Hybrid methods for stock index modeling
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Yuehui | - |
dc.contributor.author | Abraham, Ajith | - |
dc.contributor.author | Yang, Ju | - |
dc.contributor.author | Yang, Bo | - |
dc.date.accessioned | 2023-03-09T00:42:03Z | - |
dc.date.available | 2023-03-09T00:42:03Z | - |
dc.date.issued | 2006 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65535 | - |
dc.description.abstract | In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using neural network, TS fuzzy system and hierarchical TS fuzzy techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock Market(SM) and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. The parameters of the different techniques are optimized by the particle swarm optimization algorithm. This paper briefly explains how the different learning paradigms could be formulated using various methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the models considered could represent the stock indices behavior very accurately. | - |
dc.format.extent | 4 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.title | Hybrid methods for stock index modeling | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/11540007_137 | - |
dc.identifier.bibliographicCitation | FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 2, PROCEEDINGS, v.3614, pp 1067 - 1070 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000232218400137 | - |
dc.identifier.scopusid | 2-s2.0-33749015191 | - |
dc.citation.endPage | 1070 | - |
dc.citation.startPage | 1067 | - |
dc.citation.title | FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 2, PROCEEDINGS | - |
dc.citation.volume | 3614 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.publisher.location | 독일 | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.