Predicting Internal Control Weaknesses Using a Deep Learning Approach:New Theory and New Method
Internal control weakness(ICW)prediction is important to prevent major risks of listed firms and maintain the stable development of the capital market.Prior research ignored the challenges of complex nonlinear relationships and dynamic time sequences in ICW prediction.To address these problems,we develop a state-of-the-art ICW prediction model using one of the most powerful deep learning methods,recurrent neural network(RNN).We also identify a comprehensive set of ICW-related variables based on Pressure-Opportunity-Predisposition framework.We show that our new RNN model outperforms the logistic model from 14.11％to 28.26％,which is also economically significant.We further investigate the mechanisms of the superiority of our RNN model.From the perspective of structural interpretability,analyses suggest that the outperformance relative to the logistic model stems from both nonlinear mechanism and intertemporal mechanism.In addition,from the perspective of variable interpretability,we show that Opportunity variables play a more important role in predicting ICWs than other input variables.For application,we find that the results of our RNN model can better predict corporate future operation risk,market risk,and crash risk.This paper is the first to combine deep learning algorithms with internal control research,which provides an effective tool for regulators to prevent major risks of listed firms.
Deep LearningInternal ControlRecurrent Neural NetworkInterpretability
万方数据
维普
