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Understanding the Plott-Wit-Yang Paradox

Author

Listed:
  • Katarína Kálovcová
  • Andreas Ortmann

Abstract

Plott, Wit & Yang (2003) conduct a betting market experiment and find: First, information was aggregated. This suggests that traders updated their private information based on observed market odds. Second, a model based only on the use of private information seems to fit their data best. The authors call this paradoxical. Because the original data are lost, we replicate their experiment. Our results suggest that the paradox seems due to aggregate rather than individual level data analysis. We analyze the individual level data and explain the paradoxical results reported in Plott et al. (2003).

Suggested Citation

  • Katarína Kálovcová & Andreas Ortmann, 2009. "Understanding the Plott-Wit-Yang Paradox," Journal of Prediction Markets, University of Buckingham Press, vol. 3(3), pages 33-44, December.
  • Handle: RePEc:buc:jpredm:v:3:y:2009:i:3:p:33-44
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    References listed on IDEAS

    as
    1. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    2. Charles R. Plott & Jorgen Wit & Winston C. Yang, 2003. "Parimutuel betting markets as information aggregation devices: experimental results," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 22(2), pages 311-351, September.
    3. Glenn W. Harrison & Eric Johnson & Melayne M. McInnes & E. Elisabet Rutström, 2005. "Risk Aversion and Incentive Effects: Comment," American Economic Review, American Economic Association, vol. 95(3), pages 897-901, June.
    4. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
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    Cited by:

    1. Buckley, Patrick, 2016. "Harnessing the wisdom of crowds: Decision spaces for prediction markets," Business Horizons, Elsevier, vol. 59(1), pages 85-94.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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