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Mathematical Problems in Engineering
Volume 2018, Article ID 3967525, 14 pages
https://doi.org/10.1155/2018/3967525
Research Article

A Bimodel Algorithm with Data-Divider to Predict Stock Index

School of Computer Science & Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China

Correspondence should be addressed to Jinsong Hu; nc.ude.tucs@sjhsc

Received 12 August 2017; Accepted 30 January 2018; Published 5 March 2018

Academic Editor: Daniela Boso

Copyright © 2018 Zhaoyue Wang 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. B. Luo, Y. Chen, and W. Jiang, “Stock market forecasting algorithm based on improved neural network,” in Proceedings of the 8th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA '16), pp. 628–631, China, March 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Qiu, Y. Song, and F. Akagi, “Application of artificial neural network for the prediction of stock market returns: the case of the Japanese stock market,” Chaos, Solitons & Fractals, vol. 85, pp. 1–7, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  3. J. Wang and J. Wang, “Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks,” Neurocomputing, vol. 156, pp. 68–78, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Al-Hnaity and M. Abbod, “A novel hybrid ensemble model to predict FTSE100 index by combining neural network and EEMD,” in Proceedings of the European Control Conference (ECC '15), pp. 3021–3028, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Wang, Y. Zhang, H. Xiao, L. Kuang, and Y. Lai, “Enhancing Stock Price Prediction with a Hybrid Approach Based Extreme Learning Machine,” in Proceedings of the 15th IEEE International Conference on Data Mining Workshop (ICDMW '15), pp. 1568–1575, November 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Sun and Y. Gao, “An improved hybrid algorithm based on PSO and BP for stock price forecasting,” Open Cybernetics and Systemics Journal, vol. 9, no. 1, pp. 2565–2568, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. V. W. Chu, F. Chen, R. K. Wong, I. Ho, and J. Lee, “Enhancing portfolio return based on market-sentiment linked topics,” in Proceedings of the International Conference on Big Data and Smart Computing (BigComp '16), pp. 85–92, January 2016. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Shynkevich, T. M. McGinnity, S. Coleman, and A. Belatreche, “Stock price prediction based on stock-specific and sub-industry-specific news articles,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '15), July 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Wu and X. Diao, “Technical analysis of three stock oscillators testing MACD, RSI and KDJ rules in SH & SZ stock markets,” in Proceedings of the 4th International Conference on Computer Science and Network Technology (ICCSNT '15), pp. 320–323, December 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Li, J. Bo, N. Tao, and Y. Bo, “A BP Neural Network Predictor Model for Stock Price,” in in proceeding of the Intelligent Computing Methodologies - 10th International Conference (ICIC '14), pp. 362–368, August 2014.
  11. W. Ma, Y. Wang, and N. Dong, “Study on stock price prediction based on BP neural network,” in Proceedings of the 2010 IEEE International Conference on Emergency Management and Management Sciences (ICEMMS '10), pp. 57–60, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Liu and Y. Ren, “Bp neural network model for prediction of listing Corporation stock price of Qinghai province,” in Proceedings of the International Conference on Logistics, Informatics and Service Science (LISS '15), July 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. M.-T. Wu and Y. Yong, “The research on stock price forecast model based on data mining of BP neural networks,” in Proceedings of the 3rd IEEE International Conference on Intelligent System Design and Engineering Applications (ISDEA '13), pp. 1526–1529, January 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. Q. Mingyue, A study on prediction of stock market index and portfolio selection, Fukuoka Institute of Technology, 2014.
  15. L. Zhang, T. Liu, and J. Zhang, “Analysis of Momentum Factor in Neural Network Blind Equalization Algorithm,” in proceeding of the 2009 WRI International Conference on Communications and Mobile Computing (CMC '09), vol. 1, pp. 345–348, 2009.
  16. H. Kuang, J. Jin, and Y. Su, “Improving Crossover and Mutation for Adaptive Genetic Algorithm,” Computer Engineering and Applications, pp. 93–96, 2006. View at Google Scholar
  17. A. Brindle, Genetic algorithms for function optimization, Computer Science Dept., University Of Alberta, 1981.
  18. T. Yalcinoz and H. Altun, “Environmentally constrained economic dispatch via a genetic algorithm with arithmetic crossover,” IEEE AFRICON Conference, vol. 2, article no. 79, pp. 923–928, 2002. View at Publisher · View at Google Scholar · View at Scopus