计算机工程与应用2024，Vol.60Issue(11)：281-289,9.DOI:10.3778/j.issn.1002-8331.2308-0007
基于SMOTE-XGBoost的外贸企业财务危机预警模型
Research on Financial Crisis Early Warning Model for Foreign Trade Listed Companies Based on SMOTE-XGBoost Algorithm
摘要
Abstract
The operational risk of foreign trade enterprises is increasing under the context of external demand contraction and intensive protectionism,leading to a greater risk of financial crisis.In response to the challenge of low accuracy in predicting financial crises for foreign trade enterprises,the early warning indicator system is optimized,and a combined model based on SMOTE-XGBoost is proposed.Firstly,a financial crisis early warning indicator system is established by integrating financial indicators and macro foreign trade indicators.Secondly,a combined model integrating synthetic minority over-sampling technique(SMOTE)and extreme gradient boosting algorithm(XGBoost)is constructed to ana-lyze data from foreign trade listed enterprises in China.The results show that this combined model can achieve more accu-rate prediction and better overall stability than other models,with superior ACC,recall,F1-score,and AUC.This model can be used to assist foreign trade enterprises in proactively identifying potential financial risks and avoiding falling into financial crises.关键词
财务危机预警/外贸企业/不平衡数据/XGBoostKey words
financial crisis early warning/foreign trade listed companies/imbalanced data/XGBoost分类
信息技术与安全科学引用本文复制引用
吴增源,金灵敏,韩香丽,王泽林,伍蓓..基于SMOTE-XGBoost的外贸企业财务危机预警模型[J].计算机工程与应用,2024,60(11):281-289,9.基金项目
国家社会科学基金重点项目(22AGL002). （22AGL002）
