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

Pavel Netolický, Jonáš Petrovský and František Dařena
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Pavel Netolický: Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
Jonáš Petrovský: Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2018, vol. 66, issue 6, 1573-1580

Abstract: Each 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 %.

Keywords: machine learning; text mining; stock market; data stream (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:mup:actaun:actaun_2018066061573

DOI: 10.11118/actaun201866061573

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