Optimal Quoting under Adverse Selection and Price Reading

ArXiv ID: 2508.20225 “View on arXiv”

Authors: Alexander Barzykin, Philippe Bergault, Olivier Guéant, Malo Lemmel

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.

Keywords: Market Making, Adverse Selection, Inventory Management, Limit Order Book, Algorithmic Trading, Equities

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper relies heavily on advanced stochastic optimal control theory, stochastic calculus, and complex Hamiltonian-based derivations, indicating high mathematical density. However, it lacks backtesting results, implementation details, or empirical datasets, focusing instead on theoretical model derivation and first-order approximations.

  flowchart TD
    A["Research Goal"] --> B["Data & Inputs"]
    B --> C["Computational Process"]
    C --> D["Key Findings"]
    
    A["Research Goal<br/>Model adverse selection &amp; price reading<br/>in algorithmic market making"]
    
    B["Data &amp; Inputs<br/>• RFQ flow data<br/>• Trade size distributions<br/>• Inventory levels<br/>• Adverse selection signals"]
    
    C["Computational Process<br/>• Extend Avellaneda-Stoikov model<br/>• Solve optimization problem<br/>• Implement quote adjustment<br/>• Simulate market scenarios"]
    
    D["Key Findings<br/>• Tractable framework for quote adjustment<br/>• Quantified adverse selection costs<br/>• Inventory-aware quoting strategy<br/>• Implementation guidelines for dealers"]