# Learning Market Making with Closing Auctions

arXiv: https://arxiv.org/abs/2601.17247 · HTML: https://mlsanity.com/paper/2601.17247/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 24 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 17 of 17)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [A Theoretical Analysis of Deep Q-Learning](https://mlsanity.com/paper/1901.00137/) (2019)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Double Deep Q-Learning for Optimal Execution](https://mlsanity.com/paper/1812.06600/) (2018)
- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [Learning a functional control for high-frequency finance](https://mlsanity.com/paper/2006.09611/) (2020)
- [Market Making without Regret](https://mlsanity.com/paper/2411.13993/) (2024)
- [Logarithmic regret in the ergodic Avellaneda-Stoikov market making model](https://mlsanity.com/paper/2409.02025/) (2024)
- [Optimal auction duration: A price formation viewpoint](https://mlsanity.com/paper/1906.01713/) (2019)
- [AHEAD : Ad-Hoc Electronic Auction Design](https://mlsanity.com/paper/2010.02827/) (2020)
- [Clearing time randomization and transaction fees for auction market design](https://mlsanity.com/paper/2405.09764/) (2024)
- [Optimal Rebate Design: Incentives, Competition and Efficiency in Auction Markets](https://mlsanity.com/paper/2501.12591/) (2025)

## Built on by (most cited first, 1 of 1)

- [Continuous-Time Reinforcement Learning for Controlled Hawkes Jump-Diffusions](https://mlsanity.com/paper/2608.19151/) (2026)

## Abstract (arXiv)

In this work, we investigate a market making execution 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 reinforcement learning framework, consisting of a Deep Q-Network and its continuous-control actor-critic extensions (DDPG, TD3 and SAC), 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 these deep reinforcement learning methods are 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 stylized reference benchmarks from optimal market making.