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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Wu Haijian, Department of Statistics, Beijing Wuzi University, Beijing, China
Li Qianqian, Department of Statistics, Beijing Wuzi University, Beijing, China
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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.
Shanghai Composite Index, ARIMA Model, Forecast, Time Series Analysis
To cite this article
Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model, Journal of World Economic Research.
Vol. 6, No. 6,
2017, pp. 71-74.
Copyright © 2017 Authors retain the copyright of this article.
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Yao Ting. Analysis and Prediction of the Macro-factors of Stock Prices [D]. Bengbu: Anhui University of Finance and Economics, 2014 (05).
You Zuojun. Research and Application of Time Series Analysis in Stock [D]. Shenyang: Shenyang University of Technology, 2014 (02).
Li Yujing, Cheng Zongmao. The Application of Time Series Model on Stock Price Forecast [J]. Market Modernization, 2011 (11).
Zhang Chao. Stock Price Forecast Based on ARMA-GARCH Model [J]. Journal of Nanjing University of Aeronautics and Astronautics, 2014 (09).
Dong Bolun, Xu Dongyu. Prediction and Analysis of Stock Price of Agricultural Products Based on ARIMA Model [J]. Modern Business, 2015 (03).
Gao Yuan. Empirical Study on LETV Stock Price Forecast Based on ARMA Model [J]. Modern Economic Information, 2015 (07).
Sun Xianqiang. The Application of Time Series Model in Stock Price Forecasting [D]. Kunming: Yunnan University, 2016 (05).
Zhang Nan. Research on Stock Trend Forecasting Based on Time Series and R Language Application [J]. Modern Business, 2016 (08).
Wu Yuxia, Wen Xin. Short-term Stock Price Forecasting Based on ARIMA Model [J]. Statistics & Decision, 2016 (12).
Ma Yanna, Zeng Jiying. Prediction and Analysis of the Shanghai Composite Index Based on the ARIMA Model [J]. Economic and Trade Practice, 2017 (02). Journal of Hunan University of Arts and Science (Natural Science Edition), 2017 (09).
Zhang Jie. Analysis of Volatility of Stock Price Based on Time Series Model [J]. Journal of Hunan University of Arts and Science, 2017 (09).