Forecasting the U.S. Stock Market via Levenberg-Marquardt and Haken Artificial Neural Networks Using ICA&PCA Pre-processing Techniques

  • Sergey Golovachev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

Artificial neural networks (ANN) is an approach to solving different tasks. In this paper we forecast U.S. stock market movements using two types of artificial neural networks: a network based on the Levenberg-Marquardt learning mechanism and a synergetic network which was described by German scientist Herman Haken. The Levenberg-Marquardt ANN is widely used for forecasting financial markets, while the Haken ANN is mostly known for the tasks of image recognition. In this paper we apply the Haken ANN for the prediction of the stock market movements. Furthermore, we introduce a novation concerning pre-processing of the input data in order to enhance the predicting power of the abovementioned networks. For this purpose we use Independent Component Analysis (ICA) and Principal Component Analysis (PCA). We also suggest using ANNs to reveal the “mean reversion” phenomenon in the stock returns. The results of the forecasting are compared with the forecasts of the simple auto-regression model and market index dynamics.

Keywords

artificial neural network back-propagation independent component analysis principal component analysis forecast 

References

  1. 1.
    Back, A.D., Weigend, A.S.: A First Application of Independent Component Analysis to Extracting Structure from Stock Returns. International Journal of Neural Systems 8(5) (October 1997)Google Scholar
  2. 2.
    Bishop, C.M.: Neural Networks for Pattern Recognition, p. 483. Oxford University Press, Oxford (1995)Google Scholar
  3. 3.
    Bell, J.I., Sejnowsi, T.J.: An information-maximisation approach to blind separation and blind deconvolution. Neural Computation 7(6), 1004–1034 (1995)CrossRefGoogle Scholar
  4. 4.
    Górriz, J.M., Puntonet, C.G., Moisés Salmerón, E.W.: Lang Time Series Prediction using ICA Algorithms. In: IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Lviv, Ukraine, September 8-10 (2003)Google Scholar
  5. 5.
    Hyvärinen, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13(4-5), 411–430 (2000)CrossRefGoogle Scholar
  6. 6.
    Kröse, B., van der Smagt, P.: An Introduction To Neural Networks, 8th edn. (November 1996)Google Scholar
  7. 7.
    Lu, C.-J., Le, T.-S., Chiu, C.-C.: Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems 47, 115–125 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sergey Golovachev
    • 1
  1. 1.National Research University – Higher School of Economics, Department of World Economics and International AffairsMoscowRussia

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