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Web search queries can predict stock market volumes

Author

Listed:
  • Ilaria Bordino
  • Stefano Battiston
  • Guido Caldarelli
  • Matthieu Cristelli
  • Antti Ukkonen
  • Ingmar Weber

Abstract

We live in a computerized and networked society where many of our actions leave a digital trace and affect other people's actions. This has lead to the emergence of a new data-driven research field: mathematical methods of computer science, statistical physics and sociometry provide insights on a wide range of disciplines ranging from social science to human mobility. A recent important discovery is that query volumes (i.e., the number of requests submitted by users to search engines on the www) can be used to track and, in some cases, to anticipate the dynamics of social phenomena. Successful exemples include unemployment levels, car and home sales, and epidemics spreading. Few recent works applied this approach to stock prices and market sentiment. However, it remains unclear if trends in financial markets can be anticipated by the collective wisdom of on-line users on the web. Here we show that trading volumes of stocks traded in NASDAQ-100 are correlated with the volumes of queries related to the same stocks. In particular, query volumes anticipate in many cases peaks of trading by one day or more. Our analysis is carried out on a unique dataset of queries, submitted to an important web search engine, which enable us to investigate also the user behavior. We show that the query volume dynamics emerges from the collective but seemingly uncoordinated activity of many users. These findings contribute to the debate on the identification of early warnings of financial systemic risk, based on the activity of users of the www.

Suggested Citation

  • Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2011. "Web search queries can predict stock market volumes," Papers 1110.4784, arXiv.org, revised Jun 2012.
  • Handle: RePEc:arx:papers:1110.4784
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    File URL: http://arxiv.org/pdf/1110.4784
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    References listed on IDEAS

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    1. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    2. Garlaschelli, Diego & Battiston, Stefano & Castri, Maurizio & Servedio, Vito D.P. & Caldarelli, Guido, 2005. "The scale-free topology of market investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 491-499.
    3. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
    4. Jean-Philippe Bouchaud, 2009. "The (unfortunate) complexity of the economy," Papers 0904.0805, arXiv.org.
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    Citations

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    Cited by:

    1. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    2. Pavlicek, Jaroslav & Kristoufek, Ladislav, 2015. "Nowcasting unemployment rates with Google searches: Evidence from the Visegrad Group countries," FinMaP-Working Papers 34, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    3. Damien Challet & Ahmed Bel Hadj Ayed, 2014. "Do Google Trend data contain more predictability than price returns?," Papers 1403.1715, arXiv.org.
    4. Ji, Qiang & Guo, Jian-Feng, 2015. "Oil price volatility and oil-related events: An Internet concern study perspective," Applied Energy, Elsevier, vol. 137(C), pages 256-264.
    5. Dirk Ulbricht & Konstantin A. Kholodilin & Tobias Thomas, 2017. "Do Media Data Help to Predict German Industrial Production?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 483-496, August.
    6. Ming-Yuan Yang & Sai-Ping Li & Li-Xin Zhong & Fei Ren, 2018. "Modelling stock correlations with expected returns from investors," Papers 1803.02019, arXiv.org, revised Mar 2018.
    7. Federico Garzarelli & Matthieu Cristelli & Andrea Zaccaria & Luciano Pietronero, 2011. "Memory effects in stock price dynamics: evidences of technical trading," Papers 1110.5197, arXiv.org.
    8. Kristoufek, Ladislav, 2015. "Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 194-205.
    9. Matteo Accornero & Mirko Moscatelli, 2018. "Listening to the buzz: social media sentiment and retail depositors' trust," Temi di discussione (Economic working papers) 1165, Bank of Italy, Economic Research and International Relations Area.
    10. Piñeiro-Chousa, Juan Ramón & López-Cabarcos, M. Ángeles & Pérez-Pico, Ada María, 2016. "Examining the influence of stock market variables on microblogging sentiment," Journal of Business Research, Elsevier, vol. 69(6), pages 2087-2092.
    11. T. T. Chen & B. Zheng & Y. Li & X. F. Jiang, 2017. "New approaches in agent-based modeling of complex financial systems," Papers 1703.06840, arXiv.org.
    12. Shan Lu & Jichang Zhao & Huiwen Wang, 2018. "The Power of Trading Polarity: Evidence from China Stock Market Crash," Papers 1802.01143, arXiv.org.
    13. Matija Piv{s}korec & Nino Antulov-Fantulin & Petra Kralj Novak & Igor Mozetiv{c} & Miha Grv{c}ar & Irena Vodenska & Tomislav v{S}muc, 2014. "News Cohesiveness: an Indicator of Systemic Risk in Financial Markets," Papers 1402.3483, arXiv.org.
    14. Paola Cerchiello & Paolo Giudici, 2016. "How to measure the quality of financial tweets," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(4), pages 1695-1713, July.
    15. Guo, Jian-Feng & Ji, Qiang, 2013. "How does market concern derived from the Internet affect oil prices?," Applied Energy, Elsevier, vol. 112(C), pages 1536-1543.
    16. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    17. Milla Siikanen & Kestutis Baltakys & Hannu Karkkainen & Jari Jussila & Ravi Vatrapu & Raghava Mukkamala & Abid Hussain & Juho Kanniainen, 2017. "How Facebook drives investor behavior," Papers 1709.07300, arXiv.org, revised Feb 2018.
    18. Latoeiro, Pedro & Ramos, Sofía B. & Veiga, Helena, 2013. "Predictability of stock market activity using Google search queries," DES - Working Papers. Statistics and Econometrics. WS ws130605, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
    20. repec:spr:qualqt:v:51:y:2017:i:4:d:10.1007_s11135-016-0354-x is not listed on IDEAS
    21. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    22. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    23. Semen Son-Turan, 2016. "The Impact of Investor Sentiment on the "Leverage Effect"," International Econometric Review (IER), Econometric Research Association, vol. 8(1), pages 4-18, April.

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