多标准等级判别模型在信用评级中的应用研究

在对目前我国信用评级方法应用现状分析的基础上,提出改进的多标准等级判别模型.并将该模型应用于商业银行信用风险评估中.通过对银行五级分类贷款样本的实证研究,证实了该判别模型的有效性和先进性.     

A multicriteria approach for rating the credit risk of financial institutions

Within the new bank regulatory context, the assessment of the credit risk of financial institutions is an important issue for supervising authorities and investors. This study explores the possibility of a developing risk assessment model for financial institutions using a multicriteria classification method. The analysis is based on publicly available financial data for UK firms. The results indicate that the proposed multicriteria methodology provides promising results compared to well known statistical methods.     

Multi-group discrimination using multi-criteria analysis: Illustrations from the field of finance

The primary objective in the discrimination problem is to assign a set of alternatives into predefined classes. During the last two decades several new approaches, such as mathematical programming, neural networks, machine learning, rough sets, multi-criteria decision aid (MCDA), etc., have been proposed to overcome the shortcomings of traditional, statistical and econometric techniques that have dominated this field since the 1930s. This paper focuses on the MCDA approach. A new method to achieve multi-group discrimination based on an iterative binary segmentation procedure is proposed. Five real world applications from the field of finance (credit cards assessment, country risk evaluation, credit risk assessment, corporate acquisitions, business failure prediction) are used to illustrate the efficiency of the proposed method as opposed to discriminant analysis.     

基于多目标进化聚类的信用风险特征识别

在信用风险识别领域,聚类算法常被用于区分不同风险等级的样本并识别风险特征。然而该领域中通常面临高维数据处理问题,导致传统聚类算法存在不适应此类问题的缺陷:易陷入局部最优、受冗余特征干扰、鲁棒性不强等。采用高维信用风险数据,研究上市公司信用风险,建立信用风险特征识别的三目标优化模型,设计基于分解的多目标子空间聚类算法进行求解。通过算法的横向对比实验,展示了所提出的算法在聚类精度和鲁棒性方面的优势,并根据聚类算法的权重分配结果,归纳总结上市公司信用风险评估过程中应重点关注的指标。     

Measuring retail company performance using credit scoring techniques

This paper discusses models for evaluating credit risk in relation to the retailing industry. Hunt’s [Hunt, S.D., 2000. A General Theory of Competition. Sage Publications Inc., California] Resource–Advantage Theory of Competition is used as a basis for variable selection, given the theory’s relevancy to retail competition. The study focuses on the US retail market. Four standard credit scoring methodologies: Naïve Bayes, Logistic Regression, Recursive Partitioning and Artificial Neural Network, are compared with Sequential Minimal Optimization (SMO), using a sample of 195 healthy companies and 51 distressed firms over five time periods from 1994 to 2002.     

基于模糊子集的贷款组合优化方法

遵照国际银行业大多数银行的做法,信用风险评估包括对债务人和债项两个方面.以模糊集理论为基础,通过试算与比较,构造隶属函数,对各指标进行无量纲化处理,建立距离判别函数,评估债务人信用风险.根据债项特征,考察风险四因素:违约概率,特定违约损失,违约敞口,期限,建立0-1整数规划模型,对债项进行风险评估,确定最佳贷款组合,以解决组合贷款的优化决策问题.     

A classification problem of credit risk rating investigated and solved by optimisation of the ROC curve

Estimation of probability of default has considerable importance in risk management applications where default risk is referred to as credit risk. Basel II (Committee on Banking Supervision) proposes a revision to the international capital accord that implies a more prominent role for internal credit risk assessments based on the determination of default probability of borrowers. In our study, we classify borrower firms into rating classes with respect to their default probability. The classification of firms into rating classes necessitates the finding of threshold values separating the rating classes. We aim at solving two problems: to distinguish the defaults from non-defaults, and to put the firms in an order based on their credit quality and classify them into sub-rating classes. For using a model to obtain the probability of default of each firm, Receiver Operating Characteristics (ROC) analysis is employed to assess the distinction power of our model. In our new functional approach, we optimise the area under the ROC curve for a balanced choice of the thresholds; and we include accuracy of the solution into the program. Thus, a constrained optimisation problem on the area under the curve (or its complement) is carefully modelled, discretised and turned into a penalized sum-of-squares problem of nonlinear regression; we apply the Levenberg–Marquardt algorithm. We present numerical evaluations and their interpretations based on real-world data from firms in the Turkish manufacturing sector. We conclude with a discussion of structural frontiers, parametrical and computational features, and an invitation to future work.     

Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment

The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. The present paper deals with the optimization of Nearest Neighbor Classifiers via Metaheuristic Algorithms. The Metaheuristic Algorithms used include tabu search, genetic algorithms and ant colony optimization. The performance of the proposed algorithms is tested using data from 1411 firms derived from the loan portfolio of a leading Greek Commercial Bank in order to classify the firms in different groups representing different levels of credit risk. Also, a comparison of the algorithm with other methods such as UTADIS, SVM, CART, and other classification methods is performed using these data.     

Credit contagion in a network of firms with spatial interaction

This contribution studies the effects of credit contagion on the credit risk of a portfolio of bank loans. To this aim we introduce a model that takes into account the counterparty risk in a network of interdependent firms that describes the presence of business relations among different firms. The location of the firms is simulated with probabilities computed using an entropy spatial interaction model. By means of a wide simulation analysis we investigate the behavior of the model proposed and study the effects of default contagion on the loss distribution of a portfolio of bank loans.     

Model combination for credit risk assessment: A stacked generalization approach

The development of credit risk assessment models is often considered within a classification context. Recent studies on the development of classification models have shown that a combination of methods often provides improved classification results compared to a single-method approach. Within this context, this study explores the combination of different classification methods in developing efficient models for credit risk assessment. A variety of methods are considered in the combination, including machine learning approaches and statistical techniques. The results illustrate that combined models can outperform individual models for credit risk analysis. The analysis also covers important issues such as the impact of using different parameters for the combined models, the effect of attribute selection, as well as the effects of combining strong or weak models.     

Credit risk assessment: a challenge for financial institutions

^** E-mail: vangeli3{at}eaee.gr This study explores financial credit risk assessment. This isan important issue because there is currently no standardizedmethod used by financial institutions for the assessment ofcredit risk. A critical evaluation of the most popular creditrisk assessment methods—the judgmental method, credit-scoringand portfolio models—highlights a number of limitationswhen used on their own. Several interviewees confirm that creditrisk assessment methods should be combined for effective creditrisk assessment. Accordingly, the study proposes a frameworkfor improving credit risk assessment, which combines the strengthsof these methods and copes successfully with their limitations.     

The pricing of credit risky securities under stochastic interest rate model with default correlation

In this paper, we study the pricing of credit risky securities under a three-firms contagion model. The interacting default intensities not only depend on the defaults of other firms in the system, but also depend on the default-free interest rate which follows jump diffusion stochastic differential equation, which extends the previous three-firms models (see R.A. Jarrow and F.Yu (2001), S.Y.Leung and Y.K.Kwok (2005), A.Wang and Z.Ye (2011)). By using the method of change of measure and the technology (H. S.Park (2008), R.Hao and Z.Ye (2011)) of dealing with jump diffusion processes, we obtain the analytic pricing formulas of defaultable zero-coupon bonds. Moreover, by the “total hazard construction”, we give the analytic pricing formulas of credit default swap (CDS).     

基于数据的银行信贷行业的信用风险研究

目前多数研究利用美国旧金山市KMV公司于1997年建立的模型(KMV模型)计算企业年违约距离来评估具体企业的信用风险,但缺乏信贷行业的信用风险评估方法,也不能给出随时间变化的信用风险.首先提出基于数据的信贷行业随时间动态演化的信用风险评估模型,然后利用2016年18个行业的数据得到了中国信贷行业动态演化的信用风险,该信用风险随时间演化特征可分为波动上升、下降后波动、下降后稳定、稳定四种类型.进一步研究发现金融业、科学研究和技术服务业、信息传输软件和技术服务业这三个行业动态演化的信用风险平均值高且不稳定,住宿和餐饮业的信用风险很高但是比较平稳,其他行业的信用风险较低且较平稳.     

Joint logistics and financial services by a 3PL firm

Integrated logistics and financial services have been practiced by third party logistics (3PL) firms for years; however, the literature has been silent on the value of 3PL firms as credit providers in budget-constrained supply chains. This paper investigates an extended supply chain model with a supplier, a budget-constrained retailer, a bank, and a 3PL firm, in which the retailer has insufficient initial budget and may borrow or obtain trade credit from either a bank (traditional role) or a 3PL firm (control role). Our analysis indicates that the control role model yields higher profits not only for the 3PL firm but also for the supplier, the retailer, and the entire supply chain. In comparison with a supplier credit model where the supplier provides the trade credit, the control role model yields a better performance for the supply chain as long as the 3PL firm’s marginal profit is greater than that of the supplier. We further demonstrate that, for all players, both the control role and supplier credit models can outperform the classic newsvendor model without budget constraint.     

农村信用社信贷违约识别模型及其应用

构建农村信用社信用风险模型对完善农村金融风险管理体系、提高农村信用社经营管理意义重大.基于还款意愿和还款能力两方面,系统分析了影响农信社贷款债务人违约率的主要因素,在此基础上应用logistic方法建立农信社债务人违约率预测模型,并通过Gini系数对模型区分能力和识别能力进行验证评估.实证结果表明,模型中债务人年龄、所在地区、贷款额所占家庭收入比例、与信用社信贷关系密切程度以及户口状况等因素都表现显著;违约率预测模型在样本内和样本外均有较好的违约识别能力,从而可为农信社放贷前的债务人信用评估、贷款发放和风险管理提供有力参考.     

Enhanced decision support in credit scoring using Bayesian binary quantile regression

Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.     

Time to default in credit scoring using survival analysis: a benchmark study

We investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context.     

A Two-stage screening procedure for mailing credit assessment

Direct mailing is one of the tools of business marketing. Itcan stimulate the purchase of mail-order products or financialservices. When selecting a mailing list, we hope to obtain ahigher mailing response; on the other hand, we also have toconsider the risk of defaulting. In the case of selling financialservices, customers who respond must be examined for credit.In order to avoid jeopardizing the existing relations with suchcustomers, companies try to reduce their rate of declining suchapplicants by controlling, at the mailing stage, for the riskof choosing customers who will default. In this article, a two-stage screening procedure is constructedto solve a problem of mailing credit assessment with mailingand credit-assessment stages;A screening method can be appliedto select a mailing list at the mailing stage, while the needfor a credit assessment depends on the types of product or service.Therefore, this problem may include four possible models: screeningwith or without credit assessment and random sampling with orwithout credit assessment. Moreover, the optimal cutoff scoresare determined by maximizing total profit. A mailing exampleis then given to illustrate the use of the proposed models formailing credit assessment. Compared with a random sampling method,a screening method has a significant improvement in terms ofresponse rate, decline rate, bad rate, and total profit.     

Artificial immune algorithm-based credit evaluation for mobile telephone customers

The arrearage problem is a critical concern for China’s mobile communication services industry. Analysis of customer credit evaluation provides this study with a potential viable solution to the arrearage problem in China. By employing an artificial immune algorithm (AIA), a measure of customer credit based on customer attributes is proposed. This method was applied to one China mobile communication services company with approximately 400?000 customers yielding satisfying results. Utilizing traditional predictive accuracy and alternative metrics, performance comparisons of the proposed AIA were made using the feed-forward back propagation artificial neural network and the logistic regression model. A decision tree analysis of anticipated benefits was performed and indicates workability of the proposed method based on customer credit evaluation.     

TOPSIS-GRA法下的企业动态信用评价方法及其应用

针对传统信用评价方法较少考虑到时间的延续性,且只注重信用基础值而忽视其发展趋势的问题,本文提出了一种具有风险抗性信用奖惩特征的TOPSIS-GRA的动态信用评价方法。首先,利用指标信息量诱导密度算子对静态数据进行综合集成,得到静态综合信用评价值,在此基础上构造动态信用评价加权决策矩阵;其次,在对矩阵进行TOPSIS法验算的过程中嵌入企业风险抗性信用奖惩点,进而得到包含奖惩性质的相对贴近度;再以GRA方法得到各受评企业理想的信用发展趋势关联度,结合两者最终得到融合风险抗性奖惩量、信用基础值和信用发展趋势三项特征的稳定科学的企业动态信用评价结果。最后,给出了一个实证分析,验证了该方法的有效性及合理性。