# Research on the Correlation Model and Algorithm between Intangible Assets and Enterprise Value of Sports Listed Enterprises Based on Deep Learning

Authors: Xiaoyan Dong; Ziqi Xu
Journal: Mobile Information Systems
 (2022)

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## Abstract

In order to explore the relationship between intangible assets and enterprise value of sports listed enterprises and build a multiple regression model, obtain the enterprise value of enterprise variables through parallel neural fuzzy network calculation, so as to verify the correlation between intangible value and enterprise value. With the development of economy, the intangibility of enterprises has been widely concerned. No matter what angle scholars study it from, our common concern is the research on the value of intangible assets and the definition of its existence. Taking sports listed companies as the research object, this study analyzes and discusses the correlation between intangible assets and enterprise value of sports listed companies by using the statistical analysis model algorithm based on deep learning and the method of constructing multiple regression model. The results show that intangible assets have a significant correlation with enterprise value, which is conducive for enterprises to promote the realization of enterprise value by improving intangible assets and provide favorable support for the sustainable development of the company.


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## Body

## 1. Introduction

Intangible assets of enterprises are the inevitable product of economic development to a certain stage. There is no standard definition for them, but they are defined in a wide range relative to tangible assets [1]. Sports listed companies rely on the concept of sports to circulate and trade in the secondary market. The main body of the transaction is all shareholders and institutions. The value trend of sports listed companies in the secondary market is an embodiment of the intangible assets of enterprises. The stock houses and institutions trade the shares of enterprises according to the annual reports and operating conditions of listed companies, which is helpful to promote the development of enterprises in the primary market.The value trend of listed enterprises in the secondary market is generally based on the operation status of the main body of the company, and the sharp decline of share price will cause panic among shareholders and sell a large number of shares, thus affecting the image and operation of the enterprise. On the contrary, the operating condition of the company will also be directly related to the stock price fluctuation of the enterprise in the secondary market [2]. The intangible assets of listed sports enterprises are not only measured by the stock price, but all things that can produce economic efficiency for listed enterprises can be called intangible assets, such as brand effect, intellectual property rights, patented technology, and trademark [3].Under the guidance of policies, the emerging sports industry has added color to national life and entertainment. Creating a model of health combined with leisure has not only promoted the revitalization of sports, but also promoted the development of national economy [4]. Based on the perspective of enterprise growth, the development of sports industry can improve the vitality and market competitiveness of enterprises, optimize the market environment, promote technological innovation, create a good environment for the growth of enterprises, and strive to help enterprises become bigger and stronger. At the same time, listed companies also need to understand the value status and influencing factors of enterprises themselves. This paper analyzes the influencing factors of the assets of listed companies in the sports industry, which helps investors to effectively judge and control the risks in the investment process. At the same time, the benign development of the secondary market can also promote the benign development of listed sports enterprises in the market. There are only dozens of sports industry companies listed in Shanghai stock market, mainly because the revenue capacity of the sports industry is relatively weak compared with other industries, and the sports industry has only developed in recent years. Its technology and operation mode basically rely on its own exploration and learn from western experience. In the process of development, the state gives policy dividend support, but the difficulties in reality still restrict the development of some enterprises. This paper discusses the profit ratio, industrial structure, operation efficiency, and debt repayment ability of the sports industry in Shanghai stock market. The results show that the industrial structure of these sports listed enterprises is relatively single, but due to different business models, the profitability varies greatly, and the repayment ability is also different [5].The intangible assets of listed enterprises are directly related to the value of the enterprise. Although the listed companies will have certain benefits under the influence of national policies and the market, the value related to the enterprise is the development status of the enterprise itself and the operation efficiency of the enterprise [6]. Due to the late start of China’s sports listed companies, their scale and investment in other aspects are relatively simple, which limits their operating income [7]. At the present stage, there are structural contradictions such as insufficient effective supply of sports capital market, system structure deviation and dislocation of risk appetite, and the development is facing functional constraints [8]. By constructing a model and analyzing variables, this study discusses the causal relationship between intangible assets and enterprise value of sports listed enterprises. At the same time, the machine learning algorithm is used for regression analysis, so as to obtain the impact of the intangible assets of sports listed enterprises on the value of enterprises, which is helpful for enterprises to optimize the industrial structure and improve the earning ability in the process of development.


## 2. Influencing Factors of Intangible Assets of Sports Listed Enterprises

### 2.1. Description of Enterprise Value Influencing Variables

The intangible assets of listed sports enterprises cover a wide range. Some scholars deliberately emphasize the existence form of intangible assets of sports listed enterprises; some scholars pay attention to the economic function of enterprise intangible assets; some scholars have discussed the relevant factors in the market [9]. All these views are to analyze the existing form and value of intangible assets of sports listed enterprises. The value evaluation of intangible assets of sports listed companies is also the direction of more attention in the research field of this industry.The main indicators to measure the value of listed enterprises include net asset profit rate, profit per share, total asset turnover rate, total asset growth rate, and net income growth rate [10]. Using the principal component analysis model, this paper reduces the dimension of each index of the enterprise value of sports listed companies, extracts the principal component, and obtains the enterprise value of listed companies through calculation. At the same time, the correlation between them is analyzed to determine whether there is a certain correlation between enterprise value and various variables [11]. Carry out multiple regression analysis on the relevant variables, introduce machine learning, use neural network algorithm to analyze the impact of variables on enterprise value, and explain the above selected variables, as shown in Table 1:Table 1
Description of enterprise value variables.
VariableSymbolic expressionExplainRevenue capacityEarnings per share (yuan)EPSAfter tax income and total sharesNet income per share (yuan)BPSShareholders’ equity and total outstanding sharesNet interest rate of assets (%)ROENet interest rate and net assetsSolvencyCash flow ratioCRCurrent assets, the need to repay debts, and the company’s sustainable resourcesAsset liability ratio (%)LEVTotal liabilities, total assets, and operating conditions of the companyEnterprise operation capabilityAccounts receivable periodARThe company’s sales volume, amount of receivables, and bad debtsInventory turnover cycleRSLCompany sales, inventory, and capital occupation cycleTotal asset turnoverTATThe company’s sales volume and total assets and the proportion of the flow cycle of sales volume to capitalDevelopment capacityGrowth rate of total assets (%)NRGrowth rate of current total liabilities compared with the same period last yearNet income growth rate (%)RGRGrowth rate of current net income compared with the same period last yearTurnover growth rate (%)GDLThe current growth rate of enterprise sales compared with the same period last year


### 2.2. Model Construction

Through principal component analysis, the dimension of the four indicators of the explained variables is reduced, and the total score is calculated to obtain the variable enterprise value (). Therefore, the constructed multiple regression model is as follows:(1)EV=α+βIIA+γ1BPS+γ2IFA+γ3AF+γ4PDV+γ5PB+γ6SIZE+ε,where α is a constant term, β is the regression coefficient before the explanatory variable, γi,i=1,2,⋯,6 is the regression coefficient before the control variable, and ε is the random term of error.At the same time, machine learning algorithms such as spatial convolution and fuzzy multicolumn convolution are introduced to push forward the timet axis for a certain period to form the variable prediction values of multiple intangible assets based on deep iterative regression from T1 to TN. The logic of the algorithm is shown in Figure 1:Figure 1
Logic diagram of data prediction algorithm.Algorithm of convolution function is shown in Figure2; the node function of FNN fuzzy neural network selects the sixth-order polynomial depth iterative regression basis function, as shown in formula (3); the node function of multicolumn fuzzy neural network selects the logarithmic depth iterative regression basis function, as shown in formula (4).
(2)y=∫−∞+∞gxqt−xdt,where gx: convoluted array function; qt−x: convolution kernel function; t: convolution pointer; x: convolution control variable; y: convolution results.
(3)y=∑i=1n∑j=05Ajxij,(4)y=∑i=1nA∙logexi+B,where xi: input value of the ith node of the previous neural network; y: output value of this node; n: the number of nodes of the previous neural network; A,B: the variable to be regressed refers to the variable to be regressed of the jth order polynomial.Figure 2
Statistical analysis of relevant variables of these sports listed enterprises.


## 2.1. Description of Enterprise Value Influencing Variables

The intangible assets of listed sports enterprises cover a wide range. Some scholars deliberately emphasize the existence form of intangible assets of sports listed enterprises; some scholars pay attention to the economic function of enterprise intangible assets; some scholars have discussed the relevant factors in the market [9]. All these views are to analyze the existing form and value of intangible assets of sports listed enterprises. The value evaluation of intangible assets of sports listed companies is also the direction of more attention in the research field of this industry.The main indicators to measure the value of listed enterprises include net asset profit rate, profit per share, total asset turnover rate, total asset growth rate, and net income growth rate [10]. Using the principal component analysis model, this paper reduces the dimension of each index of the enterprise value of sports listed companies, extracts the principal component, and obtains the enterprise value of listed companies through calculation. At the same time, the correlation between them is analyzed to determine whether there is a certain correlation between enterprise value and various variables [11]. Carry out multiple regression analysis on the relevant variables, introduce machine learning, use neural network algorithm to analyze the impact of variables on enterprise value, and explain the above selected variables, as shown in Table 1:Table 1
Description of enterprise value variables.
VariableSymbolic expressionExplainRevenue capacityEarnings per share (yuan)EPSAfter tax income and total sharesNet income per share (yuan)BPSShareholders’ equity and total outstanding sharesNet interest rate of assets (%)ROENet interest rate and net assetsSolvencyCash flow ratioCRCurrent assets, the need to repay debts, and the company’s sustainable resourcesAsset liability ratio (%)LEVTotal liabilities, total assets, and operating conditions of the companyEnterprise operation capabilityAccounts receivable periodARThe company’s sales volume, amount of receivables, and bad debtsInventory turnover cycleRSLCompany sales, inventory, and capital occupation cycleTotal asset turnoverTATThe company’s sales volume and total assets and the proportion of the flow cycle of sales volume to capitalDevelopment capacityGrowth rate of total assets (%)NRGrowth rate of current total liabilities compared with the same period last yearNet income growth rate (%)RGRGrowth rate of current net income compared with the same period last yearTurnover growth rate (%)GDLThe current growth rate of enterprise sales compared with the same period last year


## 2.2. Model Construction

Through principal component analysis, the dimension of the four indicators of the explained variables is reduced, and the total score is calculated to obtain the variable enterprise value (). Therefore, the constructed multiple regression model is as follows:(1)EV=α+βIIA+γ1BPS+γ2IFA+γ3AF+γ4PDV+γ5PB+γ6SIZE+ε,where α is a constant term, β is the regression coefficient before the explanatory variable, γi,i=1,2,⋯,6 is the regression coefficient before the control variable, and ε is the random term of error.At the same time, machine learning algorithms such as spatial convolution and fuzzy multicolumn convolution are introduced to push forward the timet axis for a certain period to form the variable prediction values of multiple intangible assets based on deep iterative regression from T1 to TN. The logic of the algorithm is shown in Figure 1:Figure 1
Logic diagram of data prediction algorithm.Algorithm of convolution function is shown in Figure2; the node function of FNN fuzzy neural network selects the sixth-order polynomial depth iterative regression basis function, as shown in formula (3); the node function of multicolumn fuzzy neural network selects the logarithmic depth iterative regression basis function, as shown in formula (4).
(2)y=∫−∞+∞gxqt−xdt,where gx: convoluted array function; qt−x: convolution kernel function; t: convolution pointer; x: convolution control variable; y: convolution results.
(3)y=∑i=1n∑j=05Ajxij,(4)y=∑i=1nA∙logexi+B,where xi: input value of the ith node of the previous neural network; y: output value of this node; n: the number of nodes of the previous neural network; A,B: the variable to be regressed refers to the variable to be regressed of the jth order polynomial.Figure 2
Statistical analysis of relevant variables of these sports listed enterprises.


## 3. Empirical Analysis on the Correlation between Intangible Assets and Enterprise Value of Sports Listed Enterprises

This paper collects the data of 27 listed sports enterprises such as shenseg, Rhine sports, and TEDA from 2010 to 2019. After excluding the data of st∗, gold certificate, and significant changes in the research range, a total of 157 effective data are collected. Descriptive statistical analysis and multiple regression analysis of these data were based on deep learning model algorithm.


### 3.1. Descriptive Statistical Analysis Results of the Correlation between Intangible

#### 3.1.1. Assets and Enterprise Value

Descriptive statistical analysis was carried out on the effective data of 157 listed sports enterprises after the above treatment, mainly including return on net assets (ROE), return on total assets (ROA), earnings per share (EPS), total asset turnover (TAT), quick ratio (AR), net asset growth rate (NAGR), net profit growth rate (NPGR), intangible asset growth rate (IIA), net asset per share (BPS), and fixed asset growth rate (IFA). The mean value and dispersion degree of these variables, such as amortization amount (AF), impairment provision (PDV), net asset ratio (PB), and asset size (SIZE), are statistically analyzed to obtain the data in Table2.Table 2
Descriptive analysis data of listed sports enterprises.
VariableMinimum valueMaximum valueAverage valueStandard deviationCoefficient of variationROE-73.042832.3847.006210.3271.474ROA-64.673825.88665.3517.80171.458EPS-4.20631.620.25520.50491.9784TAT0.01622.0010.54350.36920.6792AR0.139614.80472.11922.25311.0632NAGR-53.7632609.10423.627977.70433.2887NPGR-3152.1981922.45141.3873387.3294279.1942IIA-11.59390.07490.42755.7096BPS0.76619.68043.71662.04840.5511IFA-0.96611.98680.09740.41624.275AF2774137.72.723E+092324532035056269612.1752PDV-830050223.002E+091230500063857811883.1352PB0.718918.64.17182.87980.6903SIZE19.224427.76722.19911.61430.0727Table2 shows that ROE, ROA, EPS, TAT, AR, NAGR, and NPGR of these listed sports enterprises are the explained variables, and their average values are 7.0062, 5.3510, 0.2552, 0.5435, 2.1192, 23.6279, and 1.3873, respectively; IIA is the explanatory variable, and its mean value is 0.0749; BPS, IFA, AF, PDV, PB, and SIZE are control variables, and their mean values are 3.7166, 0.0974, 232453202.9459, 123050006.1349, 4.1718, and 22.1991. According to the data in the above table, draw Figure 2 below.In Figure2, the dimensional difference between different variables is large, so it is not suitable to use standard deviation to express the dispersion degree of variables. Therefore, the coefficient of variation is used to express the dispersion degree of variables. The larger the coefficient of variation, the higher the dispersion degree of variables. In this study, the coefficient of variation of NAGR is the largest, which shows that the dispersion degree of NAGR is high and the difference is the largest, while the dispersion degree of other variables is not much, which is relatively low.


### 3.2. Principal Component Analysis Results of Relevant Factors between Intangible Assets and Enterprise Value

#### 3.2.1. Principal Component Analysis of Original Variable Data

Before the multiple regression analysis of the relevant factors between intangible assets and enterprise value, the principal component analysis method should be used to reduce the dimension of the seven indicators measuring enterprise value. Firstly, we should extract the principal components of all variables to obtain the principal components with strong correlation, and get the data in Table3.Table 3
Extraction results of principal components.
VariableInitial eigenvalueExtract10.906ROA10.914EPS10.875TAT10.946AR10.726NAGR10.736NPGR10.569In Table3, when the initial eigenvalue of the principal component to be selected for extracting the principal component by the principal component analysis method is ≥1, it has the significance of extraction. The seven variables in the above table have the initial eigenvalue of 1, so they can be used as the extracted principal component. Among them, the extracted value is the common degree of all original variables. It can be seen from the table that the common degree of all variables is greater than 0.7, and only the common degree of three variables is close to 1. According to the data in the above table, Figure 3 can facilitate readers to compare the original data.Figure 3
Extraction results of original variables.In Figure3, it can be seen from the figure that the common value of only three variables is close to 1, so only three principal components are extracted, and the common value of all variables shows the proportion that these original variables can be explained by principal components, which also shows that most of the information of variables can be explained by principal components to reduce the loss of information.


#### 3.2.2. Score Analysis of Principal Components of Variables

Because of the large dimensional difference of all the original variables selected, in order to avoid the impact of dimensional difference on principal component analysis, all variables will be processed in a unified way to obtain the data in Table4.Table 4
Data table of principal component standardization of variables.
VariableComponent123ROE0.949-0.019-0.067ROA0.9530.021-0.078EPS0.9310.042-0.084TAT0.226-0.3730.869AR-0.0350.8510.026NAGR0.3070.6930.401NPGR0.714-0.194-0.144In Table4, from the principal component load matrix analysis in the above table, three components are extracted, of which the maximum load of the first principal component is 0.953, the maximum load of the second principal component is 0.851, and the maximum load of the third principal component is 0.869. Figure 4 was made according to the data in the above table.Figure 4
Data diagram of principal component standardized processing of variables.In Figure4, it can be seen that among the three extracted components, the first principal component has a greater load on the principal component of profitability, the second has a greater load on the principal component of growth solvency, and the third has a greater load on the principal component of operational capacity.


### 3.3. Multiple Regression Analysis

The significance test of multiple regression model is carried out for the variables. In the early stage, the linear regression modelF test will be carried out for the variables, and the multiple regression model test will be carried out according to the results of the linear regression model.Table5 shows that there is a strong correlation between the enterprise value and the growth rate of intangible assets, net assets per share, growth rate of fixed assets, and provision for impairment, in which PDV, IIA, BPS, and IFA are the prediction variables and EV is the dependent variable. And the significance level α is 0.05. In the linear regression model, the probability p value of the significance t test of the regression coefficient of the growth rate of intangible assets, net assets per share, and impairment provision is less than the significance level α; the probability p value of the significance test of the growth rate of fixed assets is greater than 0.05. Therefore, we believe that there is a significant difference between the regression coefficient of the growth rate of intangible assets, net assets per share, and impairment provision and 0, and there is no significant difference between the regression coefficient of the growth rate of fixed assets and 0. At the same time, from the perspective of collinearity statistics, the tolerances of DV, IIA, BPS, and IFA are greater than 0.05. This paper holds that the multicollinearity between explanatory variables and control variables is not serious.Table 5
Significance test of multiple regression.
ModelNon standardized coefficientStandard coefficienttSignificanceCollinearity statisticsBStandard errorBetaToleranceVIF1(Constant)-0.6030.167-3.6090IIA0.6180.2140.2352.8950.0040.7691.301BPS0.1630.0420.2983.91100.8791.137IFA0.2290.2190.0851.0470.2970.7711.296PDV-5.80E-100-0.199-2.7030.0080.9361.069


## 3.1. Descriptive Statistical Analysis Results of the Correlation between Intangible

### 3.1.1. Assets and Enterprise Value

Descriptive statistical analysis was carried out on the effective data of 157 listed sports enterprises after the above treatment, mainly including return on net assets (ROE), return on total assets (ROA), earnings per share (EPS), total asset turnover (TAT), quick ratio (AR), net asset growth rate (NAGR), net profit growth rate (NPGR), intangible asset growth rate (IIA), net asset per share (BPS), and fixed asset growth rate (IFA). The mean value and dispersion degree of these variables, such as amortization amount (AF), impairment provision (PDV), net asset ratio (PB), and asset size (SIZE), are statistically analyzed to obtain the data in Table2.Table 2
Descriptive analysis data of listed sports enterprises.
VariableMinimum valueMaximum valueAverage valueStandard deviationCoefficient of variationROE-73.042832.3847.006210.3271.474ROA-64.673825.88665.3517.80171.458EPS-4.20631.620.25520.50491.9784TAT0.01622.0010.54350.36920.6792AR0.139614.80472.11922.25311.0632NAGR-53.7632609.10423.627977.70433.2887NPGR-3152.1981922.45141.3873387.3294279.1942IIA-11.59390.07490.42755.7096BPS0.76619.68043.71662.04840.5511IFA-0.96611.98680.09740.41624.275AF2774137.72.723E+092324532035056269612.1752PDV-830050223.002E+091230500063857811883.1352PB0.718918.64.17182.87980.6903SIZE19.224427.76722.19911.61430.0727Table2 shows that ROE, ROA, EPS, TAT, AR, NAGR, and NPGR of these listed sports enterprises are the explained variables, and their average values are 7.0062, 5.3510, 0.2552, 0.5435, 2.1192, 23.6279, and 1.3873, respectively; IIA is the explanatory variable, and its mean value is 0.0749; BPS, IFA, AF, PDV, PB, and SIZE are control variables, and their mean values are 3.7166, 0.0974, 232453202.9459, 123050006.1349, 4.1718, and 22.1991. According to the data in the above table, draw Figure 2 below.In Figure2, the dimensional difference between different variables is large, so it is not suitable to use standard deviation to express the dispersion degree of variables. Therefore, the coefficient of variation is used to express the dispersion degree of variables. The larger the coefficient of variation, the higher the dispersion degree of variables. In this study, the coefficient of variation of NAGR is the largest, which shows that the dispersion degree of NAGR is high and the difference is the largest, while the dispersion degree of other variables is not much, which is relatively low.


## 3.1.1. Assets and Enterprise Value

Descriptive statistical analysis was carried out on the effective data of 157 listed sports enterprises after the above treatment, mainly including return on net assets (ROE), return on total assets (ROA), earnings per share (EPS), total asset turnover (TAT), quick ratio (AR), net asset growth rate (NAGR), net profit growth rate (NPGR), intangible asset growth rate (IIA), net asset per share (BPS), and fixed asset growth rate (IFA). The mean value and dispersion degree of these variables, such as amortization amount (AF), impairment provision (PDV), net asset ratio (PB), and asset size (SIZE), are statistically analyzed to obtain the data in Table2.Table 2
Descriptive analysis data of listed sports enterprises.
VariableMinimum valueMaximum valueAverage valueStandard deviationCoefficient of variationROE-73.042832.3847.006210.3271.474ROA-64.673825.88665.3517.80171.458EPS-4.20631.620.25520.50491.9784TAT0.01622.0010.54350.36920.6792AR0.139614.80472.11922.25311.0632NAGR-53.7632609.10423.627977.70433.2887NPGR-3152.1981922.45141.3873387.3294279.1942IIA-11.59390.07490.42755.7096BPS0.76619.68043.71662.04840.5511IFA-0.96611.98680.09740.41624.275AF2774137.72.723E+092324532035056269612.1752PDV-830050223.002E+091230500063857811883.1352PB0.718918.64.17182.87980.6903SIZE19.224427.76722.19911.61430.0727Table2 shows that ROE, ROA, EPS, TAT, AR, NAGR, and NPGR of these listed sports enterprises are the explained variables, and their average values are 7.0062, 5.3510, 0.2552, 0.5435, 2.1192, 23.6279, and 1.3873, respectively; IIA is the explanatory variable, and its mean value is 0.0749; BPS, IFA, AF, PDV, PB, and SIZE are control variables, and their mean values are 3.7166, 0.0974, 232453202.9459, 123050006.1349, 4.1718, and 22.1991. According to the data in the above table, draw Figure 2 below.In Figure2, the dimensional difference between different variables is large, so it is not suitable to use standard deviation to express the dispersion degree of variables. Therefore, the coefficient of variation is used to express the dispersion degree of variables. The larger the coefficient of variation, the higher the dispersion degree of variables. In this study, the coefficient of variation of NAGR is the largest, which shows that the dispersion degree of NAGR is high and the difference is the largest, while the dispersion degree of other variables is not much, which is relatively low.


## 3.2. Principal Component Analysis Results of Relevant Factors between Intangible Assets and Enterprise Value

### 3.2.1. Principal Component Analysis of Original Variable Data

Before the multiple regression analysis of the relevant factors between intangible assets and enterprise value, the principal component analysis method should be used to reduce the dimension of the seven indicators measuring enterprise value. Firstly, we should extract the principal components of all variables to obtain the principal components with strong correlation, and get the data in Table3.Table 3
Extraction results of principal components.
VariableInitial eigenvalueExtract10.906ROA10.914EPS10.875TAT10.946AR10.726NAGR10.736NPGR10.569In Table3, when the initial eigenvalue of the principal component to be selected for extracting the principal component by the principal component analysis method is ≥1, it has the significance of extraction. The seven variables in the above table have the initial eigenvalue of 1, so they can be used as the extracted principal component. Among them, the extracted value is the common degree of all original variables. It can be seen from the table that the common degree of all variables is greater than 0.7, and only the common degree of three variables is close to 1. According to the data in the above table, Figure 3 can facilitate readers to compare the original data.Figure 3
Extraction results of original variables.In Figure3, it can be seen from the figure that the common value of only three variables is close to 1, so only three principal components are extracted, and the common value of all variables shows the proportion that these original variables can be explained by principal components, which also shows that most of the information of variables can be explained by principal components to reduce the loss of information.


### 3.2.2. Score Analysis of Principal Components of Variables

Because of the large dimensional difference of all the original variables selected, in order to avoid the impact of dimensional difference on principal component analysis, all variables will be processed in a unified way to obtain the data in Table4.Table 4
Data table of principal component standardization of variables.
VariableComponent123ROE0.949-0.019-0.067ROA0.9530.021-0.078EPS0.9310.042-0.084TAT0.226-0.3730.869AR-0.0350.8510.026NAGR0.3070.6930.401NPGR0.714-0.194-0.144In Table4, from the principal component load matrix analysis in the above table, three components are extracted, of which the maximum load of the first principal component is 0.953, the maximum load of the second principal component is 0.851, and the maximum load of the third principal component is 0.869. Figure 4 was made according to the data in the above table.Figure 4
Data diagram of principal component standardized processing of variables.In Figure4, it can be seen that among the three extracted components, the first principal component has a greater load on the principal component of profitability, the second has a greater load on the principal component of growth solvency, and the third has a greater load on the principal component of operational capacity.


## 3.2.1. Principal Component Analysis of Original Variable Data

Before the multiple regression analysis of the relevant factors between intangible assets and enterprise value, the principal component analysis method should be used to reduce the dimension of the seven indicators measuring enterprise value. Firstly, we should extract the principal components of all variables to obtain the principal components with strong correlation, and get the data in Table3.Table 3
Extraction results of principal components.
VariableInitial eigenvalueExtract10.906ROA10.914EPS10.875TAT10.946AR10.726NAGR10.736NPGR10.569In Table3, when the initial eigenvalue of the principal component to be selected for extracting the principal component by the principal component analysis method is ≥1, it has the significance of extraction. The seven variables in the above table have the initial eigenvalue of 1, so they can be used as the extracted principal component. Among them, the extracted value is the common degree of all original variables. It can be seen from the table that the common degree of all variables is greater than 0.7, and only the common degree of three variables is close to 1. According to the data in the above table, Figure 3 can facilitate readers to compare the original data.Figure 3
Extraction results of original variables.In Figure3, it can be seen from the figure that the common value of only three variables is close to 1, so only three principal components are extracted, and the common value of all variables shows the proportion that these original variables can be explained by principal components, which also shows that most of the information of variables can be explained by principal components to reduce the loss of information.


## 3.2.2. Score Analysis of Principal Components of Variables

Because of the large dimensional difference of all the original variables selected, in order to avoid the impact of dimensional difference on principal component analysis, all variables will be processed in a unified way to obtain the data in Table4.Table 4
Data table of principal component standardization of variables.
VariableComponent123ROE0.949-0.019-0.067ROA0.9530.021-0.078EPS0.9310.042-0.084TAT0.226-0.3730.869AR-0.0350.8510.026NAGR0.3070.6930.401NPGR0.714-0.194-0.144In Table4, from the principal component load matrix analysis in the above table, three components are extracted, of which the maximum load of the first principal component is 0.953, the maximum load of the second principal component is 0.851, and the maximum load of the third principal component is 0.869. Figure 4 was made according to the data in the above table.Figure 4
Data diagram of principal component standardized processing of variables.In Figure4, it can be seen that among the three extracted components, the first principal component has a greater load on the principal component of profitability, the second has a greater load on the principal component of growth solvency, and the third has a greater load on the principal component of operational capacity.


## 3.3. Multiple Regression Analysis

The significance test of multiple regression model is carried out for the variables. In the early stage, the linear regression modelF test will be carried out for the variables, and the multiple regression model test will be carried out according to the results of the linear regression model.Table5 shows that there is a strong correlation between the enterprise value and the growth rate of intangible assets, net assets per share, growth rate of fixed assets, and provision for impairment, in which PDV, IIA, BPS, and IFA are the prediction variables and EV is the dependent variable. And the significance level α is 0.05. In the linear regression model, the probability p value of the significance t test of the regression coefficient of the growth rate of intangible assets, net assets per share, and impairment provision is less than the significance level α; the probability p value of the significance test of the growth rate of fixed assets is greater than 0.05. Therefore, we believe that there is a significant difference between the regression coefficient of the growth rate of intangible assets, net assets per share, and impairment provision and 0, and there is no significant difference between the regression coefficient of the growth rate of fixed assets and 0. At the same time, from the perspective of collinearity statistics, the tolerances of DV, IIA, BPS, and IFA are greater than 0.05. This paper holds that the multicollinearity between explanatory variables and control variables is not serious.Table 5
Significance test of multiple regression.
ModelNon standardized coefficientStandard coefficienttSignificanceCollinearity statisticsBStandard errorBetaToleranceVIF1(Constant)-0.6030.167-3.6090IIA0.6180.2140.2352.8950.0040.7691.301BPS0.1630.0420.2983.91100.8791.137IFA0.2290.2190.0851.0470.2970.7711.296PDV-5.80E-100-0.199-2.7030.0080.9361.069


## 4. Discussion

After the successful hosting of the Olympic Games in 2008, the world witnessed the achievements of China’s sports development. At the same time, it also stimulated the people’s love and participation in sports, put forward the call for health and sports, and made sports a part of people’s life and entertainment. At the same time, under the guidance of national policies, it has stimulated the market demand for the sports industry, so as to drive the development of the sports industry. The development of China’s sports industry is relatively backward. So far, there are only dozens of sports listed enterprises, and the sports industry is relatively single, which restricts the development of enterprises. With the economic development, the industrial model is also changing. When intangible assets are mentioned more and more, the focus of enterprise development is not only limited to entities, but also the brand and the patented technology of enterprises which have become a part of enterprise assets. At the same time, it can bring substantial profits to enterprise development. Moreover, with the social progress and the intensification of the process of business diversification, people’s way of thinking should also change.Under the prosperity and development of the sports industry, listed companies in China’s sports industry have brought rare development opportunities, stimulating people’s enthusiasm for sports. With the increasing fitness, the demand for sports is increasing, ushering in excellent development opportunities for listed companies in China’s sports industry. With the support of policies, the market has increased investment in the sports industry and analyzed the value and influencing factors of listed companies in the sports industry. After all, the development trend of listed companies in the sports industry can further help investors invest in the sports industry reasonably, reduce investment risks, and promote its long-term development.The intangible assets of sports listed companies are relatively empty, which must be combined with the real industry to better play its role. The market has increasingly recognized the binding mode between intangible assets and enterprise value, which also directly reflects that the impact of intangible assets on enterprise value is positive. Therefore, in the operation of sports listed companies, giving effective play to the role of intangible assets is conducive to the stable operation and development of enterprises.Market demand dominates the change of economic model. Due to the incomplete management and law of intangible assets, people cannot quantify intangible assets and give standardized definitions, which seriously underestimates the value and role of intangible assets. When sports listed companies develop to a certain stage, they will pay attention to western countries and learn from their advanced ideas to drive the development of their own enterprises. Quantifying the intangible assets of the sports industry can help investors effectively predict the sports listed companies and reduce investment risks. At the same time, it can also help sports listed companies raise funds in the financing process and contribute to the sound development of sports listed companies.


## 5. Conclusion

It is concluded that there is a significant positive correlation between enterprise value and the growth rate of intangible assets, which shows that intangible assets can significantly promote enterprise value under the influence of various factors. With the rapid development of economy and the continuous change of industrial structure mode, the value of intangible assets has attracted more and more attention and plays a vital role in the development of enterprises, which helps to improve the core competitiveness of enterprises. The intangible assets of sports listed enterprises are a kind of wealth in the process of enterprise development. Compared with tangible assets, intangible assets are not specifically quantified. They often need to be combined with fixed assets to realize their value, so they cannot be evaluated separately [12]. However, there is no clear standard for the definition and value analysis of intangible assets. Although intangible assets can promote the realization of enterprise value in the process of economic development, the management of intangible assets is relatively backward due to the late start of China’s sports listed enterprises compared with western countries [13]. By constructing a model, this paper analyzes the correlation between intangible assets and enterprise value of sports listed enterprises and uses neuro-fuzzy algorithm to calculate the data. In the future, with our people’s love for sports, the sports industry will drive the sustainable development of the economy, and the management system and relevant policies of intangible assets of sports listed enterprises will be gradually completed to promote the cultivation of talents, so as to improve the development and construction of intangible assets and better promote the benign development of enterprise economy.



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*Source: 3540011-2022-05-14.xml*