Reinforcement Learning in Repeated Portfolio Decisions
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.
|Date of creation:||16 Feb 2011|
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- Bossaerts, Peter & Plott, Charles, 2002. "The CAPM in thin experimental financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 26(7-8), pages 1093-1112, July.
- Ed Hopkins, 2002.
"Two Competing Models of How People Learn in Games,"
Econometric Society, vol. 70(6), pages 2141-2166, November.
- Ed Hopkins, 2000. "Two Competing Models of How People Learn in Games," ESE Discussion Papers 51, Edinburgh School of Economics, University of Edinburgh.
- Ed Hopkins, 2001. "Two Competing Models of How People Learn in Games," Levine's Working Paper Archive 625018000000000226, David K. Levine.
- Ed Hopkins, 2001. "Two Competing Models of How People Learn in Games," NajEcon Working Paper Reviews 625018000000000226, www.najecon.org.
- Peter Bossaerts & Charles Plott, 2004. "Basic Principles of Asset Pricing Theory: Evidence from Large-Scale Experimental Financial Markets," Review of Finance, Springer, vol. 8(2), pages 135-169.
- Peter Bossaerts & Charles Plott, 2004. "Basic Principles of Asset Pricing Theory: Evidence from Large-Scale Experimental Financial Markets," Review of Finance, European Finance Association, vol. 8(2), pages 135-169.
- Bossaerts, Peter & Plott, Charles, 2000. "Basic Principles Of Asset Pricing Theory: Evidence From Large-Scale Experimental Financial Markets," CEPR Discussion Papers 2578, C.E.P.R. Discussion Papers.
- Bossaerts, Peter & Plott, Charles R., 2000. "Basic Principles of Asset Pricing Theory: Evidence From Large-Scale Experimental Financial Markets," Working Papers 1070, California Institute of Technology, Division of the Humanities and Social Sciences.
- Markku Kaustia & Samuli Knüpfer, 2008. "Do Investors Overweight Personal Experience? Evidence from IPO Subscriptions," Journal of Finance, American Finance Association, vol. 63(6), pages 2679-2702, December.
- Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
- Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May. Full references (including those not matched with items on IDEAS)
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