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Predicting Stock Price Volatility by Analyzing Semantic Content in Media

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.

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File URL: http://www.lusem.lu.se/media/kwc/working-papers/kwc-wp-2013-16.pdf
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Paper provided by Knut Wicksell Centre for Financial Studies, Lund University in its series Knut Wicksell Working Paper Series with number 2013/16.

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Length: 43 pages
Date of creation: 13 Sep 2013
Date of revision:
Handle: RePEc:hhs:luwick:2013_016
Contact details of provider: Postal: Knut Wicksell Centre for Financial Studies, Lund University School of Economics and Management, P.O. Box 7080, S-220 07 Lund, Sweden
Phone: +46 46-222 32 61
Fax: +46 46-222 34 06
Web page: http://www.lusem.lu.se/kwc

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  1. Adrian R. Pagan & G. William Schwert, 1989. "Alternative Models For Conditional Stock Volatility," NBER Working Papers 2955, National Bureau of Economic Research, Inc.
  2. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
  3. Amos Tversky & Daniel Kahneman, 1979. "Prospect Theory: An Analysis of Decision under Risk," Levine's Working Paper Archive 7656, David K. Levine.
  4. Charlotte Strunk Hansen, 2001. "The relation between implied and realised volatility in the Danish option and equity markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 41(3), pages 197-228.
  5. West, K.D. & Cho, D., 1993. "The Predictive Ability of Several Models of Exchange Rate Volatility," Working papers 9317r, Wisconsin Madison - Social Systems.
  6. Gita Persand & Chris Brooks, 2003. "Volatility forecasting for risk management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 1-22.
  7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  8. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
  9. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  10. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
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