Sophisticated Investor Attention and Market Reaction to Earnings Announcements: Evidence From the SEC’s EDGAR Log Files

Ruihai Li, Xuewu (Wesley) Wang, Zhipeng Yan and Yan Zhao

Journal of Behavioral Finance, 2019, vol. 20, issue 4, 490-503

Abstract: The Securities and Exchange Commission’s (SEC) Electronic Data Gathering and Retrieval (EDGAR) log files provide a direct, powerful measure of attention from relatively sophisticated investors. The authors apply this measure to a sample of earnings announcements from 2003 to 2016. The authors find that the stock market is less surprised, and the post–earnings-announcement drift is weaker for earnings announcements receiving more preannouncement investor attention, measured in downloads by humans from EDGAR. The authors further show that it is profitable to utilize the different drift patterns. An attention-based portfolio without the SEC reporting lag that longs stocks with the lowest investor attention and most positive earnings surprises and shorts stocks with the lowest attention and most negative earnings surprises generates a statistically significant monthly alpha of 1.24% after adjusting for standard asset pricing factors.

Date: 2019
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DOI: 10.1080/15427560.2019.1575829

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