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

Demetrios Papakostas, P. Richard Hahn, Jared Murray, Frank Zhou, Joseph Gerakos

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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 forecast-ers with unique access to highly predictive covariates. In this paper we in-vestigate the causal effect of bankruptcy forecasts on bankruptcy using non-parametric 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 corre-sponding 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.

Original languageEnglish (US)
Pages (from-to)711-739
Number of pages29
JournalAnnals of Applied Statistics
Volume17
Issue number1
DOIs
StatePublished - Mar 2023

Keywords

  • BART
  • causal inference
  • heterogeneous treatment effects
  • self-fulfilling prophecy
  • sensitivity analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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