Published April 2021
| Submitted
Journal Article
Open
Extrapolative Beliefs in the Cross-Section: What Can We Learn from the Crowds?
- Creators
- Da, Zhi
- Huang, Xing
- Jin, Lawrence J.
Abstract
Using novel data from a crowdsourcing platform for ranking stocks, we investigate how investors form expectations about stock returns over the next week. We find that investors extrapolate from stocks' recent past returns, with more weight on more recent returns, especially when recent returns are negative, salient, or from a dispersed cross-section. Such extrapolative beliefs are stronger among nonprofessionals and large stocks. Moreover, consensus rankings negatively predict returns over the next week, more so among stocks with low institutional ownership and a high degree of extrapolation. A trading strategy that sorts stocks on investor beliefs generates an economically significant profit.
Additional Information
© 2020 Elsevier B.V. Received 28 October 2019, Revised 25 March 2020, Accepted 26 March 2020, Available online 17 October 2020. We thank Bill Schwert (the editor), an anonymous referee, Nicholas Barberis, Colin Camerer, Julien Cujean, Peter Cziraki, Michael Ewens, Samuel Hartzmark, Burton Hollifield, Stephen Karolyi, Lisa Kramer, Juhani Linnainmaa, Yueran Ma, Abhiroop Mukherjee, Marina Niessner, Terrance Odean, Cameron Peng, Jesse Shapiro, David Solomon, Noah Stoffman, and seminar and conference participants at Aalto University, Baylor University, Boston College, Caltech, CKGSB, CUHK Shenzhen, CUNY Baruch, Florida International University, Georgetown University, SWUFE, UC Berkeley, UC Irvine, UCLA Anderson, UC Riverside, University of Iowa, University of Washington, WUSTL, AFA 2019, CICF 2018, EFA 2018, FIRS 2018, MFA 2019, the 15th Annual Conference in Financial Economics Research, the 2018 LA Finance Day Conference, the 2019 Mitsui Finance Symposium, the 2019 PKU-CCER Summer Institute, the 2019 SFS Cavalcade, the 2018 TAU Finance Conference, the 2019 Utah Winter Finance Conference, the WAPFIN conference at NYU Stern, and the Yale Junior Finance Conference for helpful comments and suggestions. We are grateful to Leigh Dorgen, Josh Dulberger, and Aram Balian for providing data from Forcerank.Attached Files
Submitted - SSRN-id3144849.pdf
Files
SSRN-id3144849.pdf
Files
(939.1 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:7041fb8ed2c1030a3eb1d9773048fe48
|
939.1 kB | Preview Download |
Additional details
- Eprint ID
- 106172
- Resolver ID
- CaltechAUTHORS:20201020-124457711
- Created
-
2020-10-20Created from EPrint's datestamp field
- Updated
-
2023-06-01Created from EPrint's last_modified field