基于csQCA的P2P网络借贷平台信用影响因素
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摘要
针对P2P网络借贷平台成交量及数量减少的现象,运用网络爬虫技术等方法,获取了上市系、国资系、风投系、创业系4个类别共40个P2P网络借贷平台的实际数据;建立了基于品牌信用、交易信用、人气信用、服务信用4个维度的P2P网络借贷平台信用评估模型;运用csQCA方法对其平台信用影响因素进行构型分析。研究表明:交易信用中的信息透明度是影响P2P网络借贷平台信用的主要要素,其次是品牌信用中的合规程度;高品牌信用是上市系和创业系P2P网络借贷平台高信用值产生的必要条件;高交易信用是国资系P2P网络借贷平台高信用值产生的必要条件。政府和P2P网络借贷平台应重点关注品牌信用和交易信用的变化,为借款人和出借人营造良好的交易氛围。
The article focuses on the phenomenon of the decrease in the volume and quantity of the P2 P network lending platform. It uses the methods of web crawling technology to obtain the actual data of 40 P2 P network lending platforms in the four categories of listing,state-owned,venture capital and entrepreneurship. The paper establishes a credit evaluation model for P2 P network lending platform based on four dimensions:brand credit,transaction credit,popular credit and service credit. It uses the csQCA method to analyze the configuration factors of its platform credit. Research shows that the transparency of information in transaction credit is the main factor affecting the credit of P2 P network lending platform,and secondly,the degree of compliance in brand credit. High brand credit is a necessary condition for the high credit value of the listing system and the entrepreneurial P2 P network lending platform.High transaction credit is a necessary condition for the high credit value of the P2 P network lending platform of the state-owned assets. Therefore,the government and P2 P network lending platforms should focus on changes in brand credit and transaction credit,creating a good trading atmosphere for borrowers and lenders.
The article focuses on the phenomenon of the decrease in the volume and quantity of the P2 P network lending platform. It uses the methods of web crawling technology to obtain the actual data of 40 P2 P network lending platforms in the four categories of listing,state-owned,venture capital and entrepreneurship. The paper establishes a credit evaluation model for P2 P network lending platform based on four dimensions:brand credit,transaction credit,popular credit and service credit. It uses the csQCA method to analyze the configuration factors of its platform credit. Research shows that the transparency of information in transaction credit is the main factor affecting the credit of P2 P network lending platform,and secondly,the degree of compliance in brand credit. High brand credit is a necessary condition for the high credit value of the listing system and the entrepreneurial P2 P network lending platform.High transaction credit is a necessary condition for the high credit value of the P2 P network lending platform of the state-owned assets. Therefore,the government and P2 P network lending platforms should focus on changes in brand credit and transaction credit,creating a good trading atmosphere for borrowers and lenders.
引文
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[14]吴庆田,罗璨,陈宜瑄. P2P网贷平台特征与平台运营的稳健性———基于中国1706家P2P网贷平台的证据[J].金融理论与实践,2018(4):62-70.
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[16]孙宝文,牛超群,赵宣凯,等.财务困境识别:中国P2P平台的风险特征研究[J].中央财经大学学报,2016(7):32-43+55.
[17]郭海凤,陈霄. P2P网贷平台综合竞争力评价研究[J].金融论坛,2015,20(2):12-23.
[18]曹业奇.我国P2P网贷行业规范发展程度测评与建议[J].经济纵横,2016(8):110-113.
[19]朱宗元,王景裕. P2P网络借贷平台效率的综合评价———基于AHP-DEA方法[J].南方金融,2016(4):31-38.
[20]王重润,孔兵. P2P网络借贷平台融资效率及其影响因素分析———基于DEA-Tobit方法[J].河北经贸大学学报,2017,38(5):54-60.
[21]伯努瓦·里豪克斯,查尔斯C,拉金. QCA设计原理与应用[M].杜运周,李永发,译.北京:机械工业出版社,2017.
[2] SLATTERY P. Square pegs in a round hole:sec regulation of online peer-to-peer lending and the CFPB alternative[J]. Yale Journal on Regulation,2013,30(1):233-275.
[3]贾丽平,邵利敏. P2P网络借贷的监管边界:理论探讨与中国的检验[J].经济社会体制比较,2015(3):175-184.
[4]李先玲.资本充足、从业背景与平台倒闭———来自中国231个P2P网络借贷平台的证据[J].经济经纬,2016,33(6):137-142.
[5]王会娟,廖理.中国P2P网络借贷平台信用认证机制研究———来自“人人贷”的经验证据[J].中国工业经济,2014(4):136-147.
[6]中国人民银行征信中心与金融研究所联合课题组,纪志宏,王晓明,曹凝蓉,等.互联网信贷、信用风险管理与征信[J].金融研究,2014(10):133-147.
[7]李焰,高弋君,李珍妮,等.借款人描述性信息对投资人决策的影响———基于P2P网络借贷平台的分析[J].经济研究,2014,49(S1):143-155.
[8]廖理,吉霖,张伟强.借贷市场能准确识别学历的价值吗?———来自P2P平台的经验证据[J].金融研究,2015(3):146-159.
[9]谢雪梅,许悦. P2P网贷平台借款信息对投资决策的影响研究[J].科技和产业,2016,16(12):163-168.
[10]孟娜娜,粟勤.软信息对网贷平台借款人的借款获得与违约风险的影响———来自“人人贷”的经验证据[J].中国流通经济,2018,32(3):85-94.
[11]陈冬宇,朱浩,郑海超.风险、信任和出借意愿———基于拍拍贷注册用户的实证研究[J].管理评论,2014,26(1):150-158.
[12]闫春英,张佳睿.完善我国P2P网络借贷平台风险控制体系的策略研究[J].经济学家,2015(10):78-83.
[13]何光辉,杨咸月,蒲嘉杰.中国P2P网络借贷平台风险及其决定因素研究[J].数量经济技术经济研究,2017,34(11):44-62.
[14]吴庆田,罗璨,陈宜瑄. P2P网贷平台特征与平台运营的稳健性———基于中国1706家P2P网贷平台的证据[J].金融理论与实践,2018(4):62-70.
[15]邓小峰. P2P网贷平台发展的多维度分析[J].商业经济研究,2015(18):83-85.
[16]孙宝文,牛超群,赵宣凯,等.财务困境识别:中国P2P平台的风险特征研究[J].中央财经大学学报,2016(7):32-43+55.
[17]郭海凤,陈霄. P2P网贷平台综合竞争力评价研究[J].金融论坛,2015,20(2):12-23.
[18]曹业奇.我国P2P网贷行业规范发展程度测评与建议[J].经济纵横,2016(8):110-113.
[19]朱宗元,王景裕. P2P网络借贷平台效率的综合评价———基于AHP-DEA方法[J].南方金融,2016(4):31-38.
[20]王重润,孔兵. P2P网络借贷平台融资效率及其影响因素分析———基于DEA-Tobit方法[J].河北经贸大学学报,2017,38(5):54-60.
[21]伯努瓦·里豪克斯,查尔斯C,拉金. QCA设计原理与应用[M].杜运周,李永发,译.北京:机械工业出版社,2017.