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Research of Enterprise Credit Rating Based on K-Means GMDH Model

Received: 11 April 2017     Published: 12 April 2017
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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.

Published in Science Journal of Education (Volume 5, Issue 3)
DOI 10.11648/j.sjedu.20170503.15
Page(s) 105-110
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

K-Means GMDH Model, Enterprise Credit Rating, Credit Risk Management

References
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[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.
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  • APA Style

    Xiangyun Zhou, Yixiang Tian. (2017). Research of Enterprise Credit Rating Based on K-Means GMDH Model. Science Journal of Education, 5(3), 105-110. https://doi.org/10.11648/j.sjedu.20170503.15

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    ACS Style

    Xiangyun Zhou; Yixiang Tian. Research of Enterprise Credit Rating Based on K-Means GMDH Model. Sci. J. Educ. 2017, 5(3), 105-110. doi: 10.11648/j.sjedu.20170503.15

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    AMA Style

    Xiangyun Zhou, Yixiang Tian. Research of Enterprise Credit Rating Based on K-Means GMDH Model. Sci J Educ. 2017;5(3):105-110. doi: 10.11648/j.sjedu.20170503.15

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  • @article{10.11648/j.sjedu.20170503.15,
      author = {Xiangyun Zhou and Yixiang Tian},
      title = {Research of Enterprise Credit Rating Based on K-Means GMDH Model},
      journal = {Science Journal of Education},
      volume = {5},
      number = {3},
      pages = {105-110},
      doi = {10.11648/j.sjedu.20170503.15},
      url = {https://doi.org/10.11648/j.sjedu.20170503.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjedu.20170503.15},
      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.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Research of Enterprise Credit Rating Based on K-Means GMDH Model
    AU  - Xiangyun Zhou
    AU  - Yixiang Tian
    Y1  - 2017/04/12
    PY  - 2017
    N1  - https://doi.org/10.11648/j.sjedu.20170503.15
    DO  - 10.11648/j.sjedu.20170503.15
    T2  - Science Journal of Education
    JF  - Science Journal of Education
    JO  - Science Journal of Education
    SP  - 105
    EP  - 110
    PB  - Science Publishing Group
    SN  - 2329-0897
    UR  - https://doi.org/10.11648/j.sjedu.20170503.15
    AB  - 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.
    VL  - 5
    IS  - 3
    ER  - 

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Author Information
  • College of Economics and Management, University of Electronic Sc

  • College of Economics and Management, University of Electronic Sc

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