Open Access
Issue
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 01024
Number of page(s) 6
Section Network Security System, Neural Network and Data Information
DOI https://doi.org/10.1051/matecconf/201823201024
Published online 19 November 2018
  1. E. L. De Faria, Marcelo P. Albuquerque, J. L. Gonzalez, J. T. P. Cavalcante, and Marcio P. Albuquerque. Predicting the brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Systems with Applications, 36(10): 12506-12509, 2015. [CrossRef] [Google Scholar]
  2. R W Rebello and Y V Reddy. Multivariate regression: A tool for forecasting stock prices. Iup Journal of Accounting Research Audit Practices, ix: 7-32, 2010. [Google Scholar]
  3. Yichen Dong, Siyi Li, and Xueqin Gong. Time series analysis: An application of arima model in stock price forecasting. In International Conference on Innovations in Economic Management and Social Science, 2017. [Google Scholar]
  4. Bhowmik Roni, Chao Wu, Roy Kumar Jewel, and Shouyang Wang. A study on the volatility of the bangladesh stock market based on garch type models. Journal of Systems Science Information, 5, 2017. [Google Scholar]
  5. H. U. Zhongyi, Yukun Bao, Chiong Raymond, and Tao Xiong. Profit guided or statistical error guided a study of stock index forecasting using support vector regression. Journal of Systems Science Complexity, 30(6): 1-18, 2017. [CrossRef] [Google Scholar]
  6. Xiaoxia Xie. Research on the explanation power of accounting information of annual report of listed companies of stock price based on econometric model concept of empirical accounting theory. In International Conference on Business Management and Electronic Information, pages 588-592, 2011. [Google Scholar]
  7. Kazuhiro Kohara, Yoshimi Fukuhara, and Yukihiro Nakamura. Selective presentation learning for neural network forecasting of stock markets. Neural Computing Applications, 4(3): 143-148, 2016. [CrossRef] [Google Scholar]
  8. Yu Fang, Kamaladdin Fataliyev, Lipo wang, Xiuju Fu, and Yaoli Wang. Improving the genetic-algorithm-optimized wavelet neural network for stock market prediction. In International Joint Conference on Neural Networks, pages 3038-3042, 2014. [Google Scholar]
  9. Xing Chen, Lifang Wu, and Fuming Wang. The research of stock prediction based on ga-bp neural network. Shanxi Electronic Technology, 2014. [Google Scholar]
  10. Kai Sheng Tai, Richard Socher, and Christopher D. Manning. Improved semantic representations from tree-structured long short-term memory networks. Computer Science, 5(1): 36, 2015. [Google Scholar]
  11. Hongmei Shao and Gaofeng Zheng. Convergence analysis of a back-propagation algorithms with adaptive momentum. Neurocomputing, 74(5): 749-752, 2011. [CrossRef] [Google Scholar]

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