جستجوی مقالات مرتبط با کلیدواژه « مدل بنیش » در نشریات گروه « حسابداری »

Restatement of Financial Statements , which means re-presenting last year's financial information in order to correct incorrect information or increase comparability, is one of the common phenomena in Iran, which causes investors to lose confidence in the credibility and competence of management and the quality of reported profits. The purpose of this research is to present the development model of Benish fraud in companies admitted to the Tehran Stock Exchange between 2008 and 2019. The data of 265 companies were extracted using the selected and final indicators of Benish's (1999) fraud model, and neural network and genetic algorithms were also used to develop the prediction model. The results of the research indicate that based on the confusion matrix, the accuracy and efficiency of the improved Benish model with the combined algorithm of neural network and genetics in predicting unrepresented companies was 78.15%, and re-presented companies was 29.63%. . Also, the overall accuracy of the model presented with the combined algorithm of neural network and genetics was 73.21%.

From a theoretical point of view, the meaning of financial disorder is to try to reduce the opportunistic behavior of managers; Therefore, it seems that many companies are involved in financial irregularities, and for this reason, it is very important to determine the factors affecting the level of financial irregularities in companies, for this purpose, the indigenous model of Hanifi et al. (2022) was used. For this purpose, a questionnaire was prepared and distributed among experts to ask the opinion of the experts about the weight and importance of the criteria considered for measuring the financial irregularity of the company, and the weight and importance of the criteria were determined using one of the methods of the multi-criteria decision-making model (Shannon's entropy). Also, in order to measure the relationships of the hidden variable, which in this research is financial irregularity, confirmatory factor analysis was used; Also, in this research, we will try to measure the impact of financial risk on the presented model of financial disorder. Therefore, in this research, data from 167 companies during the years 1390 to 1400 has been used. The obtained results show that the proposed model is a suitable indicator for financial disorder. Also, the results include that financial risk increases financial irregularity

As the business process becomes more complex, the risk of financial statements being distorted increases with each passing day. In this regard, researchers have been looking for models to detect fraud in financial statements. Benish (1997) used a combination of financial ratios and accruals to predict profit manipulation methods. since auditors are presented as external oversight in the corporate governance structure of the company's performance, in this study the model is developed based on the qualitative characteristics of the auditor, which include the auditor's size, auditor tenure, reporting delay, Auditor Class and Auditor Change.The fitting of the stochastic vector machine, random forest and neural network has been used to fit the extended model. The results show that the coefficients obtained from the random forest model are 99% and more than the two neural network and vector model 94%.

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.

According to Beneish (1999), “earnings manipulation happens as an instance where management violates Generally Accepted Accounting Principles (GAAP)inordertobeneficiallyrepresentthefirm’s financial performance.” In this research, the development of the Beneish model (DBM) was done through emphasizing non-accounting variables,including the Information Asymmetry (IS) and Product Market Competition (PMC).

The data was collected for 184 companies listed in the Tehran Stock Exchange (TSE) during the past 11 years 2006-2017. The coefficients of models were estimated by trained Artificial Neural Network (ANN) through PSO algorithm. In order to provide the potential of comparability, ten run with 300 iterations in each run were done for both the Beneish model (BM) and (DBM), then were stopped after convergence.

Research results indicate that training error of ANN trained by PSO algorithm measured by mean square error (MSE) was reduced from 0/0807 to 0/0777 by the development of BM. The area under Receiver Operating Characteristic (ROC) for BM was calculated up to 0/5792, which is located in very low confidence range of 0/5-0/6, indicating failed test result and high prediction error up to 39/74 percent. Consequently the best cut-off point and the best precision for BM were estimated to be 0/5021, 60/26 percent, by the maximum accuracy method, respectively. Furthermore, the results show that the AUC for DBM was increased to 0/6335 through incorporating environmental variables of Product Market Competition (PMC) and information symmetry (IS) to the BM, which is still out of an acceptable range of 0/7–0/8 for a relatively good test, indicating poor test result and high model prediction error up to 32/58 percent. Consequently the best cut-off point and the best precision for DBM were estimated to be 0/5304, 67/42 percent by the intersection point of minimum distance and Youden's index, respectively. Incorporating PMC and IS variables to the original model of Beneish decreased model prediction error from 39/74 to 32/58 percent, which is not statistically significant. Nevertheless, this fact improved the predictive power of the BM slightly insignificant.

The findings indicate that the BM is a random model in Iranian capital market and impotent to detect two groups of earning manipulator and non-earning manipulator companies. Although findings indicate that the DBM is a little bit more powerful than the BM and confirm that the impact of environmental variables of PMC and IS is slightly insignificant, indicating the test outcome is still weak and the DBM is an approximately random model in identifying two groups of earning manipulator and non-earning manipulator companies.

Earnings Manipulation within GAAP same of Earnings Management, that may be Efficient or Opportunistic. But Earnings manipulation is fraud through violation of GAAP. However, Earnings Manipulation is prevented from properly evaluate the company's performance. The development of a model that can be used to help predict earnings manipulation, it can be provide possible to better evaluate the company's performance. Therefore, the aim of this paper is to adjustment coefficients of beneish model and development of a new model for prediction of earnings manipulation based on the best predictor variables. For this purpose, all manufacturing firms of Tehran Stock Exchange during the years 2002- 2012, include 990 Observation, with the discriminant and logit analysis, were studied. The results indicate that on Iran economic environment, beneish model, not able to identify earnings manipulation. the adjusted beneish model and developed models of discriminant and logit analysis approach, respectively with overall accuracy 72%, 75% and 81%, is able to identify earnings manipulator and non- manipulator firms. Also evidence showed that accounting information is useful for prediction of earnings manipulation.