Integrating Ensemble of Intelligent Systems for Modeling Stock Indices

  • Ajith Abraham
  • Andy AuYeung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2687)


The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well-represented using ensemble of intelligent paradigms. To demonstrate the proposed technique, we considered Nasdaq-100 index of Nasdaq Stock MarketSM and the Samp;P CNX NIFTY stock index. The intelligent paradigms considered were an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Ta- kagi-Sugeno neuro-fuzzy model and a difference boosting neural network. The different paradigms were combined using two different ensemble approaches so as to optimize the performance by reducing the different error measures. The first approach is based on a direct error measure and the second method is based on an evolutionary algorithm to search the optimal linear combination of the different intelligent paradigms. Experimental results reveal that the ensemble techniques performed better than the individual methods and the direct ensemble approach seems to work well for the problem considered.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [I]
    Abraham A., Nath B. and Mahanti P.K., Hybrid Intelligent Systems for Stock Market Analysis, Computational Science, Springer-Verlag Germany, Vassil N Alexandrov et al (Editors), USA, pp. 337–345, May 2001.Google Scholar
  2. [2]
    Abraham A., Neuro-Fuzzy Systems: State-of-the-Art Modeling Techniques, Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Springer-Verlag Germany, Jose Mira and Alberto Prieto (Eds.), Granada, Spain, pp. 269–276, 2001.Google Scholar
  3. [3]
    Abraham A., Philip N.S., Nath B. and Saratchandran P., Performance Analysis of Con-nectionist Paradigms for Modeling Chaotic Behavior of Stock Indices, Computational Intelligence and Applications, Dynamic Publishers Inc., USA, pp. 181–186, 2002.Google Scholar
  4. [4]
    Abraham A., Philip N.S. and Saratchandran P., Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms, International Journal of Neural, Parallel & Scientific Computations, USA, Volume 11, Issue (1&2), 2003.Google Scholar
  5. [5]
    Francis E.H. Tay and L.J. Cao, Modified Support Vector Machines in Financial Time Series Forecasting, Neurocomputing 48(1–4): pp. 847–861, 2002.CrossRefzbMATHGoogle Scholar
  6. [6]
    Hashem, S., Optimal Linear Combination of Neural Networks, Neural Network, Volume 10, No. 3. pp. 792–994, 1995.CrossRefGoogle Scholar
  7. [7]
    Jang J. S. R., Sun C. T. and Mizutani E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall Inc, USA, 1997.Google Scholar
  8. [8]
    Joachims T., Making large-Scale SVM Learning Practical. Advances in Kernel Methods— Support Vector Learning, B. Schölkopf and C. Burges and A. Smola (Eds.), MIT-Press, 1999.Google Scholar
  9. [11]
    Philip N.S. and Joseph K.B., Boosting the Differences: A Fast Bayesian classifier neural network, Intelligent Data Analysis, Vol. 4, pp. 463–473, IOS Press, 2000.zbMATHGoogle Scholar
  10. [12]
    Vapnik V. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ajith Abraham
    • 1
  • Andy AuYeung
    • 1
  1. 1.Department of Computer ScienceOklahoma State UniversityUSA

Personalised recommendations