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), 2024. Published by Science Publishing Group |
Shanghai Composite Index, ARIMA Model, Forecast, Time Series Analysis
| [1] | Yao Ting. Analysis and Prediction of the Macro-factors of Stock Prices [D]. Bengbu: Anhui University of Finance and Economics, 2014 (05). |
| [2] | You Zuojun. Research and Application of Time Series Analysis in Stock [D]. Shenyang: Shenyang University of Technology, 2014 (02). |
| [3] | Li Yujing, Cheng Zongmao. The Application of Time Series Model on Stock Price Forecast [J]. Market Modernization, 2011 (11). |
| [4] | Zhang Chao. Stock Price Forecast Based on ARMA-GARCH Model [J]. Journal of Nanjing University of Aeronautics and Astronautics, 2014 (09). |
| [5] | Dong Bolun, Xu Dongyu. Prediction and Analysis of Stock Price of Agricultural Products Based on ARIMA Model [J]. Modern Business, 2015 (03). |
| [6] | Gao Yuan. Empirical Study on LETV Stock Price Forecast Based on ARMA Model [J]. Modern Economic Information, 2015 (07). |
| [7] | Sun Xianqiang. The Application of Time Series Model in Stock Price Forecasting [D]. Kunming: Yunnan University, 2016 (05). |
| [8] | Zhang Nan. Research on Stock Trend Forecasting Based on Time Series and R Language Application [J]. Modern Business, 2016 (08). |
| [9] | Wu Yuxia, Wen Xin. Short-term Stock Price Forecasting Based on ARIMA Model [J]. Statistics & Decision, 2016 (12). |
| [10] | 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). |
| [11] | Zhang Jie. Analysis of Volatility of Stock Price Based on Time Series Model [J]. Journal of Hunan University of Arts and Science, 2017 (09). |
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
Share This Article
Twitter
Linked In
Facebook
Copy
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 -
Information
Email: