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Text-Mining in Streams of Textual Data Using Time Series Applied to Stock Market

dc.contributor.authorNetolický, Pavel
dc.contributor.authorPetrovský, Jonáš
dc.contributor.authorDařena, František
dc.date.accessioned2021-07-02T00:02:15Z
dc.date.available2021-07-02T00:02:15Z
dc.date.issued2018
dc.identifier43916215
dc.identifier.issn1211-8516 Sherpa/RoMEO, JCR
dc.identifier.urihttps://repozitar.mendelu.cz/xmlui/handle/20.500.12698/1369
dc.description.abstractEach day, a lot of text data is generated. This data comes from various sources and may contain valuable information. In this article, we use text mining methods to discover if there is a connection between news articles and changes of the S&P 500 stock index. The index values and documents were divided into time windows according to the direction of the index value changes. We achieved a classification accuracy of 65-74 %.en
dc.format1573-1580
dc.publisherMendelova univerzita v Brně
dc.relation.ispartofActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
dc.relation.urihttps://doi.org/10.11118/actaun201866061573
dc.rightsCC BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectmachine learningen
dc.subjecttext miningen
dc.subjectstock marketen
dc.subjectdata streamen
dc.titleText-Mining in Streams of Textual Data Using Time Series Applied to Stock Marketen
dc.typeJ_ČLÁNEK
dc.date.updated2021-07-02T00:02:15Z
dc.description.versionOA
local.identifier.doi10.11118/actaun201866061573
local.identifier.scopus2-s2.0-85060693556
local.number6
local.volume66
local.identifier.obd43916215
local.identifier.e-issn2464-8310
dc.project.IDGA16-26353S
dc.project.IDPEF_DP_2018002
dc.project.IDSentiment a jeho vliv na akciové trhy
dc.project.IDDolování znalosti z kontinuálních textových zdrojů s měnícím se konceptem
dc.identifier.orcidDařena, František 0000-0001-8892-4256
local.contributor.affiliationPEF


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CC BY-NC-ND 4.0
Except where otherwise noted, this item's license is described as CC BY-NC-ND 4.0