Abstract
In this paper we study the interactions between how companies are mentioned in news, their presence on social media, and daily fluctuation in their stock prices. Our experiments demonstrate that for some entities these time series can be correlated in interesting ways, though for others the correspondences are more opaque. In this study, social media presence is measured by counting Wikipedia page hits. This work is done in a context of building a system for aggregating and analyzing news text, which aims to help the user track business trends; we show results obtainable by the system.
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Notes
- 1.
The Pattern Understanding and learning System: http://puls.cs.helsinki.fi.
- 2.
We use standard R ccp function to calculate cross-correlation.
References
Boudoukh, J., Feldman, R., Kogan, S., Richardson, M.: Which news moves stock prices?. A textual analysis. Technical report, National Bureau of Economic Research (2013)
Du, M., Kangasharju, J., Karkulahti, O., Pivovarova, L., Yangarber, R.: Combined analysis of news and Twitter messages. In: Joint Workshop on NLP&LOD and SWAIE: Semantic Web, Linked Open Data and Information Extraction (2013)
Du, M., Pierce, M., Pivovarova, L., Yangarber, R.: Improving supervised classification using information extraction. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds.) NLDB 2015. LNCS, vol. 9103, pp. 3–18. Springer, Heidelberg (2015)
Guo, W., Li, H., Ji, H., Diab, M.T.: Linking tweets to news: A framework to enrich short text data in social media. In: Proceedings of ACL-2013 (2013)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web. ACM (2010)
Moat, H.S., Curme, C., Stanley, H., Preis, T.: Anticipating stock market movements with Google and Wikipedia. In: Matrasulov, D., Stanley, H.E., (eds.) Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale, pp. 47–59 (2014)
Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining for market prediction: a systematic review. Expert Syst. Appl. 41(16), 7653–7670 (2014)
Tanev, H., Ehrmann, M., Piskorski, J., Zavarella, V.: Enhancing event descriptions through Twitter mining. In: Sixth International AAAI Conference on Weblogs and Social Media (2012)
Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Financ. 62(3), 1139–1168 (2007)
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Karkulahti, O., Pivovarova, L., Du, M., Kangasharju, J., Yangarber, R. (2016). Tracking Interactions Across Business News, Social Media, and Stock Fluctuations. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_61
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DOI: https://doi.org/10.1007/978-3-319-30671-1_61
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30670-4
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