Learning, Fast or Slow

Brad Barber, Yi-Tsung Lee, Yu-Jane Liu, Terrance Odean and Ke Zhang

The Review of Asset Pricing Studies, 2020, vol. 10, issue 1, 61-93

Abstract: Rational models claim “trading to learn” explains widespread excessive speculative trading and challenge behavioral explanations of excessive trading. We argue rational learning models do not explain speculative trading by studying day traders in Taiwan. Consistent with previous studies of learning, unprofitable day traders are more likely than profitable traders to quit. Consistent with models of overconfidence and biased learning (but not with rational learning), the aggregate performance of day traders is negative; 74% of day trading volume is generated by traders with a history of losses; and 97% of day traders are likely to lose money in future day trading. Received: March 4, 2019; Editorial decision: May 16, 2019 by Editor: Jeffrey Pontiff. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

JEL-codes: D14 G12 G15 G41 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1093/rapstu/raz006 (application/pdf)
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:oup:rasset:v:10:y:2020:i:1:p:61-93.

Access Statistics for this article

The Review of Asset Pricing Studies is currently edited by Zhiguo He

More articles in The Review of Asset Pricing Studies from Society for Financial Studies
Bibliographic data for series maintained by Oxford University Press ().