Optimization of big data analysis resources supported by XGBoost algorithm: Comprehensive analysis of industry 5.0 and ESG performance

Abstract

To enable state-owned enterprises in Industry 5.0 to better carry out M&A activities, it is important and necessary to provide early warning of M&A risks, which directly affects the interests of both parties and even affects the effectiveness of state-owned enterprise reform. The author proposes the optimization of big data analysis resources supported by the XGBoost algorithm: a comprehensive analysis of Industry 5.0 and ESG performance. Design a comprehensive evaluation system to measure the M&A risk of state-owned listed companies. Using Python programming language to achieve data crawling and processing. Build an early warning model using the XGBoost algorithm. To further evaluate the effectiveness of the early warning model, comparative experiments were conducted. Using multiple linear regression models to study the significant factors of merger and acquisition risk. The experimental results show that the prediction accuracy based on the XGBoost algorithm is 80 %, which performs the best among all models and has stronger reliability and applicability. Conclusion: The return on investment capital, operating profit margin, and net profit from paid consideration are more important and effective in predicting merger and acquisition risks; The total asset turnover rate, return on investment capital, equity balance, and audit quality are more conducive to suppressing merger and acquisition risks.

Keywords

• State-owned enterprises
• Merger and acquisition risk warning
• Influencing factors
• Data mining
• XGBoost algorithm