Learning, Fast or Slow

B-Tier
Journal: Review of Asset Pricing Studies
Year: 2020
Volume: 10
Issue: 1
Pages: 61-93

Authors (5)

Brad M Barber (University of California-Davis) Yi-Tsung Lee (Peking University) Yu-Jane Liu (not in RePEc) Terrance Odean (not in RePEc) Ke Zhang (not in RePEc)

Score contribution per author:

0.402 = (α=2.01 / 5 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:oup:rasset:v:10:y:2020:i:1:p:61-93.
Journal Field
Finance
Author Count
5
Added to Database
2026-01-24