Graham Kendall
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Professor Graham Kendall

Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

News

A Conversation article celebrating Pi
http://bit.ly/1DXuXbV
I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Transportation & Logistics: Dissertation Prize 2013

Publication(s)

Introducing a Round Robin Tournament into Evolutionary Individual and Social Learning Checkers
http://bit.ly/1cJw2am
General Video Game Playing
http://bit.ly/1a2Vjvz
Iterated Local Search Using an Add and Delete Hyper-heuristic for University Course Timetabling
http://bit.ly/1mlRZo4
Measuring the Robustness of Airline Fleet Schedules
http://bit.ly/1mlqXcv

Graham Kendall: Details of Requested Publication


Citation

Kendall, G and Su, Y A multi-agent based simulated stock market - testing on different types of stocks. In Proceedings of the The IEEE 2003 Congress on Evolutionary Computation (CEC2003), pages 2298-2305, Canberra, Australia, 2003.


Abstract

In our previous work, we have developed a multi-agent based simulated stock market where artificial stock traders coevolve by means of individual and social learning and learn to trade stock profitably. We tested our model on a single stock (British Petroleum) from the LSE (London Stock Exchange) where our artificial agents demonstrated dynamic learning behaviours and strong learning abilities. In this paper, we extend our previous work by testing the model on different types of stocks from different sections of the stock market. The results from the experiments show that the artificial traders demonstrate stable and satisfactory learning abilities during the simulation regardless of the different types of stocks. The results from this paper lays the foundation for our future work developing an effecient portfolio manager from a multi-agent based simulated stock market.


pdf

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doi

The doi for this publication is 10.1109/CEC.2003.1299375 You can link directly to the original paper, via the doi, from here

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URL

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Bibtex

@INPROCEEDINGS{ks2003a, author = {G. Kendall and Y. Su},
title = {A multi-agent based simulated stock market - testing on different types of stocks},
booktitle = {Proceedings of the The IEEE 2003 Congress on Evolutionary Computation (CEC2003)},
year = {2003},
volume = {3},
pages = {2298--2305},
address = {Canberra, Australia},
month = {Dec 8 - 12},
abstract = {In our previous work, we have developed a multi-agent based simulated stock market where artificial stock traders coevolve by means of individual and social learning and learn to trade stock profitably. We tested our model on a single stock (British Petroleum) from the LSE (London Stock Exchange) where our artificial agents demonstrated dynamic learning behaviours and strong learning abilities. In this paper, we extend our previous work by testing the model on different types of stocks from different sections of the stock market. The results from the experiments show that the artificial traders demonstrate stable and satisfactory learning abilities during the simulation regardless of the different types of stocks. The results from this paper lays the foundation for our future work developing an effecient portfolio manager from a multi-agent based simulated stock market.},
comment = {IEEE Catalog Number: 03TH8674, ISBN: 0-7803-7804-0},
doi = {10.1109/CEC.2003.1299375},
keywords = {stock market, London Stock Exxhange, LSE, evolution, individual learning, social learning, artificial neural networks},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/ks2003a.pdf} }