Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model
Journal of World Economic Research
Volume 6, Issue 6, December 2017, Pages: 71-74
Received: Nov. 7, 2017; Accepted: Nov. 29, 2017; Published: Jan. 2, 2018
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Authors
Wu Haijian, Department of Statistics, Beijing Wuzi University, Beijing, China
Li Qianqian, Department of Statistics, Beijing Wuzi University, Beijing, China
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Abstract
Time series analysis is an important research tool in the field of stock price prediction. It analyzes the historical data to find out its development rules and guide people's future decision-making. This paper selects the monthly average closing price of the Shanghai Composite Index from January 1991 to September 2017 as the research object. By using EViews 7.2 software, the stationary non-white noise sequence is obtained after the first-order difference of the non-stationary raw data, and then establishing the autoregressive integrated moving average (ARIMA) model to forecast the future trend of Shanghai Stock Index.
Keywords
Shanghai Composite Index, ARIMA Model, Forecast, Time Series Analysis
To cite this article
Wu Haijian, Li Qianqian, Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model, Journal of World Economic Research. Vol. 6, No. 6, 2017, pp. 71-74. doi: 10.11648/j.jwer.20170606.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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