Exploring the financial indicators to improve the pattern recognition of economic data based on machine learning

• Published in: Neural computing & applications Vol. 33; no. 2; pp. 723 - 737
• Main Authors: Wei, Xiaohui, Chen, Wanling, Li, Xiao
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Economic development; Economics; Financing; Image Processing and Computer Vision; Indicators; Machine learning; Multiple regression models; Pattern recognition; Probability and Statistics in Computer Science; S.I. : DPTA Conference 2019; Statistical analysis; Statistical models; Pattern recognition; Data mining; Economic data; Machine learning
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-05094-0

Various economic data in the financial market need to be pattern-recognized to improve the efficiency of economic data pattern recognition, further improve the accuracy of economic-related decisions, and promote stable economic development. Based on machine learning technology, this study establishes a statistical model by establishing a multiple regression model to extract financial indicators that have significant effects on the financing trade of listed companies. Moreover, this study provides a preliminary empirical model for judging whether a company conducts financing trade based on some company’s financial indicators and uses data to verify the consistency of the model. In addition, this study conducts research and demonstration of the algorithm model of this research through empirical research. The research results show that the model shows high reliability and validity in accurately identifying whether the enterprise has the characteristics of conducting financing trade.