Cooperation between Independent Market Makers

TL;DR

This paper explores how independent AI-driven market makers can learn to cooperate in financial markets through repeated game interactions, potentially leading to supra-competitive spreads without explicit communication.

Contribution

It demonstrates that independent Q-learning agents can develop cooperative strategies in market making, challenging assumptions about collusion and competition.

Findings

Cooperative strategies emerge without communication among agents.

High spreads can dominate even when lower spreads are Nash equilibria.

Adding more agents does not always reduce supra-competitive spreads.

Abstract

With the digitalization of the financial market, dealers are increasingly handling market-making activities by algorithms. Recent antitrust literature raises concerns on collusion caused by artificial intelligence. This paper studies the possibility of cooperation between market makers via independent Q-learning. Market making with inventory risk is formulated as a repeated general-sum game. Under a stag-hunt type payoff, we find that market makers can learn cooperative strategies without communication. In general, high spreads can have the largest probability even when the lowest spread is the unique Nash equilibrium. Moreover, introducing more agents into the game does not necessarily eliminate the presence of supra-competitive spreads.