Research on financing efficiency and influencing factors of equipment manufacturing industry—

Abstract

Equipment manufacturing industry is the core industry of national economy. The development of artificial intelligence technology provides new development opportunities for the transformation and upgrading of equipment manufacturing industry, but in this process, China’s equipment manufacturing enterprises are faced with serious financing constraints and financing efficiency needs to be improved. Based on the panel data of Listed Companies in equipment manufacturing industry from 2009 to 2018, the article constructs a panel data regression model by using stochastic frontier analysis to measure the financing efficiency of equipment manufacturing industry and study its influencing factors. The results show that the average financing efficiency of China’s equipment manufacturing enterprises is in the medium level, while the traditional equipment manufacturing industry is lower; external financing has a positive impact on the financing efficiency of enterprises, and labor input has a negative impact; in the analysis of influencing factors, the Capital structure, R&D investment, Accounts receivable turnover rate, Fixed assets turnover rate have a great impact on the financing efficiency. The research results have a certain reference significance for equipment manufacturing enterprises to improve financing efficiency.

Keywords

Equipment manufacturing financing efficiency stochastic frontier analysis

1 Introduction

The equipment manufacturing industry is the core industry that provides equipment for the production of various sectors of the national economy. It is in a key position in the industrial chain and plays a pivotal role in the construction of the national economy. With the development of artificial intelligence technology, the traditional equipment manufacturing industry’s production and operation model has been greatly affected. Industrial transformation and up-grading have become the only way for China’s equipment manufacturing industry to break through the development problems. Problems such as insufficient resource allocation efficiency and severe financing constraints have become critical issues that need to be resolved in the process of transformation and upgrading of China’s equipment manufacturing industry [1, 2].

Capital is the key resource for equipment manufacturing enterprises to realize transformation and upgrading, and is an important driving force for them to create value. On the one hand, equipment manufacturing companies need a lot of capital to support their project investment and technological innovation to maintain their competitive advantage; On the other hand, equipment manufacturing companies need huge capital investment to maintain the healthy operation of their production and operation activities. Therefore, for equipment manufacturing enterprises characterized by capital-intensive, labor-intensive, and technology-intensive features, the stability of financing channels and the effectiveness of capital allocation capabilities play a key role [3]. However, Financing issues still restrict the development of equipment manufacturing companies, such as uneven capital market support, lack of funds, and high degree of external financing dependence [4, 5].

Therefore, this paper focuses on the financing efficiency of equipment manufacturing industry, dis-cusses the ability of equipment manufacturing enterprises to use different financing methods to integrate funds and use funds to create income. The stochastic frontier method (SFA) is used to measure the financing efficiency of equipment manufacturing enterprises and identify its influencing factors, so as to ease the financing constraints of equipment manufacturing enterprises and promote industrial up-grading.

2 Literature review

There is currently no unified definition of the concept of financing efficiency in academic circles. Gregory (1990) believes that financing efficiency is the process of effectively using the integrated funds, that is, the allocation efficiency of funds [6]; Wang (2002) believes that corporate financing efficiency refers to whether the company can obtain funds and the way to obtain funds, reflecting the enterprise Financing capacity [7]; Thomas (2005) pointed out that financing efficiency includes not only the efficiency of the allocation of corporate funds, but also the efficiency of dynamic use, companies only use the funds on the basis of fully weighing the relationship between the allocation of funds and dynamic use, In order to achieve effective financing [8]; Fang and Zeng (2005) believed that the financing efficiency of an enterprise not only includes the ability of an enterprise to integrate capital with the lowest possible risk and the highest ratio of return to cost, but also includes three parts: the utilization ratio of capital, the financing cost and the degree of freedom after financing [9].

At present, the research on financing efficiency mainly focuses on the technology based enterprises in the GEM and the New OTC Market. For example, Lin and Liu (2019) use the BCC model in the data envelopment analysis method to compare and analyze the financing efficiency of science and technology enterprises from the perspectives of regions and levels of capital markets [10]; Cao (2015) measured the financing efficiency of 130 companies listed on the New OTC Market in 2013, and concluded that the overall financing efficiency of China’s New OTC Market companies was low [11].

The predecessors also conducted a more in-depth study on the influencing factors of corporate financing efficiency. Li et al. (2008), Wang et al. (2016), and Yang (2017) conducted asset efficiency analysis using asset size as an influencing factor [12 –14]. Gong et al. (2017) and Li (2014) studied asset-liability ratio as influencing factors [15, 16]; Chen et al. (2006), Cui et al. (2014), Song et al. (2017), Li Et al. (2017) used internal and external financing as input variables to measure the efficiency of financing [17 –20]; Yang (2019), Wu et al. (2019) from the perspective of business management capabilities, Development capacity is analyzed as an influencing factor [21, 22]; Feng et al. (2019) analyzed the degree of impact on corporate financing efficiency using equity concentration as an indicator [23]. Wang and Geng (2017) discussed the impact of the financial ecological environment on the financing efficiency of state-owned and non-state-owned listed companies [24]; Liu (2019) and Gui et al. (2019) used the level of economic development as an external factor to finance enterprises Research on efficiency [25, 26].

At the level of research methods of corporate financing efficiency, Tian (2011), on the basis of analyzing the financing characteristics of enterprise groups and commonly used financing methods, uses fuzzy mathematics methods to establish a fuzzy comprehensive evaluation model of financing efficiency [27]; Zeng and Geng (2019) fully utilized the advantages of Super-SBM to accurately measure and rank the financing efficiency value, and used the data of listed companies to calculate the financing efficiency difference of high-end equipment manufacturing enterprises [2]; Liu et al. (2019) adopted the DEA method The financing efficiency of listed companies in the smart industry is measured, and the Malmquist index method is used to reflect the changes in the financing of listed companies in the artificial intelligence industry from a dynamic perspective, and then the Tobit method is used to construct a model of factors affecting financing efficiency [28]; Gui and Wu (2019) use the Three-stage DEA model and Malmquist index to compare the financing efficiency from multiple perspectives such as industry segmentation, region, hierarchy and transfer mode [26].

In summary, scholars at home and abroad have conducted extensive research on corporate financing efficiency from multiple angles, but the specific connotation of the concept of corporate financing efficiency is different. In view of the research results of Thomas (2005) and others, this paper defines the financing efficiency as the ability of the enterprise to use different financing methods to integrate funds into the enterprise and use the funds to bring the highest returns, Which is ultimately reflected in the long-term value creation efficiency of the enterprise; The research on the influencing factors of financing efficiency is more comprehensive. However, because the influencing factors of different industry financing efficiency may have obvious differences, the selected indicators lack of targeted industrial characteristics analysis; In terms of efficiency research objects, research in recent years has mainly focused on technological innovation enterprises on the GEM and the New OTC Market, with relatively little research on the equipment manufacturing industry. However, the equipment manufacturing industry, which is the core industry of the national economy, is also facing financing pressures to a certain extent, such as uneven capital market support, high dependence on external financing, and insufficient resource allocation efficiency. Therefore, this paper believes that equipment manufacturing enterprises financing efficiency has certain research value; as far as research methods are concerned, the methods that scholars mostly use are concentrated on DEA method, fuzzy comprehensive evaluation method, linear regression method and entropy method, among which DEA method is the main evaluation method. SFA is used less frequently. Compared with the DEA method, the stochastic frontier analysis method not only considers the effect of random factors on efficiency, but also overcomes the problems of low measurement accuracy and 1 efficiency value of traditional DEA model [29]. It can analyze the variables that affect the efficiency, and then find out the direction and intensity of the factors that affect the efficiency. Therefore, this paper chooses the stochastic frontier method to conduct an empirical study on the financing efficiency and influencing factors of the equipment manufacturing industry.

3 Methodology, materials and data description

3.1 Methodology

In this paper, the stochastic frontier analysis method (SFA) is used to set the frontier function of the production unit. The model error term is divided into management error term and random error term. The SFA model mainly includes the BC92 model and BC95 model established by Battle and Coelli (1995) [30]. Among them, the BC92 model assumes that the technical inefficiency in the error term follows a non-negative truncated normal distribution and changes with time, and requires that the random error and the management error are independent of each other; and the BC95 model can not only measure the specific value of efficiency, You can study the related influencing factors of technical efficiency. Therefore, this article selects the BC95 model in the SFA method, the specific form is as follows: $Y_{it} = β X_{it} + (V_{it} - U_{it})$ (1) $U_{it} = \sum δ z_{it} + ɛ$ (2) ${TE}_{t} = exp (- U_{it}) = exp (- δ z_{it} - ɛ_{t})$ (3) $γ = \frac{σ_{u}^{2}}{σ_{u}^{2} + σ_{v}^{2}}$ (4)

In Equation (1), Y_it represents the output of production unit _i in period _t , and X_it is the input of production element_i in production unit_t. β is the parameter to be estimated; V_it and U_it represent the random error term and the inefficiency term, respectively, and the two are independent of each other, V_it obey N(u,σ _v ²), U_it ≥0, obey the truncated normal distribution N(u,σ _u ²).

Equation (2) is an inefficiency model. In this model, the larger the value of U_it, the lower the financing efficiency, and the lower the output of the conversion of input equivalent factors; where z_it represents the variable factor of financing efficiency, and δ is the estimated parameter of the variable factor, δ is a positive value, indicating that the variable has a positive effect on the inefficiency term, and a negative effect on efficiency, otherwise, it has a positive effect [31].

Equation (3) is the efficiency level. TE_t is between 0∼1. The greater the value of TE_t, the higher the efficiency level. When u_t = 0, TE_t = 1, indicating that the actual output and The distance between the maximum output is 0, the input factor falls on the frontier of production, that is, the input activity is effective, otherwise the input activity is invalid.

In Equation (4), γ is used to test the proportion of invalid term to composite disturbance term, and its value is between 0∼1. If γ=0 is accepted, it indicates that the distance between the actual output and the maximum output is all from the random factor v_t. In this case, the SFA model is meaningless. It can be estimated directly by OLS. If γ passes the likelihood ratio (LR) significance test, it means that it is meaningful to use the SFA model.

The SFA method mainly depends on Cobb-douglas production function and transcendental logarithmic production function to measure financing efficiency. However, the factors considered by Cobb-douglas production function are not comprehensive enough. The transcendental logarithm function not only takes into account the changes of input-output elasticity, the interaction between input factors and their mutual substitution, but also reduces the estimation error caused by function setting. Its basic form is as follows:

$\begin{matrix} Lnf = & β_{0} + β_{1} lnK + β_{2} lnL + β_{3} (lnK)^{2} \\ + β_{4} (lnL)^{2} + β_{5} lnKlnL + (V_{it} - U_{it}) \end{matrix}$ (5)

In Equation (5), the effect of input variable interaction on output is considered, which overcomes the shortcoming that Cobb-douglas production function fixes substitution elasticity as 1. Therefore, this paper chooses the transcendental logarithmic production function. On this basis, in order to obtain a better fitting effect, the specific form of the financing efficiency evaluation model of the equipment manufacturing enterprise is determined through calculation: $\begin{matrix} Ln (Y_{it}) = β_{0} + β_{1} Ln (K_{it}) + β_{2} Ln (L_{it}) + \\ β_{3} (Ln (K_{it}))^{2} + β_{4} (Ln (L_{it}))^{2} \\ + \frac{1}{2} β_{5} Ln (K_{it}) Ln (L_{it}) + v_{it} - u_{it} . \end{matrix}$ (6) $U_{it} = δ_{0} + δ_{it} Z_{it} + ɛ$ (7) ${TE}_{it} = exp (- u_{it}) = exp (- δ_{it} Z_{it} - ɛ)$ (8) $γ = \frac{σ_{u}^{2}}{σ_{u}^{2} + σ_{v}^{2}}$ (9)

In Equation (6), Y_it represents the output value of the company ’s financing activities, K_it and L_it represents the input elements of financing activities, t is the time, and β is the parameter to be estimated by the model. for reference.

In Equation (7), U_it is a function of exogenous variables, which represents the non-efficiency terms of financing for listed companies in the equipment manufacturing industry, δ _it is the coefficient to be estimated, and Z_it is the variable affecting the financing efficiency.

In Equation (8), TE_it is the level of financing efficiency, which is between 0–1. The higher the value is, the higher the level of financing efficiency is. At that time, the distance between the actual output and the maximum output is 0, and the input elements fall on the production frontier, that is, the input activities are effective, otherwise the input activities are invalid.

In Equation (9), γ is used to test the ratio of invalid term to compound disturbance term, and its value is between 0–1, which is used to test the practicability of the model.

3.2 Materials

Based on the research results of scholars such as song [19], Wang [30], this paper takes business in-come as output index, external financing and labor as input index; on the basis of research results of Li [12], Feng [23], Yan [3], Li [16] and Tian [27], this paper considers the impact of operational capacity on financing efficiency of equipment manufacturing industry, and selects the influencing factor indicators. As shown in Table 1.

3.3 Data description

This article selects 2009–2018 equipment manufacturing listed companies as samples to discuss the issue of China’s equipment manufacturing enterprises financing efficiency. When selecting sample companies, a total of 167 listed companies in the equipment manufacturing enterprises were collected from the CSMAR and the RESSET. Considering the continuity of the data, the number of samples and the representativeness of the selection period, the semi annual data of 107 listed companies in equipment manufacturing industry are selected. The number of samples selected in this paper conforms to the rule of thumb that the number of decision-making units is greater than twice the sum of output and input indicators. In addition, compared with the data about 5 years that most scholars at home and abroad have used to study the financing efficiency, the length of the period selected in this paper is more representative. Considering the time lag of output, this paper sets the time lag of the output index of financing efficiency as one lag period.

Compared with DEA method, SFA method does not require the input and output data to be non negative, but the transcendental logarithm production function model of SFA method used in this paper requires the original data to be logarithmic. However, some of the original data are non positive, which makes it impossible to log the original data. In addition, considering the dimensional relationship among variables, the input variables and influencing factors are processed by Min-Max method, and the original data are classified into a fixed interval according to a certain functional relationship. On the one hand, the original data can meet the requirements of logarithmic processing, on the other hand, the data can be comparable. The specific processing formula is as follows: $X_{it} = \frac{x_{it} - Min (x_{it})}{Max (x_{it}) - Min (x_{it})} + 0.001$ (10)

In Equation (10), x_it represents various values of relevant indicators in period_t, Min (x_it) is the minimum value among them, and Max (x_it) is the maximum value among them. On this basis, each value is shifted to the right by 0.001 units on the number axis, which does not change the relationship between the data and ensures that the processed data is greater than 0.

After the above-mentioned normalization processing is performed on the data, the natural logarithm is substituted into the model.

4 Empirical analysis

4.1 Model testing and parameter estimation

Using Frontier4.1, the production function and technical inefficiency function with “operating income” as output are simultaneously regressed, and the parameter estimates and related test results in Table 2 are obtained. Among them, the statistics γ=0.234, which passed the statistical test with a significance level of 1%, indicating that technological inefficiency is common in the financing activities of equipment manufacturing enterprises. It is reasonable to use the stochastic frontier production function model for financing efficiency analysis. LR Statistics at the 1% significance level all fall into the rejection domain of the mixed χ distribution, which further shows that the stochastic frontier production function model fits the sample data better and is more suitable to express the transformation process of input and output of financing activities of equipment manufacturing enterprises. According to the parameter test results of the stochastic frontier equation, the quadratic coefficients β ₃ and β₄ of the logarithmic production function all passed the significance test, indicating that the transcendental logarithmic production function model is suitable for measuring the financing efficiency of equipment manufacturing industry.

Table 1
Variable selection and indicator description

Variable type Variable name Symbol Variable description

Output Operating results Y Operating income

Input External financing K Interest liabilities+equity+capital reserve+other equity instruments

Labor input L Number of employees

Influence factors Capital structure Z₁ Liabilities / Equity

R &D Z₂ R &D investment / Operating income

Equity incentive Z₃ Management shareholding ratio

Equity concentration Z₄ Shareholding ratio of the largest shareholder / Shareholding ratio of the second largest shareholder

Turnover rate of accounts receivable Z₅ Operating income / Accounts receivable

Inventory turnover Z₆ Operating cost / Inventory

Turnover rate of fixed assets Z₇ Operating income / Net fixed assets

Financing cost Z₈ Cash outflow for Interest and dividend payment in each period

Macroeconomic situation Z₉ GDP growth

Variable type	Variable name	Symbol	Variable description
Output	Operating results	Y	Operating income
Input	External financing	K	Interest liabilities+equity+capital reserve+other equity instruments
	Labor input	L	Number of employees
Influence factors	Capital structure	Z₁	Liabilities / Equity
	R &D	Z₂	R &D investment / Operating income
	Equity incentive	Z₃	Management shareholding ratio
	Equity concentration	Z₄	Shareholding ratio of the largest shareholder / Shareholding ratio of the second largest shareholder
	Turnover rate of accounts receivable	Z₅	Operating income / Accounts receivable
	Inventory turnover	Z₆	Operating cost / Inventory
	Turnover rate of fixed assets	Z₇	Operating income / Net fixed assets
	Financing cost	Z₈	Cash outflow for Interest and dividend payment in each period
	Macroeconomic situation	Z₉	GDP growth

Table 2

SFA model parameter estimation and related test results

Item	Parameter to be estimated	Take operating income as output
		Coefficient	SD	T
Stochastic frontier equation	β ₀	27.527***	0.509	54.103
	β ₁	4.951***	0.911	5.436
	β ₂	–1.236**	0.906	–2.364
	β ₃	–23.061***	0.536	–43.026
	β ₄	–19.900***	0.534	–37.259
	β ₅	41.574***	0.730	56.982
Inefficiency equation	δ ₀	0.751	0.776	0.968
	δ ₁	0.492***	0.668	2.737
	δ ₂	0.207***	0.028	7.414
	δ ₃	–0.038***	0.014	–2.763
	δ ₄	–0.007	0.016	–0.436
	δ ₅	–0.155***	0.059	–2.628
	δ ₆	–0.009	0.035	–0.253
	δ ₇	–0.092***	0.030	–3.092
	δ ₈	0.072**	0.028	2.528
	δ ₉	0.050***	0.014	3.564
Variance parameter	Sigma-squared	0.368***	0.017	21.398
	Gamma	0.234***	0.031	7.446
	LR One-sided error test	4508.339

Note: *, **, and *** represent 10%, 5%, and 1% levels respectively, and LR is the likelihood ratio test statistic, which conforms to the mixed chi-square distribution.

In Table 2, both β ₁ and β ₂ have passed the significance test, indicating that each input variable has a significant impact on output. From the elasticity of input variables, the output elasticity of external financing is positive and greater than 1, indicating that the increase of external financing scale is conducive to improving the financing efficiency of equipment manufacturing industry. The increase of external financing scale provides the equipment manufacturing industry with the cash flow needed for business activities, which makes enterprises have sufficient funds for project investment, independent R&D. The elasticity of labor output is negative, which indicates that the labor intensive characteristics of equipment manufacturing industry are no longer suitable for the current development trend. With the development of production technology, labor productivity has been greatly improved, and the competition key of equipment manufacturing industry has evolved into the competition of core technology. The increase of labor cost increases the pressure of cash flow, and also has a negative impact on the improvement of financing efficiency.

4.2 Measurement results of financing efficiency

As can be seen from Table 3, the average financing efficiency of Equipment manufacturing industry from is 0.665.Traditional equipment manufacturing enterprises is 0.627, while the high-end equipment manufacturing enterprises is 0.702. The financing efficiency of traditional equipment manufacturing enterprises is lower than the average level of the whole industry.

Table 3
Equipment manufacturing company financing efficiency calculation results

Time Overall level Output based on operating income

High end equipment manufacturing industry Traditional equipment manufacturing industry

2009.07-2009.12 0.719 0.751 0.687

2010.01-2010.06 0.727 0.759 0.694

2010.07-2010.12 0.715 0.748 0.682

2011.01-2011.06 0.720 0.751 0.689

2011.07-2011.12 0.693 0.722 0.664

2012.01-2012.06 0.672 0.699 0.644

2012.07-2012.12 0.642 0.677 0.607

2013.01-2013.06 0.662 0.693 0.630

2013.07-2013.12 0.637 0.672 0.602

2014.01-2014.06 0.649 0.685 0.612

2014.07-2014.12 0.615 0.658 0.571

2015.01-2015.06 0.635 0.669 0.601

2015.07-2015.12 0.602 0.643 0.561

2016.01-2016.06 0.648 0.686 0.609

2016.07-2016.12 0.624 0.671 0.576

2017.01-2017.06 0.652 0.704 0.599

2017.07-2017.12 0.626 0.680 0.571

2018.01-2018.06 0.722 0.766 0.678

Average 0.665 0.702 0.627

Time	Overall level	Output based on operating income
2009.07-2009.12	0.719	0.751	0.687
2010.01-2010.06	0.727	0.759	0.694
2010.07-2010.12	0.715	0.748	0.682
2011.01-2011.06	0.720	0.751	0.689
2011.07-2011.12	0.693	0.722	0.664
2012.01-2012.06	0.672	0.699	0.644
2012.07-2012.12	0.642	0.677	0.607
2013.01-2013.06	0.662	0.693	0.630
2013.07-2013.12	0.637	0.672	0.602
2014.01-2014.06	0.649	0.685	0.612
2014.07-2014.12	0.615	0.658	0.571
2015.01-2015.06	0.635	0.669	0.601
2015.07-2015.12	0.602	0.643	0.561
2016.01-2016.06	0.648	0.686	0.609
2016.07-2016.12	0.624	0.671	0.576
2017.01-2017.06	0.652	0.704	0.599
2017.07-2017.12	0.626	0.680	0.571
2018.01-2018.06	0.722	0.766	0.678
Average	0.665	0.702	0.627

As can be seen from Table 4, the efficiency value of 5.607% of the sample enterprises is lower than 0.5, indicating that the equipment manufacturing enterprises in this part are not utilized. The efficiency of capital value creation is not high, and there are big problems in the process of capital transformation and utilization; 84.112% of the sample enterprises’ efficiency value is between 0.5 and 0.8, the financing efficiency level is general, and there is still room for improvement; only 10.280% of the sample enterprises’ efficiency value is higher than 0.8, which can effectively use and transform funds.

Table 4

Statistical distribution of efficiency values of sample companies

Efficiency interval	Number of samples	Proportion of total sample (%)
0.4–0.5	6	5.607
0.5–0.6	21	19.626
0.6–0.7	37	34.579
0.7–0.8	32	29.907
0.8–0.9	11	10.280
Total	107	100

As shown in Fig. 1, the financing efficiency of equipment manufacturing industry shows a trend of first decreasing and then increasing, which is basically consistent with the change trend of average return on equity of equipment manufacturing enterprises. Since the economic crisis, affected by the global economic downturn, many enterprises have reduced the frequency of equipment replacement, which has led to the situation of overcapacity in China’s equipment manufacturing enterprises. Especially in the traditional equipment manufacturing enterprises, the phenomenon of low-cost malicious competition is frequent, and even domestic equipment manufacturing enterprises are killing each other in overseas markets. In this context, some enterprises blindly embark on new projects for self-protection, resulting in a substantial increase in business risk. Since 2015, China has begun to deploy and implement “the strategy of manufacturing power”, the operating conditions of equipment manufacturing industry have been improved. To sum up, this paper believes that the trend of financing efficiency is reasonable.

Fig. 1

Trends in equipment manufacturing enterprises’ financing efficiency.

To sum up, according to the above analysis of the financing efficiency results, it is found that although the financing efficiency of the equipment manufacturing industry has shown a good trend in recent years, the financing efficiency of the equipment manufacturing enterprises belongs to the upper middle level, and the financing efficiency level of the traditional equipment manufacturing industry is far from that of the high-end equipment manufacturing industry. Therefore, the financing efficiency of Chinese equipment manufacturing enterprises needs to be improved.

4.3 Analysis of factors affecting financing efficiency

According to Table 2, except for Equity concentration and Inventory turnover rate, the coefficient of other influencing factors has passed the significance test at different levels. Among them, the degree of influence on financing efficiency of equipment manufacturing industry is as follows: Capital structure, R&D investment, Turnover rate of accounts receivable, Turnover rate of fixed assets, Financing cost, Macroeconomic situation and Equity incentive. The specific analysis is as follows:

(1) The influence of capital structure on financing efficiency

The estimated coefficient of capital structure is 0.492, which is significant at the level of 1%, indicating that there is efficiency loss in the capital structure of equipment manufacturing industry. This paper measures the capital structure by debt equity ratio. The empirical results show that the debt ratio of equipment manufacturing industry is too high, which is not conducive to the improvement of financing efficiency. Through the analysis of the asset liability ratio and debt structure of sample enterprises, it is found that the asset liability ratio of equipment manufacturing industry is on the rise, and the proportion of short-term debt financing loans is large, which makes enterprises face greater debt repayment pressure and has a negative impact on the improvement of performance.

(2) The impact of R&D investment on financing efficiency

The estimated coefficient of R&D investment is 0.207, which is significant at the level of 1%, indicating that there is a certain efficiency loss in R&D investment. This paper measures the R&D investment by the proportion of R&D investment in operating revenue. The empirical results show that the R&D investment of equipment manufacturing industry has not fully played the role of improving the financing efficiency. The overall level of R&D efficiency of China’s equipment manufacturing enterprises is low, most of the enterprises’ R&D activities management level is relatively backward, the ability to use R&D investment is poor, and the R&D investment does not play the best benefit. Because many technologies and key equipment of equipment manufacturing enterprises in China have been imported from abroad for a long time, a large part of R&D funds are caused by technology introduction and technological transformation, which to a certain extent disperses the investment of enterprises in independent R&D.

(3) The influence of accounts receivable turnover rate on financing efficiency

The estimated coefficient of accounts receivable turnover rate is –0.155, which is significant at the level of 1%, indicating that there is no efficiency loss in the accounts receivable turnover rate. The improvement of accounts receivable turnover rate is conducive to improving the financing efficiency of equipment manufacturing industry. The increase of the turnover rate of accounts receivable means that the collection period of the sales revenue of the enterprise is shortened, which brings a continuous flow of cash to the enterprise. It is plays a positive role in relieving the debt paying pressure of the enterprise and carrying out project investment, so as to pro-mote the improvement of the financing efficiency of the enterprise.

(4) The influence of fixed assets turnover rate on financing efficiency

The estimated coefficient of the fixed assets turn-over rate is –0.092, which is significant at the level of 1%, indicating that there is no efficiency loss in the fixed assets turnover rate. The improvement of the fixed assets turnover rate is conducive to improving the financing efficiency of the equipment manufacturing industry. Especially for the equipment manufacturing enterprises, the operation level of fixed assets has an important impact on their profitability. The improvement of the turnover rate of fixed assets means that the utilization degree of assets is high, the value creation ability of assets is stronger, and the financing efficiency of enterprises is higher.

(5) The influence of financing cost on financing efficiency

The estimated coefficient of financing cost is 0.072, which is significant at the level of 5%, indicating that the financing cost has efficiency loss. Too high financing cost is not conducive to the improvement of financing efficiency of equipment manufacturing industry. Previous studies mostly take financial expense as the measurement index of financing cost, while financial expense only reflects the cost of debt financing, and is also affected by interest income. This paper argues that the cash expenditure for interest and dividend can not only reflect the financing cost comprehensively, but also reflect the influence of financing cost on cash flow. The increase of financing cost that enterprises need to pay every year aggravates the situation of cash shortage, thus inhibiting the improvement of financing efficiency.

(6) The influence of macroeconomic situation on financing efficiency

The estimated coefficient of macroeconomic situation is 0.050, which is significant at the level of 1%. There is efficiency loss. In the period of macroeconomic situation is better, the financing efficiency level of equipment manufacturing industry has not been improved. From 2009 to 2016, China’s GDP growth rate has always maintained above 7%, while the financing efficiency of equipment manufacturing industry in this period showed a downward trend. This paper holds the reason for this contrast is that the extensive economic development mode has no longer adapted to the development requirements of the equipment manufacturing industry. The equipment manufacturing industry no longer wins by the cost advantage, no longer at the cost of high pollution and high energy consumption, nor should it be limited to the local market.

(7) The influence of equity incentive on financing efficiency

The estimated coefficient of equity incentive is -0.038, which is significant at the level of 1%, indicating that there is no efficiency loss in equity incentive. On the one hand, the implementation purpose of management equity incentive is to drive the management and shareholders of listed companies to form a community of interests, so as to reduce agency costs and promote the value growth of enterprises; on the other hand, it is to attract and retain talents to maintain the long term and stable development of enterprises. From the above empirical results, we can see that from 2009 to 2018, the equity incentive system of equipment manufacturing enterprises has achieved the expected implementation purpose and played a positive role in improving the financing efficiency.

5 Conclusion

Based on the transcendental logarithm stochastic frontier production function model, this paper estimates the financing efficiency of equipment manufacturing enterprises and analyzes the influencing factors of financing efficiency by taking the semi annual panel data of China’s equipment manufacturing industry listed companies from 2009 to 2018 as samples. The main conclusions are as follows:

(1) From 2009 to 2018, the average financing efficiency of equipment manufacturing enterprises is 0.668, and 84.112% of the sample enterprises’ efficiency value is between 0.5 and 0.8. It can be seen that the financing efficiency of equipment manufacturing enterprises still has some room for improvement. Since “One belt, one road”, “China made 2025”, and other policies put into practice, the financing efficiency of equipment manufacturing enterprises is increasing. Equipment manufacturing enterprises should comply with the industrial policy, take various measures to improve the external financing ability and improve the quality of employees.

(2) The regression results of inefficiency equation show that there are loss of financing efficiency in Capital structure, R&D investment, Financing cost and Macroeconomic situation; there is no efficiency loss in the Turnover rate of accounts receivable, Turnover rate of fixed assets and Equity incentive. According to the above analysis, this paper believes that the government should improve the capital market system, broaden the financing channels of equipment manufacturing industry, reduce financing costs, and promote the optimization of capital structure of equipment manufacturing industry. In addition, it is necessary to change the mode of economic development and boost the transformation of the development mode of equipment manufacturing industry with the transformation of national economy; the equipment manufacturing industry should optimize the capital structure and reduce the proportion of debt financing; strictly control the R&D activities, coordinate the relationship between R&D investment, R&D cycle and innovation degree, pay attention to the application, transformation and improvement of R&D achievements, and transform the development mode of enterprises to intellectualization. In order to improve the financing efficiency, the equipment manufacturing industry should focus on the operation and management of accounts receivable and fixed assets, and further improve the incentive system to play the incentive role of share-based payment to improve the financing efficiency.

Footnotes

Acknowledgments

National Natural Science Foundation of China (No. 71771112, 71371092, 71571091), Liaoning Planning Foundation of Philosophy and Social Science (No. L18AJY001), Natural Science Foundation of Liaoning Province of China (20180550274), Innovative Talents Project of Liaoning Province(WCR2018003) and Public Welfare Research Fund for Science of Liaoning Provincial (2020JH4/10100008).

References

Miao

Cao

, Research on the financing efficiency and influencing factors of China’s new third board enterprises. South China University of Technology, 2015.

Xiaohong

Chen

and Jian

Liu

, Research on the choice of financing methods for SMEs in different growth stages, Journal of Management Engineering (01) (2006), 6–11.

Jie

Cui

, Haiqing

and Daohong

Zhang

, Research on the influencing factors of financing efficiency of non-listed small and medium-sized enterprises——evidence from manufacturing non-listed small and medium-sized enterprises, Soft Science 28(12) (2014), 84–88.

Fang

and Hui

Zeng

, Comparison of financing methods and financing efficiency of SMEs, Economic Theory and Economic Management (4) (2005), 38–42.

Taozhu

Feng

and Jingxuan

, Research on the financing efficiency of military listed companies from the perspective of military-civilian integration——based on the two-stage chain DEA model, Friends of Accounting (06) (2019), 77–82.

Gregory

P.R.

and Stusrt

R.C.

, The role of debt and preferred stock as a solution to adverse investment incentives, Journal of. Financial and Quantitative (25) (1990), 143–148.

Xingguo

Gong

and Xinming

Sun

, Research on the financing efficiency of logistics enterprises based on the two-stage DEA model——Taking 31 listed logistics companies as an example, Friends of Accounting (5) (2017), 103–107.

Jiawei

Gui

and Qunqi

, Research on financing efficiency and financial risk of new third board technology service enterprises, Science and Technology Progress and Countermeasures, 36(12) (2019).

Yan

Lin

and Xia

Liu

, Research on multi-level capital markets supporting financing efficiency of technology enterprises——Based on the Comparative Perspective of Beijing, Tianjin and Hebei, Friends of Accounting (20) (2019), 101–106.

10.

Zhigang

, Xianliang

Chi

and Chunzhou

, Empirical analysis of financing efficiency of small and medium-sized enterprises, Industrial Technology and Economy 27(9) (2008), 64–66.

11.

Fang

and Chao

Wang

, Construction of an innovative evaluation system for financing efficiency of small and medium-sized enterprises, Statistics and Decision (02) (2014), 172–175.

12.

Zheng

, Fei

and Huamin

, The financing efficiency, derivative risks and system correction of the New Third Board enterprises, Economic Survey (5) (2017), 153–158.

13.

Bingfeng

Liu

and Xiaomei

Wang

, Research on the operation efficiency of listed companies of cultural media based on the three-stage DEA model, Mathematics in Practice and Understanding 49(02) (2019), 119–133.

14.

Chao

Liu

, Ruoyu

, Jiahui

, et al., Research on the financing efficiency of listed companies in the artificial intelligence industry based on the DEA-Tobit method, Operations Research and Management (6) (2019), 144–152.

15.

Xiaolin

and Zhifang

Wan

, Industrial science and technology innovation efficiency measurement based on stochastic frontier analysis, Statistics and Decision (03) (2017), 158–161.

16.

Jiqiu

Ren

, Promote high-quality development of equipment manufacturing industry, Macroeconomic Management (05) (2019), 24–29.

17.

Guanghui

Song

, Hongfa

and Lin

, Research on the financing efficiency of technology-based SMEs based on two-stage DEA, Science and Technology Management Research 99(2) (2017), 191–195.

18.

Thomas , The last sustainable competitive advantage, CA: SRI International (2) (2005), 352–356.

19.

Fen

Tian

, Comparison of enterprise group financing efficiency based on multi-factors, Statistics and Decision (8) (2011), 179–182.

20.

Xiaobing

Tang

, Research on financing efficiency and influencing factors of China’s new energy industry. 2019.

21.

Donghua

Wang

and Chunling

Zheng

, Empirical evidence of corporate financing efficiency and financing model, Statistics and Decision (21) (2008), 185–187.

22.

Wang Xiaoning

Turin

and Qitong

Wang

, Financing efficiency analysis of SMEs based on the three-stage DEA model, Statistics and Decision (5) (2016), 179–182.

23.

Qiong

Wang

and Chengxuan

Geng

, Financial ecological environment, property rights nature and financing efficiency of strategic emerging industries, Economic Survey 34(03) (2017), 87–92.

24.

Baoqian

Wang

and Zhujun

Wang

, Equity crowdfunding financing efficiency measurement and influencing factors: based on SFA-panel regression model, Industrial Technology and Economy 38(05) (2019), 31–39.

25.

Minghua

Wang

, Enterprise financing efficiency. Beijing: China Economic Publishing House, 2002.

26.

Yangfen

and Fanhua

Zeng

, A study on the measurement of the financing efficiency of China ’s New Third Board SMEs, Hubei Social Sciences (1) (2019), 69–77.

27.

Guoyi

Xiu

and Tongtong

, Research on the measurement of financial support for the development of China’s equipment manufacturing industry, Industrial Technology and Economy 35(03) (2016), 73–77.

28.

Zhicang

, Technical efficiency analysis of manufacturing based on transcendental logarithmic production function, Statistics and Decision (5) (2015), 139–143.

29.

Fuhai

Yan

and Ningxin

, Research on the impact of investment efficiency on the transformation of equipment manufacturing enterprises, Friends of Accounting (14) (2019), 97–103.

30.

Ting

Yang

, Yi

Yang

and Qiuhua

Zhu

, Research on the financing methods and financing efficiency of listed companies on the Growth Enterprise Market based on growth ability, Journal of Guangxi University of Science and Technology 30(01) (2019), 118–125.

31.

Guozuo

Yang

, Feng

Zhang

and Ziyi

Chen

, Empirical analysis of financing efficiency of listed companies on the New Third Board, Finance Theory and Practice 38(2) (2017), 51–56.

32.

Gang

Zeng

and Chengxuan

Geng

, Empirical measurement of financing efficiency of Chinese high-end equipment manufacturing listed companies——Based on Super-SBM and Malquist model, Science and Technology Management Research 39(10) (2019).

Research on financing efficiency and influencing factors of equipment manufacturing industry——Regression model based on SFA panel data

Abstract

Keywords

1 Introduction

2 Literature review

3 Methodology, materials and data description

3.1 Methodology

3.3 Data description

4.1 Model testing and parameter estimation

5 Conclusion

Footnotes

Acknowledgments

References