eprintid: 1328
rev_number: 12
eprint_status: archive
userid: 6
dir: disk0/00/00/13/28
datestamp: 2012-07-30 11:14:52
lastmod: 2016-04-07 09:29:17
status_changed: 2012-07-30 11:14:52
type: article
metadata_visibility: show
creators_name: Bordino, Ilaria
creators_name: Battiston, Stefano 
creators_name: Caldarelli, Guido
creators_name: Cristelli, Matthieu
creators_name: Ukkonen, Antti
creators_name: Weber, Ingmar
creators_id: 
creators_id: 
creators_id: guido.caldarelli@imtlucca.it
creators_id: 
creators_id: 
creators_id: 
title: Web Search Queries Can Predict Stock Market Volumes
ispublished: pub
subjects: HB
subjects: QA75
subjects: QC
divisions: EIC
full_text_status: public
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 search engine traffic (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 examples 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 daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily 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.
date: 2012-07-19
date_type: published
publication: PloS One
volume: 7
number: 7
publisher: Public Library of Science
pagerange: e40014
refereed: TRUE
issn: 1932-6203
official_url: http://dx.doi.org/10.1371%2Fjournal.pone.0040014
projects: EC FET Open Project “FOC” No. 255987
citation:   Bordino, Ilaria and Battiston, Stefano  and Caldarelli, Guido and Cristelli, Matthieu and Ukkonen, Antti and Weber, Ingmar  Web Search Queries Can Predict Stock Market Volumes.  PloS One, 7 (7).  e40014.  ISSN 1932-6203      (2012)  
document_url: http://eprints.imtlucca.it/1328/1/Web_search_PlosOne_2012_Caldarelli.pdf