From linear to machine learning models: an empirical study on real earnings management detection in Indian listed firms
TL;DR
Empirical results have revealed the fact that DBNNs outperform other models in both classification and prediction of R.E.M. in Indian listed firms and the results have remained robust across a dataset of US listed firms.
Abstract
Earnings Management practices degrade the reporting quality and potentially deceive stakeholders. This paper addresses the evaluation of multiple models to identify the most effective model for detecting Real Earnings Management (R.E.M.). Financial data of non-financial BSE 500 listed companies from April 1, 2014, to March 31, 2024, was utilized for analysis. The study has compared the prediction and classification rate of Linear Regression (LR), logistic regression, support vector machines, random forests and Deep Belief Neural Networks (DBNNs). Further, the empirical analysis has been re-conducted using a dataset of S&P 500 firms for the period 2020–2024, to assess the robustness of the results. Empirical results have revealed the fact that DBNNs outperform other models in both classification and prediction of R.E.M. in Indian listed firms and the results have remained robust across a dataset of US listed firms. The present research offers empirical support to the relatively scarce literature by employing deep neural networks for the prediction and categorization of R.E.M.