prediction accuracy. The results of the ANN prediction model exhibit better prediction accuracy. It is expected that the results of this study will be useful for managers, depositors, regulatory bodies and shareholders to better manage their interests in the banking sector of the country.

During the recent years, value of financial assets has grown exponentially when compared to physical assets indicating that intangibles are growing in importance in their contribution to economic growth. The evidence in support of this phenomenon can be found in the increasing gap between market and book valuation of firms. The present study attempts to measure the intellectual capital (IC) of publicly listed firms in India and empirically examine the relationship among IC, financial performance and market valuation of these firms. Value creation efficiency of the firms listed on CNX Nifty over the period ranging from 2004–2005 to 2013–2014 has been estimated using Pulic’s Value Added Intellectual Coefficient (VAIC). It was observed that firms operating in sectors such as financial services, mining and energy had the highest VAIC scores. Further, there was a positive association between VAIC and all the measures of financial performance—profitability, productivity and market valuations. Efficiency of physical capital employed had a significant positive relation with profitability, market valuation as well as productivity. Human capital efficiency was found to have a strong positive association with profitability, while structural capital efficiency did not have any significant impact on any of the measures of financial performance.

The financial crisis and resulting failure of large banks worldwide has shaken the entire world. Improper management of operational risk has been touted as one of the reasons for this failure. In light of the rising importance of operational risk management (ORM) in banks, the study explores the range of ORM practices followed by a cross section of Indian banks and compares them with the banks worldwide. The study also analyses the impact of size and ownership of banks on these practices. Reliability analysis using Cronbach alpha model, Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity was used to test reliability of questionnaire and justifies the use of factor analysis. Factor analysis was performed to extract the most important variables in ORM. The small size of bank was observed to be a deterrent to deep involvement of operational risk functionaries, collection and usage of external loss data and data collection and analysis. Further, the performance/preparedness of public sector and old private sector banks lagged behind peers in usage of key reporting components, such as risk and control self-assessment (RCSA), key risk indicators (KRI), scenarios, collection and usage of external loss data, data collection and analysis and quantification and modelling of operational risk.

Indian economy is one of the fastest growing economies in the world today. In line with global trade trends, Indian export sector has been growing and contributing significantly to the economy. Given its exports structure, India is well positioned to benefit from the structural changes in technology and emerging forces of globalization. Indian economy has shown remarkable progress in terms of foreign trade after the introduction of economic reforms in 1991. The European Union (EU) is a very important trading partner of India. The trade volumes between India and EU have shown remarkable improvement in last one and a half decade. After starting out at a relatively low level in the 1990’s, the trade volumes, both with respect to Indian exports to the EU as well as with respect to Indian imports from the EU, started to increase most noticeably after the year 2001.Use of non-tariff measures (NTMs) as means of protection has captured a lot of focus after reduction of tariffs in the world trade. India even after being a strategic partner for European Union (EU) has to face lot of NTMs on its exports. Based on studies in the past, link between the incidence of NTMs imposed by the home country and the income level of the foreign country has been established. The interplay of incidence of NTMs and the GDP remains largely unexplored in the context of India-EU trade relationship. This paper tries to fill this gap and show the importance of the study in policy decisions. Authors have used UNCTAD’s NTM data and Spearman’s correlation coefficient to measure the strength and direction of the relationship between incidence of NTM with per capita GDP of the exporting country (India). The authors have used different permutations of data from the main data set (1994-95 to 2016-17) for analysis and have concluded that incidence of NTMs on Indian exports to EU is positively co-related to the per capita GDP of India.