Using a hybrid model to detect earnings management for Polish public companies

Abstract

This paper analyses the role of non-financial variables in the detection of earnings management in Poland. Previous research on earnings management in Poland concentrated on the use of the Beneish and Roxas models. The sample comprises 63 non-financial Polish companies listed on the Warsaw Stock Exchange for the years 2010-2021. The author uses the hybrid model with elements of decision trees and logistic regression as a proxy for earnings management detection. The results indicate that using a hybrid model increases the accuracy more than standard methods such as decision trees and logistic regression do. Accordingly, inclusion of non-financial variables related to the shareholding structure and the audit increases model accuracy and has a significant impact on the construction of the hybrid model. The findings suggest that using only financial variables worsens model accuracy. The author makes a significant contribution to accounting literature by providing new empirical evidence on the importance of non-financial variables in earnings management detection and their impact on model construction.