Deep learning solution to mean field game of optimal liquidation

Shuhua Zhang, Shenghua Qian, Xinyu Wang and Yilin Cheng

Finance Research Letters, 2025, vol. 73, issue C

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.

Keywords: Deep learning; Deep Galerkin method; High-dimensionality; Mean field games; Optimal liquidation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:73:y:2025:i:c:s1544612324016921

DOI: 10.1016/j.frl.2024.106663

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