Dr Steve Phelps
Telephone: +44 020 7848 1800
Office: (N)7.06, Bush House, Strand Campus.
Title: Lecturer in Computational Finance/Economics
Research Group: Distributed Artificial Intelligence
I joined King's in September 2015 as a lecturer. Prior to this I have commercial experience of the electronic-commerce and financial sectors, having worked for a number of SMEs and Blue-chip companies, with a total of over ten years of commercial software engineering experience. I co-founded a startup company, Ripple Software Ltd., which developed econometric analysis tools for power-sellers in the eBay market place, and later Victria Ltd which delivered a prototype dark-pool trading platform.
The core focus of my research is using agent-based modelling to understand real-world complex-adaptive systems which are composed of interacting autonomous agents. The key research question that I am interested in is how, and if, these systems maintain macroscopic homeostatic behaviour despite the fact that their constituent agents often face an incentive to disrupt the rest of the system for their own gain. This question pervades the biological and social sciences, as well as many areas of engineering and computing. Accordingly, I work with a diverse range of collaborators in different disciplines. I am particularly interested in whether models of learning and cooperation can be validated against empirical studies, and I have had the opportunity to apply many different modelling techniques to a diverse range of data.
The financial markets present a unique opportunity for studying complex-adaptive systems with the recent availability of high-frequency tick-data which records every transaction in the market, and can run to many billions of events per exchange per annum. I am interested in developing methods for using these big data sets to systematically calibrate agent-based simulation models, in order to try and better understand the role of learning and adaptation in explaining some of the phenomena that are observed in empirical financial time-series data, which cannot be accounted-for by the classical theoretical models in this field.
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