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State-dependent Preferences in Prediction Markets and Prices as Aggregate Statistic

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  • Urmee Khan

    (Department of Economics, University of California Riverside)

Abstract

If traders in prediction markets have state-dependent preferences so that marginal utility of money varies across states, prices in a Rational Expectation equilibrium are quantile statistics of distributions that de- rive from both the distribution of realized signals, and the distribution of state-dependence parameters. As a result, even with a common prior and regardless of whether prices reveal realized signals fully or not, the interpretation of prices as posterior probabilities remains problematic.

Suggested Citation

  • Urmee Khan, 2016. "State-dependent Preferences in Prediction Markets and Prices as Aggregate Statistic," Working Papers 201609, University of California at Riverside, Department of Economics.
  • Handle: RePEc:ucr:wpaper:201609
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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. Biais, Bruno & Glosten, Larry & Spatt, Chester, 2005. "Market microstructure: A survey of microfoundations, empirical results, and policy implications," Journal of Financial Markets, Elsevier, vol. 8(2), pages 217-264, May.
    3. Justin Wolfers & Eric Zitzewitz, 2006. "Interpreting prediction market prices as probabilities," Working Paper Series 2006-11, Federal Reserve Bank of San Francisco.
    4. Marco Ottaviani & Peter Norman Sørensen, 2015. "Price Reaction to Information with Heterogeneous Beliefs and Wealth Effects: Underreaction, Momentum, and Reversal," American Economic Review, American Economic Association, vol. 105(1), pages 1-34, January.
    5. Radner, Roy, 1979. "Rational Expectations Equilibrium: Generic Existence and the Information Revealed by Prices," Econometrica, Econometric Society, vol. 47(3), pages 655-678, May.
    6. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    7. Marco Ottaviani & Peter Norman Sørensen, 2007. "Outcome Manipulation in Corporate Prediction Markets," Journal of the European Economic Association, MIT Press, vol. 5(2-3), pages 554-563, 04-05.
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    Keywords

    Prediction markets; information aggregation; state-dependent preferences;
    All these keywords.

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