Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/260134 
Year of Publication: 
2014
Series/Report no.: 
Working Paper No. 2014:38
Publisher: 
Lund University, School of Economics and Management, Department of Economics, Lund
Abstract: 
Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. We suggest a method to quantify the semantic content of words in news articles about a company and use this as a predictor of its stock volatility. The results show that future stock volatility is better predicted by our method than the conventional models. We also analyze the functional role of text in media either as a passive documentation of past information flow or as an active source for new information influencing future volatility. Our data suggest that semantic content may take both roles.
Subjects: 
volatility
information flow
latent semantic analysis
GARCH
JEL: 
G19
Document Type: 
Working Paper

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