Forecasting Financial Market Volatility Using a Dynamic Topic Model

Takayuki Morimoto () and Yoshinori Kawasaki ()
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Takayuki Morimoto: Kwansei Gakuin University
Yoshinori Kawasaki: The Institute of Statistical Mathematics and SOKENDAI

Asia-Pacific Financial Markets, 2017, vol. 24, issue 3, No 1, 149-167

Abstract: 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.

Keywords: Big data; Online news; Dynamic topic model; Topic score; Forecasting; Realized volatility (search for similar items in EconPapers)
JEL-codes: C10 C80 G17 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10690-017-9228-z

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