Predicting earnings management through machine learning ensemble classifiers

In this paper, we utilize six novel ensemble classifiers to predict earnings management (EM) in both its forms, accrual-based earnings management (AEM) and real earnings management (REM), and then compare the EM prediction accuracy of wrapper feature selection (FS) and filtering FS techniques in the context of EM. Specifically, we integrate three well-known filtering FS techniques (information gain [IG], principal component analysis [PCA], and relief [Re]) and three popular wrapper FS techniques (particle swarm optimization [PSO], genetic algorithm [GA], and artificial bee colony [ABC]) with the support vector machine (SVM) to generate our ensemble classifiers. We then assess the performance of each of the six ensemble classifiers to predict AEM and REM based on three criteria: type Iota error, type Iota Iota error, and average accuracy. The results show that the ABC-SVM ensemble classifier outperforms the others in predicting both AEM and REM. We also find that, overall, wrapper FS ensemble classifiers outperform filtering FS ensemble classifiers in predicting AEM and REM and that it is more difficult for our ensemble classifiers to predict REM than to predict AEM. This paper contributes to the literature on EM prediction by introducing six new ensemble classifiers. It is also the first work (to the best of our knowledge) in the domain of ensemble classifiers' applications (a) to consider both REM and AEM in one context and to show that REM is more difficult to predict than AEM and (b) to compare the performance of wrapper and filtering FS techniques in the EM prediction setting.

Automated summary

In this paper, six novel ensemble classifiers are used to predict earnings management (EM) in both forms, accrual-based earnings management (AEM) and real earnings management (REM). The paper compares the EM prediction accuracy of wrapper feature selection (FS) and filtering FS techniques. The results show that the ABC-SVM ensemble classifier outperforms others in predicting both AEM and REM. It is also found that wrapper FS ensemble classifiers generally outperform filtering FS ensemble classifiers in predicting AEM and REM, and that predicting REM is more difficult than predicting AEM. This paper contributes to the literature on EM prediction by introducing six new ensemble classifiers, and it is the first work (to the best of our knowledge) to consider both REM and AEM in one context and to compare the performance of wrapper and filtering FS techniques in the EM prediction setting.