DWARFB: A Dynamic Weight-Adjusted Random Forest Boost for Predicting Financial Distress in Chinese Listed Companies

Two key challenges in financial distress prediction are pronounced class imbalance between majority and minority classes and the persistent misclassification of hard-to-learn samples. To tackle these issues, this study proposes an ensemble framework called Dynamic Weight-Adjusted Random Forest Boost (DWARFB). The proposed method incorporates a dynamic sample-weighting mechanism that leverages cumulative misclassification information, adaptive minority–majority class ratios to address class imbalance issue, and a real-time performance-driven strategy to integrate models’ prediction results. The effectiveness of DWARFB is evaluated using a financial dataset from the China Stock Market & Accounting Research (CSMAR) database. Comparative experiments against eight benchmark Random Forest (RF) approaches show that DWARFB delivers superior balanced performance, and a stable precision–recall trade-off, which effectively reduces both false negatives and false positives in the prediction. Moreover, a loss-based feature contribution metric provides economically meaningful insights into the key financial determinants of distress, enhancing model interpretability. Overall, DWARFB demonstrates strong reliability and adaptability and offers a practical solution for early financial distress warning in imbalanced and dynamic financial environments.