Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/26786

TitleSome experiments on modeling stock market behavior using investor sentiment analysis and posting volume from twitter
Author(s)Oliveira, Nuno
Cortez, Paulo
Areal, Nelson
KeywordsText mining
Sentiment analysis
Microblogging data
Returns
Trading volume
Volatility
Issue date2013
PublisherACM
CitationN. Oliveira, P. Cortez and N. Areal. Some Experiments on Modeling Stock Market Behavior Using Investor Sentiment Analysis and Posting Volume from Twitter. In Proceedings of the 3rd Internationa Conference on Web Intelligence, Mining and Semantics (WIMS’13), article no. 31, Madrid, Spain, June, 2013, ACM, ISBN 978-1-4503-1850-1.
Abstract(s)The analysis of microblogging data related with stock mar- kets can reveal relevant new signals of investor sentiment and attention. It may also provide sentiment and attention indicators in a more rapid and cost-effective manner than other sources. In this study, we created several indicators using Twitter data and investigated their value when model- ing relevant stock market variables, namely returns, trading volume and volatility. We collected recent data from nine ma jor technological companies. Several sentiment analy- sis methods were explored, by comparing 5 popular lexical resources and two novel lexicons (emoticon based and the merge of all 6 lexicons) and sentiment indicators produced using two strategies (based on daily words and individual tweet classifications). Also, we measured posting volume associated with tweets related to the analyzed companies. While a short time period is considered (32 days), we found scarce evidence that sentiment indicators can explain these stock returns. However, interesting results were obtained when measuring the value of using posting volume for fit- ting trading volume and, in particular, volatility.
TypeConference paper
URIhttp://hdl.handle.net/1822/26786
ISBN978-1-4503-1850-1
DOI10.1145/2479787.2479811
Publisher versionhttps://dl.acm.org/citation.cfm?doid=2479787.2479811
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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