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.
翻译:在半现实的市场模拟器中,独立强化学习算法可能有利于市场制造者保持广泛的利差,即使没有沟通。这种意想不到的结果挑战了目前的反托拉斯法框架。我们研究了制造者-投标者收费模式在防止通过算法合作方面的有效性。在将市场建模成一个反复的普通和游戏之后,我们实验性地表明,净交易成本和制造者回扣之间的关系不一定是单调的。除了收受者收费的上限外,我们还需要降低对购买者回扣的限制,以破坏合作的稳定。我们还考虑到收购者-投标者模式以及中价波动、库存风险和代理商数量的影响。