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Research on Early Warning of Financial Risk of Local Financial Enterprises

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DOI: 10.23977/ferm.2022.050604 | Downloads: 51 | Views: 454


Liquan Yang 1, Zhuosheng Zhong 2


1 School of Economics, Shandong University, Jinan, 250100, China
2 School of Information, Central University of Finance and Economics, Beijing, 102206, China

Corresponding Author

Liquan Yang


In recent years, the situation of the domestic financial industry is becoming more and more severe, especially the small and medium-sized local financial enterprises have been seriously impacted, and this situation is mostly due to the lack of early warning measures for financial risks, so financial risk early warning is very crucial for financial enterprises. In this paper, the financial index system of local financial enterprises is established from the five dimensions: operational solvency, profitability, development ability, local financial enterprise indexes and other important indexes. Random forests establish the model. Although the model has good accuracy, the model is poor in interpretability. Then the method of Fischer discriminant based on random forest optimization is adopted to build the model, which improves the interpretability of the model and obtains a relatively good accuracy. At the end of the paper, according to the established model for developing small and medium-sized local financial enterprises, put forward some suggestions, such as paying attention to development, adapting to change and so on.


Local Financial Enterprises, Financial Early-Warning Model, Random Forests, Fisher discriminant approach, Leave-One-Out Cross Validation


Liquan Yang, Zhuosheng Zhong, Research on Early Warning of Financial Risk of Local Financial Enterprises. Financial Engineering and Risk Management (2022) Vol. 5: 27-33. DOI:


[1] Wang Xiaoyan, Yao Jiahan. Punishment constraints based on clustering analysis financial risk early warning model [J]. Journal of statistics and decision, 2020, 36(02): 153-156. DOI: 10.13546/j.cnki.tjyjc.2020.02.035.
[2] Guan Yin, Li Xinyue, Zhu Jiaming. Based on Bayes discriminant method for manufacturing the early financial warning and econometric analysis [J]. Journal of neijiang normal college, 2019, 36(06): 68-72. The DOI: 10.13603 / j. cnki.51-1621 / z. 2019.06.012.
[3] Zhang Lu, Liu Jiapeng, Jiang Minqi. Integrated machine learning model in early financial warning of unbalanced sample application [J]. Journal of electronic technology applications, 2021, 47 (08): 34-38, DOI: 10.16157/j.issn. 0258-7998.201234.
[4] Zhang Yinjie, Chuai Jinhua, Zhai Xiaohui. Binary prediction of malware infection based on ensemble learning algorithm [J]. Computer technology and development, 2021, 31(05): 15-20.
[5] Liu Xin, Zhang Weijun, Shi Quan, Zhou Le. Optimization of operation parameters of blast furnace based on data mining and cleaning [J]. Journal of Northeastern University (natural science edition), 2020, 41(08): 1153-1160.
[6] ZHANG Qian. Research on housing rent prediction model based on random forest regression model [D]. Northeast Normal University, 2019.

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