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我国互联网金融上市公司信用评价研究

发布时间:2018-09-18 07:13
【摘要】:自进入21世纪以来,互联网信息技术被广泛应用于各行各业,大大提升了人们的工作效率,降低了工作强度。其中,互联网与传统金融相结合催生的互联网金融,以发展较为健全的第三方支付、P2P网络借贷和众筹融资作为三大主流业务模式,凭借对计算机信息技术的熟练运用,快速完成信息的采集、分类和反馈处理,降低了部分交易成本和开设营业网点的实际运营成本;专业的网络平台使资金供求双方能够迅速完成匹配、议价和交易,标准化的操作流程也有利于提高业务完成的效率,推动互联网金融的进一步发展。众多第三方支付平台和个人融资平台聚集了大量的消费支付资金和社会闲散资金,一旦平台信用坍塌,将会产生很多的不安全因素,危及到各方利益。为此,对互联网金融公司的信用进行评价,便于投资者进行投资选择,涉及到广大投资者的切身利益,也深切影响着社会资金的安全。本文的理论分析主要包括两个部分。第一个部分为国内外研究综述,从互联网金融和企业信用评价两个角度对国内外已有研究进行梳理、总结和评价。其中,互联网金融的研究主要围绕其概念与模式、对传统金融的影响、风险与监管及未来发展四个方面展开;企业信用评价则主要包括评价方法和评价指标的建立两个方面。第二个部分为我国互联网金融的特点、发展历程和主要运作模式分析,笔者通过对我国互联网金融的发展进行系统分析,归纳总结为三个重要阶段,分别为:兴起阶段、快速扩张阶段和理性发展阶段,在此基础上阐述了第三方支付、P2P网络借贷和众筹融资三大主流业务模式的发展、风险和监管情况等。论文以我国沪深股票市场上的互联网金融上市公司为研究对象,以2013年至2015年共142组数据为研究样本,选取财务与非财务评价指标作为原始变量,利用因子分析方法提取出八个主成分因子,分别代表我国互联网金融公司的营运能力、偿债能力、盈利能力、发展能力、股权结构、企业影响力、董事会结构和管理者素质;在此基础上,将提取出的八个主成分因子作为新的解释变量纳入Logistic回归方程,得出了适应我国互联网金融公司的信用评价模型;最后将实际样本数据代入信用评价模型,将计算得到的概率值与预测值相比较,发现模型对总体样本的判断准确率达到了95.1%,说明本文构建的信用评价模型对我国互联网金融上市公司的信用状况具有良好的预测效果。本文研究结论有:(1)通过因子分析法提取的八个主成分因子最终都留在了回归模型当中,且根据回归系数的正负发现这八个主成分因子与我国互联网金融公司的信用为正相关关系;(2)所有财务类主成分因子对我国互联网金融公司的影响程度均大于非财务类指标的主成分因子,其中,财务类指标的主成分因子按回归系数的大小排序依次为盈利能力、发展能力、偿债能力和营运能力,非财务类指标的主成分因子对互联网金融公司信用影响大小排序为:管理者素质、股权结构、企业自身素质和董事会结构因素;(3)在所有的主成分因子中,盈利能力和发展能力的回归系数远高于其他因子,对我国互联网金融公司信用的影响程度最深。
[Abstract]:Since the beginning of the 21st century, Internet information technology has been widely used in all walks of life, greatly improving people's work efficiency and reducing the intensity of work. With the skilled use of computer information technology, we can collect, classify and feedback information quickly, reduce some transaction costs and the actual operating costs of opening business outlets; professional network platform enables both the supply and demand of funds to quickly complete matching, bargaining and trading, standardized operating procedures are also conducive to improving the industry. Many third-party payment platforms and personal financing platforms have accumulated a large number of consumer payment funds and social idle funds. Once the platform credit collapses, there will be a lot of unsafe factors, endangering the interests of all parties. Price, which is convenient for investors to make investment choices, involves the vital interests of investors and deeply affects the security of social funds. The theoretical analysis of this paper mainly includes two parts. The first part is the research summary at home and abroad, which combs and summarizes the existing research at home and abroad from the perspective of Internet finance and enterprise credit evaluation. Among them, the study of Internet finance mainly focuses on its concepts and modes, its impact on traditional finance, risk and supervision, and its future development; enterprise credit evaluation mainly includes two aspects: evaluation methods and the establishment of evaluation indicators. The second part is the characteristics, development process and main aspects of China's Internet finance. Based on the analysis of the operation mode, the author summarizes the development of Internet finance in China into three important stages: the rising stage, the rapid expansion stage and the rational development stage. On this basis, the author expounds the development, risk and supervision of the three main business modes: third-party payment, P2P network lending and crowd financing. Based on 142 sets of data from 2013 to 2015, this paper selects financial and non-financial evaluation indicators as the original variables and extracts eight principal component factors by factor analysis to represent the operation of China's Internet financial companies. On this basis, eight principal component factors are put into Logistic regression equation as new explanatory variables, and a credit evaluation model adapted to China's Internet financial companies is obtained. Finally, the actual sample is given. This data is substituted into the credit evaluation model. Comparing the calculated probability value with the predicted value, it is found that the accuracy rate of the model is 95.1% for the overall sample. This shows that the credit evaluation model constructed in this paper has a good prediction effect on the credit status of China's Internet financial listed companies. The eight principal component factors extracted by the analysis method are finally left in the regression model, and according to the positive and negative regression coefficients found that these eight principal component factors and the credit of China's Internet financial companies are positively correlated; (2) All the financial principal component factors of China's Internet financial companies are greater than the impact of non-financial indicators. Among them, the principal component factors of financial indicators are profitability, development ability, solvency and operation ability, and the principal component factors of non-financial indicators are ranked as follows: managerial quality, ownership structure, enterprise quality and board of directors. Structural factors; (3) Among all the principal components, the regression coefficients of profitability and development ability are much higher than other factors, which have the deepest impact on the credit of China's Internet financial companies.
【学位授予单位】:安徽大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:F724.6;F832

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