Reinforcement Learning with Hawkes-Structured Networks for Market Making
We propose a model-free reinforcement learning (RL) framework for market making with general Hawkes order-flow dynamics.
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Key points
- 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 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.
- 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.
Sources (1)
- [1]Reinforcement Learning with Hawkes-Structured Networks for Market MakingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 10:14 AM
We propose a model-free reinforcement learning (RL) framework for market making with general Hawkes order-flow dynamics.
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.
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