درخت حوزه‌های تخصصی

One of the most important aspects of investment is determining the “optimal investment portfolio”. To date, scientific research has been conducted to determine the optimal portfolio with “artificial intelligence” and “Fuzzy logic”. However, we determine the optimal portfolio based on Dynamic Stochastic General Equilibrium (DSGE) model. On the other hand, several factors affect returns, which is one of the most important issues in investment decision-makings, and various models have been developed to analyze the return of “capital” and “other assets”. In this regard, some of the most important models include linear and non-linear models, artificial neural network models, Fama–French model, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, and optimal stable model. All of these models are indicative of the use of quantitative methods and models in the investment industry. One of the causes of using these models is developing the financial economy. In fact, the development of financial economy has more increased with the concept of “portfolio optimization”. In fact, the portfolio optimization and diversification concept is the basis of developing and expanding classical finance and financial decision-making. Financial markets, especially the capital market, can have a great deal of relevance to other sectors of the economy. In the present study, we design and calibrate a new Keynesian DSGE model in relation to the optimal investment portfolio and effectiveness of shocks (e.g., productivity and foreign exchange earnings fluctuation) on macroeconomics variables.

Chief executive officers (CEO), as the central pillar directing companies at the capital market level, plays a critical role in making investment decisions to de-crease agency costs and maximize shareholders’ wealth; however, their psycho-logical attitudes and perceptions lead to different investment functions of these companies in a competitive market. Accordingly, this study aimed to detect the propositional arrays of the investment functions using Q analysis in order to identify the CEOs’ mental typology in distinguishing investment functions. The present study was carried out with the participation of 13 CEOs from Tehran Stock Exchange companies during a one-year period (2018-2019). The study encompassed two different phases. In the first phase, content analysis was used to identify the phrase Q of the CEOs’ investment functions. In the second phase, Q analysis was adopted to typify the CEOs’ investment functions, which was based on the subjective cognition of the target population and contributed to the development of the approaches in line with the research objectives. The results confirmed the existence of three mental patterns in the CEOs regarding the in-vestment functions in the capital market. These mental patterns were ‘investment function in stock market indices’ as the first mental pattern, ‘investment function in risk control’ as the second mental pattern, ‘strategic investment function’ as the third mental pat-tern. The study results revealed different types of investment functions among the CEOs’ of stock exchange companies and thus contributed to the development of financial theories from the perspective of CEOs’ cognition.

This study examines the criterion of value at risk from another perspective and presents a new type of mean-value at Risk model. To solve the portfolio optimization problem in Tehran Stock Exchange, we use NSGA II, MOACO, and MOABC algorithms and then compare the mean-pVaR model with the mean-SV model. Given that, finding the best answer is very important in meta-heuristic methods, we use the concept of dominance in the discussion of multi-objective optimization to find the best answers and show that, at low iterations, the performance of the NSGA II algorithm is better than the MOABC and MOACO algorithms in solving the portfolio optimization problem. As the iteration increases, the performance of the algorithms improves, but the rate of improvement is not the same, in a way, the performance of the MOABC algorithm is better than that of the NSGA II and MOACO algorithms. Then, to compare the performance of the “mean-percentage of Value at Risk” model and the “mean-semi variance” model, we examine both models in the standard mean-variance model and show that the mean-pVaR model, compared to the mean-SV model, Has better performance in stock portfolio optimization.

The Supply Chain Network Design (SCND) with perishability is an active research topic. The Agri-fresh Food Supply Chain (AFSC) is a relevant topic to SCND and this study aims to model a new AFSC for a real-world case study. Regarding the traditional AFSC, the geographically dispersed small farmers transport their product individually to market for selling. This leads to a higher transportation cost, which is the major cause of farmers’ low profitability. This paper formulates a traditional product movement model to represent the existing AFSC. The concept of sharing economic approach is employed by the aggregate and collaborative transportation of products to minimize transportation inefficiency. This paper proposes an aggregate product movement with the vehicle routing model to re-design an AFSC for a case study in Iran based on the data of ETKA Company-the largest domestic agri-fresh food supply chain. A four-echelon, multi-period, Mixed Integer Non-Linear Programming (MINLP) approach for the proposed location-inventory-routing model is formulated for perishable products via considering the clustering of farmers to minimize the total distribution cost.

Price return and P/E are two interesting factors for a lot of investors; The Bohmian quantum mechanics used referring to the time correlation of return and P/E of the stock market under consideration. In this study, we extend the quantum potential concept to determine the behaviour of P/E and also price return in two different industry of Tehran stock market during a time interval of April. 2008 to march 2019. The obtained results show that the quantum potential behaves in the same manner for P/E and price return, also confines the variations of the P/E and price return into a specific domain. Furthermore, a joint quantum potential as a function of return and P/E is derived by the probability distribution function (PDF) constructed by the real data of a given market. It serves as a suitable instrument to investigate the relationship between these variables. The resultant PDF and the corresponding joint quantum potential illustrate that where we have light points in joint quantum potential chart, the probability of those amount of P/E and price return are more than other points. In addition, because of the rectangular shape of the joint quantum potential chart we can say that these two variables behave as two independent variables in the Market.