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Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

Author

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  • Gabriele Ranco
  • Ilaria Bordino
  • Giacomo Bormetti
  • Guido Caldarelli
  • Fabrizio Lillo
  • Michele Treccani

Abstract

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

Suggested Citation

  • Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2016. "Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-14, January.
  • Handle: RePEc:plo:pone00:0146576
    DOI: 10.1371/journal.pone.0146576
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    References listed on IDEAS

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    Cited by:

    1. Mario Gutiérrez-Roig & Carlota Segura & Jordi Duch & Josep Perelló, 2016. "Market Imitation and Win-Stay Lose-Shift Strategies Emerge as Unintended Patterns in Market Direction Guesses," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-19, August.
    2. Agarwal, Arvind & Gupta, Aparna & Kumar, Arun & Tamilselvam, Srikanth G., 2019. "Learning risk culture of banks using news analytics," European Journal of Operational Research, Elsevier, vol. 277(2), pages 770-783.
    3. Zhen-Hua Yang & Jian-Guo Liu & Chang-Rui Yu & Jing-Ti Han, 2017. "Quantifying the effect of investors’ attention on stock market," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-16, May.
    4. Ana Fern'andez Vilas & Rebeca D'iaz Redondo & Ant'on Lorenzo Garc'ia, 2023. "The irruption of cryptocurrencies into Twitter cashtags: a classifying solution," Papers 2312.11531, arXiv.org.
    5. Shen, Dehua & Liu, Lanbiao & Zhang, Yongjie, 2018. "Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 928-934.
    6. Danilo Vassallo & Giacomo Bormetti & Fabrizio Lillo, 2019. "A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics," Papers 1910.01407, arXiv.org, revised Sep 2020.

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