Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61444
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dc.contributor.authorChiong, Raymonden
dc.contributor.authorAdam, Marc T Pen
dc.contributor.authorFan, Zongwenen
dc.contributor.authorLutz, Bernharden
dc.contributor.authorHu, Zhongyien
dc.contributor.authorNeumann, Dirken
local.source.editorEditor(s): Hernan Aguirreen
dc.date.accessioned2024-07-10T01:04:25Z-
dc.date.available2024-07-10T01:04:25Z-
dc.date.issued2018-
dc.identifier.citationGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, p. 278-279en
dc.identifier.isbn9781450357647en
dc.identifier.urihttps://hdl.handle.net/1959.11/61444-
dc.description.abstract<p>Stock market prediction plays an important role in financial decisionmaking for investors. Many of them rely on news disclosures to make their decisions in buying or selling stocks. However, accurate modelling of stock market trends via news disclosures is a challenging task, considering the complexity and ambiguity of natural languages used. Unlike previous work along this line of research, which typically applies bag-of-words to extract tens of thousands of features to build a prediction model, we propose a sentiment analysis-based approach for financial market prediction using news disclosures. Specifically, sentiment analysis is carried out in the pre-processing phase to extract sentiment-related features from financial news. Historical stock market data from the perspective of time series analysis is also included as an input feature. With the extracted features, we use a support vector machine (SVM) to build the prediction model, with its parameters optimised through particle swarm optimisation (PSO). Experimental results show that our proposed SVM and PSO-based model is able to obtain better results than a deep learning model in terms of time and accuracy. The results presented here are to date the best in the literature based on the financial news dataset tested. This excellent performance is attributed to the sentiment analysis done during the pre-processing stage, as it reduces the feature dimensions significantly.</p>en
dc.languageenen
dc.publisherAssociation for Computing Machineryen
dc.relation.ispartofGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companionen
dc.titleA sentiment analysis-based machine learning approach for financial market prediction via news disclosuresen
dc.typeConference Publicationen
dc.relation.conferenceGECCO '18: Genetic and Evolutionary Computation Conferenceen
dc.identifier.doi10.1145/3205651.3205682en
local.contributor.firstnameRaymonden
local.contributor.firstnameMarc T Pen
local.contributor.firstnameZongwenen
local.contributor.firstnameBernharden
local.contributor.firstnameZhongyien
local.contributor.firstnameDirken
local.profile.schoolSchool of Science & Technologyen
local.profile.emailrchiong@une.edu.auen
local.output.categoryE5en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference15th - 19th July, 2018en
local.publisher.placeNew York, NY, United Statesen
local.format.startpage278en
local.format.endpage279en
local.contributor.lastnameChiongen
local.contributor.lastnameAdamen
local.contributor.lastnameFanen
local.contributor.lastnameLutzen
local.contributor.lastnameHuen
local.contributor.lastnameNeumannen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
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local.identifier.unepublicationidune:1959.11/61444en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleA sentiment analysis-based machine learning approach for financial market prediction via news disclosuresen
local.output.categorydescriptionE5 Conference Posteren
local.conference.detailsGECCO '18: Genetic and Evolutionary Computation Conference, 15th - 19th July, 2018en
local.search.authorChiong, Raymonden
local.search.authorAdam, Marc T Pen
local.search.authorFan, Zongwenen
local.search.authorLutz, Bernharden
local.search.authorHu, Zhongyien
local.search.authorNeumann, Dirken
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2018en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/a35d2dcf-ef3a-406f-9138-b03a6b096f88en
local.subject.for20204602 Artificial intelligenceen
local.date.start2018-07-15-
local.date.end2018-07-19-
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-28en
Appears in Collections:Conference Publication
School of Science and Technology
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