Advances in Mathematical Finance and Application (AMFA)

The gaining returns in line with risks is always a major concern for market play-ers. This study compared the selection of stock portfolios based on the strategy of buying and retaining winning stocks and the purchase strategy based on the level of investment risks. In this study, the two-step optimization algorithms NSGA-II and SPEA-II were used to optimize the stock portfolios. In order to determine the winning algorithm, the performance indexes, Set coverage and the Mean Ideal Distance were used. Finally, the active shares of 50 Tehran Stock Exchange com-panies were analysed (2007-2016). The results indicate that the SPEA-II algo-rithm can perform optimization and achieve a better performance than the NSGA-II. This algorithm could achieve better outcomes than the winning strategy during the selection period based on the risk-taking strategies in different months

The present study models the risk of investment in the petrochemical industry considering the impacts of exchange rate (US dollar to Iran's Rial) movements using the time series data from November 2008 to March 2019 and ARFIMA-FIGARCH framework. The empirical results prove the existence of the Fractal Market Hypothesis, FMH, and the Long Memory property in both the risk and return of the petrochemical stock index. These findings can be culminated in reaching a reliable and significant model to evaluate the investment risk in the petrochemical industry. In line with this, to analyze the idea whether considering the exchange rate movements matter for assessing the risk management in the petrochemical industry, the effects of exchange rate movements as a crucial source of systematic risk in Iran has been taken into consideration in the process of modelling the risk of investment in that industry. Our results demonstrate that the exchange rate movements have had a direct and significant effect on the investment risk of that industry so that if, on average, one percent change occurs in the exchange rate, the investment risk in this industry changes by 57% in the same direction.

The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-βRisk (MShβR) model and the Multi-Objective MeanSharp-βRisk (MOMShβR) model base on Range Directional Measure (RDM) that can take positive and negative values. We utilize different risk measures in these models consist of variance, semivariance, Value at Risk (VaR) and Conditional Value at Risk (CVaR) to find the best one as input. After using our proposed models, the efficient stock companies will be selected for making the portfolio. Then, by using Multi-Objective Decision Making (MODM) model we specified the capital allocation to the stock companies that selected for the portfolio. Finally, a numerical example of the Iranian stock companies is presented to demonstrate the usefulness and effectiveness of our models, and compare different risk measures together in our models and allocate assets.

he financial management field has witnessed significant developments in recent years to help decision makers, managers and investors, to made optimal decisions. In this regard, the institutions investment strategies and their evaluation methods continuously change with the rapid transfer of information and access to the fi- nancial data. When information is available as several inputs and output factors, the data envelopment analysis (DEA) applies to calculate the efficiency of com- panies. Distinguishing efficient companies from inefficient ones, makes it possi- ble for the financial managers to select suitable portfolios. The discriminating power of DEA depends on the number of companies under evaluation and the number of inputs and outputs. When the number of inputs and outputs are high compared to the number of units, most of the units will be evaluated as efficient, thus the discriminating power of DEA decreases and the results are not reliable. To deal with this problem, the Quick-Reduct algorithm of the rough set theory (RST) was used in this study to reduce inputs or outputs. It should be noted that the advantage of this algorithm is its ability to use negative data.

Fraudulent financial reporting has been one of the most sensitive issues on the business world. Financial statements that conceal the company's facts have caused great losses to its stakeholders. The ranking of companies based on fraudulent financial reporting is one of the key issues for performance analysis. This study, by using financial variables and the data envelopment analysis methodology, ranked the pharmaceutical companies listed on the Tehran Stock Exchange in terms of fraudulent financial reporting. The data and theoretical framework of the research are based on library studies and data analysis has been done based on the data envelopment analysis model. The results of this study indicate that the most manipulation of profits in 2015 was in the Daru Amin and Faravordehaye Tazrighi companies, as well as Iran Daru and Sina Daru in 2016. This will lead to the companies being ranked at the highest level of fraudulent financial reporting.

This Study seeks to scrutinize whether surplus free cash flow is correlated with earnings management, if auditor size moderates this relationship. To do so, modified Jones discretionary accrual model (1995) and audit firm size are used as audit quality indicator to measure earnings management. The research hypotheses are built upon a sample of 103 companies listed on the Tehran Stock Exchange during the years 2013 to 2017 and then tested using multiple regression model based on panel data techniques. The results reveal that earnings management is significantly associated with surplus free cash flow. Furthermore, the findings confirm that auditor size exerts no significant impact on the relationship between surplus free cash flow and corporate earnings management.

The purpose of this research is to provide a model for reporting quality of financial information based on behavioral and value accounting of listed companies in Tehran Stock Exchange which is based on Structural Equation Modeling. This research in terms of applied purpose is applied research and in terms of data collection method is post-semi experimental research in the field of proofing accounting research with deductive- inductive approach. The statistical population of this is including the sample of 128 listed companies in the Tehran Stock Exchange between 2007 and 2017. The behavioral characteristics of managers (hidden variables) is measured by observable variables of myopia, opportunistic behavior and over confidence of managers and the value content of accounting information (hidden variable) is evaluated by the observable variables of relevance of profit and relevance of book value. And reporting quality of financial information is also investigated based on the scores accrued to each company and the announcement published by the Tehran Stock Exchange based on the companies' rating in terms of the quality of reporting and proper notification. After insuring the acceptable fitness of the measurement pattern and the structure of research in both approaches; Structural Equations Modeling and regression, the results indicate that there is a significant negative relationship between the behavioral characteristics of managers and the reporting quality financial information. Also, there is a positive and significant relationship between value content of accounting information and reporting quality of financial information.

Audit market concentration causes to decrease the scope of companies' au-thorities for selecting the audit institutions but instead it increases the power of auditors' market that it turns, leads to a decrease in quality and an increase in auditors' stress. The purpose of the present research is to study the effect of audit market concentration on auditors's job stress and audit quality of Tehran Stock Exchange (TSE) Listed Companies performed on a total of 97 companies between the years of 2013-2017. In order to evaluate the audit market concentration, the ratio of company's audit fees to total industry audit fees was used. The Accruals Quality model was also used to evaluate the audit quality. The results of the study showed that the audit market concentration had a negative and significant effect on the audit quality so that with the increase in audit market concentration, the audit quality is decreased. Also it was found that the audit market concentration had a posi-tive and significant effect on the auditors' job stress: it means that the in-crease of concentration on audit market as a result of time pressure can in-crease the auditors' job stress and thus the risk of financial statement assess-ment.