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The Scientific World Journal
Volume 2014, Article ID 124523, 9 pages
Research Article

Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

1School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, China
2Shanghai Financial Information Technology Key Research Laboratory, 777 Guoding Road, Shanghai 200433, China
3School of Management, Fudan University, 220 Handan Road, Shanghai 200433, China

Received 30 August 2013; Accepted 10 March 2014; Published 23 March 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Yonghui Dai et al. 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. A. Lendasse, E. de Bodt, V. Wertz, and M. Verleysen, “Non-linear financial time series forecasting-application to the Bel 20 stock market index,” European Journal of Economic and Social Systems, vol. 14, no. 1, pp. 81–91, 2000. View at Google Scholar
  2. K. J. Lee, A. Y. Chi, S. Yoo, and J. J. Jin, “Forecasting Korean stock price index (kospi) using back propagation neural network model, Bayesian Chiao's model, and ASRIMA model,” Academy of Information and Management Sciences Journal, vol. 11, no. 2, pp. 53–62, 2008. View at Google Scholar
  3. Y. Fan and F. Gao, “A stock index forecasting model based on grey relation theory and GNNM (1, N),” in Proceedings of the International Conference in Electrics, Communication and Automatic Control Proceedings, pp. 1625–1633, 2011.
  4. C. W. Li and J. Zhang, “ANN-based mid-term stock forecasting,” Computer Engineering & Science, vol. 28, no. 5, pp. 115–117, 2006. View at Google Scholar
  5. M. Hanias, P. Curtis, and E. Thalassinos, “Time series prediction with neural networks for the athens stock exchange indicator,” European Research Studies Journal, vol. 15, no. 2, pp. 23–32, 2012. View at Google Scholar
  6. W. Liu and B. Morley, “Volatility forecasting in the hang seng index using the GARCH approach,” Asia-Pacific Financial Markets, vol. 16, no. 1, pp. 51–63, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. P. Srinivasan, “Modeling and forecasting the stock market volatility of S&P 500 index using GARCH models,” The IUP Journal of Behavioral Finance, vol. 8, no. 1, pp. 51–69, 2011. View at Google Scholar
  8. B. B. Nair, V. P. Mohandas, and N. R. Sakthivel, “A decision tree—rough set hybrid system for stock market trend prediction,” International Journal of Computer Applications, vol. 6, no. 9, pp. 1–6, 2010. View at Google Scholar
  9. J. Ying, L. Kuo, and G. S. Seow, “Forecasting stock prices using a hierarchical Bayesian approach,” Journal of Forecasting, vol. 24, no. 1, pp. 39–59, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. M. A. Kaboudan, “Genetic programming prediction of stock prices,” Computational Economics, vol. 16, no. 3, pp. 207–236, 2000. View at Google Scholar · View at Scopus
  11. H. Hwang and J. Oh, “Fuzzy models for predicting time series stock price index,” International Journal of Control, Automation and Systems, vol. 8, no. 3, pp. 702–706, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Alizadeh and N. Nomikos, “A markov regime switching approach for hedging stock indices,” Journal of Futures Markets, vol. 24, no. 7, pp. 649–674, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Li and X. Hui, “A new stock index fuzzy stochastic prediction model developed by introducing a Markov chain,” Journal of Harbin Engineering University, vol. 32, no. 8, pp. 1086–1090, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Gong and C. H. Ma, “A hidden Markov chain modeling of shanghai stock index,” Finance, vol. 2, pp. 45–49, 2012. View at Google Scholar
  15. M. J. Kim, I. Han, and K. C. Lee, “Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea stock price index,” Intelligent Systems in Accounting, Finance and Management, vol. 12, no. 1, pp. 43–60, 2004. View at Google Scholar
  16. P.-F. Pai and C.-S. Lin, “A hybrid ARIMA and support vector machines model in stock price forecasting,” Omega, vol. 33, no. 6, pp. 497–505, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. M. R. Hassan, B. Nath, and M. Kirley, “A fusion model of HMM, ANN and GA for stock market forecasting,” Expert Systems with Applications, vol. 33, no. 1, pp. 171–180, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Google Scholar · View at Scopus
  19. J. P. Yang, The Research of Improved BP Algorithm Based on Self-Adaptive Learning Rate, Tianjin University, Tianjin, China, 2008.
  20. G. L. Su and F. P. Deng, “On the improving back propagation algorithms of the neural networks based on MATLAB language: a review,” Bulletin of Science and Technology, vol. 19, no. 2, pp. 130–135, 2003. View at Google Scholar
  21. M. M. He, The Application on Some Economic Prediction With Markov Chain Model, Harbin Institute of Technology, Harbin, China, 2008.
  22. W. H. Dai, “The public cognitive mechanism of emotion on city emergency events and coping strategy,” Urban Management, no. 1, pp. 34–37, 2014. View at Google Scholar
  23. W. H. Dai, X. Q. Wan, and X. Y. Liu, “Emergency event: internet spread, psychological impacts and emergency management,” Journal of Computers, vol. 6, no. 8, pp. 1748–1755, 2011. View at Publisher · View at Google Scholar · View at Scopus