Academic Journal of Computing & Information Science, 2022, 5(3); doi: 10.25236/AJCIS.2022.050311.
Haoyu Bi1, Shihao Fang2, Xinyi Bi3
1Central South University, Changsha, Hunan, China
2Beijing Institute of Technology, Zhuhai, Guangdong, China
3Hong Kong Baptist University, Hong Kong, China
This paper predicts the default behavior of the financial market, reduces the bad debt rate in bank loans and securities investment, and discovers potential risks in time. The currently used technologies mainly rely on models with static weights, such as simple linear models. The advantage of these algorithms is that they are fast. But in a large number of samples, these algorithms also face inaccurate problems, requiring the use of machine learning modeling methods to train models. This paper proposes a modeling framework for financial data mining algorithms based on random forests, which can accurately predict microscopic behaviors and reduce financial risks. The experimental results show that the method proposed in this paper has certain application value, the prediction accuracy (precision) reaches 85%, and the recall rate (recall) reaches 90%.
Decision tree, Random forest, Logistic regression, Risk prediction
Haoyu Bi, Shihao Fang, Xinyi Bi. Prediction method of financial market risk behavior based on big data mining algorithm. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 3: 78-84. https://doi.org/10.25236/AJCIS.2022.050311.
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