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

If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg
How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Twitter: Identifying Potential Followers

Publication(s)

Hybridising heuristics within an estimation distribution algorithm for examination timetabling
http://bit.ly/1Plbd56
Exploring hyper-heuristic methodologies with genetic programming
http://bit.ly/Kmibw0
An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-heuristics
http://bit.ly/erH9W8
An Ant Based Hyper-heuristic for the Travelling Tournament Problem
http://bit.ly/gPYAJl

Graham Kendall: Details of Requested Publication


Citation

Kendall, G and Su, Y Co-evolution of Successful Trading Strategies in A Simulated Stock Market. In Proceedings of the 2003 International Conference on Machine Learning and Applications (ICMLA'03), pages 200-206, 23-24 June 2003, Sheraton Gateway Hotel, Los Angeles, California, USA, 2003.


Abstract

In this paper we present a multi-agent based model of a simulated stock market within which active stock traders are modelled as heterogeneous adaptive artificial agents. We employ the approach of integrating individual learning and social learning to co-evolve these artificial agents with the aim of evolving successful trading strategies. The proposed model was tested on the British Petroleum (BP.L) share from the LSE (London Stock Exchange). Throughout the experiment we see successful trading strategies emerge among the artificial traders. These artificial agents also demonstrate rich dynamic learning behaviours during the simulation. On average, 80% of the artificial stock traders were able to trade using successful trading strategies which brings the investors higher returns compared to a baseline buy-and-hold strategy.


pdf

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doi

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What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information



URL

The URL for additional information is http://www.icmla-conference.org/icmla03/

The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.


Bibtex

@INPROCEEDINGS{ks2003b, author = {G. Kendall and Y. Su},
title = {Co-evolution of Successful Trading Strategies in A Simulated Stock Market},
booktitle = {Proceedings of the 2003 International Conference on Machine Learning and Applications (ICMLA'03)},
year = {2003},
pages = {200--206},
address = {23-24 June 2003, Sheraton Gateway Hotel, Los Angeles, California, USA},
abstract = {In this paper we present a multi-agent based model of a simulated stock market within which active stock traders are modelled as heterogeneous adaptive artificial agents. We employ the approach of integrating individual learning and social learning to co-evolve these artificial agents with the aim of evolving successful trading strategies. The proposed model was tested on the British Petroleum (BP.L) share from the LSE (London Stock Exchange). Throughout the experiment we see successful trading strategies emerge among the artificial traders. These artificial agents also demonstrate rich dynamic learning behaviours during the simulation. On average, 80% of the artificial stock traders were able to trade using successful trading strategies which brings the investors higher returns compared to a baseline buy-and-hold strategy.},
keywords = {agent, stock exchange, trading, London Stock Exchange, co-evolution, coevolution, individual learning, social learning, neural networks, artificial neural networks, shares},
timestamp = {2007.03.29},
url = {http://www.icmla-conference.org/icmla03/},
webpdf = {http://www.graham-kendall.com/papers/ks2003b.pdf} }