| Abstrakt: |
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 analyze data from foreign trade listed enterprises in China. The results show that this combined model can achieve more accurate 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. [ABSTRACT FROM AUTHOR] |
