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

Author

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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)

  • František Dařena

    (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic)

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 %.

Suggested Citation

  • Pavel Netolický & Jonáš Petrovský & František Dařena, 2018. "Text-Mining in Streams of Textual Data Using Time Series Applied to Stock Market," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(6), pages 1573-1580.
    • Handle: RePEc:mup:actaun:actaun_2018066061573
    DOI: 10.11118/actaun201866061573
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    References listed on IDEAS

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    1. Pierre Giot, 2005. "Market risk models for intraday data," The European Journal of Finance, Taylor & Francis Journals, vol. 11(4), pages 309-324.
    2. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    3. František Dařena & Jan Přichystal, 2018. "Analysis of the Association between Topics in Online Documents and Stock Price Movements," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(6), pages 1431-1439.
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