درخت حوزه‌های تخصصی

The portfolio selection problem is one of the main investment management prob-lems. In the portfolio selection problem, robustness is sought against uncertainty or variability in the value of the parameters of the problem. This paper has been conducted for Robust portfolio optimization based on the mean-cvar approach. And introduces the linear mean-cvar model as a criterion for calculating risk and provides an optimal Robust mean-cvar model. Robust approach used in this research is the Bertsimas and Sim. In this approach, Robust counterpart presented for a linear programming model remains linear, maintaining the advantages of the linear programming model in the optimal model. The model developed in this research is randomly selected by real data of 20 stocks of the S&P 500 index for three years, this development help portfolio selection problem to consider uncertainty. Interval optimization is modeling approach to consider parameters uncertainty in this paper. Considering uncertainty make model more realistic. The results of model show that this approach has computational efficiency and on the other hand proposed model produce better solution in risk and portfolio rate of return point of view

One of the biggest shortcomings of urban spaces in most cities of the country is the lack of suitable sports spaces, which in addition to improving the health of the general public, especially the youth, creates a lively environment and can boost the development of the tourism industry. Many projects in the country, especially sports, are slow or stopped due to not evaluating the relevant risks, so the purpose of this study is to identify and prioritize investment risks in the country's sports projects. The present study is a positive research in terms of philosophical foundations and is applied in terms of orientation. The statistical population of the study includes experts in the field of sports tourism and the sampling method has been done judgmentally. To conduct the research, in the first stage, the risks of investing in sports projects were assessed through literature review. The number of these risks was 15, and after screening with a Binominal test, 6 factors were excluded. The remaining 9 factors were evaluated in terms of degree of impact with Dematel technique and 5 factors, i.e. market risks, economic risks, legal risks, financing risks and stakeholder conflict risks were selected as the most effective risks in terms of net effect index. Finally, these 5 risks were ranked by Aras decision technique and it was observed that the economic, market and financing risks, had the highest priority.

There has not been presented any comprehensive index for Corporate Governance in spite of its crucial role in resolution and responsiveness. Although diverse methods have been sought for measuring the Corporate Governance in the previous studies, in this study, a comprehensive index is proposed for measuring Corporate Governance taking other indexes into account through mathematical equations. For testing the designed model’s functionality, a set of data related to 149 firms from 2014 to 2019 has been used jointly, too. To do so, a questionnaire, which asks the experts their idea about the weight and significance of the criteria of measuring the Corporate Governance of the firm, is made and distributed and the weight and significance of the criteria were determined through one method of multi-criteria decision model (Shannon Entropy). To measure the changeable hidden relationship which exists in this study of the Corporate Governance, provided with the measurement items, a confirmatory factorial analysis was taken, too; results show that the supposed model has been a proper index for Corporate Governance. This mixed index includes criteria such as the board of directors' size, independence, meetings, and its education, the CEO's permanence, the size, independence, financial specialty, financial experience, tenure and the meetings of the auditors hip committee, ownership concentration, organizational ownership, governmental ownership, deals with the dependent persons, the quality of the disclosure, proper information and weaknesses if the internal controls,

The purpose of this study was to present an optimal model Predicting Future Cash Flows optimized neural network with genetic (ANN+GA) and particle swarm algorithms (ANN+PSO). In this study, due to the nonlinear relationship among accounting information, we have tried to predict future cash flows by combining artificial intelligence algorithms. Variables of accruals components and operating cash flows were employed to investigate this prediction; therefore, the data of 137 companies listed in Tehran Stock Exchange during (2009-2017) were analysed. The results of this study showed that both neural network models optimized by genetic and particle swarm algorithms with all variables presented in this study (with 15 predictor variables) are able to provide an optimal model Predicting Future Cash Flows. The results of fitting models also showed that neural network optimized with particle swarm algorithm (ANN+PSO) has lower error coefficient (better efficiency and higher prediction accuracy) than neural network optimized with ge-netic algorithms (ANN+GA).