Tracking multiple social media for stock market event prediction

Fang Jin, Wei Wang, Prithwish Chakraborty, Nathan Self, Feng Chen, Naren Ramakrishnan

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

12 Scopus citations

Abstract

The problem of modeling the continuously changing trends in finance markets and generating real-time, meaningful predictions about significant changes in those markets has drawn considerable interest from economists and data scientists alike. In addition to traditional market indicators, growth of varied social media has enabled economists to leverage micro- and real-time indicators about factors possibly influencing the market, such as public emotion, anticipations and behaviors. We propose several specific market related features that can be mined from varied sources such as news, Google search volumes and Twitter. We further investigate the correlation between these features and financial market fluctuations. In this paper, we present a Delta Naive Bayes (DNB) approach to generate prediction about financial markets. We present a detailed prospective analysis of prediction accuracy generated from multiple, combined sources with those generated from a single source. We find that multi-source predictions consistently outperform single-source predictions, even though with some limitations.

Original languageEnglish
Title of host publicationAdvances in Data Mining
Subtitle of host publicationApplications and Theoretical Aspects - 17th Industrial Conference, ICDM 2017, Proceedings
EditorsPetra Perner
PublisherSpringer-Verlag
Pages16-30
Number of pages15
ISBN (Print)9783319627007
DOIs
StatePublished - 2017
Event17th Industrial Conference on Advances in Data Mining, ICDM 2017 - New York, United States
Duration: Jul 12 2017Jul 13 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10357 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Industrial Conference on Advances in Data Mining, ICDM 2017
CountryUnited States
CityNew York
Period07/12/1707/13/17

Keywords

  • Features combination
  • Google trends
  • Market prediction
  • Multiple social media
  • News sentiment
  • Twitter burst

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  • Cite this

    Jin, F., Wang, W., Chakraborty, P., Self, N., Chen, F., & Ramakrishnan, N. (2017). Tracking multiple social media for stock market event prediction. In P. Perner (Ed.), Advances in Data Mining: Applications and Theoretical Aspects - 17th Industrial Conference, ICDM 2017, Proceedings (pp. 16-30). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10357 LNAI). Springer-Verlag. https://doi.org/10.1007/978-3-319-62701-4_2