Textual analysis of stock market prediction using financial news articles

Robert P. Schumaker, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations

Abstract

This paper examines the role of financial news articles on three different textual representations; Bag of Words, Noun Phrases, and Named Entities and their ability to predict discrete number stock prices twenty minutes after an article release. Using a Support Vector Machine (SVM) derivative, we show that our model had a statistically significant impact on predicting future stock prices compared to linear regression. We further demonstrate that using a Noun Phrase representation scheme performs better than the de facto standard of Bag of Words.

Original languageEnglish (US)
Title of host publicationAssociation for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006
Pages1422-1430
Number of pages9
StatePublished - Dec 1 2006
Event12th Americas Conference on Information Systems, AMCIS 2006 - Acapulco, Mexico
Duration: Aug 4 2006Aug 6 2006

Publication series

NameAssociation for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006
Volume3

Other

Other12th Americas Conference on Information Systems, AMCIS 2006
CountryMexico
CityAcapulco
Period8/4/068/6/06

Keywords

  • Bag of words
  • Named entities
  • Noun phrases
  • Prediction
  • SVM
  • Stock market

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Library and Information Sciences
  • Information Systems

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  • Cite this

    Schumaker, R. P., & Chen, H. (2006). Textual analysis of stock market prediction using financial news articles. In Association for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006 (pp. 1422-1430). (Association for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006; Vol. 3).