Graham Kendall
Various Images

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

I have published some papers on timetabling.
http://bit.ly/hSGAhZ
I have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Tracking Paper Downloads: Database

Publication(s)

Learning with imperfections - a multi-agent neural-genetic trading system with differing levels of social learning
http://bit.ly/hBQypU
Providing a memory mechanism to enhance the evolutionary design of heuristics
http://bit.ly/fd4uYt
On Nash equilibrium and evolutionarily stable states that are not characterised by the folk theorem
http://bit.ly/1J4KNC0
On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets
http://bit.ly/2kqxtD3

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.


pdf

There is no pdf available for this paper. You might like to try to obtain the original source (see the doi, for example)


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

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


Journal Rankings



URL

This pubication does not have a URL associated with it.

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

@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} }