ABSTRACT
This paper uses event study based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to study the impact of the COVID-19 outbreak on China's financial market. It finds that the pandemic had an overall significant and negative impact on the stock prices of firms listed on SSE, SZSE and ChiNext. However, such impact appeared to be heterogeneous across industries, affecting listed firms in industries such as pharmaceutical and telecommunications positively, but those in services industries such as accommodation, catering, and commercial services negatively. Apparently, a crisis for some had been an opportunity for others. In addition, this paper seeks to understand the micro mechanism behind the heterogeneity of pandemic shock from the perspective of firms' financial position. It finds that listed firms with higher debt level were hit harder, whereas those with more net cash flow had displayed higher resilience against the blow of the pandemic. However, the opposite pattern is found among those listed on ChiNext and in industries severely devastated by the pandemic. These findings have policy implications in terms of preventing systemic financial risks and facilitating recovery during pandemic-induced economic downturns. It also helps investor adjust investment strategies, hedge against risks, and secure gains when the market conditions in general are unfavorable.
Subject(s)
COVID-19/economics , COVID-19/epidemiology , Models, Economic , China/epidemiology , Financial Management , Industry , InvestmentsABSTRACT
This paper investigates the impact of stock liquidity on firm value in the time of COVID-19 pandemic. Using data from A-share listed companies in China, we calculate the firm value of Cumulative Abnormal Returns through the event study method and stock liquidity by the Amihud illiquidity. We find that significant negative relationships between stock liquidity and firm value exist in the first three days of the COVID-19 outbreak, while significant positive relationships in the following days. We also find that these negative relationships are more significant in severely impacted regions, small companies, and non-state-owned enterprises.