Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model
This addresses market making for quantitative finance, but it is incremental as it builds on existing weakly consistent limit order book models.
The paper tackled the problem of optimal market making in quantitative finance by training a deep reinforcement learning controller on a Hawkes process-based limit order book simulator, achieving superior performance compared to benchmarks in risk-reward metrics under high transaction costs.
The stochastic control problem of optimal market making is among the central problems in quantitative finance. In this paper, a deep reinforcement learning-based controller is trained on a weakly consistent, multivariate Hawkes process-based limit order book simulator to obtain market making controls. The proposed approach leverages the advantages of Monte Carlo backtesting and contributes to the line of research on market making under weakly consistent limit order book models. The ensuing deep reinforcement learning controller is compared to multiple market making benchmarks, with the results indicating its superior performance with respect to various risk-reward metrics, even under significant transaction costs.