AI-driven liquidity provision in OTC financial markets

Abstract

Providing liquidity in over-the-counter markets is a challenging under-taking, in large part because a market maker does not observe where their competitors quote, nor do they typically know how many rivals they compete with or what the trader's overall liquidity demand is. Optimal pricing strategies can be derived in theory assuming full knowledge of the competitive environment, but these results do not translate into practice where information is incomplete and asymmetric. This paper studies whether artificial intelligence, in the form of multi-armed bandit reinforcement learning algorithms, can be used by liquidity providers to dynamically set spreads using only information that is commonly available to them. We also investigate whether collusive effects can arise when competing liquidity providers all employ such algorithms. Our findings are as follows. In a single-agent setup where only one liquidity provider is optimising pricing in an otherwise static environment, all the algorithms considered are able to locate the theoretically optimal pricing policy, albeit they do so quite inefficiently when compared to a model-based approach. In a multi-agent setting where competing liquidity providers simultaneously and independently use algorithms to optimise pricing, we demonstrate that for one class of algorithms (pseudo) collusion can not arise, while for another it can theoretically arise in certain circumstances and we provide examples where it does. The scenarios where collusive effects appear, however, are fragile and sensitive to the specific configuration and exceedingly unlikely to occur in practice. Moreover, with a modest number of competitors, collusive effects that might otherwise arise in some of the most contrived scenarios are largely or entirely eliminated.