List of Articles ارزش در معرض خطر مشروط

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  1 - Two-objective optimization of petrochemical portfolio with Strength Pareto Evolutionary Algorithm (SPEA2) by different approaches in portfolio selection
  Arezou Karimi Fatemeh Zakipour
  10.30495/jik.2024.23151
  The issue of choosing a portfolio is a multi-objective issue; Therefore, the need to know the methods of solving portfolio selection models is of great importance. Ultra-innovative algorithms are new ideas that were introduced in this regard. The multi-objective SPEA2 a More

  The issue of choosing a portfolio is a multi-objective issue; Therefore, the need to know the methods of solving portfolio selection models is of great importance. Ultra-innovative algorithms are new ideas that were introduced in this regard. The multi-objective SPEA2 algorithm is one of the algorithms that solves the portfolio optimization problem. The purpose of this study is to use the SPEA2 multi-objective algorithm to achieve the desired combination of petrochemical companies in the petrochemical portfolio. The objective functions of the problem under study include the two objectives of maximizing returns and minimizing risk. The statistical sample includes data of 900 days of 12 petrochemical companies allowed to operate from 1/12/94 to 12/12/98, which by transferring this data to MATLAB software, the logarithmic return each stock is calculated and is the input of SPEA2 algorithm. Then the SPEA2 algorithm is implemented for each of the models of Mean-Variance, Mean-Semi Variance, Mean-Absolute Deviation, Mean- Conditional Value at Risk and the weight of each stock and risk and return of each portfolio are calculated. Then, using SPSS software, the mean difference between risk and return of the models was tested. The results show that the returns obtained by SPEA2 algorithm under different risk models are not statistically significant; However, the portfolio risk created by the SPEA2 algorithm under the Conditional Value at Risk model is significantly different from other risk measures and shows more risk. Manuscript profile
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  2 - Examining the Efficiency Models, Genetic Algorithm under MSV Risk and Particle Swarm Optimization Algorithm under CVAR Risk Criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange
  Dariush Adinevand Ebrahim Ali Razini Mahmoud Khodam Fereydoun Ohadi Elham Elsadat Hashemizadeh
  10.30495/fed.2023.707996
  Abstract Choosing the optimal stock portfolio is one of the main goals of capital management. Today, There are several tools and techniques for measuring portfolio risk and selecting the optimal stock portfolio. In this article, using data of 15 shares selected by purp More

  Abstract Choosing the optimal stock portfolio is one of the main goals of capital management. Today, There are several tools and techniques for measuring portfolio risk and selecting the optimal stock portfolio. In this article, using data of 15 shares selected by purposeful sampling method from the top companies of Tehran Stock Exchange Organization including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days and then using MATLAB software models The Metaheuristic Optimization of the Genetic Algorithm under the MSV Risk Criterion and the Particle Swarm Algorithm under the CVaR risk Criterion are Compared. The results show that the genetic algorithm model under MSV risk criterion is more efficient and less risky, therefore the genetic algorithm model under MSV risk criterion is more efficient than the particle swarm algorithm model under CVaR risk criterion. Manuscript profile
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  3 - Stock portfolio optimization using Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) under Conditional Value at Risk (CVaR)
  Arezou Karimi sara goodarzi dahrizi
  The choice of stock portfolio is a special issue in the field of investment. Given the wide range of options in the stock market, one of the major concerns of investment groups is the optimal allocation of assets. Therefore, most of these collections use portfolio selec More

  The choice of stock portfolio is a special issue in the field of investment. Given the wide range of options in the stock market, one of the major concerns of investment groups is the optimal allocation of assets. Therefore, most of these collections use portfolio selection models. The conditional value at Risk, which is one of the models of portfolio selection, follows the Quadratic Programming. Given that Quadratic Programming requires extensive computations, the use of metaheuristic algorithms in solving these problems increases the speed and accuracy of computations. The aim of this study is to minimize the Conditional Value at Risk by using two algorithms of Imperialist Competitive Algorithm and Particle Swarm Optimization. Therefore, using 800 days of data from 12 companies listed on the Tehran Stock Exchange in the period of 2/5/92 to 1/28/98, portfolio has been formed, and the weight of each stock in the optimal portfolio and the risk and return of the portfolio has been calculated using MATLAB2018 software. Then, using SPSS software, the average difference between risk and return of the two algorithms was tested.The results showed that the risk and return of the two algorithms were not statistically significant,. Manuscript profile
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  4 - Stock portfolio optimization using multi-objective genetic algorithm (NSGA II) and maximum Sharp ratio
  Arezou Karimi
  One of the most important issues in finance is how to choose an investment portfolio. Activists in this field are seeking to select a portfolio that controls risk with high return. Due to the increasing limitations of the capital market, the efficiency of classical meth More

  One of the most important issues in finance is how to choose an investment portfolio. Activists in this field are seeking to select a portfolio that controls risk with high return. Due to the increasing limitations of the capital market, the efficiency of classical methods has been discussed. Hence, researchers have turned their attention to metaheuristic algorithms. The aim of this study is to determine the optimal portfolio of pharmaceutical companies accepted in the Tehran Stock Exchange by two methods of multi-objective genetic algorithm (NSGA-II) and maximum Sharp ratio. In this study, the multi-objective genetic algorithm (NSGA-II) is under Conditional Value at Risk criterion. Also, the data of 13 companies in the period of 90-97 were used to form the portfolio. The results show that in the multi-objective genetic algorithm (NSGA-II) method, the stock with the lowest Value at Risk gains the most weight in the optimal portfolio. Also, the optimized portfolio by multi-objective genetic algorithm is more return and at the same time less risky. Manuscript profile
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  5 - Examining the Efficiency Models, Conditional Value at Risk and Mean Absolute Deviation and Particle Swarm Optimization Algorithm under CVAR and MAD risk criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange
  dariuosh adinehvand Ebrahim ali Razini Rahmani Mahmod khoddam Fereydon Ohadi alhamsadat hashemizadeh
  Choosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including More

  Choosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days. Then and their portfolio risk is calculated using the models of absolute deviation risk and conditional value at risk, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the following, the deviation - absolute risk model and conditional value at risk model of metaheuristic method using MATLAB (R2019) software are compared. The results show that the PSO model of metaheuristic method compared to the classical method in solving portfolio optimization problem showed more return in PSO-MAD criteria and therefore it is a better method to solve such portfolio optimization problems. Manuscript profile
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  6 - Examining the efficiency of optimization models of multi objective genetic algorithm and particle swarm algorithm under the risk criteria of conditional value at risk and mean smai variance in determining the optimal stock portfolio
  Dariush Adinehvand Ebrahim Ali Razini Rahmani Mahmoud Khoddam Fereydoun Ohadi Elham Sadat Hashemizadeh
  10.30495/afi.2023.1988982.1233
  Objective: The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.Research Methodology: Attaini More

  Objective: The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.Research Methodology: Attaining an efficient portfolio involves solving an optimization problem. There are numerous techniques and tools available to solve this issue. In this study, 15 stocks from companies listed on the Tehran Stock Exchange, including symbols such as Khapars, Khazamiya, Vepasar, Foulad, Akhabar, Kegel, Femli, Tapiko, Sepaha, Fazer, Fakhas, Shohbaran, Shefan, Qamro and Qathabat, were selected using cluster sampling. First, the daily returns of these stocks were calculated over a 5-year period from 2015 to 2020 (1183 days). The risk of the optimal investment portfolio was then calculated using the Mean-Semi Variance and Conditional Value at Risk models. These two criteria were compared using a classic solution method. Subsequently, the output data obtained from these calculations were compared using MATLAB software, employing the Particle Swarm Optimization algorithm under the Mean-Semi Variance risk criterion and the Genetic Algorithm under the Conditional Value at Risk criterion.Findings: The results of this study indicate that the meta-heuristic Particle Swarm Optimization method yields a higher portfolio return ratio compared to the Genetic Algorithm in the Mean-Semi Variance risk criterion.Originality / Value: This research utilizes multi-objective genetic algorithms and Particle Swarm Optimization, which are intelligent and novel algorithms, to minimize the objective function value using Conditional Value at Risk and Mean-Semi Variance criteria. These algorithms optimize the return and risk ratios of the stocks in the investment portfolio with the highest possible accuracy. Additionally, the efficiency comparison of these models using MATLAB software contributes an innovative aspect to this study. Manuscript profile