Suresha B.

Geopolitical tensions between nations play a crucial role in triggering volatility and affecting the investors’ behavior in stock markets. This empirical work attempts to detect the traces of herding and bubble embedded in the Indian stock indices of CNX Nifty 50 and CNX Nifty 100 (both in High-Frequency Trading domains) during the latest events of geopolitical tensions escalated between India-China and India-Pakistan. An event window approach is employed to capture the impact of these events on herding behavior and information uncertainty in the considered stock indices. Multifractal Detrended Fluctuation Analysis (MFDFA) is applied to compute the Hurst value in all the trading days of the event window. The results of both indices exhibit conclusive evidence of herding and bubble formation during the overall period of geopolitical tensions between India-China and India-Pakistan. However, the degree of herding in the stock indices intensifies to a profound pattern when the tensions between India and China escalated into deadly violent clashes, and also during the heightened tensions between India and Pakistan that eventually ended up in airstrikes across the boundaries. The overall level of information uncertainty depicted by entropy is within control. The volatility in these stock indices has been confirmed to follow a unidirectional pattern.

Acknowledgements
The authors express their sincere thanks of gratitude to Dr. Bikramaditya Ghosh (Professor, Department of Finance and Analytics, RV Institute of Management, Bangalore, India) for his instrumental role in encouraging and motivating them to accomplish this research task. The authors also extend their sincere thanks to Dr. Manu K.S. (Assistant Professor, School of business and management, CHRIST (Deemed to be university), Bangalore, India) for his continued support throughout this empirical investigation.

The recent India-China geopolitical conflicts have presented enormous uncertainty to the investors in various sectoral indices of the Indian stock market. This empirical study aims to examine the impact of intensified India-China geopolitical conflicts 2020 on investors’ herding behavior in the National Stock Exchange sectoral indices. The high-frequency data of three major NIFTY sectoral indices (Auto, Energy, and Pharma) are used in an intensified geopolitical event window to spot precisely the traces of the investors’ herding behavior. Furthermore, multifractal detrended fluctuation analysis (MFDFA) is employed to obtain Hurst Exponent values (h(q)) for the NIFTY sectoral indices. The findings reveal that these NIFTY sectoral indices exhibited profound traces of herding behavior on the event day (t = 0) due to the heightened India-China geopolitical clashes. In addition, these indices depicted an overall higher level herding behavior with the (h(q)) values close to 0.72 throughout the intensified geopolitical event window. The study concludes that the sectors highly reliant on the Chinese supplies and with significant trade linkages with China depicted a higher level of herding behavior in their indices. Further, the presence of herding behavior in these sectoral indices is due to the operational and supply-chain risks posed by the geopolitical event.

Acknowledgments
The authors express their sincere thanks of gratitude to Dr. Bikramaditya Ghosh (Associate Professor, Symbiosis Institute of Business and Management, Bangalore, India) and Dr. Iqbal Thonse Hawaldar (Professor, College of Business Administration, Kingdom University, Riffa, Bahrain) for their instrumental role in encouraging and motivating them to accomplish this publication. The authors also extend their sincere thanks to Dr. Manu K.S and Dr. Surekha Nayak (Assistant Professor, School of Business and Management, CHRIST (Deemed to be university), Bangalore, India) for their continued support throughout this empirical investigation.

In recent years, investors have perceived that Environmental, Social, and Governance (ESG) practices significantly increase the value of companies’ stocks. This study investigates the impact of ESG inclusion on the price, liquidity and financial performance of stocks listed in the Indian ESG indices. Two major Indian benchmark ESG Indices, the BSE100 ESG and Nifty 100 ESG, were considered for the study. A total sample of 64 firms from the BSE100 ESG index and 86 firms from the Nifty100 ESG index were selected. The market model of the event study methodology was employed to measure AAR and CAAR and to demonstrate the effect before and after the inclusion of the stocks in the ESG indices. The empirical results show a highly significant negative AAR on the announcement day, i.e., on (day = 0) for BSE100 ESG index stocks and an insignificant positive AAR for Nifty100 ESG index stocks. In addition, the results also document a significant negative CAAR for BSE 100 ESG stocks and a positive insignificant CAAR for Nifty100 ESG stocks. Moreover, the liquidity test results revealed a considerable liquidity enhancement in the stocks posts their inclusion in the BSE100 ESG. At the same time, there were no significant changes in the liquidity ratio of stocks after being included in the Nifty100 ESG index. This study concludes that there will be a substantial improvement in the companies’ financial performance as indicated by EPS and market capitalization after their inclusion in the ESG indices.

In this digital era, customers in the insurance sector always look for better insurance products and services at an affordable price. When customers are unsure about service, they switch over to a better service provider. This behavior is more relevant to non-life insurance. However, the switching behavior of customers is hampered by certain switchover barriers such as “brand consciousness”, “brand pride”, “brand loyalty”, etc. This study focuses on exploring switching intentions and switching behaviors of adults in India keeping “brand love” as a mediator. A structured questionnaire was employed to collect the primary data from adults having non-life insurance products to analyze switching intentions and switching behaviors. The collected data were analyzed employing SPSS software and Hayes Process Model and appropriate statistical tools. The study results show that the switching intentions of adults vary based on their age, annual income, and education. Mean scores reveal that the lesser the age, the higher the intention to switch over. Further, based on annual income, adults who earn up to Rs 2 lakhs annually have more switching-over intentions (Mean score: 3.9719) followed by adults who earn Rs more than 2 lakhs to 5 lakhs annually (Mean score: 3.7590). Mean scores of education levels regarding switching intentions are higher among more educated adults and less among those who are qualified up to the school level.

The announcement of a buyback informs the market about the company’s decision to repurchase its own shares. This announcement highlights the company’s price valuation and the inefficiencies that exist in the market. This study examines the share buyback announcement effect during the COVID-19 period. The study considered the stocks listed in the National Stock Exchange (NSE) that offered share buyback under tender offer mode during the pre-pandemic period between April 2016 and February 2020 and the post-pandemic period between March 2020 and March 2022. 75 firms in the pre-pandemic period and 43 in the post-pandemic period that announced share buyback under the tender offer method were analyzed. The event study methodology using a market model was employed to determine the presence of abnormal returns during the event period, which consisted of –21 days and +21 days. The findings of the study revealed the existence of abnormal returns in and around the announcement date. Besides, statistically significant cumulative abnormal average returns (CAAR) were also found on the event day, i.e., on Day 0. The study found that the impact of buyback announcements on stock returns significantly differed before and after COVID-19 for 10 and 21-day periods, with no significant differences for shorter periods. These insights can help traders and fund managers make informed portfolio adjustments during turbulent market periods surrounding buyback announcements.

Acknowledgement
The authors express their sincere gratitude and special thanks to Dr. Krishna T.A., Assistant Professor, Department of Professional Studies, School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bangalore, India, for encouraging, motivating and providing all the required support throughout this empirical investigation and to accomplish this research task.