Market Making

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9 papers5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Towards Robust Reinforcement Learning Market Making: Lessons from Loss Function Experimentation
Nov 6, 2026Siddharth Nand, Bora GuneyMarket Making Algorithmic Trading
Learning AI Agents, Market Makers, and Stress in Automated Markets
Nov 1, 2026Irene AldridgeMarket Making Finance
RiskAICenter and Cornell Tech, Cambridge and Johns Hopkins
Equilibria and Learning in Competitive Market Making
Oct 22, 2026Matteo Onger, Luigi FoscariMarket Making Nash Equilibrium
Department of Computer Science, University of Milan
Cooperative Multi-Agent Reinforcement Learning for Market Making on Prediction-Market Order Books
Oct 19, 2026Skander LejmiMarket Making Algorithmic Trading
Department of Statistics, London School of Economics and Political Science
No-Regret Revenue Maximization in Sequential Market Making Under Inventory Constraints
Oct 17, 2026Davide Maran, Marcello Restelli, Francesco Emanuele StradiMarket Making Algorithmic Trading
Politecnico di Milano
Reinforcement Learning with Hawkes-Structured Networks for Market Making
Oct 12, 2026Fernando Acero, Philipp Plank, Yufei ZhangMarket Making Hawkes Processes
Department of Mathematics, Imperial College London, United Kingdom
Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning
Sep 19, 2026Hao Yang, Zhenguo XuHawkes Processes Adversarial MARL
North China Institute of Computer System Engineering, Beijing, China · University of Science and Technology of China, Hefei, China
Zero-shot adaptation to order book dynamics
May 20, 2026Arip AsadulaevSequential Decision Making Stochastic Optimal Control
Optimal Adaptive Market Making: A Theoretical Framework for High-Yield Liquidity Provision in Perpetual Futures Markets
Apr 5, 2026Minmin Zeng, Yi LiuMarket Making Algorithmic Trading
tsaftech.

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Towards Robust Reinforcement Learning Market Making: Lessons from Loss Function Experimentation

Siddharth Nand, Bora Guney

Market Making Algorithmic Trading Risk-Sensitive RL Robust Loss Functions Robust RL

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Market makers play a vital role in ensuring efficient trade within financial markets. Recent literature has delved into training market-making systems using reinforcement learning. This paper delves into the impact of various loss function formulations on risk management behavior within reinforcement-based market making. Through empirical evaluation, we contrast profit-maximizing strategies with risk-averse approaches, finding that penalties for inventory and cash management foster more resilient and adaptable market-making strategies. Our results underscore the significance of striking a balance between profit maximization and risk management in algorithmic trading, providing actionable insights for refining strategies in practical settings.