Predicting stock index movement using twin support vector machine as an integral part of enterprise system.
In order to improve the predicting performance of stock index movement, this study proposes a new predicting model called Twin Support Vector Machines (TWSVM), which will be used to predict the trend of Shanghai Securities Composite Index (SSCI) and Standard and Poor's 500 Index (S&P500 Index), resp...
| Publicado en: | Systems Research & Behavioral Science Vol. 39; no. 3; pp. 428 - 440 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Wiley-Blackwell
May2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=157710424&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 157710424 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10927026 2SN jtl: Systems Research & Behavioral Science issn: 10927026 maglogo: Y pubinfo: dt: May2022 vid: 39 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 157710424 10.1002/sres.2862 ppf: 428 ppct: 12 formats: tig: atl: Predicting stock index movement using twin support vector machine as an integral part of enterprise system. aug: au: Zou, Borong Wang, Hong Li, Hui Li, Ling Zhao, Yuhan affil: School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, China College of Business and Economics, Department of Management, North Carolina A&T State University, Greensboro North Carolina,, USA Department of Information Technology and Decision Science, Old Dominion University, Norfolk Virginia,, USA su: Investments Support vector machines Decision trees Random forest algorithms Prediction models Artificial neural networks sug: subj: Investments Investment Advice Miscellaneous Financial Investment Activities Support vector machines Decision trees Random forest algorithms Prediction models Artificial neural networks keyword: decision tree Naive‐Bayes probabilistic neural network random forest stock index trend predicting twin support vector machine decision tree Naive‐Bayes probabilistic neural network random forest stock index trend predicting twin support vector machine ab: In order to improve the predicting performance of stock index movement, this study proposes a new predicting model called Twin Support Vector Machines (TWSVM), which will be used to predict the trend of Shanghai Securities Composite Index (SSCI) and Standard and Poor's 500 Index (S&P500 Index), respectively. Thirteen indicators constructed by stock index historical data are selected as input features of the predicting model. The predicting target is the stock index daily movement, up or down. The decision tree (DT), Naive‐Bayes (NB), random forests (RF), probabilistic neural network (PNN) and support vector machine (SVM) are set as contrast experiments. The experiment results indicate that the TWSVM predicting model has a better predicting performance on both stock price and index daily movement. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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