支付宝用户蚂蚁借呗平台使用意愿影响原因的实证研究

随着国内互联网行业的飞速发展,网络上各种信贷平台应运而生,其中具有代表性的便是阿里巴巴旗下的蚂蚁借呗平台。蚂蚁借呗作为国内使用人数最多的金融现金贷产品,相较于其他的信贷平台具有更为全面的综合评价信用机制——芝麻信用评分,评分越高借贷额度也就越高。论文在分析支付宝用户对蚂蚁借呗平台使用意愿的基础上,对其产生原因进行归纳总结。     

The Use of Generalizability Theory to Improve International Lending Decisions

This paper demonstrates the application of generalizability theory, a statistical technique which explicitly recognizes multiple sources of measurement error, to the identification of error or inconsistency in commercial bank loan ratings. The approach allows decision makers to assess the reliability of the credit analysis process used in either international or domestic lending. A sample obtained from a large commercial bank is analyzed to determine whether. the credit analysts' evaluative skills or the rating criteria utilized contribute to variability in the score for each loan. The results indicate that differences in credit analysts' loan evaluations are an important source of error, suggesting a possible tradeoff between increased analysis costs and improved rating consistency.     

A bottom-up simulation on competition of online interpersonal communication platforms

The competition of interpersonal communication platforms is a complex process affected by various factors. This paper aims to simulate and analyze this process from a bottom-up perspective. Individual platform selection serves as the micro-foundation for the study. The evolution of online interpersonal communication networks, and innovations proposed by online interpersonal communication platforms, would also impact this process by affecting individual selection on those platforms. Three scenarios were designed for this study to simulate typical modes of competition. In this regard, the simulation results were compared to practical cases. Taken together, this bottom-up simulation model could reproduce and anticipate the applied competition process associated with such platforms. Based on this model, it was found that, in any case, one online interpersonal communication platform will eventually monopolize the market, either partly or entirely. The late entrant platform, comprising a major innovation, tends to fail when competing with the incumbent monopoly due to “network externalities.” Even when two competing platforms continue to propose innovations, and they will alternately lead the competition due to those innovations, this type of replacement of their competitive positions in the market may only occur a few times and then disappear completely.

     

评级“斗法”

8月的第一个周末,印度人迪万·沙马掌舵下的标准普尔公司（Standard＆Poors）连续对美国主权信用、贷款抵押发难。6日,标普把美国长期主权信用评级评价由AAA降到AA＋,史无前例。     

Measuring Credit Risk of a Bank's Corporate Loan Portfolio Using Advanced Internal Ratings Base Approach

Abstract

The use of technical and advanced approaches in the measurement of credit risk of banks' portfolios has nowadays become a very hot issue. The most recent technical report issued by the Basel Committee in May 2003 has concentrated heavily on the measurement of credit risk using either foundation or advanced Internal Ratings Base (IRB) approaches. This empirical research study attempts to measure credit risk of a bank's corporate loan portfolio, including firms from 10 different Turkish sectors. The monthly observations of the total amount of corporate loans and the total amount of corporate loans at default across various sectors are downloaded from the web page of Central Bank of Turkey (CBT) in a period of 1999-2002. This period covers 47 monthly observations since CBT has captured sectoral corporate loans beginning of 1999. Therefore, the observed sectoral default rates are needed to be simulated to obtain a nicely shaped distribution. Monte Carlo simulation is applied for 1,000 times. Based on the simulated default rates, the expected sectoral default rates are computed. Next, a credit quality rating scale is fitted into sectoral default rates distributions. Finally, the sectoral weights in the whole loan portfolio are multiplied by the expected sectoral default rates matrix, considering cross-sectoral correlations to get the total amount of the bank's credit risk and capital requirement. It is assumed that sectoral monthly default rates are a good representative of the default risk of a sample bank's corporate loan portfolio since no publicly available data on any particular bank's corporate loan portfolio composition exists. Nevertheless, this research may be a good application for measuring the credit risk of banks' corporate loan portfolios using advanced IRB approach.     

基于AHP对客户信用评级可行性分析

AHP作为一种评级排序工具,在信用评级的过程中有着重要的重用。它能对定性问题与定量问题进行综合分析处理,并得到明确的定量化结论,以优劣排序的形式表现出来。[1]通过它对金融行业中两个客户的基本情况进行分析和比较,重点说明AHP在客户信用评价体系中的可行性。     

客户信用评级系统的经济计量模型检验

客户信用评级系统是银行业为规范授信业务降低贷款风险而采用的内部评级系统，必须考虑评级系统中指标设置的科学性和合理性。本文利用排序多元离散选择模型，结合实际数据，对中国银行的客户信用评级系统进行了检验。检验结果表明，该系统总体可行，但还可以进一步修订完善。     

Using your regular contacts as collateral: The information value of call logs

This paper aims to uncover the deterrent effect of external social ties on online peer-to-peer lending by extracting valuable information from the phone usage data. Contrary to the studies viewing online internal friendships as the signal of credit quality, we find that the total number of outgoing calls made in the month before loan requests is positively associated with the probability of default, which suggests that more social interactions offline mediated by mobile phones are more likely to signal poor trustworthiness. Only the features pertaining to the calls made during the morning for a given day have the potential as an effective predictor for the borrower’s credible social collateral. However, the online peer-to-peer lending platform studied by us fails to perfectly judge borrowers by their external social ties since the total number of outgoing calls generally has a negative relationship with the interest rates charged for funded loans. Overall, our work advances the understanding of the economic value carried by call logs in the context of disintermediated financial markets with information asymmetry.     

A network based fintech inclusion platform

The paper evaluates, from a sustainable finance viewpoint, a machine learning model implemented in a fintech platform, whose aim is to assign credit ratings. The aim of the model is to learn from both micro economic data and macro economic trends the credit rating of companies that ask for credit. We show that the proposed model is able to reward the companies that have better financial performances with better ratings and, therefore, a higher probability/lower cost of obtaining credit. At the same time, the model correctly takes into account the overall evolution of the economy, favoring financial inclusion for the more penalized economic sectors, particularly during crisis times. The model, its application to credit rating, and its evaluation, are illustrated with reference to more than 100,000 European companies before and during the COVID-19 pandemic crisis. The results shows that, while the impact of the financial variables does not change over time, and particularly during the pandemic, the impact of sectors changes considerably, favoring financial inclusion and resilience.     

Feelings of Pride and Respect as Drivers of Ongoing Member Activity on Crowdsourcing Platforms

Crowdsourcing platforms are online organizations that organize work by sourcing tasks to their members. As participation on crowdsourcing platforms is completely voluntary, getting members to actively participate in tasks on an ongoing basis is essential for the survival of these online platforms. Why members would be active on an ongoing basis, however, is currently not well understood. To understand ongoing member activity on crowdsourcing platforms, we build on the group engagement model, which postulates that feelings of pride and respect influence engagement because they foster identification with the group. We argue that, although in general the nature of crowdsourcing platforms limits the effects of identification processes on member behaviour, feelings of pride and respect will still play central roles in such online organizations, because feelings of pride and respect can directly drive members’ cooperative behaviors towards the platform organization. Moreover, we posit that the way in which platform organizations communicate with their members affects these feelings of pride and respect. We test these ideas in a longitudinal, multisource field study and find that feelings of pride drive ongoing member activity on an online crowdsourcing platform directly and that platform management can increase members’ feelings of pride and respect by engaging in specific organizational communication practices.     

商业银行国家助学贷款之风险分析

文章着重论述了国家助学贷款的信贷风险问题,指出国家助学贷款是一种财政贴息无担保的信用贷款,是一项公共政策;提出防范助学贷款的信贷风险,需要构建以国家征信体系为基础的社会诚信环境,完善全国性的个人征信信息系统,大力推广生源地贷款,创建助学贷款的风险管理系统。     

商业银行贷款风险及其防范

作为一种经济现象,商业银行贷款风险具有客观性。一直以来,商业银行的不良贷款比率都较高,情况十分不乐观。商业银行贷款风险一方面给商业银行的运营带来了不利影响,一方面还阻碍到了银行经济效益以及市场信誉的提升。因此,为了符合新协议的相关标准,在日益激烈的市场竞争中处于优势地位,商业银行就必须采取措施对贷款风险进行防范。文中从实际情况出发对商业银行贷款风险的相关现状进行了阐述,并在此基础之上分析了造成贷款风险的原因,进一步提出了相关的应对措施。     

货币渠道与信贷渠道的比较研究——基于1994年至2011年季度数据

货币政策传导机制素有货币渠道和信贷渠道之分.我国GDP受到货币供应量和贷款余额的共同影响,在两个标准差的货币政策变量冲击下,贷款余额对我国GDP的影响大于货币供应量,而随着时间的推移,货币渠道的影响在逐渐加大,信贷渠道的影响则相对减弱.     

Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach

Corporate credit-rating assessment plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. This paper presents a new approach for small industrial enterprises’ credit-rating assessment using fuzzy decision-making methods and then tests this novel approach using real bank loan data from 1820 small industrial enterprises in China. The procedure of the proposed rating approach includes (1) using triangular fuzzy numbers to quantify the qualitative evaluation indicators; (2) adopting a correlation analysis, univariate analysis, and stepping backward feature selection method to select the input features; (3) employing the best-worst method (BWM) combined with the entropy weight method (EWM), the fuzzy c-means algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) to classify small enterprises into different rating classes; and (4) applying the lattice degree of nearness to predict a new loan applicant’s rating. We also conduct 10-fold cross-validation to evaluate the predictive performance of our proposed approach. The predictive results demonstrate that our proposed data-processing and feature selection approaches have better accuracy than the alternative approaches in predicting default, offering bankers a new valuable rating system to assist their decision making.     

国家审计与商业银行信贷行为

采用2008—2020年中国商业银行非平衡面板数据,考察国家审计对银行信贷行为的影响。研究发现,国家审计可以促使银行提供更为宽松的信贷,且这种效应具有延续性。机制分析结果表明,国家审计可以通过提高管理效率、减少违规行为、加快经济增长三条渠道促进银行扩充信贷。异质性分析发现,国家审计对内部治理较好、东部地区、外部法律环境较好的银行信贷行为的正向影响更加显著。调节效应研究发现,社会审计增强了国家审计对银行信贷的促进作用,媒体监督削弱了国家审计的促进效应,而公众监督的调节效应不显著。进一步研究发现,虽然国家审计推动了银行信贷投放,但对贷款集中度产生了抑制作用,且相较于商业贷款和非农户贷款,国家审计提升了消费贷款和农户贷款,国家审计与银行信贷的关系在降低不良贷款率的同时提升了银行财务业绩,说明国家审计可以助推银行信贷业务的高质量发展。     

银行不良贷款违约损失率结构特征研究

本文对中国银行业面临的信用风险违约损失率(LGD)展开研究,以温州某商业银行不良贷款数据为样本,通过描述性统计,对LGD的结构特征:信用风险暴露规模特征、期限特征、地域特征以及担保特征等进行了详细分析。结果表明LGD与风险暴露规模呈负相关,LGD与贷款期限呈正相关,不同地域、不同担保方式的违约贷款其LGD差异性显著。以上这些结论可为商业银行信用风险管理、信贷投放导向以及信用风险监管提供现实帮助。     

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.     

Sequential optimization three-way decision model with information gain for credit default risk evaluation

To categorize credit applications into defaulters or non-defaulters, most credit evaluation models have employed binary classification methods based on default probabilities. However, while some loan applications can be directly accepted or rejected, there are others on which immediate accurate credit status decisions cannot be made using existing information. To resolve these issues, this study developed an optimized sequential three-way decision model. First, an information gain objective function was built for the three-way decision, after which a genetic algorithm (GA) was applied to determine the optimal decision thresholds. Then, appropriate accept or reject decisions for some applicants were made using basic credit information, with the remaining applicants, whose credit status was difficult to determine, being divided into a boundary region (BND). Supplementary information was then added to reevaluate the credit applicants in the BND, and a sequential optimization process was employed to ensure more accurate predictions. Therefore, the model’s predictive abilities were improved and the information acquisition costs controlled. The empirical results demonstrated that the proposed model was able to outperform other benchmarking credit models based on performance indicators.     

The valuation effects of bank loan ratings in the presence of multiple monitors

Studies have shown that when two information providers or outside auditors exist, the value provided by the second one will be decreased by the actions of the first. Credit rating agencies have been rating bank loans since 1996. Capitalizing on the highly similar functions performed by banks and these agencies, the informational value of bank loan ratings is examined. Further, evidence is provided on whether rating agencies duplicate the certifying and monitoring roles played by banks. The significant market reaction to negative bank loan rating announcements suggests these rating actions convey information beyond that provided via bank loan approvals and renewals. The authors wish to thank Richard Robinson (the Editor), an anonymous referee, Mary A. Lawrence, Abdullah Mamun, Brian Murphy, Lawrence Rose, Mark Vaughn, Massey University-Albany seminar, 2004 Financial Management Association and 2005 Academy of Financial Services participants for their helpful comments.     

The multi-state latent factor intensity model for credit rating transitions

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions in a 7 grade rating system. The model includes a common dynamic component that can be interpreted as the credit cycle. Asymmetric effects of this cycle across rating grades and additional semi-Markov dynamics are found to be statistically significant. Finally, we investigate whether the common factor model suffices to capture systematic risk in rating transition data by introducing multiple factors in the model.