A Study on the Forecast of Earning Management Based on Deep Learning by Reflecting Information on Corporate Litigation Cases

Abstract

This paper aims to find that predictive performance is better when earning management predictions using deep learning technology, including litigation case information filed with companies. Unlike previous accounting forensics studies, this study designed a basic earning management prediction model using related financial variables by referring to various previous studies on earning management in accounting. To present more objective and reliable verification results, this study used two measures of accrual earning management (AEM) as proxy variables for corporate earning management. It used four deep-learning classifiers: RNN, GRU, LSTM, and Transformers. In addition, before predicting earning management through deep learning analysis, regression analysis proved that the number and amount of litigation cases filed against the company significantly reduced accrual earning management. In this study, the number of litigation cases filed with the company and the amount of litigation were used as information on the company's litigation cases. The main findings of this paper are as follows. First, as expected, when predicting the earning management level, including the company's litigation case information, accuracy, recall, precision, AUC (area under the ROC curve), and F1 score, all showed high overall predictive performance. Second, overall predictive accuracy was higher when predicting the level of earning management, including the number of litigation cases, than when predicting the level of earning management, including the number of litigation cases. Overall predictive performance was the highest when predicting the earning management level, including the number and amount of litigation cases. Third, among the four classifiers of deep learning, Transformers' predictive performance was the best, followed by LSTM, GRU, and RNN.