After the 19th National Congress of the Communist Party of China, the government has successively introduced ‘Pilot Project for Leading Enterprises to Drive Industrial Development’ ‘Small and Medium-sized Enterprise Technology Innovation Fund Modern Agriculture Project’ and other agricultural support policies. Related agricultural products are listed on the market [1]. As a result, the company has also become the focus of attention. Therefore, studying the financial performance of agricultural-related listed companies helps public investors understand the development status of agricultural listed companies and can also provide a particular reference for the government to formulate relevant policies.
Some scholars analysed the company's financial performance that completed the share-trading reform, used the entropy method to explore its correlation and concluded that the correlation between financial performance and stock price is very weak. Some scholars have chosen multiple linear regression methods to discuss and analyse the impact of company performance and stock prices [2]. They concluded that the most influential is the profitability and development ability of listed companies in agricultural products processing. Some scholars have used panel data models to analyse the relationship between company stock prices and profit levels to prove a significant correlation between the two. The CCR model verifies the correlation between financial indexes and stocks and evaluates the stock selection in the portfolio based on the correlation. Some scholars use multiple regression analysis models to find that earnings per share (EPS) and return on net assets are essential factors that affect listed companies’ stock price fluctuations. Finally, they analysed whether the monetary policy issued by the country has a particular impact on the company's stock price. They obtained the conclusion that the impact of monetary policy on the stock price varies according to regional differences [3]. They used a variety of methods to examine the impact of financial performance on stock prices. They found that EPS, book value, dividend coverage, growth rates, and dividend yields positively correlate with stock prices. In contrast, dividends per share and price-earnings ratios are negatively correlated with stock prices.
There are relatively few studies on the impact of financial indicators on stock prices of agricultural listed companies. Our selection of financial indicators is not comprehensive enough, and the analysis method is mainly based on multiple statistical regression. Because of this, this article analyses from the four perspectives of profitability, growth ability, operating ability and solvency [4]. We select appropriate financial information indicators and apply the econometric panel data model to analyse the relevance of stock prices of listed agricultural companies in China to financial performance. We use the above methods to study the impact of the financial performance of Chinese agricultural listed companies on stock prices.
The data in this article comes from the Sina Finance Market Center. After excluding companies with insufficient financial data, we selected 8 financial indicators of 14 listed agricultural companies from 2013 to 2019 as sample data.
This article selects a total of 8 financial indicators from 4 aspects of profitability, growth ability, operating ability and solvency: EPS (
Profitability refers to the company's ability to make profits under normal operating conditions. In today's market, the competition between industries and products is becoming increasingly fierce, and the stock market is volatile [5]. Therefore, analysing the company's profitability is of great significance to the company's sustainable development and operation. In addition, this can provide a reference for stakeholders to make financial decisions and play a key role in predicting future cash flows. Among the various indicators for analysing profitability, EPS and paramount business profitability have been widely used as core indicators.
EPS (
Growth ability refers to the development trend of the company at this stage and in the future. Whether it can expand its scale and increase profits can reflect the company's development prospects. The company's growth capability analysis can judge the company's future cash flow changes in operating activities and better analyse the company's future financial fluctuations [6].
Primary business income growth rate (
Operational capacity refers to the company's ability to use its assets to obtain benefits. Functional ability plays a decisive role in the company's solvency and profitability, and it is the core content of financial analysis. Operating ability is mainly manifested in the turnover rate of various assets. Among various asset turnover rates, total asset turnover and current asset turnover are the leading indicators. As a result, they can better reflect the company's operational capabilities.
The turnover rate of total assets (
Solvency refers to the ability of the company to repay the debts it borrows from the outside world when it reaches the time of return. Is it possible to repay the due debts promptly [10]? The former is the primary indicator to measure short-term debt solvency, and the latter is the primary indicator to measure long-term debt solvency.
Quick ratio (
The panel data is an X×Y data matrix. What is recorded in the matrix is a particular data index of X objects at Y time nodes. In recent decades, a variety of statistical methods have emerged, including panel data analysis methods. Panel data is generally analysed by Eviews software, so this paper selects Eviews6.0 software for research and analysis. The models used in this article are as follows:
We must analyse whether there are unit roots in a sequence we choose. Once we find that there is a unit root, we call this series a non-stationary time series. Such a sequence will lead to spurious regression when performing regression analysis [11]. Therefore, to ensure the data's stability, we must first perform a unit root test on the selected sequence.
First, we use the obtained P-value to indicate whether the data contain unit roots. When the obtained P-value is <0.05, we think that the data does not have a unit root, proving that the data is stable. Otherwise, it is considered that the data has a unit root, and the data is not stable.
As can be seen from Table 1, the P-value obtained by LLC inspection of the stock prices of 14 agricultural listed companies and eight financial index data from 2013 to 2019 is 0.0045, and that of IPS inspection, Fisher-ADF inspection, and Fisher-PP inspection. The P values are all 0.0000, and the P values of the 4 test methods are all <0.05. The research results show that the data in this paper do not contain unit roots. Therefore, the data in this article are all stable, and we can conduct an empirical analysis on them.
Unit root test results of 14 listed agricultural companies’ stock prices and eight financial indicators from 2013 to 2019
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 |
According to the characteristics of this article, we select the variable intercept model as the evaluation method. The Hausman test method is usually used to select a specific model [12]. Assume no correlation between the individual influence and the independent variable in the random influence model. The first step is to do a regression analysis of the original data. Then, we selected and used the random influence variable intercept model on the model, and the estimated results are shown in Table 2.
Random influence variable intercept model estimation results
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 |
In the second step, Hausman's test method is used to determine whether the conclusion obtained by the random influence model analysis is appropriate. The results obtained by this method are shown in Table 3.
It can be seen from Table 3 that the model statistic is 17.111761, and the P-value is 0.0290 and <0.05. Therefore, a fixed-effect variable-intercept model should be established. The calculated results of the model are shown in Table 4.
Hausman test results
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 |
Fixed influence variable intercept model estimation results
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 |
From Table 4, the model equation can be obtained as:
The coefficient of determination R2 in the process of the fitness test is 0.669120. This result is still relatively ideal in the time series model. The reason may be that the company's finances are sometimes affected by some uncertain factors, causing the current stock price to fluctuate. However, from the perspective of the degree of fit, these fluctuations have little effect on the test index data, and they can be effectively analysed and studied.
The DW statistic is 0.945865, indicating that the residuals of the model follow a normal distribution. Thus, the data has a solid ability to explain the model.
Among the selected eight financial indicators, the P-value of EPS () is 0.0060, and the P-value of the profit margin of the leading business () is 0.0060. They have passed the significance test, and the regression coefficient is positive, showing a positive correlation with the stock price (
The P-value of the interest payment multiple (
In the remaining indicators, the net asset growth rate (
This article takes the financial data released by domestic agricultural listed companies in the 20 quarters from 2013 to 2019 as a sample, selects a panel data model and explores the impact of financial performance on stock prices from four aspects: profitability, growth ability, operating ability, and solvency. EPS are the most critical indicator of the profitability of agricultural listed companies, and investors are very concerned about EPS. Therefore, the quality of these three indicators will directly affect the level of profitability. However, only from a systematic perspective can we scientifically evaluate the factors affecting stock prices. Therefore, to prevent blind investment, investors should conduct a comprehensive inspection of the capabilities of agricultural listed companies in all aspects.