Investor Attention, Divergence of Opinions, and Stock Returns

Zhen Cao, Osman Kilic and Xuewu (Wesley) Wang

Journal of Behavioral Finance, 2021, vol. 22, issue 3, 265-279

Abstract: Using a direct measure of investor attention generated from the Securities and Exchange Commission’s EDGAR (Electronic Data Gathering, Analysis, and Retrieval) log files, the authors revisit the stock return predictability of the divergence of opinions in the presence of varying degree of investor attention and information acquisition. They document a positive relationship between the divergence of opinions and future stock returns, consistent with the risk hypothesis, as opposed to the overvaluation hypothesis. More importantly, the authors find that the predictive power of divergence of opinions is more pronounced in stocks with lower investor attention. They further document the construction and profitability of divergence of opinions portfolios augmented with investor attention. A portfolio that goes long on stocks with low investor attention and the highest divergence of opinions and short on stocks with low attention and the lowest divergence of opinions generates a Fama-French 5-factor monthly alpha of 1.14%.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/15427560.2020.1772263 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:hbhfxx:v:22:y:2021:i:3:p:265-279

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/hbhf20

DOI: 10.1080/15427560.2020.1772263

Access Statistics for this article

Journal of Behavioral Finance is currently edited by Brian Bruce

More articles in Journal of Behavioral Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().