A Multicriteria Outranking Modeling Approach for Credit Rating

Rating models are widely used by credit institutions to obtain estimates for the probabilities of default for their clients (firms, organizations, individuals) and to assess the risk of credit portfolios. Several statistical and data mining methods are used to develop such models. In this article, the potential of an outranking multicriteria decision‐aiding approach is explored. An evolutionary algorithm is used to fit a credit rating model on the basis of the ELimination Et Choix Traduisant la REalité trichotomique method. The methodology is applied to a large sample of Greek firms. The results indicate that outranking models are well suited to credit rating, providing good classification results and useful insight on the relative importance of the evaluation criteria.     

Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from “nominal predictions” due to “upsetting events” such as the 2008 global banking crisis.     

付费主体差异对信用评级结果的影响机制研究

2010年我国首家投资人付费评级机构——中债资信成立,由于我国投资人付费评级机构成立时间不长,目前国内鲜有研究投资人付费与发行人付费评级差异及产生原因的文献,并且国外相关文献主要关注评级方式对某一类债券评级结果的影响,而没有探讨两种评级方式对不同类型债券的影响之间是否存在差别。本文针对投资人付费与发行人付费评级之间的差异,基于声誉效应、竞争机制对发行人付费评级机构迎合选择进行了博弈分析,并分别以信贷资产支持证券和企业债主体的评级数据为样本,对评级差异及其产生原因进行了实证检验。研究结果表明：投资人付费评级结果显著低于发行人付费评级结果;不同付费模式下评级结果的差异受到是否为首次评级和发行人付费评级机构市场份额的影响,发行人付费评级机构对发行人的迎合是产生评级差异的主要原因;两种付费评级方式在企业债主体中的评级差异显著高于在信贷资产支持证券中的评级差异,表明付费评级方式对企业债主体的影响更大。本文较为全面地研究了我国投资人付费与发行付费评级之间的差异,使投资人、监管机构对不同付费模式下的评级结果有更加清晰的认识。     

Bagging方法在基于日常交易数据解决中小企业的信用评价问题中的应用

关于信用风险评价问题至今已经做了很多研究,各种信用评价模型与方法也已被开发.但是这些模型与方法几乎都是基于财务数据、股票价格或风险调研机构发表的各种调查结果.因为几乎所有的中小企业的财务数据都是非公开的,至今开发的信用评价模型与方法都不免成为无米之炊.为此,本文提出了一个新的途径,只需要根据销售额、顾客付款额、拖欠款额等日常业务处理数据来评价顾客企业的信用度.本文提出一个应用Bgging方法评价顾客信用的系统,其目的在于解决由于异常顾客数比正常顾客要少很多而带来的问题,提高分辨异常顾客的能力.本文所提出的信用评价系统将应用到一个实际企业的信用评价问题中,借此来验证系统的性能和效果.     

CDS and rating announcements: changing signaling during the crisis?

In parallel with the development of credit derivatives market, researchers have begun to explore the relationship between Credit Default Swap (CDS) market and rating events. Many papers, via classical event-study methodology, show that CDS market is able to signal future negative rating events announced by credit rating agencies. In this work, we incorporate into the event-study methodology the ability of Markov switching models in modeling state-dependent means and variances. This approach allows to get over the drawbacks of the classical methodology, which ignores the heteroscedasticity and volatility clustering often affecting financial time series. The proposed methodology is applied to study the reactions of CDS quotes to reviews for downgrading and effective downgradings announced by the three major credit rating agencies (Fitch Ratings, Moody’s, Standard and Poor’s), in order to examine if and to what extent CDS market anticipates announcements related with a company’s creditworthiness. The analysis, focusing mainly on volatility, is performed on two periods, 2004–2006 and 2007–2009, in order to verify whether a change in the signaling power of CDS quotes can be ascribed to recent financial turmoils.     

Rating the Raters: Conflicts of Interest in the Credit Rating Firms

The major credit rating agencies contributed substantially to the sub‐prime mortgage crisis by giving their highest rating (AAA) to most of the collateralized debt obligations (CDO) securities that were backed by these sub‐prime mortgages. Because the rating agencies are compensated by the issuers whose CDO bonds they rate, this relationship creates a prima facie conflict of interest, one that is compounded when the rating agency also consults for the issuers on designing the CDOs. While Congress and the Securities Exchange Commission investigate possible wrongdoing, various reforms have been proposed. This article analyzes these conflicts of interest and the cognate corporate governance issues. It then categorizes and critiques several of the reform proposals—which range in severity from requiring more disclosure to the suspension of the rating agency's license.     

信息披露的作用 ——来自拍拍贷的经验证据

  信息披露对于降低信息不对称、减少市场的非效率的作用已经得到广泛的认可。因为中国依然缺乏完善的征信体系,P2P市场上也存在着更严重的信息不对称,所以信息披露在P2P借贷市场中发挥着更为重要的作用,值得学界和业界更深入的研究。         基于拍拍贷的数据,对信息披露在P2P市场上的作用进行研究。为了比较可验证的和不可验证的标准信息披露的不同作用,选择Logistic和Tobit等不同的回归模型以及不同的模型设置,实证检验其对于是否借款成功、是否违约、内部收益率的影响以及与借款利率之间的关系。         研究结果表明,信息披露对借款成功有正向影响,可验证的标准信息披露影响更大;信息披露具有的可验证性对借款成功有正向影响。同时,信息披露多的借款者更愿意提高借款利率,表明他们可能具有更低的信用。信息披露并不总是降低违约风险,部分信息及其可验证性反而导致更高的违约率。为了得到更可靠的结果,进一步对信息披露和内部收益率进行研究,结果同样表明,部分信息披露及其可验证性会带来更低的内部收益率。在稳健性检验中,上述结论依然不变。         研究结果丰富了信息披露、信息经济学、行为经济的研究内容,有利于平台设计更好的信息披露机制,即应控制披露信息条目,只披露能反映风险的信息,从而减少信息不对称,降低由投资人决策偏差导致的损失。同时,也为监管部门制定监管条例去纠正市场偏差和保护投资者权益提供了实证依据。     

基于风险环境的企业多层交叉信用评分模型与应用

研究基于风险环境的企多层交叉信用评分模型与信用评级方法,解决同一地区具有多个地域、多个行业和多个企业的企业、行业和地域等具有二级或以上层级结构的企业、行业和地域信用评级问题.定义了地域信用形象,针对同一地域同一行业、不同地域同一行业、同一地域不同行业和不同地域不同行业等4种不同的企业层级结构,分别建立了企业信用评分模型、行业信用评分模型和地域信用评分模型,用以对企业、行业和地域进行信用评级.以某一地区某一行业的集团公司进行项目贷款申请为例,假定该公司同时在"好"、"中"和"差"3个不同的经济发展区域分别建立3个子公司,并分别计算了该公司及其3个子公司在不同地域信用环境影响下的信用评分值,然后综合计算了在不同地域同一行业下的具有多层级结构的公司多级信用评分值,给出公司相应的信用评级结果和银行相应的信贷策略.最后还给出了集团公司具有贷款申请资格的数值条件.该方法对集团公司的信用评级方法以及银行对集团公司的信贷策略及相应决策具有科学参考依据.     

内部控制、过度投资与公司信用风险

本文以2009-2014年中国深沪A股上市公司为样本,研究过度投资对公司信用风险的影响,并检验内部控制质量水平的提高能否有效抑制过度投资导致的信用风险,以及内部控制的这种风险管控作用是否因公司产权性质的不同而有所差异。检验结果发现,过度投资行为会显著增加公司信用风险,内部控制质量水平的提高能有效抑制过度投资导致的信用风险,但上市公司的民营性质会弱化内部控制的风险管控作用。表明在政府的推动下,国企内部控制体系建设已经取得了一定的果效,而民营企业的内控需要进一步提升以降低其发展风险。     

现代信用风险度量模型的实证比较与适用性分析

本文通过实证比较分析发现,现代信用风险度量模型对银行贷款的违约率、贷款损失和损失率的预测结果的差异性较大;但信用监测模型和信用风险附加法所预测的经济资本配置比例不仅符合巴塞尔协议对银行贷款经济资本的要求,也略大于实际应该配置的比例,实证表明了它们对度量我国商业银行贷款组合的信用风险具有较好的适用性.此外,本文也充分验证了借款人信用等级的不同,银行贷款经济资本配置的比例会有显著性的差异.     

Evaluation of corporate governance systems by credit rating agencies

In periods of high market volatility, and in order to minimize their risks, some investors prefer to invest their funds in well-governed companies. This paper aims to describe the methodology used by rating agencies to assess corporate governance systems (CGS) and to compare agencies’ practices. More and more the shareholders and creditors incorporate mechanisms related to CGS in the assessment of risks. Using a sample of five rating agencies, we distill 51 governance criteria to two governance factors using principal components analysis. The first factor represents the “shareholders rights and board of directors”. The second one is related to “remuneration policy and convergence of interests for shareholders and managers”. We identify three different business models for the corporate governance rating process.     

Robust multi-criteria sorting with the outranking preference model and characteristic profiles

We present a new multiple criteria sorting approach that uses characteristic profiles for defining the classes and outranking relation as the preference model, similarly to the Electre Tri-C method. We reformulate the conditions for the worst and best class assignments of Electre Tri-C to increase comprehensibility of the method and interpretability of the results it delivers. Then, we present a disaggregation procedure for inferring the set of outranking models compatible with the given preference information, and use the set in deriving, for each decision alternative, the necessary and possible assignments. Furthermore, we introduce simplified assignment procedures and prove that they maintain a no class jumps-property in the possible assignments. Application of the proposed approach is demonstrated by classifying 40 land zones in 4 classes representing different risk levels.     

基于指标体系违约鉴别能力最大的小企业债信评级体系及实证

债信评级是通过评级体系确定债务违约的可能性大小。根据单个指标违约鉴别能力大小的遴选来建立评级指标体系看上去似乎是一个不错的选择，但事实并非如此。因为用违约鉴别能力强的单个指标组成的一组指标，其指标体系的违约鉴别能力却不一定强。本研究基于一组指标构成的指标体系的违约鉴别能力最大标准，构建了小企业债信评级体系。创新与特色：一是通过将单个违约鉴别力最强的指标组成的指标体系的b值，与本文建立的整体违约鉴别力最大的指标体系的b值对比，证明了单个违约鉴别力强的指标，组合起来的体系违约鉴别力不一定也强。在构建债信评级指标体系时，应关注指标体系整体的违约鉴别力，而非单个指标的违约鉴别力。二是在由n和n-1个指标构成的两组指标中，根据两组指标体系的非违约客户的综合得分S_i越高，则S_i偏离非违约状态（y_i=0）的距离越大；违约客户的综合得分S_i越低，S_i偏离违约状态（y_i=1）的距离越大；则布莱尔分数b越大，指标体系鉴别力越强的思路来遴选违约鉴别能力最大、而不是单个指标违约鉴别能力最大的一组指标体系，确保了评级体系具有最大的违约鉴别能力。三是在相关系数大于阈值的一对指标中，删除违约鉴别能力b值小、即区分违约状态能力弱的指标，既避免了指标体系的信息冗余、又避免了误删区分违约状态能力强的指标。实证表明：一是由于指标间的相互影响，单个违约鉴别力强的指标，组合起来的体系违约鉴别力不一定也强。二是非财务指标在小企业债信评级中的地位更加重要。     

基于最大指标区分度与最优相对隶属度的上市公司信用风险研究

信用风险又称违约风险，是评价一笔债务由于某些因素遭受损失的可能性，因此信用评价体系既要有违约鉴别能力，又能给出违约产生的原因。本文涉及的问题一是如何在众多指标中筛选出对违约状态鉴别最大的指标组合，二是如何对指标赋予权重，使得权重体现指标真实的重要程度，三是如何选取兼顾分类性能和可解释性的模型。本文的方法一是通过指标区分度最大的广义F-Score、指标子集间冗余度最小的平均相关系数遴选出对违约状态鉴别能力最大的指标体系；二是通过构建组合赋权模型，以指标信息含量最大、赋权结果一致性最大为目标函数，得到一组最优的指标权重；三是以客户与类别的广义加权距离最小为目标函数，求解非线性目标规划得到客户在不同类别中的相对隶属度，以此得到客户所属类别。上市公司样本实证分析表明：（1）从610个指标中筛选出的31个指标既具有违约状态的区分能力，又符合"信用5C"原则，是一个科学、合理的指标体系；（2）通过组合赋权得到31个指标的权重，企业内部财务因素对信用评价影响最大，"营运资本周转率"的权重值最大，是影响企业信用评价的关键指标，"监事会会议次数"、"派息比"、"审计意见类型"等指标都是影响信用评价的重要指标；（3）与逻辑回归、支持向量机等8种模型对比发现，本文提出的相对隶属度模型不仅具有良好的分类性能，而且是一个平衡分类性能、可解释性的模型。     

Multicriteria Preference Disaggregation for Classification Problems with an Application to Global Investing Risk*

Mathematical programming and multicriteria approaches to classification and discrimination are reviewed, with an emphasis on preference disaggregation. The latter include the UTADIS family and a new method, Multigroup Hierarchical DIScrimination (MHDIS). They are used to assess investing risk in 51 countries that have stock exchanges, according to 27 criteria. These criteria include quantitative and qualitative measures of market risk (volatility and currency fluctuations); range of investment opportunities; quantity and quality on market information; investor protection (security regulations treatment of minority shareholders); and administrative “headaches” (custody, settlement, and taxes). The model parameters are determined so that the results best match the risk level assigned to those countries by experienced international investment managers commissioned by The Wall Street Journal. Among the six evaluation models developed, one (MHDIS) classifies correctly all countries into the appropriate groups. Thus, this model is able to reproduce consistently the evaluation of the expert investment analysts. The most significant criteria and their weights for assessing global risk investing are also presented, along with their marginal utilities, leading to identifiers of risk groups and global utilities portraying the strength of each country's risk classification. The same method, MHDIS, outperformed the other five methods in a 10‐fold validation experiment. These results are promising for the study of emerging new markets in fast‐growing regions, which present fertile areas for investment growth but also     

基于幂风险谱和蒙特卡洛模拟的贷款优化配置模型

极端风险对于银行资产配置至关重要,尤其是次贷危机之后尾部风险以惨重的代价引起了金融机构的重视,传统条件风险价值CVaR、风险价值VaR不能有效度量尾部极端风险,因此本文基于幂风险谱和蒙特卡洛模拟构建了贷款组合优化配置模型,同时控制尾部极端风险和信用风险。本文一是通过损失-X_i越大、其风险权重φ_i也就越大的思路,构建幂风险谱PSR （Power Spectral Risk）最小的目标函数对极端风险进行控制,即弥补了CVaR同等看待尾部风险、忽略风险较大的损失应予以更大权重的不足,也同时弥补了VaR仅提供某一置信水平下资产损失的最大值、无法反映一旦超过这一数值的可能损失的弊端。二是通过蒙特卡洛模拟信用等级迁移引起贷款收益的变化情景,并以信用等级迁移后贷款组合损失越大、则风险厌恶权重越大的思路构建幂风险谱PSR最小为目标函数,以贷款组合的收益大于目标收益为约束,构建贷款优化配置模型,改变了现有研究贷款配置时没有同时控制信用风险和尾部风险的不足。对比分析结果表明：本文模型能够实现更高的收益风险比,即在单位幂风险谱PSR下能够实现较高的收益。     

中国企业部门信用风险溢价期限结构与宏观经济因子

近年来，中国企业部门债务风险不断暴露，其是否会引发系统性信用危机正成为焦点。本文着眼于中国企业部门信用风险累积与暴露背后的宏观经济现实，在简约模型中引入结构向量自回归模型（SVAR），将经济冲击区分为总供给冲击、总需求冲击和货币政策冲击，以此研究各宏观经济因子对中国企业部门信用风险溢价期限结构的影响特征，从而揭示中国企业部门信用风险定价的宏观经济机理。本文发现，正向的总供给冲击和货币政策冲击有助于降低中国企业部门信用风险溢价，但正向的总需求冲击则会推高中国企业部门信用风险溢价，自2011年以来持续处于高位水平的信用风险溢价的主要根源正是4万亿经济刺激计划所带来的扩张性总需求，因此欲从根本上降低中国企业部门信用风险水平，应紧缩社会总需求，并通过制度改革和结构调整，改善社会总供给。     

A multicriteria decision analysis model for faculty evaluation

In the context of increasing demands for social and financial accountability of universities, the required implementation of transparent faculty evaluation systems constitutes a challenge and an opportunity for universities strategically aligning the activity of academic staff with the university goals. However, despite growing interest in the performance appraisal of faculty, only a few reported studies propose models that cover the full range of academic activities and the models in use are typically based on ad hoc scoring systems that lack theoretical soundness. This article approaches faculty evaluation from an innovative comprehensive perspective. Based on the concepts and methods of multiple criteria value measurement, it proposes a new faculty evaluation model that addresses the whole range of academic activities and can be applied within and across distinct scientific areas, while respecting their specificities. Constructed through a socio-technical process, the model was designed for and adopted by the Instituto Superior Técnico, the engineering school of the Technical University of Lisbon. The model has a two-level hierarchical additive structure, with top-level evaluation areas specified by second-level evaluation criteria. A bottom non-additive third level accounts for the quantitative and qualitative dimensions of academic activity related to each evaluation criterion. The model allows (a) the comparison of the performance of academic staff with performance targets reflecting the strategic policy concerns of university management; (b) the definition of the multicriteria value profile of each faculty member at the top level of the evaluation areas; (c) the computation of an overall value score for each faculty member, through an optimisation procedure that makes use of a flexible system of weights and (d) the assignment of faculty members to rating categories.     

An extension of the Electre I method for group decision-making under a fuzzy environment

Many real-world decision problems involve conflicting systems of criteria, uncertainty and imprecise information. Some also involve a group of decision makers (DMs) where a reduction of different individual preferences on a given set to a single collective preference is required. Multi-criteria decision analysis (MCDA) is a widely used decision methodology that can improve the quality of group multiple criteria decisions by making the process more explicit, rational and efficient. One family of MCDA models uses what is known as “outranking relations” to rank a set of actions. The Electre method and its derivatives are prominent outranking methods in MCDA. In this study, we propose an alternative fuzzy outranking method by extending the Electre I method to take into account the uncertain, imprecise and linguistic assessments provided by a group of DMs. The contribution of this paper is fivefold: (1) we address the gap in the Electre literature for problems involving conflicting systems of criteria, uncertainty and imprecise information; (2) we extend the Electre I method to take into account the uncertain, imprecise and linguistic assessments; (3) we define outranking relations by pairwise comparisons and use decision graphs to determine which action is preferable, incomparable or indifferent in the fuzzy environment; (4) we show that contrary to the TOPSIS rankings, the Electre approach reveals more useful information including the incomparability among the actions; and (5) we provide a numerical example to elucidate the details of the proposed method.     

跳跃-扩散条件下信用风险相关性度量的变结构Copula模型

针对现有研究大多只考虑扩散条件的不足,构建了跳跃-扩散条件下信用风险相关性度量的变结构Copula模型。运用1991~2010年中国上市公司的数据构建了行业信用风险指数,运用双指数跳跃扩散模型来识别行业信用风险的跳跃扩散点,发现在样本期,共同因素与行业特质因素引发了行业信用风险的多次跳跃。在识别跳跃点的基础上,构建了变结构Copula模型,该模型能较准确地描述信用风险相关性的变化,各行业之间的信用风险相关系数在0.5以上,并且上市公司信用风险的变化呈现出"一损俱损"的特征,而"一荣俱荣"的特征并不明显。构建的模型及实证结论将有助于理解信用风险相关或传染,从而为信贷组合管理和风险管理提供更多的方法与经验。