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Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

Overview of attention for article published in PLoS ONE, January 2016
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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
1 news outlet
twitter
36 tweeters
facebook
3 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
46 Mendeley
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Title
Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics
Published in
PLoS ONE, January 2016
DOI 10.1371/journal.pone.0146576
Pubmed ID
Authors

Gabriele Ranco, Ilaria Bordino, Giacomo Bormetti, Guido Caldarelli, Fabrizio Lillo, Michele Treccani

Abstract

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

Twitter Demographics

The data shown below were collected from the profiles of 36 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 2%
United States 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 26%
Student > Master 11 24%
Lecturer 6 13%
Researcher 5 11%
Student > Doctoral Student 4 9%
Other 8 17%
Readers by discipline Count As %
Computer Science 13 28%
Economics, Econometrics and Finance 8 17%
Business, Management and Accounting 8 17%
Unspecified 5 11%
Engineering 2 4%
Other 10 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 35. 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 21 November 2016.
All research outputs
#451,628
of 12,984,283 outputs
Outputs from PLoS ONE
#8,465
of 140,125 outputs
Outputs of similar age
#15,611
of 334,486 outputs
Outputs of similar age from PLoS ONE
#336
of 5,062 outputs
Altmetric has tracked 12,984,283 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 140,125 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.9. This one has done particularly well, scoring higher than 93% 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 334,486 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 95% of its contemporaries.
We're also able to compare this research output to 5,062 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 93% of its contemporaries.