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Open Access Article

1 - Modeling and Comparison of Fuzzy and Non-Fuzzy Multi-Objective Evolution Optimization Portfolios in Tehran Stock Exchange
Mohammad Fallah Hadi Khajezadeh Dezfuli Hamed Nozari

Selecting the optimal stock portfolio is one of the most important issues in the field of financial research, which tries to choose the optimal combination of assets in order to create maximum utility for the investor, Given that the return on securities in the real wor More

Selecting the optimal stock portfolio is one of the most important issues in the field of financial research, which tries to choose the optimal combination of assets in order to create maximum utility for the investor, Given that the return on securities in the real world is often vague and inaccurate, one of the most important investment challenges is uncertainty about the future. In this paper the problem of selecting and optimizing securities portfolios with different modeling goals has been solved and compared. The designed models have considered both the nature of the portfolio selection issue and the considerations considered by the shareholder in the portfolio selection. The uncertainty quality of the future return of a given portfolio is estimated using fuzzy LR numbers, while its return torques are measured using possibility theory. The most important purpose of this paper is to solve the problem and compare portfolio selection models with simultaneous optimization of two, three, and four objectives. For this purpose, the NSGA-II genetic algorithm is used and the mutation and intersection operators are designed specifically to generate possible solutions to the cardinality constraint of the problem. Finally, the efficiency and performance of the models in case of using fuzzy logic and not using it have been compared and it has been determined that the use of fuzzy logic and possibility theory leads to the formation of portfolios with higher performance and higher efficiency. Manuscript profile

Open Access Article

2 - Three steps method for portfolio optimization by using Conditional Value at Risk measure
S. Navidi sh. Banihashemi M. Sanei

Comprehensive methods must be used for portfolio optimization. For this purpose, financial data of stock companies, inputs and outputs variable, the risk measure and investor’s preferences must be considered. By considering these items, we propose a method for por More

Comprehensive methods must be used for portfolio optimization. For this purpose, financial data of stock companies, inputs and outputs variable, the risk measure and investor’s preferences must be considered. By considering these items, we propose a method for portfolio optimization. In this paper, we used financial data of companies for screening the stock companies. We used Conditional Value at Risk (CVaR) as a risk measure, because of its advantages. Data Envelopment Analysis (DEA) can be used to calculate the efficiency of stock companies. Conventional DEA models assume non-negative data. However, many of these data take the negative value, therefore we propose the MeanSharp- CVaR (MSh CV) model and the Multi Objective MeanSharp- CVaR (MOMSh CV) model base on Range Directional Measure (RDM) that can take positive and negative values. By using Multi Objective Decision Making (MODM) model, investors can allocate their capital to the stocks of portfolio as they like. Finally, a numerical example of the purposed method is applied to Iran’s financial market. Manuscript profile

Open Access Article

3 - Efficiency analysis of the meta-heuristic algorithms in portfolio optimization
Sina Shirtavani Mehdi Homayonfar Keyhan Azadi amir daneshvar

The most important goal of every investor in the stock market is to increase returns and reduce investment risk. Therefore, the purpose of this research is to analyze the effectiveness of meta-heuristic algorithms in stock portfolio optimization. Considering that in thi More

The most important goal of every investor in the stock market is to increase returns and reduce investment risk. Therefore, the purpose of this research is to analyze the effectiveness of meta-heuristic algorithms in stock portfolio optimization. Considering that in this research, the past performance of Tehran Stock Exchange companies is examined in past studies from 1390-1399, therefore, in terms of the research design, this research was post-event using Delphi and meta-analysis techniques. The statistical community of this research Academic researchers in the field of finance and active in the Tehran Stock Exchange, and the sampling method in this research was targeted with a volume of 30 people. The data collection tool was a researcher-made questionnaire. The method of collecting information was structured interview of researchers and review of the results of various studies in the field of determining the optimal stock portfolio in Tehran Stock Exchange. In order to analyze the data, Spss software version 23 and Laserl version 5.7 were used. The results showed that among meta-heuristic algorithms of genetic algorithm, ant colony and bee colony are the most suitable tools with the aim of not stopping at local optimal points and not premature convergence. Finally, after evaluating the appropriate algorithms, a comparison of the average risk and returns of the stock portfolio in genetic algorithms, ant colony and bee colony was done in the study unit, they showed that in terms of the criteria of reducing the risk of genetic and bee algorithms and in terms of increasing the return of the optimal portfolio Stock bee algorithm has worked more efficiently. Manuscript profile

Open Access Article

4 - Predicting Financial Contagion from Generating shock in Investment Institutions Activated in Capital Market due to Overlapping Portfolios Risk
Alireza Rayati Shavazi Abbas Rezaei Pandari

The risk of maintaining shared assets or overlapping portfolios risk is one of channels that cause financial contagion. Since a shock in an investor institution can spread to other investment institutions and cause great damage to them and the entire stock market and ev More

The risk of maintaining shared assets or overlapping portfolios risk is one of channels that cause financial contagion. Since a shock in an investor institution can spread to other investment institutions and cause great damage to them and the entire stock market and even cause a crisis in the economy, therefore; The main goal of this research is to provide a model for predicting financial contagion caused by a shock in investor institutions in Tehran Stock Exchange based on overlapping portfolios risk. This research is an analytical survey that was conducted using the statistical method of discriminant analysis. In order to investigate the goal, based on the data related to the stock portfolio of the investing institutions in the Tehran Stock Exchange, a multi-variable discriminant model for predicting financial contagion based on shocks in financial institutions has been presented. The results indicate that "risky assets value of the investment institution", "Debt value of the investment institution" and "Degree of the investing institution portfolio" have been validated as independent variables. Supervision departments can use the models presented in this study to identify industrial groups that have a high risk of overlapping portfolios and maintain the stability of the financial system by taking appropriate decisions. Manuscript profile

Open Access Article

5 - Explaining the fuzzy genetic model of choosing a resilient supplier portfolio in the supply chain of the construction industry under recession conditions
amir mohtasham taghi torabi reza radfar mohammadereza motadel nazanin pilehvari

The purpose of this paper is to present a new technique to the portfolio selection using Genetic Algorithm and Fuzzy Synthetic Evaluation. Portfolio selection is a multi-objective/criteria decision-making problem in financial management. The proposed approach (Genetic A More

The purpose of this paper is to present a new technique to the portfolio selection using Genetic Algorithm and Fuzzy Synthetic Evaluation. Portfolio selection is a multi-objective/criteria decision-making problem in financial management. The proposed approach (Genetic Algorithm and Fuzzy Synthetic Evaluation) solves the problem in two stages. In the first stage، by using genetic algorithm and fuzzy synthetic evaluation، weight of criteria will be calculated. In second stage، using Fuzzy Synthetic Evaluation، Portfolios will be prioritized. A multi objective genetic algorithm is used to determine return and risk in the efficient frontier in Tehran stock market. In this research, we have used of firms’ performance between 1396-1400 in civil engineering, construction, investment and construction materials and tools manufacturers in order to determine portfolio selection. The main advantage of proposed approach is helping an investor to find a portfolio which have Best performance، portfolio selection doesn’t rely to expert knowledge. Manuscript profile

Open Access Article

6 - Portfolio optimization with differential evolution and conditional value at risk approach
Shahin Ramtinnia Romina Atrchi

Portfolio selection, in order to maximize the profit from investment, is an important concern for minor and institutional investors.Therefore; efficient and secure optimization of financial assets is one of the most important new and modern, financial topics, trying to More

Portfolio selection, in order to maximize the profit from investment, is an important concern for minor and institutional investors.Therefore; efficient and secure optimization of financial assets is one of the most important new and modern, financial topics, trying to improve the portfolio performance using modern approaches of other sciences. Accordingly, this article aimed to optimize the index returns of top 10 companies of Tehran Stock Exchange from 2011 to 2015 using portfolio risk minimization approach with the maximum yield according to conditional value at risk and differential evolution algorithm(DE-CVaR) on a monthly basis. The results showed that differential evolution algorithm with the conditional value at risk approach, had better Sharpe and returns ratios by CVaR value compared to the random algorithm. The results of posttest with monthly approach also showed that DE-CVaR was better than random algorithm in terms of the criteria for selecting the optimal portfolio. Manuscript profile

Open Access Article

7 - The two-stage Approach for Stock Selection and Portfolio Composition (Enriched Promethee Method)
Saeed Khodamoradi Mahdi Bashiri Hossein Reisi

Deciding about portfolio selection and composition are the main challenge ofinvestment institutions. The nature of analysis and effective criteria in industry andcompany are different. They should rank the industry and companies options with regardto multiple and confli More

Deciding about portfolio selection and composition are the main challenge ofinvestment institutions. The nature of analysis and effective criteria in industry andcompany are different. They should rank the industry and companies options with regardto multiple and conflict criteria that they have different weights and priorities. This articleseeks to provide two-stage approach for stock selection and portfolio composition. Weenriched promethee (compensatory MCDM method) by weight of criteria. Then we usedit in intended approach. The expert opinions and objective data used to criteria ranking.An ANP was used to determining weight of criteria that merge in promethee method.After designing we tried to validate our approach with real data. The results show wehave 7% (simple mode) and 11% (enriched mode) return more than actual performance.Using of Porter five forces analysis for indentifying and verify industry criteria, toinclude some indices from corporate governance and technical analysis, weighting thecriteria and integrate them in the promethee are our research novelty. Manuscript profile

Open Access Article

8 - Selection and Portfolio Optimization by Mean–Variance Markowitz Model and Using the Different Algorithms
Jamal Bahri Sales Askar Pakmaram Mostafa Valizadeh

One of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not onl More

One of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society.In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this.In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk.Since the objective function of particle swarm algorithms was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of this algorithms in the selection of the optimal portfolio. Manuscript profile

Open Access Article

9 - Developing an uncertain mean-chance model for portfolio optimization using forecasted returns
Hosein Didehkhani Amir Shiri-ghehi Behzad Miran

The purpose of this research is to present a portfolio optimization model within the framework of uncertainty theory. To estimate the return on assets, a prospective approach was used based on expert opinions. Also, a different risk-based approach based on uncertainty ( More

The purpose of this research is to present a portfolio optimization model within the framework of uncertainty theory. To estimate the return on assets, a prospective approach was used based on expert opinions. Also, a different risk-based approach based on uncertainty (chance model) was used to model risk. The theory used to model the uncertainty in model parameters is the uncertainty theory. The team of experts involved in this research was required to complete the required information on the projections used, including 30 managers of the portfolio of active investment funds in the Tehran Stock Exchange. In the end, to demonstrate the applicability, the model was designed in Tehran Stock Exchange and according to the nonlinear nature of the model, the hyper bacterial method of the genetic algorithm was used to solve it. Finally, by generating randomized portfolios and comparing them with the optimal portfolio for solving the model, we conclude that the optimized portfolio achieves a higher level of efficiency while delivering better performance. Manuscript profile

Open Access Article

10 - Portfolio Optimization in Capital Market Bubble Condition
Abdollah Daryabor frydoon Rahnama Roodposhti Hashem Nikoomaram Farhad Ghaffari

Financial markets, especially capital markets, are considered the main tools for equipping and allocating financial resources. With regard to the strategic, financial and economic importance of such markets, whenever a widespread disruption or deviation occurs, it becom More

Financial markets, especially capital markets, are considered the main tools for equipping and allocating financial resources. With regard to the strategic, financial and economic importance of such markets, whenever a widespread disruption or deviation occurs, it becomes extremely difficult to equip and allocate a country’s financial resources. One of the contributing factors is price bubble. In fact, the essence of price bubbles lies in the reactions to price hikes. Thus, the increase in prices leads to greater investor appetite, higher demand and ultimately another price hike.In such occasions, the investment managers plan to optimize their stock portfolios. In other words, they intend to bring about maximum return for customers and shareholders in exchange for a certain level of risk. This study attempted to examine several variables such as stock price, stock monthly return, overall market return, variance, standard deviation, var and Downside Risk to a new model within the bubble space at Tehran Stock Exchange (TSE) for period (2000-2015). At first, the effects of bubble were proven and the junctures were identified for 7 periods. Then, the variables were analyzed to achieve an optimization model, adopting an approach similar to Sharpe’s, where the extracted optimum portfolio brought about a far more desirable position for the investors than other portfolios under non-bubble conditions involving return, Sharpe, Treynor and Jensen. The main hypothesis was proven and a new model was proposed to achieve the ideal results through analyzing the model within an ascending bubble space as well as a descending bubble space, which were then compared against a non-bubble space. Manuscript profile

Open Access Article

11 - Comparing the performance of optimization models with equity investment funds: evidence from the Tehran Stock Exchange
Mahmood Pakbaz kataj Daryush Farid

10.30495/jfksa.2022.20220

Since portfolio optimization models are based on past information, the efficiency of these models has always been questioned. In this study, first, an optimization model based on investor views is introduced and then the performance of all optimization models are compar More

Since portfolio optimization models are based on past information, the efficiency of these models has always been questioned. In this study, first, an optimization model based on investor views is introduced and then the performance of all optimization models are compared with the performance mutual funds to both measure the effectiveness of these models and to achieve a practical model for this purpose. The research period is between 2016 and 1400 and MATLAB software has been used to obtain the optimal portfolio. The results show that using different evaluation criteria, the optimal portfolio of Black Literman model performs better than other optimization models and mutual funds; Also, the returns generated by all optimization models at the market risk level were significantly higher than the average returns of equity mutual funds and top mutual funds. Manuscript profile

Open Access Article

12 - Optimization of Network-Based Matrix Investment Portfolio and Comparison with Fuzzy Neural Combination Pattern and Genetic Algorithm(ANFIS)
ALI SheidaeiNarmigi Fraydoon Rahnamay Roodposhti Reza Radfar

Researchers have been researching portfolio optimization issues for several years. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, ie to minimize investment risk and maximize investment profit. The aim of More

Researchers have been researching portfolio optimization issues for several years. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, ie to minimize investment risk and maximize investment profit. The aim of this study is to investigate the strategic capability of network matrix and fuzzy genetic neural model (ANFIS) in optimizing the investment portfolio among companies on the Tehran Stock Exchange. Grouping stocks by network matrix based on new variables including aggressive, indifferent and defensive stocks provided by Roodpashti (2009) and traditional variables including growth, growth-value and value stocks and classification of companies based on their market value and use. From the law of quarters and finally their weighting is considered in proportion to the return of that share. The design and presentation of a stock portfolio optimization model using adaptive fuzzy neural inference system and its combination with genetic algorithm (ANFIS) in which two different categories of technical and fundamental variables are used as model inputs. Research outputs show that these systems have the necessary ability to optimize the stock portfolio. Therefore, a combined model of neural networks and fuzzy reasoning theory with genetic algorithm has been used to weight the factors affecting stock portfolio optimization in the 7 years leading up to 1398. Manuscript profile

Open Access Article

13 - Hybrid Portfolio Optimization using Analytic Hierarchy Process (AHP), Combined Compromise Solution (CoCoSo) and Markowitz Model (Case study of Tehran Stock Exchange)
Nasimeh Abdi mehdi Moradzadeh Fard Hamid Ahmadzadeh Mahmoud Khoddam

Using effective and efficient criteria in choosing the investment portfolio can provide the most profitability for individual and institutional investors. Therefore, it seems necessary to choose a hybrid method to create a portfolio that shows better performance. The pu More

Using effective and efficient criteria in choosing the investment portfolio can provide the most profitability for individual and institutional investors. Therefore, it seems necessary to choose a hybrid method to create a portfolio that shows better performance. The purpose of this study is to provide a model that can combine Multi-criteria decision-making techniques and Markowitz's mean-variance model, in different periods, to create an optimal portfolio that maximizes shareholder profits. The proposed model was implemented in three steps. In the first step, using the AHP technique, utilizing the opinion of experts, comparing different decision options based on the fundamental and technical criteria effective in decision making and prioritizing the mentioned criteria during the period from June 2016 to June 2021, among industries Activists in the Tehran Stock Exchange were selected as top industries. In the second step, from selected industries, three portfolios with one-month, six-month, and one-year periods were selected using the CoCoSo technique. In the third step, using the Markowitz model in the expressed time period, optimal portfolios were created on the efficient frontier. The results of this study showed that this hybrid proposed model will give more returns to investors according to the risk in different time periods. Manuscript profile

Open Access Article

14 - An Investigation of methods to reduce transaction costs in Tehran Stock Exchange
Romina Atrchi Shahin Ramtinnia

Among 37 methods to reduce transaction costs introduced by Cha (2007), we recursively choose the best method for next period's investment in each of three portfolio strategies: Mean-Variance Optimization, Mean-CVaR Optimization, and the equally-weighted market. We ident More

Among 37 methods to reduce transaction costs introduced by Cha (2007), we recursively choose the best method for next period's investment in each of three portfolio strategies: Mean-Variance Optimization, Mean-CVaR Optimization, and the equally-weighted market. We identify a few of the best methods and offer a framework by which additional methods can be considered. Within our framework, the best methods recapture a substantial amount of wealth and significantly improve risk-adjusted performance, both economically and statistically. We used prices and returns of the 10 most active firms of Tehran Stock Exchange market, from 1391 to 1394 on a monthly basis in this research. The transactions costs reduction methods will be applied on them and the best methods will be identified. Also, a framework will be offered for comparison and investigation of new methods.     Manuscript profile

Open Access Article

15 - Presentation of a model for the active optimization of stock portfolios using value at risk exposure; Application of Convergence Variance Difference Models Approach Based on Algorithm DE Approach
Saeid Fallahpour Reza Raei M. Esmaeil Fadaeinejad Reza Monajati

Active management is one of the issues that is important in terms of violating effectiveness of financial markets financial markets. Because inefficient market, there is a potential to generate abnormal returns through active portfolio management. In many studies in thi More

Active management is one of the issues that is important in terms of violating effectiveness of financial markets financial markets. Because inefficient market, there is a potential to generate abnormal returns through active portfolio management. In many studies in this regard the reason for the surplus return compared to the baseline portfolio by minimizing the tracking error variance (TEV) in this regard, the risk of the entire portfolio is not taken into account. In this study, by using the differential evolution algorithm (DE) to optimize the active portfolio, with the goal of maximizing portfolio surplus returns compared to the standard portfolio, considering the risk of the entire portfolio from the calculated conditional risk value criterion (CVaR) based on the GARCH approach is used. The results of the portfolio consist of 14 stocks with a positive average yield from the beginning of 2011 to the end of June of 2017 from the top 50 stock exchanges on a monthly shows that subject to risk portfolio based on CVaR, causes better performance in the active optimization of the portfolio, based on backtesting method.     Manuscript profile

Open Access Article

16 - Performance Evaluation of risk premium measurement models: q-theory asset pricing model against three factor model of fama and french
Gholamreza kordestani Mozhde Ghasemi

Financial scholars have made valuable efforts to measure risk premium. Recently, Chen et al (2010) proposed a three factor model based on market factor, investment factor, and profitability factor for explaining stock return and called it q-theory model. Prior researche More

Financial scholars have made valuable efforts to measure risk premium. Recently, Chen et al (2010) proposed a three factor model based on market factor, investment factor, and profitability factor for explaining stock return and called it q-theory model. Prior researches have shown that this model reduces the magnitude of the abnormal returns of a wide range of anomalies. This research examines the performance of new model in explaining the risk premium of the individual stock and portfolio of stock, and compares it with the performance of CAPM and three factor model of Fama and French in stock exchange market. Sample under investigation consist of 72 listed companies for the period of 1386-1391. The results show that risk premium of stocks has a significant relationship with the sensitivity of its returns to investment and profitability factors. Furthermore, q-theory model significantly excel CAPM in explaining risk premium of firm size, book to market value and momentum portfolios. But it significantly excels three factor model of Fama and French just in explaining the risk premium of momentum portfolios. Manuscript profile

Open Access Article

17 - Selection of optimal portfolio by using improved Non-Dominated Sorting Genetic Algorithm and Evolutionary Algorithm Strength Pareto By taking risk on the basis of conditional value at risk
Mojtaba Moradi Maryam Ghavidel

Portfolio selection problem is one of the most important economic issues. The right combination of stock or other asset portfolio is that an investor pays to buy it. Selection of an optimal portfolio is based on the principle that the investor decides to accept one or s More

Portfolio selection problem is one of the most important economic issues. The right combination of stock or other asset portfolio is that an investor pays to buy it. Selection of an optimal portfolio is based on the principle that the investor decides to accept one or several investments among different investment depending on the tolerance of risk and expected a reasonable amount of stock returns. In this study, improved Non-Dominated Sorting multi-objective genetic algorithms and Evolutionary Algorithm Strength Pareto are used to create an optimum portfolio. These algorithms are improved version of their previous versions and have a better solution than its previous versions. The value of the portfolio and its risk, as optimization purposes and conditional value at risk as the basis risk, have been used. Two applied conditions consider to Portfolio and shown that the Evolutionary Algorithm Strength Pareto‌ has better results than the Non-Dominated Sorting Genetic Algorithm II.       Manuscript profile

Open Access Article

18 - The Impact of Investor’s Perception of Risk on Portfolio Management (Case Study: Active Investor’s Mashhad Stock brokers)
Ahmad Nategh Golestan Elahe Damghani Ahmad Sabahi

Investor’s decision making is a subject of considerable debates in behavioral finance. Behavioral finance refers to how people make decisions under uncertainty conditions. This paper is devoted to The impact of investor’s Perception of Risk on Portfolio Mana More

Investor’s decision making is a subject of considerable debates in behavioral finance. Behavioral finance refers to how people make decisions under uncertainty conditions. This paper is devoted to The impact of investor’s Perception of Risk on Portfolio Management. It is an applied study of descriptive-surveying type used to gather the required data through questionnaires. A simple sampling was used and the final sample included 104 investors. Investors were questioned about 24 possibly influencing factors of investor’s reception of risk and 21 influencing factors Portfolio Management in the form of a Likert Scale. The data from questionnaires with SPSS and Smart PLS software using structural equation modeling (path analysis) were analyzed. Results of the study indicated that risk perceived risk factors directly and affecting the perceived risk indirectly is effective on the portfolio management.     Manuscript profile

Open Access Article

19 - Optimizing Stock Portfolio with regard to Minimum Level of Total Risk using Genetic Algorithm
Maedeh Kiani Harchegani Seyed Ali Nabavi Chashmi Erfan Memarian

Risk and return are two main factors that have always been considered in the field of investment. Simultaneously with the advent of different models for portfolio optimization which the Markowitz model is the most important of those, the necessity to identify methods fo More

Risk and return are two main factors that have always been considered in the field of investment. Simultaneously with the advent of different models for portfolio optimization which the Markowitz model is the most important of those, the necessity to identify methods for solving these models gained great Importance. Genetic Algorithm is one of the most important metaheuristic methods used for the solution of the portfolio optimization models.This study aimed at evaluating the level of efficiency of this metaheuristic model in portfolio optimization. Therefore, in this study once we have calculated the optimal efficient frontier by the use of the genetic algorithm, and then we compared this optimal efficient frontier with the efficient frontier which was obtained through exact solution method. To achieve this purpose, 25 companies were selected from companies in Tehran Stock Exchange. The results of our study shows that the optimal efficient frontier gained through genetic algorithm is equal to the efficient frontier obtained using the exact solution method, and thereby indicating the high efficiency of genetic algorithm in portfolio optimization. The other result of the present study is that the comparison of the optimal portfolio gained through exact solution with the systematic and unsystematic risk, also revealed that Stock diversity in portfolios with unsystematic risk is much greater than portfolios with systematic risk. Manuscript profile

Open Access Article

20 - Multi-objective Portfolio Optimization Model by Fruit Fly Optimization Algorithm
Amir Amini alireza alinezhad

One of the most famous optimization problems in the field of financial engineering is portfolio selection problem. In its simplest form, while trying to minimize risk in the portfolio selection according to defined constraints such as budget and integer constraints it d More

One of the most famous optimization problems in the field of financial engineering is portfolio selection problem. In its simplest form, while trying to minimize risk in the portfolio selection according to defined constraints such as budget and integer constraints it deals with selecting a basket of various assets. Generally, investors prefer to invest in some assets rather than investing in only one asset to reduce unsystematic risk by diversifying their investment. Complex computational models have been developed to solve this problem and there is not an optimal solution for many of them. In this paper, a new and innovative approach known as fruit fly optimization algorithm (FOA) is used for multi-objective problem solving based on mean-variance Markowitz problem with class and cardinality constraints. Fruit fly optimization algorithm is a new way to find the overall optimal solution based on the behavior of the fruit fly in finding food. So far, few studies have been done on this algorithm and almost none of them used this algorithm for portfolio optimization problem. The results indicated the better comparative performance of the algorithm compared to the genetic algorithm for data set of Tehran stock exchange.JEL classification: G1, P5, O3 Manuscript profile

Open Access Article

21 - Developing an Optimized Portfolio Model using Modified Risk Aversion Coefficient
Roohollah Mehralizadeh shiadehi hosein didehkhani Ali Khozain arash naderian

In this paper, we propose a modification to the use of the risk aversion coefficient in optimization models, based on research literature and mathematical methods. The modified risk aversion coefficient introduced in this paper can be applied in the maximization part of More

In this paper, we propose a modification to the use of the risk aversion coefficient in optimization models, based on research literature and mathematical methods. The modified risk aversion coefficient introduced in this paper can be applied in the maximization part of the model without any adverse effects. By doing so, it can improve the accuracy of meta-heuristic algorithms in finding optimal solutions. To test the efficacy of our proposed model, we applied it to 30 shares of the Tehran Stock Exchange, along with a zero-risk asset, taking into account some limitations in the market. We used a genetic meta-heuristic optimization method to solve the model, and to measure its efficiency, we compared the results of the optimization process with 2500 randomly generated portfolios that were within the problem's constraints. Our results show that our model outperforms the random portfolios in terms of both risk factors and return. In conclusion, our proposed modification to the risk aversion coefficient can improve the accuracy of optimization models, and our results demonstrate its effectiveness in generating optimal portfolios in the market. Manuscript profile

Open Access Article

22 - A Comparative Study of Dynamic Portfolio Optimization Using Grey Relational Analysis Methods and Basic Methods (Average, Moving Average and Moving Average) in Tehran Stock Exchange
Reza Adak Mehdi Meshki Miavaghi Mohammad Hassan Qolizadeh

Objective: The Ranking Of Financial Assets For Investment Decision Making Is One Of The Most Important Stages For Portfolio Formation.Different And Different Methods Are Used To Perform Ranking. In This Research, A New Integrated Method Is Used For Ranking Which Investo More

Objective: The Ranking Of Financial Assets For Investment Decision Making Is One Of The Most Important Stages For Portfolio Formation.Different And Different Methods Are Used To Perform Ranking. In This Research, A New Integrated Method Is Used For Ranking Which Investors Using It Can Determine Their Specific Goals By Considering The Return, Risk And Profit. This Research Is Based On Two Main Goals: First, Ranking Portfolios Based On The Grey Relational Analysis And The Second Method Is Compared To Classic Methods, Which Are Named Fundamental Methods In This Research. Methodology: To Implement The Research Topic, 11 Weight And Investment Strategies Were Defined For The Method Of Grey Relational Analysis And 3 Strategies For Basic Methods.The Study Population Includes Companies Listed In Tehran Stock Exchange And The Sample Consisted Of Five Top Industries At The Beginning Of The Study Including Investment, Chemical, Iron And Steel Industries, Banks And Oil Products And Products.In These Five Industries With The Conditions That Were Considered In The Study, 160 Companies Were Selected As The Sample And To Examine The Hypotheses, Mann - Whitney U Test Was Used To Compare The Results Of Grey Analysis Method With The Basic Method Of Data Envelopment Analysis. Results: In General, The Results Of The Study Show That The Grey Relational Algorithm Is A More Efficient Method Than The Baseline Method When The Goal Of Investing In The Best Part Is The Grey Relational Algorithm (2020), As Well As Hamza cebiza Abi And Pekkaya (2011) Manuscript profile

Open Access Article

23 - Stock portfolio optimization using prohibited search algorithms and itinerant trader
fatemeh samadi fatemeh khosravi Hossein Eslami Mofid Abadi

In this thesis, modeling and forecasting of stock market fluctuations using the combination of neural network and conditional variance patterns (case, Tehran Stock Exchange) have been used from April 2008 to April 2012. According to the predicted results, this hypothesi More

In this thesis, modeling and forecasting of stock market fluctuations using the combination of neural network and conditional variance patterns (case, Tehran Stock Exchange) have been used from April 2008 to April 2012. According to the predicted results, this hypothesis is confirmed, but its accuracy is not as large as the composition of the neural network and the conditional variance pattern. In the return time series, both GRACH and ARCH conditional variances are rejected, but in the GRACH time series, ARCH is rejected. Given the artificial neural network simulation and conditional variance, the error value of the least squares is the return value of 18, that is, an error is required to estimate future returns. An important parameter of the opacifying factor is the dependence of our input and output at each stage, which indicates a number close to 1 and shows a complete dependence. According to the artificial neural network simulation and conditional variance, the least squares risk error value is 0.001, that is, to estimate the returns for the future, this error is error. Another important parameter of this regression table is R, which shows the dependence of our input and output in each stage, where 0 means a random relationship and 1 means dependence. Manuscript profile

Open Access Article

24 - Optimizing the investment portfolio using ccc, dcc and Markowitz algorithm models : Evidence from the stock exchange
zahra ghorbani Alireza Daghighi Asli Marjan Damankeshideh roya seifipour

10.30495/ECOMAG.1402.1045588

Extended Abstract This study investigates the impact of the capital market using multivariate GARCH models and the Markowitz algorithm to optimize the stock portfolio. The statistical population of this research includes stock exchange companies that were admitted More

Extended Abstract This study investigates the impact of the capital market using multivariate GARCH models and the Markowitz algorithm to optimize the stock portfolio. The statistical population of this research includes stock exchange companies that were admitted to the stock exchange before 1395 and were active until the end of 1399 and had the following characteristics: The financial year of the companies should have ended on March 20th and the companies' shares should have been traded on the stock exchange during each year of the research period and the end-of-period price was available. In addition, the financial information of the companies must also be available. Considering the above characteristics, 4 top industries, including the automotive and parts manufacturing industry, the selected electrical machinery industry, the metal mining and oil products industry, were selected as the screening population in our portfolio based on a combination of stock liquidity, stock trading volume in the trading hall, stock trading frequency in the trading hall, and the company's impact on the market. The sample size is 800 and is daily during the period from 1395 to 1399. Purpose The results of this study show that the optimal weights are more allocated to stocks with less volatility in the stock return trend of that industry. In fact, lower weights are allocated to industries with more volatile returns among the four industries, namely the automotive and parts manufacturing and oil products industries. Conversely, the largest optimal average share of the portfolio among the four industries is for the non-metallic minerals industry with the least return volatility. Methodology The results of this study also show that industry stock return shocks have reciprocal effects on each other. For example, a positive shock to the stock return of the non-metallic minerals industry leads to a negative shock to the stock return of the automotive and parts manufacturing industry. In addition, the results of this study show that the CCC and DCC models have different results in estimating the optimal weights of the industries and risk-free assets that make up the investment portfolio. So that, the DCC model, compared to the CCC model, allocates less weight to the stocks of the automotive and parts manufacturing and oil products industries and, conversely, allocates more weight to the stocks of the non-metallic minerals industry. Finally, the results of this study show that the portfolio formed using the Markowitz optimization algorithms can track the risk-averse individual's utility to maximize profit. And Based on the results of this study, it is suggested that investors pay attention to the volatility of the stock return of that industry when selecting stocks for investment and allocate a greater share to stocks of industries with less return volatility. Finding It is also suggested that DCC models be used alongside CCC models to estimate the optimal weights of the investment portfolio. In addition, it is suggested that Markowitz optimization algorithms be used to form an investment portfolio that matches the risk-averse individual's utility. Now, let’s address the limitations of this study, that one of the limitations of this study is the use of daily stock return data. It is suggested that in future research, data with higher frequency such as hourly or minute data be used. Another limitation of this study is the non-consideration of other factors affecting stock returns, such as macroeconomic factors. It is suggested that in future research, these factors should also be considered. Conclusion The results of this study have important implications for investors and portfolio managers. The use of multivariate GARCH models and the Markowitz algorithm can help to optimize stock portfolios and improve risk-adjusted returns. Investors should consider the volatility of stock returns and the correlation between industries when making investment decisions. DCC models can be used to estimate optimal portfolio weights, and Markowitz optimization algorithms can be used to form portfolios that match the risk-averse individual's utility. Future research should focus on using higher frequency data and considering other factors affecting stock returns. Manuscript profile

Open Access Article

25 - Portfolio Optimization of Listed Industries in Tehran Stock Exchange using Orthogonal GARCH
sahar abedini esmaiel abounoori Gh. Reza Keshavarz Haddad

10.30495/fed.2024.709335

Abstract The development of financial markets and the stock market play an essential role in economic development. Considering that financial markets are always associated with risk and uncertainty, and shocks and turbulence in one market affect other markets, therefor More

Abstract The development of financial markets and the stock market play an essential role in economic development. Considering that financial markets are always associated with risk and uncertainty, and shocks and turbulence in one market affect other markets, therefore, one of the main objectives of this research is to identify the type of distribution of financial series (stock returns of different industries) and estimate their uncertainty and risk (turbulence), determining the weight of stocks in the investment portfolio, as well as accurately identifying how the volatility changes and the intensity of correlation and interactions between the stocks of different industries over time in order to maximize the interests of investors and provide the necessary solutions to planners and policy makers Investors are for managing and developing the stock market.In order to optimize, statistics related to the weekly price index data of  selected industries (mass housing, banks and credit institutions, chemical, automotive, pharmaceutical and basic metals) have been used. For this purpose, using orthogonal GARCH model and weekly data of stock price index of different industries in the period March 27, 2010 and January 18, 2021, the elements of the variance-conditional covariance matrix were estimated, Then, the stock portfolio was optimized using the obtained information and the distribution of general hyperbolic (GH) skewed t, in the framework of the static and dynamic classical Mean-Variance model as well as the static Mean-CVAR model. The results of fitting (estimation) of the data distribution show that the return distribution of the price index of the studied industries follows the distribution of the general hyperbolic skewed t; Based on the dynamic classical mean-variance model, the highest weight in the stock portfolio in the study period was related to the pharmaceutical (0/6336) and chemical industries (0/3539), respectively. Manuscript profile

Open Access Article

26 - Application of Threshold-based Filtered Networks in Stock Portfolio Selection and Performance Evaluation
Marzieh Noorahmadi Hojatullah Sadeghi

10.30495/fed.2023.705588

Abstract Network analysis is one of the methods of attention of analysts to analyze complex relationships in data in an intuitive way. One of the applications of network analysis is illustrating the relationships between different classes of assets. Identifying stock m More

Abstract Network analysis is one of the methods of attention of analysts to analyze complex relationships in data in an intuitive way. One of the applications of network analysis is illustrating the relationships between different classes of assets. Identifying stock market dynamics is essential for actors, investors, and financial policymakers. The stock market is considered a complex system that shows its complex dynamics. The complexity of the stock market can have several reasons that the interdependence of stocks can be one of the most prominent of these factors. One of the most important concerns of people in the capital market is finding a way to present and analyze stock data of different companies. There are different companies in the stock market and portfolio managers and investors, in choosing the right stock portfolio, need to consider the best way to form a stock portfolio. This article discusses the formation of diverse and non-diverse portfolios through network theory. To conduct this research, the adjusted final price of 138 listed companies for the period 2017-01-01 to 2021-07-06, equivalent to 1648 trading days, has been used. To describe the effect between stocks, the Adjacency Matrix is used and using the optimal threshold, diverse and non-diverse portfolios are obtained. We implement the results of selected stocks for the portfolio using the Hierarchical Risk Parity (HRP) approach based on clustering methods and the results with three methods of Minimum Variance (MVP), Uniform Distribution (UNIF), and Risk Parity (RP) for both in-sample and out-of-sample periods are compared for both diverse and non-diversified portfolios. Finally, the results have been compared using the four criteria of Sortino, Sharpe, Maximum DD, and Calmar. The results show the superiority of the non-diversified portfolio approach in market downturns and the superiority of the diversified portfolio approach in other periods. Manuscript profile

Open Access Article

27 - Optimization portfolio selection model with financial and ethical considerations
elham fallahi ganzagh Farimah Mokhatab Rafiei

The moral investment movement that began in the 1960s in the United States has recently led to a massive move around the world. Growing cases of corporate scams and scandals have pushed investors to consider the quality of corporate governance and the ethics of their be More

The moral investment movement that began in the 1960s in the United States has recently led to a massive move around the world. Growing cases of corporate scams and scandals have pushed investors to consider the quality of corporate governance and the ethics of their behavior. Also, investors are becoming aware of the desirability of moral aberration of assets.The growing influence of institutional investors has strengthened this awareness. Hence, in order to research in this field, there should be an understanding of the progress made in constructing models that are consistent with financially ethical considerations. We use multiple methodologies to achieve this goal. To obtain the ethical performance scores of each asset, based on the investor's preferences, a hierarchical process approach has been used. A multi-faceted decision-making method is used to obtain the rating of each asset based on the investor's rate on the financial benchmark. Model of portfolio optimization is available to obtain diverse, reliable, and well-matched portfolio portfolios. The purpose of this model is to maximize the financial purpose as the primary purpose and maximize the ethical goal adopted by the investor. Manuscript profile

Open Access Article

28 - A new Method for Sustainable Portfolio Selection with DEA, TOPSIS and MIP in Stock exchange
Soghra Rezaei Mohsen Vaez-Ghasemi

In today's highly competitive world, the condition of survival and participation in the field of activity, functioning and efficient and effective, is not achieved except through continuous planning, monitoring, control and evaluation. In this regard, we tried to presen More

In today's highly competitive world, the condition of survival and participation in the field of activity, functioning and efficient and effective, is not achieved except through continuous planning, monitoring, control and evaluation. In this regard, we tried to present a mathematical hybrid model for selecting and planning an optimal composition of the shares according to the goals and priorities, in order to obtain the highest compatibility between the final selection and the initial ranking of each share. The proposed model consists of three steps and several steps, the SBM method of data envelopment analysis (DEA) (for initial stock revisions, multi-factor decision-making (TOPSIS)) in uncertainty conditions, for the assessment and ranking of shares in two individual steps and categorized and integer linear programming (IP) for choosing the best stock portfolio with increased scores according to the organization's priorities and constraints. Collect information from reputable sites of five industries active automotive, pharmacy, petrochemical, cement and food industries to the best stock portfolio for investment, due to the impact of algorithms and methods. Manuscript profile

Open Access Article

29 - Portfolio Optimization Based on Robust Probablistic Planning Model Using Genetic Algorithm and Shuffled Frog-leaping Algorithm
MohammadSaeed Heidari Javad Validi Seyed Babak Ebrahimi

Portfolio selection problem which is one of the most important issues in finance, using a model that considers conditions of the real world is important. In financial markets, severe and frequent fluctuations cause frequent changes in the portfolio selection models outp More

Portfolio selection problem which is one of the most important issues in finance, using a model that considers conditions of the real world is important. In financial markets, severe and frequent fluctuations cause frequent changes in the portfolio selection models outputs, which increases the number of times to change the weight of portfolio's assets, and so that incurs high management and transaction costs. In the literature of portfolio selection models, one of the approaches to prevent this kind of high costs is robust optimization approach. In this study, in order to optimize the portfolio, genetic algorithm and shuffled frog-leaping algorithm are used to solve robust probablistic planning model presented by Amiri and Heidari (1399) in higher dimensions. To this end, 15 specific problems with different dimensions (number of companies and time periods) are designed and processed. The results of the implementation of two algorithms on the above 15 problems were compared using T-test, which shows no significant difference between two algorithms in portfolio selection problem, but the combined approach of TOPSIS and entropy weighting selects the genetic algorithm as superior algorithm. Manuscript profile

Open Access Article

30 - Adaptive Neural Inference System (ANFIS) and Grid Matrix (GA) Strategies Approach in Optimizing the Investment Portfolio in Tehran Stock Exchange and OTC Iran
ALI SHEIDAEI NARMIGI Fereydun Rahnama roodposhti Reza Radfar

Portfolio optimization is a process in which the investor seeks to maximize return on investment or minimize risk. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, which means minimizing investment risk an More

Portfolio optimization is a process in which the investor seeks to maximize return on investment or minimize risk. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, which means minimizing investment risk and maximizing investment profit. The aim of this study was to investigate the capability of adaptive fuzzy neural inference system (ANFIS) and grid matrix (GA) strategies in selecting and optimizing the investment portfolio from among selected Tehran Stock Exchange and OTC companies. The grouping of stocks by the network matrix and the classification of companies based on their market value and the use of the law of quarters and finally their weighting is considered in proportion to the forecast return for the next month of that share. Also, a stock portfolio optimization model has been designed and presented using an adaptive fuzzy neural inference system and its combination with a genetic algorithm in which three different categories of time, technical and fundamental series variables are used as model inputs. It becomes. Research outputs show that these systems have the ability to optimize the stock portfolio. Manuscript profile

Open Access Article

31 - Stock portfolio optimization of companies listed on the Tehran Stock Exchange based on a combination of two-level ensemble machine learning methods and multi-objective meta-innovative algorithms based on market timing approach
sanaz faridi amir daneshvar Mahdi Madanchi Zaj Shadi Shahverdiani

In this article, using the market timing approach and homogeneous and inhomogeneous collective learning methods, the purchase, maintenance and sales signal and market forecast are presented based on the basic characteristics, technical characteristics and time series of More

In this article, using the market timing approach and homogeneous and inhomogeneous collective learning methods, the purchase, maintenance and sales signal and market forecast are presented based on the basic characteristics, technical characteristics and time series of returns of each company in the 100 days leading to the current day. . Based on this, 208 companies were selected as active companies between 1390 and 1399 To teach data by two-level ensemble learning machine (HHEL) and market trend forecasting based on market timing strategy, use data from 5 years 1390 to 1394 and to test the data as stock portfolio optimization based on stock portfolio maximization and risk minimization. The investment portfolio uses MOPSO and NSGA II algorithms and is compared with the obtained investment portfolio with the buy and hold strategy. The results showed that the MOPSO algorithm achieved the highest stock portfolio yield with 96.437% compared to the NSGA II algorithm with a yield of 91.157% and the same investment method with a yield of 13.058%. Also, the portfolio risk in NSGA II algorithm was much lower than the portfolio risk in MOPSO algorithm with 0.792% and 1.367%, respectively Manuscript profile

Open Access Article

32 - Two stage combination model for portfolio optimization via smart BETA strategies.
mohammad sharafi Nowrouz Nourollahzadeh fatemeh sarraf

The issue of stock portfolio selection has always been one of the most attractive and practical issues in financial markets. The present article introduces a two-stage model for stock portfolio optimization by using a combination of the six smart beta strategies founded More

The issue of stock portfolio selection has always been one of the most attractive and practical issues in financial markets. The present article introduces a two-stage model for stock portfolio optimization by using a combination of the six smart beta strategies founded in the literature and fuzzy approach. In this article, first, the six factors of smart beta factores, for 76 pharmaceutical and steel companies active in the stock market, are calculated by using the financial information in the financial statements of 2016 and 2017 and their trading information in the period 2016 to 2017. Then, by combining the six factors of smart beta and fuzzy logic, the final weight of each share in the portfolio is determined. In order to evaluate the model, using SPSS software and Levin statistical test and based on yield information of the mentioned companies, during 2017 year, the difference between the efficiency of the proposed model and the index portfolio based on the market index was discussed. The results showed that at 95% confidence level, a higher profit can be obtained from the portfolio based on the proposed hybrid model. Manuscript profile

Open Access Article

33 - Development of stock portfolio trading systems using machine learning methods
Ali Heidarian Mohadeseh Moradi Mehr Ali Farhadian

Investment portfolio theory is an important foundation for portfolio management, which is a well-studied but not saturated topic in the academic community. Integrating return forecasting in investment portfolio formation can improve the performance of portfolio optimiza More

Investment portfolio theory is an important foundation for portfolio management, which is a well-studied but not saturated topic in the academic community. Integrating return forecasting in investment portfolio formation can improve the performance of portfolio optimization model. Since machine learning models have shown a superiority over statistical models, in this research, a approach of forming the stock portfolio in two stages is presented. first step, by implementing neural network, suitable stocks are selected for purchase, in the second step, using the (MV) model, the optimal weight in investment portfolio is determined for them. In particular, the stages of selecting suitable stocks and forming a stock portfolio are the two main stages of the model developed in this research. first step, a convolutional neural network model is proposed to predict stock buy and sell points for the next period.second step, stocks that are labeled as buys are selected as stocks suitable for buying, and MV model is used to determine their optimal weight in the stock portfolio. The results obtained using 5 shares of Tehran stock market as a study sample show that the efficiency and Sharpe ratio of proposed method is significantly better than traditional methods (without filtering suitable stocks) Manuscript profile

Open Access Article

34 - Portfolio optimization with Fraction of Expectation to Risk of future financial strength based on Eigen Vector of Pairwise Comparisons Matrix
Keikhosro Yakideh Gholamreza Mahfoozi Mahshid Goodarzi

The aim of this study is to propose a new method for portfolio optimization based on financial ratios. In this method, cross efficiency scores are produced from financial ratios, using Data Envelopment Analysis. Mathematical interpretation of these cross efficiency scor More

The aim of this study is to propose a new method for portfolio optimization based on financial ratios. In this method, cross efficiency scores are produced from financial ratios, using Data Envelopment Analysis. Mathematical interpretation of these cross efficiency scores that allocates several score to each company is efficiency of company in probably future situations. Efficiency scores calculated based on proper financial ratios can be considered as financial strength. Thus cross efficiency scores produced from financial ratios, can be considered as potential financial strength. As future is not clear, potential financial strength can be presented in expectation and risk indices that are mean and variance of cross efficiencies. Fraction of expectation to risk for potential financial strengths can be used as a criterion for pairwise comparison of companies. Eigenvector associated with the biggest eigenvalue of pairwise comparison matrix reflects relative importance weights of companies. This paper proposes relative importance weights of companies as a basis for portfolio optimization.  Based on sharp index Performance of proposed method is acceptable and better than marker portfolio and portfolio of one similar method.  Manuscript profile

Open Access Article

35 - Portfolio Optimization Using the Whale Algorithm with Expected Shortfall as the Measure of Risk
saeed fallahpour sepehr asefi sima fallahtafti MohammadReza Bagherikazemabad

Portfolio Selection is  one of the most  important decisions that institutional investors have to face. Markowitz was the first to introduce risk into the portfolio selection decision by introducing the Mean-Variance Model. This created one of the most importa More

Portfolio Selection is  one of the most  important decisions that institutional investors have to face. Markowitz was the first to introduce risk into the portfolio selection decision by introducing the Mean-Variance Model. This created one of the most important fields in finance, that is Portfolio Optimization and finding the efficient frontier. In the next researches, adding real world constraints to the model broadened this field. With increasing the number of assets or the constraints, Portfolio Optimization becomes an NP-hard problem which is impossible to solve with derivative-based methods, therefore, numerical and metaheuristic methods should be used for solving it. The aim of this research is optimizing portfolio using Whale optimization  algorithm.  This  metaheuristic  algorithm is  inspired  by the behavior of Whales and was introduced in 2016. This research implements the algorithm in the top 50 index in Tehran Stock Exchange and tries to find the efficient portfolio in this index. We also compare the performance of this method to two other metaheuristic algorithms and explain the advantages of the proposed method in portfolio optimization. Manuscript profile

Open Access Article

36 - رویکرد دو مرحله ای ریاضی در بهینه سازی سبد سهام
سعید خدامرادی محمد ترابی گودرزی محمدابراهیم راعی عزآبادی

Manuscript profile

Open Access Article

37 - The Application of Robust Optimization and Goal Programming in Multi Period Portfolio Selection Problem
Saghar Homaeifar Emad Roghanian

Portfolio selection is one of the most important area in financial world. Investors always want to make the best decisions which are compatible with conditions of real world. In the real world, data are usually under uncertainty. On the other hand, the most of strategie More

Portfolio selection is one of the most important area in financial world. Investors always want to make the best decisions which are compatible with conditions of real world. In the real world, data are usually under uncertainty. On the other hand, the most of strategies for portfolio selection are multi-period. Therefore, investors should rebalance their portfolios during investment horizon. In this research we present a multi-period portfolio optimization model which considers transaction costs and deal with uncertainty by application of robust programming. This model is a mean-CVaR multi objective model that is solved by goal programming. Furthermore, most of previous researches have used regression or time series models to forecast future returns of stocks for solving numerical examples, however, in this paper we forecast future returns by using Artificial Neural Networks (ANNs). Finally, solutions of robust model are compared with results of nominal one. These results show that consideration of data uncertainty and other real assumptions lead to more practical solutions.    Manuscript profile

Open Access Article

38 - Portfolio Optimization Using Chance Constrained Compromise Programming
mojtaba nouri Emran Mohammadi

One of the key issues for investors is the issue of creating an optimal stock portfolio. In the issue of choosing an portfolio, the decision maker faces different and sometimes conflicting goals such as rate of return, liquidity, dividend, and risk. In portfolio optimiz More

One of the key issues for investors is the issue of creating an optimal stock portfolio. In the issue of choosing an portfolio, the decision maker faces different and sometimes conflicting goals such as rate of return, liquidity, dividend, and risk. In portfolio optimization, the main issue is the optimal choice of assets and securities that can be made with a certain amount of capital, but on the one hand, the uncertainties associated with each share, and, on the other hand, the multiplicity of the optimal portfolio selection model, on the complexity of the problem increases. In this paper, the portfolio optimization under uncertainty has been studied. A randomized approach to converting uncertainty into a state of definiteness and agreeing to plan for a single objective is used in combination. Information about 20 pharmaceutical companies from the Tehran Stock Exchange has been used and the validity of the model has been investigated. The results show that the stock portfolio offered has a high performance. Manuscript profile

Open Access Article

39 - Foster-Hart Optimal Portfolio
sepehr asefi reza eivazlu reza tehrani

This essay is going to optimize the portfolio of stocks similar to the Markowitz approach. Nonetheless, the way in which the risk is measured is Foster-Hart risk. This measure was proposed by Foster and Hart in 2009. It takes into account the extreme events of losses. T More

This essay is going to optimize the portfolio of stocks similar to the Markowitz approach. Nonetheless, the way in which the risk is measured is Foster-Hart risk. This measure was proposed by Foster and Hart in 2009. It takes into account the extreme events of losses. The theoretical definition could be as a minimum wealth that an investor should have in order not to face with bankruptcy. Our sample consists of adjusted daily data from thirty-four companies chosen from Tehran Stock Exchange’s Top 50 Index in the period between 1391/07/01 and 1396/06/31. Data has been collected from Rahavard Novin software which is widely used in finance studies in Iran. Different optimal portfolios has been achieved in this essay. Each of which uses a different method of risk like Cvar and Semi-Variance besides Foster-Hart. Results of this essay show that Foster-Hart optimal portfolio could have higher sharp ratio in comparison with the others. Manuscript profile

Open Access Article

40 - Solving the multiobjective portfolio rebalancing model with fuzzy parameters to develop the expected value by genetic algorithm
Hosein Didehkhani zeynab Fereidooni koochaksaraei

The ability to choose the most optimal change in the composition of the portfolio of assets, brings the investor to the highest level of investment in terms of efficiency and effectiveness in the dynamic and changing market. Rebalancing the portfolio occur through a cha More

The ability to choose the most optimal change in the composition of the portfolio of assets, brings the investor to the highest level of investment in terms of efficiency and effectiveness in the dynamic and changing market. Rebalancing the portfolio occur through a change in the composition of assets weights, remove the assets, bought and sold assets and etc. Therefore, in this study solving the multiobjective portfolio rebalancing model with fuzzy parameters. The return, risk, liquidity and uncertainty as a key financial criteria are considered. Due to its importance as well as transaction costs, the net return of the portfolio are adjusted. the multiobjective portfolio rebalancing model with fuzzy parameters is solved by fuzzy goal programming and a hybrid intelligent algorithm that combines fuzzy simulation with a genetic algorithm. The results demonstrated the effectiveness of the solution approach and effciency of the model in practical applications of rebalancing an existing portfolio. Manuscript profile

Open Access Article

41 - Developing Meta-heuristic AntLion-Genetic and PBILDE Algorithms to Portfolio Optimization in Tehran Stock Exchange
Mahdi Homayounfar Amir Daneshvar Jafar Rahmani

In financial studies, portfolio can be defined as a set of investments that are selected and accepted by an individual or institution. Portfolio selection is one of the main concerns of investors in financial markets. The average-variance model with bound restrictions i More

In financial studies, portfolio can be defined as a set of investments that are selected and accepted by an individual or institution. Portfolio selection is one of the main concerns of investors in financial markets. The average-variance model with bound restrictions is considered as one of the main models in solving the portfolio optimization problem. In terms of complexity, this model is a polynomials NP-hard non-linear problem that cannot be accurately solved. In this study, an Antlion optimizer- Genetic algorithm (ALOGA) and a population based incremental learning and differential evolution algorithm (PBILDE), which are modern meta-heuristic models for solving optimization problem, are used to optimize the investment portfolio through increase the return and reduce the risk. Among 591 companies listed on Tehran stock exchange from April 2012 through March 2015, 150 companies were selected as the final sample using screening method. The data of these companies were analyzed using the applied algorithms in this research and their efficiency was compared together. The results indicate that ALOGA and PBILDE algorithms both are suitable for solving the portfolio optimization problem. In addition, using the ALOGA algorithm, it is possible to create an optimal portfolio with high accuracy and efficiency. Manuscript profile

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