Financial Distress Early Warning for Chinese Enterprises from a Systemic Risk Perspective: Based on the Adaptive Weighted XGBoost-Bagging Model

Abstract

This paper aims to tackle the problem of low accuracy in predicting financial distress in Chinese industrial enterprises, attributable to data imbalance and insufficient information. It utilizes annual data on systemic risk indicators and financial metrics of Chinese industrial enterprises listed on the China’s A-share market between 2008 and 2022 to construct the adaptive weighted XGBoost-Bagging model for corporate financial distress prediction. Empirical findings demonstrate that systemic risk indicators possess predictive potential independent of traditional financial information, rendering them valuable non-financial early warning indicators for China’s industrial sector; moreover, they help to enhance the predictive accuracy of various comparative models. The adaptive weighted XGBoost-Bagging model incorporating systemic risk indicators effectively addresses challenges arising from data imbalance and information scarcity, significantly improving the accuracy of financial distress prediction in Chinese industrial enterprises under the 2015 Chinese stock market crash, the Sino-US trade friction, and the COVID-19 epidemic; as such, it can be used as an efficient risk early warning tool for China’s industrial sector.