Dynamics of market making algorithms in dealer markets: Learning and tacit collusion

Abstract

The widespread use of market‐making algorithms in electronic over‐the‐counter markets may give rise to unexpected effects resulting from the autonomous learning dynamics of these algorithms. In particular the possibility of “tacit collusion” among market makers has increasingly received regulatory scrutiny. We model the interaction of market makers in a dealer market as a stochastic differential game of intensity control with partial information and study the resulting dynamics of bid‐ask spreads. Competition among dealers is modeled as a Nash equilibrium, while collusion is described in terms of Pareto optima. Using a decentralized multi‐agent deep reinforcement learning algorithm to model how competing market makers learn to adjust their quotes, we show that the interaction of market making algorithms via market prices, without any sharing of information, may give rise to tacit collusion, with spread levels strictly above the competitive equilibrium level.

Towards multi‐agent reinforcement learning‐driven over‐the‐counter market simulations

“…(2018) have developed RL‐based market making approaches for limit order book markets; however, they do not explicitly model the competing market makers or study different competitive scenarios. Cont and Xiong (2023) recently study competition and collusion among a set of market makers, and use reinforcement learning as a mean to solve for the equilibrium of the game. Spooner and Savani (2020) study a discrete‐time zero‐sum game between a market maker and an adversary and show that adversarial reinforcement learning can help produce more robust policies.…”