Research of Enterprise Credit Rating Based on K-Means GMDH Model
Science Journal of Education
Volume 5, Issue 3, June 2017, Pages: 105-110
Received: Apr. 11, 2017; Published: Apr. 12, 2017
Views 1494      Downloads 52
Authors
Xiangyun Zhou, College of Economics and Management, University of Electronic Sc
Yixiang Tian, College of Economics and Management, University of Electronic Sc
Article Tools
Follow on us
Abstract
Since the outbreak of credit risk, researching on corporate credit rating has been brought into investors, the government and scholars focus. This paper constructs an optimal K-means clustering Group Method of Data Handling model can effectively improve the accuracy of rating results, reduce the computational complexity, and this paper proves the model under the least squares estimation can get the optimal results. This article uses Chinese corporate credit rating and financial indexes to study, comparing its results with the concequences of Hidden Markov GMDH model and other traditional neural network models. The empirical outcomes show that the K-means clustering GMDH model is better than Hidden Markov GMDH model and the remaining four neural network models, indicating that the method can effectively improve the accuracy of corporate credit rating assessment and reduce the cost of rating.
Keywords
K-Means GMDH Model, Enterprise Credit Rating, Credit Risk Management
To cite this article
Xiangyun Zhou, Yixiang Tian, Research of Enterprise Credit Rating Based on K-Means GMDH Model, Science Journal of Education. Vol. 5, No. 3, 2017, pp. 105-110. doi: 10.11648/j.sjedu.20170503.15
References
[1]
Xiaoyang Zhou et al. The issuer-pays business model and competitive rating market: rating network structure [J]. The Journal of Real Estate Finance and Economics,2016,53:1-26.
[2]
Bolton Patrick, Freixas Xavier, Shapiro Joel. The credit ratings game [J]. The Journal of Finance,2012, 67(1):85-111.
[3]
Ping He, Meng Jin. The influence of credit rating in Chinese bond market [J]. Financial Research,2010,358(4):15-28.
[4]
Zonglai Kou, Yuzhang Pan, Does China's credit rating really affect the cost of issuing bonds? [J]. Financial Research, 2015, 424(10):81-98.
[5]
B. Shi, G. Chi. A Credit risk evaluation index screening model of petty loans for small private business and its application [J]. Advances in information Sciences and Service Science- s, 2013, 5(7):1116-1124.
[6]
K. Kim, H. Ahn. A corporate credit rating using multi-class support vector machines with an ordinal pairwise partitioning approach [J]. Computer &Operation Research, 2012, 39(8):1800-1811.
[7]
P. Hajek, K. Michalak. Feature selection in corporate credit rating prediction [J]. Knowledge-Based Systems,2013,51:72-84.
[8]
Ge-Er Teng, Chang-Zheng He. Jin Xiao. Customer credit scoring based on HMM/GMDH hybrid model [J]. Expert Systems with Applications, 2013,36:731-747.
[9]
Anastasios Petropoulos, Sotirios P. Chatzis, Stylianos Xanthopoulos. A novel corporate credit rating system based on Student’s t hidden Markov models [J]. Expert Systems with Application, 2016,53:87-105.
[10]
Robert J. Elliott, Tak Kuen Siud,e, Eric S. Fung. A Double HMM approach to Altman Z-scores and credit ratings [J]. Expert Systems with Applications, 2014, 41:1553-1560.
[11]
Yixiang Tian. Comparative analysis and empirical analysis of the different level of GMDH algorithm in the medium and long term forecasting model [J]. Forecasting,1999,6:73-75.
[12]
Geer Teng, Changzheng He, Jin Xiao,Yue He, Bing Zhu, Xiaoyi Jiang. Cluster ensemble framework based on the group method of data handling [J]. Applised Soft Computing, 2016,43:35-46.
[13]
Hao Zhang, Guanglong Dai. Improvement of distributed clustering algorithm based on min-cluster [J]. Optik, 2016, 127:3878-3881.
[14]
Jiali Lin. Analysis on the factors influencing the rating quality of credit rating agencies [D]. Hang Zhou: Zhe Jiang University, 2014:30-56.
[15]
Yechen Qin,Reza Langari,Liang Gu. A new modeling algorithm based on ANFIS and GMDH [J]. Journal of Intelligent & Fuzzy Systems, 2015, 29:1321-1329.
[16]
Michele Azzollini, Vincenzo Pacelli. An Artifical Neural Network Approach for Credit Risk Management [J]. Journal of Intelligent Learning Systems and Applications, 2011, 3(2):103-112.
[17]
Qiumin Li, Yixiang Tian. The k-nearest neighbor-based GMDH prediction model and its application [J]. International Journal of Systems Science, 2014, 45(11):2301-2308.
[18]
Zengguang Li, Jin Wang. Improvement of GMDH parameter model and its application in coal price research [J]. Systems Engineering, 2012. 30(6):105-110.
[19]
Wei Hu. Improved hierarchical K means clustering algorithm [J]. Computer Engineering and Applications, 2013, 49(2):157-159.
[20]
Miaojing Li, Jiyi Wu. Credit evaluation of small and medium sized enterprises in e-commerce environment [J]. Systems Engineering Theory and Practice, 2012, 32(3):555-560.
[21]
Haoming. Zhang, Chunyan Miao, Zhiqi Shen. Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings [J]. Neurocomputing, 2014, 128(5):285-295.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186