Extreme Categories and Overreaction to News
Abstract What characteristics of news generate over-or-underreaction? We study the asset-pricing consequences of diagnostic expectations, a model of belief formation based on the representativeness heuristic, in a setting where news events are drawn from categories with extreme distributions of fundamentals. Our model predicts greater overreaction to news belonging to categories with more extreme outliers, or tail events. We test our theory on a comprehensive database of corporate news that includes news from twenty-four different categories, including earnings announcements, product launches, mergers and acquisition, business expansions, and client-related news. We find theory-consistent heterogeneity in investor reaction to news, with more overreaction in the form of greater post-announcement return reversals and trading volume for news categories with more extreme distributions of fundamentals.
- DOI
- 10.1093/restud/rdaf037
- Volume
- 93 (2)
- Pages
- 1137-1166
- Language
- en
- Export
- BibTeX
- Sources
- openalex crossref