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Information Aggregation in Arrow-Debreu Markets: An Experiment

Author

Listed:
  • Ro’i Zultan

    (BGU)

  • Todd R. Kaplan

    (University of Exeter, Exeter, EX4 4PU, UK, and University of Haifa)

  • Lawrence Choo

    (Germany)

Abstract

Studies of experimental and betting markets have shown that markets are able to efficiently aggregate information dispersed over many traders. We study information aggregation in Arrow–Debreu markets using a novel information structure. Compared to previous studies, the information structure is more complex, allows for heterogeneity in information among traders—which provides insights into the way in which information is gradually disseminated in the market—and generates situations in which all traders hold identical beliefs over the traded assets’ values, thus providing a harsh stress test for belief updating. We find little evidence for information aggregation and dissemination in early rounds. Nonetheless, after traders gain experience with the market mechanism and structure, prices converge to reveal the true state of the world. Elicited post-market beliefs reveal that markets are able to efficiently aggregate dispersed information even if individual traders remain uninformed, consistent with the marginal trader hypothesis.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Ro’i Zultan & Todd R. Kaplan & Lawrence Choo, 2018. "Information Aggregation in Arrow-Debreu Markets: An Experiment," Working Papers 1807, Ben-Gurion University of the Negev, Department of Economics.
    • Handle: RePEc:bgu:wpaper:1807
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    Cited by:

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    2. 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.
    3. 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.
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    6. Stefan Palan & Jürgen Huber & Larissa Senninger, 2020. "Aggregation mechanisms for crowd predictions," Experimental Economics, Springer;Economic Science Association, vol. 23(3), pages 788-814, September.

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    More about this item

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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