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The Review of Asset Pricing Studies Vol. 10 No. 1 2020

Learning, Fast or Slow

Brad M. Barber1; Yi-Tsung Lee2; Yu-Jane Liu2; Terrance Odean3; Ke Zhang4

1 Graduate School of Management, University of California , Davis · 2 Guanghua School of Management, Peking University · 3 Haas School of Business, University of California, Berkeley · 4 School of Management and Engineering, Nanjing University

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.

DOI
10.1093/rapstu/raz006
Volume
10
Issue
1
Pages
61-93
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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