School of Computing

Michael Kampouridis

Lecturer / Director of Graduate Studies (Taught) / Chief Examiner PGT / Advanced MSc Programme Director (UKM)

Photo of M Kampouridis, if available
  • Room M3-35
    Medway Building
    University of Kent,
    ME4 4AG


My publications are available from the University of Kent's Academic Repository.

You can also find a list of my publications here.

Research Interests

I belong to the following research groups:

My research focuses on the use of Computational Intelligence to Business applications, such as Finance and Economics. Areas of particular interest are financial forecasting and intelligent decision support systems for business.


I joined the School in September 2012. Before, I worked as a Research Assistant at the School of Computer Science and Electronic Engineering at the University of Essex, under the BT NetDesign project funded by British Telecommunications plc. Furthermore, previous academic experience includes two 3-month research visits to AI-Econ Center, Department of Economics, at National Cheng Chi University of Taiwan.


I am interested in the use of Computational Intelligence (CI) techniques in business-related problems. So far, I have used Evolutionary Algorithm and Artificial Neural Network techniques in the fields of Finance, Economics, and Telecommunications. Methods I am interested in include: Genetic Programming, Genetic Algorithms, Heuristic Search Optimisation and Hyper-Heuristics.

NEW PhD funding

Funding available for a 3-year PhD project on the use of Machine Learning for Portfolio Optimisation. More information can be found here.

Recent publications

  • Cramer, S., Kampouridis, M., Freitas, A.A., "Decomposition Genetic Programming: An Extensive Evaluation on Rainfall Prediction in the COntext of Weather Derivatives",Applied Soft Computing, Elsevier, Vol. 70C, pp. 208-224 (2018)
  • Kampouridis, M., Otero, F.E.B., "Evolving Trading Strategies Using Directional Changes", Expert Systems with Applications, Elsevier, Vol. 73, pp. 145-160 (2017)
    Final publication available at Elsevier via

Current projects

My current research can be divided into two areas: algorithmic trading, and weather derivatives pricing.

With regards to algorithmic trading and financial forecasting in general, now, in the aftermath of a global financial crisis, it is more important than ever to have a better understanding of the markets and be able to forecast their movement. Directional changes is a new concept, which is based on the idea that an event-based system can capture significant points in price movements that the traditional physical time methods cannot. I am currently using evolutionary algorithms to create trading rules, by taking advantage of this new concept.

Another area I am heavily involved with is weather derivatives. Weather derivatives are financial instruments used as part of a risk management strategy to reduce risk associated with adverse or unexpected weather conditions. My aim is to develop a model of pricing weather derivative contracts by the use of genetic programming methods. Until now, there is no generally accepted framework for pricing such derivatives, as it happens with other (non-weather) derivatives (i.e. Black-Scholes model). This is a major problem, as it leads to incorrect predictions of the contract prices, thus resulting to significant financial loses. On the other hand, developing a novel genetic programming algorithm to create a generic pricing framework has the potential of great impact in the sector, by solving a problem that has existed since the introduction of weather derivatives in 1997, much like the Black-Scholes model did for options pricing in 1973. This is a significant problem, which is getting more and more attention by both industry and academia.

Past projects

I have used Genetic Programming to develop a financial forecasting tool, named EDDIE, which I used for predicting buy opportunities based on data from daily closing prices. I am deeply interested in the field of financial forecasting, and I continuously look for new search methods that can improve the predictability of forecasting tools such as EDDIE.

In addition, I have developed a financial model to study market dynamics and market behaviour by using Genetic Programming, along with Self-Organizing Maps. Financial modeling is another area that interests me and I believe that it has much to offer in understanding the financial markets and the decision-making process.

Furthermore, I have applied Genetic Algorithms and other heuristic techniques to a Telecommunications problem, where I built an intelligent system alongside an economic model, for British Telecoms (BT). This system acts as a decision support tool for the investment of Fibre Optic Networks, by advising BT on the optimal time and location for deploying a network.

Lastly, I have also been working on Automated Bargaining for Price-Speed (P-S) Optimised Negotiation. While Price-only optimisation is very popular and well-known, Price-Speed optimisation is relatively new. I am the first to bring GP into P-S optimisation and results have already demonstrated the GP’s superiority against other state-of-the-art algorithms.

My interests are not, however, limited only in the above applications. I am always open in any type of interdisciplinary research that includes the use of Computational Intelligence. I am thus very keen on bringing CI into business, economics, and finance.

PhD Supervision

I am very interested in supervising PhD students on topics related to the application of Computational Intelligence to Business, Economics and Finance. I currently have 2 projects available on algorithmic trading and weather derivatives. If you are interested for more information in these projects or any other topics related to my research interests, feel free to contact me. As mentioned earlier, there is funding available for a specific project on machine learning for portofolio optimisation. Please follow the relevant link for more information.

Application Procedure: For details of the application procedure, visit

Funding opportunities: Please visit

Current PhD student(s)

Adesola Adegboye is working on genetic programming and a new technique in algorithmic trading, called directional changes. He is jointly supervised by Dr Colin Johnson and me. He started in February 2016.

Previous PhD student(s)

Sam Cramer worked on genetic programming and weather derivatives. He was jointly supervised by Professor Alex Freitas and myself. He started in October 2013 and successfully defended his thesis in September 2017.


Current teaching

Modules that I am currently involved (in one way or the other, e.g. lecturer, class supervisor, project supervisor):

Past teaching

School of Computing, University of Kent, Canterbury, Kent, CT2 7NF

Enquiries: +44 (0)1227 824180 or contact us.

Last Updated: 23/02/2020