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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

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  • Yuanxin Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing 102206, China)

  • FengYun Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Xinhua Yu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jiahai Yuan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Dong Zhou

    (School of Economics and Management, North China Electric Power University, Beijing 102206, 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 hybrid model for assessing the credit risk of NEE bonds based on factor analysis and logistic regress analysis techniques, and verifies the applicability and effectiveness of the model employing relevant data from 46 Chinese NEEs. The results show that the main factors affecting the credit risk of NEE bonds are internal factors involving the company’s profitability, solvency, operational ability, growth potential, asset structure and viability, and external factors including macroeconomic environment and energy policy support. Based on the empirical results and the exact situation of China’s NEE bonds, this article finally puts forward several targeted recommendations.

Suggested Citation

  • Yuanxin Liu & FengYun Li & Xinhua Yu & Jiahai Yuan & Dong Zhou, 2018. "Assessing the Credit Risk of Corporate Bonds Based on Factor Analysis and Logistic Regress Analysis Techniques: Evidence from New Energy Enterprises in China," Sustainability, MDPI, vol. 10(5), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1457-:d:145006
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    2. Kui Wang & Jie Wan & Gang Li & Hao Sun, 2022. "A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry," Energies, MDPI, vol. 15(14), pages 1-18, July.

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