Author(s)

Peng Dong ¹

Affiliation(s)

¹ School of Business, Stevens Institute of Technology, Hoboken, New Jersey, 7030, United States

Corresponding Author

Peng Dong

ABSTRACT

This article investigates the relationship between centralized equity structure and deleveraging in non-financial listed companies in China's Shanghai and Shenzhen A-shares. The results show that a highly concentrated equity structure gives major shareholders stronger ability and motivation to drive deleveraging in the company, thereby having a supportive impact on the company's long-term profitability. This impact is particularly important in financial management, as deleveraging not only reduces financial risks but also helps improve the stability of capital structure. The study also suggests that excessively high levels of debt significantly moderate this main effect, possibly due to the increased financial risk caused by high debt, prompting major shareholders to take deleveraging measures. At the same time, the implementation of mandatory deleveraging policies further strengthens the connection between centralized equity structure and enterprise deleveraging. The heterogeneity analysis results show that this relationship is particularly evident in non-state-owned enterprises and companies with separated ownership, suggesting that policy makers should pay attention to the equity structure and industry characteristics of enterprises when designing deleveraging strategies, in order to formulate more targeted policies.

KEYWORDS

Centralized equity, deleveraging of enterprises, deleveraging policies, separation of ownership, excessive debt

CITE THIS PAPER

Peng Dong, Research on Corporate Bankruptcy Prediction Combining Financial Data and Algorithmic Models Based on the Impact of Deleveraging. Accounting and Corporate Management (2024) Vol. 6: 76-81. DOI: http://dx.doi.org/10.23977/acccm.2024.060410.

REFERENCES

[1] LIU P, LI Y. Comparative analysis of machine learning models for bond default forecasting based on financial data of Chinese listed companies[J]. BCP Business & Management, 2022, 34: 1151-1158.
[2] HUAN Z. On the effectiveness of graph statistics of shareholder relation network in predicting bond default risk[J]. Journal of Control Science and Engineering, 2022, 2022: 8401354.
[3] LEI S, LIANG X, WANG X, et al. A short-term net load forecasting method based on two-stage feature selection and LightGBM with hyperparameter auto-tuning[C]. IEEE/IAS 59th Industrial and Commercial Power Systems Technical Conference, 2023: 1-6.
[4] TANG M, ZENG W, ZHAO R. Corporate credit risk rating model based on financial big data[J]. BCP Business & Management, 2023, 48: 33-42.
[5] Chen, H., Yang, Y., & Shao, C. (2021). Multi-task learning for data-efficient spatiotemporal modeling of tool surface progression in ultrasonic metal welding. Journal of Manufacturing Systems, 58, 306-315. 
[6] Varatharajah, Y., Chen, H., Trotter, A., & Iyer, R. K. (2020). A Dynamic Human-in-the-loop Recommender System for Evidence-based Clinical Staging of COVID-19. In HealthRecSys@ RecSys (pp. 21-22).
[7] Zhang X , Zhao D , Ma Y ,et al.Research on high-precision angular measurement based on machine learning and optical vortex interference technology[J].IOP Publishing Ltd, 2024.DOI:10.1088/1361-6501/ad6207.

Sponsors, Associates, and Links

---

• Information Systems and Economics
  
  
• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Tourism Management and Technology Economy
  
  
• Journal of Computational and Financial Econometrics
  
  
• Financial Engineering and Risk Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Agricultural & Forestry Economics and Management
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics