1. Classification of financial insolvency using data mining techniques.
- Author
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Abdullah, Dalya Abdulkarim and AL-Anber, Nashaat Jasim
- Subjects
- *
DATA mining , *ARTIFICIAL neural networks , *BANKRUPTCY , *PSYCHOLOGICAL distress , *ERROR rates - Abstract
The constant change in the components and variables of the economic environment is one of the most important phenomena that characterize this environment at present, and these changes have made the goal of survival in the market a priority due to the prevalence of financial distress in companies. The main purpose of this paper is to test the impact and effectiveness of the model of artificial neural networks and Naive Bayes technology and to see how accurate they are and their ability to classify companies. In this paper, the information of the companies listed on the Iraq stock exchange for 2017, which represents 36 companies with high financial distress, 20 companies with medium financial distress, in addition to 43 non-distressed companies for a group of 99 companies. According to the experimental results, the model of artificial neural networks gives the highest rating accuracy of 96.97% and an error rate compared to Naive Bayes, which gave a rating accuracy of 74.74%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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