Hanxin Hu, Ting Sun, Miklos V. Vasarhelyi, Min Zhang
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Measuring Audit Quality with Surprise Scores: Evidence from China and the U.S.
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.
期刊介绍:
The Journal of Information Systems (JIS) is the academic journal of the Accounting Information Systems (AIS) Section of the American Accounting Association. Its goal is to support, promote, and advance Accounting Information Systems knowledge. The primary criterion for publication in JIS is contribution to the accounting information systems (AIS), accounting and auditing domains by the application or understanding of information technology theory and practice. AIS research draws upon and is informed by research and practice in management information systems, computer science, accounting, auditing as well as cognate disciplines including philosophy, psychology, and management science. JIS welcomes research that employs a wide variety of research methods including qualitative, field study, case study, behavioral, experimental, archival, analytical and markets-based.