Introduction: Fund or capital which is needed to finance a business or invest in a plan is prepared by different groups, those groups expecting a significant rate of return. Cost of equity of a business is equal to the minimum expected returns which a plan should include, and in this case investors will become eager to pay their fund for business. Research shows that various factors have effect on expected rate of return. Basu (1977) mentioned in his studies that those stocks that have larger P/E RATIOS will have a larger expected return. Bhandari (1988) described in his studies that depth ratio to total equity of shareholders as one of leverage RATIOS, have a relationship with expected return....

In this paper, the fraud detection model in FINANCIAL statements is examined by using the FINANCIAL RATIOS tool. The purpose of the fraud detection model is to examine the FINANCIAL statements using the FINANCIAL instrumnet tool. The period of this research is considered to be 1339-1394. The statistical population includes all companies accepted in Tehran Stock Exchange. The statistical sample consisted of 35 healthy companies with healthy, non fraudulent statements in the Tehran Stock Exchange, which had been listed on the Stock Exchange before 1387, and during this period, at least once in the list of the Top Vocational Companies Declared by the Stock Exchange. Research in terms of the purpose of the theoretical type and in terms of analytical-experimental methodology. In order to investigate the relationship between independent variables and dependent variable, two – way independent t-test and Mann-Whitney test as well as developed logistic regression model were used. The results of the research showed that by using the FINANCIAL statements analysis of listed companies Securities can be discovered in fraudulent FINANCIAL reporting. The results of the research indicate that all FINANCIAL RATIOS include the ratio of total debt to total assets, the ratio of liquidity to current debt, the FINANCIAL RATIOS of current assets to current debt, and the ratio of net profit to equity in determining fraud. Has an effective role.

Designing a system which is capable of predicting FINANCIAL insolvency in insurance companies is a major task of each national insurance supervisory institute all around the world. Although there are many models and systems in different countries to do so, using these models in Iran's insurance industry is not possible. The reason is some restrictions and impediments related to this industry in Iran. However, a system that is able to assess FINANCIAL solvency or FINANCIAL health of companies and inform regulators is necessary. In the first step the prerequisites of using such systems and models are identifying factors affecting FINANCIAL stability and health of insurance companies. In this paper, twenty four FINANCIAL variables in five factors (capital adequacy RATIOS, profitability RATIOS, liquidity RATIOS, operational and risk RATIOS and other basic factors) affecting FINANCIAL health were derived from the literature and tested in their relation to the independent variable.

The purpose of this research is to investigate the prediction of corporate social responsibility reporting using FINANCIAL RATIOS. To answer the research question, four prediction models of linear regression, K Nearest Neighbor, decision tree, and deep learning were investigated. Also, 61 FINANCIAL RATIOS were used according to previous research using data related to listed and non-listed companies of Iran from the years 2012 to 2018. According to the re-sults obtained from the estimation of each of the proposed prediction mod-els, it can be stated that the k-nearest neighbor model has the lowest RMSE value, and in fact, this model predicts the amount of social responsibility with less error than other models. The linear regression model with the high-est RMSE value has a weaker performance than other models. LSTM model and decision tree respectively had the lowest RMSE value after the k-nearest neighbor model. As a result, since the LSTM model requires a large number of test sam-ples for deeper learning, it could not achieve high performance in the evaluated data set. Based on the investigations, it can be stated that the current research does not have a similar example inside or outside of Iran.

Using logit analysis in cross section data, this paper examine the role of accounting data to develop a model for detecting factors associated with fraudulent FINANCIAL reporting. The sample is comprised of 178 firms (66 with fraudulent FINANCIAL reporting and 112 with non-fraudulent FINANCIAL reporting) listed in Tehran Stock Exchange (TSE) during the period of 1383-1386. Firms with fraudulent FINANCIAL reports selected on the basis of 1) Inclution of company in TSE lists for reasons associated with falsification of FINANCIAL data and 2) doing insider trading and the existence of court proceedings pending with respect to fraudulent FINANCIAL reporting. After doing analysis ten FINANCIAL RATIOS introduced for examination as potential predictors of fraudulent FINANCIAL reporting.The results indicate that the model is accurate in classifying the total sample correctly with accuracy rate exceeding 82.98 percent. Also, results demonstrate that the models function effectively in detecting fraudulent FINANCIAL reporting and can be of assistance to various users such as auditors, taxation and other state authorities, the banking system, and so on.

We examines published data to develop a model for detecting factors associated with Fraudmental FINANCIAL statements (FFS). Most Fraudmental FINANCIAL statements in Tehran Stocks Exchange (Tse) Listed Companies, can be identified on the basis of the FINANCIAL RATIOS. We use a sample of 68 firms includes 34 with FFS and 34 non-FFS. Nine FINANCIAL variables are selected for examination as potential predictors of FFS. Univariate and multivariate statistical techniques (logistic regression) are used to develop a model to identify factors associated with FFS. The model is accurate in classifying the total sample correctly with accuracy rates 83.8 per cent. The results therefore demonstrate that the models function effectively in detecting FFS and could be of assistance to Investors and auditors, both internal and external and taxation, Government bodies and to the banking system.

This study aims to identify the best FINANCIAL RATIOS and the most efficient method for fraud risk detection in the FINANCIAL statements of the listed companies by considering the FINANCIAL importance of decision-making as well as the growing fraud statistics and detrimental effects. The statistical sample included 180 companies listed in the Tehran Stock Exchange from 2014 to 2021 (532 fiscal years suspected of fraud and 908 non-fraudulent fiscal years). Theoretical foundations were first taken into account to extract 96 FINANCIAL RATIOS. The k-NN algorithm, Bayesian network, support vector machine, and bagging method were then employed for fraud risk detection in FINANCIAL statements. According to the findings, the adopted methods failed to meet the evaluation standards in general. With an accuracy of 70. 60% and a proportionality function value of 0. 2940, the gray wolf optimization (GWO) algorithm was then utilized to reduce the RATIOS in order to improve performance. After 31 iterations, nine appropriate FINANCIAL RATIOS were determined. The extracted FINANCIAL RATIOS were then used to reevaluate the effectiveness of the proposed fraud detection strategies. After the FINANCIAL RATIOS were reduced, all of the proposed approaches yielded better results. The accuracy and efficiency of the bagging method, support vector machine, Bayesian network, and k-NN algorithm were reported 79. 25% and 81. 70%, 75. 83% and 80. 30%, 72. 01% and 74. 60%, and 74. 55% % and 75. 60%, respectively. In conclusion, the bagging method outperformed the other approaches in terms of accuracy and efficiency.