Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Mingxia Jiang,Xuexia Wang,Jian Su

doi:10.1155/2021/3807480

Abstract

Traditional financial crisis prediction approaches have a tough time extracting the properties of financial data, resulting in financial crisis prediction with insufficient accuracy. As a result, based on the random forest algorithm, an intelligent financial crisis prediction approach for listed enterprises is proposed. The random forest method is used to mine the characteristics of financial data based on financial index data from publicly traded companies. This research develops a financial crisis prediction index system based on the findings of data feature mining. The CCR model is used to assess the efficiency of listed firms’ decision-making units with more input and output, and the efficiency index of each decision-making unit is calculated. The efficiency evaluation index of publicly traded companies is used to divide the severity of the financial crisis. The experimental results reveal that, when compared to standard prediction methods, this method’s forecast accuracy is commensurate with the actual state of businesses, and it can reduce the time it takes to predict financial crises.

Highlights

E likelihood of a financial crisis for publicly traded companies is gradually increasing as market competition becomes more intense
Support vector machines and artificial neural networks based on artificial intelligence have been widely applied in the field of financial crisis prediction in recent years, which has improved prediction efficiency significantly. ese methods, are challenging to generate satisfying results due to the imbalance of financial forecasting difficulties and the complexity of data noise and distribution [3,4,5]
Security and Communication Networks financial crisis prediction approach based on particle swarm optimization algorithm and nuclear limit learning machine is proposed in reference [7]

Summary

Mingxia Jiang and Xuexia Wang

Shijiazhuang Information Engineering Vocational College, Shijiazhuang 059000, China. Received 1 September 2021; Revised 28 September 2021; Accepted 18 October 2021; Published 29 October 2021. As a result, based on the random forest algorithm, an intelligent financial crisis prediction approach for listed enterprises is proposed. As a result, based on the random forest algorithm, this research provides a financial crisis prediction approach for the listed enterprises. 2. Intelligent Prediction of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm. E goal of this study is to create an intelligent financial crisis prediction model using financial index data from publicly traded companies and the random forest algorithm in order to generate an accurate prediction of the financial state of target publicly traded companies. E random forest approach is used to produce a similarity matrix by measuring the similarity of financial data samples from publicly traded companies. E sample points of the principal data subject are commonly referred to as remote samples. ey are classified

Random Features Selection for Node Splitting

Calculation formula

Safer Safer Crisis Crisis Crisis Crisis Crisis Safer

Findings

Conclusion