Acta Univ. Agric. Silvic. Mendelianae Brun. 2018, 66(6), 1573-1580 | DOI: 10.11118/actaun201866061573

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

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

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
Grants and funding:

This research was supported by the Czech Science Foundation [grant No. 16-26353S "Sentiment and its Impact on Stock Markets"] and Internal Grant Agency of Mendel University [No. PEF_DP_2018002 "Knowledge mining in continuous textual sources with a changing concept"] and Internal Grant Agency of Mendel University [No. PEF_DP_2018016 "Text analysis by machine learning with a focus on the stock market"].

Published: December 19, 2018  Show citation

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Netolický, P., Petrovský, J., & Dařena, F. (2018). Text-Mining in Streams of Textual Data Using Time Series Applied to Stock Market. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(6), 1573-1580. doi: 10.11118/actaun201866061573
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