News or Noise? Using Twitter to Identify and Understand Company‐specific News Flow
提出一种基于微博消息识别真实世界新闻事件的方法，利用40多万条标普500股票相关推特消息区分好坏新闻，发现好消息前的回报更显著，且不同类别新闻对股市影响差异大。
Abstract This study presents a methodology for identifying a broad range of real‐world news events based on microblogging messages. Applying computational linguistics to a unique dataset of more than 400,000 S&P 500 stock‐related Twitter messages, we distinguish between good and bad news and demonstrate that the returns prior to good news events are more pronounced than for bad news events. We show that the stock market impact of news events differs substantially across different categories.