Nahid Maleki Nia; reza tehrani; Akbar Tabriz Akbar; Mirfeiz Fallah shams
Abstract
Extended abstract1- INTRODUCTIONAccurately predicting earning manipulation in order to detect and identify manipulation of financial statements has always been one of the most fundamental challenges ahead of financial reports users. Because of increasing financial reporting fraud, this fact resulted ...
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Extended abstract1- INTRODUCTIONAccurately predicting earning manipulation in order to detect and identify manipulation of financial statements has always been one of the most fundamental challenges ahead of financial reports users. Because of increasing financial reporting fraud, this fact resulted in investor distrust of capital markets in recent years. The purpose of this study is to answer the questions whether it is possible to detect earning manipulation in financial statements based on the Beneish model? is it possible to detect earning manipulation in financial statements based on the proposed model? and does the proposed model predict better than the Beneish model in detecting earning manipulation? The findings of this research can be concidered by investors, creditors, auditors, regulators and other users to help them in making decisions and offering appropriate solutions. In order to detect earning manipulation and enhance the predictive accuracy of the earning manipulation model, it has been designed and presented a developed model based on Beneish model (1999). this study uses corporate governance variables i.e., audit committee structure, legal inspector and independent auditor, board of director's structure and corporate ownership structure requirements.2- THEORETICAL FRAMEWORK Benish (1999) investigated 74 earning-manipulator companies applying probit analysis during 1982-1992. He assigned the number 1 to the manipulative companies and the number zero to the non-manipulative companies and calculated the coefficients of the independent variables. The cut-off point of this model was -1.78. Therefore, if the M-score is greater than -1.78, it is likely that the company is earning manipulator. The overall accuracy of the model was confirmed at 76%.By using eight accounting variables in its model, Beneish showed that the probability of earning manipulation increases with unusual increase in receivables, decrease in gross profit margin, decrease in asset quality, sales growth and increase in accruals. But what is hidden from view in this model, is the attention to the control of mechanism to reduce transaction and agency costs. Studies conducted to develop the Beneish model have also been based solely on accounting data and have ignored the implications of control mechanisms in model development. Therefore, in order to improve the predictive power of the beneish model, corporate governance system can be considered as a deterrent factor from earning manipulation.3- METHODOLOGY The data of this study are drawn from the annual financial statements and reports of a sample of 81 non-financial listed companies on TSE over the period 2012-2018 i.e., 567 firm-year observations and analyzed by the hybrid multi-layer perceptron (MLP) neural network and cosmology based algorithms i.e., black-hole based optimization (BHBO), big bang-big crunch (BBBC) and galactic swarm optimization (GSO). It has been applied feed forward net to design the initial and final neural network model by structure of 8-17-1-1 for Beneish model (1999) and of 25-17-1-1 for the proposed model.This study also compares models based on hybrid neural networks and cosmological algorithms and the best and weakest cosmological algorithms are determined in neural network training to detect earning manipulation.4- RESULTS & DISCUSSIONThe numerical value of the area under the curve (AUC) of the receiver operating characteristic gives an idea about the detection power which it has been in the rejected range of 0.74 to 0.55 for the Beneish model (1999) and in the accepted range of 0.97 to 0.75 for the proposed model. The best cut-off points and the best accuracy of the Beneish model (1999) have been estimated up to 0.4014, 63.49%, respectively, by the hybrid multi-layer perceptron neural network and big bang-big crunch algorithm (MLP-BBBC). The best cut-off point and the best accuracy of the proposed model have been estimated up to 0.4023 and 87.30 percent, respectively, by the hybrid multi-layer perceptron neural network and galactic swarm optimization algorithm (MLP-GSO). The estimated accuracy of the model by the hybrid methods of multi-layer perceptron neural network and galactic swarm optimization algorithm (MLP-GSO), hybrid multi-layer perceptron neural network and big bang-big crunch algorithm (MLP-BBBC) and hybrid multi-layer perceptron neural network and black-hole based optimization algorithm (MLP-BHBO) has been increased from 59.08, 63.49 and 57.5 percentages to 87.3, 79.72 and 74.25 percentages, respectively.5- CONCLUSIONS & SUGGESTIONSThis evidence indicates that predictive power of the model has been enhanced in detecting earning-manipulator companies and training error of the network has been decreased up to 12.7 percentages by hybrid method of multi-layer perceptron neural network and galactic swarm optimization algorithm (MLP-GSO). Therefore, it can be concluded that the integration of corporate governance variables as non-accounting variables to the Beneish model (1999) has been more effective in detecting and identifying earning manipulation. This evidence is consistent with this fact that a significant reduction in mean square error (MSE) is up to 23.81% and as a result the predictive power of proposed model has been significantly improved. The area under the curve of the black-hole based optimization (BHBO) and big bang-big crunch (BBBC) algorithms is covered by the galactic swarm optimization (GSO) algorithm in the proposed model. Also the area under the curve of the black-hole based optimization (BHBO) and galactic swarm optimization (GSO) algorithms is covered by the big bang-big crunch (BBBC) algorithm in Beneish model(1999). Therefore, the best algorithms for training the multi-layer perceptron neural network belong to the galactic swarm optimization (GSO) algorithm in the proposed model by the 12.7% error and to the Big Bang- Big Crunch (BB-BC) algorithm in Beneish model (1999) by the 36.51% error compared to the other cosmological algorithms in this study to detect and identify of manipulator companies.
Hosein Asgari Alouj; Mohammadreza Nikbakht; Gholamreza Karami; Mansoor Momeni
Abstract
Extended abstract
1- INTRODUCTION
Earning of companies is one of the important factors in economic growth and development and earning manipulation is one of the main challenges of market efficiency that researchers often use accounting data to predict earning manipulation, while non-accounting data ...
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Extended abstract
1- INTRODUCTION
Earning of companies is one of the important factors in economic growth and development and earning manipulation is one of the main challenges of market efficiency that researchers often use accounting data to predict earning manipulation, while non-accounting data also play an important role in predicting earning manipulation. Due to the fact of the conducted research in order to develop the Beneish model has been formed solely because of accounting data, so the effects and consequences of non-accounting variables in all models have been ignored. This study tries to examine the nonlinear relationships of accounting and non-accounting variables and examine the effect of both variables simultaneously. The purpose of this study is to measure the predictive power of Beneish model and the development of the Beneish model (DBM) by non-accounting variables and to compare the accuracy of earning manipulation prediction of the research models using a hybrid Artificial Neural Network trained by Particle Swarm Optimization (PSO) algorithm and Imperialist Competition Algorithm (ICA).
2- THEORETICAL FRAMEWORK
The development of the Beneish model (DBM) was done through emphasizing non-accounting variables, including the Information Asymmetry (IS) and Product Market Competition (PMC). (Asgari Alouj et al, 2020). Another study by (Pourali & Kouchaki Tajani, 2021) was conducted to compare the accuracy of companies' profit manipulation predictions using colonial competition algorithm and genetic algorithm. The results showed that colonial competition algorithm with 93% accuracy and 7% error and genetic algorithm with 76% accuracy And 24% error could have predicted the coefficients of the variables of the profit manipulation model. The results also showed that the ability to predict the accuracy of profit manipulation model coefficients by colonial and genetic competition algorithms is more accurate than the prediction of the initial model of Banish (1999) and the modified model of Banish (Kurdistani & Tatli, 2016).
3- METHODOLOGY
This research has been developed the Beneish model) BM) with non-accounting variables including information asymmetry (IS) and competition in the product market (PMC). The data of 184 companies listed on the Tehran Stock Exchange during 2006-2017 has been collected and the prediction accuracy of research models has been compared by two algorithms in training of Artificial Neural Network (ANN): Particle Swarm Optimization (PSO) and Imperialist Competition Algorithm (ICA) in detecting and identification of earning-manipulator companies. In this research, the auditor's report has been used as an alternative solution and the review process has been done such that the audit report of the sample companies has been fully reviewed and studied and if there were the cases as an index of earning manipulation (regardless of the type of report acceptable - adjusted - rejected and no comment), the sample companies would be selected as the earning-manipulator firm and the number 1 would be allocated. Also, if there were no clauses as an index of earning manipulation, for example, the report is adjusted for another reason, it would be selected as a non-earning manipulator and the number zero would be allocated.
4- RESULTS & DISCUSSION
After reviewing and auditing the audit reports of the sample companies of 1840 data-year, 900 data-year companies has been classified at the low level of earning manipulator companies and 940 data -year companies has been classified at the high level of earning manipulator companies. In this study, the prediction power of earning manipulation companies has been investigated by hybrid Artificial Neural Network method and Particle Swarm Optimization (PSO) algorithm and also by hybrid Artificial Neural Network method and Imperialist Competition Algorithm (ICA) and a comparison has been made between the accuracy of the research models. The areas under Receiver operating characteristic (ROC) curve of the Beneish model have been estimated up to 0.6001 and 0.5538 using the hybrid neural network trained by Imperialist competition algorithm and particle swarm optimization algorithm, respectively. The area under the ROC curve in the Beneish model has been estimated in the range of 0.5 - 0.6 and indicates the Beneish model test has been rejected in detecting and identifying earning manipulator companies. Therefore, it can be seen that the separation of the two groups of earning manipulator and non-manipulator companies is not significantly different from the separation of the chance model and it can be said that the Beneish model is a completely random model in the Tehran Stock Exchange and cannot be used to identify earning manipulator companies. Also, the best prediction accuracy of the Beneish model has been estimated up to 57.55 and 55.71 percentages using the hybrid neural network method trained by the Imperialist competition algorithm and the particle swarm optimization algorithm, respectively.
5- CONCLUSIONS & SUGGESTIONS
Findings indicate that the prediction accuracy of the proposed model has increased from 57.55 to 63.86 percentages and 55.71 to 59.84 percentages by the ANN-ICA and ANN-PSO, respectively. Development of the model, area under curve (AUC) of ROC has been increased and the prediction error has been reduced to 6.31 percentages by the ANN-ICA and to 4.13 percentages by the ANN-PSO, but the test result is still poor. In fact, the accuracy of model prediction by the ANN-PSO has been improved compared to the ANN-ICA.
However, it can be seen that relying on these variables by itself could not easily identify earning manipulator and non-manipulator companies. Considering that the proposed model with the variables of Competition in the Product Market and Information Asymmetry has not significantly improved the accuracy of the prediction model, it can be seen that there is not a significant relationship between these variables and earning manipulation variable.
In order to judgement whether or not the results of ANN-ICA and ANN-PSO of research models are significantly different, the Wilcoxon test has been performed at a significance level of 5% as the statistical method of non-parametric. The results of Wilcoxon test show that the normal statistic of Wilcoxon test is more than the critical value of 1.64 and the significance level is less than 0.05 in both methods .Also, the average rank has been calculated up to 548.5 before the development of the model and has been calculated up to 5549.7 after the development of the model, so the research hypothesis is confirmed.