Losing from Naive Reinforcement Learning: A Survival Analysis of Individual Repurchase Decisions

Peiran Jiao

No 765, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: This paper applies survival analysis to individual trading data from a discount brokerage firm, and documents significant individual-level repurchase bias, investors' tendency to disproportionately repurchase more previously sold winners than losers. Investor sophistication and experience mitigated the bias, but generated asymmetric effects: the most sophisticated/experienced investors' tendency to avoid prior losers were almost completely eliminated, but they were still over twice more likely to repurchase prior winners. Limited attention, chasing past performance and risk-adjusted returns could not justify the asymmetry. This suggests one reason for loss from frequent trading was persistent naive reinforcement learning in repurchasing prior winners.

Keywords: Repurchase Bias; Reinforcement Learning; Sophistication; Experience. (search for similar items in EconPapers)
JEL-codes: D10 D14 G10 (search for similar items in EconPapers)
Date: 2015-11-18
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://ora.ox.ac.uk/objects/uuid:5b12392c-20ef-4d9b-b609-672db0024218 (text/html)

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:oxf:wpaper:765

Access Statistics for this paper

More papers in Economics Series Working Papers from University of Oxford, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Anne Pouliquen ( this e-mail address is bad, please contact ).