Machine Learning
A case-based approach using inductive indexing for corporate bond rating
Decision Support Systems - Decision-making and E-commerce systems
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Review: A new training method for support vector machines: Clustering k-NN support vector machines
Expert Systems with Applications: An International Journal
Credit Risk Evaluation Using Support Vector Machine with Mixture of Kernel
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Predicting financial activity with evolutionary fuzzy case-based reasoning
Expert Systems with Applications: An International Journal
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Computers and Operations Research
Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis
Expert Systems with Applications: An International Journal
Probabilistic and discriminative group-wise feature selection methods for credit risk analysis
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
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.