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Improving trading saystems using the RSI financial indicator and neural networks.

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: SoftLabes
dc.contributor.authorRodríguez-González, Alejandro
dc.contributor.authorGuldrís-Iglesias, Fernando
dc.contributor.authorColomo Palacios, Ricardo
dc.contributor.authorGómez Berbís, Juan Miguel
dc.contributor.authorJiménez Domingo, Enrique
dc.contributor.authorAlor-Hernández, Giner
dc.contributor.authorPosada-Gómez, Rubén
dc.contributor.authorCortes-Robles, Guillermo
dc.date.accessioned2012-06-21T08:07:35Z
dc.date.available2012-06-21T08:07:35Z
dc.date.issued2010-08-12
dc.descriptionProceedings of: 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010), 20 August-3 September 2010, Daegu (Korea)
dc.description.abstractTrading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of computational intelligence which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof-of-concept architecture and implementation of a Trading Decision Support System based on the RSI and Feed-Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the RSI calculation and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.
dc.description.sponsorshipThis work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148), SONAR2 (TSI-020100-2008-665 and GO2 (TSI-020400-2009-127). Furthermore, this work is supported by the General Council of Superior Technological Education of Mexico (DGEST). Additionally, this work is sponsored by the National Council of Science and Technology (CONACYT) and the Public Education Secretary (SEP) through PROMEP.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationByeong-Ho Kang et al. (eds.), Knowledge management and acquisition for smart systems and services. 11th International Workshop, PKAW 2010, Daegu, Korea, August 20 - September 3, 2010 (pp. 27-37). Proceedings. Berlin: Springer, 2010
dc.identifier.doi10.1007/978-3-642-15037-1_3
dc.identifier.isbn3-642-15036-5
dc.identifier.isbn978-3-642-15036-4
dc.identifier.issn1611-3349 (Online)
dc.identifier.issn0302-9743 (Print)
dc.identifier.publicationfirstpage27
dc.identifier.publicationlastpage37
dc.identifier.publicationtitleKnowledge management and acquisition for smart systems and services. 11th International Workshop, PKAW 2010, Daegu, Korea, August 20 - September 3, 2010. Proceedings
dc.identifier.urihttps://hdl.handle.net/10016/14617
dc.language.isoeng
dc.publisherSpringer
dc.relation.eventdate20 August-3 September, 2010
dc.relation.eventnumber11
dc.relation.eventplaceDaegu (Korea)
dc.relation.eventtitleInternational Workshop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010)
dc.relation.ispartofseriesLecture Notes in Computer Science. Lecture Notes in Artificial Intelligence
dc.relation.ispartofseries6232
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-642-15037-1_3
dc.rights© Springer
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherNeural networks
dc.subject.otherRSI financial indicator
dc.titleImproving trading saystems using the RSI financial indicator and neural networks.
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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