Misstatement Detection Lag and Prediction Evaluation

ABSTRACT 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 data-generating 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. Data Availability: Data are available from the public sources identified in the study. JEL Classifications: C53; G32; G38; M41.