Extreme Categories and Overreaction to News
研究了新闻的极端分布特征如何导致投资者过度反应，基于诊断性预期模型，发现属于极端类别（如尾部事件）的新闻会引发更强的价格反转和交易量异常，对理解市场异象和投资者行为有参考价值。
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.