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Private information in futures markets: An experimental study

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  • Anna Dodonova
  • Yuri Khoroshilov

Abstract

This paper presents the results of an experimental study on how people use their private information to estimate the “fair†futures price and how the quality of this information affects the traders' behavior and desire to trade. It finds that subjects are able to use their information correctly and that their desire to rely on it depends positively on the information precision. It shows that subjects are able to recognize that they are expected to lose money on futures trading when other traders have better quality information. However, subjects failed to recognize the symmetry of the futures contracts.

Suggested Citation

  • Anna Dodonova & Yuri Khoroshilov, 2018. "Private information in futures markets: An experimental study," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 39(1), pages 65-70, January.
    • Handle: RePEc:wly:mgtdec:v:39:y:2018:i:1:p:65-70
    DOI: 10.1002/mde.2868
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    Cited by:

    1. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    2. Alessio Emanuele Biondo, 2019. "Order book modeling and financial stability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 469-489, September.
    3. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
    4. repec:hal:journl:hal-04790290 is not listed on IDEAS
    5. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.

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