The Experts below are selected from a list of 22974 Experts worldwide ranked by ideXlab platform
Chinghsue Cheng - One of the best experts on this subject based on the ideXlab platform.
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hybrid models based on rough set classifiers for setting Credit Rating decision rules in the global banking industry
Knowledge Based Systems, 2013Co-Authors: Youshyang Chen, Chinghsue ChengAbstract:Banks are important to national, and even global, economic stability. Banking panics that follow bank insolvency or bankruptcy, especially of large banks, can severely jeopardize economic stability. Therefore, issuers and investors urgently need a Credit Rating indicator to help identify the financial status and operational competence of banks. A Credit Rating provides financial entities with an assessment of Credit worthiness, investment risk, and default probability. Although numerous models have been proposed to solve Credit Rating problems, they have the following drawbacks: (1) lack of explanatory power; (2) reliance on the restrictive assumptions of statistical techniques; and (3) numerous variables, which result in multiple dimensions and complex data. To overcome these shortcomings, this work applies two hybrid models that solve the practical problems in Credit Rating classification. For model verification, this work uses an experimental dataset collected from the Bankscope database for the period 1998-2007. Experimental results demonstrate that the proposed hybrid models for Credit Rating classification outperform the listing models in this work. A set of decision rules for classifying Credit Ratings is extracted. Finally, study findings and managerial implications are provided for academics and practitioners.
Petr Hajek - One of the best experts on this subject based on the ideXlab platform.
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feature selection in corporate Credit Rating prediction
Knowledge Based Systems, 2013Co-Authors: Petr Hajek, Krzysztof MichalakAbstract:Credit Rating assessment is a complicated process in which many parameters describing a company are taken into consideration and a grade is assigned, which represents the reliability of a potential client. Such assessment is expensive, because domain experts have to be employed to perform the Rating. One way of lowering the costs of performing the Rating is to use an automated Rating procedure. In this paper, we assess several automatic classification methods for Credit Rating assessment. The methods presented in this paper follow a well-known paradigm of supervised machine learning, where they are first trained on a dataset representing companies with a known credibility, and then applied to companies with unknown credibility. We employed a procedure of feature selection that improved the accuracy of the Ratings obtained as a result of classification. In addition, feature selection reduced the number of parameters describing a company that have to be known before the automatic Rating can be performed. Wrappers performed better than filters for both US and European datasets. However, better classification performance was achieved at a cost of additional computational time. Our results also suggest that US Rating methodology prefers the size of companies and market value ratios, whereas the European methodology relies more on profitability and leverage ratios.
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municipal Credit Rating modelling by neural networks
Decision Support Systems, 2011Co-Authors: Petr HajekAbstract:The paper presents the modelling possibilities of neural networks on a complex real-world problem, i.e. municipal Credit Rating modelling. First, current approaches in Credit Rating modelling are introduced. Second, previous studies on municipal Credit Rating modelling are analyzed. Based on this analysis, the model is designed to classify US municipalities (located in the State of Connecticut) into Rating classes. The model includes data pre-processing, the selection process of input variables, and the design of various neural networks' structures for classification. The selection of input variables is realized using genetic algorithms. The input variables are extracted from financial statements and statistical reports in line with previous studies. These variables represent the inputs of neural networks, while the Rating classes from Moody's Rating agency stand for the outputs. In addition to exact Rating classes, data are also labelled by four basic Rating classes. As a result, the classification accuracies and the contributions of input variables are studied for the different number of classes. The results show that the Rating classes assigned to bond issuers can be classified with a high accuracy rate using a limited subset of input variables.
Chihyu Hsu - One of the best experts on this subject based on the ideXlab platform.
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a hybrid kmv model random forests and rough set theory approach for Credit Rating
Knowledge Based Systems, 2012Co-Authors: Chingchiang Yeh, Fengyi Lin, Chihyu HsuAbstract:In current Credit Ratings models, various accounting-based information are usually selected as prediction variables, based on historical information rather than the market's assessment for future. In the study, we propose Credit Rating prediction model using market-based information as a predictive variable. In the proposed method, Moody's KMV (KMV) is employed as a tool to evaluate the market-based information of each corporation. To verify the proposed method, using the hybrid model, which combine random forests (RF) and rough set theory (RST) to extract useful information for Credit Rating. The results show that market-based information does provide valuable information in Credit Rating predictions. Moreover, the proposed approach provides better classification results and generates meaningful rules for Credit Ratings.
Youngchan Lee - One of the best experts on this subject based on the ideXlab platform.
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application of support vector machines to corporate Credit Rating prediction
Expert Systems With Applications, 2007Co-Authors: Youngchan LeeAbstract:Corporate Credit Rating analysis has drawn a lot of research interests in previous studies, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the corporate Credit Rating problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, the researcher uses a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of RBF kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, the researcher compares its performance with those of multiple discriminant analysis (MDA), case-based reasoning (CBR), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.
Jenying Shih - One of the best experts on this subject based on the ideXlab platform.
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a study of taiwan s issuer Credit Rating systems using support vector machines
Expert Systems With Applications, 2006Co-Authors: Wunhwa Chen, Jenying ShihAbstract:By providing Credit risk information, Credit Rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries. In this paper, we propose an automatic classification model for issuer Credit Ratings, a type of fundamental Credit Rating information, by applying the support vector machine (SVM) method. This is a novel classification algorithm that is famous for dealing with high dimension classifications. We also use three new variables: stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification. Previous research has seldom considered these variables. The data period of the input variables used in this study covers three years, while most previous research has only considered one year. We compare our SVM model with the back propagation neural network (BP), a well-known Credit Rating classification method. Our experiment results show that the SVM classification model performs better than the BP model. The accuracy rate (84.62%) is also higher than previous research.
