Dynamics of market making algorithms in dealer markets: Learning and tacit collusion
研究了电子场外交易中做市算法的自主学习动态可能导致隐性合谋，使买卖价差高于竞争均衡水平，对监管有启示。
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.