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Stock index trend analysis based on signal decomposition
Zhang L.2; Zhang D.3; Li W.1
2014
Source PublicationIEICE Transactions on Information and Systems
ISSN17451361 09168532
VolumeE97-DIssue:8Pages:2187-2190
Abstract

A new stock index trend analysis approach is proposed in this paper, which is based on a newly developed signal decomposition approach - adaptive Fourier decomposition (AFD). AFD can effectively extract the signal's primary trend, which specifically suits the Dow Theory based technique analysis. The proposed approach integrates two different kinds of forecasting approaches, including the Dow theory the RBF neural network. Effectiveness of the proposed approach is assessed through comparison with the direct RBF neural network approach. The result is proved to be promising. Copyright © 2014 The Institute of Electronics, Information and Communication Engineers.

KeywordAdaptive Fourier Decomposition Rbf Neural Network Stock Index Trend Forecasting The Dow Theory
DOI10.1587/transinf.E97.D.2187
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering
WOS IDWOS:000342784500032
Scopus ID2-s2.0-84906223552
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorZhang L.
Affiliation1.Tsinghua University
2.Universidade de Macau
3.Xiamen University
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhang L.,Zhang D.,Li W.. Stock index trend analysis based on signal decomposition[J]. IEICE Transactions on Information and Systems, 2014, E97-D(8), 2187-2190.
APA Zhang L.., Zhang D.., & Li W. (2014). Stock index trend analysis based on signal decomposition. IEICE Transactions on Information and Systems, E97-D(8), 2187-2190.
MLA Zhang L.,et al."Stock index trend analysis based on signal decomposition".IEICE Transactions on Information and Systems E97-D.8(2014):2187-2190.
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