Suman Chakraborty

Determinants of dividend policy have been a topic of debate in the academic literature for several decades, but the studies have not been able to give a concluding result on the topic. Existing literature reveals that one of the most challenging decisions, dividend payout, is affected by multiple determinants thereby impacting the value of stock, among which proficatibility, capital structure and level of cash flows are identified to be significant factors. The aim of this study is to evaluate empirically the determinants of dividend payout among the companies in the Indian auto components sector which are listed in major Indian bourses. This paper constitutes a modest attempt to explore the relationship between dividend policy (dividend pay-out ratio) of the companies and the variables representing profitability, capital structure, investments, liquidity and cash flows. The other salient feature of the study is that it examines casual relationship of financial performance, operational efficiencies and investment strategies on decision of paying the dividend. ANOVA, correlation analysis and regression analysis have been used to explore the relationship between the identified variables. The study finds that the dividend policy of the companies in the Indian auto components sector is largely influenced by the operating profit, cash from operations, proportion of cash from operations used for financing the investment activities and the proportion of equity in the capital structure of the companies. The study addresses the Indian auto components sector, which is not researched much, and suggests rejuvenation in dividend policy after accounting a derived variable of cash flow to capital expenditure, as identified relevant to the group of auto manufacturers selected for the study.

The unprecedented outbreak of COVID-19 has affected every aspect of the human life, be it health, social, or economic dimensions. The anxiety and uncertainty wobbled the economies of affected countries worldwide. This study attempts to quantify the impact of COVID-19 on the performance of major stock markets of G-7 nations vis-à-vis BRICS nations. An event study methodology is employed to capture the effect of the systematic event in the form of Buy and Hold Abnormal Returns (BHAR) and Average Buy and Hold Abnormal Returns (ABHAR). The study considers a 90-day observation window, consisting of six sub-event windows after the COVID-19 news up-doves the world, and 120 days prior to the selected event date to estimate average expected returns. BHAR values in the four event windows are statistically significant, covering stock markets from panic and nosedive to their correction and recovery. ABHAR values reported are significantly negative in the event window ranging from –0.15% to –38.43% for G-7 and –0.06% to –37.12% for BRICS nations. Despite similar ABHAR trends, the BHAR values and correlation matrix exhibit a diverse reaction in BRICS nations compared to the highly synchronized reaction in the G-7 group of nations in the COVID period.

The crumble of financial markets due to the recent crises has wobbled precariousness in the stock market and intensified the returns vulnerability of banking indices. Against this backdrop, this study intends to model the volatility of the Indian Bank Nifty returns using a battery of GARCH specifications. The finding of the present research contributes to the literature in three ways. First, volatility during the sample period, which corresponds to a time of stress (a bear market), is more persistent, with an estimated coefficient of 0.995695. Moreover, when volatility rises, it persists for a long time before returning to the mean in an average of 16 days. Second, for a positive γ, the results insinuate the possibility of an “anti-leverage effect” with a coefficient of 0.139638. Thus, the volatility of the Bank Nifty returns tends to rise in response to positive shocks relative to negative shocks of equal magnitude in India. Finally, the findings demonstrate that EGARCH with Student’s t-distribution offers lower forecast errors in modeling conditional volatility.