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sis 23(6):

Research Article

Strategies for Analyzing Financial Data of Listed Companies Based on Data Mining

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  • @ARTICLE{10.4108/eetsis.3827,
        author={Panke Xie and Shujuan Zheng},
        title={Strategies for Analyzing Financial Data of Listed Companies Based on Data Mining},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={6},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={10},
        keywords={data mining, financial analysis, cluster analysis},
        doi={10.4108/eetsis.3827}
    }
    
  • Panke Xie
    Shujuan Zheng
    Year: 2023
    Strategies for Analyzing Financial Data of Listed Companies Based on Data Mining
    SIS
    EAI
    DOI: 10.4108/eetsis.3827
Panke Xie1,*, Shujuan Zheng1
  • 1: Modern Business School of Jiaxing Vocational &Technical College
*Contact email: 903016250@qq.com

Abstract

INTRODUCTION: A company's net profit is a significant factor in measuring whether the company is performing well or not. How to improve the company's return on assets, strengthen the company's operations, improve the company's capital structure, enhance the company's marketing strength, and accelerate the company's financing speed is an inevitable choice for the company to avoid falling into a financial crisis. OBJECTIVES: Forecasting the financial crisis of listed companies based on the financial situation of selected listed companies. METHODS: The return on assets, shareholders' equity ratio, return on net worth and other company factors have been studied empirically using data mining techniques. A mathematical model for financial risk identification was developed and evaluated. RESULTS: The results show that the accuracy is above 90%. CONCLUSION: The study found that the lower the return on capital, the higher the financial risk the firm faces; the lower the financial debt ratio, the higher the chance of financial difficulties, and the two are positively correlated.

Keywords
data mining, financial analysis, cluster analysis
Received
2023-10-06
Accepted
2023-10-06
Published
2023-10-06
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.3827

Copyright © 2023 Xie et al., licensed to EAI. This open-access article is distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transforming, and building upon the material in any medium so long as the original work is properly cited.

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