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

Ximei Liu, Zahid Latif, Daoqi Xiong, Sehrish Khan Saddozai and Kaif Ul Wara
Volume: 15, No: 5, Page: 1201 ~ 1210, Year: 2019
10.3745/JIPS.04.0135
Keywords: ARIMA Model, Neural Network, Non-linear Sequence, Stock Price
Full Text:

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.

Article Statistics
Multiple requests among the same broswer session are counted as one view (or download).
If you mouse over a chart, a box will show the data point's value.


Cite this article
IEEE Style
X. Liu, Z. Latif, D. Xiong, S. K. Saddozai and K. U. Wara, "Mean-VaR Portfolio: An Empirical Analysis of Price Forecasting of the Shanghai and Shenzhen Stock Markets," Journal of Information Processing Systems, vol. 15, no. 5, pp. 1201~1210, 2019. DOI: 10.3745/JIPS.04.0135.

ACM Style
Ximei Liu, Zahid Latif, Daoqi Xiong, Sehrish Khan Saddozai, and Kaif Ul Wara. 2019. Mean-VaR Portfolio: An Empirical Analysis of Price Forecasting of the Shanghai and Shenzhen Stock Markets, Journal of Information Processing Systems, 15, 5, (2019), 1201~1210. DOI: 10.3745/JIPS.04.0135.