Do forecasts of bankruptcy cause bankruptcy? A machine learning sensitivity analysis
Do forecasts of bankruptcy cause bankruptcy? A machine learning sensitivity analysis
复制标题
DOI：
10.1214/22-aoas1648
复制
发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Demetrios V. Papakostas;P. Hahn;Jared S. Murray;Frank S. Zhou;Joseph J. Gerakos
中科院分区：
文献类型：
--
作者：
Demetrios V. Papakostas;P. Hahn;Jared S. Murray;Frank S. Zhou;Joseph J. Gerakos
人们普遍猜测，审计师对破产的公开预测至少在一定程度上是自我实现的预言，因为它们实际上可能会导致本来不会发生的破产。然而，这一猜想很难证明，因为破产和破产预测之间的紧密联系可能简单地表明审计师是熟练的预测者，拥有获得高度预测协变量的独特途径。在本文中，我们使用非参数敏感性分析研究破产预测对破产的因果影响。我们将我们的分析与两种替代方法进行对比：具有内生回归量的线性二变量概率模型，以及最近开发的称为 E 值的风险比界限。此外，我们的机器学习方法包含了单调性约束，该约束对应于破产预测不会降低破产可能性的假设。最后，基于树的处理效果估计的后验总结使我们能够探索哪些可观察到的公司特征调节了诱导效应。
It is widely speculated that auditors' public forecasts of bankruptcy are, at least in part, self-fulfilling prophecies in the sense that they might actually cause bankruptcies that would not have otherwise occurred. This conjecture is hard to prove, however, because the strong association between bankruptcies and bankruptcy forecasts could simply indicate that auditors are skillful forecasters with unique access to highly predictive covariates. In this paper, we investigate the causal effect of bankruptcy forecasts on bankruptcy using nonparametric sensitivity analysis. We contrast our analysis with two alternative approaches: a linear bivariate probit model with an endogenous regressor, and a recently developed bound on risk ratios called E-values. Additionally, our machine learning approach incorporates a monotonicity constraint corresponding to the assumption that bankruptcy forecasts do not make bankruptcies less likely. Finally, a tree-based posterior summary of the treatment effect estimates allows us to explore which observable firm characteristics moderate the inducement effect.