Reinforcement Learning with Hawkes-Structured Networks for Market Making

Abstract:Market making under realistic order-flow dynamics is challenging due to the clustered, self-exciting nature of order arrivals, which are naturally modeled by non-Markovian Hawkes processes. We propose a model-free reinforcement learning (RL) framework for market making with general Hawkes order-flow dynamics. We first establish non-asymptotic performance bounds that quantify the loss incurred by approximating the infinite-dimensional Hawkes state using a finite collection of exponentially weighted features. Guided by this analysis, we introduce the Hawkes-Structured Network (HSN), a novel neural architecture that encodes historical order flow through a learnable mixture-of-exponentials filter. HSN is trained end-to-end using model-free RL, jointly optimizing the feature representation and quoting policy directly from reward feedback. Experiments on single- and multi-asset market-making problems with mixture-of-exponentials, power-law, and regime-switching kernels demonstrate that HSN outperforms general-purpose neural architectures, achieving higher trading rewards while implicitly recovering the underlying order-flow intensities.

Machine Learning (cs.LG); Computational Finance (q-fin.CP)

arXiv:2610.16524 [cs.LG]

(or arXiv:2610.16524v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.16524 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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