Edward Tsang

Edward Tsang has a first degree in Business Administration (Major in Finance) and a PhD in Computer Science. He has broad interest in applied artificial intelligence, in particularly computational finance, heuristic search, constraint satisfaction and scheduling. He is currently a Professor in the Department of Computing and Electronic Systems at the University of Essex where he leads the Computational Finance Group and Constraint Satisfaction and Optimization Group. He is the Director of the Centre for Computational Finance and Economic Agents (CCFEA), an interdisciplinary centre which he co-founded in 2002. He chaired the Technical Committee for Computational Finance under the IEEE Computational Intelligence Society in 2004-2005.

Publications

Simulation in Computational Finance and Economics: Tools and Emerging Applications
Simulation in Computational Finance and Economics: Tools and Emerging Applications
Biliana Alexandrova-Kabadjova, Serafin Martinez-Jaramillo, Alma Lilia Garcia-Almanza, Edward Tsang. © 2013. 378 pages.
Simulation has become a tool difficult to substitute in many scientific areas like manufacturing, medicine, telecommunications, games, etc. Finance is one of such areas where...
Modeling the FX Market Traders’ Behavior: An Agent-Based Approach
Modeling the FX Market Traders’ Behavior: An Agent-Based Approach
Monira Aloud, Edward Tsang, Richard Olsen. © 2013. 36 pages.
In this chapter, the authors use an Agent-Based Modeling (ABM) approach to model trading behavior in the Foreign Exchange (FX) market. They establish statistical properties...
The Market Fraction Hypothesis under Different Genetic Programming Algorithms
The Market Fraction Hypothesis under Different Genetic Programming Algorithms
Michael Kampouridis, Shu-Heng Chen, Edward Tsang. © 2012. 18 pages.
In a previous work, inspired by observations made in many agent-based financial models, we formulated and presented the Market Fraction Hypothesis, which basically predicts a...
Using Genetic Programming Systems as Early Warning to Prevent Bank Failure
Using Genetic Programming Systems as Early Warning to Prevent Bank Failure
Alma Lilia Garcia Almanza, Serafín Martínez Jaramillo, Biliana Alexandrova-Kabadjova, Edward Tsang. © 2012. 14 pages.
The main advantage of creating understandable rules is that users are able to interpret and identify the events that may trigger bankruptcy. By using the method that we propose...