Summary:
In recent years, the bond market has played an important role in serving the real economy, optimizing resource allocation, and supporting macroeconomic policy regulation. However, since China terminated rigid payments in 2014, bond defaults have occurred frequently. In this context, the identification of bond default risk has become a new and key issue for the capital market and economic development. At the same time, financial technology (fintech) is becoming an important method to enhance the prevention and control of financial risks. In this context, this paper proposes the use of fintech, such as big data and machine learning, to develop an early warning model for bond default risk that fits the current context. This paper systematically explores the performance of big data and machine learning models in predicting bond default risk. In terms of data, in this paper, we examine the general enterprise bonds, enterprise bonds, medium-term notes, and commercial papers issued by China's A-share listed companies in the interbank and exchange markets. We construct a macro and micro mixed big dataset with a total of 1,245 variables, including 15 macroeconomic indicators, 70 enterprise characteristic variables, and 12 bond characteristic variables. Specifically, this paper adds macroeconomic indicators that reflect the willingness and ability of local governments to rescue enterprises subject to bond defaults. In addition, we construct enterprise characteristic variables based on six categories of indicators, including valuation and growth, investment, profit, inertia, transaction friction, and intangible assets. Furthermore, we cross-multiply macro and micro indicators to construct interactive indicators. In terms of model construction, we select 10 machine learning models, including PCA, PLS, Ridge, LASSO, ENet, SVR, RF, GBDT, XGBoost, and AdaBoost. Based on the above models, we examine an early warning model of bond default risk based on big data and machine learning, and explore the economic mechanism behind machine learning. The empirical results show that a machine learning model can predict China's bond default risk better than the classical Altman, Merton, and credit rating models. Moreover, nonlinear machine learning models perform better. The above conclusions remain valid under the modified Diebold-Mariano statistic test. We find that machine learning can distinguish differences in bond risk more effectively than the portfolio analysis method of empirical asset pricing. In addition, we determine that the advantages of machine learning models over benchmark models increase over time, and that bond default risk prediction gradually requires an increasingly complex modeling process. Finally, this paper establishes an “improved credit rating” based on machine learning spread predictions and finds that increasing rating discrimination can improve the effectiveness of ratings. This paper further explores the economic mechanism behind the model. First, the heterogeneity analysis finds that the machine learning model has stronger predictive ability for bonds with low ratings, long issuance maturities, and high coupon rates, issuances by non-state-owned enterprises, and bonds issued in the interbank market. Moreover, its predictive ability is stronger during periods of higher economic policy uncertainty than during other periods. Second, the variable importance analysis reveals that indicators related to valuation and growth, investment, profit, intangible assets, and bond characteristics provide good warning signals of default risk in the context of machine learning, but the role of the inertia and transaction friction indicators (other than stock liquidity) is relatively insignificant. Third, machine learning models can achieve accurate predictions through default bond identification, short-term signal identification (bond trading volume), and long-term feature identification (financing constraints, internal control), and their sensitivity to “negative information” is better than that of classical models. The contributions of this paper are as follows. First, this paper promotes the application of big data and machine learning in finance. From a theoretical perspective, we point out that classical models lack dynamic time-varying parameters and variable diversity, and therefore proceed to empirically test the effectiveness of using machine learning models. Second, this paper expands the research perspective on bond default risk. We use the continuous variable “credit spread” to quantify bond risk, and enrich bond default risk identification from a high-dimensional perspective. Third, this paper deepens the discussion on credit rating in China's bond market and finds that improving rating discrimination can improve rating effectiveness. Based on our research, future studies could further expand the prediction indicators for bond default risk from additional perspectives, such as text analysis, and use more advanced machine learning and deep learning models to improve the accuracy of predictions of bond default risk.
姜富伟, 林奕皓, 马甜. “去刚兑”背景下的企业债券违约风险:机器学习预警和经济机制探究[J]. 金融研究, 2023, 521(10): 85-103.
JIANG Fuwei, LIN Yihao, MA Tian. Research on an Early Warning Model of Corporate Bond Default and its Economic Mechanism Based on Machine Learning. Journal of Financial Research, 2023, 521(10): 85-103.
[1]陈诗一,2008,《德国公司违约概率预测及其对我国信用风险管理的启示》,《金融研究》第8期,第53~71页。 [2]丁志国、丁垣竹和金龙,2021,《违约边界与效率缺口:企业债务违约风险识别》,《中国工业经济》第4期,第175~192页。 [3]黄振和郭晔,2021,《央行担保品框架、债券信用利差与企业融资成本》,《经济研究》第1期,第105~121页。 [4]姜富伟、丁慧和靳馥境,2023,《参照点效应、公司治理与上市公司财务重述》,《经济研究》第10期,第201~217页。 [5]姜富伟、马甜和张宏伟,2021,《高风险低收益？ 基于机器学习的动态CAPM模型解释》,《管理科学学报》第1期,第109~126页。 [6]姜富伟、薛浩和周明,2022,《大数据提升了多因子模型定价能力吗？——基于机器学习方法对我国A股市场的探究》,《系统工程理论与实践》第8期,第2037~2048页。 [7]寇宗来、盘宇章和刘学悦,2015,《中国的信用评级真的影响发债成本吗？》,《金融研究》第10期,第81~98页。 [8]李斌、邵新月和李玥阳,2019,《机器学习驱动的基本面量化投资研究》,《中国工业经济》第8期,第61~79页。 [9]林晚发、刘岩和赵仲匡,2022,《债券评级包装与“担保正溢价”之谜》,《经济研究》第2期,第192~208页。 [10]刘晓光和刘元春,2019,《杠杆率、短债长用与企业表现》,《经济研究》第7期,第127~141页。 [11]陆瑶和施函青,2022,《我国科技企业融资的决定因素研究——基于科创板企业的机器学习分析》,《金融研究》第9期,第132~151页。 [12]马甜、姜富伟和唐国豪,2022,《深度学习与中国股票市场因子投资——基于生成式对抗网络方法》,《经济学(季刊)》第3期,第819~842页。 [13]史永东、郑世杰和袁绍锋,2021,《中债估值识别了债券信用风险吗？——基于跳跃视角的实证分析》,《金融研究》第7期,第115~133页。 [14]汪莉和陈诗一,2015,《政府隐性担保、债务违约与利率决定》,《金融研究》第9期,第66~81页。 [15]杨国超和刘琪,2022,《中国债券市场信用评级制度有效性研究》,《经济研究》第10期,第191~208页。 [16]杨子晖、张平淼和林师涵,2022,《系统性风险与企业财务危机预警——基于前沿机器学习的新视角》,《金融研究》第8期,第152~170页。 [17]Altman E. I.,G. Haldeman Robert and P. Narayanan, 1977, “Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations”, Journal of Banking ＆ Finance, 10, pp.29~54. [18]Altman E. I., 1968, “Financial ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy”, The Journal of Finance, 23(4), pp.589~609. [19]Bali T. G.,A. Goyal,D. Huang et al, 2022, “Predicting Corporate Bond Returns: Merton Meets Machine Learning”, Georgetown McDonough School of Business Research Paper: 20~110. [20]Bharath S. T. and T. Shumway, 2008, “Forecasting Default with the Merton Distance to Default Model”, The Review of Financial Studies, 21(3), pp.1339~1369. [21]Bianchi D.,M. Büchner and A. Tamoni, 2021, “Bond Risk Premiums with Machine Learning”, The Review of Financial Studies, 34(2), pp.1046~1089. [22]Brogaard J.,D. Li and Y. Xia, 2017, “Stock Liquidity and Default Risk”, Journal of Financial Economics, 124(3), pp.486~502. [23]Fama E. F. and K. R. French, 1993, “Common Risk Factors in the Returns on Stocks and Bonds”, Journal of Financial Economics, 33(1), pp.3~56. [24]Gu S.,B. Kelly and D. Xiu, 2020, “Empirical Asset Pricing via Machine Learning”, The Review of Financial Studies, 33(5), pp.2223~2273. [25]Jiang X. and F. Packer, 2019, “Credit Ratings of Chinese Firms by Domestic and Global Agencies: Assessing the Determinants and Impact”, Journal of Banking ＆ Finance, 105, pp.178~193. [26]Leippold M.,Q. Wang and W. Zhou, 2022, “Machine Learning in the Chinese Stock Market”, Journal of Financial Economics, 145(2), pp.64~82. [27]Livingston M.,W. P. H. Poon and L. Zhou, 2018, “Are Chinese Credit Ratings Relevant？ A Study of the Chinese Bond Market and Credit Rating Industry”, Journal of Banking ＆ Finance, 87, pp.216~232. [28]Merton R. C., 1974, “On the Pricing of Corporate Debt: the Risk Structure of Interest Rates”, The Journal of Finance, 29(2), pp.449~470.
