🤖 AI Summary
This study addresses the limitations of existing public datasets for corporate bankruptcy prediction—namely, their small scale, extreme class imbalance, and lack of a unified multi-horizon evaluation benchmark—by introducing V4FinBench, the first large-scale, publicly available, cross-regional (covering four countries in China and Europe) benchmark. V4FinBench incorporates 131 features, six prediction horizons, and a composite default definition. Using this benchmark, the authors systematically evaluate tabular foundation models (e.g., TabPFN), Llama-3-8B fine-tuned with QLoRA, and traditional gradient boosting methods under imbalance-aware training protocols. Results show that domain-adapted TabPFN matches or exceeds gradient boosting models in F1 and ROC-AUC for long-term predictions and demonstrates strong cross-regional generalization, whereas Llama-3-8B exhibits consistently weaker performance, particularly over longer horizons.
📝 Abstract
Corporate bankruptcy prediction is a high-stakes financial task characterized by severe class imbalance and multi-horizon forecasting demands. Public datasets supporting it remain scarce and small: widely used free benchmarks contain between 6,000 and 80,000 company-year observations, while larger resources are behind subscription paywalls. To address this gap, we introduce V4FinBench, a benchmark of over one million company-year records from the Visegràd Group (V4) economies (2006-2021), with 131 financial and non-financial features, six prediction horizons, and a composite distress criterion jointly capturing solvency, profitability, and liquidity deterioration. V4FinBench is designed to support the evaluation of tabular and foundation-model methods under realistic class imbalance, with positive rates between 0.19% and 0.36%. We provide reference evaluations of standard tabular baselines, finetuned TabPFN, and QLoRA-finetuned Llama-3-8B. With imbalance-aware finetuning, TabPFN matches or exceeds gradient boosting at longer time horizons on both $F_1$-score and ROC-AUC. In contrast, Llama-3-8B trails gradient boosting on ROC-AUC at every horizon and is generally weaker on $F_1$-score, with the gap widening sharply beyond the immediate horizon. In an external evaluation on the American Bankruptcy Dataset, the V4FinBench-finetuned TabPFN checkpoint improves over vanilla TabPFN, suggesting that adaptation captures transferable financial-distress structure rather than only V4-specific patterns. V4FinBench is publicly released to support further evaluation and development of prediction methods on realistic financial data.