Acta Univ. Agric. Silvic. Mendelianae Brun. 2018, 66, 1573-1580

https://doi.org/10.11118/actaun201866061573
Published online 2018-12-19

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 %.

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”].

References

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