Paper Info
Title
Some experiments on modeling stock market behavior using investor sentiment analysis and posting volume from Twitter
Abstract
The analysis of microblogging data related with stock markets 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 modeling relevant stock market variables, namely returns, trading volume and volatility. We collected recent data from nine major technological companies. Several sentiment analysis 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 fitting trading volume and, in particular, volatility.
Year
DOI
Venue
2013
10.1145/2479787.2479811
WIMS
Keywords
Field
DocType
investor sentiment analysis,fitting trading volume,investor sentiment,sentiment analysis method,recent data,twitter data,trading volume,relevant stock market variable,microblogging data,sentiment indicator,stock market behavior,stock market,sentiment analysis,text mining,volatility
Econometrics,Text mining,Social media,Emoticon,Information retrieval,Sentiment analysis,Computer science,Microblogging,Merge (version control),Volatility (finance),Stock market
Conference
Citations 
PageRank 
References 
6
0.51
9
Authors
3
Name
Order
Citations
PageRank
Nuno Oliveira116015.80
Paulo Cortez2156.45
Nelson Areal3763.91