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Predicting Stock Price Volatility by Analyzing Semantic Content in Media.

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Author

Summary, in English

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.

Publishing year

2014

Language

English

Publication/Series

Working Paper / Department of Economics, School of Economics and Management, Lund University

Issue

38

Document type

Working paper

Publisher

Department of Economics, Lund University

Topic

  • Psychology
  • Economics

Keywords

  • latent semantic analysis
  • information flow
  • volatility
  • GARCH

Status

Published