Research on enterprise financial economics early warning based on machine learning method

被引:0
作者
Yi, Jian [1 ]
机构
[1] Sichuan Top IT Vocat Inst, Dept Econ Management, Chengdu, Sichuan, Peoples R China
关键词
Machine learning; early financial warning; combined model; back-propagation; STOCK; SYSTEM;
D O I
10.3233/JCM-215783
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.
引用
收藏
页码:529 / 539
页数:11
相关论文
共 21 条
  • [1] Cfa MAW., 2015, J BANK FINANC, V56, P123
  • [2] Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression
    Chou, Jui-Sheng
    Thi-Kha Nguyen
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (07) : 3132 - 3142
  • [3] A novel hybrid model using teaching-learning-based optimization and a support vector machine for commodity futures index forecasting
    Das, Shom Prasad
    Padhy, Sudarsan
    [J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2018, 9 (01) : 97 - 111
  • [4] Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified technique
    Dash, Rajashree
    Dash, PradiptaKishore
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2016, 52 : 75 - 90
  • [5] Huang C. H., 2015, APPL MECH MAT, V764-765, P7
  • [6] Klibanov M. V., 2015, PAPERS, V32
  • [7] Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection
    Kou, Gang
    Xu, Yong
    Peng, Yi
    Shen, Feng
    Chen, Yang
    Chang, Kun
    Kou, Shaomin
    [J]. DECISION SUPPORT SYSTEMS, 2021, 140
  • [8] Evaluation of clustering algorithms for financial risk analysis using MCDM methods
    Kou, Gang
    Peng, Yi
    Wang, Guoxun
    [J]. INFORMATION SCIENCES, 2014, 275 : 1 - 12
  • [9] Gold price volatility: A forecasting approach using the Artificial Neural Network-GARCH model
    Kristjanpoller, Werner
    Minutolo, Marcel C.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (20) : 7245 - 7251
  • [10] Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks
    Laboissiere, Leonel A.
    Fernandes, Ricardo A. S.
    Lage, Guilherme G.
    [J]. APPLIED SOFT COMPUTING, 2015, 35 : 66 - 74