Vandana Bhama

India witnessed the first major wave of COVID-19 in 2020. The second major wave during April 2021 caused a higher number of infected cases across the country. These waves of COVID-19, rising cases and lockdown announcements severely impacted the Indian economy. Moreover, huge volatility was observed in the prices of oil and exchange rates during the similar period. Thus, this study tests the effect of selected macroeconomic variables and the COVID-19 pandemic on the performance of the Indian stock market. Using co-integration and the vector error correction model on the NIFTY 100 firms, the findings suggest co-integration and long-term association among variables. The Indian stock market experienced an inverse connection with the exchange rate volatility; the coefficient value is 57.582. The exchange rates rose heavily (with a value of Indian rupee being 76.95 against US dollar) with the onset of COVID-19 cases. Further, these cases do hurt the sentiments of the stock market; however, the relationship is relatively infirm (the value is 0.22) as compared to that of the exchange rate. The accumulated major negative influence of COVID-19 on the economy had a weak impact on the stock market. In conclusion, it should be noted that after the first wave, businesses were more prepared and therefore incorporated the required changes that saw them through the second wave.

Acknowledgment
The infrastructural support provided by the FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

Although the tender offer buyback method has gained significance over time, many companies still prefer open market repurchases. The existing literature focuses mainly on the impact of buyback announcements, specifically on stock returns; however, buyback announcements and abnormal returns in the case of open market repurchases have not yet been studied in detail, especially across industries in the Indian context. This study, therefore, attempts to analyze the impact of open market repurchase announcements on the stock returns of Indian firms. To that end, the event study methodology has been used for a period of 31 days, i.e. 15 days prior to and 15 days after the buyback announcement on a filtered sample of 100 firms during the period 2010–2020. The results of the study indicate that the returns were more favorable in the short run. The findings do not support the undervaluation rationale of firms behind the open buyback statement. The low-profit opportunities in the prior event window convey investors’ predictions about the repurchase announcement. In the context of industries, the manufacturing sector seemed to be far better than IT & telecom, chemical, and pharma firms as the returns were statistically significant for five (5) out of 31 days. The industry-specific results also suggest that the profit opportunities are majorly in the pre-announcement phase. The overall findings corroborate that share repurchases might be irrelevant to shareholders’ wealth. Therefore, open market buybacks may support decisions related to capital structure changes.

Acknowledgment
The infrastructural support provided by the FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

The efficient market hypothesis states that in the efficient markets, participants cannot make extra-normal returns by exploiting any publicly available information. However, traders are constantly looking to exploit publicly available information to generate abnormal returns for themselves and their clients. One such event is share buyback announcement, which traders can utilize to create profitable trading strategies. The authors undertake the present study to examine if share buyback announcements provide profitable trading strategies to traders. Event study methodology has been adopted to analyze buyback announcements by Indian companies from January 2012 to December 2018. Forty-one (41) day window period comprising of 20 days pre-event, an announcement day, and 20 days post-event period is created to analyze the risk-adjusted average abnormal returns. The empirical findings suggest that there are negligible trading opportunities available for investors post announcements. However, significant risk-adjusted returns are found in the pre-event window, indicating that if investors can predict buyback announcements, they may earn extra-normal returns. The study confirms that Indian stock markets are in the semi-strong form of efficiency. The study also provides a profitable trading strategy for investors in the pre-event window. Finally, it also draws the regulators’ attention to see if insider trading could be the reason for abnormal returns in the pre-event window. The authors conclude the results by confirming that Indian markets are semi-strong in market efficiency and by indicating regulatory interventions to control insider trading.

Acknowledgement
The infrastructural support provided by FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

Corporates express their intention to reward shareholders during repurchase announcements by maximizing their wealth. However, most empirical research finds that stocks’ performance is poor when repurchase announcements are made, and there are no significant abnormal returns. In the Indian context, the present study examines firms’ real intention behind repurchase decisions. The sample comprises 132 firms listed on the Bombay Stock Exchange (BSE) from 2012 to 2018. A Tobit regression model has been used on different firm set-up. The empirical results reveal that low stock valuation is the prominent reason for buybacks among corporates. Firms prefer repurchases to provide abnormal returns to the investors; however, the Indian market does not react much positively to the repurchases, and this might be the reason for less encouraging buybacks in the Indian market. Further, the tender offer is the most preferred mode to open market repurchases. In the case of service firms, undervaluation, low earnings, and low debt ratios are the contributing factors impacting repurchases. Firms with low dividend intend to have more buybacks to reduce their tax burden.

Acknowledgment
The infrastructural support provided by FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

The classical view of experts associates greater risks with greater rewards. The present study explores whether increased volatility in portfolios can create more returns for investors by using technical indicators or the buy-and-hold (BH) strategy. The study used closing prices of National Stock Exchange (NSE) 500 index firms for a period of 16 years (2007–2022). Five portfolios ranging from low to high volatility were created using standard deviation as a key measure. Findings indicate that as the volatility of the portfolios increases, the moving average (MA) returns seem to be higher. Across the various MA time frames, the 20-day MA seems to have generated the highest return annually (36.53% before transaction costs and 31.05% after transaction costs) due to reasonable trading opportunities with adjustable transaction costs. The CAPM also generated positive alpha (after bearing transaction costs) in the case of 20, 50, and 100 days MA, with the values being 16.66%, 13.29%, and 12.09%, respectively, in the case of highly volatile portfolios. On the other hand, while the BH strategy created substantial returns in all scenarios, the risk factor was extremely high due to the high standard deviation. Hence, it is suggested that investors/traders consider the BH strategy more cautiously while choosing between technical analysis returns and BH returns. Investors with high-risk preferences may have BH as their choice, while day traders with managed risk appetites may prefer technical tools over BH returns.

Acknowledgment
The infrastructural support provided by the FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.