Abstract
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.
| Original language | American English |
|---|---|
| Pages (from-to) | 51-78 |
| Number of pages | 28 |
| Journal | Journal of Information Systems |
| Volume | 38 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2024 |
ASJC Scopus subject areas
- Management Information Systems
- Software
- Information Systems
- Accounting
- Human-Computer Interaction
- Information Systems and Management
- Management of Technology and Innovation
Keywords
- audit adjustment
- audit failure
- audit quality
- machine learning
- misstatement
- nonclean opinion