• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.7
• /
• pp.1569-1574
• /
• 2005

In this paper, I propose an intelligent credit rating system using a bankruptcy prediction model based on support vector machines (SVMs). SVMs are promising methods because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study examines the feasibility of applying SVM in Predicting corporate bankruptcies by comparing it with other data mining techniques. In addition. this study presents architecture and prototype of intelligeht credit rating systems based on SVM models.

• The Journal of Information Systems
• /
• v.14 no.1
• /
• pp.41-65
• /
• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• Journal of Information Technology Applications and Management
• /
• v.14 no.2
• /
• pp.151-168
• /
• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• Asia pacific journal of information systems
• /
• v.25 no.3
• /
• pp.579-596
• /
• 2015

Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.

• Journal of Contemporary Eastern Asia
• /
• v.20 no.1
• /
• pp.102-119
• /
• 2021

Social credit rating in China (SCRC) has been criticized as "dystopian" and "Orwellian," an attempt by the Communist Party to hold onto power by exerting ever greater control over its citizens. To explain such measures, value differences are often invoked, that Chinese value stability and cooperation over privacy and freedom. However, these explanations are oversimplifications that result in ethical impasses. This article argues social credit rating should be understood in terms of the commonly human problem of large-scale cooperation. To do so, this paper relies on a cultural evolutionary framework and is an exercise in interpretive pros hen ethical pluralism, attempting to understand how apparently irresolvable cultural differences stem from common human concerns. Wholesale condemnation of SCRC fails to acknowledge the serious, intractable nature of problems resulting from a lack of trust in China. They take for granted the existence of institutions ensuring largescale, anonymous cooperation characteristic of - but somewhat unique to - Western Educated Industrialized Rich and Democratic (WEIRD) cultures. Because of its history and rapid development, China lacks the institutions necessary to ensure such cooperation, and because of anti-social punishment, social credit rating might be one of the few ways to ensure cooperation at this scale. The point is not to defend social credit rating in general, but to raise the possibility of its defense in China and show one way this would be done.

• Management & Information Systems Review
• /
• v.35 no.4
• /
• pp.37-56
• /
• 2016

This study investigates how adapting of K-IFRS effects NI and OCI affecting of credit rating on changing of the period and variable by using samples of around adapting of K-IFRS. First of all, after adapting of K-IFRS(2011-2013), it was noticeable that how NI affecting after adapting of K-IFRS(2007-2010) had been increased more than that of before affecting of K-IFRS. However, there was not a single difference in affecting OCI on credit rating comparing to the past of adapting of K-IFRS. Second, it seemed like NI affected more after adapting of K-IFRS(2011-2013). The first year of K-IFRS had bigger incremental effect than after adapting of K-IFRS. However, after adapting of K-IFRS, OCI affecting on credit rating had no ncremental effect. Third, it seemed like NI in the first year affected more than OCI on credit rating. After adapting(2012-2013) of K-IFRS, it seemed like NI and OCI do not affect on credit rating. To interpret this, NI and OCI affected the first year of adapting of K-IFRS; therefore, adapting of K-IFRS affected without affecting financial ratio on adapting credit rating. As the time goes on, it can be expected that adapting K-IFRS became stable; therefore, extra incremental effect will not be seen comparing to the early adaption. The implication of this study is when information users use credit rating, they have to concern of affecting of K-IFRS. This is because NI in financial ratio is affecting on credit rating.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.4
• /
• pp.1-12
• /
• 2022

Our study adds to the body of knowledge about the relationship between credit ratings and the capital structure of bond issuers. Using Bloomberg and Datastream databases and employing panel regression models, we study the capital structure changes of Japanese enterprises after credit rating changes by global rating agencies (S&P and Moody's) as well as their local counterparts (R&I and JCR) from 1998 to 2016. We find that after rating downgrades, Japanese enterprises considerably reduce net debt or net debt relative to net equity, similar to the findings of Kisgen (2009), who focused on U.S. industrial firms. They do not, however, make adjustments to their financial structure as a result of rating improvements. In comparison to downgrades by S&P and Moody's, Japanese corporations issue 1.89 percent less net debt and 1.50 percent less net debt relative to net equity after R&I and JCR rating downgrades. To put it another way, Japanese companies consider rating adjustments made by local agencies to be more significant than those made by global rating organizations. Our findings contradict earlier research that suggests S&P and Moody's are more prominent in the investment community than R&I and JCR in Japan.

• Journal of Digital Convergence
• /
• v.20 no.5
• /
• pp.585-592
• /
• 2022

The purpose of this paper is to examine the relation between executive excesss compensation and credit rating. According to the prior research which show the negative effects of excess compensation on a firm's future performance, this paper expects the negative effect of excess compensation on credit rating. Using a sample of Korean listed non-financial firms from 2014 to 2019, I perform the multivariate regressions analysis of excess compensation on credit rating. I find that excess compensation is negatively related to credit rating when executive compensation exceed expected executive compensation. Moreover, I find that the result is constant when a fim belongs to small-medium business. These results show that credit rating is affected by executive excess compensation and the relation could be different by the type of firm's size. Therefore, this study contributes to the literature by suggesting the possibility that capital market is aware of negative effect of executive excess compensation.

• The Korean Journal of Applied Statistics
• /
• v.22 no.3
• /
• pp.443-461
• /
• 2009

In order to overcome the lack of Korean credit rating migration data, we consider an empirical Bayes procedure to estimate credit rating migration matrices. We derive the posterior probabilities of Korean credit rating transitions by utilizing the Moody's rating migration data and the credit rating assignments from Korean rating agency as prior information and likelihood, respectively. Metrics based upon the average transition probability are developed to characterize the migration matrices and compare our Bayesian migration matrices with some given matrices. Time series data for the metrics show that our Bayesian matrices are stable, while the matrices based on Korean data have large variation in time. The bootstrap tests demonstrate that the results from the three estimation methods are significantly different and the Bayesian matrices are more affected by Korean data than the Moody's data. Finally, Monte Carlo simulations for computing the values of a portfolio and its credit VaRs are performed to compare these migration matrices.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.347-354
• /
• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.