Analysis of Shanghai Composite Index Variation Based on Regression Analysis
Abstract
In this paper, through collecting data of Shanghai Composite Index since 2007, we analyze overall trend of the Shanghai stock market after the financial crisis, and carry on the forecast to the future trend in order to provide a meaningful guidance for people’s investment securities. Because the fitting results of simple regression is not good, we consider the long-term trend, seasonal fluctuations, cyclical fluctuations, irregular variables and other factors. We also add lagged variables and establish an ARIMA model through SPSS statistical analysis software. The fitting degree of model we built is good and the effect of prediction is significant improvement in the analysis.
Keywords
Full Text:
PDFReferences
Cryer, J. D., & Beasley, K. S. (2008). Time Series Analysis With Applications in R. Springer Texts in Statistics.
Dai, W. S., Shao,Y. J. E., & Lu, C. J. (2013). Incorporating feature selection method into support vector regression for stock index forecasting. Neural Computing & Applications, 23(6), 1551-1561.
Mezali, H., & Beasley, J. E. (2013). Quantile regression for index tracking and enhanced indexation. Journal of the Operational Research Society, 64(11), 1676-1692.
DOI: http://dx.doi.org/10.3968/4991
Refbacks
- There are currently no refbacks.
Copyright (c)
Please send your manuscripts to hess@cscanada.net,or hess@cscanada.org for consideration. We look forward to receiving your work.
Articles published in Higher Education of Social Science are licensed under Creative Commons Attribution 4.0 (CC-BY).
HIGHER EDUCATION OF SOCIAL SCIENCE Editorial Office
Address: 1055 Rue Lucien-L'Allier, Unit #772, Montreal, QC H3G 3C4, Canada.
Telephone: 1-514-558 6138
Website: Http://www.cscanada.net Http://www.cscanada.org
E-mail: caooc@hotmail.com; office@cscanada.net
Copyright © 2010 Canadian Research & Development Center of Sciences and Cultures