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

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.

Probabilistic modelling permeates the Financial services, healthcare, technology and other Service industries crucial to the UK's continuing social and economic prosperity, which are major users of stochastic algorithms for data analysis, simulation, systems design and optimisation. There is a major and growing skills shortage of experts in this area, and the success of the UK in addressing this shortage in cross-disciplinary research and industry expertise in computing, analytics and finance will directly impact the international competitiveness of UK companies and the quality of services delivered by government institutions.
By training highly skilled experts equipped to build, analyse and deploy probabilistic models, the CDT in Mathematics of Random Systems will contribute to
- sharpening the UK's research lead in this area and
- meeting the needs of industry across the technology, finance, government and healthcare sectors

MATHEMATICS, THEORETICAL PHYSICS and MATHEMATICAL BIOLOGY

The explosion of novel research areas in stochastic analysis requires the training of young researchers capable of facing the new scientific challenges and maintaining the UK's lead in this area. The partners are at the forefront of many recent developments and ideally positioned to successfully train the next generation of UK scientists for tackling these exciting challenges.
The theory of regularity structures, pioneered by Hairer (Imperial), has generated a ground-breaking approach to singular stochastic partial differential equations (SPDEs) and opened the way to solve longstanding problems in physics of random interface growth and quantum field theory, spearheaded by Hairer's group at Imperial. The theory of rough paths, initiated by TJ Lyons (Oxford), is undergoing a renewal spurred by applications in Data Science and systems control, led by the Oxford group in conjunction with Cass (Imperial). Pathwise methods and infinite dimensional methods in stochastic analysis with applications to robust modelling in finance and control have been developed by both groups.
Applications of probabilistic modelling in population genetics, mathematical ecology and precision healthcare, are active areas in which our groups have recognized expertise.

FINANCIAL SERVICES and GOVERNMENT

The large-scale computerisation of financial markets and retail finance and the advent of massive financial data sets are radically changing the landscape of financial services, requiring new profiles of experts with strong analytical and computing skills as well as familiarity with Big Data analysis and data-driven modelling, not matched by current MSc and PhD programs. Financial regulators (Bank of England, FCA, ECB) are investing in analytics and modelling to face this challenge. We will develop a novel training and research agenda adapted to these needs by leveraging the considerable expertise of our teams in quantitative modelling in finance and our extensive experience in partnerships with the financial institutions and regulators.

DATA SCIENCE:

Probabilistic algorithms, such as Stochastic gradient descent and Monte Carlo Tree Search, underlie the impressive achievements of Deep Learning methods. Stochastic control provides the theoretical framework for understanding and designing Reinforcement Learning algorithms. Deeper understanding of these algorithms can pave the way to designing improved algorithms with higher predictability and 'explainable' results, crucial for applications.
We will train experts who can blend a deeper understanding of algorithms with knowledge of the application at hand to go beyond pure data analysis and develop data-driven models and decision aid tools
There is a high demand for such expertise in technology, healthcare and finance sectors and great enthusiasm from our industry partners. Knowledge transfer will be enhanced through internships, co-funded studentships and paths to entrepreneurs