Predict Audit Quality Using Machine Learning Algorithms

40 Pages Posted: 24 Sep 2019

See all articles by Chanyuan (Abigail) Zhang

Chanyuan (Abigail) Zhang

State University of New Jersey, Rutgers Business School at Newark & New Brunswick, Accounting & Information Systems

Date Written: 2018

Abstract

Audit quality has always been the focus of audit research, especially since the passage of the Sarbanes-Oxley Act in 2002. Much research has been done to measure and predict audit quality, and the existing predictive models commonly use regression. By contrast, this paper uses various supervised learning algorithms to predict audit quality, which is proxied by restatements, the best measure of audit quality that is publicly available (Aobdia, 2015). Using 14,028 firm-year observations from 2008 to 2016 in the United States and ten different supervised learning algorithms, the research mainly shows that Random Forest algorithm can predict audit quality more accurately than logistic regression and that audit-related variables are better than financial variables in predicting audit quality. The results of this paper can provide regulators, investors, and other stakeholders a more effective tool than the traditional logistic regression to assess and predict audit quality, thus better protecting the benefit of the general public and ensuring the healthy functioning of the capital market.

Keywords: Audit Quality, Machine Learning Algorithms, Restatements

Suggested Citation

Zhang, Chanyuan (Abigail), Predict Audit Quality Using Machine Learning Algorithms (2018). Available at SSRN: https://ssrn.com/abstract=3449848 or http://dx.doi.org/10.2139/ssrn.3449848

Chanyuan (Abigail) Zhang (Contact Author)

State University of New Jersey, Rutgers Business School at Newark & New Brunswick, Accounting & Information Systems ( email )

96 New England Avenue, #18
Summit, NJ 07901-1825
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
49
Abstract Views
186
PlumX Metrics