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Tracking Multiple Social Media for Stock Market Event Prediction

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2017)

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Texas Tech University, Lubbock, USA

    Fang Jin

  2. Discovery Analytics Center, Department of Computer Science, Virginia Tech, Blacksburg, USA

    Wei Wang, Prithwish Chakraborty, Nathan Self & Naren Ramakrishnan

  3. Department of Computer Science, University at Albany, SUNY, Albany, USA

    Feng Chen

Authors
  1. Fang Jin
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  2. Wei Wang
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  3. Prithwish Chakraborty
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  4. Nathan Self
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  5. Feng Chen
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  6. Naren Ramakrishnan
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Corresponding author

Correspondence to Fang Jin .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Sachsen, Germany

    Petra Perner

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Jin, F., Wang, W., Chakraborty, P., Self, N., Chen, F., Ramakrishnan, N. (2017). Tracking Multiple Social Media for Stock Market Event Prediction. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2017. Lecture Notes in Computer Science(), vol 10357. Springer, Cham. https://doi.org/10.1007/978-3-319-62701-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-62701-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62700-7

  • Online ISBN: 978-3-319-62701-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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