eprintid: 3555
rev_number: 8
eprint_status: archive
userid: 69
dir: disk0/00/00/35/55
datestamp: 2016-10-04 12:24:38
lastmod: 2016-10-04 12:24:38
status_changed: 2016-10-04 12:24:38
type: article
metadata_visibility: show
creators_name: Zhou, Wei-Xing
creators_name: Ranco, Gabriele
creators_name: Bordino, Ilaria
creators_name: Bormetti, Giacomo
creators_name: Caldarelli, Guido
creators_name: Lillo, Fabrizio
creators_name: Treccani, Michele
creators_id: 
creators_id: gabriele.ranco@imtlucca.it
creators_id: 
creators_id: 
creators_id: guido.caldarelli@imtlucca.it
creators_id: 
creators_id: 
title: Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics
ispublished: pub
subjects: QC
divisions: EIC
full_text_status: public
keywords: Finance, Forecasting, Twitter, Financial firms, Financial markets, Internet, Noise reduction
note: WOS ID: WOS:000369527800026
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.
date: 2016
publication: PLOS ONE
volume: 11
number: 1
pagerange: e0146576
id_number: doi:10.1371/journal.pone.0146576
refereed: TRUE
issn: 1932-6203
official_url: http://doi.org/10.1371/journal.pone.0146576
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citation:   Zhou, Wei-Xing and Ranco, Gabriele and Bordino, Ilaria and Bormetti, Giacomo and Caldarelli, Guido and Lillo, Fabrizio and Treccani, Michele  Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics.  PLOS ONE, 11 (1).  e0146576.  ISSN 1932-6203      (2016)  
document_url: http://eprints.imtlucca.it/3555/1/journal.pone.0146576.PDF