Title: An evaluation of UK risky money: an artificial intelligence approach

Authors: Jane M. Binner, Alicia M. Gazely, Graham Kendall

Addresses: Economics and Strategy Group, Aston Business School, Birmingham, B4 7ET, UK. ' Department of Information Management & Systems, The Nottingham Trent University, Nottingham, NG1 4BU, UK. ' School of Computer Science, Jubilee Campus, The University of Nottingham, Nottingham, NG8 1BB, UK

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 information. 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.

Keywords: risky money index; artificial intelligence: inflation forecasting; neural networks: evolution strategies; Bank of England; Divisia index; interest rates.

DOI: 10.1504/GBER.2009.025378

Global Business and Economics Review, 2009 Vol.11 No.1, pp.1 - 18

Published online: 20 May 2009 *

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