Abstract:
It has been hypothesized that the salience of policy related events on online media and social networks is influenced by the sign and magnitude of co-occurring stock market fluctuations. This hypothesis relies on the intuition that stock markets are used by online media and by the general public as thermometers, i.e. sensor devices, for gauging the “relevance” of policy-related events and their expected economic impacts.
The aim of this paper is to investigate this hypothesis by studying the relation between U.S. media coverage and public perception of events related to U.S.-China trade relations and U.S. market fluctuations, using English data from an online social network. By applying text mining tools and NLP techniques to Twitter posts published by major U.S. information outlets and key political figures, I analyze chronologically the online reactions to signals concerning trade related events and public announcements from the beginning of March to the end of April 2019 and relate them to U.S. stock market fluctuations.
