Application of Intervention Analysis on Stock Market Forecasting

  • Mahesh S. Khadka
  • K. M. George
  • N. Park
  • J. B. Kim
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


In today’s financial market, different financial events have direct impact on stock market values. Even a slight change in those events may result a huge difference in stock prices. So consideration of those effects is very important in forecasting stock values. Most of the researches as of now only consider about forecasting but not these effects. This paper studies the effects of some of those events in financial market forecasting. In this paper, we focused our study on the effects of financial events such as GDP, Consumer Sentiments and Jobless Claims on stock market forecasting and analyze them. These events are considered as intervention effects. The intervention effect is described in this study as temporary but immediate and abrupt. So we have tried to estimate not only the period of effect of these events but also use intervening values on forecasting. These forecasted values are then compared to forecasted values obtained from fusion model based on Concordance and Genetic Algorithm (GA). The concept is validated using financial time series data (S&P 500 Index and NASDAQ) as the sample data sets. We also have analyzed how often our forecasting values have the same movement as that of actual market values. The developed tool can be used not only for forecasting but also for in depth analysis of the stock market.


Root Mean Square Error Recurrent Neural Network Fusion Model Joint Estimation Time Series Prediction 
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.
    Chen, G., Abraham, B., Bennett, G.W.: Parametric and Non-Parametric Modelling of Time Series - An Empirical Study. Environ. Metrics 8, 63–74 (1997)zbMATHGoogle Scholar
  2. 2.
    Fan, J., Yao, Q.: Non-Linear Time Series. Springer, New York (2003)Google Scholar
  3. 3.
    Quek, C., Kumar, N., Pasquier, M.: Novel Recurrent Neural Network Based Prediction System for Trading. In: International Joint Conference on Neural Networks, pp. 2090–2097. IEEE (2006)Google Scholar
  4. 4.
    White, H.: Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns. In: Proceedings of the 2nd Annual IEEE Conference on Neural Networks, vol. II, pp. 451–458 (1988)Google Scholar
  5. 5.
    Giles, C.L., Lawrence, S., Tsoi, A.C.: Noisy Time Series Prediction using a Recurrent Neural Network and Grammatical Inference. Machine Learning 44, 161–183 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Zhang, J., Chung, S.H., Lo, W.: Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates. IEEE Transactions on Knowledge and Data Engineering 20, 956–964 (2008)CrossRefGoogle Scholar
  7. 7.
    Yu, T.K., Huarng, K.H.: A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems with Applications 34, 2945–2952 (2008)CrossRefGoogle Scholar
  8. 8.
    Germán, A., Vieu, P.: Nonparametric time series prediction: A semi-functional partial linear modeling. Journal of Multivariate Analysis 99, 834–857 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Choudhary, R., Garg, K.: A Hybrid Machine Learning System for Stock Market Forecasting. World Academy of Science, Engineering and Technology 39, 315–318 (2008)Google Scholar
  10. 10.
    Hassan, M.R., Kirley, M., Nath, B.: A fusion model of HMM, ANN and GA for Stock Market Forecasting. Expert Systems with Applications 33, 171–180 (2007)CrossRefGoogle Scholar
  11. 11.
    Lan, B.L., Tan, O.T.: Statistical Properties of Stock Market Indices of different Economies. Physica A: Statistical Mechanics and its Applications 375, 605–611 (2007)CrossRefGoogle Scholar
  12. 12.
    Yahoo Finance Website Historical Prices, (cited January 29, 2011)
  13. 13.
    Khadka, M.S., George, K.M., Park, N., Popp, B.: A New Approach for Time Series Forecasting Based on Genetic Algorithm. In: Proceedings of 23rd Annual CAINE Conference, Las Vegas, USA (2010)Google Scholar
  14. 14.
    Kendall, M.: A New Measure of Rank Correlation. Biometrika 30, 81–89 (1938)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Mahesh S. Khadka
    • 1
  • K. M. George
    • 1
  • N. Park
    • 1
  • J. B. Kim
    • 2
  1. 1.Computer Science DepartmentOklahoma State UniversityStillwaterUSA
  2. 2.Department of Economics and Legal Studies in BusinessOklahoma State UniversityStillwaterUSA

Personalised recommendations