The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credi...The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators,including platform transaction volume and average expected rate of return.We also consider two qualitative indicators of online loan background,namely platform background and guarantee mode,that reflect Chinese characteristics.Subsequently,a factor analysis was conducted to reduce the 14 indicators dimensions.The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor,fund dispersion factor,security factor,and profitability factor.Finally,a K-means clustering algorithm was employed to cluster the factor scores of each OLP,thereby obtaining credit rating results.The empirical results indicate that the proposed machine learning-based credit rating method effectively provides early warnings of problem platforms,yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China,namely,Wangdaitianyan and Wangdaizhijia.展开更多
P2P(peer to peer)网贷平台,以网络平台为中介在互联网金融领域得到快速发展,从其引入我国之后,与我国特殊的金融环境相适应,逐渐具有其"本土性"特点。但由于缺乏法律规范、市场监管等必要的制度约束,逐渐使其走向"异化&...P2P(peer to peer)网贷平台,以网络平台为中介在互联网金融领域得到快速发展,从其引入我国之后,与我国特殊的金融环境相适应,逐渐具有其"本土性"特点。但由于缺乏法律规范、市场监管等必要的制度约束,逐渐使其走向"异化"的道路。通过对我国P2P网贷平台不规范问题的提出,运用数据分析和资料收集等方法,提出适合我国P2P网贷平台发展的相关性建议,完善其在法律制度方面的缺陷,确保我国P2P网贷市场完成本土性优化。展开更多
基金supported by grants from Major Program of National Social Science Foundation(No.22&ZDo73)the key program of the National Natural Science Foundation of China(NSFC No.71631005).
文摘The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators,including platform transaction volume and average expected rate of return.We also consider two qualitative indicators of online loan background,namely platform background and guarantee mode,that reflect Chinese characteristics.Subsequently,a factor analysis was conducted to reduce the 14 indicators dimensions.The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor,fund dispersion factor,security factor,and profitability factor.Finally,a K-means clustering algorithm was employed to cluster the factor scores of each OLP,thereby obtaining credit rating results.The empirical results indicate that the proposed machine learning-based credit rating method effectively provides early warnings of problem platforms,yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China,namely,Wangdaitianyan and Wangdaizhijia.
文摘P2P(peer to peer)网贷平台,以网络平台为中介在互联网金融领域得到快速发展,从其引入我国之后,与我国特殊的金融环境相适应,逐渐具有其"本土性"特点。但由于缺乏法律规范、市场监管等必要的制度约束,逐渐使其走向"异化"的道路。通过对我国P2P网贷平台不规范问题的提出,运用数据分析和资料收集等方法,提出适合我国P2P网贷平台发展的相关性建议,完善其在法律制度方面的缺陷,确保我国P2P网贷市场完成本土性优化。