Learning, Fast or Slow. (12th August 2019)

• Record Type: Journal Article
• Authors: Barber, Brad M; Lee, Yi-Tsung; Liu, Yu-Jane; Odean, Terrance; Zhang, Ke
• Is Part Of: Review of asset pricing studies. Volume 10:Number 1(2020:Feb.)
• Page Start: 61
• Page End: 93
• Publication Date: 2019-08-12
• Subjects: D14 -- G12 -- G15 -- G41; Capital assets pricing model -- Periodicals; Stocks -- Prices -- Periodicals; Investments -- Valuation -- Periodicals; Finance -- Periodicals; 332.632042
• DOI: 10.1093/rapstu/raz006
• Languages: English
• ISSNs: 2045-9939

Abstract

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.