In this paper, we developed robust optimization approach that departs from the randomness assumption used in other methods of optimization under uncertainty and describe uncertainty in parameters through uncertainty sets; for PORTFOLIO SELECTION problem. The model can control the conservativeness of investor for PORTFOLIO SELECTION by a defined parameter. We used 50 active company of Tehran exchange stock in 3 first months of 1392 to study the performance of model. The results of paired comparisons in out of sample experiments shows that Markowitz PORTFOLIO which has same expected return by robust PORTFOLIO, has lower Sharpe ratio.

Choosing a stock PORTFOLIO is always one of the most important issues for investors. Theoretically, selecting a stock PORTFOLIO can be solved by minimizing risk assumptions with the help of mathematical relationships, but with the variety of choices in the capital market, mathematical relationships alone are not an effective solution. The variety of investment tools and the differences in the functionality of investors’ complexity have complicated the SELECTION process. Now the expansion of financial and capital markets, the use of rule-based systems for quick decisions, with minimal risk and away from human error, design, development, or improvement of these systems can be a competitive advantage. In the present study, neural network algorithms and genetic programming algorithms have been used to identify effective features and the decision tree to improve id3 has been proposed as a method for predicting price and trend of stock price change to select the OPTIMAL basket. The research results show that in addition to reducing computational and memory overhead, the proposed method is able to accurately predict severe fluctuations with nonlinear patterns and compared to modern methods such as nearest neighbor search, linear regression, autoregressive integrated moving average, and time series prophet algorithm will do better.

Modifications are an integrated part of economic evolution in financial section which include reforming the capital market structure, innovating in tools and an environment with more comprehensive discipline. Two dominant schools of thought in the literature on stock markets are fundamental and technical analysis. SELECTION the PORTFOLIO would be so important. So we use both fundamental and technical analysis to evaluate companies' stock and then in order to form a PORTFOLIO which consider different risk states and investor’s preferences utilize Mamdani Fuzzy and mixed integer linear programming model. The reasons for the use Mamdani Fuzzy system are its capability of working in vague environment and using human knowledge and for mixed integer linear programming model is its capability in finding the optimum solution among the several available ones. The results of evaluating the performance of formed PORTFOLIO for three cases of Risk averse, Risk neutral and Risk prone investor show that the performance of proposed PORTFOLIO is positive and proper, but in a more accurate scale the formed PORTFOLIO has a more proper condition for the risk averse investor.

The aim of this study was to "study behavioral factors in the SELECTION of OPTIMAL PORTFOLIO Iran capital market" is. In order to achieve this goal, the dominant factors for mental accounting and loss aversion behavior and investment in stock and OPTIMAL PORTFOLIO SELECTION with high efficiency compared to standard finance Using data from 106 firms listed in the Tehran Stock Exchange during the period of 5 years from 2011 to 2014 And regression analysis, variance analysis, was measured.The results showed that the expected return on PORTFOLIO behavioral model with an emphasis on mental accounting and loss aversion (as an indicator of behavioral factors) is greater than the expected return of the Standard Model and The hypothesis was accepted.

In this paper an OPTIMAL PORTFOLIO SELECTION is obtained so that it provides the maximal yield and at the same time satisfies the constraints on the value at risk. Value at risk is an important measure of extent to which a given PORTFOLIO is subject to different kinds of risk present in financial markets. The OPTIMAL weights of each share have been obtained using Genetic Algorithm (Gas). Actually GAs is stochastic parallel global-search algorithms based on the mechanism of natural genetics and the biological theory of evolution. Because GAs exploits strategies of genetic information and survival of the fittest to guide their search, they need not calculate the gradient or assume that the search space is differentiable or continuous. GAs simultaneously evaluates many points in the parameter space, so they are more likely to converge toward a global solution. Gas is very suitable for searching discrete, noisy, multimodal and complex space. The PORTFOLIO which is considered in this article has been selected from 12 various companies in the Tehran stock exchange. Simulation results show that the high performance of the VaR approach risk modeling and GA optimization method to SELECTION an OPTIMAL PORTFOLIO under a pre-specified constraint on the value at risk.    

Expected shortfall measures the loss of a PORTFOLIO in mathematical expectation when the amount of loss exceeds a threshold for a certain level of confidence and in a certain time horizon. Multi-horizon expected shortfall extends the concept of expected shortfall for an investment with a set of maturity horizons. In the present study, two stock PORTFOLIO models are designed based on the multi-horizon expected shortfall, the first is based on historical simulation and the second is parametric and based on the distribution of normal-Laplace mixture for proper fitting of tail data. Also, expectile is used to compute the expected shortfall in parametric form. The result of the experimental study of stock PORTFOLIO models designed on a stock PORTFOLIO with eight indices of the Tehran Stock Exchange by coding in the MATLAB software in the period 1390 to 1399 shows that the parametric approach in the test data in average return and Sharpe ratio criterions has a better performance than the historical scenario. Also, the relative error between the period expected shortfall predicted by the stock PORTFOLIO and its estimate in the test data in the parametric approach is less.

Selecting effective criteria on prices and consequently on investor’ s decision making is one of the most debating topics in PORTFOLIO optimization debate. Both of market and industry price changing and the information releasement by companies can affect stock prices. As a result, the goal of this study is surveying the role of market and industry price changing on stock prices and its effect on PORTFOLIO optimization. So we use stock price synchronicity. Stock price synchronicity measures the degree to which the market change can explain stock price movement. So by collecting price changing data from 130 sample companies during 10 years and with solving regression equations we computed stock price synchronicity. Then by collecting financial and nonfinancial data from the samples and analyzing these data and using data envelopment analysis (DEA) technique we made several PORTFOLIO to compare them by sharp ratio. The result shows that stock price synchronicity is about 59% in the sample and it is in conformity with international surveys like Jin and Myers (2006). Also the result shows that if stock price synchronicity affection be considered in PORTFOLIO SELECTION then PORTFOLIOs will have better return. In this research we use data envelopment analysis to choose PORTFOLIOs and use Fuzzy Delphi to choose effective criteria on stock price changing.