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In a semi-realistic market simulator, independent reinforcement learningalgorithms may facilitate market makers to maintain wide spreads even withoutcommunication. This unexpected outcome challenges the current antitrust lawframework. We study the effectiveness of maker-taker fee models in preventingcooperation via algorithms. After modeling market making as a repeatedgeneral-sum game, we experimentally show that the relation between nettransaction costs and maker rebates is not necessarily monotone. Besides anupper bound on taker fees, we may also need a lower bound on maker rebates todestabilize the cooperation. We also consider the taker-maker model and theeffects of mid-price volatility, inventory risk, and the number of agents.

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