A0321
Title: Interactions of market making algorithms: A study on perceived collusion
Authors: Wei Xiong - University of Oxford (United Kingdom) [presenting]
Abstract: The widespread use of market-making algorithms and the associated feedback effects may have unexpected consequences which need to be better understood. In particular, the phenomenon of `tacit collusion' in which the interaction of algorithms leads to an outcome similar to collusion among market makers, has increasingly received regulatory scrutiny. We propose a game-theoretic model of a financial market in which multiple market-makers compete for market share and learn from market data to adjust their spreads. We model this learning process through a decentralized multi-agent reinforcement learning algorithm and show that, even in absence of information sharing, market prices may converge to levels that are similar to a collision situation, resulting in `tacit collusion'. We briefly discuss the implications of our research for market regulators.
