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.

For standard statistical problems, we provide new evidence documenting (1) multimodality and (2) instability in probability estimates, including from irrelevant changes in problem description. The evidence motivates a model in which, when solving a problem, people represent each hypothesis by attending to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used. The model unifies biases in judgments about i.i.d. draws, such as the Gambler's Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions for how changes in the salience of specific features jointly shapes known biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Salience-driven attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of Bayes' rule.