DO FORECASTS OF BANKRUPTCY CAUSE BANKRUPTCY? A MACHINE LEARNING SENSITIVITY ANALYSIS

• 成果类型: Article
• 署名作者: Papakostas, Demetrios; Hahn, P. Richard; Murry, Jared; Zhou, Frank; Gerakos, Joseph
• 署名单位: Arizona State University; Arizona State University-Tempe; University of Texas System; University of Texas Austin; University of Pennsylvania; Dartmouth College
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1648
• 发表日期: 2023
• 页码: 711-739
• 关键词: audit opinions; regression; variables

摘要

It is widely speculated that auditors' public forecasts of bankruptcy are, at least in part, self-fulfilling prophecies in the sense that they 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.