网络借贷中羊群效应的存在性、驱动机制与投资者投资效率*

本文探究了网络借贷中羊群效应的存在性、背后的驱动机制以及对投资者投资效率的影响。实证结果表明，在控制了标的流标风险和时间固定效应后，我国网络借贷投资者群体中存在显著的羊群效应。进一步，标的羊群效应程度与借款人信息以及投资者类型紧密相关，借款人的还款能力越低，参与投资的投资者风险厌恶程度越低，则该标的的羊群效应越显著，说明羊群效应是“风险厌恶程度较低的投资者期望通过模仿他人的投资选择来消除由于信息不对称带来的违约风险”造成的结果。最后，网络借贷中的羊群效应有利于提升投资者的投资效率，具体表现在羊群效应提高了投资者成功投资的概率，并且有助于投资者在低质量标的中将资金投资到潜在违约风险更低的标的之中。     

Lending policies of informal, formal and semiformal lenders

This paper compares lending policies of formal, informal and semiformal lenders with respect to household lending in Vietnam. The analysis suggests that the probability of using formal or semiformal credit increases if borrowers provide collateral, a guarantor and/or borrow for business‐related activities. The probability of using informal credit increases for female borrowers. It also appears that the probability of using formal credit increases in household welfare up to a certain threshold, but at a decreasing rate. In addition, the paper discerns the determinants of probability of default across lender types. Default risk of formal credit appears to be strongly affected by formal loan contract terms, e.g., loan interest rate and form of loan repayment, whereas default risk on informal loans is significantly related to the presence of propinquity and other internal characteristics of the borrowing household. Overall, the study raises several important implications for the screening, monitoring and enforcement instruments that may be employed by different types of lenders.     

Heterogeneity of farm loan packaging term decisions: a finite mixture approach

This article revisits the minority borrowers’ discrimination issue in farm lending by departing from traditional loan approval-rejection or default rate-based analytical models to focus on loan packaging decisions. This study analyses such decisions using a Finite Mixture Model that optimally separates the borrowers into two sub-classes allowing for a priori unspecified heterogeneity in borrowers’ data, which has not been accounted for in previous loan discrimination analyses. Results show that non-white farm borrowers tend to receive larger loans among those in the lower loan latent class, but receive relatively lower loans in the larger loans borrower category. These farmers are also charged higher interest rates vis-à-vis their peers in both the low and high interest rate latent classes. This study’s results also indicate that male borrowers are accommodated with larger loans and longer maturities in all loan amount and maturity latent classes. This study validates the interplay among significant trends in loan packaging terms for racial and gender minority borrowers that seems logical from the lenders’ credit risk management perspective.     

基于K-S检验与距离相关分析的网络借贷信用评价指标体系构建

网络借贷作为一种新型互联网金融模式,提升了金融资源使用效率,缓解了小企业融资难的困局。构建合理的网络借贷信用评价指标体系,从而对网络借贷的潜在风险及时甄别与预防,对互联网金融健康持续发展意义重大。本文根据K-S检验与距离相关分析相结合,筛选对借款客户违约状态甄别能力强的指标,建立了网络借贷信用评价指标体系,通过P2P网络借贷(peer to peer lending,个人对个人借贷)平台LendingClub交易数据进行实证研究,结果表明:不仅借款金额、借款利率等借款标的特征对借贷者违约具有显著相关性,借款者年龄等个人特征、借款者年收入等财务特征以及借款者违约次数等信用特征均对借贷者违约风险产生显著影响。投资者在出借资金时,往往青睐于已婚、年龄适中、具有一定工作经历、历史违约次数较少的借款人。因此,风险监管部门应构建网络借贷违约风险评估模型,对P2P平台进行风险监测,同时建立关键信息共享机制,融合多源数据,明确审查范围,实现P2P网络借贷行业健康有序发展。     

Two stages credit evaluation in bank loan appraisal

Traditionally, banks conduct standard credit evaluation such as credit scoring following the receipt of loan request and make the accept/reject decision accordingly. This research explores the possibility of two stages credit evaluation in lending process. When the evaluation cost drops below the trigger cost, it pays to conduct the second-stage loan appraisal. We derive two trigger cost thresholds for borrowers who are rated as credible and default in the first stage, respectively. Contingent on the share of good borrowers relative to the bad ones, the optimal strategy of the bank can be differentiated to implement second-stage evaluation on either (1) both types, or (2) only one type, or (3) neither type of the borrowers. We find that during severe economic contractions or in geographic areas/industries which are in deep troubles, whilst the borrowers who repay the loan are out-numbered by the borrowers who fail to pay, the trigger cost for good borrower is higher than that of default borrower. In this scenario, the banks are more inclined to undertake the second-stage credit evaluation on good borrowers. On the other hand, if the percentage of credible borrowers is higher than that of default borrowers, the trigger cost for good borrower lies below the trigger cost of default borrower. As a result, the banks are less inclined to undertake the second-stage evaluation on good borrowers.     

Adverse selection as an explanation of credit rationing and different lender types

An adverse selection model is utilized to demonstrate that informational asymmetry may make it wealth optimal for the financial intermediary (FI) to credit ration and to rationalize the existence of different lenders in the credit market. The crucial assumption is that borrowers differ in their tolerance for a lender-imposed default penalty, the severity of which also varies with the lender. The credit rationing portion proves that the FI will: 1) be forced by a binding regulatory constraint to overinvest in capital; 2) ration its worst risk class borrowers; 3) establish its optimal loan interest rate on the basis of the average quality of its loans and the interest rate elasticity of the borrower demand in its best risk category; and 4) decrease the total loan volume and increase the loan interest rate due to an increase in the capital requirement, but the effect on the default risk quality of its loan portfolio is ambiguous. The existence result is that if a lender has a high default penalty, he can charge a lower rate and attract only “good” borrowers, i.e., heterogeneous lender types encourage the screening of borrowers and vice versa.     

Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China

Recent years have witnessed the popularity of online peer-to-peer lending, which allows individuals to borrow from and lend to each other on an Internet-based platform. Using data from a large P2P platform in China, this article explores the factors that determine the default risk based on the demographic characteristics of borrowers. Moreover, we propose a credit risk evaluation model, which can quantify the default risk of each P2P loan. Empirical results reveal that gender, age, marital status, educational level, working years, company size, monthly payment, loan amount, debt to income ratio and delinquency history play a significant role in loan defaults. Finally, we analyse the relationship between default risk and these contributory variables, and the possible causes are also discussed in this study.     

群组制度对P2P网贷平台借款成功率和借款利率的影响*——基于Prospercom样本数据的实证检验

2013年后,中国的P2P网络借贷（简称P2P网贷）异军突起,在激活金融要素和拓展投融资渠道的同时,也面临着不少理论与实践问题。本文将群组制度视为重要的社会资本,并以此为理论视角,对世界上规模最大的P2P网贷平台Prospercom①超过126万个借款标的进行实证检验,得出的主要结论是：加入群组,特别是加入借款标的经过群组组长审阅和信誉评分较高的群组,不仅能提高借款人获得贷款的成功率,而且能降低借款人的借款利率。本文认为,Prospercom的群组制度能够为中国P2P网贷平台健康发展提供有益的启示与借鉴。     

On the rationale of bank lending in pre-crisis Thailand

Evidence from credit files is provided to examine bank lending determinants of Thai commercial banks. Their lending practice follows reasonable patterns as a standard set of variables, including indirect risk variables, explains much of the variance in interest rate spread. Reflecting institutional differences with mature markets, we find a higher importance of relationship banking and risk control via credit availability. Information about later default reveals prudent relationship lending. However, banks could have made better use of available information about borrowers’ riskiness. These findings do not support a general verdict of bad banking but indicate room to improve lending decisions.     

Do interest rate controls work? Evidence from Kenya

This paper reviews the impact of interest rate controls in Kenya, introduced in September 2016. The intent of the controls was to reduce the cost of borrowing, expand access to credit, and increase the return on savings. However, we find that the law on interest rate controls has had the opposite effect of what was intended. Specifically, it has led to a collapse of credit to micro‐, small‐, and medium‐sized enterprises; shrinking of the loan book of the small banks; and reduced financial intermediation. Because of their adverse effects on bank lending, we estimate that the interest rate controls have reduced economic growth by ¼–¾ percentage points on an annual basis. We also show that interest rate caps reduced the signaling effects of monetary policy. These suggest that (1) the adverse effects could largely be avoided if the ceiling was high enough to facilitate lending to higher‐risk borrowers and (2) alternative policies could be preferable to address concerns about the high cost of credit.     

The suspension of borrowing: an implicit penalty for loan default under imperfect information

A credit seeker may be suspended from borrowing for a period of time due to a previous default. Such suspension is widely used in bank lending through credit check. Our work analyses the effects of suspension on the investment choice of borrowers under uncertainty and on the lending policy of banks facing asymmetric information. We show that suspension should be tightened at low loan rates, but loosened otherwise, to improve the repayment performance of borrowers. We also show that although credit rationing may not be completely removed due to imperfect information, the excess demand for credit or transitive waiting in the market can actually be attenuated by such efficient use of suspension. Our theoretical predictions are consistent with observed cyclical patterns of changes in lendingrates and suspension severity.     

Social heterogeneity and local bias in peer-to-peer lending – evidence from China

This paper investigates the presence of local bias in the peer-to-peer (P2P) lending market and explores the social heterogeneous factors that may affect the formulation of the investor's local bias. We find that local biases are commonly present among investors. Investors have a 9.3% higher probability and put 105% more money in lending to local borrowers. We also find that overinvesting in local loans is correlated with higher default risk, lower recovery rate, and lower realized return, suggesting the underperformance of these locally biased investors. By taking advantage of the diverse local culture and institutional features in China, we further show that social heterogeneity, including geography, language, and social trust, affects the degree of local biases in the P2P lending market. We propose two debiasing techniques from the P2P platforms’ perspective.     

Borrower credit access and credit performance after loan modifications

While the preventive effect of loan modifications on mortgage default has been well-documented, evidence on the broad consequences of modifications has been fairly limited. Based on two unique loan-level data sets with borrower credit profiles, this study reports novel empirical evidence on how homeowners manage their credit before and after receiving modifications. The paper has several main findings. First, loan modifications improve borrowers’ overall credit standing and access to credit. Modifications that provide principal reduction, rate reduction, or greater payment relief, as well as those received by borrowers not in financial catastrophe, lead to a larger improvement in borrowers’ credit rating than others. Second, loan modifications lead to a slight increase in borrowers’ debts, primarily on home equity line of credit accounts and auto loans. Third, borrowers’ performance on nonmortgage accounts, however, has not been negatively impacted by modifications. This study demonstrates that interventions designed to improve household balance sheets could have a direct and sizeable impact on borrower financial outcomes.     

LGB-BAG模型在P2P网贷借款者信用风险评估中的应用

立足于P2P平台,利用P2P平台个人借款人的信息建立了一套系统的信用风险评估指标体系来甄别可能违约的借款人。本文基于LightGBM（一种基于决策树的Boosting模型）和Bagging而提出一种新的LGB-BAG模型,有效结合了Boosting和Bagging的优势。结果表明,在N增大到一定程度的时候,LGB-BAG的F1均值（预测效果）要高于LightGBM和随机森林;并且LGB-BAG的F1方差也要小于其余两种模型。LGB-BAG的F1均值最高可达到0.71175,且LGB-BAG模型能够显著提高信用风险预测效率。     

Progressive Lending in Microfinance—What about the Farmers?

Dynamic incentives have become a common measure in microfinance institutions (MFI) to counteract the risk of default and to strengthen the borrower's identification with his micro‐lender. This article focuses on progressive lending over the course of the bank–borrower relationship. As the agricultural sector is increasingly important for Azerbaijan's economy, this study differentiates between the lending policies faced by farmers and non‐farmers, and matches the findings with the repayment performances of both client groups. By means of a rich data set spanning from 2007 through 2012 provided by an MFI in Azerbaijan, it can be demonstrated that farmers face a higher degree of loan volume rationing that cannot be justified by our findings on repayment performances. Moreover, we find that repeated borrowing increases the default probabilities of both client groups. In conclusion, we deduce that the MFI and borrowers could benefit from reconsidering the current lending policies.     

Individual versus Village Lending: Evidence from Montenegro

This paper analyzes differences in loan performance across two Montenegrin microfinance institutions with different lending techniques using a sample of individuals borrowing from both institutions. We make use of administrative data from both institutions over the period 2004–2013. While one institution relies on village associations for screening and monitoring of borrowers, the other institution uses the individual liability approach. We find that the likelihood to go into arrears is higher for the institution with a strictly individual lending technique, while the likelihood of going into arrears over 30 days is higher for the institution working with village associations. These results are robust to a variety of additional tests, including different definitions of arrears and subsamples. Our findings suggest that the institution using an individual lending technique provides certain flexibility to its clients, while the village‐based microfinance institution might face more strategic default behavior. We provide evidence that once a borrower is in arrears, (s)he is more likely to stay in arrears for more than 30 days in branches with a higher share of borrowers in arrears and in the village‐based lender. Our findings provide evidence that a village‐ or group‐based lending technique is not necessarily superior to the individual lending technique in terms of loan performance.     

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.     

Credit union loan rate determinants in the United States

ABSTRACT

Credit union participation in the consumer lending market continues to grow as an increasing number of consumers and small businesses become members and open accounts. This study investigates the determinants of credit union loan rates during a period of economic expansion in the United States using fourth quarter 2015 data for 5,942 credit unions. Five different interest rate categories are analysed using nine potential loan rate determinants. Results indicate that loan rates tend to be lower as credit union size increases, while high ratios for net charge-offs and operating costs cause interest rates to increase. Opposite to what is expected, loan rates are positively correlated with regional unemployment rates. A possible explanation for this outcome is that weak labour markets are associated with elevated loan delinquency rates and, therefore, greater default risks resulting in higher interest rates.     

Economic effects of positive credit information sharing: the case of Korea

In many countries, in addition to negative credit information such as loan default and arrears, positive credit information is also exchanged on a voluntary and reciprocal basis. Employing optimal credit decision models of profit maximizing banks, and utilizing a unique dataset of 2?million consumer loan obligors in Korea, we investigate the economic effects of sharing positive credit information in addition to negative credit information already exchanged. We find that the discriminatory power of the credit scoring model improves significantly. We proceed to investigate the economic effects of the information gap in a competitive credit market by assuming two representative banks that differ only in the level of credit information sharing. The bank that utilizes negative information only suffers from deterioration of the borrower pool and reduced profit, as high credit risk borrowers are more concentrated on this bank due to underpricing of risks. Our finding suggests that banks have incentives to voluntarily participate in the positive information sharing mechanism, since even a small difference in discriminatory power stemming from the information gap may lead to a significant fall in profitability as the distribution of borrower quality changes endogenously due to adverse selection problems.