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Predicting Stock Price Volatility by Analyzing Semantic Content in Media

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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.

Suggested Citation

  • Asgharian, Hossein & Sikström, Sverker, 2013. "Predicting Stock Price Volatility by Analyzing Semantic Content in Media," Knut Wicksell Working Paper Series 2013/16, Lund University, Knut Wicksell Centre for Financial Studies.
  • Handle: RePEc:hhs:luwick:2013_016
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    1. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
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    Keywords

    volatility; information flow; latent semantic analysis; GARCH;

    JEL classification:

    • G19 - Financial Economics - - General Financial Markets - - - Other

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