Abstract

Stock market news and investing tips are popular topics in Twitter. In this paper, first we utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website for the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content. Then we proceed to prove that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in the hourly directional prediction accuracies for the prices of DJI stocks mentioned in these articles. Secondly, we show that using document-level sentiment extraction does not yield to a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages523-530
Number of pages8
Volume1
ISBN (Electronic)9781467396172
DOIs
StatePublished - Feb 2 2016
Event2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015 - Singapore, Singapore
Duration: Dec 6 2015Dec 9 2015

Other

Other2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015
CountrySingapore
CitySingapore
Period12/6/1512/9/15

Keywords

  • Breaking news
  • Stock prediction
  • Stock trading
  • Text mining
  • Twitter analysis
  • Twitter volume spike

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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  • Cite this

    Alostad, H., & Davulcu, H. (2016). Directional prediction of stock prices using breaking news on twitter. In Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015 (Vol. 1, pp. 523-530). [7396858] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WI-IAT.2015.82