Stock Recommendation, Contributor Information Disclosure and Stock Abnormal Returns in Online Investment Communities – A Signaling Theory Perspective

PI: Prof. Yulin Fang, HKU

Project Abstract: 

The development of information technology has reformed many industries. Social media, as a representative of the Web 2.0 era, reshaped the way people obtain financial information with online investment communities (OICs), such as Seeking Alpha and StockTwits. 

Existing research has suggested that the contents in OICs own prediction value for stock returns. However, since the content volume is huge and the prediction qualities are varying, investors will suffer losses if they are misguided by valueless content. Over the long term, valueless content poses threats to the development of platforms and the financial market. To date, limited research has focused on valuable content screening in OICs.

To fill this research gap, we will leverage SeekingAlpha as our research context, where contributors share stock opinions and recommendations and readers consume these contents. Based on signaling theory, we propose that contributors’ disclosed information will serve as a quality signal that helps investors screen content with high prediction value. Specifically, we will examine the moderating effects of contributors’ disclosed information on the relationships between stock recommendations and short-/long-term cumulative abnormal returns. Moreover, the outcomes of investment strategies based on our findings will be illustrated to show their effectiveness and practical relevance.