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Forecasting Financial Market Volatility Using a Dynamic Topic Model

Asia-Pacific Financial Markets volume 24, pages 149–167 (2017)Cite this article

Abstract

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological evolution of the topic in the set of articles using a dynamic topic model. After calculating a topic score, we develop time series models that incorporate the score to estimate and forecast realized volatility. The results of our empirical analysis suggest that the proposed models can contribute to improving forecasting accuracy.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 6691337, Japan

    Takayuki Morimoto

  2. Department of Statistical Modeling, The Institute of Statistical Mathematics and SOKENDAI, 10-3 Midori-cho, Tachikawa, Tokyo, 1908562, Japan

    Yoshinori Kawasaki

Authors
  1. Takayuki Morimoto
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  2. Yoshinori Kawasaki
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Correspondence to Takayuki Morimoto.

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Cite this article

Morimoto, T., Kawasaki, Y. Forecasting Financial Market Volatility Using a Dynamic Topic Model. Asia-Pac Financ Markets 24, 149–167 (2017). https://doi.org/10.1007/s10690-017-9228-z

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  • DOI: https://doi.org/10.1007/s10690-017-9228-z

Keywords

  • Big data
  • Online news
  • Dynamic topic model
  • Topic score
  • Forecasting
  • Realized volatility

JEL Classification

  • C10
  • C80
  • G17