Comprehensive financial health assessment using Advanced machine learning techniques: Evidence based on private...

Area of Science:

• Financial analysis
• Machine learning applications in finance
• Corporate finance

Background:

• Assessing financial health (FH) in privately-owned companies is crucial for investment decisions and risk mitigation.
• Existing models like the Altman Z-score may not fully capture the complexities of emerging markets like ChiNext.
• Identifying financial fraud and earnings management requires robust and specific assessment tools.

Purpose of the Study:

• To develop and validate a specific, measurable framework for assessing the financial health (FH) of ChiNext-listed private companies.
• To enhance investor ability to identify financially sound enterprises and avoid losses.
• To provide a superior alternative to existing financial soundness prediction models.

Main Methods:

• Utilized gradient boosting machines and random forests for predictive modeling.
• Developed a framework incorporating four pairs of financial indicators and two non-financial indicators.
• Employed iterative learning to ensure model robustness and prevent overfitting.

Main Results:

• The developed model achieved a 96% accuracy rate in identifying 72 out of 75 sub-healthy or unhealthy companies.
• The framework significantly outperformed the Altman Z-score model in predicting financial soundness.
• The model demonstrated high accuracy and robustness, validated with 2022 data.

Conclusions:

• The proposed framework offers a practical and effective solution for assessing the financial health of private companies.
• The study provides valuable insights for enterprise managers, investors, and policymakers in financial decision-making.
• The model's success suggests potential for cross-market applications in financial risk management.