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CO-EVOLVING NEURAL NETWORKS WITH EVOLUTIONARY STRATEGIES: A NEW APPLICATION TO DIVISIA MONEY

Applications of Artificial Intelligence in Finance and Economics

ISBN: 978-0-76231-150-7, eISBN: 978-1-84950-303-7

Publication date: 1 January 2004

Abstract

This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices vis-à-vis their simple sum counterparts in a simple inflation forecasting experiment. We develop a new approach that uses co-evolution (using neural networks and evolutionary strategies) as a predictive tool. This approach is simple to implement yet produces results that outperform stand-alone neural network predictions. Results suggest that superior tracking of inflation is possible for models that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money outperform their simple sum counterparts as macroeconomic indicators.

Citation

Binner, J.M., Kendall, G. and Gazely, A. (2004), "CO-EVOLVING NEURAL NETWORKS WITH EVOLUTIONARY STRATEGIES: A NEW APPLICATION TO DIVISIA MONEY", Binner, J.M., Kendall, G. and Chen, S.-H. (Ed.) Applications of Artificial Intelligence in Finance and Economics (Advances in Econometrics, Vol. 19), Emerald Group Publishing Limited, Leeds, pp. 127-143. https://doi.org/10.1016/S0731-9053(04)19005-1

Publisher

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Emerald Group Publishing Limited

Copyright © 2004, Emerald Group Publishing Limited