Research on financing risk of mining enterprises based on fuzzy comprehensive evaluation

Abstract

Mining is the important basic industry about economy, people’s livelihood and social development in our country. Its development degree will directly affect the level of industrial development in China. There are financing difficult problems in most mining companies which restrict its further development because of the imperfect capital market in China as well as the mining industry’s development level and high risk characteristic of environmental impact. Through the use of the combination of theoretical and empirical research methods, this study takes Zijin Mining as an example and analyzes its financing risk systematically, which based on the fuzzy comprehensive evaluation method combined with the current financing situation of China’s mining industry. The purpose is to evaluate the financing risk effectively so as to help the management make more effective financing decisions.

Keywords

Financing risk mining enterprises Zijin mining fuzzy comprehensive evaluation

1 Introduction

As one of the important pillar industries in China’s secondary industry, mineral enterprises provide necessary energy and raw materials for the national economy and social life, which play an irreplaceable important role in China’s national economy. Mineral enterprises are basically capital-intensive enterprises, which have a long input-output cycle and high fixed costs. With the prosperity and development of the world economy, the demand for copper, iron ore, precious metals and other mineral resources is booming, the transaction scale is expanding day by day, and the financing demand of mining enterprises is increasingly booming. Financing will inevitably produce financing risks, and the sources and manifestations of financing risks are diverse. Enterprises are in different life cycles, and the choice of different financing methods will affect the financing risk to different degrees [1]. Moreover, mining enterprises in different stages have totally different demands for capital. Therefore, financing risk is a problem that can not be ignored in the process of raising funds for mineral enterprises. If the risk is too high, it will be likely to produce adverse effects and hinder the development of enterprises.

In the era of rapid adjustment of market economy, how to effectively alleviate and avoid the risk in the financing process of mining enterprises has become a difficult problem today. Financing activities related to the development of the enterprise, to scientific and proper financing, capital chain needed for mining companies in different stages are guaranteed, but a lot of mining companies in the process of financing, only pay attention to the choice of the financing way, ignore the financing risk assessment analysis and control, the results of financing not only without benefits, but also to the enterprise to bring the people big losses. Based on this, considering the different demands for funds in different stages of the mining industry, this study adopts the fuzzy comprehensive evaluation method to comprehensively evaluate the financing risks of mining enterprises, so as to promote the effective decision-making of the management.

2 Literature review

Mining industry is an industry developed according to mineral resources, which main mineral resources include: energy, mineral, metal, mineral, nonmetal, mineral, water, gas, mineral, etc. Mineral resources are widely distributed in the world, but relatively concentrated. Financing of mineral enterprises refers to the activities of raising funds and using managed funds actively in different stages of production and operation [2]. At present, the common financing methods of mining enterprises include the following: internal and external financing, project financing and venture investment. Internal financing refers to the internal use of funds accumulated in production and other links of the enterprise for financing; External financing refers to raising funds from the external environment of enterprises, including financial financing, debt financing, equity financing and so on. In addition, mining industry capital market is an indispensable and important financial tool for mining enterprises to finance. The particularity of mining, compared with other industries in its long development cycle time, large capital demand, many uncertain factors, investment and financing risk is bigger, so the research of mineral enterprise financing risk, need according to the characteristics of the mining activities of mining enterprises financing stage is divided into: exploration stage, mining stage and operation stage.

Financing risk mainly refers to the risk that an enterprise loses its solvency due to financing activities and its profit is affected. The higher the financing risk, the more difficult it is for the enterprise to repay the debt and continue to operate. There are many evaluation methods for financing risk, such as analytic hierarchy process, fuzzy comprehensive evaluation, gray comprehensive evaluation, artificial neural network evaluation, data envelopment analysis [3]. These methods are widely used in the risk assessment of various industries, as far as the financing risk is concerned: Lin studied the early warning of financing risks of listed mining companies by using BP neural network; Yan and Meng used the fuzzy comprehensive evaluation method to evaluate the financing risks of small and medium-sized technology-based enterprises; Guo and Duan [4] conducted a fuzzy comprehensive evaluation on the financial risks of overseas mining investment; Wang [5]also studied the risk evaluation of asset securitization by using analytic hierarchy process and fuzzy comprehensive evaluation. Huang and Zheng [6] used ANP and gray fuzzy comprehensive evaluation to evaluate the risk of mining project investment; Bi [7] also used analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method to conduct fuzzy comprehensive evaluation on the network financing risk of small and micro science and technology enterprises.

3 Establishing evaluation system

3.1 Identification of financing risks of mining enterprises

The financing risk of mineral enterprises refers to the financing risk that may or will occur during the exploration, exploitation, operation and other stages of mineral enterprises by choosing different financing methods. At first, the first step is to identify the financing risks of mining enterprises. Since the capital needs and financing methods of mining enterprises are different at different stages of development, the financing risks at different stages are also different: (1) Exploration stage financing risk. The main task of the exploration stage is to find the mineral resources worth mining and the amount of mineral resources that can be determined, so the financing risks of this stage mainly include: resource risk, reserves risk, exploration risk, capital risk and exploration technology risk.

(2) Mining stage financing risk. The main possible financing risk in the mining stage is: production risk, safety risk, environmental protection risk and capital risk. Among them:

Production risk: it is the main core risk of mineral enterprise financing, including production technology, raw material supply, labor management and other risk factors.

Safety risk: the mining of ore requires a large number of artificial and technical equipment, and there are many uncertainties in the process of work and high accident safety risk. Therefore, the safety risk in the mining stage is higher than that in other stages.

Environmental protection risk: mining and processing of mineral resources will cause pollution and harm to the natural environment, cultural and social environment to varying degrees.

Capital risk: although the mineral exploitation stage is not as long as the exploration stage in the development cycle, its capital demand is larger than the exploration stage.

(3) Operation stage financing risk. Financing risk in the operation stage is basically similar to other industries in the capital market, mainly including debt financing risk and equity financing risk. Debt financing risks include the risk of fund supply, debt service, rising cost of capital. Equity financing risks include operational risks, capital risks, market environment risks, etc.

3.2 Evaluating indicators system

Indicator factor is an important component, which is essentially the quantitative significance and specific value of risk factors. Therefore, in the process of the formation of the risk evaluation index system, the selection of indicator factors should conform to the six principles of relevance, authenticity, scientific, systematic, typicality and practicality, and the construction of the mining enterprise financing risk evaluation index system is shown in the table below:

3.3 Evaluation model construction

3.3.1 Evaluation index weight

Through the analytic hierarchy process to determine the weight of the mineral enterprise financing risk evaluation index, the specific process is as follows:

Table 1
Evaluation indexes system of mineral enterprise financing risk evaluation

Objective First grade indexes Second grade indexes

Mining industry financing risk Exploration stage financing risk Resource risk

Reserves risk

Exploration risk

Exploration technology risk

Mining stage financing risk Production risk

Safety risk

Environmental protection risk

Capital risk

Operation stage financing risk Debt financing risk

Equity financing risk

Legal policy risk

Industry market risk

Firstly, establish the judgment matrix. Relevant data were obtained by sending questionnaires to relevant experts, and then MCE program software was used to calculate and process the obtained data, and the final weight was determined through consistency test. The scale and description of the judgment matrix are shown in the Table 2.

Table 2

Judgment matrix scale and its meaning

Scale	Definition	Introductions
1	Equal Importance	Both contribute equally to the goal.
3	Moderate Importance	The former element is slightly more important than the latter.
5	Obviously Importance	The former element is significantly more important than the latter.
7	More Important	The former element is much more important than the latter.
9	Strong Importance	The former element is strongly more important than the latter.
2,4,6,8	Intermediate Value of two adjacent degrees	When a compromise is needed

Notes: If the ratio of the importance of element i and element j is a_ij, then the importance ratio of element j and element i is: a_ji = 1/a_ij.

Table 3

List of phase judgment matrix RI values table

N	3	4	5	6	7	8	9	10
RI	0.58	0.91	1.12	1.24	1.32	1.41	1.45	1.53

Table 4

First-order index judgment matrix

	Exploration stage financing risk	Mining stage financing risk	Operation stage financing risk	W
Exploration stage financing risk	1	1/4	1/7	0.0754
Mining stage financing risk	4	1	1/4	0.2290
Operation stage financing risk	7	4	1	0.6955

CR = 0.0659 < 1, λ_max = 3.0764, CI = 0.0382, RI = 0.58.

Table 5

Evaluation matrix of financing risk in Exploration Stage

	Resource risk	Reserves risk	Exploration risk	Exploration technology risk	W
Resource risk	1	1/4	3	4	0.2350
Reserves risk	4	1	6	5	0.5909
Exploration risk	1/3	1/6	1	2	0.1031
Exploration technology risk	1/4	1/5	1/2	1	0.0710

CR = 0.0650 < 0.1, λ_max = 4.1755, CI = 0.0585, RI = 0.9.

Table 6

Evaluation matrix of financing risk in mining stage

	Production risk	Safety risk	Environmental protection risk	Capital risk	W
Production risk	1	3	2	7	0.4776
Safety risk	1/3	1	2	5	0.2535
Environmental protection risk	1/2	1/2	1	8	0.2231
Capital risk	1/7	1/5	1/8	1	0.0459

CR = 0.0904 < 0.1, λ_max = 4.2440, CI = 0.0813, RI = 0.9.

Table 7

Evaluation matrix of financing risk in operation stage

	Debt financing risk	Equity financing risk	Legal policy risk	Industry market risk	W
Debt financing risk	1	3	2	3	0.4167
Equity financing risk	1/3	1	1/2	1/3	0.1087
Legal policy risk	1/2	2	1	2	0.2662
Industry market risk	3	1/2	1/2	1	0.2084

CR = 0.0528 < 0.1, λ_max = 4.1425, CI = 0.0475, RI = 0.9.

Secondly, calculating the single-level weight and testing the consistency. In the hierarchy, if there is an element in the non-bottom level, and there are A₁, A₂,…, A_n elements in the next level, then the pairwise comparison judgment matrix A can be established, and the eigenvector W and the maximum Eigen root λ_max of the matrix A can be found. The n components of the eigenvector W are the relative importance weights of A₁, A₂,…, A_n, the ranking of the relative importance of this level and the weight of the relative importance of this level are given by the weight from large to small, that is, the maximum Eigen root λ_max and corresponding eigenvectors are calculated for each pair comparison matrix.

$\bar{W} = (\begin{matrix} W_{1} \\ ⋮ \\ W_{n} \end{matrix})$ (1)

The consistency test is to test the degree to which a pairwise comparison matrix satisfies the consistency. In practical application, the consistency test of pairwise comparison matrix is often needed to check whether the weight is reasonable. For example, there are three factors: A, B, and C. if A is twice as important as B, and B is twice as important as C, then under normal circumstances A is four times more important than C. However, in the actual determination of the weight, the pairwise comparison of factors is only an estimate, and it is likely that A and C are equally important, which results in A contradiction, and the pairwise comparison judgment matrix needs to be adjusted [8].

The consistency test of the pairwise comparison matrix can be carried out according to the following methods. Let the characteristic roots of the pairwise comparison matrix of the importance of n elements be set λ = n. We can show that any pairwise comparison matrix, when the matrices are exactly the same, has the maximum Eigen root λ_max = n, the judgment matrix that is not completely consistent is λ_max > n. In general, the higher the order of the matrix is, the greater the inconsistency. To eliminate this effect, the following consistency test indicators are defined:

$CI = (λ_{max} - n) / - (N - 1)$ (2)

Table 8

The weight of the index of financing risk evaluation of mineral enterprises

Objective	First grade indexes	Second grade indexes	Second grade indexes risk weight	Weighting
Mining industry financing risk	Exploration stage financing risk (0.0754)	Resource risk	0.2350	0.0177
		Reserves risk	0.5909	0.0446
		Exploration risk	0.1030	0.0078
		Exploration technology risk	0.0710	0.0054
	Mining stage financing risk (0.2290)	Production risk	0.4776	0.1098
		Safety risk	0.2535	0.0583
		Environmental protection risk	0.2231	0.0513
		Capital risk	0.0459	0.0106
	Operation stage financing risk (0.6955)	Debt financing risk	0.4167	0.2898
		Equity financing risk	0.1087	0.0756
		Legal policy risk	0.2662	0.1851
		Industry market risk	0.2084	0.1449

Table 9

Statistical table of expert scoring results

	Influencing factors	Low	Lower	Generally	Higher	High
Exploration stage (0.0754)	Resource (0.2350)	2	1	3	4	0
	Reserves (0.5909)	2	1	3	3	1
	Exploration (0.1031)	0	3	7	0	0
	Exploration technology (0.0710)	0	3	3	3	1
Mining stage (0.2290)	Production (0.4776)	0	5	3	2	0
	Safety (0.2535)	0	1	3	3	3
	Environmental protection (0.2231)	1	2	2	4	1
	Capital (0.0459)	0	3	2	2	3
Operation stage (0.6955)	Debt (0.4167)	0	3	4	3	0
	Equity (0.1087)	0	3	4	3	0
	Legal policy (0.2662)	1	3	5	1	0
	Industry market (0.2084)	0	1	3	6	0

When the matrix is completely consistent, CI = 0, the greater the CI, the more serious the inconsistency. In order to measure whether the judgment matrix satisfies the consistency, we introduce the RI value of the average random consistency index of the judgment matrix. The specific contents are shown in the following table:

In the case where CI, RI are known, the consistency index CI and the mean random consistency index RI can be used for consistency test (CR = CI/ -RI). If $CR = \frac{CI}{RI} < 0.10$ , then the consistency is satisfied. And then, the upper initial weight vector $\bar{W} = (\begin{matrix} W_{1} \\ ⋮ \\ W_{n} \end{matrix})$ is normalized as a single sort weight vector: If $CR = \frac{CI}{RI} > 0.10$ , then the pairwise comparison matrix needs to be modified until a satisfactory consistency is obtained.

Finally, the pairwise comparison matrix can be obtained by summarizing the analytic hierarchy process and analyzing the evaluation index system.

3.3.2 Fuzzy comprehensive evaluation

Fuzzy comprehensive evaluation method is a systematic analysis method to analyze and evaluate ‘fuzzy’ things by using fuzzy mathematics principle. The main advantages of this method are different from the uniqueness of traditional mathematical results, its processing results contain rich information to a large extent, which solves the problem of fuzziness and uncertainty of judgment, and has been widely used in the comprehensive evaluation system. The basic steps are as follows:

Firstly, the fuzzy comprehensive evaluation factor set should be determined. This study has determined the set of factors that affect the financing risk of mining enterprises as: First grade indexes factor: U = {Exploration stage financing risk U₁, Mining stage financing risk U₂, Operation stage financing risk U₃}. Second grade indexes factors: U₁ = {Resource risk U₁₁, Reserves risk U₁₂, Exploration risk U₁₃, Exploration technology risk U₁₄}; U₂ = {Production risk U₂₁, Safety risk U₂₂, Environmental protection risk U₂₃, Capital risk U₂₄}; U₃ = {Debt financing risk U₃₁, Equity financing risk U₃₂, Legal policy risk U₃₃, Industry market risk U₃₄}.

Secondly, establish the comprehensive evaluation set. Set up a set of all kinds of total evaluation results that the evaluator may make to the evaluation object, that is, the evaluation set of financing risk of mineral enterprises V = {Low risk V₁, Lower risk V₂, Generally risk V₃, Higher risk V₄, High risk V₅}.

Thirdly, the single factor fuzzy judgment is carried out to obtain the judgment matrix R. The five comments in the evaluation set are regarded as five fuzzy sets, and the membership of a single index at each evaluation level is calculated. The membership degree was determined by the expert scoring method, and the fuzzy relation matrix R was obtained through the evaluation of each factor and the normalization of the evaluation results.

$R = [\begin{matrix} R_{1} \\ R_{2} \\ ⋮ \\ R_{m} \end{matrix}] = [\begin{matrix} r_{11} & r_{12} & \dots & r_{1 n} \\ r_{21} & r_{22} & \dots & r_{2 n} \\ ⋮ & ⋮ & ⋮ & ⋮ \\ r_{m 1} & r_{m 2} & \dots & r_{mn} \end{matrix}]$ (3)

Fourthly, the establishment of a judgment model for comprehensive evaluation. Using the weight W calculated in the study and the single factor fuzzy evaluation results to calculate the fuzzy comprehensive evaluation B = W∗R, and then the corresponding evaluation set was set to calculate the final score of financing risk S = B∗V, so as to determine the level of financing risk of mining enterprises.

4 Empirical test

4.1 Case enterprise selection

Zijin Mining is a large mining group mainly engaged in the exploration and development of gold, copper, zinc and other metal mineral resources. The company has listed A shares in Shanghai and H shares in Hong Kong overall, the main production of gold and copper zinc metal reserves minerals have been into the top three domestic mining industry, the asset scale and sales income over 100 billion yuan, which is one of the largest and most competitive mining companies in China, which has the best industry benefits and controls the most metal mineral resources. The company’s main investment projects are distributed in 18 provinces and 11 overseas countries. At present, the company controls the output and profit of resource mineral products overseas, accounting for more than one third of the group, and has become one of the largest enterprises in the output and resource reserves of mineral products overseas in China.

The financing balance of Zijin Mining experienced a peak at the end of June 2019, when the allowance for short was relatively high. And the allowance for short reached a peak at the end of November 2019, but the financing balance at this time was low and the gap was large.

4.2 Analysis process

4.2.1 Term weight

The objects of the questionnaire are all experienced mining experts, entrepreneurs and university professors who are familiar with the financing risks of the mining industry, which are highly authoritative and widely representative. The trend (mean value) of the experts’ overall opinions on a risk factor indicator in the collected valid questionnaires was taken as the consultation result, and the pairwise comparison judgment matrix at all levels was constructed as follows:

4.2.2 Combined weight

That is to calculate the composition weight of each level element to the system target as shown in the table below:

4.2.3 Fuzzy comprehensive evaluation results

The membership degree of evaluation set was determined through the scoring of secondary indexes by 10 experts, combined with the average value of enterprise’s actual indexes and peers, and the specific scoring results of fuzzy membership matrix R of secondary evaluation indexes were summarized as follows: B₁ = W₁ ∗ R₁ = (0.165, 0.135, 0.341, 0.293, 0.066) In the same way: B₂ = W₂ ∗ R₂ = (0.022, 0.323, 0.273, 0.270, 0.112) B₃ = W₃ ∗ R₃ = (0.027, 0.258, 0.406, 0.309, 0) So, B = (0.036, 0.264, 0.371, 0.299, 0.031) Finally, the fuzzy comprehensive score is: S = 0.036∗90 + 0.264∗70 + 0.371∗50 + 0.299∗30 +0.031∗10 = 49.45

4.3 Result analysis

According to the fuzzy comprehensive score obtained in the previous step and the evaluation rating system table in the previous step, it can be seen that the enterprise’s score of 49.45 is within the range of [40,60), which belongs to the financing risk evaluation level of general risk. According to the actual financing situation of Zijin mining, the financing risk of the enterprise this year is general and the asset condition is good, which can basically meet the capital needs of the enterprise, which is consistent with the model evaluation results.

5 Conclusions

Due to the characteristics of mining enterprises, such as the longtime of high-risk investment return, the uncertainty of mineral resources and the complexity of enterprise development stage, the factors influencing the financing risk of mining enterprises are numerous and complex. Therefore, in the process of evaluating the financing risks of mining enterprises, mathematical models and experts’ professional scientific scores are used to improve the rationality and accuracy of the final results and lay a solid foundation for the management to make appropriate financing decisions.

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