Atlantis Press Journal style Research on Evaluation of Equity Financing Efficiency of Listed Companies in Strategic Emerging Industries Yaxi Huang*, Mu Zhang School of Finance, Guizhou University of Finance and Economics, Guiyang 550025, China Abstract This paper chooses 198 listed companies in strategic emerging industries, using DEA model to study the efficiency of equity financing, and carries on efficiency analysis, investment redundancy and output shortage analysis and industry comparative analysis. The results show that the efficiency of equity financing of listed companies in strategic emerging industries is inefficient. The comprehensive efficiency, pure technical efficiency and scale efficiency are 0.370, 0.603 and 0.563. From the scale pay, the economic scale of Chuanrungufen should be increased, Zhongguobaoan and other 179 decision-making units should be reduced; Dongxulantian and other 169 decision-making units have different levels of input redundancy and lack of output; equity financing efficiency is unevenly developed between different industries. Keywords: Strategic emerging industries, Financing efficiency, Equity financing efficiency evaluation, Data envelopment analysis (DEA) 1. Introduction Strategic emerging industries refer to breaking new ground in major cutting-edge technologies, representing the new direction for the development of science and technology and industry, and embodying the trend of development of the knowledge-based economy, circular economy and low-carbon economy in the world today. September 8, 2010 Premier Wen Jiabao chaired a meeting of the Standing Committee of the State Council to consider and adopt the "Accelerate the Cultivation and Development of Strategic Emerging Industries Decision" will be energy saving and environmental protection, a new generation of information technology, biomedicine, high-end equipment manufacturing, new energy sources , new materials and new energy vehicles and other seven industries designated as China's key strategic development of new industries. This injected a new * Corresponding author: E-mail: 1308113622@qq.com. force into promoting the upgrading of China's industrial structure and effectively meeting social needs and supply. Strategic emerging industries are guided by Deng Xiaoping theory and the important thought of “Three Represents”. They insist on giving full play to the fundamental role of the market and promoting the government-led role, insist on combining scientific and technological innovation with industrialization, and adhering to the overall promotion the four basic principles of combining the development across key areas with each other and insisting on enhancing the long-term competitiveness of the national economy and supporting the current development are committed to providing strong support for the sustainable economic and social development. The theory of pecking order in corporate financing argues that the best financing order should be endogenous financing first and then exogenous financing. Exogenous financing should first be debt financing, and then equity financing [1]. However, the choice of financing methods in Chinese companies is Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 189 Received 11 September 2017 Accepted 3 November 2017 Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). the opposite. The financing structure of China's listed companies is mainly based on external financing, and the external financing is up to 80% of the financing structure. The internal financing is generally less than 20% of the financing structure, and a few even depend entirely on external financing. In terms of foreign financing, listed companies in China generally prefer equity financing, and debt financing is not considered. Therefore, it is impossible to optimize the company's financing structure by coordinating the proportion of equity financing and debt financing [2]. The sustainable development of strategic emerging industries cannot be separated from the choice of financing methods. And the capital-intensive and technology-intensive strategic emerging industries prefer the of equity financing. [3] Equity financing efficiency can effectively measure the degree of perfection of China's capital market and the degree of resource allocation, which is of great significance for national economic growth and sustainable development of strategic emerging industries.[4] Therefore, how to accurately and effectively evaluate the efficiency of equity financing of strategic new industry listed companies has become a major issue in the sustainable development of new strategic industries. 2. Literature review As a way of exogenous financing, equity financing mainly refers to the behavior of listed companies raising funds by issuing shares, including two ways: public offering and private offering. In order to effectively study the efficiency of equity financing of listed companies in strategic new industries, this paper finds that many domestic and foreign scholars have studied equity financing and equity financing efficiency. The famous American financial expert Modigliani and Miller (1958) [5] in the "American economic review published entitled" the cost of capital, corporate finance and investment theory "of the thesis, the thesis put forward the famous MM theory, they think, in the condition of perfect capital market, because of arbitrage mechanism, the company issued shares regardless of financing or bond financing will not affect the value of the company, namely, capital structure and company value. Sayuri Shirai (2004) [6] based on the theory of financing constraints, constructs a regression model of the three ways of equity financing, bank lending and debt financing, which affect the company's investment decisions. The empirical results show that equity financing has not played a significant role, and thus lack of financing efficiency. Charnes et al (1989) [7] first introduced the DEA method into the evaluation of urban economic growth efficiency, and compared the economic performance level of 28 cities in China in 1983 and 1984. Sueyoshi (1992) [8] expanded the application of DEA in the area of urban efficiency evaluation, and investigated the resource allocation efficiency of 35 cities in China using DEA/AR model. The domestic scholar Zhengde Xiong, Fangjuan Yang and Jun Wan (2014) [9] using two stage relational network DEA model, with the cost of debt financing, debt financing risk as input indexes, rate of return on assets, total assets turnover, operating income growth rate as output indexes, the debt financing efficiency of China's new energy automotive industry listed companies and the corresponding sub stage efficiency was calculated. Li Jingwen, Wang Yuchun et al (2014) [10] took the 51 strategic emerging industries listed companies in Beijing as samples, selected ten quarterly financial data since 2011, and took total assets, asset- liability ratio and total operating costs as input indexes, return on net assets, total asset turnover, total revenue growth as output indexes, using DEA method to measure and analyze financing efficiency. According to the total assets, assets and liabilities ratio as input indexes, return on net assets and Tobin's Q as the output indexes, Xiaoyan Qiao and Dongjun Mao (2015) [11] used DEA method to compare and analyze the efficiency of equity financing for 2010-2013 years in Jiangsu province 15 listed companies of the new energy, and based on the above results, the influence factors of efficiency are analyzed by the fixed effect model. Qiong Wang, Chengxuan Geng (2016) [12] extended the multi-stage DEA model, taking non-flow accountable and capital public reserve as input indexes and net profit and total operating revenue as output indexes to build a six-stage Super-SBM model Malmquist index model, the static and dynamic evaluation of the financing efficiency of 29 listed companies in strategic emerging industries in Jiangsu Province for 2009-2014. Ruibo Liu and Xuemei Zhang (2009) [13] from the perspective of financing efficiency, Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 190 apply data envelopment analysis (DEA) to the analysis and evaluation of the efficiency of equity financing of Expressway listed companies. The empirical results show that the efficiency of equity financing of our expressway listed companies is relatively low. We should improve the efficiency of equity financing from reforming input scale of equity financing, equity concentration and other input indexes, main business income growth rate and net assets yield index. Lichang Liu and Genfu Feng et al (2004) [14] used the data envelopment analysis method to take the 47 listed companies that initially listed in Shanghai stock market for 1998 as the research object, taking asset-liability ratio and ownership concentration as input indexes, net asset return rate, Tobin's Q value as the output indexes, a comprehensive evaluation of China's equity financing efficiency. Haokun Yan and Honghong Zhao (2014) [15] take the total assets, asset liability ratio and financial cost as input indexes, main business revenue growth rate and net assets yield ratio as output indexes. They use DEA model to analyze the financing efficiency of 23 listed companies in Inner Mongolia. The results show that the overall financing efficiency of the listed companies in Inner Mongolia is low, and only 5 of the 23 companies are satisfied with the DEA. On the basis of this, the basic idea of optimizing the financing efficiency of the listed companies is put forward. Wen Han, Kaihong Liu (2014) [16] take labor force, operating expenses, paid in capital as input indexes, premium income, payments for the output indexes by DEA model showed that DEA evaluation sample period the insurance business property insurance company operating performance; then, using two step cluster analysis, clustering analysis, clustering results were obtained clear. The empirical results demonstrate the feasibility and applicability of the DEA clustering method in the performance evaluation of the insurance business of property insurance companies. Yueping Dai and Hongmei Zhang (2013) [17] choose the data from 2012 and 2011 of China high tech industry statistical yearbook, and use DEA model to evaluate the efficiency of input-output of 31 provinces in China. The results show that the independent innovation efficiency of all the provinces in the last 5 years is generally low, and the national efficiency of Guizhou Province in the last 5 years has been greatly fluctuated and lack of stability. In summary, most foreign scholars focus on the research on the enterprise value and its influencing factors, and research on the financing efficiency of enterprises. Domestic scholars have studied the financing efficiency of Chinese enterprises, especially the equity financing efficiency from different angles. However, most of them involve strategic emerging industries and their listed companies or their sample size is too small, but they also do not carry out a detailed analysis of their inputs, outputs and industries. Therefore, this article chooses 198 representative listed companies in China's strategic emerging industries as the research object, taking equity financing net value, ownership concentration, asset-liability ratio as input indexes, return on net assets, growth rate of main business, Tobin's Q value as output indexes, using data envelopment analysis (DEA) study on the efficiency of equity financing, and analysis of the efficiency, input redundancy and output deficiency of industry analysis and comparative analysis of the financing efficiency of the three aspects of strategic emerging industries listed companies analysis. This is of great theoretical and practical significance to improve the equity financing efficiency of listed companies in strategic emerging industries and to improve the strategic emerging industry market. 3. Introduction of DEA model 3.1. Fundamental Data Envelopment Analysis (DEA) was proposed by Charnes, Coopor and Rhodes in 1978. The principle of this method is to maintain the input or input of DMU (Decision Making Units) by means of Mathematical programming and statistics identify relatively efficient frontiers of production, project each decision-making unit onto the DEA production frontier and evaluate their relative validity by comparing the extent to which decision-making units deviate from the DEA frontier. Based on the concept of relative efficiency, DEA method uses convex analysis and linear programming as a method of evaluation. The mathematical programming model is used to calculate and compare the relative efficiency between the decision-making units and evaluate the evaluation objects. It can give full consideration to the optimal input-output plan for the decision-making unit itself, so it can reflect the information and characteristics of the evaluation object Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 191 more ideally. Meanwhile, it has its uniqueness for evaluating multi-input and multi-output of complex systems. It has the following characteristics: First, it is applicable to the comprehensive evaluation of the effectiveness of multi-output and multi-input. It has an absolute advantage in evaluating the effectiveness of multi-output and multi-inputs. Second, the DEA method does not integrate the data directly. Therefore, the optimal efficiency index of decision-making unit has nothing to do with the dimension selection of input index and output index. There is no need to dimensionless the data before establishing the model using the DEA method. Third, no weight hypothesis is required. The decision-making unit input and output of the actual data to obtain the optimal weight, excluding a lot of subjective factors, with strong objectivity. Fourth, the DEA method assumes that each input is associated with one or more outputs. And there is indeed some connection between the input and output, but do not have to determine the display of this relationship. 3.2. Construction of the model 3.2.1. Integrated efficiency model (CCR model) There are n decision units DMU𝑗, where𝑗 = 1,2, … , 𝑛. Any DMU has m input vectors (input production factors) and s output vectors (output obtained), then 𝑋𝑗 = （𝑥1𝑗，𝑥2𝑗，𝑥3𝑗 … 𝑥𝑚𝑗） > 0 , 𝑗 = 1,2, … , 𝑛 𝑌𝑗 = （𝑦1𝑗，𝑦2𝑗，𝑦3𝑗 … 𝑦𝑠𝑗） > 0, 𝑗 = 1,2, … , 𝑛 Where𝑗 = 1,2, … , 𝑛, X𝑚𝑗denotes that the jth decision unit has m kinds of inputs, and 𝑌𝑠𝑗 denotes the sth input of the jth decision unit. So for the jthDMU𝑗 decision unit based on the minimum, inefficient, convex hypothesis production set: 𝑇 = {(𝑋, 𝑌)| �𝑋𝑗𝜆𝑗 𝑛 𝑗=1 ≤ X, �𝑋𝑗𝜆𝑗 𝑛 𝑗=1 ≥ 𝑌, 𝜆𝑗 ≥ 0, 𝑗 = 1,2, … , 𝑛 The input validity model of the DMU has the following CCRs: ⎩ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎧ 𝑚𝑚𝑛𝑚 �𝑋𝑗𝜆𝑗 + 𝑆− 𝑛 𝑗=1 = 𝑚𝑋0 �𝑌𝑗𝜆𝑗 − 𝑆+ 𝑛 𝑗=1 = 𝑌0 𝜆𝑗 ≥ 0, j = 1,2 … n 𝑆+ ≥ 0, 𝑆− ≥ 0 The CCR model is "comprehensively effective" in terms of the effective and technologically efficient DMU. Assuming that the optimal solution is θ, λ, 𝑆+ , 𝑆− , the effective judgments and economic explanations are as follows: (1) Ifθ = 1,𝑆+ = 0,𝑆− = 0, then the decision unit DMU𝑗 is said to be valid for the DEA under the CCR model, indicating that the decision unit is comprehensive and effective, that is, both the scale efficiency and the technical efficiency Best, there is no "excess" investment and "deficit" output; (2) Ifθ = 1, 𝑆+ ≠ 0,𝑆− ≠ 0 , the decision unit DMU𝑗 is said to be weakly valid for the DEA in the CCR model. Although there is no need for isometric compression in terms of input, there are some aspects of "excess" Input or "deficit" output; (3) Ifθ < 1, then the decision unit DMU𝑗 is said to be valid for non-DEA under the CCR model, indicating that the input can be fully compressed by θ [18]. 3.2.2. Technical efficiency model (CCGSS model) There are n decision unitsDMU𝑗, where 𝑗 = 1,2, … , 𝑛, And 𝑋𝑗 = （𝑥1𝑗，𝑥2𝑗，𝑥3𝑗 … 𝑥𝑚𝑗） > 0 , 𝑌𝑗 = （𝑦1𝑗，𝑦2𝑗，𝑦3𝑗 … 𝑦𝑠𝑗） > 0, the CCGSS model is ⎩ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎧ 𝑚𝑚𝑛𝑚 𝑠. 𝑡. �𝑋𝑗𝜆𝑗 + 𝑆− 𝑛 𝑗=1 = 𝑚𝑋0 �𝑌𝑗𝜆𝐽 − 𝑆+ 𝑛 𝑗=1 = 𝑌0 �𝜆𝑗 𝑛 𝑗=1 = 1 𝜆𝑗 ≥ 0, 𝑗 = 1,2, … , 𝑛 𝑆+ ≥ 0, 𝑆− ≥ 0 The optimal value can be obtained by this model. When ∑ 𝜆𝑗𝑛𝑗=1 < 1 is the scale returns increasing, Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 192 ∑ 𝜆𝑗𝑛𝑗=1 = 1 is the scale returns constant; ∑ 𝜆𝑗 𝑛 𝑗=1 > 1is the decreasing returns to scale. Suppose the optimal solution is θ, λ, 𝑆+ , 𝑆− , the effective judgments and economic explanations are as follows: (1)If θ < 1 or 𝑆+ ≠ 0 , 𝑆− ≠ 0 , DMU𝑗 is non-DEA valid (CCGSS); (2)Ifθ = 1,𝑆+ = 0, 𝑆− = 0, thenDMU𝑗 is DEA valid (CCGSS). When (2) is satisfied, it indicates that DMU𝑗 is purely technical, otherwise non-technical. When 𝑆− ≠ 0 , it indicates that there is excess investment; when𝑆+ ≠ 0, it indicates that there is a loss output. When 0<θ<1, it indicates that the DMU is improperly input and can be compressed in equal ratio, which is non-technical and effective. To sum up, the CCR model is used to evaluate whether the decision-making unit is both efficient and technically effective. However, the CCGSS model is only used to evaluate whether the technical efficiency is the best. Combining the two can make the combination of technical efficiency and economies of scale Analysis [19]. 3.3. General steps 3.3.1. Determine the evaluation objectives Evaluation is the most basic function of DEA model, which is the basis of our correct application of DEA model. Only by determining the purpose of evaluation, can we find the right direction, select the appropriate model and collect the appropriate data to substantiate the problems in production and life. This requires that we be able to accurately translate the information in economic activity into the information required by the DEA model or to correspond one-on-one with the relevant concepts of DEA. 3.3.2. Select the decision unit DMU Since DEA evaluates the relative validity of DMUs of the same type, the following two points need to be followed in the selection of DMUs: First, DMUs must be of the same type, DMUs of the same nature or DMUs with the same time interval; Then, the number of DMU should be selected as the input and output data of the sum of 2 times is appropriate. 3.3.3. Establish input and output index system The establishment of the input and output index system needs to pay attention to the following points: First of all, it is necessary to reflect the purpose of evaluation truthfully and comprehensively. Secondly, attention should be paid to the relationship between input indicators and output indicators. At the same time, we should try to avoid the multiple linear relationship between input and output indicators; Finally, to ensure the diversity and availability of input and output indicators. 3.3.4. Select the DEA model The choice of DEA model to follow the following two requirements: first, pay attention to the actual production and life background; the second is to choose the DEA model for evaluation purposes. In addition, different models can be applied for multi-angle analysis in order to arrive at a more comprehensive evaluation. 3.3.5. Evaluation and analysis of DEA results This is the most critical step in the application of DEA model. By collecting data and calculating models, we get the result of DEA model. Based on this result, we analyze the real economic problems, and provide an accurate direction for policymakers to formulate effective policies and solve practical problems. 3.3.6. Adjust the input and output index system When the result of DEA evaluation and analysis is unsatisfactory, we should adjust the input and output index system appropriately and reconsider it without violating the purpose of evaluation. By using a variety of DEA models to analyze different angles, the different results are compared and the important factors that affect the decision making unit are observed. 3.3.7. Draw a comprehensive analysis and evaluate the conclusion By calculating the DEA model, we can get the following information: the DEA validity of each DMU, the relationship between the relative efficiency of DMU and the input and output indexes, the relative effective production frontier and the projection of DMU on the Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 193 effective production frontier. To make a comprehensive analysis and evaluation of these results, we can formulate a scientific and reasonable policy [20]. 4. Empirical analysis 4.1. Determination of evaluation index 4.1.1. Input indexes (1) Equity financing net value Since this paper mainly investigates the efficiency of equity financing, after we choose the total amount of equity financing acquired by the company after IPO, deducting the weighted financing cost, we get the net equity financing of the company. In order to ensure the validity of the model, it is transformed into a dimensionless the amount. (2) Ownership concentration The degree of ownership concentration affects the company's financing efficiency through its influence on the company's daily operation, so it is used as an input index to investigate the efficiency of financing. The index mainly uses the largest shareholders to share the total number of shares issued. (3) Asset liability ratio The target is to reflect the impact of the company's financial structure on equity financing. The calculation method is to divide the total liabilities of the listed company by the total assets. This index mainly investigates the relationship between the tax credit effect and the financing cost of the creditor's rights. 4.1.2 Output indexes (1)Return on equity This index reflects the profitability of shareholder investment in production and operation, which is the ratio of the company's net profit to the average net asset over a period of time. This paper introduces this index in the empirical evidence, intended to use this index to measure whether the profitability of the company after the equity financing has been enhanced. (2) Main business revenue growth rate This index reflects the company's growth ability. It can be calculated from the increase of income from main business over the previous year. (3) Tobin Q Tobin's Q reflects the allocation efficiency of equity financing, equal to the total market price divided by the replacement value. Since the replacement value of the company is assessed through acquisition, the replacement value is replaced by the net asset value of the company, which is the ratio of total turnover to total turnover over the years since the equity financing. Table 4-1 Input and Output Indexes Input indexes Output indexes Equity financing net value Return on equity Ownership concentration Main business revenue growth rate Asset liability ratio Tobin Q 4.2. Sample Selection and Data Sources This paper chooses 198 companies with certain representativeness and comparability as the research object after excluding the changes of the financial statements before and after the listing of the company, the negative output indicators and the financial indicators such as the secondary issuance. According to DEA's experience, it is only meaningful to analyze the sample size at least twice to three times more than the total number of input variables and output variables. The total number of input and output variables in this paper is 6, which meets the requirements. The data in this paper are all from the database of Tai`an (CSMAR) series research database. The sample table of the company in this paper is shown in table 1 of appendix. Strategic new industries are divided into seven industries: energy saving, environmental protection, a new generation of information technology, biomedicine, high-end equipment manufacturing, new energy, new materials and new energy vehicles. Among the research objects in this sample, there are 29 energy saving and environmental protection industries, a total of 25 new generation of information technology industry, 34 biomedical industry, 30 high-end equipment manufacturing industry, 27 new energy industry, 25 new material industry, 28 new energy vehicle industry. The proportion of sample companies selected by each industry is shown in Fig.4-1. Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 194 Fig.4-1 Distribution of Selected Samples by Industry In this paper, the database of Tai`an (CSMAR) series research database are selected as input indexes and output indexes of each sample company in 2016, and the original data of each sample company are shown in table 2 of appendix. 4.3. Evaluation results of equity financing efficiency This article intends to use DEAP Version 2.1 software to research the issue of equity financing efficiency of strategic new listed companies. The original data of input and output indexes in table 2 of appendix are substituted into the model software for calculation, and the shares of the strategic new industry listed companies financing evaluation results and the overall situation (see table 4-2), the evaluation results of equity financing of listed companies in strategic emerging industries are shown in table 3 of appendix. 4.4. Analysis of the evaluation results of equity financing efficiency 4.4.1 Efficiency analysis (1) Comprehensive efficiency The comprehensive efficiency is a comprehensive measure and evaluation of various aspects of the ability of resource allocation and resource utilization of decision-making units. As seen from table 3 of appendix and table 4-2, from the comprehensive efficiency of DEA measurement, in 2016, the comprehensive efficiency of 18 decision-making units such as Shenzhennengyuan, Yichengxinneng, Shangqijituan Zhenhuazhonggong, Xugongjixie, Liugong, Haimaqiche, yinxinnengyuan, Hebeixuangong, Haigetongxin and Wandongyiliao and so on reached 1, indicating that the inputs and outputs of the above decision-making units are comprehensive and effective , that is both technically effective and scale effective. The proportion of comprehensive and effective decision-making units is 9.09%. However, the average comprehensive efficiency of the 198 publicly listed companies in strategic emerging industries is 0.370, indicating that the input and output of listed companies in strategic emerging industries are not comprehensively and effectively implemented. Among them, Zhongguobaoan, Dongxulantian, Desaidianchi, Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Xujidianchi, Yingtejituan, Zhongyuanhuanbao and other 180 decision-making units still has some space for improvement and improvement. (2) Pure technical efficiency Pure technical efficiency is the production efficiency of decision-making unit due to factors such as management and technology. It can be seen from table 3 of appendix and table 4-2,from the purely technical efficiency of DEA measurement, in 2016, the pure technical efficiency of 29 decision-making units such as Shenzhenneneyuan, Yamadun, Yinengxincheng, Yaxingkeche, Hongduhankong, Zhenhuazhonggong, Xugongjixie, Liugong, Haimaqiche, Yinxing Energy, Ankaikeche, HebeixuanongHager and so on reached 1, indicating that at the current technical level, the above- mentioned decision-making unit invested in the use of resources is efficient. The proportion of purely technical and effective decision making units is 14.65%. However, the average pure technical efficiency of the Table 4-2 The overall efficiency of equity financing efficiency in strategic emerging industries Comprehensive efficiency Pure technical efficiency Scale efficiency DEA effective 18（9.09%） 29（14.65%） 18（9.09%） Non DEA effective 180（90.91%） 169（85.35%） 180（90.91%） Mean 0.370 0.603 0.563 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 195 198 publicly listed companies in the strategic emerging industries is 0.603, which is not entirely technically effective. Among them, the management and technical level of 169 decision-making units such as Zhongguobaoan, Dongxulantian, Desaidianchi, Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Xujidianchi, Yingtejituan, Zhongyuanhuanbao, Wanxiangqianchao, Chinaguangheji, Huanruishiji, Kaidishentai, Xinxianghuaxian and Zhongkesanhuan should be improved. (3) Scale efficiency Scale efficiency is the production efficiency that is affected by the size of the decision unit. it can be seen from table 3 of appendix and table 4-2, from the scale efficiency of DEA measurement, in 2016, , The scale efficiency of 18 decision making units such as Shenzhennengyuan, Yinengxincheng, Zhenhuazhonggong, Xugongjixie, Liugong, Haimaqiche, Yinxingnengyuan, Hebeiuangong, Haigetongxin, Daomingguangxue and Wandongyiliao reached 1, indicating that these decision-making units are effective in scale. The proportion of effective decision-making units is 9.09%. However, the average of the scale efficiency of the 198 listed companies that represent the strategic emerging industries is 0.563, indicating that the overall performance of strategic emerging industries listed companies is not achieved. Among them, the scale efficiency of 180 decision- making units such as Zhongguobaoan, Dongxulantian, Desaidianchi, Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Xujidianqi, Yingtejituan, Zhongyuanhuanbao, Wanxiangqianchao, Fenghuagaoke, Huitianredian and Zhongtaiqiche still has some room for improvement and improvement. (4) Returns to scale From the returns to scale of view, Chuanrungufen shows the increasing returns to scale in production within the boundaries, that should be appropriate to increase the size of its economy, the scale and the input and output matching; Zhongguobaoan, Dongxulantian, Desaidianchi Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Xujidianchi, Yingtejituan, Zhongyuanhuanbao, Wanxiangqianchao, Fenghuagaoke, Zhongguangheji, Huanruishiji, Kaidishentai, Xinxianghuaxian, Zhongkesanhuan, Zhongtaiqiche, Huagongkeji, Jingxinyaoye, Xinhaiyi, Jinzhikeji, Leibaogaoje, Wohuayiyao, Sanweitongxin and other 179 decision-making unit in the production boundary performance of scale returns diminishing, indicating that its economic size should be appropriately reduced to make the scale and investment matching; the remaining 18 decision-making units in the production boundary performance for the same scale returns, then the economy should remain the same size. 4.4.2 Analysis of insufficient input redundant output DEAP Version 2.1 software gives the DEA evaluation value of equity financing efficiency of listed companies in strategic emerging industries, and also gives the values of slack variables of inputs and outputs of each decision unit, that is, inputting redundant values and outputs Insufficient value, the results see table 4 of appendix. Table 4 shows that Zhongguobaoan, Dongxulantian, Desaidianchi, Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Xujidianchi, Yingtejituan,Zhongyuanhuanbao, Wanxiangqianchao, Fenghuagaoke Huitianredian, Yingluohua, Zhongguangheji, Huanruishiji, Kaidishentai, Xinxianghuaxian, Zhongkesanhuan, Zhongtaiqiche, Huagongkeji, Jingxinyaoye and other 169 decision- making unit there are varying degrees of input redundancy and output deficiencies. In the case of Shenwuhuanbao, there were 0.092 million yuan of net investment in equity financing. There was 1.447% investment redundancy in the ownership concentration and 4.079 in the asset-liability ratio; net assets yield was 91340.018 output deficiency, there are 29.955 output deficits in the main business yield, 0.342 output deficit in Tobin Q. Only after eliminating the above input redundancy and insufficient output can Shenwuhuanbao Company reach purely technical and effective. The remaining 168 decision-making unit input redundant output analysis, and so on. 4.4.3 Industry comparative analysis September 8, 2010 Premier Wen Jiabao chaired a meeting of the Standing Committee of the State Council to consider and adopt the "Accelerate the Cultivation and Development of Strategic Emerging Industries Decision" will be energy saving and environmental protection, a new generation of Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 196 information technology, biomedicine, high-end equipment manufacturing, new energy sources , new materials and new energy vehicles and other seven industries designated as China's key strategic development of new industries. Among them, the energy-saving and environmental protection industries include 29 companies such as Dongxulantian, Zhongyuanhuanbao, Kaidishentai, Huitianxincai, Xianhehuanbao, Shenwuhuanbao, Zhongdianhuanbao, Tianhaohianjing, Zhongcaijieneng and so on; the new generation of information technology industry include 25 companies such as Tefaxinxi, Huanruishiji, Xinhaiyi, Sanweitongxin, Beiweikeji, Guangxunkeji, Shensunda A and so on. The biopharmaceutical industry includes 34 companies such as Zhongguobaoan, Haiwangshengwu, Fengyuanyaoye, Jingxinoyey, Wohuayiyao and so on. The high-end equipment manufacturing industries include30 companies such as Xujidianqi, Huagongkeji, Hezhongsizhuang, Siweituxin, Teruide, Zhongguoweixin and so on. The new energy industries include 27 companies such as Shenzhennengyuan, Huitianredian, Yingluohua, Jinzhikeji, Tuorixinneng, Yamadun and so on. The new materials industry include25 companies such as Desaidianchi, Fenghuagaoke, Zhongguangheji, Xinxianghuaxian, Zhongkesanhuan, Xinyegufen, Zhonggangtianhuan and so on. The new energy automotive industries include 28 companies such as Wanxiangqianchao, Zhongtaiqiche, Yinlungufen, Yataigufen and so on. Based on the data in table 4-3, we can calculate the DEA average of the financing efficiency of the seven major industries such as energy saving and environmental protection, new generation of information technology, biomedicine, high-end equipment manufacturing, new energy, new materials and new energy vehicles. The results, as shown in figures 4-2, 4-3 and 4-4, show that the equity financing efficiency of listed companies in china's strategic emerging industries has an unbalanced development between industries. Among them, the average value of comprehensive efficiency from high to low is the high- end equipment manufacturing industry, new energy industry, energy saving and environmental protection industry, new energy automotive industry, a new generation of information technology industry, bio pharmaceutical industry, new materials industry; pure technical average value from high to low is the energy saving and environmental protection industry, high-end equipment manufacturing industry, the new energy automotive industry, a new generation of information technology industry, new energy industry, new material industry, bio pharmaceutical industry; the average scale efficiency from high to low is the energy saving and environmental protection industry, high-end equipment manufacturing industry, new energy industry, new energy automotive industry, new material industry, a new generation of information technology industry, bio pharmaceutical industry. Fig.4-3 The average of pure technical efficiency of each industry Fig.4-4 The average of scale efficiency of each industry Fig.4-2 The average of the comprehensive efficiency of each industry 0,8728 0,6434 0,5393 0,7811 0,6273 0,5492 0,7324 0 0,2 0,4 0,6 0,8 1 0,786 0,5245 0,5158 0,6783 0,6273 0,5333 0,6143 0 0,2 0,4 0,6 0,8 1 0,4772 0,3866 0,3137 0,5634 0,4778 0,3127 0,4624 0 0,1 0,2 0,3 0,4 0,5 0,6 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 197 5. Conclusions This paper selects a representative 198 listed companies from the strategic emerging industries as the research object, starting from the efficiency of equity financing based on capital input and output efficiency, with the previous definition of the financing efficiency as the theoretical basis established the evaluation index system of equity financing efficiency, and used DEA- BCC model to evaluate the equity financing efficiency of listed companies in strategic emerging industries, thus draw the following conclusions: First, the average comprehensive efficiency of listed companies in strategic emerging industries is 0.370, indicating that the input and output of listed companies in strategic emerging industries are not comprehensively and effectively implemented. Among them, Shenzhennengyuan, Yichengcinneng, Zhenhuazhonggong, Xugongjixie, Liugong, Haimaqiche, Yinxingnengyuan, Hebeixuanong, Haigetongxin, Daomingguangxue, Metainuo, Nandudianyuan, Wandongyiliao and other 18 decision- making unit to achieve comprehensive and effective; Zhongguobaoan, Dongxulantian, Desaidianchi, Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Xujidianqi, Yingtejituan, Zhongyuanhuanbao and other 180 decision-making units have not been comprehensively and effectively implemented. Second, the average net technical efficiency of listed companies in strategic emerging industries is 0.603, which is not entirely purely technical and effective. Among them, 29 decision-making units such as Shenzhennengyuan, Yamadun, Yichengxinneng, Yaxingkeche, Hongduhankong, Zhenhuazhonggong, Xugongjixie, Liugong, Haimaqiche, Yinxingnengyuan, Ankaikeche and Haigetongxin reached the purely technical and effective level. 169 decision-making units such as Zhongguobaoan, Dongxulantian, Desaidianchi, Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Yingtejituan, Zhongyuanhuanbao Wanxiangqianchao, Fenghuagaoke, Huitianredian, Yingluohua, Huanruishiji, Kaidishentai, Xinxianghuaxian and Zhongkesanhuan did not reach pure Effective technology. Thirdly, the average size efficiency of listed companies in strategic emerging industries is 0.563, indicating that the listed companies in strategic emerging industries are not achieving the overall scale effective. Among them, Shenzhennengyuan, Yichengxinneng, Zhenhuazhonggong, Xugongjixie, Liugong, Haimaqiche, Yinxingnengyuan, Hebeixuanong, Haigetongxin, Daomingguangxue, Metainuo, Nandudianyuan, Wandongyiliao and other 18 decision-making unit to achieve the scale of effective. 180 decision-making units such as Zhongguobaoan, Dongxulantian, Desaidianchi, Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Xujidianqi, Yingtejituan, Zongyuanhuanbao, Wangxiangqianchao, Fenghuagaoke, Huitianredian, Yingluohua, Huanruishiji, Kaidishentai, Xinxianghuaxian, Zhongkesanhuan and Zhongtaiqiche have not achieved the scale effective. Fourth, From the returns to scale of view, Chuanrungufen`s economies of scale should be increased; Zhongguobaoan, Dongxulantian, Desaidianchi, Haiwangshengwu, Fengyuanyaoye, Xujidianchi, Yingtejituan, Zhongyuanhuabao, Wanxiangqianchao, Fenghuagaoke, Huitianredian, , Huanruishiji, Kadishentai, Xinxianghuaxian, Zongtaiqiche, Huagongkeji, Jingxinyaoye, Xinhaiyi and other 179 decision making units should be reduced; the remaining 18 decision making units economic scale should remain unchanged. Fifth, Zhongguobaoan, Dongxulantian, Desaidianchi, Tefaxinxi, Haiwangshenwu, Fengyuanyaoye, Xujidianqi, Yingtejituan, Zhongyuanhuanbao, Wanxiangqianchao, Fenghuagaoke, Huitianredian, Yingluohua, Huanruishiji, Kaidishentai, Xinxianghuaxian, Zhongkesanhuan, Zhongtaiqiche, Jingxinyaoye, Xinhaiyi and other 169 decision-making units have varying degrees of input redundancy and output deficiencies. Sixthly, the equity financing efficiency of the listed companies in the strategic emerging industries has the unbalanced development among industries. Among them, the average value of comprehensive efficiency from high to low is the high-end equipment manufacturing industry, new energy industry, energy saving and environmental protection industry, new energy automotive industry, a new generation of information technology industry, bio pharmaceutical industry, new materials industry; pure technical average value from high to low is the energy saving and environmental protection industry, high-end equipment Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 198 manufacturing industry, the new energy automotive industry, a new generation of information technology industry, new energy industry, new material industry, bio pharmaceutical industry; the average scale efficiency from high to low is the energy saving and environmental protection industry, high-end equipment manufacturing industry, new energy industry, new energy automotive industry, new material industry, a new generation of information technology industry, bio pharmaceutical industry.[21] References [1] S C Myers, N S Majluf. 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Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 199 Appendices Table 1: 198 listed companies of strategic emerging industries Enterprise name Stock code Enterprise name Stock code Zhongguobaoan 000009 Shenzhennengyuan 000027 Dongxuliantian 000040 Desaidianchi 000049 Tefaxinxi 000070 Haiwangshenwu 000078 Fenyuanyaoye 000153 Xujidianqi 000400 Yingtejituan 000411 Zhongyuanhuanbao 000544 Wanxiangqianchao 000559 Fenhuagongke 000636 Huitianredian 000692 Yingluohua 000795 Zhonguangheji 000881 Huanruishiji 000892 Kaidishentai 000939 Xinxianghuanxian 000949 Zhongkesanhuan 000970 Zhongtaiqiche 000980 Huagongkeji 000988 Jingxinyaoye 002020 Xinhaiyi 002089 Jinzhikeji 002090 Laibaogaoke 002106 Wohuayiyao 002107 Sanweitongxin 002115 Yinlungufen 002126 Tuobangufen 002139 Beiweikeji 002148 Laiyinshenwu 002166 Tuorixinneng 002218 Guanxunkeji 002281 Yataigufen 002284 Gelinmei 002340 Hezhongsizhuang 002383 Siweituxin 002405 Duofuduo 002407 Kanshenggufen 002418 Shuanghuanchuangdong 002472 Rongjiruanjian 002474 Jiangfencicai 002600 Yamadun 002623 Teruide 300001 Yiweilineng 300014 Huitianxincai 300041 Shuzizhengtong 300075 Yichengxinneng 300080 Dongfangrishen 300118 Xianhehuanbao 300137 Shenwuhuanbao 300156 Zhengdongzhiyao 300158 Zhongdianhuanbao 300172 Chulingxixin 300250 Tianhaohuanjing 300332 Kanxinxincai 600076 Shangqijituan 600104 Dongkuigufen 600114 Zhongguoweixing 600118 Mingjiangshuidian 600131 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 200 Futianqiche 600166 Yaxingkeche 600213 Hongduhangkong 600316 Zhneghuazhonggong 600320 Hangtiandongli 600343 Kunyaojituan 600422 Hengtongguangdian 600487 Guihanggufen 600523 Yijingguangdian 600537 Guanguyuan 600771 ShensangdaA 000032 Xugongjixie 000425 Liugong 000528 Haimaqiche 000572 Qidiguhan 000590 Shantuigufen 000680 Xinyegufen 000751 Xinhuazhiyao 000756 Zhonghanfeiji 000768 Bohaihuosai 600960 Qinchuangjichuang 000837 Yinxingnengyuan 000862 Ankaiqiche 000868 Faershen 000890 Yunneidongli 000903 Shandahuate 000915 Hebeixuangong 000923 Zhongguozhongqi 000951 Fousukeji 000973 Jiuzhitang 000989 Shirongzhaoye 002016 Zhouyankeji 002046 Hengdiandongci 002056 Zhonggangtianyuan 002057 Suzhougude 002079 Longjigufen 601012 Zhongcaijieneng 603126 Woerhecai 002130 Yunhaijinshu 002182 Zhengtongdianzi 002197 Feimaguoji 002210 Aotexun 002227 Aoweitongxin 002231 Dahuagufen 002236 Chuanrungufen 002272 Zhongdianxinlong 002298 Dongfangyuanlin 002310 Gellinmei 002340 Longjijixie 002363 Dongshanjingmi 002384 Neimengyiji 600967 Shenglutongxin 002446 Haigetongxin 002465 Fuchunhuanbao 002479 Keshida 002518 Tianshunfengneng 002531 Yataikeji 002540 Yishengyaoye 002566 Qinxinghuanjing 002573 Shenyanggufen 002580 Daomingguangxue 002632 Maoshuodianyuan 002660 Jingweigufen 002662 Teyiyaoye 002728 Ankeshenwu 300009 Jiqiren 300024 Meitainuo 300038 Hekanxinneng 300048 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 201 Yujingangshi 300064 Nandudianyuan 300068 Danshenkeji 300073 Shengyunhuanbao 300090 Jiayugufen 300117 Zhejiangdingli 603338 Dafukeji 300134 Weiminghuanbao 603568 Yongqinghuanbao 300187 Xinwangda 300207 Qianshanyaoji 300216 Dongfangredian 300217 Meichengkeji 300237 Dianzhenduan 300244 Chanshanyaoye 300255 Jinduankeji 300258 Baanshuiwu 300262 Hejiagufen 300273 Yangguangdianyuan 300274 Sannuoshenwu 300298 Zhongjizhuangbei 300308 Bohuichuangxin 300318 Jinmokeji 300334 Mengcaoshentai 300355 Xuelanghuanjing 300385 Zhonglaigufen 300393 Feilihua 300395 Huannengkeji 300425 Sitongxincai 300428 Shanheyaopu 300452 Maikeshanwu 300463 Zhongfeigufen 300489 Meishangshentai 300495 Gaolangufen 300499 Wandongyiliao 600055 Huarunshuanghe 600062 Yutongkeche 600066 Jinhuagufen 600080 Yongdinggufen 600105 Beifangxitu 600111 Juhuagufen 600160 Jiangsuwuzhong 600200 Guangshengyouse 600259 Haizhengyaoye 600267 Guodiannanzi 600268 Hengruiyiyao 600276 Taihuagufen 600281 Shiyinggufen 603688 Hangfakeji 600391 Hanlianhuanjing 600323 Changjiangtongxin 600345 Lianchuangguangdian 600363 Ningboyunshen 600366 Shangongjintai 600385 Wukuangziben 600390 Sanyouhuagong 600409 Jianghuiqiche 600418 Peilingdianli 600452 Baotaigufen 600456 Guiyanboye 600459 Laobaixing 603883 Fenhuotongxin 600498 Zhongtiankeji 600522 Changyuanjituan 600525 Feidahuanbao 600526 Xiamengwuye 600549 Tiandikeji 600582 Nanjingxiongmao 600775 Shangchaigufen 600841 Hangfadongli 600893 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 202 Table 2: Input,output indexes and related raw datas Company abbreviation Input indexes Output indexes Equity finan- cing net value (Ten thousand yuan) Ownership concentration （%） Asset liability ratio Return on equity Main business revenue growth rate Tobin Q Zhongguobaoan 18000 11.9113 0.623791 0.051567 0.317839 1.029797 Shenzhennengyuan 36830 47.8246 0.592068 0.056656 0.016903 0.447504 Dongxulantian 12140 30.9812 0.365806 0.015992 1.265375 0.950595 Desaidianchi 6436 45.2277 0.708479 0.233108 0.034411 1.677195 Tefaxinxi 53460.62 39.1841 0.594942 0.111641 0.882775 1.565169 Haiwangshenwu 9667.6 45.9346 0.647212 0.084867 0.223803 1.001336 Fenyuanyaoye 45325 11.4827 0.537108 0.039653 0.308429 1.615621 Xujidianqi 44700 41.276 0.472021 0.124559 0.307734 1.279725 Yingtejituan 4646.4 21.5432 0.757153 0.113082 0.115792 0.656154 Zhongyuanhuanbao 15075 56.618 0.152379 0.056745 0.807481 1.82053 Wanxiangqianchao 11100 51.5291 0.589862 0.188514 0.053155 2.641354 Fenhuagaoke 11002.5 20.0286 0.316424 0.031979 0.430391 1.30635 Huitianredian 12719.46 35.1048 0.705711 0.042107 0.166339 0.700617 Yingluohua 28325 39.3785 0.20797 0.01569 0.465252 2.841968 Zhongguangheji 23625 27.6 0.53008 0.060528 0.439343 0.760075 Huanruishiji 24186.25 5.9062 0.178561 0.098234 53.968966 4.174999 Kaidishentai 28683 28.4657 0.68962 0.025709 0.430543 0.605557 Xinxianghuaxian 56752 30.1696 0.372869 0.032147 0.199627 1.370864 Zhongkesanhuan 25215 23.1744 0.133647 0.078054 0.011009 2.544025 Zhongtaiqiche 30681.79 19.9883 0.473969 0.04028 0.041599 1.852857 Huagongkeji 40680 32.3575 0.417369 0.073051 0.264986 2.496118 Jingxinyaoye 16517 23.1578 0.262634 0.089321 0.324762 2.198813 Xinhaiyi 13490.32 18.0506 0.62116 0.028052 0.091058 1.814412 Jinzhikeji 22973 36.916 0.644611 0.105423 0.509782 1.472001 Laibaogaoke 93265.7 20.8423 0.199965 0.059913 0.383746 1.707126 Wohuayiyao 17907 50.2671 0.18847 0.107317 0.200293 8.440842 Sanweitongxin 16648.17 19.0872 0.621308 0.025444 0.143054 1.679945 Yinlungufen 23043.88 11.156 0.466098 0.105559 0.145714 1.457458 Tuobangufen 17327.23 19.6085 0.32204 0.083375 0.263597 2.204443 Beiweikeji 20715.509 21.2504 0.090645 0.071153 1.037182 3.988281 Laiyinshenwu 15016.1 17.5723 0.652109 0.082234 0.110749 2.224834 Tuorixinneng 41068.4 32.5529 0.437158 0.047604 0.568097 1.156847 Guanxunkeji 61214.91 45.4344 0.395116 0.094016 0.292752 3.416419 Yataigufen 42211.337 38.8217 0.4493 0.055966 0.117497 2.110095 Gelinmei 70353.976 12.54 0.622371 0.041603 0.531296 0.999681 Hezhongsizhuang 104927.53 39.4551 0.270369 0.027372 0.545808 2.467085 Siweituxin 136777.3 12.2128 0.230614 0.036656 0.052553 5.006329 Duofuduo 99084.837 13.9279 0.452753 0.165225 0.3093 3.259756 Kanshengufen 66022.47 15.5804 0.698482 0.099706 0.287662 1.591019 Shuanghuanchuangdong 77277.998 8.8614 0.228187 0.061629 0.247183 1.965198 Rongjiruanjian 90566.32 20.6293 0.338254 0.017677 0.126668 3.951408 Jangfencicai 59961.75 18.4646 0.577755 0.046733 1.475008 0.945552 Yamadun 146868 45 0.493956 0.007921 0.27809 1.549758 Teruide 77928.3 43.9969 0.748315 0.065754 1.034825 1.473806 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 203 Tianlongguangqi 37392.79 42.1827 0.522712 0.150541 0.734491 2.84242 Huitianxincai 57117.407 23.2827 0.134061 0.061031 0.160359 2.741069 Shuzizhengtong 70255.3 30.4462 0.385291 0.098264 0.485614 3.133993 Yichengxinneng 148008 20.0219 0.476753 0.008974 0.405479 0.791892 Dongfengrishen 183757.98 32.5652 0.60289 0.183408 0.334125 1.116291 Xianhehuanbao 62650.328 13.8599 0.162212 0.078113 0.256952 2.816268 Shenwuhuanbao 110894.32 42.6771 0.487197 0.27446 1.572772 5.019875 Zhendongzhiyao 130505.44 43.7861 0.233178 0.036636 0.451311 1.480393 Zhongdianhuanbao 53860.62 28.9767 0.336518 0.102676 0.06689 2.9211 Chulingxinxi 22050.3 42.7341 0.098137 0.082983 0.229803 4.049243 Tianhaohuanjing 60990.82 21.2635 0.529938 0.013597 0.770685 0.958072 Kanxinxincai 26700 21.3127 0.192468 0.13107 0.268388 2.678145 Shangqijituan 208200 74.295 0.601955 0.186995 0.128313 0.437754 Dongkuigufen 43199.69 19.4549 0.148709 0.074687 0.072161 2.585874 Zhongguoweixin 12370 51.017 0.4703 0.080102 0.163146 3.402302 Mingjiangshuidian 17569 23.9211 0.565189 0.153201 0.214414 2.286302 Futianqiche 31000 27.0653 0.646346 0.02686 0.368691 0.382292 Yaxingkeche 36999.8 51 0.956045 0.319506 0.684865 0.737739 Hongduhangkong 87827 43.7703 0.522279 0.002468 0.307081 1.32524 Zhenhuanzhonggong 83800 28.8281 0.728936 0.018641 0.046222 0.324244 Hangtiandongli 22857.772 28.7781 0.39091 0.013658 0.199757 3.080362 Kunyaojituan 39343 29.7873 0.33139 0.113377 0.037617 1.972355 Hengtongguangdian 38016.6 19.3351 0.656001 0.224366 0.423001 1.173695 Guihanggufen 32755.42 37.0069 0.384845 0.082063 0.050131 1.752411 Yijinguangdian 28587.636 30.3608 0.572383 0.118439 0.050501 1.232354 Guangyuyuan 6750 23.2198 0.193013 0.089001 1.187008 5.51039 ShensangdaA 8400 27.8079 0.18994 0.021402 0.093641 3.226157 Xugongjixie 11040 42.5567 0.43458 0.016345 0.010659 0.538602 Liugong 20000 34.9758 0.45215 0.012268 0.090789 0.399605 Haimaqiche 16000 28.7966 0.1066 0.001935 0.114369 0.484404 Qidiguhan 5800 18.6099 0.55682 0.029638 0.022667 6.958307 Shantuigufen 37300.6 12.8127 0.53704 0.033263 0.290749 0.740825 Xinyegufen 65280 23.591 0.47083 0.061276 0.142051 1.935407 Xinhuazhiyao 3275 34.4595 0.56293 0.041142 0.146104 1.200706 Zhonghangfeiji 35700 38.1795 0.53861 0.023972 0.093937 1.500922 Qinchuangjichuang 19783 4.999 0.48565 0.00598 0.26126 0.751772 Yingxingnengyuan 28140 14.6453 0.62288 0.008091 0.078713 0.459694 Ankaiqiche 32820 21.1339 0.82949 0.041277 0.22801 0.509507 Faershen 36948.6 21.0656 0.75692 0.144655 0.12084 0.519541 Yunneidongli 37500 30.9655 0.44611 0.051304 0.381352 0.88804 Shandahuate 13499 20.7161 0.12023 0.168022 0.181025 3.059428 Hebeixuangong 19567.33 35.5402 0.7105 0.005127 0.02375 3.36523 Zhongguozhongqi 32396.54 63.7759 0.73288 0.096596 0.07623 0.444266 Fuosukeji 58045 5.8586 0.49031 0.071475 0.036139 1.489305 Jiuzhitang 34530 5.8439 0.08758 0.034818 0.114623 3.933827 Shirongzhaoye 17737.83 53.5709 0.20849 0.108033 0.432934 1.077128 Zhouyankeji 14783.99 39.5579 0.33056 0.034476 0.083552 1.775413 Hengdiancidong 61040.62 50.146 0.31203 0.111907 0.128831 1.961213 Zhonggangtianyuan 12829.22 25.9393 0.16786 0.019791 0.11988 4.923638 Suzhougude 22537.68 34.284 0.12605 0.087356 0.211722 3.80147 Longjigufen 151310.5 14.9446 0.38142 0.061057 0.95973 1.670124 Zhongcaijieneng 25120 2.2383 0.39708 0.081635 0.088168 2.091867 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 204 Wowehecai 20823.57 2.806 0.46708 0.018855 0.119996 1.851254 Yunhaijinshu 49632.15 6.4141 0.57318 0.011782 0.310188 2.036891 Zhengtongdianzi 23343.26 5.779 0.43747 0.017756 0.700349 1.784756 Feimaguoji 25387.15 50.0595 0.84064 0.072063 0.073705 0.911288 Aotexun 37539.58 57.5726 0.27216 0.024992 0.068753 5.328808 Aoweitongxin 21531.88 18.4978 0.22478 0.011085 0.23733 6.246577 Dahuagufen 38955.3 10.3138 0.18514 0.233542 0.302168 2.581213 Chuanrungufen 22181.79 3.8122 0.02606 0.003113 0.025215 2.452324 Zhongdianxinlong 24013.97 5.2163 0.1709 0.008062 0.074614 1.542406 Dongfangyuanlin 80103.85 8.2513 0.60675 0.09416 0.275175 2.016332 Gelinmei 70353.98 10.3159 0.53233 0.003037 0.471734 1.109946 Longjijixie 50896 45.5516 0.30383 0.03472 0.066615 1.908261 Dongshanjimi 95882.38 8.7742 0.76089 0.004494 0.361346 1.097664 Neimengyiji 34527 23.6185 0.4673 0.064681 24.638863 2.927052 Shenlutongxin 43002.5 4.5006 0.17994 0.030051 0.099744 4.009343 Haigetongxin 314314.4 16.9693 0.26216 0.082313 0.041283 2.764868 Fuchunhuanbao 133771.2 34.7541 0.20614 0.1019 0.192276 2.220042 Keshida 88362.82 60.1006 0.29619 0.132879 0.107002 3.596084 Tianshunfenneng 122682.9 29.8115 0.22284 0.058555 0.108446 1.573325 Yataikeji 154722.5 10.623 0.06154 0.07064 0.134139 2.717388 Yishenyaoye 103922.1 9.771 0.40291 0.028825 0.077862 1.559103 Qinxinhuanjiang 159048.6 45.0273 0.58945 0.217156 0.45448 1.887238 Shenyanggufen 43837.11 2.8958 0.41865 0.045569 0.119271 2.056385 Daomingguangxue 56736.49 42.182 0.17677 0.004946 0.036836 4.526761 Maoshuodianyuan 41418.55 7.5847 0.42547 0.015636 0.020701 1.932026 Jingweigufen 143213.8 30 0.43974 0.093117 0.199524 1.331809 Teyiyaoye 32180.69 2.9 0.44267 0.075389 0.18795 3.176194 Ankeshenwu 32109.5 6.7488 0.25408 0.106903 0.156789 7.523596 Jiqiren 57590 25.27 0.14804 0.072172 0.204218 5.255439 Meitainuo 55130 5.867 0.44249 0.013416 0.001622 2.410953 Hekanxinneng 96456.42 21.7151 0.19407 0.039542 0.052915 1.881521 Yujingongshi 74502.18 20.4566 0.21699 0.018039 0.464152 1.643184 Nandudianyuan 196564.9 4.3561 0.27284 0.022368 0.032202 1.862394 Danshengkeji 65537.19 27.0571 0.38001 0.025615 0.41984 4.759231 Shwnyunhuanbao 51262.02 6.7223 0.47692 0.008596 0.524326 1.247397 Jiayugufen 67769.9 39.6643 0.6294 0.064815 0.0408 1.067788 Zhenjiangdingli 43987.43 1.575 0.18195 0.160318 0.415431 5.58102 Dafukeji 186988.5 43.3888 0.21451 0.028971 0.082131 2.528019 Weiminghuanbao 45147.32 1.6236 0.06583 0.247014 0.09413 5.270439 Yongqinghuanbao 61352.57 58.2045 0.39453 0.065618 0.291912 3.375329 Xinwangda 82334.43 6.2204 0.71533 0.22284 0.363935 2.80671 Qianshanyaoji 46468.2 3.4452 0.65659 0.196326 0.868597 3.403831 Dongfangdianre 55924.64 3.9023 0.17367 0.032649 0.003564 2.56007 Meichengkeji 33231.09 3.7468 0.24912 0.048871 0.367727 2.276939 Dianzhenduan 27027.73 8.3066 0.55446 0.039967 0.299982 3.347799 Chanshangyaoye 69726.11 12.5569 0.25662 0.074874 0.28552 2.486892 Jingduankeji 58793.76 48.3557 0.23397 0.092779 0.156354 2.923013 Baanshuiwu 26742.12 10.4186 0.44756 0.062255 0.408375 2.176984 Hejiagufen 61004.08 5.0013 0.2824 0.044699 0.119007 3.763279 Yangguangdianyuan 127124.4 7.9717 0.47948 0.098043 0.485882 1.553448 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 205 Sannuoshenwu 58083.6 27.6498 0.1304 0.094702 0.234857 4.712694 Zhongjizhuangbei 29766.16 46.0071 0.12081 0.01809 0.084099 4.693377 Bohuichuangxin 35854.15 19.8865 0.19687 0.016975 0.192963 3.820974 Jinmokeji 44544.75 23.1869 0.38736 0.034106 0.210806 2.407568 Mengcaoshentai 37361.41 5.9687 0.50505 0.09888 0.600747 1.855393 Xuelanghuanjing 25810.54 1.0918 0.48881 0.113478 0.298417 2.804849 Zhonglaigufen 35000 7.6141 0.51244 0.221942 0.893895 2.596604 Feilihua 26983.01 7.0122 0.21285 0.115276 0.119594 5.003353 Huannengkeji 24403 1.2456 0.09319 0.031894 0.308065 4.260348 Sitongxincai 26842.62 0.2054 0.13374 0.111696 0.207206 8.550455 Shanheyaopu 14868.6 1.8982 0.21451 0.118519 0.085339 9.237657 Maikeshenwu 99732 5.9588 0.08749 0.14305 0.137017 5.640249 Zhongfeigufen 16203.37 6.7488 0.268 0.063789 0.138595 5.551527 Meishangshentai 49591.66 5.1021 0.33097 0.053404 0.193698 4.627519 Gaolaigufen 22761.43 1.9472 0.49794 0.120104 0.305244 5.135788 Wandongyiliao 12330 46.6818 0.15173 0.058092 0.010364 3.419443 Huanrunshuanghe 30503 38.8981 0.11882 0.098426 0.013767 1.78957 Yutongkeche 33075 27.114 0.60509 0.303712 0.155955 1.751181 Jinhuangufen 17880 15.7225 0.22583 0.026587 0.276149 2.53229 Yongdinggufen 23996.03 25.7401 0.21915 0.052393 0.041948 2.727364 Beifangxitu 34240 30 0.25873 0.084874 0.05082 2.841912 Juhuagufen 44341.68 48.7474 0.09062 0.006217 0.049251 1.927764 Jangsuwuzhong 25047.5 17.0103 0.19168 0.022737 0.147961 2.171001 Guangshengyouse 29710.11 37.2164 0.37972 0.025531 2.337913 3.147939 Haizhengyaoye 45979.83 33.2235 0.44983 0.000534 0.035507 0.700826 Guodiannanzi 30771 50.2603 0.74765 0.086113 0.112207 0.488345 Hengruiyiyao 46660 24.3076 0.08688 0.22595 0.209272 7.586455 Taihuagufen 55755 43.4783 0.69624 0.078889 0.17965 1.62128 Shiyinggufen 33084.48 2.828 0.04865 0.07237 0.081654 3.683754 Hangfakeji 24523 36.0184 0.61126 0.031833 0.088985 2.336412 Hanlanhuanjing 41825 17.9806 0.44463 0.035094 0.143528 0.907461 Changjiangtongxin 35460 28.6274 0.07628 0.019998 0.369324 2.519341 Lianchuangguangdain 38722 21.7288 0.22522 0.08026 0.168225 2.196961 Ningboshenrun 34845.57 31.3721 0.1723 0.137232 0.370272 2.053032 Shandongjintai 3977.72 17.3819 8.38 0.025617 0.051392 17.658202 Wukuangziben 58120 27.8393 0.10632 0.003036 0.278969 2.069426 Sanyouhuangong 57694.5 39.9839 0.4762 0.037353 0.441365 0.803604 Jianghuaiqiche 84119.15 7.0963 0.64123 0.073875 0.144266 0.511175 Peilingdianli 24348.16 51.6438 0.76181 0.188405 0.332232 2.104195 Baotaigufen 31088.4 56.0419 0.47841 0.018563 0.08157 1.150653 Guiyanboye 25939.8 39.3433 0.41914 0.038357 0.148653 1.971349 Laobaixing 101002.8 2.0548 0.50295 0.034679 0.273318 2.917035 Fenghuotongxin 179734.2 46.5749 0.70317 0.054282 0.370349 1.380452 Zhongtiankeji 35992 16.5882 0.33742 0.078633 0.268934 1.429016 Changyuanjituan 18085.6 3.9149 0.59015 0.038324 0.211132 1.265562 Feidahuanbao 27297.22 17.6519 0.64511 0.010392 0.154221 0.918951 Xiamengwuye 33354.83 26.0363 0.48819 0.030018 0.563182 1.751385 Tiandikeji 30222 36.3103 0.39303 0.022878 0.175307 0.54382 Nanjingxiongmao 11199.3 26.4147 0.09628 0.02091 1.0991 2.665993 Shangchaigufen 14335 48.0509 0.37862 0.027448 0.175091 1.616131 Hangfadongli 29744 29.7823 0.32432 0.035797 0.007234 1.325377 Bohaihuosai 30397 18.191 0.26409 0.001953 0.222484 1.449027 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 206 Table 3: Evaluation results of equity financing efficiency of listed companies in strategic emerging industries Company abbreviation Comprehensive efficiency Pure technical efficiency Scale efficiency Returns to scale Zhongguobaoan 0.291 0.589 0.494 drs Shenzhennengyuan 1.000 1.000 1.000 - Dongxulantian 0.464 0.663 0.700 drs Desaidianchi 0.472 0.876 0.539 drs Tefaxinxi 0.258 0.611 0.422 drs Haiwangshenwu 0.447 0.744 0.600 drs Fenyuanyaoye 0.234 0.441 0.532 drs Xujidianqi 0.305 0.619 0.493 drs Yingtejituan 0.513 0.858 0.598 drs Zhongyuanhuanbao 0.355 0.938 0.378 drs Wanxiangqianchao 0.267 0.895 0.299 drs Fenhuagaoke 0.210 0.398 0.528 drs Huitianredian 0.543 0.850 0.639 drs Yingluohua 0.240 0.728 0.329 drs Zhongguangheji 0.370 0.591 0.625 drs Huanruishiji 0.035 0.125 0.284 drs Kaidishentai 0.619 0.850 0.728 drs Xinxianghuaxian 0.327 0.588 0.555 drs Zhongkesanhuan 0.508 0.624 0.813 drs Zhongtaiqiche 0.362 0.465 0.777 drs Huagongkeji 0.172 0.533 0.323 drs Jingxinyaoye 0.121 0.368 0.329 drs Xinhaiyi 0.355 0.489 0.725 drs Jinzhikeji 0.259 0.587 0.441 drs Laibaogaoke 0.263 0.470 0.561 drs Wohuayiyao 0.125 0.761 0.165 drs Sanweitongxin 0.343 0.521 0.659 drs Yinlungufen 0.175 0.377 0.464 drs Tuobangufen 0.107 0.322 0.333 drs Beiweikeji 0.076 0.341 0.222 drs Laiyinshenwu 0.253 0.420 0.603 drs Tuorixinneng 0.317 0.607 0.522 drs Guanxunkeji 0.184 0.708 0.260 drs Yataigufen 0.257 0.656 0.392 drs Gelinmei 0.346 0.658 0.525 drs Hezhongsizhuang 0.342 0.790 0.433 drs Siweituxin 0.481 0.636 0.756 drs Duofuduo 0.148 0.402 0.369 drs Kanshengufen 0.195 0.566 0.345 drs Shuanghuanchuangdong 0.219 0.355 0.616 drs Rongjiruanjian 0.329 0.590 0.557 drs Jangfencicai 0.296 0.593 0.499 drs Yamadun 0.901 1.000 0.901 drs Teruide 0.338 0.814 0.415 drs Tianlongguangqi 0.159 0.611 0.261 drs Huitianxincai 0.176 0.417 0.422 drs Shuzizhengtong 0.137 0.496 0.276 drs Yichengxinneng 1.000 1.000 1.000 - Dongfengrishen 0.513 0.841 0.610 drs Xianhehuanbao 0.136 0.300 0.454 drs Shenwuhuanbao 0.096 0.588 0.163 drs Zhendongzhiyao 0.519 0.871 0.596 drs Zhongdianhuanbao 0.230 0.498 0.461 drs Chulingxinxi 0.152 0.672 0.226 drs Tianhaohuanjing 0.551 0.672 0.820 drs Kanxinxincai 0.092 0.313 0.293 drs Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 207 Shangqijituan 1.000 1.000 1.000 - Dongkuigufen 0.188 0.355 0.530 drs Zhongguoweixin 0.206 0.816 0.253 drs Mingjiangshuidian 0.142 0.378 0.376 drs Futianqiche 0.765 0.860 0.890 drs Yaxingkeche 0.637 1.000 0.637 drs Hongduhangkong 0.847 1.000 0.847 drs Zhenhuanzhonggong 1.000 1.000 1.000 - Hangtiandongli 0.181 0.562 0.321 drs Kunyaojituan 0.305 0.566 0.539 drs Hengtongguangdian 0.249 0.586 0.424 drs Guihanggufen 0.303 0.657 0.461 drs Yijinguangdian 0.369 0.568 0.651 drs Guangyuyuan 0.059 0.365 0.162 drs ShensangdaA 0.215 0.490 0.439 drs Xugongjixie 1.000 1.000 1.000 - Liugong 1.000 1.000 1.000 - Haimaqiche 1.000 1.000 1.000 - Qidiguhan 0.293 0.433 0.676 drs Shantuigufen 0.369 0.584 0.632 drs Xinyegufen 0.224 0.483 0.462 drs Xinhuazhiyao 0.359 0.680 0.528 drs Zhonghangfeiji 0.409 0.736 0.556 drs Qinchuangjichuang 0.683 0.726 0.941 drs Yingxingnengyuan 1.000 1.000 1.000 - Ankaiqiche 0.724 1.000 0.724 drs Faershen 0.648 0.920 0.704 drs Yunneidongli 0.372 0.607 0.613 drs Shandahuate 0.077 0.286 0.269 drs Hebeixuangong 1.000 1.000 1.000 - Zhongguozhongqi 1.000 1.000 1.000 - Fuosukeji 0.497 0.542 0.916 drs Jiuzhitang 0.093 0.172 0.540 drs Shirongzhaoye 0.459 0.814 0.563 drs Zhouyankeji 0.327 0.684 0.478 drs Hengdiancidong 0.292 0.755 0.386 drs Zhonggangtianyuan 0.171 0.460 0.372 drs Suzhougude 0.127 0.536 0.237 drs Longjigufen 0.401 0.703 0.571 drs Zhongcaijieneng 0.177 0.268 0.660 drs Wowehecai 0.279 0.334 0.834 drs Yunhaijinshu 0.340 0.468 0.728 drs Zhengtongdianzi 0.238 0.324 0.733 drs Feimaguoji 0.582 0.922 0.632 drs Aotexun 0.495 1.000 0.495 drs Aoweitongxin 0.077 0.372 0.208 drs Dahuagufen 0.064 0.178 0.360 drs Chuanrungufen 0.427 1.000 0.427 irs Zhongdianxinlong 0.191 0.255 0.747 drs Dongfangyuanlin 0.176 0.498 0.353 drs Gelinmei 0.742 0.849 0.875 drs Longjijixie 0.436 0.846 0.515 drs Dongshanjimi 0.998 1.000 0.998 drs Neimengyiji 0.128 0.412 0.311 drs Shenlutongxin 0.134 0.223 0.601 drs Haigetongxin 1.000 1.000 1.000 - Fuchunhuanbao 0.309 0.642 0.481 drs Keshida 0.310 0.876 0.354 drs Tianshunfenneng 0.458 0.689 0.665 drs Yataikeji 0.390 0.536 0.728 drs Yishenyaoye 0.516 0.646 0.798 drs Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 208 Qinxinhuanjiang 0.288 0.724 0.398 drs Shenyanggufen 0.193 0.340 0.567 drs Daomingguangxue 1.000 1.000 1.000 - Maoshuodianyuan 0.532 0.573 0.929 irs Jingweigufen 0.405 0.707 0.572 drs Teyiyaoye 0.118 0.245 0.482 drs Ankeshenwu 0.066 0.156 0.423 drs Jiqiren 0.127 0.432 0.293 drs Meitainuo 1.000 1.000 1.000 - Hekanxinneng 0.490 0.591 0.830 drs Yujingongshi 0.319 0.465 0.687 drs Nandudianyuan 1.000 1.000 1.000 - Danshengkeji 0.154 0.547 0.281 drs Shwnyunhuanbao 0.471 0.535 0.880 drs Jiayugufen 0.647 0.841 0.769 drs Zhenjiangdingli 0.048 0.157 0.307 drs Dafukeji 0.764 1.000 0.764 drs Weiminghuanbao 0.074 0.147 0.505 drs Yongqinghuanbao 0.257 0.946 0.272 drs Xinwangda 0.122 0.443 0.276 drs Qianshanyaoji 0.086 0.330 0.260 drs Dongfangdianre 0.796 0.825 0.965 drs Meichengkeji 0.096 0.210 0.459 drs Dianzhenduan 0.164 0.281 0.583 drs Chanshangyaoye 0.161 0.319 0.503 drs Jingduankeji 0.227 0.750 0.303 drs Baanshuiwu 0.128 0.294 0.437 drs Hejiagufen 0.169 0.278 0.607 drs Yangguangdianyuan 0.313 0.631 0.495 drs Sannuoshenwu 0.122 0.439 0.279 drs Zhongjizhuangbei 0.396 0.830 0.477 drs Bohuichuangxin 0.120 0.390 0.307 drs Jinmokeji 0.168 0.448 0.374 drs Mengcaoshentai 0.126 0.369 0.343 drs Xuelanghuanjing 0.101 0.273 0.369 drs Zhonglaigufen 0.088 0.306 0.287 drs Feilihua 0.074 0.139 0.533 drs Huannengkeji 0.059 0.121 0.491 drs Sitongxincai 0.036 0.099 0.364 drs Shanheyaopu 0.050 0.074 0.678 drs Maikeshenwu 0.147 0.320 0.458 drs Zhongfeigufen 0.066 0.136 0.487 drs Meishangshentai 0.115 0.222 0.521 drs Gaolaigufen 0.082 0.180 0.457 drs Wandongyiliao 1.000 1.000 1.000 - Huanrunshuanghe 0.717 0.882 0.813 drs Yutongkeche 0.195 0.465 0.419 drs Jinhuangufen 0.106 0.293 0.363 drs Yongdinggufen 0.221 0.490 0.452 drs Beifangxitu 0.211 0.534 0.394 drs Juhuagufen 0.837 1.000 0.837 drs Jangsuwuzhong 0.140 0.323 0.432 drs Guangshengyouse 0.198 0.666 0.297 drs Haizhengyaoye 1.000 1.000 1.000 - Guodiannanzi 0.825 0.967 0.853 drs Hengruiyiyao 0.073 0.327 0.224 drs Taihuagufen 0.308 0.724 0.425 drs Shiyinggufen 0.080 0.118 0.677 drs Hangfakeji 0.333 0.652 0.511 drs Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 209 Hanlanhuanjing 0.280 0.491 0.570 drs Changjiangtongxin 0.199 0.532 0.375 drs Lianchuangguangdain 0.133 0.355 0.373 drs Ningboshenrun 0.159 0.454 0.351 drs Shandongjintai 1.000 1.000 1.000 - Wukuangziben 0.381 0.643 0.593 drs Sanyouhuangong 0.554 0.819 0.677 drs Jianghuaiqiche 0.583 0.819 0.712 drs Peilingdianli 0.240 0.727 0.330 drs Baotaigufen 0.744 1.000 0.744 drs Guiyanboye 0.290 0.683 0.424 drs Laobaixing 0.306 0.514 0.594 drs Fenghuotongxin 0.574 1.000 0.574 drs Zhongtiankeji 0.129 0.325 0.395 drs Changyuanjituan 0.285 0.509 0.560 drs Feidahuanbao 0.645 0.779 0.828 drs Xiamengwuye 0.252 0.529 0.476 drs Tiandikeji 0.740 0.866 0.855 drs Nanjingxiongmao 0.156 0.467 0.334 drs Shangchaigufen 0.436 0.841 0.519 drs Hangfadongli 1.000 1.000 1.000 - Bohaihuosai 0.290 0.461 0.629 drs Mean 0.370 0.603 0.563 Among them, the "irs" indicates that the scale of compensation increases, "-" means that the scale compensation is constant, and "drs" represents diminishing returns of scale. Table 4: The input redundancy and output insufficiency of each decision unit Company abbreviation Input redundancy Output insufficiency Equity finan- cing net value (Ten thousand yuan) Ownership concentration （%） Asset liability ratio Return on equity Main business revenue growth rate Tobin Q Zhongguobaoan 0.000 -0.077 0.000 14121.371 10.265 0.436 Shenzhennengyuan 0.000 0.000 0.000 0.000 0.000 0.000 Dongxulantian 0.000 -1.160 0.000 41564.597 15.716 0.186 Desaidianchi -0.166 0.000 -0.839 28397.577 6.402 0.100 Tefaxinxi 0.000 -0.790 -0.306 34030.814 24.943 0.379 Haiwangshenwu 0.000 0.147 -0.082 21926.638 15.803 0.223 Fenyuanyaoye 0.000 -0.212 0.000 57466.912 23.2 0.681 Xujidianqi 0.000 -0.215 -0.678 43096.172 25.399 0.290 Yingtejituan -0.086 0.000 0.000 58409.159 4.171 0.125 Zhongyuanhuanbao 0.000 -0.718 0.000 56101.867 3.727 0.312 Wanxiangqianchao -0.107 0.000 -2.065 21917.104 6.070 0.069 Fenhuagaoke 0.000 -0.358 0.000 31116.628 30.285 0.478 Huitianredian 0.000 -0.108 0.000 50156.266 6.215 0.125 Yingluohua 0.000 -0.381 -1.523 10608.972 14.749 0.196 Zhongguangheji 0.000 -0.374 0.000 31779.138 19.071 0.366 Huanruishiji 0.000 -53.880 -0.446 169317.717 41.347 1.250 Kaidishentai 0.000 -0.379 0.000 46273.698 5.013 0.121 Xinxianghuaxian 0.000 -0.009 0.000 39750.568 21.132 0.261 Zhongkesanhuan -0.028 0.000 0.000 15186.619 0.000 0.099 Zhongtaiqiche 0.000 0.000 0.000 35230.028 22.951 0.544 Huagongkeji 0.000 -0.173 -1.131 35587.270 28.307 0.365 Jingxinyaoye 0.000 -0.244 -1.553 28407.906 39.830 0.452 Xinhaiyi 0.000 -0.035 0.000 48759.343 18.834 0.648 Jinzhikeji 0.000 -0.428 -0.166 32083.533 25.976 0.454 Laibaogaoke 0.000 -0.206 0.000 105313.405 23.535 0.234 Wohuayiyao 0.000 -0.101 -5.598 106357.017 15.803 0.251 Sanweitongxin 0.000 -0.087 0.000 46424.308 17.569 0.572 Yinlungufen -0.049 0.000 0.000 43322.163 18.449 0.771 Tuobangufen 0.000 -0.180 -0.766 36438.898 41.236 0.677 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 210 Beiweikeji 0.000 -0.951 -0.053 65451.909 41.087 0.175 Laiyinshenwu -0.010 0.000 0.000 38233.825 24.307 0.902 Tuorixinneng 0.000 -0.436 0.000 26562.311 21.055 0.283 Guanxunkeji 0.000 -0.201 -2.009 25203.517 18.706 0.163 Yataigufen 0.000 -0.028 -0.794 22175.391 20.395 0.236 Gelinmei 0.000 -0.435 0.000 36575.484 23.104 0.324 Hezhongsizhuang 0.000 -0.319 -0.339 27886.559 10.486 0.194 Siweituxin 0.000 0.000 -2.558 78324.422 6.994 0.132 Duofuduo 0.000 -0.049 -0.235 147662.734 24.308 0.675 Kanshengufen -0.029 -0.216 0.000 50556.718 26.488 0.535 Shuanghuanchuangdong 0.000 0.000 0.000 140576.580 27.02 0.415 Rongjiruanjian 0.000 0.000 -1.639 62878.460 14.323 0.235 Jangfencicai -0.001 -1.415 0.000 41179.750 17.318 0.397 Yamadun 0.000 0.000 0.000 0.000 0.000 0.000 Teruide 0.000 -0.841 0.000 17773.739 10.035 0.171 Tianlongguangqi 0.000 -0.627 -1.944 102830.115 26.856 0.333 Huitianxincai 0.000 0.000 -0.329 79836.760 32.544 0.296 Shuzizhengtong 0.000 -0.339 -1.752 71314.742 30.905 0.391 Yichengxinneng 0.000 0.000 0.000 0.000 0.000 0.000 Dongfengrishen -0.014 -0.219 0.000 34793.913 32.657 0.114 Xianhehuanbao 0.000 -0.171 -0.376 146073.198 32.315 0.378 Shenwuhuanbao -0.092 -1.447 -4.079 91340.018 29.955 0.342 Zhendongzhiyao 0.000 -0.209 0.000 19294.955 6.474 0.27 Zhongdianhuanbao 0.000 0.000 -1.745 54193.470 29.156 0.339 Chulingxinxi 0.000 -0.140 -0.471 76579.311 20.824 0.292 Tianhaohuanjing 0.000 -0.619 0.000 29829.158 10.399 0.259 Kanxinxincai 0.000 -0.168 -1.645 90251.771 46.733 0.422 Shangqijituan 0.000 0.000 0.000 0.000 0.000 0.000 Dongkuigufen 0.000 0.000 -0.654 78442.475 35.326 0.279 Zhongguoweixin 0.000 0.086 -1.398 34068.421 11.520 0.106 Mingjiangshuidian -0.025 -0.118 0.000 87325.61 39.403 0.931 Futianqiche 0.000 -0.315 0.000 48534.629 4.403 0.105 Yaxingkeche 0.000 0.000 0.000 0.000 0.000 0.000 Hongduhangkong 0.000 0.000 0.000 0.000 0.000 0.000 Zhenhuanzhonggong 0.000 0.000 0.000 0.000 0.000 0.000 Hangtiandongli 0.000 -0.048 -1.451 25464.296 22.394 0.304 Kunyaojituan -0.032 0.000 -1.519 30160.349 22.835 0.261 Hengtongguangdian -0.105 -0.162 0.000 26821.650 16.456 0.463 Guihanggufen 0.000 0.000 -1.182 17122.758 19.345 0.253 Yijinguangdian -0.040 0.000 0.000 21774.204 23.125 0.436 Guangyuyuan 0.000 -1.102 -3.212 65717.872 40.481 0.336 ShensangdaA 0.000 -0.018 -0.232 25535.176 28.910 0.197 Xugongjixie 0.000 0.000 0.000 0.000 0.000 0.000 Liugong 0.000 0.000 0.000 0.000 0.000 0.000 Haimaqiche 0.000 0.000 0.000 0.000 0.000 0.000 Qidiguhan 0.000 0.000 -4.174 14566.975 24.385 0.730 Shantuigufen 0.000 -0.168 0.000 26610.877 13.689 0.383 Xinyegufen 0.000 0.000 0.000 69752.735 25.207 0.503 Xinhuazhiyao 0.000 -0.075 0.000 40120.632 16.200 0.265 Zhonghangfeiji 0.000 0.000 0.000 12798.976 13.688 0.193 Qinchuangjichuang 0.000 -0.064 0.000 38789.499 8.516 0.184 Yingxingnengyuan 0.000 0.000 0.000 0.000 0.000 0.000 Ankaiqiche 0.000 0.000 0.000 0.000 0.000 0.000 Faershen -0.117 0.000 0.000 25486.441 4.527 0.066 Yunneidongli 0.000 -0.280 0.000 24274.507 20.045 0.289 Shandahuate 0.000 -0.060 -2.049 174714.085 51.620 0.444 Hebeixuangong 0.000 0.000 0.000 0.000 0.000 0.000 Zhongguozhongqi 0.000 0.000 0.000 0.000 0.000 0.000 Fuosukeji -0.029 0.000 0.000 48994.168 26.473 0.414 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 211 Jiuzhitang 0.000 0.000 -1.248 166432.628 28.167 0.422 Shirongzhaoye 0.000 -0.328 0.000 107408.687 12.227 0.321 Zhouyankeji 0.000 -0.004 0.000 20462.589 18.245 0.152 Hengdiancidong 0.000 -0.026 0.000 68113.34 16.251 0.174 Zhonggangtianyuan 0.000 -0.041 -2.975 19491.417 30.395 0.271 Suzhougude 0.000 -0.120 -0.356 80698.624 29.726 0.273 Longjigufen 0.000 -0.688 0.000 64077.563 20.59 0.162 Zhongcaijieneng -0.038 -0.029 0.000 68721.151 32.05 1.086 Wowehecai 0.000 -0.072 0.000 53865.844 24.482 0.930 Yunhaijinshu 0.000 -0.042 0.000 56475.933 12.988 0.652 Zhengtongdianzi 0.000 -0.642 0.000 48599.723 20.119 0.911 Feimaguoji 0.000 -0.004 0.000 17575.694 4.236 0.071 Aotexun 0.000 0.000 0.000 0.000 0.000 0.000 Aoweitongxin 0.000 -0.050 0.000 36361.104 31.237 0.380 Dahuagufen -0.080 -0.198 -0.494 179403.522 47.499 0.853 Chuanrungufen 0.000 0.000 0.000 0.000 0.000 0.000 Zhongdianxinlong 0.000 0.000 0.000 70011.371 23.241 0.498 Dongfangyuanlin 0.000 -0.072 0.000 80761.497 43.21 0.612 Gelinmei 0.000 -0.196 0.000 12517.584 15.02 0.095 Longjijixie 0.000 0.000 0.000 9272.367 8.299 0.055 Dongshanjimi 0.000 0.000 0.000 0.000 0.000 0.000 Neimengyiji 0.000 -24.536 -0.650 49365.468 33.769 0.668 Shenlutongxin 0.000 0.000 -1.074 149888.536 15.687 0.627 Haigetongxin 0.000 0.000 0.000 0.000 0.000 0.000 Fuchunhuanbao 0.000 -0.094 -0.566 74531.812 19.364 0.179 Keshida 0.000 0.000 -1.729 56778.582 8.519 0.219 Tianshunfenneng 0.000 0.000 0.000 55440.784 13.472 0.216 Yataikeji 0.000 -0.064 -0.140 134068.908 9.205 0.229 Yishenyaoye 0.000 0.000 0.000 56894.906 8.25 0.221 Qinxinhuanjiang -0.054 -0.344 -0.437 60688.857 17.181 0.225 Shenyanggufen 0.000 0.000 0.000 85247.268 31.156 0.814 Daomingguangxue 0.000 0.000 0.000 0.000 0.000 0.000 Maoshuodianyuan 0.000 0.000 0.000 30861.118 17.04 0.317 Jingweigufen 0.000 0.000 0.000 59379.072 16.477 0.182 Teyiyaoye 0.000 -0.033 0.000 99377.684 41.433 1.367 Ankeshenwu 0.000 -0.074 -3.422 174345.975 36.644 1.380 Jiqiren 0.000 -0.040 -2.907 75787.729 33.255 0.313 Meitainuo 0.000 0.000 0.000 0.000 0.000 0.000 Hekanxinneng 0.000 0.000 0.000 66829.264 15.045 0.134 Yujingongshi 0.000 -0.214 0.000 85871.670 23.578 0.251 Nandudianyuan 0.000 0.000 0.000 0.000 0.000 0.000 Danshengkeji 0.000 -0.208 -3.000 54205.159 22.379 0.314 Shwnyunhuanbao 0.000 -0.238 0.000 44542.284 7.848 0.414 Jiayugufen 0.000 0.000 0.000 12784.866 7.483 0.119 Zhenjiangdingli -1.172 -0.373 -0.084 236065.005 15.439 0.976 Dafukeji 0.000 0.000 0.000 0.000 0.000 0.000 Weiminghuanbao -0.166 -0.053 -2.165 262063.597 15.355 0.382 Yongqinghuanbao 0.000 -0.210 0.000 7383.245 3.324 0.023 Xinwangda -0.062 -0.249 0.000 103385.651 58.161 0.898 Qianshanyaoji -0.081 -0.775 0.000 94193.722 49.249 1.331 Dongfangdianre -0.016 0.000 -0.132 11896.317 2.508 0.26 Meichengkeji 0.000 -0.054 0.000 124891.553 39.099 0.936 Dianzhenduan 0.000 -0.196 0.000 69150.546 25.324 1.419 Chanshangyaoye 0.000 -0.094 0.000 148625.157 26.766 0.547 Jingduankeji 0.000 -0.061 0.000 50181.755 16.143 0.194 Baanshuiwu -0.002 -0.315 0.000 64279.519 25.043 1.076 Hejiagufen 0.000 -0.037 -0.209 158250.221 12.974 0.733 Yangguangdianyuan 0.000 -0.235 0.000 74286.702 43.072 0.280 Sannuoshenwu 0.000 -0.104 -2.207 74177.356 35.311 0.327 Zhongjizhuangbei 0.000 0.000 -3.098 6087.037 9.408 0.316 Bohuichuangxin 0.000 -0.014 -2.457 55965.323 31.041 0.307 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 212 Jinmokeji 0.000 -0.042 -0.410 54844.500 28.548 0.477 Mengcaoshentai -0.046 -0.538 0.000 63979.381 30.813 0.865 Xuelanghuanjing -0.064 -0.237 0.000 68828.163 34.214 1.303 Zhonglaigufen -0.147 -0.820 0.000 79324.005 34.657 1.616 Feilihua 0.000 -0.026 -1.388 167210.279 43.454 1.319 Huannengkeji 0.000 -0.255 -1.322 177441.023 9.057 0.678 Sitongxincai -0.037 -0.165 -3.776 245600.101 6.8191 1.224 Shanheyaopu -0.039 -0.028 -2.090 185319.158 23.659 2.674 Maikeshenwu -0.058 -0.039 -2.939 211521.844 12.638 0.186 Zhongfeigufen 0.000 0.000 -1.265 102966.756 42.886 1.703 Meishangshentai 0.000 -0.095 -0.041 174085.933 17.910 1.162 Gaolaigufen -0.013 -0.215 0.000 103455.664 47.244 2.263 Wandongyiliao 0.000 0.000 0.000 0.000 0.000 0.000 Huanrunshuanghe -0.052 0.000 0.000 4082.097 5.206 0.232 Yutongkeche -0.173 0.000 0.000 38082.141 31.219 0.697 Jinhuangufen 0.000 -0.158 -0.759 43213.663 37.999 0.546 Yongdinggufen 0.000 0.000 -0.564 24992.859 26.809 0.257 Beifangxitu 0.000 0.000 -2.125 29832.154 26.138 0.347 Juhuagufen 0.000 0.000 0.000 0.000 0.000 0.000 Jangsuwuzhong 0.000 0.000 -0.694 52472.874 35.635 0.402 Guangshengyouse 0.000 -2.244 -1.830 14884.346 18.645 0.190 Haizhengyaoye 0.000 0.000 0.000 0.000 0.000 0.000 Guodiannanzi 0.000 -0.007 0.000 17710.411 1.739 0.026 Hengruiyiyao -0.039 -0.081 -7.149 161540 49.987 0.515 Taihuagufen 0.000 -0.082 0.000 21253.228 16.573 0.265 Shiyinggufen 0.000 -0.030 -0.745 246135.436 21.039 0.362 Hangfakeji 0.000 -0.007 -0.354 13096.106 19.235 0.326 Hanlanhuanjing 0.000 -0.052 0.000 43345.526 18.634 0.461 Changjiangtongxin 0.000 -0.238 0.000 31172.164 25.166 0.345 Lianchuangguangdain 0.000 -0.049 -1.045 70237.035 39.413 0.409 Ningboshenrun 0.000 -0.260 -0.113 120932.102 37.786 0.329 Shandongjintai 0.000 0.000 0.000 0.000 0.000 0.000 Wukuangziben 0.000 0.000 -0.746 32315.936 15.479 0.397 Sanyouhuangong 0.000 -0.359 0.000 12707.922 8.807 0.105 Jianghuaiqiche -0.028 -0.085 0.000 18642.877 28.835 0.142 Peilingdianli -0.011 -0.208 -0.679 172145.014 19.389 0.286 Baotaigufen 0.000 0.000 0.000 0.000 0.000 0.000 Guiyanboye 0.000 -0.066 -0.783 12054.693 18.284 0.195 Laobaixing 0.000 -0.087 0.000 95447.183 13.253 0.475 Fenghuotongxin 0.000 0.000 0.000 0.000 0.000 0.000 Zhongtiankeji 0.000 -0.110 0.000 74593.280 34.379 0.699 Changyuanjituan 0.000 -0.009 0.000 18489.78 17.813 0.570 Feidahuanbao 0.000 -0.095 0.000 20890.466 5.011 0.183 Xiamengwuye 0.000 -0.446 0.000 29729.828 23.207 0.435 Tiandikeji 0.000 -0.147 0.000 4684.167 5.628 0.114 Nanjingxiongmao 0.000 -1.022 0.000 22244.929 30.184 0.307 Shangchaigufen 0.000 -0.093 0.000 26059.281 9.054 0.081 Hangfadongli 0.000 0.000 0.000 0.000 0.000 0.000 Bohaihuosai 0.000 -0.023 -0.202 40463.024 21.251 0.309 Journal of Risk Analysis and Crisis Response, Vol. 7, No. 4 (December 2017) 189–213 ___________________________________________________________________________________________________________ 213 1. Introduction0F( 2. Literature review 3. Introduction of DEA model 3.1. Fundamental 3.2. Construction of the model 3.2.1. Integrated efficiency model (CCR model) 3.2.2. Technical efficiency model (CCGSS model) 3.3. General steps 3.3.1. Determine the evaluation objectives 3.3.2. Select the decision unit DMU 3.3.3. Establish input and output index system 3.3.4. Select the DEA model 3.3.5. Evaluation and analysis of DEA results 3.3.6. Adjust the input and output index system 3.3.7. Draw a comprehensive analysis and evaluate the conclusion 4. Empirical analysis 4.1. Determination of evaluation index 4.1.1. Input indexes 4.1.2 Output indexes 4.2. Sample Selection and Data Sources 4.3. Evaluation results of equity financing efficiency 4.4. Analysis of the evaluation results of equity financing efficiency 4.4.1 Efficiency analysis 4.4.2 Analysis of insufficient input redundant output 4.4.3 Industry comparative analysis 5. Conclusions References Appendices << /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /None /Binding /Left /CalGrayProfile (Dot Gain 20%) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.4 /CompressObjects /Tags /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends true /DetectCurves 0.0000 /ColorConversionStrategy /LeaveColorUnchanged /DoThumbnails false /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams false /MaxSubsetPct 100 /Optimize true /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments true /PreserveOverprintSettings true /StartPage 1 /SubsetFonts true /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile () /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 300 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages true /ColorImageDownsampleType /Bicubic /ColorImageResolution 300 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages true /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile () /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /CreateJDFFile false /Description << /ARA /BGR /CHS /CHT /CZE /DAN /DEU /ESP /ETI /FRA /GRE /HEB /HRV (Za stvaranje Adobe PDF dokumenata najpogodnijih za visokokvalitetni ispis prije tiskanja koristite ove postavke. 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