Abstract
With the growth of my country’s economy, the damage caused by the financial risks of listed companies has become more and more serious, which has seriously affected the survival and growth of enterprises. In order to avoid financial risks from harming the company, we need to establish an effective financial crisis early warning model. This article aims to study the construction of financial early warning models based on machine learning technology. Based on the analysis of the characteristics of financial crisis, the functions of financial early warning models, the characteristics of support vector machine algorithms and the principles of index selection, a financial early warning indicator system is constructed, and taking our country’s 117 ST companies and 117 non-ST companies as research samples, a financial early warning model based on support vector machines is established, and finally the model is verified empirically. The results show that the financial early warning model based on support vector machine is effective and financial crisis can be predicted.
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Acknowledgment
1. This Research findings is supported by the special fund of the basic research of the Central University, project number: 16ZDD01.
2. This Research findings is Supported by Science Foundation of Beijing Language and Cultural University (supported by the Fundamental Research Funds for the Central Universities. Approval number:18PT02).
3. This Research findings is supported by Cultivation plan of excellent professional courses and the special fund of the basic research of the Central University, project number: JPZ201907.
4. Construction project of “Ideological and political course” demonstration course for postgraduates of Beijing Language and Culture University, project number: KCSZ202109.
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Du, M., Liu, B., Zhou, H. (2022). Construction of Financial Early Warning Model Based on Machine Learning Technology. In: Sugumaran, V., Sreedevi, A.G., Xu, Z. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. ICMMIA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-031-05237-8_10
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DOI: https://doi.org/10.1007/978-3-031-05237-8_10
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