<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Risks in the financial market are omnipresent, and the operations of listed companies are affected by various factors, so the study of the financial risks of listed companies is also of great significance. In this paper, the statement data of listed companies and the text data of annual reports are used separately. The XGBoost model is used to analyze its classification effect, and the confusion matrix and ROC curve evaluation methods are used to compare the accuracy of the prediction results between the XGBoost model and the GBDT model, which helps corporate managers to identify the financial risks of enterprises in advance, and at the same time, improves the level of operation control. The results show that the accuracy of the XGBoost model fluctuates around 0.85, and the highest accuracy of the model is 0.883 when the number of its features is 21. The results of the confusion matrix assessment show that the accuracy of the prediction results of the risk-free company of the XGBoost model reaches 94.95%, and the accuracy of the prediction results of the XGBoost model increases by 5.15% compared with that of the GBDT model. This is in accordance with the ROC curve evaluation results. Obviously, the XGBoost model has a better prediction effect and a more stable early warning performance, and the use of the XGBoost model can help the managers of listed companies to be informed of the deterioration of the company’s financial situation as early as possible so that they can implement the corresponding operational control measures to reduce losses in time.</p>
</abstract>

Risks in the financial market are omnipresent, and the operations of listed companies are affected by various factors, so the study of the financial risks of listed companies is also of great significance. In this paper, the statement data of listed companies and the text data of annual reports are used separately. The XGBoost model is used to analyze its classification effect, and the confusion matrix and ROC curve evaluation methods are used to compare the accuracy of the prediction results between the XGBoost model and the GBDT model, which helps corporate managers to identify the financial risks of enterprises in advance, and at the same time, improves the level of operation control. The results show that the accuracy of the XGBoost model fluctuates around 0.85, and the highest accuracy of the model is 0.883 when the number of its features is 21. The results of the confusion matrix assessment show that the accuracy of the prediction results of the risk-free company of the XGBoost model reaches 94.95%, and the accuracy of the prediction results of the XGBoost model increases by 5.15% compared with that of the GBDT model. This is in accordance with the ROC curve evaluation results. Obviously, the XGBoost model has a better prediction effect and a more stable early warning performance, and the use of the XGBoost model can help the managers of listed companies to be informed of the deterioration of the company’s financial situation as early as possible so that they can implement the corresponding operational control measures to reduce losses in time.

Corporate Financial Risk Identification and Operation Control Analysis for XGBoost Modeling

Business School of Fuyang Normal University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Risks in the financial market are omnipresent, and the operations of listed companies are affected by various factors, so the study of the financial risks of...

Corporate Financial Risk Identification and Operation Control Analysis for XGBoost Modeling

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 21 mar 2024

Aceptado: 16 jun 2024

DOI: https://doi.org/10.2478/amns-2024-2247

Palabras clave
Financial Risk Identification, XGBoost, Decision Tree, Confusion Matrix Evaluation, ROC Curve Evaluation

© 2024 Yu Guan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Corporate Financial Risk Identification and Operation Control Analysis for XGBoost Modeling

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 21 mar 2024

Aceptado: 16 jun 2024

DOI: https://doi.org/10.2478/amns-2024-2247

Palabras claveFinancial Risk Identification, XGBoost, Decision Tree, Confusion Matrix Evaluation, ROC Curve Evaluation

© 2024 Yu Guan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Financial Risk Identification, XGBoost, Decision Tree, Confusion Matrix Evaluation, ROC Curve Evaluation