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Mathematical Problems in Engineering
Volume 2011, Article ID 382659, 15 pages
http://dx.doi.org/10.1155/2011/382659
Research Article

Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market

Institute of Financial Mathematics and Financial Engineering, College of Science, Beijing Jiaotong University, Beijing 100044, China

Received 24 March 2011; Revised 16 May 2011; Accepted 17 May 2011

Academic Editor: Kuppalapalle Vajravelu

Copyright © 2011 Haifan Liu and Jun Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Gamberini, F. Lolli, B. Rimini, and F. Sgarbossa, “Forecasting of sporadic demand patters with seasonality and trend components: a empirical comparison between Holt-Winters and (S) ARIMA methods,” Mathematical Problems in Engineering, vol. 2010, Article ID 579010, 14 pages, 2010. View at Publisher · View at Google Scholar
  2. R. Gaylord and P. Wellin, Computer Simulations with Mathematica: Explorations in the Physical, Biological and Social Science, Springer, New York, NY, USA, 1995.
  3. K. Ilinski, Physics of Finance: Gauge Modeling in Non-equilibrium Pricing, John Wiley, New York, NY, USA, 2001.
  4. M. F. Ji and J. Wang, “Data analysis and statistical properties of Shenzhen and Shanghai land indices,” WSEAS Transactions on Business and Economics, vol. 4, pp. 29–33, 2007. View at Google Scholar
  5. Q. D. Li and J. Wang, “Statistical properties of waiting times and returns in Chinese stock markets,” WSEAS Transactions on Business and Economics, vol. 3, pp. 758–765, 2006. View at Google Scholar
  6. T. C. Mills, The Econometric Modelling of Financial Time Series, Cambridge University Press, Cambridge, UK, 2nd edition, 1999. View at Publisher · View at Google Scholar
  7. D. Enke and S. Thawornwong, “The use of data mining and neural networks for forecasting stock market returns,” Expert Systems with Applications, vol. 29, no. 4, pp. 927–940, 2005. View at Publisher · View at Google Scholar
  8. A. Kanas and A. Yannopoulos, “Comparing linear and nonlinear forecasts for stock returns,” International Review of Economics and Finance, vol. 10, no. 4, pp. 383–398, 2001. View at Publisher · View at Google Scholar
  9. H. F. Zou, G. P. Xia, F. T. Yang, and H. Y. Wang, “An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting,” Neurocomputing, vol. 70, no. 16–18, pp. 2913–2923, 2007. View at Publisher · View at Google Scholar
  10. T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, 1986. View at Publisher · View at Google Scholar
  11. E. D. McKenzie, “General exponential smoothing and the equivalent ARMA process,” Journal of Forecasting, vol. 3, pp. 333–344, 1984. View at Google Scholar
  12. F. Leon and M. H. Zaharia, “Stacked heterogeneous neural networks for time series forecasting,” Mathematical Problems in Engineering, vol. 2010, Article ID 373648, 20 pages, 2010. View at Publisher · View at Google Scholar
  13. Z. Liao and J. Wang, “Forecasting model of global stock index by stochastic time effective neural network,” Expert Systems with Applications, vol. 37, no. 1, pp. 834–841, 2010. View at Publisher · View at Google Scholar
  14. B. Y. Lu, Y. L. Chen, and Y. Y. Li, “The forecast of the pre-processing data with BP neural network and principal component analysis,” Science & Technology Information, vol. 17, pp. 29–30, 2009. View at Google Scholar
  15. D. Olson and C. Mossman, “Neural network forecasts of Canadian stock returns using accounting ratios,” International Journal of Forecasting, vol. 19, no. 3, pp. 453–465, 2003. View at Publisher · View at Google Scholar
  16. V. D. A. Sánchez, “Frontiers of research in BSS/ICA,” Neurocomputing, vol. 49, pp. 7–23, 2002. View at Publisher · View at Google Scholar
  17. A. Back and A. Weigend, “Discovering structure in finance using independent component analysis,” in Proceedings of 5th International Conference on Neural Networks in Capital Market, pp. 15–17, Kluwer Academic Publishers, 1997.
  18. A. Hyvarinen, Independent Component Analysis, Mechanic Industry Press, 2007.
  19. Z. Q. Yang, Y. Li, and D. W. Hu, “Independent component analysis: a survey,” Acta Automatica Sinica, vol. 28, no. 5, pp. 762–772, 2002. View at Google Scholar
  20. C. F. Beckmann and S. M. Smith, “Probabilistic independent component analysis for functional magnetic resonance imaging,” IEEE Transactions on Medical Imaging, vol. 23, no. 2, pp. 137–152, 2004. View at Publisher · View at Google Scholar
  21. C. J. James and O. J. Gibson, “Temporally constrained ICA: An application to artifact rejection in electromagnetic brain signal analysis,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 9, pp. 1108–1116, 2003. View at Publisher · View at Google Scholar
  22. K. Kiviluoto and E. Oja, “Independent component analysis for parallel financial time series,” in Proceeding of the 5th International Conference on Neural Information, pp. 895–898, 1998.
  23. L. Q. Han, Theory, Design and Application of Artificial Neural Network, Chemical Industry Press, 2002.
  24. E. M. Azoff, Neural Network Time Series Forecasting of Financial Market, Wiley, New York, NY, USA, 1994.
  25. Y. Ouyang, “Evaluation of river water quality monitoring stations by principal component analysis,” Water Research, vol. 39, pp. 2621–2635, 2005. View at Google Scholar
  26. L.-N. Yang, L. Peng, L.-M. Zhang, L.-I. Zhang, and S.-S. Yang, “A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on Back Propagation Artificial Neural Network and Principal Components Analysis,” Computers and Electronics in Agriculture, vol. 68, no. 2, pp. 200–206, 2009. View at Publisher · View at Google Scholar
  27. Y.-M. Cheung and L. Xu, “Independent component ordering in ICA time series analysis,” Neurocomputing, vol. 41, pp. 145–152, 2001. View at Publisher · View at Google Scholar