Forecasting financial distress in listed Chinese construction firms: leveraging ensemble learning and non-financial variables

The construction industry, characterized by high business failure rates due to complex, capital-intensive projects, is crucial for China's economy. Predicting financial distress and early warnings is crucial to prevent crises. While financial ratios are commonly used, non-financial information remains underexplored, with limited empirical evidence supporting its effectiveness in enhancing predictive accuracy. Additionally, most studies on financial distress prediction focus on constructing single classifiers without utilizing ensemble models that integrate multiple algorithms, resulting in suboptimal prediction accuracy. To address these problems, this study presents a model that uses accounting variables and firm characteristics, construction market dynamics, and macroeconomic indicators to predict the probability of failure one to two years in advance. The model uses a soft voting-based ensemble algorithm, financial ratios refined through the recursive feature elimination with cross-validation (RFECV) algorithm, and dataset balancing via Synthetic Minority Over-Sampling Technique + Tomek Link (SMOTETomek). The comparison results indicate that the predictive performance of the soft voting ensemble model outperforms all single classifiers across all combinations of input variables and prediction years. Additionally, incorporating non-financial variables, such as firm characteristics, construction market dynamics, and macroeconomic indicators, further enhances the model's accuracy. The proposed model can be effectively employed to help stakeholders mitigate the risks associated with the financial distress of construction companies before the project implementation phase.