Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (12): 334-345.DOI: 10.3778/j.issn.1002-8331.2310-0298
• Engineering and Applications • Previous Articles Next Articles
DENG Shangkun, NING Hong, LIU Zonghua, ZHU Yingke
Online:
2024-06-15
Published:
2024-06-14
邓尚昆,宁宏,刘宗华,朱应可
DENG Shangkun, NING Hong, LIU Zonghua, ZHU Yingke. Interpretable Machine Learning Model for Default Risk Identification of Corporate Bonds[J]. Computer Engineering and Applications, 2024, 60(12): 334-345.
邓尚昆, 宁宏, 刘宗华, 朱应可. 企业债券违约风险识别的可解释机器学习模型研究[J]. 计算机工程与应用, 2024, 60(12): 334-345.
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[1] LIU P, LI Y. Comparative analysis of machine learning models for bond default forecasting based on financial data of Chinese listed companies[J]. BCP Business & Management, 2022, 34: 1151-1158. [2] HUAN Z. On the effectiveness of graph statistics of shareholder relation network in predicting bond default risk[J]. Journal of Control Science and Engineering, 2022: 8401354. [3] ALLEGRE J, RAYMOND H, RHARRABTI H. The impact of the European sovereign debt crisis on banks stocks. Some evidence of shift contagion in Europe[J]. Journal of Banking & Finance, 2017, 74: 24-37. [4] 吴世农, 卢贤义. 我国上市公司财务困境的预测模型研究[J]. 经济研究, 2001(6): 46-55. WU S N, LU X Y. A study of models for predicting financial distress in China’s listed companies[J]. Economic Research Journal, 2001(6) : 46-55. [5] 丁志国, 丁垣竹, 金龙. 违约边界与效率缺口: 企业债务违约风险识别[J]. 中国工业经济, 2021(4): 175-192. DING Z G, DING Y Z, JIN L. Default boundary and efficiency gap: debt default risk identification for enterprises[J]. China’s Industrial Economics, 2021(4): 175-192. [6] ZEITUN R, TIAN G G. Does ownership affect a firm’s performance and default risk in Jordan? corporate governance[J]. The International Journal of Business in Society, 2007, 7(1): 66-82. [7] 李萌, 王近. 内部控制质量与企业债务违约风险[J]. 国际金融研究, 2020(8): 77-86. LI M, WANG J. Internal control quality and enterprises’ debt default risk[J]. Studies of International Finance, 2020(8): 77-86. [8] 丁志国, 耿迎涛, 赵晶, 等. 上市公司财务困境时间效应的实证判别与理论猜想[J]. 会计研究, 2018(2): 62-68. DING Z G, GENG Y T, ZHAO J, et al. An empirical study and theoretical conjecture on the time effect of financial distress of China’s listed companies[J]. Accounting Research, 2018 (2): 62-68. [9] 刘晓, 周荣喜, 李玉茹. 基于Stacking算法集成的我国信用债违约预测[J]. 运筹与管理, 2023, 32(3): 163-170. LIU X, ZHOU R X, LI Y R. Default prediction of credit bond in China based on stacking algorithm integrated model[J]. Operations Research and Management Science, 2023, 32(3): 163-170. [10] SARKAR S. Can tax convexity be ignored in corporate financing decisions[J]. Journal of Banking & Finance, 2008, 32(7): 1310-1321. [11] 郭斌, 戴小敏, 曾勇, 等. 我国企业危机预警模型研究——以财务与非财务因素构建[J]. 金融研究, 2006(2): 78-87. GUO B, DAI X M, ZENG Y, et al. Warning model of Chinese enterprises: building financial and non-financial factors[J]. Journal of Financial Research, 2006(2): 78-87. [12] 谢玲玲, 范龙振. 上市公司信用债违约风险评估[J]. 管理现代化, 2020, 40(2): 104-108. XIE L L, FAN L Z. Assessment of listed companies’ debt securities[J]. Modernization of Management, 2020, 40(2): 104-108. [13] 庞春潮, 罗苑玮. 基于投资者视角的信用类债券违约风险预警[J]. 统计与决策, 2023, 39(2): 152-157. PANG C C, LUO Y W. Early warning of credit bond default risk from the investor’s perspective[J]. Statistics and Decision, 2023, 39(2): 152-157. [15] ALTMAN E I. Financial ratios discriminant analysis and the prediction of corporate bankruptcy[J]. Journal of Finance, 1968, 23: 589-609. [14] 郑煜, 吴世农. 基于财务信息和非财务信息的债券违约预警模型研究——Fisher模型与Logistic模型的实证分析与应用[J]. 财会月刊, 2023, 44(12): 22-29. ZHENG Y, WU S N. A study on bond default early warning models based on financial and non-financial information—empirical analysis and application of fisher model and logistic model[J]. Finance and Accounting Monthly, 2023, 44(12): 22-29. [16] MERTON R C. On the pricing of corporate debt: the risk structure of interest rates[J]. Journal of Finance, 1974, 29(2): 449-470. [17] JARROW R A, TURNBULL S M. Pricing derivatives on financial securities subject to credit risk[J]. Journal of Finance, 1995, 50: 53-85. [18] HUAN J Z, HUANG M. How much of the corporate-treasury yield spread is due to credit risk?[J]. Review of Asset Pricing Studies, 2012, 2(2): 153-202. [19] DUFFIE D, SINGLETON K J. Modeling term structures of defaultable bonds[J]. Review of Financial Studies, 1999, 12: 687-720. [20] 梁龙跃, 王浩竹. 基于图卷积神经网络的个人信用风险预测[J]. 计算机工程与应用, 2023, 59(17): 275-285. LIANG L Y, WANG H Z. Personal credit risk prediction based on graph convolutional network[J]. Computer Engineering and Applications, 2023, 59(17): 275-285. [21] 贾颖, 赵峰, 李博, 等. 贝叶斯优化的XGBoost信用风险评估模型[J]. 计算机工程与应用, 2023, 59(20): 283-294. JIA Y, ZHAO F, LI B, et al. XGBoost optimized by Bayesian optimization for credit scoring[J]. Computer Engineering and Applications, 2023, 59(20): 283-294. [22] 王玉龙, 周榴, 张涤霏. 企业债务违约风险预测——基于机器学习的视角[J]. 财政科学, 2022(6): 62-74. WANG Y L, ZHOU L, ZHANG D F. Enterprise debt default risk prediction—based on the perspective of machine learning[J]. Fiscal Science, 2022(6): 62-74. [23] KE G, MENG Q, FINLEY T, et al. LightGBM: a highly efficient gradient boosting decision tree[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17), Red Hook, NY, USA, 2017: 3149-3157. [24] 马晓君, 沙靖岚, 牛雪琪. 基于LightGBM算法的P2P项目信用评级模的设计及应用[J]. 数量经济技术经济研究, 2018(5): 144-160. MA X J, SHA J L, NIU X Q. Design and application of P2P project credit rating model based on LightGBM algorithm[J]. The Journal of Quantitative & Technical Economics, 2018(5): 144-160. [25] FEURE M, HUTTER F. Hyperparameter optimization[M]// Automated machine learning. Cham: Springer, 2019: 3-33. [26] MA Y, YUN W. Research progress of genetic algorithm[J]. Application Research of Computers, 2012, 29: 1201-1206. [27] KENNEDY J, EBERHAR R. Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks, 1995: 1942-1948. [28] DEB K, AGRAWAL S, PRATAP A, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6: 182-197. [29] LUNDBERG S M, LEE S. A unified approach to interpreting model predictions[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17), Red Hook, NY, USA, 2017: 4768-4777. [30] 徐舒玥, 曹艳华. 基于可解释机器学习的信用债违约研究[J]. 科学决策, 2023(5): 190-200. XU S Y, CAO Y H. Credit debt default research based on explainable machine learning[J]. Scientific Decision Making, 2023(5): 190-200. [31] LEI S, LIANG X, WANG X, et al. A short-term net load forecasting method based on two-stage feature selection and LightGBM with hyperparameter auto-tuning[C]//IEEE/IAS 59th Industrial and Commercial Power Systems Technical Conference, 2023: 1-6. [32] TANG M, ZENG W, ZHAO R. Corporate credit risk rating model based on financial big data[J]. BCP Business & Management, 2023, 48: 33-42. [33] DEMIR S, ?AHHIN E K. Predicting occurrence of liquefaction-induced lateral spreading using gradient boosting algorithms integrated with particle swarm optimization: PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost[J]. Acta Geotechnica, 2023, 18: 3403-3419. [34] 朱云伟, 黄海松, 魏建安. 基于GA-LightGBM的刀具磨损状态在线识别[J]. 组合机床与自动化加工技术, 2021(10): 83-87. ZHU Y W, HUANG H S, WEI J A. Online tool wear status identification based on GA-LightGBM[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021(10): 83-87. [35] GAO B, BALYAN V. Construction of a financial default risk prediction model based on the LightGBM algorithm[J]. Journal of Intelligent Systems, 2022, 31: 767-779. [36] 周卫华, 翟晓风, 谭皓威. 基于XGBoost的上市公司财务舞弊预测模型研究[J]. 数量经济技术经济研究, 2022, 39(7): 176-196. ZHOU W H, ZHAI X F, TAN H W. Research on financial frauds prediction model of Chinese public companies with XGBoost[J]. Journal of Quantitative & Technological Economics, 2022, 39(7): 176-196. [37] 张宁静, 顾新, 杨铖. P2P校园贷款个人违约风险因素指标探析[J]. 财会月刊, 2018(6): 82-89. ZHANG N J, GU X, YANG C. Analysis on risk factors of individual default of P2P campus loan[J]. Finance and Accounting Monthly, 2018(6): 82-89. [38] 邹旺, 江伟, 冯俊杰, 等. 基于ANN和SVM的轴承剩余使用寿命预测[J]. 组合机床与自动化加工技术, 2021(1): 32-35. ZOU W, JIANG W, FENG J J. Bearing remaining useful life prediction based on artificial neural network and support vector machine[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021(1): 32-35. [39] LI G, SHI Y, ZHANG Z. P2P default risk prediction based on XGBoost, SVM and RF fusion model[C]//Proceedings of the 1st International Conference on Business, Economics, Management Science (BEMS 2019), 2019: 470-475. |
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