Learning Market Making with Closing Auctions

TL;DR

This paper introduces a Deep Q-Learning approach for market making that explicitly accounts for closing auctions, improving risk management and price prediction in trading sessions.

Contribution

It proposes a novel framework integrating closing auction anticipation into market-making, using a generative stochastic model and deep reinforcement learning.

Findings

The method outperforms classical benchmarks in simulated Heston model environments.

It effectively predicts closing prices and manages inventory risk.

Performance is validated on real S&P 500 data.

Abstract

In this work, we investigate the market-making problem on a trading session in which a continuous phase on a limit order book is followed by a closing auction. Whereas standard optimal market-making models typically rely on terminal inventory penalties to manage end-of-day risk, ignoring the significant liquidity events available in closing auctions, we propose a Deep Q-Learning framework that explicitly incorporates this mechanism. We introduce a market-making framework designed to explicitly anticipate the closing auction, continuously refining the projected clearing price as the trading session evolves. We develop a generative stochastic market model to simulate the trading session and to emulate the market. Our theoretical model and Deep Q-Learning method is applied on the generator in two settings: (1) when the mid price follows a rough Heston model with generative data from this stochastic model; and (2) when the mid price corresponds to historical data of assets from the S&P 500 index and the performance of our algorithm is compared with classical benchmarks from optimal market making.