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Modification of the Beneish Model for Earnings Management Prediction using Logit and Probit Analysis science and research Branch
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Abstract: (276 Views) |
| This study aimed to modify the Beneish model (1999) by incorporating two environmental variables, namely information asymmetry and product market competition. Data of 184 firms listed on the Tehran Stock Exchange for 2007-2017 were collected. The model coefficients were estimated using Logit and Probit logistic regression. Given the absence of lagged dependent variables on the right side of the equations of both original and modified Beneish models, the prediction was made by the static method. In the Probit approach, the best accuracy of the original and modified Beneish models at the optimal cut-off points (0.5215 and 0.5450) was 56.18% and 68.83%, respectively. In the Logit approach, the best accuracy of the original and modified Beneish models at the optimal cut-off points (0.5216 and 0.5508) was 56.43% and 69.12%, respectively. There is a significant difference between the prediction accuracy of the Beneish model and the modified Beneish model. The Logit approach is more effective than the Probit approach in identifying earnings management levels. The results of the Wilcoxon test show a significant difference at the 5% significance level between the two models and the two approaches. |
Article number: 5 |
| Keywords: Beneish Model, Information Environment, Logit and Probit Regression |
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Full-Text [PDF 1003 kb]
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Type of Study: Research |
Subject:
Special
Received: 2022/08/17 | Accepted: 2022/08/17 | Published: 2022/10/11
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