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 over-reaction 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 24 different categories, including earnings announcements, product launches, M&A, business expansions, and client-related news.