Predicting Material Misstatements Using Machine Learning

• 成果类型: Article; Early Access
• 署名作者: Parker, Chanyuan (Abigail) Zhang; Jiang, Lanxin; Cho, Soohyun; Vasarhelyi, Miklos A.
• 署名单位: University of Texas System; University of Texas at San Antonio; State University of New York (SUNY) System; Stony Brook University; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2024-0035
• 发表日期: 2025
• 关键词: earnings management; nonaudit services; Audit quality; office size; RISK; READABILITY; accruals; BOARD; FRAUD; fees

摘要

This study uses machine learning models to forecast future material misstatements. Using raw financial data, audit variables, qualitative features, and an efficient algorithm, we design a dynamic model that continuously updates with new information. Our model outperforms the benchmarks for both one-year-ahead and two-year-ahead predictions in terms of out-of-sample predictive power and economic impact on net income. Using Explainable Artificial Intelligence, we identify key predictive features, including comprehensive income, foreign firm status, and accrued interest and penalties from unrecognized tax benefits. Results show that investors achieve better outcomes using a proactive investment strategy based on our prediction models than reactive detection models. Furthermore, our prediction model can help managers prevent internal control weaknesses, assist auditors in assessing misstatement risks in advance, and enable regulators to allocate inspection resources proactively. Our study advances the literature by moving beyond the detection of past material misstatements to the forecasting of future misstatements.