Manu K. S.

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.

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.