# Robust Market Making: To Quote, or not To Quote

arXiv: https://arxiv.org/abs/2508.16588 · HTML: https://mlsanity.com/paper/2508.16588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [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)

## Built on by (most cited first, 2 of 2)

- [Multi-Agent Reinforcement Learning for Market Making: Competition without Collusion](https://mlsanity.com/paper/2510.25929/) (2025)
- [Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2609.22785/) (2026)

## Abstract (arXiv)

Market making is a popular trading strategy, which aims to generate profit from the spread between the quotes posted at either side of the market. It has been shown that training market makers (MMs) with adversarial reinforcement learning allows to overcome the risks due to changing market conditions and to lead to robust performances. Prior work assumes, however, that MMs keep quoting throughout the trading process, but in practice this is not required, even for ``registered'' MMs (that only need to satisfy quoting ratios defined by the market rules). In this paper, we build on this line of work and enrich the strategy space of the MM by allowing to occasionally not quote or provide single-sided quotes. Towards this end, in addition to the MM agents that provide continuous bid-ask quotes, we have designed two new agents with increasingly richer action spaces. The first has the option to provide bid-ask quotes or refuse to quote. The second has the option to provide bid-ask quotes, refuse to quote, or only provide single-sided ask or bid quotes. We employ a model-driven approach to empirically compare the performance of the continuously quoting MM with the two agents above in various types of adversarial environments. We demonstrate how occasional refusal to provide bid-ask quotes improves returns and/or Sharpe ratios. The quoting ratios of well-trained MMs can basically meet any market requirements, reaching up to 99.9% in some cases.