The Impact of Market Informedness on Market Makers' Profitability

2026-06-04

AI Summary

This study investigates how the degree of market information availability affects the profitability of heterogeneous market makers. The authors develop a multi-agent market model that integrates endogenous price formation, self-exciting order flow modeled via a Hawkes process, and market makers’ heterogeneous information sets and risk preferences. Methodologically, they provide the first stability guarantee for state-dependent Hawkes-based order execution processes over a finite time horizon and employ the MAPPO algorithm under a centralized training with decentralized execution (CTDE) framework to derive optimal market-making strategies. The findings reveal that insufficient information exposes market makers to substantial adverse selection risk due to informed trading; however, as market informativeness increases, their aggregate profitability rises significantly, effectively offsetting this risk and underscoring the critical role of the information environment in shaping market microstructure efficiency.

Abstract

This paper examines the impact of market informedness on the profitability of market makers. In contrast to the existing literature, the analysis is conducted in a complex market environment featuring heterogeneous market-making agents that differ in terms of information sets and aversion to inventory risk, endogenous price formation, exogenous fundamental value dynamics, and self-exciting market order flow. The paper also establishes finite-horizon stability guarantees for the resulting state-dependent Hawkes market-taker process, including non-explosion, exponential mispricing integrability, occupation-time bounds, and a pathwise mispricing tail estimate. To address the market-making problem, the study employs a reinforcement learning framework based on the multi-agent proximal policy optimization (MAPPO) algorithm in a centralized training with decentralized execution (CTDE) setting. The study shows that informed market order flow is particularly dangerous in poorly informed markets, leading to severe adverse-selection risk. Although the complex market dynamics together with stochastic training give rise to locally non-monotonic outcomes, the results nevertheless reveal an overall upward trend in market makers' profitability as market informedness increases, suggesting that price discovery resulting from higher market informedness offsets the negative impact of adverse selection.

Problem

• market informedness
• market makers
• profitability
• adverse selection
• complex market environment

Innovation

• state-dependent Hawkes process
• multi-agent reinforcement learning
• market making
• adverse selection
• centralized training with decentralized execution