Research on Random Forest Model and Support Vector Machine Model in Financial Risk Early Warning of Listed Companies

Authors

  • Jingting Liu
  • Haixin Zhang
  • Yu Han
  • Qian Zheng

DOI:

https://doi.org/10.54691/5hw3jd51

Keywords:

Financial Risk Early Warning; Random Forest Model; SVM Model.

Abstract

On October 30, 2023, the Central Financial Work Conference was held in Beijing. One of the important contents of the meeting was to prevent and control financial market risks. Because the sustainable development of a company is closely related to the financial market , the company faces increasing financial risk challenges , and it is particularly urgent to establish and optimize an effective financial risk early warning mechanism. The establishment of a financial crisis early warning model can not only enable company managers to adjust the company's development strategy and direction in a timely manner, improve the company's performance, and reduce the company's financial risks, but also predict corporate financial crises, which can help investors evaluate the company's potential value and change the direction of investment. This paper takes the establishment of random forest model as the starting point . First, we understand the theoretical framework of financial risk of listed companies; second, we explain the theoretical basis and advantages of establishing random forest model; then, we deeply explore the theory and empirical analysis of risk indicators; then, we use support vector machine in the specific application of financial risk warning; finally , we get the theoretical analysis and empirical significance of its feature importance, as well as the limitations and future research directions of the method under the fusion of random forest model and support vector machine.

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References

[1] Zhou Yanwei. Research on Financial Risk Early Warning of Listed Companies Based on SVM Model[D]. Master's Thesis of Shanghai Normal University, 2018.

[2] Zhang Fei. Construction of financial risk warning model for environmental protection enterprises based on rough sets and random forests [J]. Chinese Agricultural Accounting, 2024(8):51-54.

[3] Duan Yixue, Peng Xuemei, Fang Kuangnan. Research on financial early warning of Chinese property insurance companies - based on random forest financial diagnosis method [J]. Insurance Research, 2024(4):20-33.

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Published

2024-09-22

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Section

Articles