A hybrid forecasting model for foreign exchange rate based on a multi-neural network

An-Pin Chen*, Yu Chia Hsu, Ko Fei Hu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

In this work, a multi-neural network model consisting of three sub-networks and one master network is proposed to combine the fundamental theorem and technical analysis in TWD/USD exchange rate forecasting. The long-term, mid-term, and short-term tendencies of exchange rate are forecasted separately by different sub-networks. Five macro economics factors of price level, interest rates, money supply, imports/exports, and productivity, and seven practical technical indicators of fifteen-day and one-day intervals are selected as the input variables of the three sub-networks. The master network then provides the integrated forecasting according to the three sub-networks. To increase forecasting accuracy, a threshold filtering mechanism was applied in this work. The experiment result shows that the multi-neural network is more effective than the random walk model and single-neural network model, and with the threshold filtering, can achieve high accuracy.

Original languageEnglish
Title of host publicationProceedings - 4th International Conference on Natural Computation, ICNC 2008
Pages293-298
Number of pages6
DOIs
StatePublished - 22 Dec 2008
Event4th International Conference on Natural Computation, ICNC 2008 - Jinan, China
Duration: 18 Oct 200820 Oct 2008

Publication series

NameProceedings - 4th International Conference on Natural Computation, ICNC 2008
Volume5

Conference

Conference4th International Conference on Natural Computation, ICNC 2008
CountryChina
CityJinan
Period18/10/0820/10/08

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