scholarly journals A Method of Enterprise Financial Risk Analysis and Early Warning Based on Decision Tree Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xianfu Wei

At present, the domestic and foreign financial crisis early-warning model research will provide only prediction accuracy as the only standard of success for early-warning model, ignoring an important problem, namely, will the financial crisis early-warning model for normal business, compared with the normal enterprise, forecast the financial crisis? This paper reviews the research situation at home and abroad from the perspective of the definition of the enterprise financial crisis, the form of expression, and so on. From the theoretical level, the relationship between the cause of the financial crisis and the change of financial indicators is established by explaining the early-warning theory, early-warning theory of financial crisis, and cost-sensitive learning theory, and the framework of early warning modeling of financial crisis based on decision tree is put forward. The decision tree model is constructed on several training subsets as the base learner so that the decision tree base learner can learn the characteristics of the healthy sample and crisis sample roughly equally. Taking the bond issuing enterprises of manufacturing industry as samples, the empirical comparison shows that the financial warning model based on decision tree integration is more accurate, which indicates that the model can improve the correct identification rate of financial crisis enterprises under the premise of higher overall warning accuracy.

Author(s):  
Liwen Wang ◽  
Soo-Jin Chung

To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.


2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Maotao Lai

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.


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