An evaluation of UK risky money: an artificial intelligence approach

Jane M. Binner*, Alicia M. Gazely, Graham Kendall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalGlobal Business and Economics Review
Volume11
Issue number1
Early online date20 May 2009
DOIs
Publication statusPublished - 2009

Keywords

  • artificial intelligence: forecasting
  • neural networks
  • risky money
  • evolution strategies

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