|Table of Contents|

Research on Financial Crisis of Enterprises Based on Random Survival Forest(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年04期
Page:
1-6
Research Field:
·数学·
Publishing date:

Info

Title:
Research on Financial Crisis of Enterprises Based on Random Survival Forest
Author(s):
Xiao Yeyu1; Zhang Shan2
(1.College of Mathematics,Jilin University,Changchun 130012,China)(2.School of Applied Mathematics,Nanjing University of Finance and Economics,Nanjing 210023,China)
Keywords:
Random Survival Forest; survival analysis; financial crisis early warning
PACS:
O213
DOI:
10.3969/j.issn.1001-4616.2021.04.001
Abstract:
Taking Shanghai and Shenzhen A-share manufacturing listed companies as a sample,the Random Survival Forest model is introduced into the research on corporate financial crisis. It is found that the growth rate of operating income and the profit before interest and tax have the greatest impact on the financial crisis by calculating the ranking of the importance of variables under the two measures. Subsequently,the Random Survival Forest was compared with Cox model,Cox model with backward stepwise variable selection and Lasso-Cox models. The prediction performance of the Random Survival Forest is better than the three Cox models. At the same time,combined with the survival function and cumulative hazard function under the random survival forest,the company is analyzed for the time when the company is ST. The results show that the model has a good early warning function,which can provide a basis for the decision-making of various stakeholders.

References:

[1] 李扬,李竟翔,马双鸽. 不平衡数据的企业财务预警模型研究[J]. 数理统计与管理,2016,35(5):893-906.
[2]马超群,何文. 基于Cox的财务困境时点预测模型研究[J]. 统计与决策,2010(21):38-42.
[3]王小燕,袁欣. 基于惩罚组变量选择的Cox财务危机预警模型[J]. 系统工程,2018,36(3):113-121.
[4]KALBFLEISCH J D,PRENTICE R L. The statistical analysis of failure time data[M]. 2nd ed. New Jersey:John Wiley & Sons,Inc,2002.
[5]SUN J. The statistical analysis of interval-censored failure time data[M]. New York:Springer,2006.
[6]高珍,柯阿香,余荣杰,等. 基于随机生存森林的交通事件持续时间预测[J]. 同济大学学报(自然科学版),2017,45(9):1304-1310.
[7]ISHWARAN H,KOGALUR U B,BLACKSTONE E H,et al. Random survival forests[J]. The annals of applied statistics,2008,2(3):841-860.
[8]王呈斌,方匡南,郑陈璐. 基于随机生存森林的房屋贷款逾期研究[J]. 上海金融,2020(2):59-63.
[9]MOGENSEN U B,ISHWARAN H,GERDS T A. uating random forests for survival analysis using prediction error curves[J]. Journal of statistical software,2012,50(11):1-23.
[10]KIM Y,PARK S,LEE J. Integrated survival model for predicting patent litigation hazard[J]. Sustainability,2021,13(4):1763.
[11]ISHWARAN H,KOGALUR U B,GORODESKI E Z,et al. High-dimensional variable selection for survival data[J]. Journal of the American statistical association,2010,105(489):205-217.
[12]鲍新中,陶秋燕,傅宏宇. 基于变量聚类和Cox比例风险模型的企业财务预警研究[J]. 系统管理学报,2015,24(4):517-523,529.

Memo

Memo:
-
Last Update: 2021-12-15