dc.contributor.author | Arratia Quesada, Argimiro Alejandro |
dc.contributor.author | Ávalos Villaseñor, Gustavo Eduardo |
dc.contributor.author | Cabaña Nigro, Ana Alejandra |
dc.contributor.author | Duarte López, Ariel |
dc.contributor.author | Renedo Mirambell, Martí |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa |
dc.date.accessioned | 2021-07-14T11:40:56Z |
dc.date.available | 2021-07-14T11:40:56Z |
dc.date.issued | 2021-06-11 |
dc.identifier.citation | Arratia, 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.isbn | 978-3-030-66891-4 |
dc.identifier.uri | http://hdl.handle.net/2117/349286 |
dc.description.abstract | This 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.sponsorship | The 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.extent | 22 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.rights | Attribution 4.0 International |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Economia i organització d'empreses::Macroeconomia::Finances |
dc.subject | Àrees temàtiques de la UPC::Informàtica |
dc.subject.lcsh | Data mining |
dc.subject.lcsh | Economic indicators |
dc.title | Sentiment analysis of financial news: mechanics and statistics |
dc.type | Part of book or chapter of book |
dc.subject.lemac | Mineria de dades |
dc.subject.lemac | Indicadors econòmics |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.contributor.group | Universitat Politècnica de Catalunya. DAMA-UPC - Data Management Group |
dc.identifier.doi | 10.1007/978-3-030-66891-4_9 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://link.springer.com/book/10.1007/978-3-030-66891-4 |
dc.rights.access | Open Access |
local.identifier.drac | 31871876 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info: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.author | Arratia, A.; Avalos, G.; Cabaña, A.; Duarte-López, A.; Renedo, M. |
local.citation.pubplace | Berlín |
local.citation.publicationName | Data science for economics and finance: methodologies and applications |
local.citation.startingPage | 195 |
local.citation.endingPage | 216 |