Market making and incentives design in the presence of a dark pool: a deep reinforcement learning approach

Bastien Baldacci; Iuliia Manziuk; Thibaut Mastrolia; Mathieu Rosenbaum

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arXiv · 1912.01129

Market making and incentives design in the presence of a dark pool: a deep reinforcement learning approach

Bastien Baldacci·Iuliia Manziuk·Thibaut Mastrolia·Mathieu Rosenbaum

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Abstract

We consider the issue of a market maker acting at the same time in the lit and dark pools of an exchange. The exchange wishes to establish a suitable make-take fees policy to attract transactions on its venues. We first solve the stochastic control problem of the market maker without the intervention of the exchange. Then we derive the equations defining the optimal contract to be set between the market maker and the exchange. This contract depends on the trading flows generated by the market maker's activity on the two venues. In both cases, we show existence and uniqueness, in the viscosity sense, of the solutions of the Hamilton-Jacobi-Bellman equations associated to the market maker and exchange's problems. We finally design deep reinforcement learning algorithms enabling us to approximate efficiently the optimal controls of the market maker and the optimal incentives to be provided by the exchange.

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BibTeX RIS

Bastien Baldacci, Iuliia Manziuk, Thibaut Mastrolia, Mathieu Rosenbaum. 2019-12-02. Market making and incentives design in the presence of a dark pool: a deep reinforcement learning approach. https://arxiv.org/abs/1912.01129

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