The Study on Credit Risk Warning of Regional Listed Companies in China Based on Logistic Model
The paper aims to propose a new method to state the credit risk characteristics of the regional listed companies in China and makes the listed companies avoid involving in credit crisis. The paper selects fifty-four listed companies of Hebei Province as the research sample and establishes the index...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/6672146 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550964831191040 |
|---|---|
| author | Qingping Zhou Long Wang Li Juan Shugong Zhou Lingli Li |
| author_facet | Qingping Zhou Long Wang Li Juan Shugong Zhou Lingli Li |
| author_sort | Qingping Zhou |
| collection | DOAJ |
| description | The paper aims to propose a new method to state the credit risk characteristics of the regional listed companies in China and makes the listed companies avoid involving in credit crisis. The paper selects fifty-four listed companies of Hebei Province as the research sample and establishes the index system of listed company’s credit risk evaluation from four financial index categories which included profitability, operating capacity, solvency, and growth capability. The paper first filtrates fifteen indexes by using the gray clustering method from the four financial categories and finds out the effective variables of the prediction model. Then the paper predicates the credit risk probability of the listed companies by using the logistic regression model. Finally, by analyzing the financial data of annual reports of fifty-four listed companies in Hebei Province from 2012 to 2017 as sample data, the simulation experiment empirical test is carried out by using SPSS software. The results show that the logistic regression model with gray clustering analysis has high predictive accuracy and has a strong predictive ability to evaluate the credit risk of listed companies. The gray logistics evaluation plays a very good role in financial early warning for regional listed companies in China. |
| format | Article |
| id | doaj-art-3c3a5ed1a30541f5b412812979b45bfb |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-3c3a5ed1a30541f5b412812979b45bfb2025-02-03T06:05:16ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/66721466672146The Study on Credit Risk Warning of Regional Listed Companies in China Based on Logistic ModelQingping Zhou0Long Wang1Li Juan2Shugong Zhou3Lingli Li4Admission and Employment Office, Tangshan Normal University, Tangshan 063000, ChinaAdmission and Employment Office, Tangshan Normal University, Tangshan 063000, ChinaSchool of Mathematics and Computational Science, Tangshan Normal University, Tangshan 063000, ChinaSchool of Mathematics and Computational Science, Tangshan Normal University, Tangshan 063000, ChinaCollege of Physics Science and Technology, Tangshan Normal University, Tangshan 063000, ChinaThe paper aims to propose a new method to state the credit risk characteristics of the regional listed companies in China and makes the listed companies avoid involving in credit crisis. The paper selects fifty-four listed companies of Hebei Province as the research sample and establishes the index system of listed company’s credit risk evaluation from four financial index categories which included profitability, operating capacity, solvency, and growth capability. The paper first filtrates fifteen indexes by using the gray clustering method from the four financial categories and finds out the effective variables of the prediction model. Then the paper predicates the credit risk probability of the listed companies by using the logistic regression model. Finally, by analyzing the financial data of annual reports of fifty-four listed companies in Hebei Province from 2012 to 2017 as sample data, the simulation experiment empirical test is carried out by using SPSS software. The results show that the logistic regression model with gray clustering analysis has high predictive accuracy and has a strong predictive ability to evaluate the credit risk of listed companies. The gray logistics evaluation plays a very good role in financial early warning for regional listed companies in China.http://dx.doi.org/10.1155/2021/6672146 |
| spellingShingle | Qingping Zhou Long Wang Li Juan Shugong Zhou Lingli Li The Study on Credit Risk Warning of Regional Listed Companies in China Based on Logistic Model Discrete Dynamics in Nature and Society |
| title | The Study on Credit Risk Warning of Regional Listed Companies in China Based on Logistic Model |
| title_full | The Study on Credit Risk Warning of Regional Listed Companies in China Based on Logistic Model |
| title_fullStr | The Study on Credit Risk Warning of Regional Listed Companies in China Based on Logistic Model |
| title_full_unstemmed | The Study on Credit Risk Warning of Regional Listed Companies in China Based on Logistic Model |
| title_short | The Study on Credit Risk Warning of Regional Listed Companies in China Based on Logistic Model |
| title_sort | study on credit risk warning of regional listed companies in china based on logistic model |
| url | http://dx.doi.org/10.1155/2021/6672146 |
| work_keys_str_mv | AT qingpingzhou thestudyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT longwang thestudyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT lijuan thestudyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT shugongzhou thestudyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT linglili thestudyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT qingpingzhou studyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT longwang studyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT lijuan studyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT shugongzhou studyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel AT linglili studyoncreditriskwarningofregionallistedcompaniesinchinabasedonlogisticmodel |