Research on Credit Risk Assessment of Commercial Banks Based on KMV Model

Yan Bingzheng; Bai Puxian

doi:doi:10.11648/j.ss.20211005.11

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Research on Credit Risk Assessment of Commercial Banks Based on KMV Model

Yan Bingzheng, Bai Puxian

Published in Social Sciences (Volume 10, Issue 5)

Received: 12 August 2021 Accepted: 6 September 2021 Published: 10 September 2021

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Abstract

Under the background of the new normal economy, the financing mode of local governments has changed, which has led to the emergence of a new pattern in China's financing market. Under this background, banks are facing the development pressure of subject diversification, financial disintermediation and cross-border competition. Commercial banks also actively promote the compliance development of emerging businesses such as financial market through the "comprehensive direction", so as to form a balanced and complementary development situation with traditional credit business. Therefore, this paper taking credit risk and related theories as the starting point, KMV model is selected to study domestic bank risk monitoring. Then, China Merchants Bank is taken as the research object, Shengjing Bank, Harbin Bank, Qingdao Bank and Chongqing Bank are taken as horizontal comparison. The data sampling time span is January to December 2018, as vertical comparison. Five banks were evaluated for credit risk, and it was found that the default distance of China Merchants Bank was relatively small and the default probability was relatively large. However, according to the calculation and analysis of KMV model, the default probability and return volatility of China Merchants Bank rank first among the five commercial banks, but at the same time, it maintains a high net profit margin of 129.3% in 2018, which shows that China Merchants Bank has a strong ability to control credit risk.

Published in	Social Sciences (Volume 10, Issue 5)
DOI	10.11648/j.ss.20211005.11
Page(s)	204-217
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), 2021. Published by Science Publishing Group

Keywords

Internet Finance, Commercial Banks, Total Factor Productivity, Malmquist Index, Pool Data

References

[1]	Zhang Wei, Li Yushuang. Credit Risk Assessment and Application of Personal Microfinance Based on Logistic Regression Model [J]. Financial Theory and Practice, 2003 (01): 33-38.
[2]	Tan Yanzhi, Zhang Yundong. An evaluation study based on the relationship between credit risk and unemployment rate —— Taking the non-performing loans of banks in China, Japan and the United States as an example [J]. Management Review, 2013 (7).
[3]	Chen Jiali. P2P online loan personal credit risk assessment model research-based on hybrid Drosophila neural network method [J]. Friends of Accounting, 2013 (21): 32-35.
[4]	Shen Yonghua. Construction of credit risk evaluation index system of listed companies [J]. Journal of neijiang normal University, 2015 (12): 65-69.
[5]	Ling Jianghuai, Liu Yanmei. Research on Credit Risk Assessment of Jinzhou Bank Based on KMV Model [J]. Cooperative Economy and Technology, 2016 (7): 59-62.
[6]	Zhang Fan. Constructing the credit risk evaluation model of small and micro enterprises based on CFaR [J]. Accounting Monthly: 2017 (5): 23-28.
[7]	Panpan Zhang, Zhou Xinmiao. Research on Internet Financial Credit Risk Assessment Based on BP Neural Network Model [J]. Science and Technology Innovation, 2017 (16): 338-338.
[8]	Zhang Jingui, Hou Yu. Research on Credit Risk Evaluation and Risk Management of SMEs under Supply Chain Finance Model [J]. Journal of Central University of Finance and Economics, 2017 (12): 36-45.
[9]	Yang Xiuyun, Jiang Yuanyuan, Duan Zhenzhen. Credit risk assessment of development finance from the perspective of default distance [J]. Financial Theory and Practice, 2018 (5).
[10]	Ma ruowai, Zhang Wei. credit risk assessment of commercial bank customers based on unbalanced samples-taking listed companies as an example [J]. financial theory and practice, 2016, No. 468 (07): 55-61.
[11]	Zhang Dabin, Zhou Zhigang, Liu Wen, Jiao Peng. Research on Bank Credit Risk Assessment Based on KMV Model [J]. Cooperative Economy and Technology, 2017 (7): 59-62.
[12]	Beixi Si. Data Processing in Model of the Bank Credit Risk Assessment Based on Asset Assessment [J]. Applied Mechanics and Materials, 1993, 707: 493-496.
[13]	Jacoby, Fowler. The Application of WN Based on PSO in Bank Credit Risk Assessment [C]// International Conference on Artificial Intelligence & Computational Intelligence. IEEE Computer Society, 2005.
[14]	Meyer, Yeager. Credit risk assessment of the microfinance industry in Nigeria: an application to Accion Microfinance Bank Limited (AMFB) [J]. Applied Physics Letters, 2006, 98 (1): 1-2.
[15]	Pesola. Assessment Research of Credit Risk in Commercial Bank Based on Fuzzy-VaR [C]// International Conference on Management & Service Science. IEEE, 2008.
[16]	Saunders. Recent developments in consumer credit risk assessment [J]. European Journal of Operational Research, 2009, 183 (3): 1447-1465.
[17]	Hoggarthetal. Multiple classifier application to credit risk assessment [J]. Expert Systems with Applications, 2013, 37 (4): 3326-3336.
[18]	David W, DavidT. Genetic algorithm-based heuristic for feature selection in credit risk assessment [J]. Expert Systems with Applications, 2015, 41 (4): 2052-2064.
[19]	Atlman. A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine [J]. Expert Systems with Applications, 1968, 39 (5): 5325-5331.
[20]	Ohlson. Credit Risk Assessment and Relationship Lending: An Empirical Analysis of German Small and Medium-Sized Enterprises [J]. 1989, 45 (2): 194–213.
[21]	Zmijewski. Credit risk assessment model for Jordanian commercial banks: Neural scoring approach [J]. Review of Development Finance, 1994, 4 (1): 20-28.
[22]	Mcquown. Non-negative matrix factorization with sparseness constraints for credit risk assessment [C]// IEEE International Conference on Grey Systems & Intelligent Services. IEEE, 2005.
[23]	Arora, et al. A Least Squares Fuzzy SVM Approach to Credit Risk Assessment [M]// Bio-Inspired Credit Risk Analysis. Springer Berlin Heidelberg, 2009.
[24]	Cao. APPLICATION OF ANT COLONY OPTIMIZATION TO CREDIT RISK ASSESSMENT [J]. New Mathematics and Natural Computation, 2012, 04 (01): 107-122.
[25]	Korablev, Dwyer. Risk Management for Central Banks and Other Public Investors \|\| Risk mitigation measures and credit risk assessment in central bank policy operations [J]. 2017, 10.1017/CBO9780511575716 (8): 303-339.

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APA Style

Yan Bingzheng, Bai Puxian. (2021). Research on Credit Risk Assessment of Commercial Banks Based on KMV Model. Social Sciences, 10(5), 204-217. https://doi.org/10.11648/j.ss.20211005.11

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ACS Style

Yan Bingzheng; Bai Puxian. Research on Credit Risk Assessment of Commercial Banks Based on KMV Model. Soc. Sci. 2021, 10(5), 204-217. doi: 10.11648/j.ss.20211005.11

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AMA Style

Yan Bingzheng, Bai Puxian. Research on Credit Risk Assessment of Commercial Banks Based on KMV Model. Soc Sci. 2021;10(5):204-217. doi: 10.11648/j.ss.20211005.11

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@article{10.11648/j.ss.20211005.11,
  author = {Yan Bingzheng and Bai Puxian},
  title = {Research on Credit Risk Assessment of Commercial Banks Based on KMV Model},
  journal = {Social Sciences},
  volume = {10},
  number = {5},
  pages = {204-217},
  doi = {10.11648/j.ss.20211005.11},
  url = {https://doi.org/10.11648/j.ss.20211005.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ss.20211005.11},
  abstract = {Under the background of the new normal economy, the financing mode of local governments has changed, which has led to the emergence of a new pattern in China's financing market. Under this background, banks are facing the development pressure of subject diversification, financial disintermediation and cross-border competition. Commercial banks also actively promote the compliance development of emerging businesses such as financial market through the "comprehensive direction", so as to form a balanced and complementary development situation with traditional credit business. Therefore, this paper taking credit risk and related theories as the starting point, KMV model is selected to study domestic bank risk monitoring. Then, China Merchants Bank is taken as the research object, Shengjing Bank, Harbin Bank, Qingdao Bank and Chongqing Bank are taken as horizontal comparison. The data sampling time span is January to December 2018, as vertical comparison. Five banks were evaluated for credit risk, and it was found that the default distance of China Merchants Bank was relatively small and the default probability was relatively large. However, according to the calculation and analysis of KMV model, the default probability and return volatility of China Merchants Bank rank first among the five commercial banks, but at the same time, it maintains a high net profit margin of 129.3% in 2018, which shows that China Merchants Bank has a strong ability to control credit risk.},
 year = {2021}
}

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TY  - JOUR
T1  - Research on Credit Risk Assessment of Commercial Banks Based on KMV Model
AU  - Yan Bingzheng
AU  - Bai Puxian
Y1  - 2021/09/10
PY  - 2021
N1  - https://doi.org/10.11648/j.ss.20211005.11
DO  - 10.11648/j.ss.20211005.11
T2  - Social Sciences
JF  - Social Sciences
JO  - Social Sciences
SP  - 204
EP  - 217
PB  - Science Publishing Group
SN  - 2326-988X
UR  - https://doi.org/10.11648/j.ss.20211005.11
AB  - Under the background of the new normal economy, the financing mode of local governments has changed, which has led to the emergence of a new pattern in China's financing market. Under this background, banks are facing the development pressure of subject diversification, financial disintermediation and cross-border competition. Commercial banks also actively promote the compliance development of emerging businesses such as financial market through the "comprehensive direction", so as to form a balanced and complementary development situation with traditional credit business. Therefore, this paper taking credit risk and related theories as the starting point, KMV model is selected to study domestic bank risk monitoring. Then, China Merchants Bank is taken as the research object, Shengjing Bank, Harbin Bank, Qingdao Bank and Chongqing Bank are taken as horizontal comparison. The data sampling time span is January to December 2018, as vertical comparison. Five banks were evaluated for credit risk, and it was found that the default distance of China Merchants Bank was relatively small and the default probability was relatively large. However, according to the calculation and analysis of KMV model, the default probability and return volatility of China Merchants Bank rank first among the five commercial banks, but at the same time, it maintains a high net profit margin of 129.3% in 2018, which shows that China Merchants Bank has a strong ability to control credit risk.
VL  - 10
IS  - 5
ER  -

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Author Information

Yan Bingzheng

College of Professional Study, Northeastern University, Boston, United States
Bai Puxian

School of Business, University of Sydney, Sydney, Australia

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APA Style

Yan Bingzheng, Bai Puxian. (2021). Research on Credit Risk Assessment of Commercial Banks Based on KMV Model. Social Sciences, 10(5), 204-217. https://doi.org/10.11648/j.ss.20211005.11

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ACS Style

Yan Bingzheng; Bai Puxian. Research on Credit Risk Assessment of Commercial Banks Based on KMV Model. Soc. Sci. 2021, 10(5), 204-217. doi: 10.11648/j.ss.20211005.11

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AMA Style

Yan Bingzheng, Bai Puxian. Research on Credit Risk Assessment of Commercial Banks Based on KMV Model. Soc Sci. 2021;10(5):204-217. doi: 10.11648/j.ss.20211005.11

Copy | Download

@article{10.11648/j.ss.20211005.11,
  author = {Yan Bingzheng and Bai Puxian},
  title = {Research on Credit Risk Assessment of Commercial Banks Based on KMV Model},
  journal = {Social Sciences},
  volume = {10},
  number = {5},
  pages = {204-217},
  doi = {10.11648/j.ss.20211005.11},
  url = {https://doi.org/10.11648/j.ss.20211005.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ss.20211005.11},
  abstract = {Under the background of the new normal economy, the financing mode of local governments has changed, which has led to the emergence of a new pattern in China's financing market. Under this background, banks are facing the development pressure of subject diversification, financial disintermediation and cross-border competition. Commercial banks also actively promote the compliance development of emerging businesses such as financial market through the "comprehensive direction", so as to form a balanced and complementary development situation with traditional credit business. Therefore, this paper taking credit risk and related theories as the starting point, KMV model is selected to study domestic bank risk monitoring. Then, China Merchants Bank is taken as the research object, Shengjing Bank, Harbin Bank, Qingdao Bank and Chongqing Bank are taken as horizontal comparison. The data sampling time span is January to December 2018, as vertical comparison. Five banks were evaluated for credit risk, and it was found that the default distance of China Merchants Bank was relatively small and the default probability was relatively large. However, according to the calculation and analysis of KMV model, the default probability and return volatility of China Merchants Bank rank first among the five commercial banks, but at the same time, it maintains a high net profit margin of 129.3% in 2018, which shows that China Merchants Bank has a strong ability to control credit risk.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Research on Credit Risk Assessment of Commercial Banks Based on KMV Model
AU  - Yan Bingzheng
AU  - Bai Puxian
Y1  - 2021/09/10
PY  - 2021
N1  - https://doi.org/10.11648/j.ss.20211005.11
DO  - 10.11648/j.ss.20211005.11
T2  - Social Sciences
JF  - Social Sciences
JO  - Social Sciences
SP  - 204
EP  - 217
PB  - Science Publishing Group
SN  - 2326-988X
UR  - https://doi.org/10.11648/j.ss.20211005.11
AB  - Under the background of the new normal economy, the financing mode of local governments has changed, which has led to the emergence of a new pattern in China's financing market. Under this background, banks are facing the development pressure of subject diversification, financial disintermediation and cross-border competition. Commercial banks also actively promote the compliance development of emerging businesses such as financial market through the "comprehensive direction", so as to form a balanced and complementary development situation with traditional credit business. Therefore, this paper taking credit risk and related theories as the starting point, KMV model is selected to study domestic bank risk monitoring. Then, China Merchants Bank is taken as the research object, Shengjing Bank, Harbin Bank, Qingdao Bank and Chongqing Bank are taken as horizontal comparison. The data sampling time span is January to December 2018, as vertical comparison. Five banks were evaluated for credit risk, and it was found that the default distance of China Merchants Bank was relatively small and the default probability was relatively large. However, according to the calculation and analysis of KMV model, the default probability and return volatility of China Merchants Bank rank first among the five commercial banks, but at the same time, it maintains a high net profit margin of 129.3% in 2018, which shows that China Merchants Bank has a strong ability to control credit risk.
VL  - 10
IS  - 5
ER  -

Copy | Download