Controlling Shareholder Characteristics and Corporate Debt Default Risk: Evidence Based on Machine Learning

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.