Open AccessPrediction Method of Enterprise Return on Net Assets Based on Improved Random Forest Algorithm
Author(s) -
Yongsong Cai,
Qi Yin,
Qing Su,
Xinyu Huang,
Yin Zhang,
Ting Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1682/1/012083
Subject(s) - computer science , net (polyhedron) , profitability index , random forest , algorithm , simulated annealing , artificial intelligence , finance , mathematics , economics , geometry
In order to accurately predict the change trend of enterprise assets, this paper proposes a prediction method of enterprise return on net assets based on improved random forest algorithm. Firstly, this paper analyzes the research background and significance of the enterprise net income rate prediction, and on this basis, it considers to use the random forest algorithm to achieve the prediction. In view of the shortcomings of the algorithm, the paper introduces the simulated annealing algorithm to improve the random forest algorithm and optimizes the prediction performance of the algorithm in the direction of feature selection, parameter optimization and weight setting, finally establishes the prediction model of enterprise return on net assets. In this paper, the proposed algorithm is compared with other algorithms and results show that the method proposed in this paper has a good prediction effect on the enterprise return on net assets, which is helpful for the capital market to evaluate the asset value and profitability of enterprises.