Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/36613

TitleBig data for stock market by means of mining techniques
Author(s)Lima, Luciana
Portela, Filipe
Santos, Manuel Filipe
Abelha, António
Machado, José Manuel
KeywordsText Mining
Stock Prediction
Big Data
Issue date2015
PublisherSpringer International Publishing
JournalAdvances in Intelligent Systems and Computing
Abstract(s)Predict and prevent future events are the major advantages to any company. Big Data comes up with huge power, not only by the ability of processes large amounts and variety of data at high velocity, but also by the capability to create value to organizations. This paper presents an approach to a Big Data based decision making in the stock market context. The correlation between news articles and stock variations it is already proved but it can be enriched with other indicators. In this use case they were collected news articles from three different web sites and the stock history from the New York Stock Exchange. In order to proceed to data mining classification algorithms the articles were labeled by their sentiment, the direct relation to a specific company and geographic market influence. With the proposed model it is possible identify the patterns between this indicators and predict stock price variations with accuracies of 100 percent. Moreover the model shown that the stock market could be sensitive to news with generic topics, such as government and society but they can also depend on the geographic cover.
TypeConference paper
URIhttp://hdl.handle.net/1822/36613
ISBN978-3-319-16485-4
978-3-319-16486-1
DOI10.1007/978-3-319-16486-1_67
ISSN2194-5357
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

Files in This Item:
File Description SizeFormat 
2015 - AISC - Big Data for Stock Market by means of Mining techniques.pdf838,1 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID