Epidemic spreads of Covid-19 narratives and their effect on financial phenomena.

We identify and retrieve the most salient Covid-19 narratives and their evolution over time via text mining on daily open-ended questionnaires sent to US stockholders since the beginning of the Covid-19 pandemic. This data is complemented with over 30,000 news outlets and blogs scrapped from the internet in order to construct a set of narrative time series (indexes) and analyze their spreading. We apply the novel view proposed by Shiller (2017, 2019) that narratives spreading through news and social media drive financial and economic phenomena. This allows us to assess whether narratives drive stock markets during the Covid-19 pandemic by examining the causal effect of each type of narrative on the dynamics of stock returns, volatility, liquidity, and uncertainty. Moreover, we examine the differing effects of narratives’ content and speed of which they spread.