Controlling Shareholder Characteristics and Corporate Debt Default Risk: Evidence Based on Machine Learning. Issue 12 (26th September 2022)

• Record Type: Journal Article
• Authors: Wang, Di; Wu, Zhanchi; Zhu, Bangzhu
• Is Part Of: Emerging markets finance & trade. Volume 58:Issue 12(2022)
• Page Start: 3324
• Page End: 3339
• Publication Date: 2022-09-26
• Subjects: Machine learning -- GWO-BP neural network -- controlling shareholder characteristics -- debt default; Balkan Peninsula -- Commerce -- Periodicals; Balkan Peninsula -- Foreign economic relations -- Periodicals; Europe, Central -- Commerce -- Periodicals; Europe, Central -- Foreign economic relations -- Periodicals; Europe, Eastern -- Commerce -- Periodicals; Europe, Eastern -- Foreign economic relations -- Periodicals; Turkey -- Commerce -- Periodicals; Turkey -- Foreign economic relations -- Periodicals; Asia -- Commerce -- Periodicals; Asia -- Foreign economic relations -- Periodicals; Eurasia -- Commerce -- Periodicals; Eurasia -- Foreign economic relations -- Periodicals; 382.05
• DOI: 10.1080/1540496X.2022.2037416
• Languages: English
• ISSNs: 1540-496X

Abstract

ABSTRACT: The influence of controlling shareholder characteristics on corporate risk has been a popular topic for discussion in academic and theoretical circles. However, current research lacks systematic and quantitative conclusions based on predictive ability, as it only focuses on the causal relationship between a single characteristic of the controlling shareholder and corporate risk. This paper utilizes the back propagation neural network based on gray wolf algorithm (GWO-BP) method in the machine learning algorithm for the first time and takes the listed companies that publicly issue bonds in the Chinese bond market as a research sample. It summarizes the qualities of controlling shareholders from the perspective of controlling shareholders' risk-taking and benefits expropriation and examines multi-dimensional controlling shareholder characteristics for predicting the debt default risk of companies. This research established that: (1) Overall, the characteristics of controlling shareholders can improve the ability to predict the debt default of a company; (2) The features of the investment portfolio of the controlling shareholder have a higher degree of predicting the debt default risk of a company, while the properties of equity structure and related transactions have a lower degree of predicting the risk of corporate debt default.This research not only uses machine learning methods to study controlling shareholders in China from a more comprehensive perspective but also provides a useful incentive for bondholders to protect their interests.