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High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility

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  • Ryohei Hisano
  • Didier Sornette
  • Takayuki Mizuno
  • Takaaki Ohnishi
  • Tsutomu Watanabe

Abstract

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information.

Suggested Citation

  • Ryohei Hisano & Didier Sornette & Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2013. "High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-12, June.
    • Handle: RePEc:plo:pone00:0064846
    DOI: 10.1371/journal.pone.0064846
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    References listed on IDEAS

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    2. Boubaker, Sabri & Liu, Zhenya & Zhai, Ling, 2021. "Big data, news diversity and financial market crash," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    3. Shinya Kawata & Yoshi Fujiwara, 2016. "Constructing of network from topics and their temporal change in the Nikkei newspaper articles," Evolutionary and Institutional Economics Review, Springer, vol. 13(2), pages 423-436, December.
    4. Poza, Carlos & Monge, Manuel, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, Elsevier, vol. 163(C), pages 163-175.
    5. Thomas J Hwang, 2013. "Stock Market Returns and Clinical Trial Results of Investigational Compounds: An Event Study Analysis of Large Biopharmaceutical Companies," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    6. Tahira, Yoshifumi & Mizuno, Takayuki, 2016. "Amount of news before stock market fluctuations," HIT-REFINED Working Paper Series 45, Institute of Economic Research, Hitotsubashi University.
    7. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Novel and topical business news and their impact on stock market activities," Papers 1507.06477, arXiv.org.
    8. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Novel and topical business news and their impact on stock market activities," CARF F-Series CARF-F-366, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    9. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 2020. "Forecasting stock market movements using Google Trend searches," Empirical Economics, Springer, vol. 59(6), pages 2821-2839, December.
    10. Andreas Dibiasi & David Iselin, 2021. "Measuring Knightian uncertainty," Empirical Economics, Springer, vol. 61(4), pages 2113-2141, October.
    11. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    12. Ka Kit Tang & Ka Ching Li & Mike K P So, 2021. "Predicting standardized absolute returns using rolling-sample textual modelling," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-28, December.
    13. repec:upd:utppwp:055 is not listed on IDEAS
    14. Kyoto Yono & Hiroki Sakaji & Hiroyasu Matsushima & Takashi Shimada & Kiyoshi Izumi, 2020. "Construction of Macroeconomic Uncertainty Indices for Financial Market Analysis Using a Supervised Topic Model," JRFM, MDPI, vol. 13(4), pages 1-18, April.
    15. Yoshifumi Tahira & Takayuki Mizuno, 2016. "Trading strategy of a stock index based on the frequency of news releases for listed companies," Evolutionary and Institutional Economics Review, Springer, vol. 13(2), pages 437-444, December.
    16. Chen, Ting-Ting & Zheng, Bo & Li, Yan & Jiang, Xiong-Fei, 2018. "Information driving force and its application in agent-based modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 593-601.

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