Efficient Asset allocation and investment portfolio selection are among the most critical and challenging issues in investment management and a continuous concern for investors. When investors invest in the capital market, they expect their portfolio to perform well. Therefore, this study determines the optimal stock portfolio of agricultural companies in the Tehran Stock Exchange (TSE). Thirty-two most important agriculture companies in the (TSE), with monthly data from 2014-2020, were selected from Iran's two most essential agriculture industries, the food and beverage industries, and the sugar industry. Two portfolios for the food and beverage industry and sugar industry goals: minimizing portfolio variance and maximizing portfolio return using the Markowitz model with two different scenarios and applying two minimum investment constraints of 1% and optimized maximum investment of 20% without considering these two constraints. The efficiency, variance, and Sharp ratios are also calculated. The results showed that both food and beverage industry portfolios and the sugar industry portfolios became more efficient when optimized to maximize portfolio returns. The result also indicates the food and beverage industry was more efficient than the portfolio of the sugar industry. In this portfolio, the amount of investment for the shares of Salmin Company was 86. 7% and for Mehram Company was 13. 3%.

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.

One of the most applicable optimization approaches used in different sciences is Meta-heuristic Algorithms. This study, by new Meta-heuristic Algorithms, Symbiotic Organisms Search (SOS), introduces the model for selection of optimum portfolio and then the result is compared with the result of older algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Therefore, the ten-month information of operation of 50 top companies in the Stock Exchange is extracted and estimated an optimized portfolio with the objectives of maximum efficiency and minimum risk by Symbiotic Organisms Search (SOS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is estimated. The results of the algorithm showed that despite the ability of these algorithms to portfolio optimization, SOS algorithm has a higher ability to optimization.

The value at risk as one of the risk measurement criteria can be used to determine the Stock Optimal Portfolio. The main objective of this study is to determine the optimum portfolio of shares using value at risk. To this end, data from the weekly prices of the stock of 17 selected cement companies (which their data have been available) has been used during the period January 2012 to March 2017. First, the value at risk for each share is calculated using a parametric approach and a variance-covariance method, and the optimal portfolio weights are comprised of the shares of the companies mentioned. Then employing nonlinear planning, optimization of the stock portfolio with the lowest value at risk was performed with respect to the expected returns. Based on the empirical results, the highest weight in the optimal portfolio belongs to the stock, which has high expected returns and has the lowest value at risk among the companies under study.

In this paper, we utilize fuzzy numbers and possibility theory to model possibility. The purpose of this work is to determine the optimal investment model based on the neural network method for fuzzy LR, trapezoidal, and triangular numbers in an optimal portfolio listed on the Tehran Stock Exchange. The aim is to maximize "returns" and minimize "risk" in order to find the optimal portfolio. Therefore, to achieve this goal, the problem of multi-objective nonlinear programming is addressed. Additionally, the mean-variance model and the standard mean deviation are substituted instead of the Markowitz mean-variance model to examine the selection of the optimal portfolio in the possible space. Finally, by calculating the possibility model of fuzzy numbers, we obtain the optimal stock portfolio, which can be used to construct the stock portfolio with the highest returns and the lowest risk.

Portfolio is combination of assets by an investor for investment. Process of selecting optimal stock basket is one of issue, which is paid attention by scholars. Various criteria of this process have changed by the time and this condition is necessary as optimal for making decision. Several criteria are included in selecting stock basket and it is necessary to use optimal tools for making better decision. Aim of this research is creating intelligent model in order to select optimal stock basket throughout adjusted differential evolution algorithm. Thus, we investigated risk and returns of companies listed at Tehran stock exchange annually. Sample study of research includes 102 companies during 2009 and 2013. Results of research showed that selected model by considering interaction between risk and expected return leads to selected optimal stock basket.

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.

According to theory of the stok exchange, supposed that investors don’t risk.It means that they select asset from between two assets with same efficiency rate that have lower risk level. Or risk against assets that they choose to be more efficient Investors that accept Theoretical basket of securities belice that con’t challenge with market. So, they keep several types of the Stock exchange until their efficiency become same average market efficiency.Therefore, this research to select optimal portfolio shares by investors in the Tehran Stock Exchange through Markowitz models and values at risk are investigated and studied To the possibility of its application to optimal portfolio choice for investors determine the stock. time period of this research is considered from 1380 to 1387 and statistics society is all of the stock companies with special conditions then and comparison of optimum stocks by two models of markovitz and worth of exposed to danger from t test. Results of this research show that selection of optimum stocks in Tehran Stock Exchange market is similar by madels of markovitz and worth of exposed to danger. Therefore, investors can for choose optimal portfolio shares equally from both models use.

In the dynamic world of financial investment, crafting an optimal stock portfolio that judiciously balances risk, return, and efficiency emerges as a critical challenge. Despite the wealth of research on financial portfolio optimization, prevailing methodologies predominantly emphasize either risk minimization or return maximization, often overlooking the imperative for a holistic strategy that simultaneously boosts efficiency and effectiveness. Addressing this gap in the literature, this study introduces an innovative four-objective model that intricately blends risk, return, and efficiency considerations for the strategic selection of stock portfolios. This model ingeniously integrates the foundational principles of Markowitz's mean-variance analysis with the sophisticated network data envelopment analysis (NDEA) techniques, significantly refining the portfolio selection methodology. It further distinguishes itself by incorporating returns represented as trapezoidal intuitionistic fuzzy numbers, adeptly capturing the inherent uncertainties in financial returns. Additionally, the model employs the network data envelopment analysis's cross-efficiency principle, providing a nuanced measure of company performance. To effectively navigate the complexities of this model, we deploy the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a multi-objective genetic algorithm, demonstrating the model's capability to unearth optimal solutions efficiently. The comparative analysis highlights that the proposed model significantly outperforms the efficiency and effectiveness of existing models, marking a substantial advancement in portfolio optimization strategies.

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.