2018
DOI: 10.3390/su10051457
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Assessing the Credit Risk of Corporate Bonds Based on Factor Analysis and Logistic Regress Analysis Techniques: Evidence from New Energy Enterprises in China

Abstract: In recent years, new energy sources have ushered in tremendous opportunities for development. The difficulties to finance new energy enterprises (NEEs) can be estimated through issuing corporate bonds. However, there are few scientific and reasonable methods to assess the credit risk of NEE bonds, which is not conducive to the healthy development of NEEs. Based on this, this paper analyzes the advantages and risks of NEEs issuing bonds and the main factors affecting the credit risk of NEE bonds, constructs a h… Show more

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Cited by 8 publications
(13 citation statements)
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“…Classification and regression trees are sensitive to data noise and training sample errors. How the selection of the kernel functions of support vector machines affects the classification accuracy remains uncertain [12]. These shortcomings affect the accuracies of the researches results.…”
Section: Introductionmentioning
confidence: 99%
“…Classification and regression trees are sensitive to data noise and training sample errors. How the selection of the kernel functions of support vector machines affects the classification accuracy remains uncertain [12]. These shortcomings affect the accuracies of the researches results.…”
Section: Introductionmentioning
confidence: 99%
“…Logistic regression is one of the most popular models in credit default prediction due to its simplicity and interpretability [3]. Logistic regression overcomes the limitation of the linear regression model, which requires that the explained variables obey a normal distribution and be continuous.…”
Section: Logistic Regressionmentioning
confidence: 99%
“…The decision boundary ensures the accuracy of correct classification while maximizing the separation between the boundary and the closest samples. The samples nearest to the optimal hyperplane are called support vectors [3]. All other training samples are irrelevant for determining the optimal hyperplane.…”
Section: Support Vector Machinementioning
confidence: 99%
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“…Therefore, the occurrence of financial exposures of listed corporations is not only a harbinger, but also completely predictable. Establishing a resultful financial meltdown warning system, judging the corporation's operating status according to the market performance and financial message of listed corporations, and getting the signal of financial deterioration as soon as possible will help relevant stakeholders to make scientific decisions and urge relevant parties to take resultful measures in time to reduce risks and losses [ 9 ]. Faced with the increasing harmfulness of financial exposures to listed corporations in China, it is a vital question that most listed corporations in China need to solve how to mine message with early warning function from a large amount of financial data generated in the process of business operation [ 10 ].…”
Section: Introductionmentioning
confidence: 99%