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Web search queries can predict stock market volumes

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  • 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.

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File URL: http://arxiv.org/pdf/1110.4784
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Bibliographic Info

Paper provided by arXiv.org in its series Papers with number 1110.4784.

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Date of creation: Oct 2011
Date of revision: Jun 2012
Handle: RePEc:arx:papers:1110.4784

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Web page: http://arxiv.org/

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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 424-38, 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. Jean-Philippe Bouchaud, 2009. "The (unfortunate) complexity of the economy," Papers 0904.0805, arXiv.org.
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Cited by:
  1. Sofía B. Ramos & Helena Veiga & Pedro Latoeiro, 2013. "Predictability of stock market activity using Google search queries," Statistics and Econometrics Working Papers ws130605, Universidad Carlos III, Departamento de Estadística y Econometría.
  2. Federico Garzarelli & Matthieu Cristelli & Andrea Zaccaria & Luciano Pietronero, 2011. "Memory effects in stock price dynamics: evidences of technical trading," Papers 1110.5197, arXiv.org.
  3. Matija Pi\v{s}korec & Nino Antulov-Fantulin & Petra Kralj Novak & Igor Mozeti\v{c} & Miha Gr\v{c}ar & Irena Vodenska & Tomislav \v{S}muc, 2014. "News Cohesiveness: an Indicator of Systemic Risk in Financial Markets," Papers 1402.3483, arXiv.org.
  4. Damien Challet & Ahmed Bel Hadj Ayed, 2014. "Do Google Trend data contain more predictability than price returns?," Papers 1403.1715, arXiv.org.

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