Learning Market Making with Closing Auctions
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918303615680512 |
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| author | Graf, Julius Mastrolia, Thibaut |
| author_facet | Graf, Julius Mastrolia, Thibaut |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_17247 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Market Making with Closing Auctions Graf, Julius Mastrolia, Thibaut Trading and Market Microstructure Optimization and Control 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. |
| title | Learning Market Making with Closing Auctions |
| topic | Trading and Market Microstructure Optimization and Control |
| url | https://arxiv.org/abs/2601.17247 |