Can maker-taker fees prevent algorithmic cooperation in market making?

TL;DR

This paper investigates whether maker-taker fee structures can prevent algorithmic cooperation among market makers, revealing complex relationships and conditions needed to destabilize collusive behaviors in simulated markets.

Contribution

It models market making as a repeated general-sum game and experimentally analyzes how fee structures influence algorithmic cooperation and market stability.

Findings

Net transaction costs and maker rebates have non-monotonic relationships.

Upper bounds on taker fees and lower bounds on maker rebates are needed to prevent cooperation.

Market volatility, inventory risk, and agent count affect cooperation dynamics.

Abstract

In a semi-realistic market simulator, independent reinforcement learning algorithms may facilitate market makers to maintain wide spreads even without communication. This unexpected outcome challenges the current antitrust law framework. We study the effectiveness of maker-taker fee models in preventing cooperation via algorithms. After modeling market making as a repeated general-sum game, we experimentally show that the relation between net transaction costs and maker rebates is not necessarily monotone. Besides an upper bound on taker fees, we may also need a lower bound on maker rebates to destabilize the cooperation. We also consider the taker-maker model and the effects of mid-price volatility, inventory risk, and the number of agents.