Welcome to Francis Academic Press

Academic Journal of Business & Management, 2022, 4(7); doi: 10.25236/AJBM.2022.040715.

Research on the Credit of High-Yield Corporate Bonds in My country Under Machine Learning


Longyifei Ye, Haolong Wu, Xiuwen Duan

Corresponding Author:
Longyifei Ye

City College, Zhejiang University, Hangzhou, Zhejiang, 310000, China


As an important part of the bond market, corporate bonds have the characteristics of high yield and high risk. This work aims to design an index system of influencing factors through the literature research on the current situation of corporate bond defaults, credit risk related theories and credit risk model theories, using corporate bond default data, and integrate machine learning models and KMV optimization models to construct the credit risk early warning model of my country's high-yield corporate bonds. Investors, regulators and other relevant parties can use this early warning model to more effectively pay attention to the potential default risk of corporate bonds, and carry out corresponding credit risk control to ensure the stable development of the bond market.


High-yield bonds, Support vector machine, KMV-Logistic model, Bond default

Cite This Paper

Longyifei Ye, Haolong Wu, Xiuwen Duan. Research on the Credit of High-Yield Corporate Bonds in My country Under Machine Learning. Academic Journal of Business & Management (2022) Vol. 4, Issue 7: 88-92. https://doi.org/10.25236/AJBM.2022.040715.


[1] Bonfim D. Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics [J]. Journal of banking & finance, 2009, 33(2): 281-299.

[2] Bakshi G, Madan D, Zhang F X. Investigating the role of systematic and firm-specific factors in default risk: Lessons from empirically evaluating credit risk models [J]. The Journal of Business, 2006, 79(4): 1955-1987.

[3] Bhojraj S, Sengupta P. Effect of corporate governance on bond ratings and yields: The role of institutional investors and outside directors [J]. The journal of Business, 2003, 76(3): 455-475.

[4] Li W, Li J. Equity, board governance and corporate credit of Chinese listed companies [J]. Management World, 2003 (9): 103-109.

[5] Wang D, Zhang X, Zhang J. Corporate debt term structure and default risk [J]. Journal of Management Science, 2009, 12(2): 77-87.

[6] Zhou H Yang M, Li Y A review of research on influencing factors of corporate bond credit risk [J]. Economics Dynamics, 2010 (12): 137-140.

[7] Yu X. Research on the influencing factors of corporate bond default based on logistic model [J]. Times Finance, 2017 (18): 194.

[8] Beaver W H. Financial ratios as predictors of failure [J]. Journal of accounting research, 1966: 71-111.

[9] Altman E I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy [J]. The journal of finance, 1968, 23(4): 589-609.

[10] Ohlson J A. Financial ratios and the probabilistic prediction of bankruptcy [J]. Journal of accounting research, 1980: 109-131.

[11] Zhang L, Hu H, Zhang D. A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance [J]. Financial Innovation, 2015, 1(1): 1-21.

[12] Tian J W, Wu K J, Zhuo Z G, et al. Application of support vector machine and Logistic regression model in personal credit prediction [J]. Regional Finance Research, 2018 (11): 25-30.

[13] Shi Y L, Zhang B J, Jiang H. Research on risk identification of P2P online lending platforms [J]. Statistics and Decision, 2018, 16.

[14] Zhao D D, Ding J C. Research on systemic risk early warning in China's banking industry——Modeling analysis based on SVM model [J]. International Business: Journal of University of International Business and Economics, 2019 (4): 100-113.