Fake News, Investor Attention, and Market Reaction
作者研究了金融市场的假新闻是否吸引更多投资者关注，并对股价产生显著影响。利用2017年4月美国证监会打击股票推广计划的事件，作者以Seeking Alpha网站的数据，比较假新闻与合法新闻文章的投资者关注度。结果发现，假新闻比合法文章获得更多关注，但文章评论者无法识别假新闻，编辑的识别能力也有限。然而，基于语言特征的机器学习算法能成功识别假新闻。此外，市场对假新闻的定价是合理的：假新闻发布时异常交易量增加，但增幅小于合法新闻，股价反应也打了折扣。
Does fake news in financial markets attract more investor attention and have a significant impact on stock prices? The authors use the SEC crackdown of stock promotion schemes in April 2017 to examine investor attention and the stock price reaction to fake news articles. Using data from Seeking Alpha, the authors find that fake news stories generate significantly more attention than a control sample of legitimate articles. The authors find no evidence that article commenters can detect fake news, and they find that Seeking Alpha editors have only modest ability to detect fake news. However, the authors implement several well-known machine learning algorithms based on linguistic characteristics and show that machine learning algorithms can successfully identify fake news. In addition, the stock market appears to price fake news correctly. While abnormal trading volume increases around the release of fake news, the increase is less than that observed for legitimate news. The stock price reaction to fake news is discounted when compared with legitimate news articles.