P2P网贷平台的信用风险评级研究

发布时间：2018-01-01 23:04

本文关键词：P2P网贷平台的信用风险评级研究　出处：《北方工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：作为互联网金融的重要组成部分,P2P网络借贷在我国得到快速的发展,网贷平台数量不断增加,但是在监管缺失的环境下,问题平台层出不穷,给整个网贷行业发展和社会稳定带来了巨大的负面影响。本文在信用风险评级理论基础上,借鉴企业和银行的信用风险评级方法,结合网贷平台的特点,选择Credit Metrics模型作为研究方法。在不同时间点分别对网贷平台做信用评级,建立信用风险转移矩阵。对网贷平台做信用风险评级时,首先分析了网贷平台的运营模式和盈利模式,结合评级机构的指标体系,选择反映网贷平台的信用风险的指标。其次分别使用非监督学习中的聚类分析方法和有监督学习中的决策树、SVM和提升算法对网贷平台的信用风险进行衡量。在未知网贷平台是否违约时,使用聚类分析方法对网贷平台做分类,根据指标选择确定各类别的信用等级,完成网贷平台的信用风险评级,并对结果进行跟踪,结果表明:研究的网贷平台样本中,出现违约的平台占7%左右,且都是评级结果较低的平台,借贷利率与评级结果呈负相关。在获得问题平台样本后,使用决策树、SVM和提升算法对网贷平台的信用风险做评级,结果显示:有监督学习算法优于聚类分析,同时该类算法可实现对网贷平台信用风险的预测,其中提升算法的准确率高。使用主成分分析对同样的网贷平台样本做评级,准确率没有聚类分析高。使用Adaboost算法对网贷平台借款人的信用风险做研究,完善网贷平台的信用风险评级。最后使用提升算法得到的信用风险评级结果,建立网贷平台的信用风险转移矩阵。加入回收率,衡量网贷平台违约后的偿还能力。信用风险转移矩阵表征网贷平台的违约概率,结合回收率,可预估网贷平台的信用风险大小。研究结果显示:利率的大小和评级结果呈反向关系,借款利率越低,网贷平台的信用等级越高,相反则越低。对网贷平台的信用风险的评级,可预估网贷平台违约的风险大小和整个行业的潜在风险,使监管部门更好的管理网贷平台,防范网贷行业风险的发生,同时可为投资者提供决策参考。
[Abstract]:As an important part of Internet finance, P2P network lending has been developing rapidly in China. The number of network lending platforms is increasing, but in the environment of lack of supervision, problem platforms emerge in endlessly. On the basis of the credit risk rating theory, this paper draws lessons from the credit risk rating methods of enterprises and banks, combined with the characteristics of the network lending platform. The Credit Metrics model is chosen as the research method. The credit rating of the network loan platform is done at different time points, and the credit risk transfer matrix is established. When the credit risk rating of the network loan platform is made, the credit risk rating of the network loan platform is made. First of all, it analyzes the operation model and profit model of the network loan platform, combined with the index system of the rating agencies. Select the index which reflects the credit risk of the network loan platform. Secondly, use the clustering analysis method in the unsupervised learning and the decision tree in the supervised learning. SVM and upgrade algorithm to measure the credit risk of the network loan platform. In the unknown network loan platform default, the use of clustering analysis to classify the network loan platform, according to the selection of indicators to determine the credit rating of each category. Complete the credit risk rating of the network loan platform, and track the results, the results show that: in the sample of the network loan platform, the default of the platform accounted for about 7%, and are the lower rating platform. The loan interest rate is negatively correlated with the rating results. After obtaining the sample of the problem platform, the credit risk of the network loan platform is rated using decision tree SVM and upgrade algorithm. The results show that the supervised learning algorithm is better than the clustering analysis, and this kind of algorithm can predict the credit risk of the network loan platform. Among them, the accuracy of the improved algorithm is high. Using principal component analysis (PCA), the sample of the same network loan platform is rated. The accuracy of clustering analysis is not high. Adaboost algorithm is used to study the credit risk of loan platform borrowers. Finally, using the credit risk rating results obtained by the upgrade algorithm, the credit risk transfer matrix of the network loan platform is established, and the recovery rate is added. The credit risk transfer matrix represents the default probability of the net loan platform, combined with the recovery rate. The research results show that the size of interest rate and rating results show a reverse relationship, the lower the borrowing rate, the higher the credit rating of the network lending platform. On the other hand, the lower the credit risk rating of the network loan platform, the risk of default and the potential risk of the whole industry can be estimated, so that the regulatory authorities can better manage the network loan platform. To prevent the occurrence of network loan industry risks, and to provide investors with decision-making reference.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F832.4;F724.6

【参考文献】