创业企业信用风险度量与贷款组合管理研究
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摘要
创业企业对经济增长和社会发展有着非常重要的意义。它在推动技术进步、产业结构调整、扩大就业等方面发挥着不可替代的作用。中国经济近二十年的快速发展,在一定程度上得益于创业精神的释放。随着我国经济和科技体制改革的不断深入,以科技成果产业化为代表的创业企业获得了蓬勃的发展,创业企业已经成为经济增长的新亮点。但是,由于创业企业所固有的特点,如创业初期资产规模较小、未来发展不确定性大、信用缺失、风险高等,使其在成长过程中的融资问题较为突出,这在很大程度上限制了它的发展及潜能的发挥。
     本文以建立适合中国国情的创业企业信用风险评估模型为研究目的,针对创业企业这一特殊的企业群体,在回顾信用风险度量的相关方法、模型和比较的基础上,探索现阶段我国创业企业信用风险度量的最可行方法,开创性地将Logit信用评分模型与投资决策理论相结合,应用于创业企业在融资过程中的信用风险度量。创业企业一般通过股权融资、债务融资以及准股权融资等多种方式解决发展中的资金问题,本文以实物期权法、市场比较法和现金流量法等股权融资定价方法解决不同生命阶段创业企业股权融资定价问题,以成本加成法和期权定价模型为导入期、成长期和成熟期创业企业的信贷融资定价提供依据,采用调整后的Black-Scholes期权定价模型对我国创业企业可转换债券进行定价。本文研究建立了针对我国创业企业的信用风险度量方法与管理的信用决策系统,为金融机构对创业企业给予各种形式的融资供给提供了理论依据和技术支持。
     本文共分七章。第一章是绪论,主要介绍论文的研究目的和意义、主要内容、论文结构及主要创新点。第二章是对国内外信用风险度量和企业生命周期不同阶段风险特征研究现状的综述和分析。第三章研究如何构建适合我国国情的创业企业信用风险度量模型。基于创业企业所处不同生命阶段的运营模式、融资方式、所需资金的性质和规模的不同,提出了构建创业企业不同生命阶段的信用风险度量模型的思想并构建模型。根据数据的可获得性以及有成长痕迹可寻的创业企业在融资方式、资金需求性质和规模等方面的研究,采用源于创业企业的深圳中小企业板75家上市公司的财务和非财务数据为研究样本,通过计算实例演示了单个企业信用风险度量的方法。第四章是创业企业股权融资、债务融资以及准股权融资定价。本文从创业企业的价值内涵和特性入手,比较分析适用于创业企业的传统和创新的定价方法,选取操作性和准确度最高的模型并加以调整,分别对导入期、成长期和成熟期(延续研究,以分析企业的潜在价值)的创业企业进行股权融资、债务融资和准股权融资定价。第五章是创业企业贷款组合风险管理研究。在组合风险管理理论基础上,将Credit Risk+与贷款企业“0-1整数规划”理论相结合,构建不同行业或不同生命阶段的创业企业贷款组合模型,为金融机构依据组合的风险与收益平衡原则做出贷款决策提供理论支持,以解决创业企业债务融资的瓶颈问题。第六章在对创业企业信用和融资方式分析的基础上,提出构建创业企业贷款风险度量与组合管理模型的配套政策建议。第七章给出了研究结论,分析了本研究的局限性,并对未来进一步地深入研究我国创业企业信用风险评估体系进行了展望。
     论文的主要创新点表现在以下四个方面:
     第一通过信用评级模型与投资决策理论的结合,将创业企业的风险定量化,建立适合于创业企业的风险度量方法,判别企业的违约概率,并结合我国实际情况进行实证分析。
     第二,运用逐步多元判别分析法对处于不同生命阶段的创业企业筛选出具有区别力的分析变量,度量出不同生命阶段的企业的信用风险。
     第三,通过对创业企业各种融资方式定价的适用性分析,找出适合不同生命阶段创业企业的定价模型,使资金拥有者可以基于风险度量对创业企业所提供的资金确定价格。
     第四,在现有贷款组合优化的原则和模型的基础上,根据创业企业不同生命阶段的特点,建立贷款组合优化决策模型,通过应用实例将所得模型与传统组合管理方法相比较,以说明该模型的有效性。
     另外,本文在研究中,将创业企业放在整个企业生命周期内去评价,针对企业不同发展阶段的特点,对其信用风险进行评估,对可行的融资组合进行分析、探讨,以客观评价创业企业的成长性和潜在价值,在符合国家支持创业企业发展的政策前提下,为金融中介提出了重要的可操作性建议,也具有明显的创新性。
     同时,本文也存在一定的局限性。第一,实证分析中的数据来源于深交所中小板企业,与创业企业存在一定的差异,可选用的样本总量也较小;第二,选取的指标体系受到了数据来源的限制;第三,选用的实证模型存在一定的局限;第四,未考虑宏观环境对信贷周期等违约情况带来的影响。因此,下一步的研究工作可以继续扩大调研范围,以提高样本准度和容量,选取更系统的指标体系,加入Probit等更多模型以选取最适合不同生命阶段企业的评分方法,同时考虑将宏观经济、金融环境加入到模型分析中。
Start-up enterprises make a great contribution to economic growth and social development. They play as an irreplaceable role in pushing the technology advances, adjusting the industrial structure and increasing the employment. To some extent, the fast economic growth in the past two decades in China benefits from the release of the spirit of start-ups. Along with the continuous progress in system reforms in economy and technology in China, the start-ups, taking the high-tech industry as a representative, have been in vigorous development and become a highlight of economic growth. However, because of their inherent characteristics, like the limit assets at the early stage of the business, the uncertainty of the future development direction, no credit record, high risks and so on, start-ups are facing the difficulties to assess the finance, which to a great extent limits their potential development.
     With the purpose to build credit risk valuation model applied to venture companies in China, the thesis is specifically focus on them and explore the“best practices”to measure their credit risks, basing on revisit methods, models, and comparison to measure credit risk. And it, in a groundbreaking way, combines the Logit credit rating with investment decision theories to apply credit risk measurement for venture companies in their process of financing. Generally speaking, start-ups use equity financing, debt financing and quasi-equity financing to assess finance. The thesis applies various equity pricing models, including real options, market comparison and cash flow analysis, to resolve the problems of equity financing pricing in different life-cycle stages of start-up enterprises. The thesis uses the risk-adjusted return on equity as a basis for pricing of credit financing; and uses the adjusted Black-Scholes option pricing model on the pricing of the convertible bonds for Chinese start-up enterprises. This study provides theoretical foundation and technical support for the establishment of the credit decision-making system of the credit risk measurement for the start-up enterprises in China.
     The thesis contains seven chapters. Chapter I is the preface, which introduces the purpose, the significance, the thesis structure and the main innovation points of the research. Chapter II summarizes and analyses the research literature on credit-risk measurements at home and abroad, and on contemporary studies of risk management at different stages of enterprise life cycle. Chapter III studies how to build the credit-risk valuation model that suites the current situation in China. Based on the differences among the business mode, the characteristics and the size of the capital required, this chapter proposes the theory and build the valuation model of credit-risk at different stages of enterprise life cycle. Considering the data availability, Chapter III demonstrates how to evaluate the credit risk of an individual enterprise by using the data of 75 SMEs listed companies in Shenzhen Stock Exchange as the sample. Chapter IV regards the pricing of equity financing, debt financing, and quasi-equity financing for start-ups. Developed from core value and characteristics of venture companies, the chapter compares and evaluates traditional and new pricing methods applied to venture companies and selects practical and precisive models with adjustments to conduct pricing for equity financing, debt financing, and quasi-equity financing separately in starts-up, growth, and mature start-ups. Chapter V investigates loan portfolio management on start-up enterprises. Based on portfolio risk management theories, loan portfolio models can be built for start-up enterprises in different industries and different life-cycle stages by combining the theories of“Credit Risk+”with“0-1 Integer Programming”. It would help ease the enterprise financing bottlenecks and decision making in loan approvals by evaluating both risks and returns. Chapter VI presents some packaged policy making suggestions regarding both financing start-up enterprises and portfolio management models, based on analytics of the enterprise credits and the means of financing. Chapter VII concludes the research and analyzes its limitations. It also makes an outlook on further studies of our risk evaluation system for start-up enterprises.
     There are mainly four innovation points in this thesis.
     Firstly, it innovatively combines the credit-rating models with the investment decision-making theory, and analyzes the credit-risk measurement of start-up enterprises, which provides significant theoretical reference to developing credit decision system for risk measurement and management on the enterprises.
     Secondly, it uses a stepwise discriminant analysis method to select discriminant variables from different stages of enterprise lifespan, and measures the credit risk of the enterprises at those points.
     Thirdly, by carrying out applicability analysis on pricing of enterprise financing, finding pricing models for different stages of enterprise life span, and helping stakeholders to find the right prices for start-up enterprises upon risk measurement.
     Fourthly, based on current model and principle of optimizing portfolio loans, the characteristics of different stages of enterprise life span, by building a decision making model on optimized portfolio loans, and comparing it with the traditional management in case studies, we proved its validity and applicability.
     On the other hand, this paper studies the start-up enterprises against their whole life-cycle. Based on their different characteristics at different stages, it evaluates their credit risks, analyzes and discusses possible financing portfolio and provides some subjective opinions on growth of enterprises and their potential value. In the background of keeping consistency with supporting the start-up enterprises, this thesis provides some applicable advices for financial intermediaries, which is also an innovative highlight.
     In the mean time, the thesis has its own limitations. Firstly, the data in the demonstration analysis came from SME board of Shenzhen Stock Exchange, which is different from their start-up counterparts, along with a very limited sample pool. Secondly, the sample threshold system is limited by data source. Thirdly, the selected demonstration model is limited. Fourthly, we did not consider the impacts brought by macro environment on defaults of credits cycles. Therefore, our next research would involve a greater scope and scale, with improved sample accuracy and capacity, a more systematic indexing mechanism, and employ PROBIT or other models that would better evaluate enterprises at different stages. And, we would incorporate macro-economic and financial environment into our analysis.
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