genetic algorithm

One of the major challenges of the performance audit is identifying the factors affecting the correct implementation and how these factors affect the operational process improvement of executive bodies. The current research aims to implement the performance audit in the Iran Audit Court. This research is applied. The present research has two communities. The first group comprises specialists and experts (audit organization auditors, Audit Court auditors, Audit Court accountants, and financial managers). The second community consists of National Audit Court employees and managers during the research period. In the first community, the snowball technique was used to determine the sample size, and 14 people were selected based on the principle of information saturation. In the second community, the convenience sampling method was used to determine the sample size using Cochran's formula (n=315). Eight effective factors in implementing performance audit in the National Audit Court were identified based on the theoretical basis. Regarding the Delphi-phase type 3 approach, the identified factors were confirmed. Then, 18 neural network, genetics, heuristic, and meta-heuristic approaches were investigated to investigate the effect of the effective factors on the implementation of performance audits in the National Audit Court. The gray wolf approach was more accurate than other methods. According to the results of the gray wolf method, infrastructure variables (0.3440); efficiency (0.1849); effectiveness (0.1134); economic efficiency (0.2947); cultivation (0.3657); specialist human resources (0.5984); organization performance (0.4609); management and leadership (0.6804) have a positive and significant effect on the implementation of performance audit.

In this paper, we propose a modification to the use of the risk aversion coefficient in optimization models, based on research literature and mathematical methods. The modified risk aversion coefficient introduced in this paper can be applied in the maximization part of the model without any adverse effects. By doing so, it can improve the accuracy of meta-heuristic algorithms in finding optimal solutions. To test the efficacy of our proposed model, we applied it to 30 shares of the Tehran Stock Exchange, along with a zero-risk asset, taking into account some limitations in the market. We used a genetic meta-heuristic optimization method to solve the model, and to measure its efficiency, we compared the results of the optimization process with 2500 randomly generated portfolios that were within the problem's constraints. Our results show that our model outperforms the random portfolios in terms of both risk factors and return. In conclusion, our proposed modification to the risk aversion coefficient can improve the accuracy of optimization models, and our results demonstrate its effectiveness in generating optimal portfolios in the market.

Financial statements fraud is increasingly a serious problem for businesses, governments and investors have become. In fact, the issue of the reliability of capital markets, bosses and even threaten the auditing profession. Auditors in particular face their apparent inability to detect large-scale fraud, and therefore various methods have been proposed to identify this problem. The aim of this study was to compare different models of traditional and modern simulation and predict fraud in the financial statements. Study the practical approach. Research period 1390 to 1398 and the model of the selected companies in the Tehran Stock Exchange has been used. In this study, based on three traditional approach, genetic algorithms and methods to predict non-linear Markov switching models have estimated fraud and accuracy.  Based on the prevailing regime in Tehran Stock Exchange regulations fraud is high; According to the results of highest accuracy in predicting fraud regime change and genetic algorithms and logistic highest and the accuracy of the model, respectively.

According to capital market research, the negative stock price shock in any market is a function of environmental factors and specific characteristics of the company, and any insight on how to describe and predict the shock can affect the decisions of investors and activists in the stock market. In this study, based on data related to 140 companies listed on the Tehran Stock Exchange.we have attempted to predict stock price shocks with emphasis on financial ratios. In order to select the optimal variables from the set of 96 variables, two evolutionary algorithms of particle swarm optimization and genetic algorithm have been used. After applying the mentioned algorithms, finally, 8 variables affecting permanent and temporary shocks were extracted, which in the regression model mentioned in the research, their effect on the predictor of shock was investigated. the results of RSME model are the permanent shock (genetic algorithm), permanent shock (particle swarm optimization), temporary shock (genetic algorithm) and temporary shock (particle swarm optimization (particle swarm optimization), 5.8433 , 5.6284 , 7.537 and 7.295 . as we observe , RSME in permanent shock based on genetic algorithm is more than RSME permanent shock model based on the evolutionary algorithm of particle swarm optimization. also in the transient shock model based on the genetic algorithm , the model is more than RSME of the temporary shock model based on the evolutionary algorithm of particle swarm optimization . It can therefore be stated that the estimated regression is based on the selected variables from the evolutionary algorithm of the particle swarm optimization, and has better predictive power than the selected variables of the genetic algorithm.

For the growth and development of countries, companies, and even individuals, investment on their part is necessary and vital, and these investments should be optimal for more benefit and effectiveness. Since the introduction of Markowitz's theory and even before that, the concept of optimal investment as a compromise between risk and return has been considered. During several decades after that, new definitions and dimensions of optimal criteria and especially risk have been proposed.In this article, an attempt has been made to present a model of liquidity risk using the concept of diversification in the form of Shannon's entropy and an econometric approach, an optimal portfolio of investments with the lowest risk and the highest return, in the form of a portfolio of 4 industrial groups of the Tehran Stock Exchange, including metal groups. Essentially, banks, oil products and metal ores, which have the highest market value of the Iranian stock market, should be provided.The statistical data of this research for selected industries include daily price index return and daily price gap return between 2015 and the end of 2019. To calculate the liquidity risk, using multivariate GARCH methods, the variance-covariance matrix of price index return and price gap, calculated and used in the presented model, and finally the optimal weight using coding in MATLAB software and using algorithm optimization method The genetics of non-excessive ranking of the second edition has been calculated for selected industries.The output results of the model show that the optimal weight of the groups with less variance in the optimal portfolio is higher. Besides, the effect of removing the concept of liquidity from the model leads to an increase in the weight of industries that have less liquidity, and along with the increase in risk, the return of the optimal portfolio also increases in this case. Also, by removing the limitation of Shannon's diversification index, the output results show that this limitation has almost no effect on the optimal weights (at least in this model).

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.

Tax revenues are one of the most important sources of governments and cover a large portion of government spending. In recent years, fraud in financial statements and tax returns has increasingly become a serious problem for businesses, governments and investors. Most taxpayers are looking for a way to manipulate their financial statements and reduce their taxable profits. Therefore, identifying tax fraudsters and companies that cheat on financial statements has become a vital issue for the government.The purpose of this study is to present a model that uses the improved ID 3decision tree algorithm. Also, to improve its performance and accuracy, it was combined with multilayer perceptron neural networks optimized by genetic algorithm to select financial ratios associated with tax fraud and reduce computational overhead. The tree in the proposed model has the lowest depth possible, so it has high velocity and low computational overhead. For this purpose, the financial statements of 06companies listed in Tehran Stock Exchange during - 4330 4331were studied and 41financial ratios were extracted. By ANOVA test, 33ratios and finally by neural networks 7ratios related to tax fraud was selected as the model input data. The proposed model, with %4411accuracy, has been successful in identifying fraudulent companies with the highest accuracy and predictive power over the adaboost algorithms.