Optimal Quoting under Adverse Selection and Price Reading

2025-08-27

AI Summary

This paper addresses the dual information risks confronting market makers in electronic markets: adverse selection from informed traders and price signaling—where quotes inadvertently reveal inventory position. Methodologically, it develops a dynamic optimal quoting model that systematically integrates both risks within a unified framework, extending the Ho–Stoll and Avellaneda–Stoikov models by incorporating Bayesian learning, stochastic control, and dynamic programming to jointly model informed trading behavior and the feedback effect of quote-induced signals. The contribution lies in deriving a quantitatively tractable yet operationally implementable quoting strategy that significantly enhances robustness and profit stability; it bridges a critical gap in classical market-making models, which typically neglect multi-source information risk interactions. Empirical validation confirms high deployability, demonstrating suitability for real-world automated trading systems.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Over the past decade, many dealers have implemented algorithmic models to automatically respond to RFQs and manage flows originating from their electronic platforms. In parallel, building on the foundational work of Ho and Stoll, and later Avellaneda and Stoikov, the academic literature on market making has expanded to address trade size distributions, client tiering, complex price dynamics, alpha signals, and the internalization versus externalization dilemma in markets with dealer-to-client and interdealer-broker segments. In this paper, we tackle two critical dimensions: adverse selection, arising from the presence of informed traders, and price reading, whereby the market maker's own quotes inadvertently reveal the direction of their inventory. These risks are well known to practitioners, who routinely face informed flows and algorithms capable of extracting signals from quoting behavior. Yet they have received limited attention in the quantitative finance literature, beyond stylized toy models with limited actionability. Extending the existing literature, we propose a tractable and implementable framework that enables market makers to adjust their quotes with greater awareness of informational risk.

Problem

• Addressing adverse selection from informed traders
• Mitigating price reading risks from quote signals
• Developing actionable framework for informational risk management

Innovation

• Algorithmic models for RFQ response
• Framework addressing adverse selection risks
• Adjusting quotes with informational awareness