Postgraduate Research
We have a vibrant research environment covering a range of areas. Our staff and PhD students publish papers in prestigious international conferences and journals, and often attract the attention of industry and government.
We are affiliated to the School of Computer Science and Electronic Engineering. We have an international reputation in machine learning, algorithmic game theory, optimisation, evolutionary computation, and agent-based technology.
Research Areas
We work in different several areas of computer science, economics and finance.
We offer postgraduate research supervision in the following areas:
Machine Learning in the Financial Domain
- Algorithmic trading and financial forecasting
We use state-of-the-art machine learning (ML) algorithms to develop financial forecasting tools for different problems, such as volatility forecasting and future cash flow growth. We also use ML algorithms to create novel and competitive trading strategies. The derived trading strategies can use information not only from financial markets, but also from news outlets and social media, by taking advantage of sentiment analysis techniques.
- Directional changes
This novel approach approaches financial data from an event-based perspective, rather than the traditional way of sampling prices at fixed intervals (e.g. daily closing prices). The advantage of this approach is that it can capture significant points in price movements that the traditional physical time methods cannot, and as a result derive novel and more profitable trading strategies.
- High frequency finance
High frequency data is the real-time record of all trading activities and their associated characteristics observed in an electronic exchange system. These “tick-by-tick” data sets provide deeper insights into the price formation process at the micro level and allow us to develop trading strategies that take advantage of the “richness” of the information that tick data provides. The use of machine learning algorithms is challenging in this domain due to the large volume of data that causes computational times to explode. Research in this area can look into how we can effectively utilise ML algorithms without excessive computational costs.
- Weather derivatives
These are financial instruments used as part of a risk management strategy to reduce risk associated with adverse or unexpected weather conditions. We create novel algorithms to predict the value of the underlying asset (e.g. temperature, rainfall) and we then use these predictions to develop pricing models for weather derivatives contracts.
Algorithmic Game Theory
Our work in Algorithmic Game Theory (AGT) focuses on the study of decentralised systems in which (artificially intelligent) self-interested agents interact with each other aiming to optimise their personal objectives.
The most important questions related to such systems are whether the dynamics induced by the interaction between the agents converges to a stable outcome, known as equilibrium, how quickly such an equilibrium is reached and how efficient it is in terms of a social objective function.
Study with us
The Centre is a superb place to support doctoral research in any area of interest that engages with its remit. This includes students wishing to research the area of computational finance and economic agents and study for a postgraduate research degree in other departments.
A number of our students have had internships with prestigious City institutions such as HSBC, Old Mutual and the Bank of England. Our students also have the opportunity to learn from and interact with industry experts during an established Expert Lecture Series. Previous students have gone on to become quantitative analysts, portfolio managers and software engineers at various institutions including major investment banks such as HSBC and Mitsubishi UFJ Securities.
Advisors at our Employability and Careers Centre will be available to help you with all aspects of planning your career, including application techniques and interview skills. Our extensive network of industrial contacts gives you the chance to work with leading figures from industry, as well as gaining and developing a solid set of skills for your career.
Also see
Previous Publications
A. Deligkas, J. Fearnley, A. Hollender and T. Melissourgos. Tight Inapproximability for Graphical Games. AAAI 2023
P. Kanellopoulos, M. Kyropoulou, H. Zhou. Debt Transfers in Financial Networks: Complexity and Equilibria. AAMAS 2023
M. Kampouridis, P. Kanellopoulos, M. Kyropoulou, T. Melissourgos, A.A. Voudouris. Multi-agent systems for computational economics and finance. AI Commun. (2022)
P. Kanellopoulos, M. Kyropoulou, H. Zhou. Forgiving Debt in Financial Network Games. IJCAI 2022
A. Deligkas, J. Fearnley, A. Hollender and T. Melissourgos. Pure-Circuit: Strong Inapproximability for PPAD. Focus 2022
A. Deligkas, J. Fearnley, A. Hollender and T. Melissourgos. Constant Inapproximability for PPA. STOC 2022
P. Kanellopoulos, M. Kyropoulou, H. Zhou. Financial network games. ICAIF 2021
A. Filos-Ratsikas, E. Micha, and A. A. Voudouris. The distortion of distributed voting. Artificial Intelligence, vol. 286, article 103343, 2020. https://doi.org/10.1016/j.artint.2020.103343
A. Brabazon, M. Kampouridis, M. O’Neill. Applications of Genetic Programming to Finance and Economics: Past, Present, Future. Genetic Programing and Evolvable Machines (Invited Article), vol. 21, Springer, pp. 33-53, 2020. https://doi.org/10.1007/s10710-019-09359-z
B. de Keijzer, M. Kyropoulou and C. Ventre. Obviously Strategyproof Single-Minded Combinatorial Auctions. Proceedings of the 47th International Colloquium on Automata, Languages and Programming (ICALP), 2020.
G. Amanatidis, G. Birmpas, A. Filos-Ratsikas, A. Hollender, and A. A. Voudouris. Maximum Nash welfare and other stories about EFX. Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020.
