The aim of this research is to examine the performance of a trading strategy based on STOCHASTIC DOMINANCE in Iran’ s stock market. To achieve this purpose, we used a sample including the daily returns of the stocks listed on Tehran stock exchange, between 2001 and 2015, and exploited the portfolio study approach. In this way, for each month portfolios are constructed based on second and third degree STOCHASTIC DOMINANCE, by buying the DOMINANCE stocks and selling the dominated ones. Then the performance of this trading strategy is tested with the respect to the obtained alphas from an extended five-factor model. The results show that the arbitrage portfolios based on STOCHASTIC DOMINANCE produce positive, statistically significant, excess returns. In addition, these returns are robust even after being adjusted to risk premiums (Market, Size, Value, Liquidity and Momentum premiums). Consequently, the results support the fact that the STOCHASTIC DOMINANCE factor is engaged in pricing of the Tehran stock exchange. Moreover, it is crucial to notice that the potential price distortions related to infrequent trading have an enormous impact on the results of the study.

The formation of the optimum portfolio based on risk and return is one of the most important decisions of investors in financial markets, for which there are various methods. Markowitz’ s Mean-Variance method was the first method introduced in this area; but because of the normality assumption for the return distribution function, it only considered specific characteristics (expected return and variance) of the return distribution function. Another method introduced years later was the STOCHASTIC DOMINANCE method which considers all of the return distribution function instead of specific characteristics such as variance. The present research investigates the “ STOCHASTIC DOMINANCE method” in portfolio optimization and compares the performance of this method with “ Markowitz Portfolio Optimization method” using performance evaluation criteria in the Tehran Stock Exchange. The performance evaluation criteria used in this article are: Sharpe, Treynor, Sortino and Omega. The results of this research indicate the advantage of Second Order STOCHASTIC DOMINANCE method on the Markowitz method in out-of-sample and in-sample approaches. Moreover the Second Order STOCHASTIC DOMINANCE method has a higher cumulative return than the Markowitz method.

This paper presents a decision making approach for mid-term scheduling of large industrial consumers based on the recently introduced class of STOCHASTIC DOMINANCE (SD) -constrained STOCHASTIC programming. In this study, the electricity price in the pool as well as the rate of availability (unavailability) of the generating unit (forced outage rate) is considered as uncertain parameters. The self-scheduling problem is formulated as a STOCHASTIC programming problem with SSD constraints by generating appropriate scenarios for pool price and self-generation unit's forced outage rate. Furthermore, while most approaches optimize the cost subject to an assumed demand profile, our method enforces the electricity consumption to follow an optimum profile for mid-term time scheduling, i.e. three months (12 weeks), so that the total production will remain constant.

One of the most important concerns of investors in the capital market is choosing a portfolio that is optimal in terms of profitability and risk. The purpose of this study is to compare the profitability and risk level of portfolios containing different percentages of shares of agricultural and food industry groups and other industries. For this purpose, the daily returns of 18 companies active in the Tehran Stock Exchange for the metals, agriculture, food industry, banking, petrochemical, and chemical groups during the period 2019-2020 were collected and classified into 11 portfolios. The returns of the portfolios were simulated and the best portfolios were determined based on STOCHASTIC DOMINANCE criteria. The results showed that if the degree of risk aversion of individuals is not known, portfolios 10 and 6, which have 30% and 25% of shares of the agriculture and food industry, respectively, are superior, and if the degree of risk aversion of individuals is clear, portfolio 11 with 30% of shares of agriculture and food industry group is the best portfolio among the risk portfolios. Also, for people with lower risk aversion, portfolio 6 with 25% of agricultural and food industry shares and 20% of petrochemical group shares, and portfolio 4 with 80% of petrochemical group without food and agriculture industry shares are superior portfolios, and for people with a higher degree of risk aversion, portfolios 10 and 2 with 30% and 10% of the shares of agriculture and food industry group, respectively, are superior to the other portfolios. The top portfolios have the highest percentage shares of in the agriculture and food industry and the highest percentage is related to the shares of the petrochemical group. Therefore, the shares of the agriculture and food industries group, along with other shares, can contribute to the superiority of the portfolio. Also, by increasing the percentage of petrochemical stocks in the portfolio, the utility can be maximized.

In this paper we study the common rules of STOCHASTIC DOMINANCE (first order STOCHASTIC DOMINANCE, second and third) and the concept of STOCHASTIC DOMINANCE are discussed in the context of real behavior of investors. Base on this, we examine the Investor's behavior toward growth-value stocks based on rules of STOCHASTIC DOMINANCE for the listed companies in TSE. The results don't show first, second and third order of STOCHASTIC DOMINANCE of growth stocks over value stocks and value stocks over growth stocks.

In this paper, for ranking Herfindahl-Hirschman concentration factor in Iran’s industries is used first, second, and third STOCHASTIC DOMINANCE approach. Features of this approach in industry-exclusive ranking is that can be use from features of probability functions and indicators inferred in order to the market structure analysis. STOCHASTIC DOMINANCE known as a form of random ranking which in that is used from the probability density functions and probability distribution functions to comprehensive evaluation of the characteristics of a variable. This approach, widely used in decision theory and analysis to decision-making. This approach, widely used in decision theory and analysis to decision-making. All comparisons between industries based on STOCHASTIC DOMINANCE approach in this study is 8515. According to research findings for the production of tobacco industry in terms of market concentration based on first-order STOCHASTIC DOMINANCE is the dominant on 127 industries and based on second-order STOCHASTIC DOMINANCE is dominant on 1 industry and overall is dominant on 128 industries of 130 industries studied and not dominated by any industries. As a result, manufacturing industries of tobacco products on the market has the highest concentration and is the most exclusive of Iran’s industry. After that Malta and alcohol free beer industries in terms of concentrating based on first-order STOCHASTIC DOMINANCE is the dominant on 123 industries and no industry in terms of concentration on the market is not dominated and in terms of concentration and market power is in second place. On the other hand, not only brick manufacturing industries aren’t' t dominant on any industrial but the concentration in the market on the basis of first-order STOCHASTIC DOMINANCE is dominated by the 128 industries and totally is dominated by 129 industries and it can be stated that there is effective competition in this industry.

Agricultural production processes inherently incorporate risks. When farmers are risk-averse as is typically the case, they allocate inputs in such a way to reduce the impact of risks. Thus it may be important to include risk as an element in farmer behavior modeling.The present study attempted to identify risk-efficient strategies for wheat in Kavar district using STOCHASTIC DOMINANCE and STOCHASTIC DOMINANCE with respect to a function.The results indicated that four deficit-irrigation based strategies had first-degree, and one second-degree STOCHASTIC DOMINANCE over the strategy based on full irrigation.

One of the problems in developing countries is unpleasant nexus between fiscal and monetary policies. Some of the economists believe that the main reason for inflation in such economies is monetaricizing of Government debt such as bounds. In this article it is tried to estimate the Degree of Fiscal DOMINANCE in Iran’s economy using General Equilibrium Dynamic STOCHASTIC Model based on Bayesian approach. Our result reaches to 77 percent for fiscal DOMINANCE which shows a low degree of central bank independence. At the end the usefulness of the obtained results are examined using some indices.

An important feature of agriculture is the high level of production, market and financial risks faced by farmers. There are different policies, programs and measures that contribute to the reduction of these risks. The farmers prefer programs that reduce the income risk and increase the income level. Crop insurance is one of the best these programs. The purpose is to determine the best option of Pistachio supplemental insurance from the perspective of Pistachio Growers in Rafsanjan district. Therefore, net income per hectar for different options was simulated and the best option was determined according to STOCHASTIC DOMINANCE criteria. Six years data of price, yield, production cost per hectar, premium and compensation per hectar were used for this analysis. The results showed that three options of Pistachio supplemental insurance exhibited first degree STOCHASTIC DOMINANCE over the refusal of insurance and results of STOCHASTIC DOMINANCE with respect to a function indicated that for risk aversion less Pistachio Growers, the third option but at risk aversion greater, the second option is the best option.