Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

Ownership discrimination and private firms financing in China

This study examines the financing/funding of private firms in China. Our results show that private firms are significantly less funded through formal financing channels such as bank loans than state-owned firms, and hence have to resort to alternative financing such as trade credit. Consistent with the theoretical expectation and literature, there is a substitution effect between trade credit and bank loans for private firms, but this effect is much weaker compared to that of state-owned firms. Moreover, while the univariate comparisons indicate that private firms obtain more notes payable than state-owned firms, the multivariate regression analyses show that the relation between bank loan and notes payable is positive and indifferent between private and state-owned firms.     

Operational risk quantified with spectral risk measures: a refined closed-form approximation

The quantification of operational risk has become an important issue as a result of the new capital charges required by the Basel Capital Accord (Basel II) to cover the potential losses of this type of risk. In this paper, we investigate second-order approximation of operational risk quantified with spectral risk measures (OpSRMs) within the theory of second-order regular variation (2RV) and second-order subexponentiality. The result shows that asymptotically two cases (the fast convergence case and the slow convergence) arise depending on the range of the second-order parameter. We also show that the second-order approximation under 2RV is asymptotically equivalent to the slow convergence case. A number of Monte Carlo simulations for a range of empirically relevant frequency and severity distributions are employed to illustrate the performance of our second-order results. The simulation results indicate that our second-order approximations tend to reduce the estimation errors to a great degree, especially for the fast convergence case, and are able to capture the sub-extremal behavior of OpSRMs better than the first-order approximation. Our asymptotic results have implications for the regulation of financial institutions, and may provide further insights into the measurement and management of operational risk.     

严监管背景下农信社合规风险管理体系建设

2018年,银行业监管环境进一步趋严,不但强调对银行的监管,更提出了对监管人员职责的监管,这种严格的外部监管环境对农信社的合规风险管理带来了极大的挑战。农信社合规风险管理体系的建设和完善已势不容缓。论文提出了农信社合规风险管理体系建设要从战略定位、文化渗透、防线搭建、专业专职化、考核和追究六个维度入手,推进合规风险管理长效机制的建设。     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

时间压力对网络冲动性购买的影响研究：交易效用和感知风险的调节

以往关于时间压力对冲动性购买影响的研究结论具有两面性，即促进或抑制。文章引入交易效用和感知风险两个调节变量，通过两个实验探讨了时间压力对网络冲动性购买倾向影响的边界条件。首先，当交易效用较高时，时间压力的增加会导致其网络冲动性购买倾向的提高；而当交易效用较低时，时间压力的增加会导致其网络冲动性购买倾向的降低（实验一）。其次，当人们感知风险较低时，时间压力的增加会导致其网络冲动性购买倾向的提高；而当人们感知风险较高时，时间压力的增加会导致其网络冲动性购买倾向的降低（实验二）。研究结论可以帮助电商企业进一步了解消费者的冲动性购买行为，充分利用时间压力的影响，制定更灵活有效的促销策略。     

Unemployment insurance benefits and income smoothing

Labor unemployment insurance reduces unemployment concerns. We argue that these benefits moderate incentives to smooth earnings to reduce employees’ concerns about unemployment risk. Using exogenous variations in unemployment insurance benefits, we find evidence consistent with this argument. We also find that the link between unemployment insurance benefits and income smoothing is stronger when there is higher unemployment risk and when the firm is likely to employ more low-wage workers, who find unemployment insurance benefits especially useful. Our paper contributes to the literature by showing that public policy decisions such as unemployment insurance have significant, albeit probably unintended, externalities on corporate financial reporting.     

A hybrid information approach to predict corporate credit risk

This study proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced‐form models to produce a model combination based on credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced‐form models contribute more in the pooled strategies for speculative‐grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names.     

延迟退休年龄下隔代抚育与劳动参与的抉择*——基于工资收入随机性假定的研究

隔代抚育会对延迟退休年龄政策在促进劳动力供给上产生挤出效应。为了对此进行验证，本文在工资收入随机性的假定及延迟退休5年的情景下，模拟了面临孙辈照护需求的女性临近退休者提前退休所产生的福利变化。进一步，本文采用CHIP和CHARLS的微观调查数据，对不同群组的收入增长率和收入风险进行了估计，继而分析了当面临孙辈照护需求时这些收入特征对退休决策者制度退休和退休后劳动参与抉择产生的影响。总体上，在延迟退休年龄政策下，隔代抚育将对女性劳动力供给产生挤出效应。当面临孙辈照护需求时，女性临近退休者的制度退休抉择主要受自身收入增长率和收入风险的影响，收入增长率越低，或收入风险越高，她们提前退休的意愿就越强烈；制度退休后的劳动参与则更多地受子女收入增长率的影响，子女收入增长率越高，劳动参与的概率越低。