# Optimal Quoting under Adverse Selection and Price Reading

## TL;DR

This paper develops a practical framework for market makers to optimize quoting strategies by accounting for adverse selection and price reading risks, enhancing decision-making in electronic trading environments.

## Contribution

It introduces a novel, implementable model that explicitly incorporates informational risks like adverse selection and price reading into market making strategies.

## Key findings

- Framework improves quoting decisions under informational risks
- Enhances understanding of market maker behavior in electronic markets
- Provides actionable insights for algorithmic trading strategies

## 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.