This study constructs a measure of audit quality that captures the effect of potential factors that are generally unobservable to people outside of the audit firm or client company. Using machine learning and a wide range of data describing audit firm characteristics, audit partners, and public companies in China, this paper constructs the “surprise score,” a new measure of audit quality, calculated as the difference between the predicted probability and the actual value of an audit quality-related event (i.e., the existence of material misstatements, audit adjustments, and nonclean audit opinions). The effectiveness of the surprise score is validated by testing the association between the surprise score and penalties or audit firm changes. The proposed approach is applied to U.S. data to generalize its application. The surprise score adds value to existing audit quality measures and can help regulators to make better-informed decisions about audit quality.

Data Availability: Except for the data privately provided by CICPA and MFC, other datasets are available from the public sources cited in the text.

JEL Classifications: M41; M42.

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