Corporate Distress Prediction in China: A Machine Learning Approach

Abstract

Rapid growth and transformation of the Chinese economy and financial markets coupled with escalating default rates, rising corporate debt and poor regulatory oversight motivates the need for more accurate distress prediction modelling in China. Given China's historical, social and cultural intolerance towards corporate failure, this study examines the Special Treatment system introduced by Chinese regulators in 1998. Regulators can assign Special Treatment status to listed Chinese companies for poor financial performance, financial abnormality and other events. Using an advanced machine learning model known as TreeNet® we model more than 90 predictor variables, including financial ratios, market returns, macro‐economic indicators, valuation multiples, audit quality factors, shareholder ownership/control, executive compensation variables, corporate social responsibility metrics and other variables. Based on out‐of‐sample tests, our TreeNet® model is 93.74 percent accurate in predicting distress (a Type I error rate of 6.26 percent) and 94.81 percent accurate in predicting active/healthy companies (a Type II error rate of 5.19 percent). Variables with the strongest predictive value in the TreeNet® model includes market capitalization and annual market returns, macro‐economic variables such as gross domestic product growth, financial ratios such as retained earnings to total assets and return on assets; and certain non‐traditional variables such as executive compensation.