IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v18y2011i3p267-272.html
   My bibliography  Save this article

What do prediction markets predict?

Author

Listed:
  • John Fountain
  • Glenn Harrison

Abstract

We show that prediction markets cannot be relied on to always elicit any interesting statistic of aggregate beliefs. Formal derivations of the bets placed in prediction markets can be viewed as demands for state-contingent commodities. We provide derivations for two popular cases, log utility and Constant Relative Risk Aversion (CRRA) utility, connecting these derivations to familiar scoring rules. We then use these results to demonstrate how the properties of prediction markets depend critically on the assumed homogeneity of participants.

Suggested Citation

  • John Fountain & Glenn Harrison, 2011. "What do prediction markets predict?," Applied Economics Letters, Taylor & Francis Journals, vol. 18(3), pages 267-272.
    • Handle: RePEc:taf:apeclt:v:18:y:2011:i:3:p:267-272
    DOI: 10.1080/13504850903559575
    as

    Download full text from publisher

    File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/13504850903559575&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504850903559575?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Forsythe, Robert & Rietz, Thomas A. & Ross, Thomas W., 1999. "Wishes, expectations and actions: a survey on price formation in election stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 83-110, May.
    3. Marco Ottaviani & Peter Norman Sørensen, 2007. "Aggregation of Information and Beliefs in Prediction Markets," FRU Working Papers 2007/01, University of Copenhagen. Department of Economics. Finance Research Unit.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He, Xue-Zhong & Treich, Nicolas, 2012. "Heterogeneous Beliefs and Prediction Market Accuracy," TSE Working Papers 13-394, Toulouse School of Economics (TSE).
    2. Antonio Filippin & Marco Mantovani, 2023. "Risk aversion and information aggregation in binary‐asset markets," Quantitative Economics, Econometric Society, vol. 14(2), pages 753-798, May.
    3. Karl Schlag & James Tremewan & Joël Weele, 2015. "A penny for your thoughts: a survey of methods for eliciting beliefs," Experimental Economics, Springer;Economic Science Association, vol. 18(3), pages 457-490, September.
    4. Karl Schlag & James Tremewan & Joël Weele, 2015. "A penny for your thoughts: a survey of methods for eliciting beliefs," Experimental Economics, Springer;Economic Science Association, vol. 18(3), pages 457-490, September.
    5. Steffen Andersen & John Fountain & Glenn Harrison & E. Rutström, 2014. "Estimating subjective probabilities," Journal of Risk and Uncertainty, Springer, vol. 48(3), pages 207-229, June.
    6. Antonio, Filippin & Marco, Mantovani, 2019. "Risk Aversion and Information Aggregation in Asset Markets," Working Papers 404, University of Milano-Bicocca, Department of Economics, revised Apr 2019.
    7. Marco Mantovani & Antonio Filippin, 2024. "When do prediction markets return average beliefs? Experimental evidence," Working Papers 532, University of Milano-Bicocca, Department of Economics.
    8. Daniel Kirste & Niclas Kannengie{ss}er & Ricky Lamberty & Ali Sunyaev, 2023. "How Automated Market Makers Approach the Thin Market Problem in Cryptoeconomic Systems," Papers 2309.12818, arXiv.org, revised Sep 2023.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Siemroth, Christoph, 2014. "Why prediction markets work : The role of information acquisition and endogenous weighting," Working Papers 14-02, University of Mannheim, Department of Economics.
    2. Mikuláš Gangur & Miroslav Plevný, 2014. "Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(36), pages 578-578, May.
    3. Franch, Fabio, 2021. "Political preferences nowcasting with factor analysis and internet data: The 2012 and 2016 US presidential elections," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    4. Werner Antweiler, 2012. "Long-Term Prediction Markets," Journal of Prediction Markets, University of Buckingham Press, vol. 6(3), pages 43-61.
    5. Boyle, Glenn & Videbeck, Steen, 2005. "A Primer on Information Markets," Working Paper Series 3853, Victoria University of Wellington, The New Zealand Institute for the Study of Competition and Regulation.
    6. Arnaud Z. Dragicevic, 2017. "Option Fund Market Dynamics for Threshold Public Goods," Dynamic Games and Applications, Springer, vol. 7(1), pages 21-33, March.
    7. Victor Tiberius & Christoph Rasche, 2011. "Prognosemärkte," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 21(4), pages 467-472, April.
    8. Joyce E. Berg & John Geweke & Thomas A. Rietz, 2010. "Memoirs of an indifferent trader: Estimating forecast distributions from prediction markets," Quantitative Economics, Econometric Society, vol. 1(1), pages 163-186, July.
    9. Amos Storkey, 2011. "Machine Learning Markets," Papers 1106.4509, arXiv.org.
    10. Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
    11. Ray C. Fair, 2006. "Interpreting the Predictive Uncertainty of Presidential Elections," Levine's Bibliography 321307000000000427, UCLA Department of Economics.
    12. Ray C. Fair, 2006. "Interpreting the Predictive Uncertainty of Elections," Cowles Foundation Discussion Papers 1579, Cowles Foundation for Research in Economics, Yale University, revised May 2007.
    13. Schneider, Mark, 2020. "A bias aggregation theorem," Economics Letters, Elsevier, vol. 196(C).
    14. Angelini, Giovanni & De Angelis, Luca & Singleton, Carl, 2022. "Informational efficiency and behaviour within in-play prediction markets," International Journal of Forecasting, Elsevier, vol. 38(1), pages 282-299.
    15. Mathias Drehmann & Jörg Oechssler & Andreas Roider, 2005. "Herding and Contrarian Behavior in Financial Markets: An Internet Experiment," American Economic Review, American Economic Association, vol. 95(5), pages 1403-1426, December.
    16. Elberse, Anita & Anand, Bharat, 2007. "The effectiveness of pre-release advertising for motion pictures: An empirical investigation using a simulated market," Information Economics and Policy, Elsevier, vol. 19(3-4), pages 319-343, October.
    17. Erik Snowberg & Justin Wolfers, 2010. "Explaining the Favorite-Long Shot Bias: Is it Risk-Love or Misperceptions?," Journal of Political Economy, University of Chicago Press, vol. 118(4), pages 723-746, August.
    18. Emerson Melo, 2021. "Learning in Random Utility Models Via Online Decision Problems," Papers 2112.10993, arXiv.org, revised Aug 2022.
    19. Coulomb, Renaud & Sangnier, Marc, 2014. "The impact of political majorities on firm value: Do electoral promises or friendship connections matter?," Journal of Public Economics, Elsevier, vol. 115(C), pages 158-170.
    20. repec:grz:wpsses:2019-01 is not listed on IDEAS
    21. Deck, Cary & Hao, Li & Porter, David, 2015. "Do prediction markets aid defenders in a weak-link contest?," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 248-258.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:apeclt:v:18:y:2011:i:3:p:267-272. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.