A News-Based Approach for Computing Historical Value-at-Risk

  • Frederik Hogenboom
  • Michael de Winter
  • Flavius Frasincar
  • Alexander Hogenboom
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 171)


Within the field of finance, Value-at-Risk (VaR) is a widely adopted tool to assess portfolio risk. When calculating VaR based on historical stock return data, the data could be sensitive to outliers caused by seldom occurring news events in the sampled period. Using a data set of news events, of which the irregular events are identified using a Poisson distribution, we research whether the VaR accuracy can be improved by considering news events as additional input in the calculation. Our experiments show that when a rare event occurs, removing the event-generated noise from the stock prices for a small, optimized time window can improve VaR predictions.


Mean Square Error Stock Price Mean Absolute Percentage Error News Event Technical Indicator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andersen, T.G., Bollerslev, T., Diebold, F.X., Ebens, H.: The Distribution of Stock Return Volatility. Journal of Financial Economics 61(1), 43–76 (2001)CrossRefGoogle Scholar
  2. 2.
    Artzner, P., Delbaen, F., Eber, J.M., Heath, D.: Coherent Measures of Risk. Mathematical Finance 9(3), 203–228 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Borsje, J., Hogenboom, F., Frasincar, F.: Semi-Automatic Financial Events Discovery Based on Lexico-Semantic Patterns. International Journal of Web Engineering and Technology 6(2), 115–140 (2010)CrossRefGoogle Scholar
  4. 4.
    Boudoukh, J., Richardson, M., Whitelaw, R.F.: The Best of Both Worlds: A Hybrid Approach to Calculating Value at Risk. Risk 11(5), 64–67 (1998)Google Scholar
  5. 5.
    Byström, H.: News Aggregators, Volatility and the Stock Market. Economics Bulletin 29(4), 2673–2682 (2009)Google Scholar
  6. 6.
    Chan, W.S.: Stock Price Reaction to News and No-News: Drift and Reversal After Headlines. Journal of Financial Economics 70(2), 223–260 (2003)CrossRefGoogle Scholar
  7. 7.
    Engelberg, J.E., Parsons, C.A.: The Causal Impact of Media in Financial Markets. Journal of Finance 66(1), 67–97 (2009)Google Scholar
  8. 8.
    Fama, E.F.: The Behavior of Stock-Market Prices. Journal of Business 38(1), 34–105 (1965)CrossRefGoogle Scholar
  9. 9.
    Goonatilake, R., Herath, S.: The Volatility of the Stock Market and News. International Research Journal of Finance and Economics 3(11), 53–65 (2007)Google Scholar
  10. 10.
    Hogenboom, A., Hogenboom, F., Frasincar, F., Kaymak, U., van der Meer, O., Schouten, K.: Detecting Economic Events Using a Semantics-Based Pipeline. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part I. LNCS, vol. 6860, pp. 440–447. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Hull, J., White, A.: Incorporating Volatility Updating into the Historical Simulation Method for Value-at-Risk. Journal of Risk 1(1), 5–19 (1998)Google Scholar
  12. 12.
    Ikenberry, D.L., Ramnath, S.: Underreaction to Self-Selected News Events: The Case of Stock Splits. Review of Financial Studies 15(2), 489–526 (2002)CrossRefGoogle Scholar
  13. 13.
    Kalev, P.S., Liu, W.M., Pham, P.K., Jarnecic, E.: Public Information Arrival and Volatility of Intraday Stock Returns. Journal of Banking & Finance 28(6), 1441–1467 (2004)CrossRefGoogle Scholar
  14. 14.
    Kupiec, P.H.: Techniques for Verifying the Accuracy of Risk Measurement Models. Journal of Derivatives 3(2), 73–84 (1995)CrossRefGoogle Scholar
  15. 15.
    Mitchell, M.L., Mulherin, J.H.: The Impact of Public Information on the Stock Market. Journal of Finance 49(3), 923–950 (1994)CrossRefGoogle Scholar
  16. 16.
    Rockafellar, R.T., Uryasev, S.: Conditional Value-at-Risk for General Loss Distributions. Journal of Banking & Finance 26(7), 1443–1471 (2002)CrossRefGoogle Scholar
  17. 17.
    Rosenberg, B., Reid, K., Lanstein, R.: Persuasive Evidence of Market Inefficiency. Journal of Portfolio Management 11(3), 9–16 (1985)CrossRefGoogle Scholar
  18. 18.
    Semlab: ViewerPro (2011),
  19. 19.
    Zhai, Y.Z., Hsu, A., Halgamuge, S.K.: Combining News and Technical Indicators in Daily Stock Price Trends Prediction. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4493, pp. 1087–1096. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Frederik Hogenboom
    • 1
  • Michael de Winter
    • 1
  • Flavius Frasincar
    • 1
  • Alexander Hogenboom
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

Personalised recommendations