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Information Aggregation with Heterogeneous Traders

Author

Listed:
  • Cary Deck

    (Department of Economics, Finance and Legal Studies, University of Alabama and Economic Science Institute, Chapman University)

  • Tae In Jun

    (Department of Economics, Finance and Legal Studies, University of Alabama)

  • Laura Razzolini

    (Department of Economics, Finance and Legal Studies, University of Alabama)

  • Tavoy Reid

    (Department of Economics, Finance and Legal Studies, University of Alabama)

Abstract

The efficient market hypothesis predicts that asset prices reflect all available information. A seminal experiment reported that contingent claim markets could yield market outcomes consistent with information aggregation when traders hold heterogeneous state-contingent values. However, a recent experiment found the rational expectation model outperformed the prior information and maxi-min models in contingent claim markets when traders hold homogeneous values despite the no trade equilibrium in that setting. But that same study failed to replicate the original result calling into question when, if ever, prices reliably reflect the aggregate information of traders with heterogeneous values. In this paper, we show contingent claim markets can robustly yield prices consistent with the efficient market hypothesis when traders hold heterogeneous values in certain circumstances. The key distinction between our environment and that of the previous studies is that we consider trader values that are correlated and not too dissimilar.

Suggested Citation

  • Cary Deck & Tae In Jun & Laura Razzolini & Tavoy Reid, 2022. "Information Aggregation with Heterogeneous Traders," Working Papers 22-13, Chapman University, Economic Science Institute.
  • Handle: RePEc:chu:wpaper:22-13
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    File URL: https://digitalcommons.chapman.edu/esi_working_papers/374/
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    References listed on IDEAS

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    1. Berg, Joyce & Forsythe, Robert & Nelson, Forrest & Rietz, Thomas, 2008. "Results from a Dozen Years of Election Futures Markets Research," Handbook of Experimental Economics Results, in: Charles R. Plott & Vernon L. Smith (ed.), Handbook of Experimental Economics Results, edition 1, volume 1, chapter 80, pages 742-751, Elsevier.
    2. Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2022. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Management Science, INFORMS, vol. 68(7), pages 5216-5232, July.
    3. Milgrom, Paul & Stokey, Nancy, 1982. "Information, trade and common knowledge," Journal of Economic Theory, Elsevier, vol. 26(1), pages 17-27, February.
    4. Plott, Charles R & Sunder, Shyam, 1982. "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 663-698, August.
    5. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    6. Corgnet, Brice & DeSantis, Mark & Porter, David, 2020. "The distribution of information and the price efficiency of markets," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    7. Brice Corgnet & Mark Desantis & David Porter, 2018. "What Makes a Good Trader? On the Role of Intuition and Reflection on Trader Performance," Journal of Finance, American Finance Association, vol. 73(3), pages 1113-1137, June.
    8. Pavel Atanasov & Phillip Rescober & Eric Stone & Samuel A. Swift & Emile Servan-Schreiber & Philip Tetlock & Lyle Ungar & Barbara Mellers, 2017. "Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls," Management Science, INFORMS, vol. 63(3), pages 691-706, March.
    9. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    10. 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.
    11. Plott, Charles R & Sunder, Shyam, 1988. "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Econometrica, Econometric Society, vol. 56(5), pages 1085-1118, September.
    12. Corgnet, Brice & DeSantis, Mark & Porter, David, 2021. "Information aggregation and the cognitive make-up of market participants," European Economic Review, Elsevier, vol. 133(C).
    13. Brice Corgnet & Mark Desantis & David Porter, 2018. "What Makes a Good Trader? On the Role of Reflection and Intuition on Trader Performance," Post-Print hal-02312062, HAL.
    14. Bo Cowgill & Eric Zitzewitz, 2015. "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1309-1341.
    15. Page, Lionel & Siemroth, Christoph, 2017. "An experimental analysis of information acquisition in prediction markets," Games and Economic Behavior, Elsevier, vol. 101(C), pages 354-378.
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    Cited by:

    1. Caporale, Guglielmo Maria & Kyriacou, Kyriacos & Spagnolo, Nicola, 2023. "Aggregate insider trading and stock market volatility in the UK," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
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    More about this item

    Keywords

    Information Aggregation; Rational Expectations; Laboratory Experiments;
    All these keywords.

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G1 - Financial Economics - - General Financial Markets

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