Mean-VaR Portfolio: An Empirical Analysis of Price Forecasting of the Shanghai and Shenzhen Stock Markets
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Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known tohave a direct influence on the stock markets globally. Given that the stock price data often contain both linearand non-linear patterns, no single model can be adequate in modelling and predicting time series data. Theautoregressive 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 seriesforecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. Asa result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing priceof the Shanghai composite index and Shenzhen component index.
Abstract
Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known tohave a direct influence on the stock markets globally. Given that the stock price data often contain both linearand non-linear patterns, no single model can be adequate in modelling and predicting time series data. Theautoregressive 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 seriesforecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. Asa result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing priceof the Shanghai composite index and Shenzhen component index.
참고문헌 (16)
- G. Xiao, "The Security of Password Computer and Communication Systems" (1993), Post & Telecom Press
- L. Cai, "Applied Cryptography" (2005), China Electric Power Press
- X. Hu, "Applied Cryptography" (2006), Pubushing House of Electronics Industry
- X. Wu Xiaowei, "Share price forecast model based on artificial neural network research", pp.2004
- Q. Sun, "The research on the making theory of stock price index", pp.2010
- G. Ma, "Trends, influence factors and seasonal analysis of gold", Times Finance, 2013(10), pp.207~224 (2013)
- X. Zhou, "Seasonal analysis of commodity futures", Futures World, 2008(14), pp.19~21 (2008)
- Y. Bai, "The research of several key problems of large oil group budget management of China", pp.2009
- N. Yang Nan, "The construction and empirical of agricultural prices seasonal index", China Price, 2007(2), pp.18~20 (2007)
- T. Xiong, "Positive analysis on ARIMA model for the CSI 300 index price", Journal of Baoji University of Arts and Sciences(Natural Science), 34(2), pp.22~25 (2014)
- M. Li, "The applications of ARIMA on stock price forecast and it's Fourier series correction", Journal of Yunnan Normal University, 31(5), pp.50~55 (2011)
- S. Ou Shide, "Statistical analysis on the rate of stock return and price forecast", pp.2006
- Y. Li, "Forecast of water resources demand in Ningxia based on BP neural network", Journal of Water Resources & Water Engineering, 25(6), pp.98~101 (2014)
- L. Wu, "The prediction of audience rating research based on BP networks", Journal of Communication University of China(Science and Technology), 18(3), pp.59~62 (2011)
- C. Cai Changjun, "Simulation realization of prediction model based on wavelet neural network", Fujian Computer, 2015(3), pp.14~15 (2015)
- Z. Chen, "The research of prediction model of short-term traffic flow based on BP neural networks", Computer Simulation, 25(6), pp.147~150 (2008)