IDEAS home Printed from https://ideas.repec.org/a/wly/mgtdec/v39y2018i1p65-70.html
   My bibliography  Save this article

Private information in futures markets: An experimental study

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/mde.2868
    Download Restriction: no

    File URL: https://libkey.io/10.1002/mde.2868?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:mgtdec:v:39:y:2018:i:1:p:65-70. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/7976 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.