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Mean-VaR Portfolio: An Empirical Analysis of Price Forecasting of the Shanghai and Shenzhen Stock Markets

  • Liu, Ximei (School of Economics and Management, Zhejiang Sci-Tech University) ;
  • Latif, Zahid (School of Economics and Management, Beijing University of Posts and Telecommunications) ;
  • Xiong, Daoqi (Zhejiang Huayun Information Technology Co. Ltd.) ;
  • Saddozai, Sehrish Khan (School of Economics and Management, Beijing University of Posts and Telecommunications) ;
  • Wara, Kaif Ul (Islamia University, Peshawar Campus)
  • Received : 2017.11.15
  • Accepted : 2018.09.02
  • Published : 2019.10.31

Abstract

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

Keywords

ARIMA Model;Neural Network;Non-linear Sequence;Stock Price

Acknowledgement

Supported by : Zhejiang Sci-Tech University

References

  1. G. Xiao, The Security of Password Computer and Communication Systems. Beijing: Post & Telecom Press, 1993.
  2. L. Cai, Applied Cryptography. Beijing: China Electric Power Press, 2005.
  3. X. Hu and Q. Wei, Applied Cryptography. Beijing: Pubushing House of Electronics Industry, 2006.
  4. X. Wu Xiaowei, "Share price forecast model based on artificial neural network research," University of Technology of Dalian, China, 2004.
  5. Q. Sun, "The research on the making theory of stock price index," Dongbei University of Finance and Economics, China, 2010.
  6. G. Ma and Y. Yang, "Trends, influence factors and seasonal analysis of gold," Times Finance, vol. 2013, no. 10, pp. 207-224, 2013.
  7. X. Zhou, "Seasonal analysis of commodity futures," Futures World, vol. 2008, no. 14, pp. 19-21, 2008.
  8. Y. Bai, "The research of several key problems of large oil group budget management of China," Tianjin University, China, 2009.
  9. N. Yang Nan, "The construction and empirical of agricultural prices seasonal index," China Price, vol. 2007, no. 2, pp. 18-20, 2007.
  10. T. Xiong and S. Shen, "Positive analysis on ARIMA model for the CSI 300 index price," Journal of Baoji University of Arts and Sciences (Natural Science), vol. 34, no. 2, pp. 22-25, 2014.
  11. M. Li, X. Gan, and X. Liu, "The applications of ARIMA on stock price forecast and it's Fourier series correction," Journal of Yunnan Normal University, vol. 31, no. 5, pp. 50-55, 2011.
  12. S. Ou Shide, "Statistical analysis on the rate of stock return and price forecast," Guangxi Normal University, China, 2006.
  13. Y. Li, X. Zhang, and L. Zhang, "Forecast of water resources demand in Ningxia based on BP neural network," Journal of Water Resources & Water Engineering, vol. 25, no. 6, pp. 98-101, 2014.
  14. L. Wu and Z. Qu, "The prediction of audience rating research based on BP networks," Journal of Communication University of China (Science and Technology), vol. 18, no. 3, pp. 59-62, 2011.
  15. C. Cai Changjun, "Simulation realization of prediction model based on wavelet neural network," Fujian Computer, vol. 2015, no. 3, pp. 14-15, 2015.
  16. Z. Chen and Z. Guo, "The research of prediction model of short-term traffic flow based on BP neural networks," Computer Simulation, vol. 25, no. 6, pp. 147-150, 2008.