Accuracy of the 'Benish' and 'Developed Benish' Models in Predicting the Probability of Restating Financial Statements in Iran's Economic Environment

Objective
Reliability is considered as one of the most important qualitative features of accounting information. According to the theoretical concepts of financial reporting, reliable information is free from errors and biases. Because of reporting scandals, the issue of financial statement restatement has garnered significant attention in accounting literature and journals. The main goal of the research is to provide a suitable criterion for predicting the re-presentation of financial statements using Benish models and Benish development.
 
Methods
This research employs an applied method and, in terms of methodology, follows an ex post facto approach. Also, this research falls within the realm of accounting evidence and is based on actual data from the financial statements of companies. The required data was extracted from the Codal Exchange website and from the published financial statements and reports of companies listed on the Tehran Stock Exchange during the period from 2008 to 2019. Furthermore, SPSS and Python software were utilized in this research to analyze the data.
 
Results
In this research, firstly, Benish's primary model was examined to predict the possibility of financial statement renewals, and then, using indicators that affect renewals, independent t-tests, and step-by-step regression were used to estimate the model using logit regression. became. The overall accuracy of Benish's initial model for predicting the probability of resubmission of financial statements was estimated at 29.43%. The overall accuracy of the model regarding the developed model of Benish was estimated at 68.68%. The research results indicate that, based on the confusion matrix, among the predictive models for financial statement restatement, the Developed Benish model with logistic regression achieves a total forecasting accuracy of 68.68%, demonstrating the highest predictive power compared to the original Benish model, which has an accuracy of only 29.43%. Therefore, the research hypothesis is confirmed and the accuracy of the developed Benish model with logit regression is more than the original Benish model in identifying renewed companies.
 
Conclusion
The correct flow of information in the capital market, as the driving engine of the economy, leads to correct and rational decision-making by the participants and finally brings economic development and improvement of social welfare. Financial reporting serves as a primary means of communication between a company and various stakeholder groups. Therefore, information transparency and the provision of correct and timely information by companies admitted to the stock exchange is one of the most important effective factors in achieving an efficient capital market. This research seeks to provide an optimal model using Benish's profit manipulation model to predict the probability of the re-presentation of financial statements. Several studies have been conducted on this topic; however, there is a notable lack of empirical evidence regarding the development of an efficient and useful model tailored to the economic conditions of Iran's capital market for predicting the likelihood of financial statement restatements. In this study, firstly, the primary Benish model was examined to predict the probability of re-presenting the financial statements, and in the second step, to present a developed model based on the Benish indices and using logit regression, eight financial indicators of the Benish model from the financial statements of the company were examined. The selected ratios were extracted, and the stepwise regression test was applied to identify the best ratios for predicting financial statement restatement. In this context, the two final financial indices chosen for the selection and development model were the total accruals index to total assets ratio and the debt ratio index. Benish's findings were presented based on two final indicators. In the third step, based on the clutter matrix and comparing them, the best model and method for prediction was selected.