شهره یزدانی

The purpose of this study is to level the optimal analytical topics for evaluating the cost of petrochemical stock in the capital market. In this study, which was synthetic and inductive-deductive in terms of data collection method, 12 accounting and financial management specialists at the university level participated as panel members in the quality department. In fact, in the qualitative part, which used meta-synthesis and Delphi analysis, the aim was to identify the optimal analytical themes for the valuation of cost-effective stocks and then, in a small section with the participation of 16 petrochemical brokers, to classify the identified statements in the form of a total interpretive structural modelling based on impact priority. Therefore, relying on meta-synthesis analysis, 12 studies were first evaluated as the basis for evaluation to determine the optimal analytical themes of cost-based stock valuation based on critical evaluation, based on which 11 themes were selected propositions and entered into Delphi analysis to determine theoretical adequacy. In this stage, 3 propositions were removed during the two stages of Delphi analysis and a total of 8 analytical propositions of stock valuation were economically entered into the quantitative analysis section, ie total interpretive structural analysis. In this section, the results show the most influential analytical themes of cost-effective stock valuation in the petrochemical industry, two statements of normal stock market price analysis based on the current rated price of "K2" and a focus on the net asset value is "K3", which is at the fourth level of this model.

both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.

Governments always affect the economic environment as legislators in the field of business. The economic conditions governing the market and business require different conditions and contexts for decision making and corporate execution practices. Companies and managers administering them under environmental conditions, achieve their goals by employing various earnings management methods. Hence, the present study examines the effect of governments’ economic performance on the earnings management methods used in listed companies of Tehran Stock Exchange (2004-2016) for a sample of 16 industries and 271 companies. To test the hypotheses, multivariate regression model was used. The results showed that during the research period, companies managed earnings, and while more than 70% of companies used the accrual earnings management method, there was a relationship between annual economic indicators and real earnings management, and the change in general level of prices and the political connections of states have affected the relationship. Also, the accruals-based earnings management method occurred independent of annual economic indicators and there is a significant relationship between governments changes and earnings management methods.