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.
| Published in | Journal of World Economic Research (Volume 6, Issue 6) |
| DOI | 10.11648/j.jwer.20170606.11 |
| Page(s) | 71-74 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2018. Published by Science Publishing Group |
Shanghai Composite Index, ARIMA Model, Forecast, Time Series Analysis
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APA Style
Wu Haijian, Li Qianqian. (2018). Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model. Journal of World Economic Research, 6(6), 71-74. https://doi.org/10.11648/j.jwer.20170606.11
ACS Style
Wu Haijian; Li Qianqian. Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model. J. World Econ. Res. 2018, 6(6), 71-74. doi: 10.11648/j.jwer.20170606.11
AMA Style
Wu Haijian, Li Qianqian. Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model. J World Econ Res. 2018;6(6):71-74. doi: 10.11648/j.jwer.20170606.11
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Download@article{10.11648/j.jwer.20170606.11,
author = {Wu Haijian and Li Qianqian},
title = {Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model},
journal = {Journal of World Economic Research},
volume = {6},
number = {6},
pages = {71-74},
doi = {10.11648/j.jwer.20170606.11},
url = {https://doi.org/10.11648/j.jwer.20170606.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jwer.20170606.11},
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.},
year = {2018}
}
TY - JOUR T1 - Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model AU - Wu Haijian AU - Li Qianqian Y1 - 2018/01/02 PY - 2018 N1 - https://doi.org/10.11648/j.jwer.20170606.11 DO - 10.11648/j.jwer.20170606.11 T2 - Journal of World Economic Research JF - Journal of World Economic Research JO - Journal of World Economic Research SP - 71 EP - 74 PB - Science Publishing Group SN - 2328-7748 UR - https://doi.org/10.11648/j.jwer.20170606.11 AB - 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. VL - 6 IS - 6 ER -
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