Extrapolative beliefs in the cross-section: What can we learn from the crowds?
By Zhi Da, Xing Huang, Lawrence J. Jin. Journal of Financial Economics, 2020
113 citations recorded by the source corpus.
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.
Subfields: Accounting, Finance, Safety Research
View Paper: https://www.semanticscholar.org/paper/1ab4ec56e0efca6e7c6e3fc451ae3df0a7f87ad3
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In Accounting
In Finance
- Nicholas Barberis
- Doron Avramov
- Allan G. Timmermann
- Massimo Guidolin
- Michael W. Brandt
- Jessica A. Wachter
- A. Shleifer
- Yihong Xia
In Safety Research
Citations in the corpus
- Psychology-based Models of Asset Prices and Trading Volume (2018, background): “…few investors monotonically increase or decrease their holdings of the bubble asset; more commonly, they alternately increase and decrease their exposure over 12See Da et al. (2018) for direct evidence of extrapolative expectations about the returns of individual stocks. time (Liao and Peng, 2018).”