Sangeetha R

This study examines the efficiency of Public Sector Banks (PSBs) in India using Data Envelopment Analysis (DEA). Analysis is carried out on a sample of 19 PSBs that are existed during the study period from 2005 to 2018. There are two different aspects deliberated, namely technical efficiency of PSBs and the growth in their productivity. Input variables envisaged for the study are deposits, borrowings, fixed assets, and the number of employees. Loans and advances along with investments act as output variables to measure technical efficiency and productivity. The results indicate that the technical efficiency of PSBs ranges between 97% and 100%. Corporation Bank, Indian Bank, and Oriental Bank of Commerce outperformed their peers with 100% technical efficiency. Productivity growth among the sampled banks during the study period stood between 0.8% and 20%. However, Corporation Bank, Indian Bank, and Oriental Bank of Commerce registered 9.1%, 5.4% and 6.4% productivity growth, respectively. The results reveal that PSBs are working hard to optimize resource utilization. Researchers around the world can use DEA as a tool to measure the efficiency of banks with different input and output variables related to financial, marketing and managerial performance.

Acknowledgment
I like to express my profound thanks to Dr Kishore Selva Babu for rendering his language expertise. I also thank all the anonymous reviewers for their valuable comments and feedback that greatly improved the manuscript.

Long-range dependence (LRD) in financial markets remains a key factor in determining whether there is market memory, herding traces, or a bubble in the economy. Usually referred to as ‘Long Memory’, LRD has remained a key parameter even today since the mid-1970s. In November 2016, a sudden and drastic demonetization measure took place in the Indian market, aimed at curbing money laundering and terrorist funding. This study is an attempt to identify market behavior using long-range dependence during those few days in demonetization. Besides, it tries to identify nascent traces of bubble and embedded herding during that time. Auto Regressive Fractionally Integrated Moving Average (ARFIMA) is used for three consecutive days around the event. Tick-by-tick data from CNX Nifty High Frequency Trading (CNX Nifty HFT) is used for three consecutive days around demonetization (approximately, 5000 data points from morning trading sessions on each of the three days). The results show a clear and profound presence of herd behavior in all three data sets. The herd intensity remained similar, indicating a unique mixture of both ‘Noah Effect’ and ‘Joseph Effect’, proving a clear regime switch. However, the results on the event day show stable and prominent herding. Mandelbrot’s specified effects were tested on an uncertain and sudden financial event in India and proved to function perfectly.