Predicting Stock Price Volatility by Analyzing Semantic Content in Media
AbstractCurrent 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.
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Bibliographic InfoPaper provided by Knut Wicksell Centre for Financial Studies, Lund University in its series Knut Wicksell Working Paper Series with number 2013/16.
Length: 43 pages
Date of creation: 13 Sep 2013
Date of revision:
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volatility; information flow; latent semantic analysis; GARCH;
Find related papers by JEL classification:
- G19 - Financial Economics - - General Financial Markets - - - Other
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