The Impact and Interactions of Artificial Intelligence Pricing Algorithms in Dealer Market

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We study the interactions of artificial intelligence pricing algorithms and their impact on market making prices in dealer market. In a dealer market, dealers, also called 'market makers', post prices at which they are willing to buy or sell assets. Bonds and FX spots are primarily traded via dealer market. Nasdaq is a typical example of equity dealer market. We are interested in the case where market makers apply artificial intelligence in algorithmic pricing of financial instruments, with a specific focus on the interactions of these algorithms and their impact on prices whether they could lead to tacit collusion. These algorithms allow market makers to automate pricing by learning from market data through trial and error. As artificial intelligence algorithms are increasingly incorporated in financial industry, there are growing concerns from regulators and market participants whether such interactions of algorithms could result in undesirable outcomes, even though the algorithms are not intended to do so by design. The objective of this research project is to tackle these concerns and provide references for market regulators on the usage of AI algorithms in dealer markets. We start by setting a mathematical framework for market makers in dealer market, and then introducing multi-agent reinforcement learning algorithms for each market maker to set prices through a pricing game. Some preliminary experimental results are achieved under discrete-time settings, and we are working at extending the model to continuous-time, where stochastic control models and multi-agent reinforcement learning will intertwine. We aim at developing theoretical framework to tackle the interactions of AI algorithms in market making. There is amplified literature on algorithmic trading from stochastic control perspective. The topic of algorithmic pricing and AI algorithms have been started only a few years ago, in which recent research focuses on tacit collusion in goods market. However, there is still no existing research on AI pricing algorithms in financial dealer market despite growing focus from regulators. Yet there is no theory explaining the possible interactions of AI pricing algorithms. The research we are working on is therefore novel. This research project falls within the following EPSRC research areas: Artificial intelligence technologies, Non-linear systems, Numerical analysis, Operational Research, Statistics and applied probability. This research project is in collaboration with FX eRisk team in HSBC.