P2P供应链金融模式下中小企业信用风险评价研究

发布时间：2019-01-09 13:39

【摘要】：伴随着互联网金融的发展,供应链金融已从最早的1.0时代发展到3.0时代,即互联网供应链金融。一方面,在现有开展供应链金融业务的主体一银行和上市公司中,银行业务对象的抓大放小、上市公司自有资金的运营弱势直接导致供应链金融业务存在空白和短缺。另一方面,2014年,P2P网络借贷平台出现“资产荒”以及P2P网络借贷问题平台的不断增加,P2P行业中逐渐成熟的平台已经开始涉水“供应链金融”。但是,在P2P供应链金融模式下,P2P网络借贷平台面对的是来自中小企业的信用风险,一旦供应链上的中小企业发生违约,将会危及P2P网络借贷平台的资金运转,进而影响投资者的资金回收。因此,评价P2P供应链金融模式下中小企业信用风险,一方面可以帮助P2P网络借贷平台发掘优质的资产,另一方面对P2P网络借贷平台有效评价供应链金融模式下中小企业信用风险评价提出可行性建议。本文在文献研究和理论研究的基础上,首先从融资企业资质、核心企业资质、融资项目资质、供应链伙伴关系以及行业环境五个方面构建了信用风险预选评价指标体系,即5个一级指标,14个二级指标以及24个三级指标,结合相关性分析和鉴别力分析,确定了的最终评价指标体系,即5个一级指标,13个二级指标以及20个三级指标。其次,选取以A股煤炭供应链上市公司瑞茂通(600180)及其部分子公司为核心企业的46家中小企业,时间跨度最长为2014-2015年的所需数据,共形成88个样本点,通过基于主成分分析的Logistic回归模型和BP神经网络模型对P2P供应链金融模式下中小企业信用风险进行了评价,实证结果表明,在相同的指标体系下,运用BP神经网络模型对P2P供应链金融模式下的中小企业信用风险评价的整体准确率较高,优于Logistic回归模型的评价效果。最后结合理论研究和实证研究结果,本文从信用风险评价自身工作、信用风险评价辅助工作以及加强防范P2P网络借贷自身法律风险三个方面提出了 P2P供应链金融模式下中小企业信用风险评价建议。
[Abstract]:With the development of Internet finance, supply chain finance has developed from 1.0 times to 3.0 times, that is, Internet supply chain finance. On the one hand, among the banks and listed companies, the main body of the supply chain financial business is banks and listed companies, and the weakness of the operation of their own funds directly leads to the blank and shortage of the supply chain financial business. On the other hand, in 2014, the P2P network loan platform appeared "assets shortage" and the P2P network loan problem platform unceasingly increased, the P2P industry gradually matured platform already began to involve the water "the supply chain finance". However, in the P2P supply chain finance model, P2P network lending platform is faced with the credit risk from small and medium-sized enterprises. Once the small and medium-sized enterprises in the supply chain default, it will endanger the capital operation of P2P network lending platform. In turn, it affects the return of funds to investors. Therefore, evaluating the credit risk of SMEs under the P2P supply chain financial model, on the one hand, can help P2P network lending platform to explore high-quality assets. On the other hand, some feasible suggestions are put forward to evaluate the credit risk of SMEs under the supply chain finance model. On the basis of literature research and theoretical research, this paper first constructs the credit risk pre-selection evaluation index system from five aspects: financing enterprise qualification, core enterprise qualification, financing project qualification, supply chain partnership and industry environment. That is, 5 first class indexes, 14 secondary indexes and 24 third grade indexes, combined with correlation analysis and discriminant analysis, the final evaluation index system is determined, that is, 5 primary index, 13 second class index and 20 third grade index. Secondly, we select 46 small and medium-sized enterprises which take the A share coal supply chain listed company Ruimao Tong (600180) and some of its subsidiaries as the core enterprises. The longest time span is the data needed in 2014-2015, forming a total of 88 sample points. Through the Logistic regression model based on principal component analysis and BP neural network model to evaluate the credit risk of SMEs in P2P supply chain finance model, the empirical results show that under the same index system, The BP neural network model is better than the Logistic regression model in evaluating the credit risk of SMEs under the P2P supply chain finance model. Finally, combined with the theoretical and empirical research results, this paper from the credit risk evaluation of their own work, This paper puts forward some suggestions on credit risk evaluation of SMEs in P2P supply chain finance model from three aspects: supporting work of credit risk evaluation and strengthening prevention of legal risk of P2P network loan.
【学位授予单位】：西安理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F832.4;F275

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