Robust Market Making: To Quote, or not To Quote

ArXiv ID: 2508.16588 “View on arXiv”

Authors: Ziyi Wang, Carmine Ventre, Maria Polukarov

Abstract

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.

Keywords: Adversarial Reinforcement Learning, Market Making, Action Space Expansion, Quoting Strategies, Sharpe Ratio, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like stochastic control, limit order book models, and adversarial reinforcement learning, requiring dense formalism. It also presents a model-driven empirical approach with sensitivity analysis on environmental variables, backtesting in simulated adversarial environments, and specific performance metrics like Sharpe ratios.

  flowchart TD
    A["Research Goal:<br>Enhance Market Making Robustness<br>by Expanding Action Space"] --> B["Methodology:<br>Adversarial Reinforcement Learning<br>with 3 MM Agents"]
    B --> C["Key Data/Inputs:<br>Various Adversarial Market Environments<br>Historical Equities Data"]
    C --> D["Computational Process:<br>Train & Compare 3 Agents:<br>1. Continuous Bid-Ask (Baseline)<br>2. Quote or Refuse<br>3. Quote, Refuse, or Single-Sided"]
    D --> E["Key Findings & Outcomes:<br>• Occasional refusal improves returns & Sharpe ratio<br>• Quoting ratios meet requirements (up to 99.9%)<br>• Enlarged action space yields robust performance"]