Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/54457

TitleThe impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices
Author(s)Oliveira, Nuno Ernesto Salgado
Cortez, Paulo
Areal, Nelson
KeywordsStock market
Twitter
Data and text mining
Regression
Issue date2017
PublisherElsevier
JournalExpert Systems with Applications
CitationIn Expert Systems with Applications, Elsevier, 73:125-144, May, 2017, ISSN 0957-4174.
Abstract(s)In this paper, we propose a robust methodology to assess the value of microblogging data to forecast stock market variables: returns, volatility and trading volume of diverse indices and portfolios. The methodology uses sentiment and attention indicators extracted from microblogs (a large Twitter dataset is adopted) and survey indices (AAII and II, USMC and Sentix), diverse forms to daily aggregate these indicators, usage of a Kalman Filter to merge microblog and survey sources, a realistic rolling windows evaluation, several Machine Learning methods and the Diebold-Mariano test to validate if the sentiment and attention based predictions are valuable when compared with an autoregressive baseline. We found that Twitter sentiment and posting volume were relevant for the forecasting of returns of S&P 500 index, portfolios of lower market capitalization and some industries. Additionally, KF sentiment was informative for the forecasting of returns. Moreover, Twitter and KF sentiment indicators were useful for the prediction of some survey sentiment indicators. These results confirm the usefulness of microblogging data for financial expert systems, allowing to predict stock market behavior and providing a valuable alternative for existing survey measures with advantages (e.g., fast and cheap creation, daily frequency).
TypeArticle
URIhttp://hdl.handle.net/1822/54457
DOI10.1016/j.eswa.2016.12.036
ISSN0957-4174
Publisher versionAvailable at Elsevier: http://dx.doi.org/10.1016/j.eswa.2016.12.036
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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