Fake News, Investor Attention, and Market Reaction

• 成果类型: Article
• 署名作者: Clarke, Jonathan; Chen, Hailiang; Du, Ding; Hu, Yu Jeffrey
• 署名单位: University System of Georgia; Georgia Institute of Technology; University of Hong Kong; Massachusetts Institute of Technology (MIT)
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0910
• 发表日期: 2021
• 页码: 35-52
• 关键词: propensity score; deception; volume; words; price

摘要

Does fake news in financial markets attract more investor attention and have a significant impact on stock prices? We use the U.S. Securities and Exchange Commission (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, we find that fake news stories generate significantly more attention than a control sample of legitimate articles. We find no evidence that article commenters can detect fake news, and we also find that Seeking Alpha editors have only modest ability to detect fake news. However, we show that machine learning algorithms can successfully identify fake news from linguistic features of the article. 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.