IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v73y2025ics1544612324016921.html
   My bibliography  Save this article

Deep learning solution to mean field game of optimal liquidation

Author

Listed:
  • Zhang, Shuhua
  • Qian, Shenghua
  • Wang, Xinyu
  • Cheng, Yilin

Abstract

This paper addresses optimal portfolio liquidation using Mean Field Games (MFGs) and presents a solution method to tackle high-dimensional challenges. We develop a deep learning approach that employs two sub-networks to approximate solutions to the relevant partial differential equations. Our method adheres to the requirements of differential operators and satisfies both initial and terminal conditions through simultaneous training. A key advantage of our approach is its mesh-free nature, which mitigates the curse of dimensionality encountered in traditional numerical methods. We validate the effectiveness of our approach through numerical experiments on multi-dimensional portfolio liquidation models.

Suggested Citation

  • Zhang, Shuhua & Qian, Shenghua & Wang, Xinyu & Cheng, Yilin, 2025. "Deep learning solution to mean field game of optimal liquidation," Finance Research Letters, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:finlet:v:73:y:2025:i:c:s1544612324016921
    DOI: 10.1016/j.frl.2024.106663
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324016921
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2024.106663?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.

    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:finlet:v:73:y:2025:i:c:s1544612324016921. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.