具有稳健显著性检验的 Group Shapley 及其在债券回收率预测中的应用
Group Shapley with Robust Significance Testing and Its Application to Bond Recovery Rate Prediction
AI总结：
本文提出面向特征组的 Group Shapley 及基于三累积量卡方近似的稳健显著性检验，并通过债券回收率预测验证其解释能力与有效性。
摘要：
我们提出 Group Shapley，这是一种扩展经典个体层面 Shapley 值框架以评估特征组重要性的指标，旨在应对商业和经济数据中常见预测变量的结构化性质。更重要的是，我们开发了一种基于三累积量卡方近似的显著性检验程序，并建立了 Group Shapley 值检验统计量的渐近性质。我们的方法能够有效处理具有挑战性的情形，包括稀疏或偏斜分布以及小样本量，并且优于 Wald 检验等替代检验。模拟结果证实，所提出的检验在多种条件下保持稳健的经验显著性水平，并表现出更强的检验功效。为说明该方法在推进可解释人工智能方面的实际相关性，我们将该框架应用于债券回收率预测，使用一个涵盖1996—2023年的全球数据集，其中包含2,094个观测值和98个特征，这些特征被划分为16个子组和五大类别：债券特征、公司基本面、行业特定因素、市场相关变量和宏观经济指标。结果表明，市场相关变量组的影响力最大。此外，Lorenz 曲线和 Gini 指数显示，与个体 Shapley 值相比，Group Shapley 分配特征重要性更加公平。
英文摘要：
We propose Group Shapley, a metric that extends the classical individual-level Shapley value framework to evaluate the importance of feature groups, addressing the structured nature of predictors commonly found in business and economic data. More importantly, we develop a significance testing procedure based on a three-cumulant chi-square approximation and establish the asymptotic properties of the test statistics for Group Shapley values. Our approach can effectively handle challenging scenarios, including sparse or skewed distributions and small sample sizes, outperforming alternative tests such as the Wald test. Simulations confirm that the proposed test maintains robust empirical size and demonstrates enhanced power under diverse conditions. To illustrate the method's practical relevance in advancing Explainable AI, we apply our framework to bond recovery rate predictions using a global dataset (1996-2023) comprising 2,094 observations and 98 features, grouped into 16 subgroups and five broader categories: bond characteristics, firm fundamentals, industry-specific factors, market-related variables, and macroeconomic indicators. Our results identify the market-related variables group as the most influential. Furthermore, Lorenz curves and Gini indices reveal that Group Shapley assigns feature importance more equitably compared to individual Shapley values.
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