COVID‑19 pandemic risk and probability of loan default:evidence from marketplace lending market

As the COVID-19 pandemic adversely affects the financial markets,a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress.Using the loan book database of Mintos(Latvia)and employing logit regression method,we provide evidence of the pandemic-induced exposure to default risk in the marketplace lending market.Our analysis indicates that the probability of default increases from 0.056 in the pre-pandemic period to 0.079 in the post-pandemic period.COVID-19 pandemic has a significant impact on default risk during May and June of 2020.We also find that the magnitude of the impact of COVID-19 risk is higher for borrowers with lower credit ratings and in countries with low levels of FinTech adoption.Our main findings are robust to sample selection bias allowing for a better understanding of and quantifying risks related to FinTech loans during the pandemic and periods of overall economic distress.

Does Mood Affect the Efficiency of Credit Approval？ Evidence from Online Peer-to-Peer Lending

Using a large dataset obtained from ＂Paipaidai,＂ an online peer-to-peer lending platform in China, we examine whether credit officers＇ mood affects the efficiency of credit approval from a perspective of individual decision-making. Refering to studies in psychology and financial economics, we employ season, temperature and weather as mood proxies, and control the variables related to the quality of the loan to study credit approval behavior under different mood conditions. The results suggest that the efficiency of credit approval by individual credit officers is significantly correlated with their mood--a positive mood improves efficiency, while a negative mood reduces it. Specifically, loans examined under better mood conditions （e.g., during spring, comfortable temperatures, and sunny days） have a significantly higher probability of approval, but a lower probability of default if approved; and those examined under poorer mood conditions show a lower probability of approval and a higher probability of default if approved. This effect of mood is even stronger when a loan application is more complex, atypical, or unusual to evaluate. Moreover, investor sentiment, denoted by closed-end fund premiums, has the same effect on credit approval as well.

How Firms’Ownership Structure Impacts Online Peer-to-Peer Lending Performance in China

A dramatic surge in online peer-to-peer(P2P)lending emerged in China,where(under conditions of credit deficiency)it took only three years for the size of the P2P lending market in China to reach four times that of the United States and ten times that of the United Kingdom.The literature indicates that ownership structure is an important factor that influences P2P lending firms’performance,while research on the underlying mechanisms remain insufficient.This study analyzes the data of P2P lending companies between June 2016 and March 2017.The results demonstrate that although ownership structure has minimal direct effect on the turnover volume and number of lenders and borrowers,it moderates the effects of firm age,interest rate,and loan term on firm performance.These results enrich the property theory and shed light on how P2P lending firms with different ownership structures could succeed when there is institutional deficiency.

Internet Financial Risks and Investors’Risk Awareness--Evidence from Transaction Data of Online Lending Platforms

For the first time,this paper uses the operation data of 575 online P2P lending platforms to test whether investors have a strong risk awareness of online lending products.It is found that investors’behavior shows a certain risk awareness,both for the individual risk of specific platforms and for the overall market risk of the industry.On the one hand,raising interest rates and shortening the term does attract more investment,but for potentially problematic platforms,the effect of attracting investment is significantly worse,with excessive interest rates on the platforms even causing investors to invest less.On the other hand,when there are more online lending platforms in the market,investors will behave more cautiously.

互联网金融风险对法拍房市场的影响——基于P2P网贷平台的实证研究

作为互联网金融的一种新业态,P2P网贷平台在提供传统银行之外的融资渠道的同时也带来了新的风险。2013—2020年P2P平台的“暴雷潮”,对法拍房市场造成了冲击。研究发现,P2P网贷平台风险事件显著增加了法拍房数量,且这一影响随着时间累积而持续存在,证明P2P网贷问题平台给法拍房市场带来了持久的金融风险。基于此,在验证坚持防范和化解金融风险的重要性的基础上,在加强金融体系监管的同时,应坚持监管的审慎渐进原则,打造住房市场和金融市场的健康关系,保障人民群众的财产安全。

Voluntary disclosures and peer-to-peer lending decisions:Evidence from the repeated game

This study investigates the effect of voluntary disclosures on lending decisions in the repeated game.Using a unique dataset from a peer-to-peer lending platform,"ppdai" (poipaidai),we document that voluntary disclosures in the repeated game play a stronger role in promoting funding success than those in the one-shot game.We argue that voluntary disclosures improve the bidding activity in the repeated game through which they increase funding success.In addition,the greater impact of voluntary disclosures on funding success in the repeated game only holds for loans without a personal guarantee attribution.Our extended results suggest that the subjective voluntary disclosures in the repeated game have greater information content only when borrowers have a successful borrowing experience.We also point out that voluntary disclosures in the repeated game are associated with a lower probability of default.Our results are robust to the Heckman two-step estimation that addresses the self-selection effect and a specification designed to rule out the alternative explanation from reputation in the repeated game.Our study provides new insights into the real effects of costless,voluntary and unverifiable disclosures on lending decisions.

我国P2P网络借贷信用风险影响因素研究——基于排序选择模型的实证分析

P2P(peer-to-peer)网络借贷是一种借助网络平台,由个人与个人间互为借贷双方的小额借贷交易。它作为互联网与民间借贷相结合的新兴金融模式,具有较高的信用风险。采用排序选择模型,基于excelVBA数据挖掘技术截取多个P2P网站数据,对平台信用风险的影响因素进行实证分析,结果表明:个人特征、信用变量、历史表现、借款信息分别对网络借贷信用风险存在正向影响,由此发现网站提供的信息对投资者避免信用风险没有起到实质作用。

征信视角下P2P网贷模式的信用风险探析

近年来,我国P2P网络贷款作为互联网金融创新模式获得爆发式增长,但同时在信息不对称、征信主体权益保护等方面存在的信用风险问题也日益突出。为促进P2P网络贷款等互联网金融的发展,应加强征信管理与服务,将P2P网贷平台纳入征信管理范畴,强化P2P网贷平台信用管理和平台资金流动监管,加大网贷平台信用信息保护力度。

P2P网络借贷模式下的小微企业融资问题研究

本文结合小微企业融资难问题,分析P2P网络借贷的信用风险及交易可能性集合,并将信号传递博弈模型引入到P2P网络借贷行为的研究当中,分析小微企业和出借方在追求各自利益最大化的前提下达到双方利益均衡的博弈过程。研究表明:P2P网络借贷模式能够降低出借方因信用风险而造成的极端大额损失;与传统融资模式相比,P2P网络借贷模式下的交易可能性集合范围更广;小微企业在P2P网络借贷平台上发出的关于借款申请列表的信号,传递了有关小微企业真实履约能力的主要信息。

网络借贷P2P:利差是否包含违约风险隐含信息?——来自人人贷交易数据的实证分析

基于Probit和Tobit模型,以人人贷2011年1月1日至2015年9月30日交易数据为基础,研究网络借贷P2P利差即包含借款人与投资人之间不对称信息的利率部分是否隐含借款人的违约风险信息。研究发现,浮动区间型利率机制下,利差与借款人违约风险具有正向相关关系,利差越大,借款人违约概率越高,表明利差确实包含借款人违约风险隐含信息。进一步探究借款人愿意增加借款资金成本的原因发现,利差与借款人获得借款的急切程度相关,具体表现为利差与满标所需时间呈反向相关关系,即利差越大,满标所需时间越短。

P2P网络借贷、流动性风险与宏观审慎政策

目前,P2P网络借贷平台合法合规发展已成为重要问题。本文依据中国P2P网络借贷平台特征事实,构建动态随机一般均衡模型,模拟分析P2P网络借贷经济影响、风险机理及宏观审慎政策有效性。研究结果表明:(1)P2P网络借贷在提高金融渗透率、促进金融普惠的同时,也放大了信贷波动,增加了银行体系外的金融脆弱性;(2)P2P网络借贷行业存在适度最优规模边界,无序发展不利于社会福祉提高;(3)与P2P网络借贷行业规模过度相比,平台债权转让模式流动性风险更严重;(4)在监管非对称下,P2P网络借贷削弱了宏观审慎政策有效性。因此,扩大宏观审慎政策框架覆盖范围,将P2P网络借贷纳入宏观审慎政策框架,与P2P网络借贷行业可持续发展并非对立关系,一定程度上可平抑信贷波动,提高宏观审慎政策有效性。

我国P2P网络借贷的信号博弈分析

针对我国P2P网络借贷中信用风险频发的现象,通过建立信号博弈模型,分析三种贝叶斯均衡下参与者的行为,找出信用风险的影响因素。研究表明:高低利率下借款者可获本金、风险担保金、修改信息使信用评级失真的成本、平台借款者总体信用等因素可以影响网络借贷信用风险,采用分离均衡策略能够降低这种风险。

我国P2P网络借贷平台风险监管及防范

随着我国经济以及互联网技术的不断发展,随着人们对投资理财需求的越来越大,P2P借贷进入我国市场后受到了广大投资者和借款人的追捧,行业规模不断扩张,交易规模快速增长,相关政策日益完善。然而,问题平台非法营运的存在以及行业监管的缺失,导致近几年我国P2P网络借贷平台问题频发。为进一步完善对我国P2P网络借贷平台风险的监管,避免给投资者造成巨大和难以弥补的财产损失,必须出台相关法律,明确监管主体,加强行业自律,防范法律风险;必须进行资金托管,设立风险准备金,防范经营风险;必须建立个人信用评价体系,设立风险分散机制,防范信用风险。

我国P2P羊群效应形成机制与破解对策研究

互联网为基础的P2P网络借贷便利了借贷双方,但也容易造成网络借贷的逆向选择,即形成网络借贷的"羊群效应"。基于"人人贷"公布的不同借款者的借款数据,构建logistic回归模型,研究当前投标次数对后期投标可能带来的影响,验证P2P网络借贷平台"羊群效应"的存在。探究"羊群效应"形成机制和影响因素,并提出完善征信体制、明确监管主体、加强行业自律等破解对策。

基于实物期权的网络借贷平台退出行为研究

近年来,我国网络借贷行业高速发展,与此同时该行业潜在风险也不断暴露,特别是网络借贷平台跑路和倒闭现象尤为突出。基于实物期权模型分析网络借贷平台退出行为及影响因素,并结合我国网贷行业平台运营数据进行实证检验可以发现,平台服务需求波动率或经营成本的增加会加大平台运营风险和退出的可能性;平台服务需求漂移率或退出成本的增加会降低平台运营风险和退出的可能性。因此,为更好地遏制当前我国网络借贷平台倒闭浪潮,从平台方面看,需要加强自身管理和风险控制水平,降低经营成本,增强产品创新能力,保持业务高增长率;从政府方面看,需要进一步完善和细化网络借贷平台退出机制和规则,加强对平台退出行为的外部监督和管理。

基于L1惩罚Logit模型的P2P网络借贷信用违约识别与预测

利用L1惩罚Logit模型,实证检验P2P网贷信用违约的关键影响因素;并利用混淆矩阵与ROC曲线等分类评价方法,检验模型的违约预测效果。研究发现:L1惩罚Logit模型具有很好的变量选择功能,可以有效地识别影响信用违约的关键因素,降低管理者的监管成本;L1惩罚Logit模型能够获得比普通Logit模型、支持向量机等更好的预测效果,既能够从总体上实现对信用违约状态的准确预测,又能够细致分析关键影响因素对违约概率造成的影响,有助于预测和控制信用风险的发生。

基于博弈模型的P2P网络借贷行业风险机理研究

互联网信息技术的进步和民间金融需求的旺盛,推动了P2P网络借贷蓬勃发展。但由于网络环境的复杂性和金融市场的波动性,也使得P2P网贷具有较高的风险。本文建立了P2P网贷市场上各主要参与主体间的博弈模型,对借款方、贷款方、平台方和监管方两两间进行了博弈分析,并结合实际案例,对P2P网贷风险行为机理进行了探究。研究结果表明,在借款方进入市场环节,借款人的申请成本和信誉成本是过滤低质量借款人的重要因素;在借贷双方借贷行为发生阶段,利率和手续费影响贷款方的出借意愿,在借款金额和利息的基础上予以一定比例的处罚和信誉损失,会影响借款方违约程度,单笔小额资金有助于缓解借款方的还款压力,提高借款方的还款信心,减少赖账行为,降低风险;监管方严格监管收益和宽松监管收益会影响监管方的监管力度和平台合规经营收益,影响平台规范化经营程度,对平台的惩罚力度和监管方转移收入,会同时影响监管方监管力度和平台规范经营程度。这些结论有望对P2P网贷市场的良性发展提供理论参考。

大学生网络借贷风险与高校学生工作应对策略研究

大学生网络借贷风行校园,存在着很多风险,酿成很多悲剧。高校管理者要加大对校园网络贷款危害性的宣传,加大校园不良网络借贷监管,引导学生树立正确的消费观,加强学生资助信贷体系的建设力度,精准帮扶贫困学生,对于已经进行网络贷款同学进行干预和正确引导,帮助大学生树立正确的消费观和人生观。

P2P投资经验与甄别违约风险的能力——基于学习的视角

本文探讨P2P投资者在经验积累过程中能否通过学习提高甄别违约风险的能力,并分析其中的传导机制。将投资者对借款标的特征的选择作为其学习结果的代表变量,构建了投资经验通过学习行为影响甄别违约风险能力的理论框架。基于"人人贷"2010-2016年的数据,本文利用中介效应分析方法比较了投资经验对甄别违约风险能力的影响效应在剔除学习效应前后的差异性。结果发现:①投资者投资经验对其甄别违约风险的能力具有正向但边际递减的总影响,且在投资经验均值处为促进作用;②投资经验通过影响学习结果,进而对投资者甄别违约风险的能力产生间接影响;③在分离出经由学习的间接影响后,投资经验对甄别违约风险能力具有负向且边际递增的直接效应,且在投资经验均值处为抑制作用。该直接效应被学习的间接影响遮盖,导致总效应与直接效应呈相反的变化趋势,说明学习行为改变了投资经验影响甄别违约风险能力的非线性形式,从经验学习中投资者可以实现甄别违约风险能力的提升。本文研究对投资者改善投资能力,对监管者规范网络借贷平台信息披露机制、制定投资者教育与保护政策,对P2P网络借贷平台构建良好的平台投资环境,均具有指导意义。