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Understanding the Plott-Wit-Yang Paradox

Author

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  • 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

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    1. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    2. Wolfers, Justin & Zitzewitz, Eric, 2006. "Five Open Questions About Prediction Markets," CEPR Discussion Papers 5562, C.E.P.R. Discussion Papers.
    3. Robert W. Hahn & Paul Tetlock, 2006. "Information Markets: A New Way of Making Decisions," Books, American Enterprise Institute, number 51409, September.
    4. Wolfers, Justin & Zitzewitz, Eric, 2006. "Interpreting Prediction Market Prices as Probabilities," IZA Discussion Papers 2092, Institute of Labor Economics (IZA).
    5. 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.
    6. 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.
    7. 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.
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    Cited by:

    1. Brice Corgnet & Cary Deck & Mark DeSantis & Kyle Hampton & Erik O. Kimbrough, 2023. "When Do Security Markets Aggregate Dispersed Information?," Management Science, INFORMS, vol. 69(6), pages 3697-3729, June.
    2. David Court & Benjamin Gillen & Jordi McKenzie & Charles R. Plott, 2018. "Two information aggregation mechanisms for predicting the opening weekend box office revenues of films: Boxoffice Prophecy and Guess of Guesses," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(1), pages 25-54, January.
    3. Buckley, Patrick, 2016. "Harnessing the wisdom of crowds: Decision spaces for prediction markets," Business Horizons, Elsevier, vol. 59(1), pages 85-94.

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    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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