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  

Systemic Risk and Corporate Financial Distress Forecasting:From the New Perspective of Machine Learning

作　　者：杨子晖[1,2] 张平森 林师涵 Yang Zihui;Zhang Pingmiao;Lin Shihan

机构地区：[1]中山大学岭南学院,广东广州510275 [2]中山大学高级金融研究院,广东广州510275

出　　处：《复印报刊资料（统计与精算）》2023年第1期113-127,共15页STATISTICS AND ACTUARIAL SCIENCE

基　　金：2021年度国家社会科学基金重大项目“‘双循环’新格局下我国金融风险演化及防控措施研究”(项目批准号:21&ZD114)资助。

摘　　要：本文采用Logit回归模型以及随机森林模型、梯度提升模型等前沿机器学习方法,深入考察系统性风险指标对我国企业财务危机的预测能力。结果表明,系统性风险对中下游企业的财务危机具有显著的预测能力,而基于因子分析构建的系统性风险指标,结合随机森林模型可取得更好的预测效果。本文进一步区分财务危机的不同成因并发现,基于随机森林模型和Logit回归模型的预测框架能够对我国大多数财务危机事件进行有效预警。在此基础上,本文对我国上市企业监管提出相关建议,从而为完善金融风险处置机制提供一定参考。The implementation of a financial security strategy was made a high priority in the 14"Five-Year Plan,which includes the aim to"improve financial risk prevention,early warning,handling and accountability sys-tems."Frequent black swan incidents have accentuated the shocks of systemic risk on global production activities and enterprise financial stability.Thus,using systemic risk indicators to improve predictions of financial distress is of academic and practical value.This paper contributes to the research on financial distress forecasting.First,few studies consider the potential impact of systemic risk on production.Thus,we introduce systemic risk indicators into our prediction of financial distress to provide a more comprehensive analysis.Second,research into financial distress mainly focuses on bankruptcy events,although reductions in corporate valuation and creditor losses typically occur before bankruptcies(Gupta and Chaudhry,2019).Instead of bankruptcy events,we regard special treatment(ST)designation as a signal of corporate financial distress,as it increases foresight and enables the early warning of potential corporate crises,thus preventing huge losses for firms and creditors.Third,current prediction models of financial distress mainly forecast bankruptcy events only one month or one quarter ahead,and thus,they cannot provide early warnings.Instead,we predict the risk event one year in advance,which gives regulators sufficient time to intervene.Finally,conventional parametric models are typically applied for predicting bankruptcies,whereas we utilize newly developed machine learning models to improve the accuracy of our prediction,and we provide references for optimizing the early warning system for corporate financial distress.Our sample consists of 3,806 Chinese listed companies,with the time period spanning from January 1,2010 to May 31,2020.All of the data are obtained from the CSMAR and Wind Databases.First,we run logit regressions and find that systemic risk indicators serve as powerful predictors

关 键 词：财务危机 系统性风险 机器学习 部分依赖图 风险防范 

分 类 号：F275[经济管理—企业管理]