Optimal Quoting under Adverse Selection and Price Reading

Abstract

Over the past decade, many dealers have implemented algorithmic models to automatically respond to RFQs and manage flows originating from 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 internalisation versus externalisation 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 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 behaviour. Yet they have received limited attention in the quantitative finance literature, beyond stylised toy models with limited actionability. Extending the existing literature, we propose a tractable framework that enables market makers to adjust their quotes with greater awareness of informational risk.