<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Research Center for International Trade and Economics

Department of Information System, Faculty of Computing and Information Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The article uses SPSS statistical analysis software to establish a multiple linear regression model of short-term stock price changes of domestic agricultural...

Test items	Chi-squared statistics	Chi-squared distribution	P value

Random section	17.111761	8	0.029000
Coefficient of determination R²	0.669120	Mean of the dependent variable	13.523210
Modified coefficient of determination R²	0.642187	The standard deviation of the dependent variable	7.541425
Regression standard error	4.511087	AIC guidelines	5.926266
Residual sum of squares	5250.276	Schwartz Guidelines	6.211857
Log-likelihood estimate	−807.6773	Hannan-Queen Criterion	6.040817
F statistics	24.844660	DW statistics	0.945865

Variable	Regression coefficient value	Standard error	t statistic	P value

C	8.561713	1.408724	6.077637	0.0000
X1	5.595259	1.723203	3.247011	0.0013
X2	0.141354	0.029785	4.745799	0.0000
X3	−0.005861	0.005546	−1.056962	0.2915
X4	0.011152	0.005377	2.074074	0.0390
X5	−0.419311	3.996603	−0.104917	0.9165
X6	0.792386	1.721791	0.460210	0.6457
X7	0.124284	0.126187	0.984921	0.3255
X8	0.000119	2.84E−05	4.172194	0.0000

Variable	Regression coefficient value	Standard error	t statistic	P value

C	8.871968	1.132411	7.834585	0.0000
X1	4.867471	1.757197	2.770021	0.0060
X2	0.140536	0.033170	4.236831	0.0000
X3	−0.007156	0.005579	−1.282692	0.2008
X4	0.009793	0.005454	1.795464	0.0738
X5	2.303726	4.240355	0.543286	0.5874
X6	−0.333951	1.840312	−0.181464	0.8561
X7	0.013605	0.136984	0.099314	0.9210
X8	0.000127	2.86E−05	4.426097	0.0000

Method	Statistics	P-value	Section	Number of samples

LLC inspection	−1.60228	0.0045	117	2039
IPS inspection	−6.18041	0.0000	117	2039
Fisher ADF test	541.92500	0.0000	117	2039
Fisher PP inspection	974.79500	0.0000	117	2223

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

Published Online: Nov 22, 2021

Page range: 267 - 274

Received: Jun 17, 2021

Accepted: Sep 24, 2021

DOI: https://doi.org/10.2478/amns.2021.2.00097

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

© 2021 Fei Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Hausman test results

Random influence variable intercept model estimation results

Fixed influence variable intercept model estimation results

Unit root test results of 14 listed agricultural companies’ stock prices and eight financial indicators from 2013 to 2019

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

Published Online: Nov 22, 2021

Page range: 267 - 274

Received: Jun 17, 2021

Accepted: Sep 24, 2021

DOI: https://doi.org/10.2478/amns.2021.2.00097

Keywordsmultiple linear regression, macroeconomic variables, listed companies, financial performance, stock prices

© 2021 Fei Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Hausman test results

Random influence variable intercept model estimation results

Fixed influence variable intercept model estimation results

Unit root test results of 14 listed agricultural companies’ stock prices and eight financial indicators from 2013 to 2019

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