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

Paper: arXiv 2611.09012

Authors: Siddharth Nand, Bora Guney

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a clear empirical study on loss function impact in RL market making. While the mathematical foundations are standard, the empirical setup and results are well-articulated, contributing practical insights into risk management in algorithmic trading.

Research Flowchart

  flowchart TD
    A[Research Goal: Impact of Loss Functions on RL Market Making Risk Management] --> B{Key Methodology: Empirical Evaluation of Loss Function Formulations};
    B --> C[Data/Inputs: Simulated Financial Market Environment];
    C --> D{Computational Process: Training RL Market-Making Agents with Varied Loss Functions};
    D --> E{Analysis: Comparison of Profit-Maximizing vs. Risk-Averse Strategies};
    E --> F[Key Finding 1: Penalties for Inventory & Cash Management Foster Resilient Strategies];
    E --> G[Key Finding 2: Balance Between Profit Maximization & Risk Management is Crucial];