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

Xiangyun Zhou; Yixiang Tian

doi:doi:10.11648/j.sjedu.20170503.15

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

Xiangyun Zhou, Yixiang Tian

Published in Science Journal of Education (Volume 5, Issue 3)

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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[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.
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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

Xiangyun Zhou

College of Economics and Management, University of Electronic Sc
Yixiang Tian

College of Economics and Management, University of Electronic Sc

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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}
}

Copy | Download

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  -

Copy | Download