Reinforcement Learning in Repeated Portfolio Decisions
AbstractHow 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.
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Bibliographic InfoPaper provided by Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics in its series Jena Economic Research Papers with number 2011-009.
Date of creation: 16 Feb 2011
Date of revision:
repeated portfolio decisions; reinforcement learning model; correlation;
Find related papers by JEL classification:
- C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
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