Abstract:Using 30-minute tick returns, we examine the impact of changes in the number of COVID-19 news on eight different stock markets during the initial two months of the coronavirus crisis 2020. We do not find evidence that stock returns are sensitive to the changes in the number of COVID-19 news. However, there is strong evidence that changes in COVID-19 news increase stock market volatility in European markets. The findings also suggest that a substantial part of market uncertainty can be explained by changes in t… Show more
“…We present a main hypothesis that the large-scale COVID-19 pandemic provokes investor sentiment, and particularly a rise in fear and anxiety, which in turn negatively impacts stock prices. Accordingly, we observe that the media coverage ( Ambros et al., 2020 ) which spreads information about the pandemic, generating fear and anxiety and even conveying fake news ( Brigida and Pratt, 2017 ), increases the level of pessimistic attitudes towards investment decisions ( Da et al., 2015 ). We structure the mechanism of sentiment and equity markets in three main pillars, which are relevant to our indicators:…”
This paper proposes a new approach to estimating investor sentiments and their implications for the global financial markets. Contextualising the COVID-19 pandemic, we draw on the six behavioural indicators (media coverage, fake news, panic, sentiment, media hype and infodemic) of the 17 largest economies and data from
January 2020 to
February 2021. Our key findings, obtained using a time-varying parameter-vector auto-regression (TVP-VAR) model, indicate the total and net connectedness for the new index, entitled ‘feverish sentiment’. This index provides us insight into economies that send or receive the sentiment shocks. The construction of the network structures indicates that the United Kingdom, China, the United States and Germany became the epicentres of the sentimental shocks that were transmitted to other economies. Furthermore, we also explore the predictive power of the newly constructed index on stock returns and volatility. It turns out that investor sentiment positively (negatively) predicts the stock volatility (return) at the onset of COVID-19. This is the first study of its kind to assess international feverish sentiments by proposing a novel approach and its impacts on the equity market. Based on empirical findings, the study also offers some policy directions to mitigate the fear and panic during the pandemic.
“…We present a main hypothesis that the large-scale COVID-19 pandemic provokes investor sentiment, and particularly a rise in fear and anxiety, which in turn negatively impacts stock prices. Accordingly, we observe that the media coverage ( Ambros et al., 2020 ) which spreads information about the pandemic, generating fear and anxiety and even conveying fake news ( Brigida and Pratt, 2017 ), increases the level of pessimistic attitudes towards investment decisions ( Da et al., 2015 ). We structure the mechanism of sentiment and equity markets in three main pillars, which are relevant to our indicators:…”
This paper proposes a new approach to estimating investor sentiments and their implications for the global financial markets. Contextualising the COVID-19 pandemic, we draw on the six behavioural indicators (media coverage, fake news, panic, sentiment, media hype and infodemic) of the 17 largest economies and data from
January 2020 to
February 2021. Our key findings, obtained using a time-varying parameter-vector auto-regression (TVP-VAR) model, indicate the total and net connectedness for the new index, entitled ‘feverish sentiment’. This index provides us insight into economies that send or receive the sentiment shocks. The construction of the network structures indicates that the United Kingdom, China, the United States and Germany became the epicentres of the sentimental shocks that were transmitted to other economies. Furthermore, we also explore the predictive power of the newly constructed index on stock returns and volatility. It turns out that investor sentiment positively (negatively) predicts the stock volatility (return) at the onset of COVID-19. This is the first study of its kind to assess international feverish sentiments by proposing a novel approach and its impacts on the equity market. Based on empirical findings, the study also offers some policy directions to mitigate the fear and panic during the pandemic.
“…When it comes to the comparison between COVID-19 event and other public health crises, Schell et al (2020) indicated that the coronavirus outbreak exhibits the significant negative abnormal returns across the majority of equity markets while this phenomenon does not exist in the remaining events such as Ebola, Zika virus, and so forth. In the same vein, the study of Ambros et al (2020) investigates the role of news on the stock markets returns and volatility. Although this paper sheds a new light on null results of potential channel between returns and pandemic news, the aforementioned paper provides an empirical evidence about the role of number of disease news significantly the European market volatility.…”
Section: Brief Literature Review Regarding Covid-19 Impacts On Financial Marketsmentioning
This paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the epidemic.
“…On that day, the Shanghai Composite Index fell by 2.75% and the Shenzhen Component Index fell by 3.52%. Therefore, this paper selects January 23, 2020 as the first day of the impact of the new epidemic on all industries in the securities market ( Ambros et al., 2020 ; Schell et al., 2020 ). As a result, all data before January 23, 2020 are used for the control group and all data after January 23 are used for the treatment group.…”
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