Abstract
Using a unique dataset of fake paid-for articles obtained from an SEC investigation, we examine the impact of fake news in financial markets. In addition to the known fake articles, we use a linguistic algorithm to detect deception in expression for a much larger set of news content with unknown authenticity. The known fake articles are used to validate the algorithm. We find increases in abnormal trading volume and temporary price impact following fake news for small firms, but no impact for large firms. Following the SEC’s announced investigation, we find a marked decrease in the probability of false content and a decrease in reaction by investors to all news, but especially content with less authenticity. These findings are most pronounced for small firms with high retail ownership. Finally, small firms engage in press releases, 8-K filings, and insider trading that coincide with fake articles, consistent with concerns of coordinated stock price manipulation. No such patterns are observed for large firms. Our setting provides a unique opportunity to quantify fake news’ impact that avoids the joint hypothesis problem plaguing tests of informational efficiency.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
