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

How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD
I have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

P vs NP

Publication(s)

Scheduling TV Commercials: Models and Solution Methodologies
http://bit.ly/idSBCA
A Hybrid Evolutionary Approach to the Nurse Rostering Problem
http://bit.ly/ey147Y
Evolving Collective Behavior in an Artificial Ecology
http://bit.ly/eNb528
Scheduling English football fixtures over holiday periods
http://bit.ly/hkJoTf

Graham Kendall: Details of Requested Publication


Citation

Binner, J.M; Gazely, A.M and Kendall, G An evaluation of UK risky money: an artificial intelligence approach. Global Business and Economics Review, 11 (1): 1-18, 2009.


Abstract

In this paper we compare the performance of three indices in an inflation forecasting experiment. The evidence not only suggests that an evolved neural network is superior to traditionally trained networks in the majority of cases, but also that a risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate informat1ion. Notably, the provision of long-term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examined.


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doi

The doi for this publication is 10.1504/GBER.2009.025378 You can link directly to the original paper, via the doi, from here

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Bibtex

@ARTICLE{bgk2009b, author = {J.M. Binner and A.M. Gazely and G. Kendall},
title = {An evaluation of UK risky money: an artificial intelligence approach},
journal = {Global Business and Economics Review},
year = {2009},
volume = {11},
pages = {1--18},
number = {1},
abstract = {In this paper we compare the performance of three indices in an inflation forecasting experiment. The evidence not only suggests that an evolved neural network is superior to traditionally trained networks in the majority of cases, but also that a risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate informat1ion. Notably, the provision of long-term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examined.},
doi = {10.1504/GBER.2009.025378},
issn = {1097-4954},
keywords = {risky money index, artificial intelligence, inflation forecasting, neural networks, evolution strategies, Bank of England, Divisia index, interest rates},
owner = {est},
timestamp = {2010.02.22} }