IDEAS home Printed from https://ideas.repec.org/a/eee/beexfi/v43y2024ics2214635024000716.html
   My bibliography  Save this article

Information aggregation with heterogeneous traders

Author

Listed:
  • Deck, Cary
  • Jun, Tae In
  • Razzolini, Laura
  • Reid, Tavoy

Abstract

The efficient market hypothesis predicts that asset prices reflect all available information. Recent experimental work found the rational expectation model to outperform the prior information model in contingent claim markets when traders hold homogeneous values, despite the no trade equilibrium. However, recent experiments have also demonstrated the inability of contingent claim markets to successfully aggregate information when traders hold highly differentiated asset values. These prior findings beg the question of whether homogeneous values are a necessary condition for efficient market outcomes in contingent claim markets. The experiments reported in this paper show that homogeneous values are not a necessary condition for information aggregation.

Suggested Citation

  • Deck, Cary & Jun, Tae In & Razzolini, Laura & Reid, Tavoy, 2024. "Information aggregation with heterogeneous traders," Journal of Behavioral and Experimental Finance, Elsevier, vol. 43(C).
  • Handle: RePEc:eee:beexfi:v:43:y:2024:i:c:s2214635024000716
    DOI: 10.1016/j.jbef.2024.100956
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214635024000716
    Download Restriction: no

    File URL: https://libkey.io/10.1016/j.jbef.2024.100956?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Corgnet, Brice & DeSantis, Mark & Porter, David, 2021. "Information aggregation and the cognitive make-up of market participants," European Economic Review, Elsevier, vol. 133(C).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Milgrom, Paul & Stokey, Nancy, 1982. "Information, trade and common knowledge," Journal of Economic Theory, Elsevier, vol. 26(1), pages 17-27, February.
    12. 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.
    13. 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.
    14. 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.
    15. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    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. Bossaerts, Peter & Bowman, Elizabeth & Fattinger, Felix & Huang, Harvey & Lee, Michelle & Murawski, Carsten & Suthakar, Anirudh & Tang, Shireen & Yadav, Nitin, 2024. "Resource allocation, computational complexity, and market design," Journal of Behavioral and Experimental Finance, Elsevier, vol. 42(C).
    2. 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).

    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. 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. 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. Corgnet, Brice & DeSantis, Mark & Porter, David, 2021. "Information aggregation and the cognitive make-up of market participants," European Economic Review, Elsevier, vol. 133(C).
    4. Brice Corgnet & Mark DeSantis & David Porter, 2020. "Information Aggregation and the Cognitive Make-up of Traders," Working Papers 20-18, Chapman University, Economic Science Institute.
    5. 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).
    6. Marquardt, Philipp & Noussair, Charles N & Weber, Martin, 2019. "Rational expectations in an experimental asset market with shocks to market trends," European Economic Review, Elsevier, vol. 114(C), pages 116-140.
    7. Lionel Page & Christoph Siemroth, 2021. "How Much Information Is Incorporated into Financial Asset Prices? Experimental Evidence," Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4412-4449.
    8. Nuzzo, Simone & Morone, Andrea, 2017. "Asset markets in the lab: A literature review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 13(C), pages 42-50.
    9. Edward Halim & Yohanes E. Riyanto & Nilanjan Roy, 2019. "Costly Information Acquisition, Social Networks, and Asset Prices: Experimental Evidence," Journal of Finance, American Finance Association, vol. 74(4), pages 1975-2010, August.
    10. Paul J. Healy & Sera Linardi & J. Richard Lowery & John O. Ledyard, 2010. "Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders," Management Science, INFORMS, vol. 56(11), pages 1977-1996, November.
    11. Frieden, B. Roy & Hawkins, Raymond J., 2010. "Asymmetric information and economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(2), pages 287-295.
    12. Morone, Andrea & Nuzzo, Simone, 2015. "Market Efficiency, Trading Institutions and Information Mirages: evidence from an experimental asset market," MPRA Paper 67448, University Library of Munich, Germany.
    13. Corgnet, Brice & Deck, Cary & DeSantis, Mark & Porter, David, 2018. "Information (non)aggregation in markets with costly signal acquisition," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 286-320.
    14. Andrea Albertazzi & Friederike Mengel & Ronald Peeters, 2021. "Benchmarking information aggregation in experimental markets," Economic Inquiry, Western Economic Association International, vol. 59(4), pages 1500-1516, October.
    15. Gerke, Wolfgang & Arneth, Stefan & Syha, Christine, 2000. "The impact of the order book privilege on traders' behavior and the market process: An experimental study," Journal of Economic Psychology, Elsevier, vol. 21(2), pages 167-189, April.
    16. Simone Alfarano & Albert Banal-Estañol & Eva Camacho & Giulia Iori & Burcu Kapar & Rohit Rahi, 2024. "Centralized vs decentralized markets: The role of connectivity," Economics Working Papers 1877, Department of Economics and Business, Universitat Pompeu Fabra.
    17. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2022. "Manipulation and (Mis)trust in Prediction Markets," Management Science, INFORMS, vol. 68(9), pages 6716-6732, September.
    18. Merl, Robert, 2022. "Literature review of experimental asset markets with insiders," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).
    19. repec:grz:wpsses:2021-04 is not listed on IDEAS
    20. Bossaerts, Peter & Bowman, Elizabeth & Fattinger, Felix & Huang, Harvey & Lee, Michelle & Murawski, Carsten & Suthakar, Anirudh & Tang, Shireen & Yadav, Nitin, 2024. "Resource allocation, computational complexity, and market design," Journal of Behavioral and Experimental Finance, Elsevier, vol. 42(C).
    21. Brice Corgnet & Mark DeSantis & David Porter, 2015. "Revisiting Information Aggregation in Asset Markets: Reflective Learning & Market Efficiency," Working Papers 15-15, Chapman University, Economic Science Institute.

    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

    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:eee:beexfi:v:43:y:2024:i:c:s2214635024000716. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-behavioral-and-experimental-finance .

    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.