Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/154644
Authors: 
Mao, Huina
Counts, Scott
Bollen, Johan
Year of Publication: 
2015
Series/Report no.: 
ECB Statistics Paper 9
Abstract: 
Computational methods to gauge investor sentiment from commonly used online data sources that rely on machine learning classifiers and lexicons have shown considerable promise, but suffer from measurement and classification errors. In our work, we develop a simple, direct and unambiguous indicator of online investor sentiment, which is based on Twitter updates and Google search queries. We examine the predictive power of this new investor bullishness indicator for international stock markets. Our results indicate several striking regularities. First, changes in Twitter bullishness predict changes in Google bullishness, indicating that Twitter information precedes Google queries. Second, Twitter and Google bullishness are positively correlated to investor sentiment and lead established investor sentiment surveys. The former, in particular, is a more powerful predictor of changes in sentiment in the stock market than the latter. Third, we observe that high Twitter bullishness predicts increases in stock returns, with these then returning to their fundamental values. We believe that our results may support the investor sentiment hypothesis in behavioural finance.
Subjects: 
big data
computational science
international financial markets
investor sentiment
social media
JEL: 
C1
C12
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-1905-0
Document Type: 
Working Paper
Social Media Mentions:

Files in This Item:
File
Size
446.62 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.