Misstatement Detection Lag and Prediction Evaluation

• 成果类型: Article
• 署名作者: Yang, Liu; Zhu, Min
• 署名单位: University of Wyoming; University of Queensland
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826; 1558-7967
• DOI: 10.2308/TAR-2023-0073
• 发表日期: 2026-03
• 页码: 395-417
• 关键词: misstatement; RESTATEMENT; detection lag; look-ahead bias; continuously updating approach; Machine Learning; hyperparameter tuning; Performance evaluation; trading strategy; FRAUD; RESTATEMENTS; Timeliness; returns

摘要

Accounting misstatements are often detected with substantial delays, leading to look-ahead bias in model predictions if the detection lag is not considered. Moreover, the misstatement data-generating process is evolving due to regulatory regime shifts, further complicating the evaluation of model predictions. We design an approach that accounts for detection lags and continuously updates models to adapt to the changing datagenerating process. By comparing with the conventional approach that ignores detection lags, we show that the look-ahead bias can substantially inflate prediction performance. We also demonstrate that although leaving a temporal gap between training and test samples can mitigate the look-ahead bias, it sacrifices the model's predictive power by disconnecting the dynamic data-generating process between training and test periods. We further implement a trading strategy to evaluate the practical utility of the continuously updating approach. Our study presents a new conceptual lens for understanding and evaluating misstatement prediction models.