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Information (Non)Aggregation in Markets with Costly Signal Acquisition

Author

Listed:
  • Brice Corgnet

    (EM Lyon Business School, GATE)

  • Cary Deck

    (University of Alabama and Chapman University)

  • Mark DeSantis

    (Chapman University)

  • David Porter

    (Chapman University)

Abstract

Markets are often viewed as a tool for aggregating disparate private knowledge, a stance supported by past laboratory experiments. However, traders’ acquisition cost of information has typically been ignored. Results from a laboratory experiment involving six treatments varying the cost of acquiring signals of an asset’s value suggest that when information is costly, markets do not succeed in aggregating it. At an individual level, having information improves trading performance, but not enough to offset the cost of obtaining the information. Although males earn more through trading than females, this differential is offset by the greater propensity of males to buy information such that total profit is similar for males and females. Looking at individual skills, we find that higher theory of mind is associated with greater trading profit, greater overall profit, and an increased likelihood of acquiring information while cognitive reflection is associated with greater profit but not a greater propensity to acquire information.

Suggested Citation

  • Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2017. "Information (Non)Aggregation in Markets with Costly Signal Acquisition," Working Papers 17-24, Chapman University, Economic Science Institute.
  • Handle: RePEc:chu:wpaper:17-24
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    References listed on IDEAS

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    Cited by:

    1. Brice Corgnet & Cary Deck & Mark Desantis & Kyle Hampton & Erik O Kimbrough, 2019. "Reconsidering Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Working Papers halshs-02146611, HAL.
    2. 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.
    3. 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.
    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. 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.
    6. 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.
    7. Alba Ruiz-Buforn & Simone Alfarano & Eva Camacho-Cuena & Andrea Morone, 2022. "Single vs. multiple disclosures in an experimental asset market with information acquisition," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1513-1539, October.
    8. Peeters, Ronald & Lopes Moreira Da Veiga, María Helena & Vorstaz, Marc, 2022. "Contagion in sequential financial markets: an experimental analysis," DES - Working Papers. Statistics and Econometrics. WS 31230, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Ruiz-Buforn, Alba & Camacho-Cuena, Eva & Morone, Andrea & Alfarano, Simone, 2021. "Overweighting of public information in financial markets: A lesson from the lab," Journal of Banking & Finance, Elsevier, vol. 133(C).
    10. Corgnet, Brice & DeSantis, Mark & Porter, David, 2021. "Information aggregation and the cognitive make-up of market participants," European Economic Review, Elsevier, vol. 133(C).
    11. 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.
    12. 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).

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    More about this item

    Keywords

    Prediction Markets; Information Acquisition; Laboratory Experiments; Behavioral Finance;
    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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