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dc.contributor.authorArratia Quesada, Argimiro Alejandro
dc.contributor.authorÁvalos Villaseñor, Gustavo Eduardo
dc.contributor.authorCabaña Nigro, Ana Alejandra
dc.contributor.authorDuarte López, Ariel
dc.contributor.authorRenedo Mirambell, Martí
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2021-07-14T11:40:56Z
dc.date.available2021-07-14T11:40:56Z
dc.date.issued2021-06-11
dc.identifier.citationArratia, A. [et al.]. Sentiment analysis of financial news: mechanics and statistics. A: "Data science for economics and finance: methodologies and applications". Berlín: Springer, 2021, p. 195-216.
dc.identifier.isbn978-3-030-66891-4
dc.identifier.urihttp://hdl.handle.net/2117/349286
dc.description.abstractThis chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks’ prices, and financial markets in general
dc.description.sponsorshipThe research of A. Arratia, G. Avalos, and M. Renedo-Mirambell is supported by grant TIN2017-89244-R from MINECO (Ministerio de Economía, Industria y Competitividad) and the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya)
dc.format.extent22 p.
dc.language.isoeng
dc.publisherSpringer
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses::Macroeconomia::Finances
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshData mining
dc.subject.lcshEconomic indicators
dc.titleSentiment analysis of financial news: mechanics and statistics
dc.typePart of book or chapter of book
dc.subject.lemacMineria de dades
dc.subject.lemacIndicadors econòmics
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.contributor.groupUniversitat Politècnica de Catalunya. DAMA-UPC - Data Management Group
dc.identifier.doi10.1007/978-3-030-66891-4_9
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/book/10.1007/978-3-030-66891-4
dc.rights.accessOpen Access
local.identifier.drac31871876
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-89244-R/ES/GESTION Y ANALISIS DE DATOS COMPLEJOS/
local.citation.authorArratia, A.; Avalos, G.; Cabaña, A.; Duarte-López, A.; Renedo, M.
local.citation.pubplaceBerlín
local.citation.publicationNameData science for economics and finance: methodologies and applications
local.citation.startingPage195
local.citation.endingPage216


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