THE EFFECT OF USING ARTIFICIAL INTELLIGENCE ON THE RELATION BETWEEN EARNING MANAGEMENT AND EARNING PERSISTENCE AND FUTURE EARNINGS PREDICTABILITY: APPLIED STUDY ON EGYPTIAN STOCK LISTED COMPANIES | ||
| Journal of Contemporary Business Research | ||
| Volume 1, Issue 1, December 2024 | ||
| Document Type: Researches | ||
| DOI: 10.21608/jcbre.2024.422433 | ||
| Authors | ||
| Mohamed S.A. AbdelGalil* 1; Alaa M. ElBatanouny2; Essam H. Amin2 | ||
| 1Department of Accounting and Auditing, ElAgamy Higher Institute of Administrative Sciences, Egypt. | ||
| 2Department of Accounting and Auditing, Faculty of Commerce, Damanhour University, Egypt. | ||
| Abstract | ||
| This research aims to know the impact of the use of various artificial intelligence tools on the accuracy of forecasting the relationship between earning management and the continuity of current earning and the predictive ability of future earning, and a sample of 112 non-financial companies registered on the Egyptian Stock Exchange. And thus the final study observations consisted of 531 observations, during the period from 2016 to 2020. In the fundamental analysis, the researcher has come to accept the first hypothesis that earning management negatively and morally affects the continuity of current earning. And the acceptance of the second hypothesis that earning management positively and morally affects the predictive ability of future earning. In the sensitivity analysis, the first hypothesis was accepted with a significant and positive effect between earning management and the continuity of current earning, and the second hypothesis was rejected with a non-significant and positive effect between earning management and the predictive ability of future earning. The third and fourth hypotheses, they were accepted which show of the artificial intelligence tools in predicting the relationship between the study variables on the regression model, as well as the actual prediction of the continuity of current earning and the predictive ability of future earning for the next year with high correct and low predictive accuracy rates. | ||
| Keywords | ||
| Earning management; Persistence of current earnings; Predictive ability of future earnings; AI tools (Artificial Neural Networks; Support Vector Machine; Decision Tree; K-Nearest Neighbor Algorithm) | ||
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