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


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.


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


Supported by : Zhejiang Sci-Tech University


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