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The distribution of information and the price efficiency of markets

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  • Brice Corgnet

    (emlyon business school, GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - CNRS - Centre National de la Recherche Scientifique - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UL2 - Université Lumière - Lyon 2 - ENS Lyon - École normale supérieure - Lyon)

  • Mark Desantis

    (Chapman University)

  • David Porter

    (Chapman University)

Abstract

Apparently contradictory evidence has accumulated regarding the extent to which financial markets are informationally efficient. Shedding new light on this old debate, we show that differences in the distribution of private information may explain why informational efficiency can vary greatly across markets. We find that markets are informationally efficient when complete information is concentrated in the hands of competing insiders whereas they are less efficient when private information is dispersed across traders. A learning model helps to illustrate why inferring others' private information from prices takes more time when information is more dispersed.

Suggested Citation

  • Brice Corgnet & Mark Desantis & David Porter, 2019. "The distribution of information and the price efficiency of markets," Post-Print halshs-02393564, HAL.
  • Handle: RePEc:hal:journl:halshs-02393564
    DOI: 10.1016/j.jedc.2019.02.006
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-02393564
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    Cited by:

    1. 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.
    2. Corgnet, Brice & DeSantis, Mark & Porter, David, 2021. "Information aggregation and the cognitive make-up of market participants," European Economic Review, Elsevier, vol. 133(C).
    3. Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2020. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Working Papers 2020, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    4. Artem Stopochkin & Inessa Sytnik & Janusz Wielki & Nataliia Zemlianska, 2021. "Methodology for Building Trader's Investment Strategy Based on Assessment of the Market Value of the Company," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 913-935.
    5. 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.
    6. Robert Merl, 2021. "Literature Review of Experimental Asset Markets with Insiders," Working Paper Series, Social and Economic Sciences 2021-04, Faculty of Social and Economic Sciences, Karl-Franzens-University Graz.
    7. Vorstaz, Marc & Lopes Moreira Da Veiga, María Helena & Peeters, Ronald, 2020. "Contagion in sequential financial markets: an experimental analysis," DES - Working Papers. Statistics and Econometrics. WS 31230, Universidad Carlos III de Madrid. Departamento de Estadística.

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    More about this item

    Keywords

    Cognitive finance; Learning models; Experimental asset markets; Market efficiency; Information aggregation; Information dispersion;
    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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