摘要
科技型中小企业是近年来经济活动中出现的最引人注目的、最具有发展前景的企业,但是目前面临的一个最普遍和最关键的制约因素是融资问题。作为股权融资主体之一的风险投资机构,迫切地需要评价科技型中小企业信用状况,以此作为投资决策的重要依据。因此,分析影响科技型中小企业信用评价关键因素,探索具有理论意义和实用价值的信用评价方法,具有重要意义。本文面向风险投资评审,通过文献回顾、发放调查问卷以及因子分析,筛选科技型中小企业信用关键影响因素,构建针对风险投资评审的科技型中小企业信用评价指标体系,探索运用不同的、可操作性强的信用评价方法进行风险投资评估,以期研究成果能够为风险投资中降低合约双方的交易成本、拓宽科技型中小企业融资渠道以及提高技术成果转化率提供决策的理论支撑。论文的主要工作和创新概括如下:
(1)通过因子分析,提取5大类公共因子:①信用品质、②组织水平、③运营水平、④研发水平、⑤网络地位,筛选并构建了科技型中小企业信用影响因素及其评价指标体系,从而为风险投资机构进行投资决策提供指标依据。
(2)针对风险投资评审情况复杂、指标种类多、既有定量又有定性的特点,提出了一种DEA模糊综合评价方法:将可以获得历年数据的定量指标先用DEA给出定量指标的效率值并模糊化;对于定性指标由隶属函数表征,最后根据权重,结合定量指标与定性指标进行总体的信用评价。
(3)针对属性权重已知、风险投资机构对各个科技型中小企业存在一定主观偏好的情况,提出了一种基于区间数排序与灰色关联分析的信用评价方法:直接从区间数决策矩阵中得出各个信用评价指标之间的相互联系和影响,采用相关系数来量化各属性之间的关联度,运用区间数多属性决策和灰色关联分析对各个科技型中小企业信用进行排序,最后给出投资决策建议。
(4)针对现有评价方法的评价结果不能全面反映客观事物的模糊性与随机性等问题,将云模型理论引入到科技型中小企业信用评价研究中,以不确定性为出发点,借鉴模糊理论中的隶属度的思想,建立了定性概念与定量表示相互转换的模型,弥补了传统评价方法中硬划分的局限性。
(5)结合CART模型输出树状结构、简单直观、处理非定量指标具有较强的优越性等优点,提出了一种基于CART的科技型中小企业信用评价预测模型,弥补了传统评价方法信用评价预测能力相对较弱的问题。
With the matchless advantages of the mechanism and efficiency of innovation, High-tech S mall a nd Medium-sized E nterprises ( High-tech S MEs) a re t he m ost remarkable and promising enterprises in recent years. Although the development of high-tech SMEs turns to be dynamic, it is realized that financing problem has become the most universal and crucial restraining factor to be faced with. As one of the most important equity f inancing investors, v enture c apital institutions urgently need t o evaluate the credit of High-tech SMEs as an important basis for investment decisions. Consequently it has theoretical and practical significance to explore the crucial factors and applicable methods of credit evaluation of High-tech SMEs. Unfortunately, the venture capital industry has just started in our country, the venture capital market is still immature, and the academia has not paid enough attention to High-tech SMEs, which lead the research in this area to a standstill.
Aiming a t ve nture capital assessment, t hrough l iterature r eview, que stionnaire survey and factor analysis, this article intends to screen the crucial influencing factors of the credit of High-tech SMEs, build the indicator system of the credit evaluation of High-tech S MEs w hich i s t argeting ve nture capital assessment, and explore using different a nd o perational c redit e valuation m ethods t o a ccomplish ve nture capital assessment. T he r esearch r esults a re ex pected t o s upport d ecision-making theoretically f or reducing t ransaction c osts of bot h pa rties i n ve nture capital, expanding the financing channels for High-tech SMEs, and increasing the conversion rate of technology achievements.
The research results are summarized as follows:
(1) By factor analysis, 5 major categories common factors is extracted:①Credit quality②Organization le vel③Operation l evel④Development l evel⑤Network status, which screens the influencing factors and builds the indicator system for credit evaluation of High-tech SMEs. Eventually the credit evaluation indicator system is designed and established to support investment decision-making for venture capital institutions.
(2) Considering the features of venture capital assessment of High-tech SMEs such as complicated conditions, variety of indicators, both qualitative and quantitative indicators, a DEA Fuzzy Comprehensive assessment is proposed: First, process the available history data of quantitative indicators by DEA, output the efficiency value on qua ntitative i ndicators f or e ach e nterprise unde r e valuated, t hen f uzzy the efficiency v alue. For t he qua litative i ndicators with unc ertainty, t he m embership functions are given by experts using fuzzy comprehensive assessment. According to the weight, the comprehensive evaluation is made on all quantitative and qualitative indicators.
(3) Considering t he c ondition t hat t he a ttributes w eights a re known, a nd subjective p reference e xists w hen v enture cap ital i nstitutions ev aluate H igh-tech SMEs’credit, by the combination of Interval Number Ranking and Grey Relational Analysis, a cr edit ev aluation method i s proposed i n t he article. T he relations and influences between each credit evaluation indicators are extracted from the interval number de cision m atrix, a nd t he r elation de grees be tween each credit e valuation indicators are quantified by correlation coefficient. Rank High-tech SMEs by Multiple Attribute Decision Making Approach and Grey Relational Analysis, and then provide the suggestion for investment decision.
(4) Considering t hat t he r esult of e xisted e valuation m ethods c annot comprehensively r eflect t he f uzziness a nd r andomness of obj ective t hings, cloud models theory is introduced in the research of credit evaluation of High-tech SMEs, aiming at uncertainty, by the idea of membership degree in fuzzy theory, establish the conversion model between qualitative concept and quantitative expression, which is breaking the division limitation of traditional evaluation methods.
(5) Considering the advantages of CART such as output tree structure, briefly and directly, processing non-quantitative indicators, a credit evaluation method for High-tech S MEs i s pr oposed, which is b reaking th e limita tion o f r elatively w eak predictive capability of traditional evaluation methods.
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