Designing Prediction Model of Financial Restatements Using Neural-Genetic Simulation Algorithm
محورهای موضوعی : Financial AccountingSasan Mehrani 1 , Akbar Rahimi poor 2
1 - Department of Accounting, University of Tehran, Tehran, Iran
2 - Department of Accounting, Tehran University Alborz Campus, Tehran, Iran.
کلید واژه: Prediction, Financial Restatement , Beneish Model, Meta-Heuristic Models,
چکیده مقاله :
The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm(S – 𝑆𝑐𝑜𝑟𝑒) had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model .
The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm(S – 𝑆𝑐𝑜𝑟𝑒) had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model .
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