Credit Assessment Research Articles

A Conceptual Framework for Transport Infrastructure PPP Project Credit Assessments

Project credit assessments have been increasingly used in order to evaluate the creditworthiness of infrastructure projects. In light of the benefits that can be achieved from changing the financial basis of such projects during their life-cycle, the accuracy and comparability of project credit assessments become of cardinal importance to the successful financing or re-financing of a project. This paper proposes a conceptual framework for assessing project creditworthiness based on the study of risks that are encountered during the life-cycle of transport infrastructure projects. The proposed framework is based on a generic classification of risks and has risk allocation among the project’s counterparties at its core. The proposed framework intends to reflect the true risk status of such projects and consider asset-specific characteristics that are important to the role and operation of transport infrastructure. The findings of this paper aim to serve as a starting point for future research on the development of transport infrastructure project credit assessments that can overcome potential limitations and accurately reflect risk bearing and coping.

Assessment of energy credits for the enhancement of the Egyptian Green Pyramid Rating System

Energy is one of the most important categories in the Green Building Rating Systems all over the world. Green Building is a building that meets the energy requirements of the present with low energy consumption and investment costs without infringing on the rights of forthcoming generations to find their own needs. Despite having more than a qualified rating system, it is clear that each system has different priorities and needs on the other. Accordingly, this paper proposes a methodology using the Analytic Hierarchy Process (AHP) for assessment of the energy credits through studying and comparing four of the common global rating systems, the British Building Research Establishment Environmental Assessment Method (BREEAM), the American Leadership in Energy and Environmental Design (LEED), the Australian Green Stars (GS), and the PEARL assessment system of the United Arab Emirates, in order to contribute to the enhancement of the Egyptian Green Pyramid Rating System (GPRS). The results show the mandatory and optional energy credits that should be considered with their proposed weights according to the present and future needs of green Egypt. The results are compared to data gathered through desk studies and results extracted from recent questionnaires.

Some Problems of Introducing European Credit Transfer System (ECTS) into Non-State Higher Institutions

The article deals with theoretical and organizational problems of introducing European credit transfer system (ECTS) into Russian non-state higher institutions. The author has come to the conclusion that the introduction of ECTS gives rise to a lot of questions on the part of professors and students of higher educational institutions. It is noted in the article that when working out the conception of credit system for Russian higher school it is important to discuss principles of credit assessment of educational curriculum content on the basis of planning the results of its realization. The suggested idea is to give higher institutions an opportunity to work out experimental educational standards within the limits of cycle (and perhaps, course) structure of federal state educational standards. DOI: 10.5901/mjss.2015.v6n5p243

The impact of Basel II on the debt costs of German SMEs

We investigate the ex-post effects of the Basel II reform on the debt costs of German small and medium-sized enterprises (SMEs). We assume that Basel II formalized the credit assessment procedure of banks and that especially SMEs might face higher costs of debt as they tend to have comparably high proportions of bank credit, and consequently lower ratings than bigger companies. Beyond that, banks might try to refinance those additional rating costs by imposing higher interest rates on debtors. The results presented in our paper indicate a significant overall rise of debt costs of SMEs that have debt relations with IRBA-certified banks since 2007. Furthermore, low-risk companies benefit from comparably lower loan rates, whereas risky firms face comparably higher loan costs than before. The results are less obvious for SMEs that are rated under RSA, but still present for highly risky companies. We test for several influences of the financial crisis. However, we do not find convincing arguments that would reason a rise of the costs of debt in regard of the crisis. Yet, we still observe higher debt costs of SMEs, particularly of highly risky companies, since 2007 that indicate a change of bank lending behavior in Germany.

State Pension Liabilities and Credit Assessments

SYNOPSIS We examine the decision relevance of a commonly suggested adjustment to how state governments report governmental pension liabilities by recalculating such pension liabilities using the return on a portfolio of high-quality municipal bonds as the discount rate. Calculated as the difference between the state's expected rate of return and the municipal bond return, we find that the discount rate adjustment associates with lower credit ratings and higher interest costs. We also find that credit rating agencies are more likely to issue conflicting ratings when the calculation of the discount rate adjustment involves greater uncertainty. Overall, while financial statement users agree about the need for and the direction of a pension liability rate adjustment, there is less consensus about the proper magnitude of this adjustment, suggesting that current accounting treatment of pensions in the public sector leads to costly uncertainty among financial statement users.

Financial institution credit assessment and implications for portfolio managers

We document systematic industry differences between the yields of bonds issued with the same credit rating. Specifically, financial firm bonds provide higher yields after controlling for issue and firm-specific characteristics. An exception is the debt of large financial issuers, consistent with the too-big-to-fail phenomenon. Evidence of higher yields extends to syndicated loans but does not translate to abnormal returns in secondary bond market trading when returns are explained by a four factor model. Our results suggest that portfolio managers could use financial institution bonds to generate greater yield within their rating constraints but doing so may increase exposure to risk.

What drives the disintegration of the loan origination value chain in the banking business

Purpose– The purpose of this paper is to analyze the vertical disintegration of the bank loan origination value chain. This paper conducts a study on the credit information market from the perspective of the bank’s decision to vertically disintegrate the loan origination value chain. The main aim is to identify the relevant drivers of the decision to vertically disintegrate the credit assessment phase in the lending business.Design/methodology/approach– Transaction cost economics and information asymmetry are the typical perspectives of analysis of the vertical scope of business value chains.Findings– This paper argues that in order to capture the drivers underlying the dynamic evolution of the vertical scope of bank loan origination business models, the above perspectives must be combined and integrated further with a resource-based view and the modularity perspective. Combining managerial and financial perspectives, this paper offers an examination of the drivers of vertical disintegration in the lending value chain and, specifically, in the credit assessment phase.Originality/value– Although the existence of substantial research on value chain vertical integration/disintegration in the literature, none has directly focussed on the credit assessment value chain. It leaves a gap that the paper aims to overcome. The value chain disintegration has deep managerial and financial implications at firm and industry levels, and the comprehension of the rational underlying it is critical to maintaining competitive business model configurations in the bank lending industry.

The Dark Side of the Four-Eyes Principle Evidence from Bank Lending

The allocation of authority affects the communication of information about clients within banks. We document that in small business lending internal control leads loan officers to propose inflated credit ratings for their clients. Inflated ratings are, however, anticipated and partly reversed by the credit officers responsible for approving credit assessments. More experienced loan officers inflate those parameters of a credit rating which are least likely to be corrected by credit officers. Our analysis covers 10,568 internal ratings for 3,661 small business clients at six retail banks. We provide empirical support to theories suggesting that internal control can induce strategic communication within organizations when senders and receivers of information have diverging interests. Our findings also point to the limits of the four-eyes principle as a risk-management tool in financial institutions.

Do Sovereign Credit Assessments Destabilize Equity Markets during Periods of Financial Crisis?

We investigate the effects of S&P’s sovereign re-ratings on the higher moments of equity market returns over recent financial crises. Using a set of intraday stock market index prices and sovereign credit ratings for a sample of 36 countries which experienced sovereign rating changes over the period from 1996 - 2013, we find that the higher moments of stock market returns are significantly more responsive to sovereign re-ratings during financial crises but the effects on stock markets are not the same across different financial crises. The effects during crises are however magnified for large downgrades and those that are associated with a loss of investment grade status. We find that there are asymmetric effects during financial crises in that downgrades are consistently more significant than upgrades in increasing realized volatility and realized kurtosis. Both upgrades and downgrades affect realized skewness in times of crises in the expected direction.

Rating Agencies and Information Efficiency: Do Multiple Credit Ratings Pay Off?

We empirically investigate why issuers solicit and pay for multiple ratings not only at issuance but also during the monitoring phase of a debt instrument. Using monthly credit rat-ing migration data from Fitch, Moody's and Standard & Poor's on all U.S. residential mort-gage-backed securities from 1985 to 2012 ever rated (154'600 individual tranches), our results provide empirical evidence that rating agencies put more effort in rating and outlook revisions when tranches have assigned multiple ratings. Further, we demonstrate that in the case of multiple ratings, agencies do a better job in discriminating tranches with respect to default risk. Our results contribute to the literature on information production of credit ratings and extend the perspective to the monitoring period after issuance. We also show that in case of multiple ratings, Moody's on average provides the most conservative credit assessment and that this relative pattern remains consistent over a tranche's lifetime.

Assessment of Informal Credit in Mobilizing Funds for Agricultural Production in Ijumu Local Government Area of Kogi State, Nigeria

Assessment of Informal Credit in Mobilizing Funds for Agricultural Production in Ijumu Local Government Area of Kogi State, Nigeria

Construction and Application Research of Isomap-RVM Credit Assessment Model

Credit assessment is the basis and premise of credit risk management systems. Accurate and scientific credit assessment is of great significance to the operational decisions of shareholders, corporate creditors, and management. Building a good and reliable credit assessment model is key to credit assessment. Traditional credit assessment models are constructed using the support vector machine (SVM) combined with certain traditional dimensionality reduction algorithms. When constructing such a model, the dimensionality reduction algorithms are first applied to reduce the dimensions of the samples, so as to prevent the correlation of the samples’ characteristic index from being too high. Then, machine learning of the samples will be conducted using the SVM, in order to carry out classification assessment. To further improve the accuracy of credit assessment methods, this paper has introduced more cutting-edge algorithms, applied isometric feature mapping (Isomap) for dimensionality reduction, and used the relevance vector machine (RVM) for credit classification. It has constructed an Isomap-RVM model and used it to conduct financial analysis of China's listed companies. The empirical analysis shows that the credit assessment accuracy of the Isomap-RVM model is significantly higher than that of the Isomap-SVM model and slightly higher than that of the PCA-RVM model. It can correctly identify the credit risks of listed companies.

Internal rating and scoring model in the function of an improved assessment of the credit standing of firms

This paper analyzes the impact of internal rating and scoring model with the aim of assessing the credit solvency of firms during the process of bank crediting and the assessment of the future repayment behavior. The bank assesses the credit risk based on the qualitative and quantitative criteria taking into consideration certain characteristics of firms, thus ensuringa clear ranking withinan appropriate risk category according to the collection rate. The internal rating represents a starting point for determining the credit risk rate of firms and a check of credit solvency, whereas scoring is a system of assigning points, the sum of which shows the quantitative value of the probability of delay in credit repayment. It will be shown in the papers that the combination of qualitative and quantitative criteria, while developing the internal rating and scoring of firms, is aimed at an improved assessment of credit standing, which reduces.

Development of Altman Five-Factor Model of Assessing the Creditworthiness of an Enterprise

In this paper, we propose a method that uses the apparatus of the theory of fuzzy sets, together with the five-factor model of Altman to assess the creditworthiness of an enterprise. Altman’s model is enhanced in two ways: applies integral approximation of the root mean square for the exact calculation of quantitative credit assessment (probability of bankruptcy), and applies the device of fuzzy sets for ordered sets according to the degree of confidence in the resulting probability. Some real examples of the methodology of applications are shown. The article is theoretical in nature, the findings made in the mathematical model have not been tested on a sufficiently large number of enterprises.

An Ensemble Learning Based Model for Real Estate Project Classification

The demand of accommodation has been increasing especially in the metro area. The process of credit assessment for loan is increasingly high. One of the criteria for banks to determine the amount of loan is a grade of the real estate project. This paper addresses the problem of determining the grade of a real estate project and proposes a real estate classification model. The model helps loaners to make a decision for the further stage of the loan. For the model construction, the data were gathered from 407 real estate projects in Thailand which are launched in 2014 and forthcoming projects that will be launched in 2015. The variables of our model involve the project infrastructure and characteristics such as the facilities, number of units, location, parking space, and size of a developer. For all 407 projects, the grades were provided by a bank. We use the supervised learning with ensemble technique and vote algorithm for training and testing against the dataset where the dataset is separated into the training set and the testing set of 307 records and 100 records, respectively. The efficiency of our proposed model is measured by classification accuracy and user satisfaction with the model.

The Impact of Credit Policy on the Performance of Nigerian Commercial Banks

The major financial intermediary in any economy is the bank. As financial intermediaries, banks provide means by which funds can be transferred from the surplus unit of the economy to the deficit unit. This role is performed primarily through the acceptance of deposits of different categories and characteristics for onward lending to the numerous customers by way of loans and credits. The study tries to access the impact of credit policy on the performance of Nigerian Commercial Banks using Zenith Bank Plc as case study. Primary data were collected through questionnaires served on sixty (60) respondents (staff: 32 and customer: 28) of the bank. The questionnaires were analysed with the use of chi-square (X2). The findings from the study show that having a good credit policy in place goes a long way in minimizing the incidence of bad debts. It was also discovered that prudent credit assessment and disbursement, dynamic credit monitoring and decisive actions when there are warning signals, have all helped the bank to maintain a high quality of assets and a high level of profitability.

Application of the algorithm based on the PSO and improved SVDD for the personal credit rating

Considering the question of personal credit rating, this paper proposes a hybrid method for credit assessment based on an improved Support Vector Data Description (SVDD) algorithm combined with the particle swarm optimization (PSO) algorithm. First, the paper carries out data preprocess, and then it solves the two problems: parameters optimization and feature selection at the same time using the PSO algorithm combined with the improved SVDD algorithm and assesses the credit data using the optimized parameters and features. Finally, the method constructed is tested through two data sets in practice, and the results show that the hybrid method constructed in this paper can obtain higher classification accuracy compared with some other existing credit scoring methods.

Quantitative Study of Bank-Specific and Social Factors of Non-Performing Loans of Pakistani Banking Sector

The Non-Performing loans presented picture of a distorted and slow-paced economy. This issue became more critical when it was observed that NPLs were declining in South East countries except for Pakistan. This study has dual purposes: Firstly to build up a model clarifying the relationship between bank-specific, social factors and non-performing loans. Secondly, to investigate this model in the setting of Pakistani Banking sector as limited interest has been given in existing literature in defining the relationship between bank specific, social factors and non-performing loans. The quantitative methodology is used as it is the most suitable for fulfillment of the study objectives. The results show that various bank-specific factors like credit assessment, credit monitoring and rapid credit growth have significant affect on Non-Performing Loans, whereas interest has a weak significance on NPLs. The social factors including political interference and bankers’ incompetence have significant affects on NPLs and these have been important factors in explaining the Non-Performing Loans. This ground-breaking work on Non-Performing Loans and its factors will not only help the entire Pakistani banking sector to control Non-Performing Loans but also its results can be generalized on other countries as well.

A Novel Credit Risk Assessment Model Using a Granular Computing Technique

Abstract Information about financial risk almost always contains a problem of class imbalance. Class-imbalanced data refers to the asymmetric categories of data, and it is divided into a major class and a minor class. If we guide all information into the training sample to model of this situation, it may happen that the accuracy rate of the major class is high, but the accuracy rate of the minor class is too low. Many risk assessment models have been developed in many studies, but most of them only use sampling methods to deal with the class-imbalanced data; this may cause the distortion of information. In order to effectively solve the problem of class imbalance in credit risk assessment, this paper proposed a novel credit risk assessment model using a granular computing technique to construct a risk assessment model to provide a better insight into the essence of data and effectively solve class imbalance problems. On the other hand, in order to improve the lack of granular computing and enhance the efficiency of the credit risk assessment model, this paper adds a new index, “% of minor class (PM),” to avoid a situation in which minor class data spread to the major class granular. Finally, this paper also compares the results of the area under the receiver operating characteristic curve (AUC) and G-means methods for dealing with class-imbalanced data. The results demonstrate that the proposed granular computing credit assessment model would have better results than other sampling models.

Intelligent Credit Assessment System by Kernel Locality Preserving Projections and Manifold-Regularized SVM Models

Intelligent Credit Assessment System by Kernel Locality Preserving Projections and Manifold-Regularized SVM Models