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

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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Publication Details

Journal
Frontiers in Science and Engineering
Published
2024-09-22
DOI
https://doi.org/10.54691/5hw3jd51
Primary Topic
Advanced Decision-Making Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Han Yu, Haixin Zhang, Jingting Liu, Qian Zheng
Frontiers in Science and Engineering
Advanced Decision-Making Techniques
article

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

Han Yu, Haixin Zhang, Jingting Liu, Qian Zheng
article en

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.

Frontiers in Science and EngineeringVol. 4(9)
Anhui University
Openalex Percentile: Top 75%
Advanced Decision-Making Techniques
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Research on Random Forest Model and Support Vector Machine Model in Financial Risk Early Warning of Listed Companies — Han Yu, Haixin Zhang, et al. · Frontiers in Science and Engineering (2024) | TGRS Research Map | TGRS