Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 6, Issue 2


Published
on

November 22, 2021


Pages

267-274


DOI

Article

Stock price analysis based on the research of multiple linear regression macroeconomic variables

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Authors

Fei Wang Affiliation:
School of Economics and Trade, Guangdong University of Foreign Studies, Guangzhou, Guangdong, 510006, China.
, Wanling Chen Affiliation:
Research Center for International Trade and Economics, Guangdong University of Foreign Studies, Guangzhou, 510006, China
, Bahjat Fakieh Affiliation:
Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
and Basel J.A Ali Affiliation:
Applied Science University, Al Eker, Kingdom of Bahrain


Abstract

The article uses SPSS statistical analysis software to establish a multiple linear regression model of short-term stock price changes of domestic agricultural listed companies. The article uses a stable time series based on the ARMA model for stable agricultural value-added, fiscal expenditure and market interest rates. The regression method is used to study its impact on the stock price index. Compared with the existing stock forecasting methods, this method has simple data collection and no specific requirements for data selection, and the prediction results have a high degree of fit. Therefore, this method is suitable for most stocks.


Keywords

multiple linear regression, macroeconomic variables, listed companies, financial performance, stock prices, 62J05


Citation

Wang, F., Chen, W., Fakieh, B., & Ali, B. J. (2021). Stock price analysis based on the research of multiple linear regression macroeconomic variables. Applied Mathematics and Nonlinear Sciences, 6(2), 267–274. https://doi.org/10.2478/amns.2021.2.00097

Published by: Engineering Journals

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