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Revisiting Information Aggregation in Asset Markets: Reflective Learning & Market Efficiency

Author

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  • Brice Corgnet

    (Economic Science Institute & Argyros School of Business and Economics, Chapman University)

  • Mark DeSantis

    (Economic Science Institute & Argyros School of Business and Economics, Chapman University)

  • David Porter

    (Economic Science Institute & Argyros School of Business and Economics, Chapman University)

Abstract

The ability of markets to aggregate disperse information leading to prices that reflect the fundamental value of an asset is key to assessing the often-debated efficiency of markets. We study information aggregation in the experimental environment originally created by Plott and Sunder (1988). Contrary to the current belief, we find that markets do not aggregate information. The model that best describes our data, as well as data on information aggregation subsequent to Plott and Sunder (1988), is prior information (Lintner, 1969). That is, traders use their private information but fail to use market prices to infer other traders’ information. We argue that reflecting on asset prices to infer others’ information requires specific skills related to the concept of cognitive reflection. We develop a learning model in which only a subset of the traders possess this reflective capacity. We show, using both simulations and laboratory experiments, that information aggregation can only be achieved when the market is populated by highly reflective traders and this high level of cognitive reflection is commonly known to all of the traders.

Suggested Citation

  • 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.
  • Handle: RePEc:chu:wpaper:15-15
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    Citations

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    Cited by:

    1. Schneider, Mark & Porter, David, 2020. "Effects of experience, choice architecture, and cognitive reflection in strategyproof mechanisms," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 361-377.
    2. 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.
    3. Brice Corgnet & Cary Deck & Mark Desantis & David Porter, 2020. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Working Papers halshs-02893291, HAL.
    4. Mark Schneider & David Porter, 2016. "Cognitive Reflection Predicts Decision Quality in Individual and Strategic Decisions," Working Papers 16-24, Chapman University, Economic Science Institute.
    5. Duchêne, Sébastien & Guerci, Eric & Hanaki, Nobuyuki & Noussair, Charles N., 2019. "The effect of short selling and borrowing on market prices and traders’ behavior," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    6. Farago, Adam & Holmén, Martin & Holzmeister, Felix & Kirchler, Michael & Razen, Michael, 2019. "Cognitive Skills and Economic Preferences in the Fund Industry," OSF Preprints 964ba, Center for Open Science.
    7. Lionel Page & Christoph Siemroth & Itay Goldstein, 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. Choo, Lawrence & Kaplan, Todd R. & Zultan, Ro'i, 2019. "Manipulation and (mis)trust in prediction markets," FAU Discussion Papers in Economics 12/2019, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Brice Corgnet & Cary Deck & Mark Desantis & Kyle Hampton & Erik Kimbrough, 2019. "Reconsidering Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Working Papers halshs-02146611, HAL.
    10. Utz Weitzel & Christoph Huber & Jürgen Huber & Michael Kirchler & Florian Lindner & Julia Rose, 2018. "Bubbles and Financial Professionals," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2018_09, Max Planck Institute for Research on Collective Goods, revised Mar 2019.
      • Utz Weitzel & Christoph Huber & Florian Lindner & Jürgen Huber & Julia Rose & Michael Kirchler, 2018. "Bubbles and financial professionals," Working Papers 2018-04, Faculty of Economics and Statistics, University of Innsbruck, revised Oct 2018.
    11. Ruiz-Buforn, Alba & Alfarano, Simone & Camacho-Cuena, Eva, 2019. "Price distortions and public information: theory, experiments and simulations," MPRA Paper 93288, University Library of Munich, Germany.
    12. Marco Cipriani & Roberta De Filippis & Antonio Guarino & Ryan Kendall, 2020. "Trading by Professional Traders: An Experiment," Staff Reports 939, Federal Reserve Bank of New York.
    13. Lionel Page & Christoph Siemroth, 2018. "How much information is incorporated in financial asset prices? Experimental Evidence," QuBE Working Papers 054, QUT Business School.
    14. Alfarano, Simone & Banal-Estanol, Albert & Camacho-Cuena, Eva & Iori, Giulia & Kapar, Burcu, 2020. "Centralized vs decentralized markets in the laboratory: The role of connectivity," MPRA Paper 99129, University Library of Munich, Germany.
    15. Mark Schneider, 2016. "Dual Process Utility Theory: A Model of Decisions Under Risk and Over Time," Working Papers 16-23, Chapman University, Economic Science Institute.
    16. Lunawat, Radhika, 2021. "Learning from trading activity in laboratory security markets with higher-order uncertainty," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    17. Ahrash Dianat & Christoph Siemroth, 2021. "Improving decisions with market information: an experiment on corporate prediction markets," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 143-176, March.
    18. Fernando Chague & Rodrigo De Losso, Bruno Giovannetti, 2018. "Individual Investors Look at Price Tags," Working Papers, Department of Economics 2018_17, University of São Paulo (FEA-USP).

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    More about this item

    Keywords

    Information aggregation; market efficiency; experimental asset markets; behavioral finance;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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