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, Kaif Ul Wara, Journal of Information Processing Systems
Vol. 15, No. 5, pp. 1201-1210, Oct. 2019
10.3745/JIPS.04.0135
Keywords: ARIMA Model, Neural Network, Non-linear Sequence, Stock Price
Fulltext:

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.


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Cite this article
[APA Style]
Ximei Liu, Zahid Latif, Daoqi Xiong, Sehrish Khan Saddozai, & 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), 1201-1210. DOI: 10.3745/JIPS.04.0135.

[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.