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Mean Field Games with Partial Information for Algorithmic Trading

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  • Philippe Casgrain
  • Sebastian Jaimungal

Abstract

Financial markets are often driven by latent factors which traders cannot observe. Here, we address an algorithmic trading problem with collections of heterogeneous agents who aim to perform optimal execution or statistical arbitrage, where all agents filter the latent states of the world, and their trading actions have permanent and temporary price impact. This leads to a large stochastic game with heterogeneous agents. We solve the stochastic game by investigating its mean-field game (MFG) limit, with sub-populations of heterogeneous agents, and, using a convex analysis approach, we show that the solution is characterized by a vector-valued forward-backward stochastic differential equation (FBSDE). We demonstrate that the FBSDE admits a unique solution, obtain it in closed-form, and characterize the optimal behaviour of the agents in the MFG equilibrium. Moreover, we prove the MFG equilibrium provides an $\epsilon$-Nash equilibrium for the finite player game. We conclude by illustrating the behaviour of agents using the optimal MFG strategy through simulated examples.

Suggested Citation

  • Philippe Casgrain & Sebastian Jaimungal, 2018. "Mean Field Games with Partial Information for Algorithmic Trading," Papers 1803.04094, arXiv.org, revised Mar 2019.
    • Handle: RePEc:arx:papers:1803.04094
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    References listed on IDEAS

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    1. Erhan Bayraktar & Alexander Munk, 2017. "Mini-Flash Crashes, Model Risk, and Optimal Execution," Papers 1705.09827, arXiv.org, revised Aug 2018.
    2. Olivier Guéant & Pierre Louis Lions & Jean-Michel Lasry, 2011. "Mean Field Games and Applications," Post-Print hal-01393103, HAL.
    3. Rene Carmona & Jean-Pierre Fouque & Li-Hsien Sun, 2013. "Mean Field Games and Systemic Risk," Papers 1308.2172, arXiv.org.
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    Cited by:

    1. Moritz Vo{ss}, 2019. "A two-player portfolio tracking game," Papers 1911.05122, arXiv.org, revised Jul 2022.
    2. Christoph Belak & Daniel Hoffmann & Frank T. Seifried, 2020. "Continuous-Time Mean Field Games with Finite StateSpace and Common Noise," Working Paper Series 2020-05, University of Trier, Research Group Quantitative Finance and Risk Analysis.
    3. Alessandro Micheli & Johannes Muhle-Karbe & Eyal Neuman, 2021. "Closed-Loop Nash Competition for Liquidity," Papers 2112.02961, arXiv.org, revised Jun 2023.
    4. Philippe Casgrain & Sebastian Jaimungal, 2018. "Mean-Field Games with Differing Beliefs for Algorithmic Trading," Papers 1810.06101, arXiv.org, revised Dec 2019.
    5. Rama Cont & Alessandro Micheli & Eyal Neuman, 2022. "Fast and Slow Optimal Trading with Exogenous Information," Papers 2210.01901, arXiv.org, revised Jun 2023.

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