Web search queries can predict stock market volumes.

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ilaria Bordino, Stefano Battiston, Guido Caldarelli, Matthieu Cristelli, Antti Ukkonen, Ingmar Weber
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/84a24faa42184fe489ad17fe2a8e30de
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:84a24faa42184fe489ad17fe2a8e30de
record_format	dspace
spelling	oai:doaj.org-article:84a24faa42184fe489ad17fe2a8e30de2021-11-18T07:11:55ZWeb search queries can predict stock market volumes.1932-620310.1371/journal.pone.0040014https://doaj.org/article/84a24faa42184fe489ad17fe2a8e30de2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22829871/?tool=EBIhttps://doaj.org/toc/1932-6203We 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.Ilaria BordinoStefano BattistonGuido CaldarelliMatthieu CristelliAntti UkkonenIngmar WeberPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 7, p e40014 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ilaria Bordino Stefano Battiston Guido Caldarelli Matthieu Cristelli Antti Ukkonen Ingmar Weber Web search queries can predict stock market volumes.
description	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.
format	article
author	Ilaria Bordino Stefano Battiston Guido Caldarelli Matthieu Cristelli Antti Ukkonen Ingmar Weber
author_facet	Ilaria Bordino Stefano Battiston Guido Caldarelli Matthieu Cristelli Antti Ukkonen Ingmar Weber
author_sort	Ilaria Bordino
title	Web search queries can predict stock market volumes.
title_short	Web search queries can predict stock market volumes.
title_full	Web search queries can predict stock market volumes.
title_fullStr	Web search queries can predict stock market volumes.
title_full_unstemmed	Web search queries can predict stock market volumes.
title_sort	web search queries can predict stock market volumes.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/84a24faa42184fe489ad17fe2a8e30de
work_keys_str_mv	AT ilariabordino websearchqueriescanpredictstockmarketvolumes AT stefanobattiston websearchqueriescanpredictstockmarketvolumes AT guidocaldarelli websearchqueriescanpredictstockmarketvolumes AT matthieucristelli websearchqueriescanpredictstockmarketvolumes AT anttiukkonen websearchqueriescanpredictstockmarketvolumes AT ingmarweber websearchqueriescanpredictstockmarketvolumes
_version_	1718423800361517056

Web search queries can predict stock market volumes.

Ejemplares similares