IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v68y2023ics1062940823000979.html
   My bibliography  Save this article

Dealer markets: A reinforcement learning mean field game approach

Author

Listed:
  • Bernasconi, Martino
  • Vittori, E.
  • Trovò, F.
  • Restelli, M.

Abstract

We study the problem of finding an equilibrium strategy in an Over The Counter (OTC) market populated by multiple strategic dealers quoting the bid–ask prices. The need for an equilibrium strategy comes from the assumption that each dealer adapts their behavior by learning the optimal quoting strategy. Hence, we model the market as a game between many agents competing for resources. Based on this framework, we propose an efficient numerical procedure using Reinforcement Learning and Mean Field Games which learns an approximate equilibrium. Through an experimental campaign, we validate the proposed method in a realistic market-making scenario against strategic dealers trained to exploit our weaknesses and evaluate their performance against non-strategic agents.

Suggested Citation

  • Bernasconi, Martino & Vittori, E. & Trovò, F. & Restelli, M., 2023. "Dealer markets: A reinforcement learning mean field game approach," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:ecofin:v:68:y:2023:i:c:s1062940823000979
    DOI: 10.1016/j.najef.2023.101974
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940823000979
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.najef.2023.101974?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2015. "Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design," Management Science, INFORMS, vol. 61(4), pages 864-884, April.
    2. Krishnamurthy Iyer & Ramesh Johari & Mukund Sundararajan, 2014. "Mean Field Equilibria of Dynamic Auctions with Learning," Management Science, INFORMS, vol. 60(12), pages 2949-2970, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Light, Bar & Weintraub, Gabriel, 2018. "Mean Field Equilibrium: Uniqueness, Existence, and Comparative Statics," Research Papers 3731, Stanford University, Graduate School of Business.
    2. W. Jason Choi & Amin Sayedi, 2019. "Learning in Online Advertising," Marketing Science, INFORMS, vol. 38(4), pages 584-608, July.
    3. Santiago R. Balseiro & Yonatan Gur, 2019. "Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium," Management Science, INFORMS, vol. 65(9), pages 3952-3968, September.
    4. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    5. Santiago R. Balseiro & Ozan Candogan, 2017. "Optimal Contracts for Intermediaries in Online Advertising," Operations Research, INFORMS, vol. 65(4), pages 878-896, August.
    6. Kostas Bimpikis & Wedad J. Elmaghraby & Ken Moon & Wenchang Zhang, 2020. "Managing Market Thickness in Online Business-to-Business Markets," Management Science, INFORMS, vol. 66(12), pages 5783-5822, December.
    7. Santiago Balseiro & Christian Kroer & Rachitesh Kumar, 2021. "Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees," Papers 2102.10476, arXiv.org, revised Oct 2022.
    8. Dragos Florin Ciocan & Krishnamurthy Iyer, 2021. "Tractable Equilibria in Sponsored Search with Endogenous Budgets," Operations Research, INFORMS, vol. 69(1), pages 227-244, January.
    9. Yash Kanoria & Daniela Saban, 2021. "Facilitating the Search for Partners on Matching Platforms," Management Science, INFORMS, vol. 67(10), pages 5990-6029, October.
    10. Nick Arnosti & Ramesh Johari & Yash Kanoria, 2021. "Managing Congestion in Matching Markets," Manufacturing & Service Operations Management, INFORMS, vol. 23(3), pages 620-636, May.
    11. Hana Choi & Carl F. Mela, 2019. "Monetizing Online Marketplaces," Marketing Science, INFORMS, vol. 38(6), pages 948-972, November.
    12. Savas Dayanik & Semih O. Sezer, 2023. "Optimal dynamic multi-keyword bidding policy of an advertiser in search-based advertising," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 97(1), pages 25-56, February.
    13. Leila Hosseini & Shaojie Tang & Vijay Mookerjee, 2024. "When Is More Merrier? A Cloud-Based Architecture to Procure Impressions from Multiple Ad Exchanges," Information Systems Research, INFORMS, vol. 35(1), pages 294-317, March.
    14. Raghav Singal & Omar Besbes & Antoine Desir & Vineet Goyal & Garud Iyengar, 2022. "Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising," Management Science, INFORMS, vol. 68(10), pages 7457-7479, October.
    15. Ilan Lobel, 2021. "Revenue Management and the Rise of the Algorithmic Economy," Management Science, INFORMS, vol. 67(9), pages 5389-5398, September.
    16. Yunke Mai & Bin Hu & Saša Pekeč, 2023. "Courteous or Crude? Managing User Conduct to Improve On-Demand Service Platform Performance," Management Science, INFORMS, vol. 69(2), pages 996-1016, February.
    17. Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2019. "Dynamic Mechanism Design with Budget-Constrained Buyers Under Limited Commitment," Operations Research, INFORMS, vol. 67(3), pages 711-730, May.
    18. Veronica Marotta & Yue Wu & Kaifu Zhang & Alessandro Acquisti, 2022. "The Welfare Impact of Targeted Advertising Technologies," Information Systems Research, INFORMS, vol. 33(1), pages 131-151, March.
    19. Meng Zhang & Deepanshu Vasal, 2020. "Mechanism Design for Large Scale Network Utility Maximization," Papers 2003.04263, arXiv.org, revised Jan 2021.
    20. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.

    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:eee:ecofin:v:68:y:2023:i:c:s1062940823000979. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    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.