Accuracy of Disclosure Level Prediction by Ant Colony Algorithm and Differential Evolution

One of the main missions of financial reporting is a proper disclosure of information in order to meet the needs of financial statement users. This study is aimed to investigate whether the company's disclosure quality can be discovered by Machine learning-based models. In this study, disclosure level rating of listed firms is predicted by Securities and Exchange Organization using the ant colony algorithm and differential evolution model. The sample consists of 171 firms listed in Tehran Stock Exchange during the period from 2010 to 2014. This study uses MATLAB software to predict a firm's disclosure quality. The algorithms fitting results show that the two algorithms have ability to predict earnings management with high accuracy of 95%. In fact, the results indicate that the ant colony algorithm has more ability (error 3.316 percent) of predicting earnings management than the differential evolution algorithm (4. 139 percent error).