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Web Search Queries Can Predict Stock Market Volumes

Overview of attention for article published in PLoS ONE, July 2012
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
2 news outlets
blogs
2 blogs
twitter
15 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
100 Dimensions

Readers on

mendeley
190 Mendeley
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Title
Web Search Queries Can Predict Stock Market Volumes
Published in
PLoS ONE, July 2012
DOI 10.1371/journal.pone.0040014
Pubmed ID
Authors

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 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.

Twitter Demographics

The data shown below were collected from the profiles of 15 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 190 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
Italy 3 2%
United Kingdom 3 2%
Portugal 2 1%
Switzerland 2 1%
China 2 1%
Brazil 1 <1%
Luxembourg 1 <1%
Finland 1 <1%
Other 2 1%
Unknown 168 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 41 22%
Student > Ph. D. Student 39 21%
Researcher 35 18%
Student > Bachelor 17 9%
Unspecified 11 6%
Other 47 25%
Readers by discipline Count As %
Economics, Econometrics and Finance 47 25%
Computer Science 40 21%
Business, Management and Accounting 23 12%
Unspecified 17 9%
Physics and Astronomy 17 9%
Other 46 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 51. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2016.
All research outputs
#282,245
of 12,378,883 outputs
Outputs from PLoS ONE
#5,693
of 135,142 outputs
Outputs of similar age
#2,208
of 121,865 outputs
Outputs of similar age from PLoS ONE
#99
of 3,316 outputs
Altmetric has tracked 12,378,883 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 135,142 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.7. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 121,865 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 3,316 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.