In order to study the changes of Shanghai Composite Stock Price Index (SCSPI) and predict the trend of stock market fluctuations, this paper constructed a time-series analysis．A non-stationary trend is found, and an ARIMA model is found to sufficiently model the data. A short trend of Shanghai composite stock price index is then predicted using the established model.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 4) |
| DOI | 10.11648/j.sjams.20150304.16 |
| Page(s) | 199-203 |
| 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. |
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Copyright © The Author(s), 2024. Published by Science Publishing Group |
The Shanghai Composite Stock Price Index (SCSPI), Prediction, ARIMA Model
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APA Style
Renhao Jin, Sha Wang, Fang Yan, Jie Zhu. (2015). The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index. Science Journal of Applied Mathematics and Statistics, 3(4), 199-203. https://doi.org/10.11648/j.sjams.20150304.16
ACS Style
Renhao Jin; Sha Wang; Fang Yan; Jie Zhu. The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index. Sci. J. Appl. Math. Stat. 2015, 3(4), 199-203. doi: 10.11648/j.sjams.20150304.16
AMA Style
Renhao Jin, Sha Wang, Fang Yan, Jie Zhu. The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index. Sci J Appl Math Stat. 2015;3(4):199-203. doi: 10.11648/j.sjams.20150304.16
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Download@article{10.11648/j.sjams.20150304.16,
author = {Renhao Jin and Sha Wang and Fang Yan and Jie Zhu},
title = {The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {3},
number = {4},
pages = {199-203},
doi = {10.11648/j.sjams.20150304.16},
url = {https://doi.org/10.11648/j.sjams.20150304.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20150304.16},
abstract = {In order to study the changes of Shanghai Composite Stock Price Index (SCSPI) and predict the trend of stock market fluctuations, this paper constructed a time-series analysis．A non-stationary trend is found, and an ARIMA model is found to sufficiently model the data. A short trend of Shanghai composite stock price index is then predicted using the established model.},
year = {2015}
}
TY - JOUR T1 - The Application of ARIMA Model in 2014 Shanghai Composite Stock Price Index AU - Renhao Jin AU - Sha Wang AU - Fang Yan AU - Jie Zhu Y1 - 2015/08/05 PY - 2015 N1 - https://doi.org/10.11648/j.sjams.20150304.16 DO - 10.11648/j.sjams.20150304.16 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 199 EP - 203 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20150304.16 AB - In order to study the changes of Shanghai Composite Stock Price Index (SCSPI) and predict the trend of stock market fluctuations, this paper constructed a time-series analysis．A non-stationary trend is found, and an ARIMA model is found to sufficiently model the data. A short trend of Shanghai composite stock price index is then predicted using the established model. VL - 3 IS - 4 ER -
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