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Predicting The possibility Of Restatement Financial statements using Benish Model And Improving The Model Through Logit Regression And Genetic Algorithm

Pizhūhish/hā-yi ḥisābdārī-i mālī (Online), 2022-08, Vol.14 (2), p.91-116

EISSN: 2322-3405 ;DOI: 10.22108/far.2023.135009.1920

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  • Title:
    Predicting The possibility Of Restatement Financial statements using Benish Model And Improving The Model Through Logit Regression And Genetic Algorithm
  • Author: sasan mehrani ; akbar rahimi poor
  • Subjects: annual adjustments ; benish model ; genetic algorithm ; logit regression ; restatement of financial statements
  • Is Part Of: Pizhūhish/hā-yi ḥisābdārī-i mālī (Online), 2022-08, Vol.14 (2), p.91-116
  • Description: The occurrence of mistakes in accounting is inevitable and factors such as diversity and complexity of economic issues, high volume of work, fatigue, etc. increase the possibility of mistakes. Also, due to the continuous changes that take place in the economic, social, etc. conditions, it may be necessary to make changes in accounting principles and methods in order to harmonize the business unit with the new conditions, all of which result in the re-presentation of financial statements. A subject that has received a lot of attention as a result of reporting scandals such as Enron and... The purpose of this research is to present the expanded model of Banish in companies admitted to the Tehran Stock Exchange between 2009 and 2019. Also, the data of 265 companies were used using Benish model and logit regression and genetic algorithm were also used to estimate the improvement of the prediction model. The results of the research indicate that, based on the confusion matrix, among the predictive models for re-presentation of financial statements, the accuracy and efficiency of the improved Benish model with the genetic algorithm has a total prediction accuracy of 73.21%, which has the highest predictive power in The comparison with the original Benish model and the presented model was with logit regression.
  • Publisher: University of Isfahan
  • Language: Persian
  • Identifier: EISSN: 2322-3405
    DOI: 10.22108/far.2023.135009.1920
  • Source: DOAJ Directory of Open Access Journals
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