Centre for Computational Finance and Economic Agents (CCFEA)


We have a vibrant research environment covering a range of areas. Our staff and PhD students publish papers in prestigious international conferences, and often attract the attention of industry and government.

We are affiliated to the School of Computer Science and Electronic Engineering, from which we benefit from expertise in computational intelligence. We have an international reputation in optimization, evolutionary computation, constraint satisfaction, games, fuzzy logic and agent-based technology.

Research Areas

We work in different several areas of computer science, economics and finance.

A first theme looks at the interplay between computation and incentives, an area usually called Algorithmic Game Theory. Here the economic and computational concepts blend to highlight meaningful and challenging problems at the intersection of the two areas (e.g. would your algorithm return good solutions in presence of selfish agents? Are equilibria computed quickly by the market?)

Other themes cover the use of computational tools to design better trading strategies.

Computational finance

Our work is oriented to the non-Gaussian characteristics of financial markets and operational aspects of financial markets, financial engineering, portfolio and risk management.

Computational methods include various Machine Learning techniques, like adaptive and reinforced learning, heuristic optimisation and evolutionary computing.

Computational economics

Our research in computational economics is focused on algorithmic game theory (AGT) and agent-based computational economics (ACE). This includes the use of artificially intelligent agents in the study of self-organising systems and risk and market-based institutions, and the design of real time trading.

This also has the potential to provide policy makers and financial institutions with a powerful interactive tool to find answers for 'what-if' questions and to do 'wind tunnel tests' for market and policy design.

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 have been widely used to study various market microstructure issues, such as price discovery, order choice behaviour of market participants and optimal order placement strategy.

Our research in this area includes:

  • developing real-time trading platforms
  • deriving new financial econometric models for real-time data, with a special focus on the asymmetric behaviour between the supply and demand sides of the market and the information set traders refer to before submitting their orders

Postgraduate Research

Along with carrying out their own research into these areas, our academics can supervise candidates who are looking to undertake a postgraduate research degree in these fields.

Find out more about
our postgraduate opportunities

Our software

Software developed by the Centre includes:

Our activity

Recent 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.

Workshops organised by our members

IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (IEEE CIFER)

Workshop on the Distortion and Information-Efficiency Tradeoffs (DIET)