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Reinforcement Learning in Repeated Portfolio Decisions

Author

Listed:
  • Linan Diao

    (Max Planck Institute of Economics, Jena, Germany)

  • Jörg Rieskamp

    (University of Basel, Switzerland)

Abstract

How do people make investment decisions when they receive outcome feedback? We examined how well the standard mean-variance model and two reinforcement models predict people's portfolio decisions. The basic reinforcement model predicts a learning process that relies solely on the portfolio's overall return, whereas the proposed extended reinforcement model also takes the risk and covariance of the investments into account. The experimental results illustrate that people reacted sensitively to different correlation structures of the investment alternatives, which was best predicted by the extended reinforcement model. The results illustrate that simple reinforcement learning is sufficient to detect correlation between investments.

Suggested Citation

  • Linan Diao & Jörg Rieskamp, 2011. "Reinforcement Learning in Repeated Portfolio Decisions," Jena Economics Research Papers 2011-009, Friedrich-Schiller-University Jena.
    • Handle: RePEc:jrp:jrpwrp:2011-009
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    File URL: https://oweb.b67.uni-jena.de/Papers/jerp2011/wp_2011_009.pdf
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    References listed on IDEAS

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    Cited by:

    1. K. Jeremy Ko & Zhijian (James) Huang, 2012. "Persistence of Beliefs in an Investment Experiment," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 2(01), pages 1-34.

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    More about this item

    Keywords

    repeated portfolio decisions; reinforcement learning model; correlation;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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