Re-visiting the efficacy of neural networks in detecting accrual and real earnings management

Aprajita Pandey, Prakash Singh and Jamini Kanta Pattanayak

International Journal of Business Information Systems, 2026, vol. 53, issue 2, 268-287

Abstract: Earnings management practices by firms to conceal their actual financial performance have always been a matter of concern for industry and academia. Therefore, over some time researchers have developed various models for the timely detection of earnings management practices. However, the efficiency of such models has been frequently challenged in numerous studies. One possible reason given in the literature for the ineffectiveness of these models is the assumption of the linear relationship between the variables used in the model. Against this backdrop, the present study explores neural network-based methods to model earnings management practices, which drops the assumption of linearity. The objective of the current work is to investigate that, while detecting earnings management, do neural network-based models show better results as compared to traditional linear regression models in. A linear regression (LR) model was compared with three neural networks, i.e., multi-layer perceptron (MLP), self-organising map (SOM), and generalised regression neural network (GRNN). The study has used Chinese IPO firms during the period 2007-2017. Using mean square errors, the findings of the study show that the neural network-based model MLP and GRNN performs best, whereas the linear regression-based model and SOM have the weakest performance.

Keywords: accrual earnings management; real earnings management; neural network; linear regression model. (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.inderscience.com/link.php?id=156827 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:53:y:2026:i:2:p:268-287

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().