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Prediction Markets

Author

Listed:
  • Justin Wolfers

    (Graduate School of Business, Stanford University)

  • Eric Zitzewitz

    (Graduate School of Business, Stanford University)

Abstract

We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of uncertain future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and that they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market’s expectations about probabilities, means and medians, and also uncertainty about these parameters. Moreover, conditional markets can effectively reveal the market’s beliefs about regression coefficients, although we still have the usual problem of disentangling correlation from causation. We discuss a number of market design issues and highlight domains in which prediction markets are most likely to be useful.

Suggested Citation

  • Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Discussion Papers 03-025, Stanford Institute for Economic Policy Research.
  • Handle: RePEc:sip:dpaper:03-025
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    References listed on IDEAS

    as
    1. Leigh, Andrew & Wolfers, Justin & Zitzewitz, Eric, 2003. "What do Financial Markets Think of War in Iraq?," Research Papers 1785, Stanford University, Graduate School of Business.
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    13. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    14. Ait-Sahalia, Yacine & Wang, Yubo & Yared, Francis, 2001. "Do option markets correctly price the probabilities of movement of the underlying asset?," Journal of Econometrics, Elsevier, vol. 102(1), pages 67-110, May.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • D7 - Microeconomics - - Analysis of Collective Decision-Making
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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