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Learning, Fast or Slow

Author

Listed:
  • Brad M Barber
  • Yi-Tsung Lee
  • Yu-Jane Liu
  • Terrance Odean
  • Ke Zhang

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.

Suggested Citation

  • Brad M Barber & Yi-Tsung Lee & Yu-Jane Liu & Terrance Odean & Ke Zhang, 2020. "Learning, Fast or Slow," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(1), pages 61-93.
    • Handle: RePEc:oup:rasset:v:10:y:2020:i:1:p:61-93.
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    File URL: http://hdl.handle.net/10.1093/rapstu/raz006
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    Citations

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    Cited by:

    1. Li, ZhouPing & Ge, RuYi & Guo, XiaoShuang & Cai, Lingfei, 2021. "Can individual investors learn from experience in online P2P lending? Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Chuang, Yi-Wei & Tsai, Wei-Che & Weng, Pei-Shih, 2020. "The impact of weather on order submissions and trading performance," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).
    3. Xing Gao & Daniel Ladley, 2022. "Noise trading and market stability," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1283-1301, October.
    4. Sarantis Tsiaplias & Qi Zeng & Guay Lim, 2021. "Retail investor expectations and trading preferences," Melbourne Institute Working Paper Series wp2021n27, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    5. Joshua E. Blumenstock & Matthew Olckers, 2020. "Gamblers Learn from Experience," Papers 2011.00432, arXiv.org, revised Aug 2021.
    6. Xindan Li & Avanidhar Subrahmanyam & Xuewei Yang & Wei Jiang, 0. "Winners, Losers, and Regulators in a Derivatives Market Bubble," Review of Economic Studies, Oxford University Press, vol. 34(1), pages 313-350.

    More about this item

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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