Stock Return Analysis Based on ARMA (2,2) Model

doi:10.1007/978-3-030-99616-1_28

	Stock Return Analysis Based on ARMA (2,2) Model
	Yan, Haorui
	2022
发表期刊	Lecture Notes on Data Engineering and Communications Technologies
ISSN	2367-4512
卷号	129 页码:213-219
摘要	With the rapid development of China’s economy, securities, stocks and other financial markets show a thriving trend. In the process of securities and stock investment, investors are more concerned about the investment return of stocks or securities. This paper selects the closing price of CSI 300 from January 5, 2015 to April 2, 2021 as the research object. Through the ARMA (2,2) model, the logarithmic rate of return of CSI 300 in this time period is fitted, and the rate of return series is predicted from two different angles outside the sample and inside the sample. The results show that there is a large difference between the predicted results and the actual results by using ARMA model alone, and the model needs to be optimized from other aspects to achieve the purpose of accurate prediction. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
关键词	Investments ARMA (2,2) Economy security Investment returns Logarithmic rates Rate of return Research object Stock returns Stocks yields Time-periods "hushen 300"
DOI	10.1007/978-3-030-99616-1_28
收录类别	EI
语种	英语
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20221511957327
EI主题词	Earnings
EI分类号	911.2 Industrial Economics
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/158435
专题	兰州理工大学
作者单位	Lanzhou University of Technology, Gansu Province, Lanzhou; 730000, China
第一作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Yan, Haorui. Stock Return Analysis Based on ARMA (2,2) Model[J]. Lecture Notes on Data Engineering and Communications Technologies,2022,129:213-219.
APA	Yan, Haorui.(2022).Stock Return Analysis Based on ARMA (2,2) Model.Lecture Notes on Data Engineering and Communications Technologies,129,213-219.
MLA	Yan, Haorui."Stock Return Analysis Based on ARMA (2,2) Model".Lecture Notes on Data Engineering and Communications Technologies 129(2022):213-219.