基础模型在破产预测中是否有效？
Are Foundation Models Useful for Bankruptcy Prediction?
AI总结：
本文研究了基础模型在破产预测中的有效性，发现传统机器学习模型在性能上优于基础模型，且基础模型在风险敏感场景中存在概率估计不可靠的问题。
摘要：
基础模型在各种金融应用中显示出潜力，但其在企业破产预测中的有效性尚未系统性地与传统方法进行评估。我们使用Llama-3.3-70B-Instruct和TabPFN研究破产预测，评估数据来自超过一百万家公司的Visegrád Group大型高不平衡数据集。我们为该任务提供了首个系统性的基础模型与经典机器学习基线的比较。我们的结果表明，XGBoost和CatBoost等模型在所有预测时间范围内均优于基础模型。基于大语言模型的方法由于概率估计不可靠，使其在风险敏感的金融环境中使用受限。TabPFN虽然与更简单的基线竞争，但需要大量计算资源，其成本未被性能提升所证明。这些发现表明，尽管基础模型具有通用性，但当前基础模型在破产预测中仍不如专门方法有效。
英文摘要：
Foundation models have shown promise across various financial applications, yet their effectiveness for corporate bankruptcy prediction remains systematically unevaluated against established methods. We study bankruptcy forecasting using Llama-3.3-70B-Instruct and TabPFN, evaluated on large, highly imbalanced datasets of over one million company records from the Visegrád Group. We provide the first systematic comparison of foundation models against classical machine learning baselines for this task. Our results show that models such as XGBoost and CatBoost consistently outperform foundation models across all prediction horizons. LLM-based approaches suffer from unreliable probability estimates, undermining their use in risk-sensitive financial settings. TabPFN, while competitive with simpler baselines, requires substantial computational resources with costs not justified by performance gains. These findings suggest that, despite their generality, current foundation models remain less effective than specialized methods for bankruptcy forecasting.
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