-
Open Access Article
1 - Portfolio Optimization Based on Cross Efficiencies By Linear Model of Conditional Value at Risk Minimization
K. Yakideh M.H . Gholizadeh M. KazmiMarkowitz model is the first modern formulation of portfolio optimization problem. Relyingon historical return of stocks as basic information and using variance as a risk measure aretow drawbacks of this model. Since Markowitz model has been presented, many effortshave MoreMarkowitz model is the first modern formulation of portfolio optimization problem. Relyingon historical return of stocks as basic information and using variance as a risk measure aretow drawbacks of this model. Since Markowitz model has been presented, many effortshave been done to remove theses drawbacks. On one hand several better risk measures havebeen introduced and proper models have been developed to detect optimized portfolio basedon them. On the other hand the idea of using generated data by data envelopment analysisinstead of historical return of stocks has been presented.In this paper, both improvements are collected by applying a conditional value at riskminimization linear model on cross efficiencies, generated by a proper model of dataenvelopment analysis model, called range adjusted model. Performance of proposedmethod, market portfolio as a benchmark and method of applying Markowitz model oncross efficiencies calculated according to sharp ratio using next year real return of eachportfolio during years of study. Results support proper performance of proposed method. Manuscript profile -
Open Access Article
2 - Three steps method for portfolio optimization by using Conditional Value at Risk measure
S. Navidi sh. Banihashemi M. SaneiComprehensive 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 MoreComprehensive 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 - Provide a robust planning model Possibility to select a stock portfolio based on Sharp ratio
Maghsoud Amiri Mohammad Saeed HeidaryPortfolio selection and asset management is one of the most important financial issues that seeks to distribute a specified budget over multiple time periods between available assets in such a way that the return of the portfolio is maximized and, at the same time, its MorePortfolio selection and asset management is one of the most important financial issues that seeks to distribute a specified budget over multiple time periods between available assets in such a way that the return of the portfolio is maximized and, at the same time, its risk does not exceed a certain amount. In this paper, we first propose a nonlinear mathematical programming model for Portfolio selection to maximize Sharpe ratios of stocks. Then, due to the uncertain nature of the input parameters of such a problem, a new robust possibilistic programming model has been developed, which is capable of adjusting the robust degree of output decisions to the uncertainty of the parameters. The proposed model was first tested and evaluated on 42 companies active in the Tehran stock market. In the end, the computational results of the proposed model show the high performance and the utility of the robust possibilistic programming model. Manuscript profile -
Open Access Article
4 - Portfolio optimization with differential evolution and conditional value at risk approach
Shahin Ramtinnia Romina AtrchiPortfolio 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 MorePortfolio 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
5 - Portfolio Selection by Means of Artificial Bee Colony Algorithm and its Comparison with Genetic Algorithm and Ant Colony Algorithm
Mahmoud Rahmani Maryam Khalili Araghi Hashem NikoomaramInvestment decision making is one of the key issues in financial management. Investor might know about different asset types when facing with various options and the ways in which investors can incorporate them in devising a strategy is significant. Selecting the approp MoreInvestment decision making is one of the key issues in financial management. Investor might know about different asset types when facing with various options and the ways in which investors can incorporate them in devising a strategy is significant. Selecting the appropriate tools and techniques that can make optimum portfolio is one of the main objectives of the investment world. In this study it is tried to optimize the decision making in stock selection or optimization of portfolio by means of artificial colony of honeybee algorithm. And to determine the effectiveness of the algorithm, Sharp criteria algorithm, the trainer criteria and its downside risk were calculated and compared with the portfolio made up of genetic and ant colony algorithms .The sample consisted of active firms listed in the Tehran Stock Exchange from 2005 to 2015. The sample was selected by the systematic removal method. The findings show that Sharp criteria algorithm formed by the artificial bee colony algorithm functions better than the genetic and ant colony algorithms in terms of portfolio formation .However, the trainer's criteria and downside risk of the stock portfolio formed through the artificial bee colony algorithm shows the optimum function, this difference is not statistically significant. Manuscript profile -
Open Access Article
6 - Developing an uncertain mean-chance model for portfolio optimization using forecasted returns
Hosein Didehkhani Amir Shiri-ghehi Behzad MiranThe 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 ( MoreThe 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
7 - The Comparison of Genetic and Weed Algorithms in Portfolio Optimization
Majid Feshari Pooria MazaherifarIn this paper, Genetic and weed algorithms are used to solve constrained mean-semi variance portfolio problem. Then AR model and simple average are compared to predict expected return of stocks. 23 active stocks from June 22, 2014 to June 21, 2016 are used as our sample MoreIn this paper, Genetic and weed algorithms are used to solve constrained mean-semi variance portfolio problem. Then AR model and simple average are compared to predict expected return of stocks. 23 active stocks from June 22, 2014 to June 21, 2016 are used as our sample. The results indicate that, weed algorithm despite its longer time consuming has better performance than Genetic algorithm. And AR (2) model has more accurate prediction than simple average in predicting expected rate of return. Finally, we compare expected and real efficient frontier, the results indicate that, in lower risk, AR model has better prediction accuracy. So in that area, we can allocate our asset with higher certainty Manuscript profile -
Open Access Article
8 - A bi-objective portfolio rebalancing model for index traking problem under transaction costs and solving it using meta-heuristic algorithms
Amir abass Najafi Ehsan Fazeli SabzevarContinuous rebalancing and optimization of the portfolio in a way that always leads to tracking the index accurately is a complex issue. Moreover, considering the transactional costs are inevitable. This paper proposes a model for optimization of the index tracking prob MoreContinuous rebalancing and optimization of the portfolio in a way that always leads to tracking the index accurately is a complex issue. Moreover, considering the transactional costs are inevitable. This paper proposes a model for optimization of the index tracking problem and a solution based on Genetic Algorithm. The proposed model is a bi-objective model that is aimed to minimize the tracking error and transactional costs. Due to complexity of the model, the Non-dominated Sorting Genetic Algorithm and the Non-Dominated ranked Genetic Algorithm are used and evaluated to solve the model. The basic metals Index of Tehran Stock Exchange at the year of 2012 is used in this research as the desired index for tracking and rebalancing. Manuscript profile -
Open Access Article
9 - The comparison of neural network, ANFIS and AR model in expected return prediction and comparison of memetic and symbiotic organism search in constrained portfolio optimization
Sayyed Mahdi Rezaei Mahmoud Baghjari Pooria MazaherifarIn this paper, Symbiotic organism search and memetic algorithms are used to solve constrained mean-semi variance portfolio problem. Then AR model, Neural network and ANFIS are compared to predict expected return of stocks. 23 active stocks from June 22, 2014 to Jan 21, MoreIn this paper, Symbiotic organism search and memetic algorithms are used to solve constrained mean-semi variance portfolio problem. Then AR model, Neural network and ANFIS are compared to predict expected return of stocks. 23 active stocks from June 22, 2014 to Jan 21, 2016 are used as our sample. The results indicate that, memetic algorithm despite its longer time consuming has better performance than SOS algorithm. And ANFIS has more accurate prediction than others in predicting expected rate of return. Finally, we compare expected and real efficient frontier, the results indicate that, in lower risk, ANFIS has better prediction accuracy. So in that area, we can allocate our asset with higher certainty. Manuscript profile -
Open Access Article
10 - Portfolio Optimization in Capital Market Bubble Condition
Abdollah Daryabor frydoon Rahnama Roodposhti Hashem Nikoomaram Farhad GhaffariFinancial 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 MoreFinancial 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 - Select the optimum stock portfolio investment based on canonical correlation analysis for member firms of Tehran Stock Exchange
Saeid Aghasi Ehsan Aghasi Sahar BiglariIn recent years, financial markets, and especially the capital market has been a significant expansion in the international and country levels and sudden changes in economic behavior and perception of investors of the market situation affected. The main problem in choos MoreIn recent years, financial markets, and especially the capital market has been a significant expansion in the international and country levels and sudden changes in economic behavior and perception of investors of the market situation affected. The main problem in choosing the optimal portfolio optimization assets and securities that can be provided with a certain amount of capital. Although minimize risk and maximize return on investment comes in plain view, but in practice has been used several approaches to portfolio optimization.In this study, to determine the optimal portfolio based on canonical correlation analysis on companies active in the Tehran Stock Exchange during the year 1394 were discussed. Methods cross-sectional study of a sample of 42 companies included in the index returns daily adjustment of the top 50 companies in the period is three months. Based on canonical correlation analysis showed, 42 samples in the form of two pairs of canonical variables, each linear combinations of the daily rates of return were, were adjusted and petrochemical allocated and the remaining 155 units will be assigned in other industries desired. Manuscript profile -
Open Access Article
12 - Comparing the performance of optimization models with equity investment funds: evidence from the Tehran Stock Exchange
Mahmood Pakbaz kataj Daryush FaridSince 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 MoreSince 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
13 - Robust Portfolio Optimization with risk measure CVAR under MGH distribution in DEA models
Morteza Robatjazi Shokoofeh Banihashemi Navideh Modarresi -
Open Access Article
14 - Portfolio optimization by using the Copula Approach and multivariate conditional value at risk in Tehran Stock Exchange
Mirfeiz Fallahshams Amir SadeghiOne of the main problems of shareholders in the stock market is the discovery, quantification and calculation of market risk. In many studies, one-way distributions are used to estimate risk metrics that usually do not give credible results to the investor. Because the MoreOne of the main problems of shareholders in the stock market is the discovery, quantification and calculation of market risk. In many studies, one-way distributions are used to estimate risk metrics that usually do not give credible results to the investor. Because the distribution of assets is generally a broad sequence, and the results of computations are not acceptable for the consideration of the univariate normal distribution and the use of parametric methods. In this paper, using the Coppola theory, we calculate risk-weighted value (VaR) and conditional value-at-risk (CVaR). After estimating the multivariate T- Copula and the normal distribution of multivariate, the Monte Carlo method is used to generate a scenario for calculating the variance of the portfolio as well as risk estimation. Also, the calculations performed using the loss function method are tested and the accuracy of the approximations is verified. Finally, the minimum value of the copula based on the variance of the portfolio as well as its CVaR value is considered as the function of the portfolio planning, and the optimal portfolio is obtained by considering the weight of each share index. In the calculation of the 1200 index, we consider a sample basket of different industries, by calculating VaR and CVaR with confidence levels of 95 and 99 percent. The results obtained from the efficiency and reliability of the Monte Carlo simulation by the Copula T-Student versus the normalized multivariate distribution. Manuscript profile -
Open Access Article
15 - Optimization of Network-Based Matrix Investment Portfolio and Comparison with Fuzzy Neural Combination Pattern and Genetic Algorithm(ANFIS)
ALI SheidaeiNarmigi Fraydoon Rahnamay Roodposhti Reza RadfarResearchers 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 MoreResearchers 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
16 - 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 KhoddamUsing 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 MoreUsing 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
17 - Application of Copula and Simulated Returns in the Portfolio Optimization with Conditional Value-at-Risk (CVaR) in Tehran Stock Exchange (TSE)
Esmaeil Lalegani Mostafa ZehtabianSeveral studies have confirmed with using criteria appropriate to the structure and characteristics of the data, the performance of the models significantly improved. The Copula function is one of the models in determining the relationship between variables has attracte MoreSeveral studies have confirmed with using criteria appropriate to the structure and characteristics of the data, the performance of the models significantly improved. The Copula function is one of the models in determining the relationship between variables has attracted a lot of attention. In this study, we investigated the portfolio of TSE industry indexes optimized to minimize the Conditional Value at Risk, simulated data based on Copula function and generalized Pareto distribution as input. The statistical analyzes implies portfolio performance improves significantly. Since the Copula functions are diverse, Compare the impact of any one risk reduction is recommended. Manuscript profile -
Open Access Article
18 - applying Imperialist competitive algorithm (ICA) for construction and optimization Portfolio
Ali Morovati Sharifabadi Shirin Azizi Nastaran AhmadiMarkowitz optimization problem and determining Efficient frontier of investment when the number of asset invested and restrictions on the market is low, is solvable with mathematical models. But when the real world restrictions is considered , the portfolio proble MoreMarkowitz optimization problem and determining Efficient frontier of investment when the number of asset invested and restrictions on the market is low, is solvable with mathematical models. But when the real world restrictions is considered , the portfolio problems cannot be easily solved with mathematical methods. For this reason, the use of innovative techniques such as neural networks, genetic and evolutionary algorithms in optimizing Algorithm portfolio is one of the main topics of discussion in recent times. The main goal of this research is to solve the portfolio optimization problem using optimization Imperialist competitive algorithm. Therefore, using price data of 30 stocks in all listed Automotive parts in the Tehran Stock Exchange from farvardin 1388 to shahrivar 1390, the graphs are plotted. Results of this study show that the optimization Imperialist competitive algorithm in the formed of a portfolio will be successful. Manuscript profile -
Open Access Article
19 - Robust model for optimal portfolio selection
Saeed Fallahpour Farid TondnevisIn this paper, we developed robust optimization approach that departs from the randomness assumption used in other methods of optimization under uncertainty and describe uncertainty in parameters through uncertainty sets; for portfolio selection problem. The model can c MoreIn this paper, we developed robust optimization approach that departs from the randomness assumption used in other methods of optimization under uncertainty and describe uncertainty in parameters through uncertainty sets; for portfolio selection problem. The model can control the conservativeness of investor for portfolio selection by a defined parameter. We used 50 active company of Tehran exchange stock in 3 first months of 1392 to study the performance of model. The results of paired comparisons in out of sample experiments shows that Markowitz portfolio which has same expected return by robust portfolio, has lower Sharpe ratio. Manuscript profile -
Open Access Article
20 - 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 GhavidelPortfolio 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 MorePortfolio 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
21 - Optimizing Stock Portfolio with regard to Minimum Level of Total Risk using Genetic Algorithm
Maedeh Kiani Harchegani Seyed Ali Nabavi Chashmi Erfan MemarianRisk 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 MoreRisk 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
22 - Fuzzy portfolio selection under down risk measure by hybrid intelligent algorithm
Hojat Ansari Adel Behzadi Mostafa EmamdoostPortfolio optimization is one of more important problems in financial area. The classic model consider that stocks is random variable with symmetric probability density function. But in real world, forecasting stock condition always faced with uncertainty and we need in MorePortfolio optimization is one of more important problems in financial area. The classic model consider that stocks is random variable with symmetric probability density function. But in real world, forecasting stock condition always faced with uncertainty and we need insert human factors in our forecasting. Fuzzy logic is one of methods that we can use this to model this condition. On other hand, experimental studies show that assets return isn’t normal and symmetric, so we should use down risk measure such as semi variance and semi absolute deviation. In this research we consider two point in portfolio selection problem. Then we use two intelligent method based genetic and deferential evolutionary algorithm for solving the models. Making use of Tehran Stock Exchange data, it is concluded that considering semi absolute deviation has higher efficiency than semi variance model and intelligent method based deferential evolutionary algorithm has higher efficiency from intelligent method based genetic algorithm. Manuscript profile -
Open Access Article
23 - تاثیر شاخص متا مالمکوئیست روی بهینه سازی سبد دارایی
زهره طائب شکوفه بنی هاشمیاز آنجائیکه تغییر ارزش در معرض خطر شرطی (CVaR) در سطوح مختلف اطمینان برای بهینه سازی سبد بسیار موثر است، شاخص متا مالمکوئیست (MMI) دراین پژوهش استفاده شده است. برای این هدف، مدلهای میانگین- ارزش در معرض خطر شرطی با شاخص متا مالمکوئیست در حضور داده منفی معرفی گردیده است. Moreاز آنجائیکه تغییر ارزش در معرض خطر شرطی (CVaR) در سطوح مختلف اطمینان برای بهینه سازی سبد بسیار موثر است، شاخص متا مالمکوئیست (MMI) دراین پژوهش استفاده شده است. برای این هدف، مدلهای میانگین- ارزش در معرض خطر شرطی با شاخص متا مالمکوئیست در حضور داده منفی معرفی گردیده است. مشابه تئوری مارکوویتزدر چارچوب میانگین- واریانس، ارزش در معرض خطر شرطی به عنوان سنجه ریسک بکار رفته و مدلها بدون در نظر گرفتن چولگی و کشیدگی بازده مطرح شده است. در این مطالعه تعدادی داده منفی وجود دارد، بنابراین مدلهای برمبنای مدل اندازه جهت دار مبنایی (RDM) است که مقادیر مثبت و منفی را می پذیرد. در این مقاله، کارائیها در همه سطوح اطمینان در مدل های میانگین- ارزش در معرض خطر شرطی و شاخص متا مالمکوئیست روی سطوح اطمینان به عنوان دوره ها در حضور داده منفی محاسبه شده است. این روش به سرمایه گذاران کمک می کند که سبدهای سودآورشان را با شاخص متا مالمکوئیست بسازند. همچنین یک مطالعه عملی روی بازار بورس ایران انجام گرفته است. Manuscript profile -
Open Access Article
24 - Using MODEA and MODM with Different Risk Measures for Portfolio Optimization
Sarah Navidi Mohsen Rostamy-Malkhalifeh Shokoofeh Banihashemi -
Open Access Article
25 - Portfolio Optimization by Means of Meta Heuristic Algorithms
Mahmoud Rahmani Maryam Khalili Eraqi Hashem Nikoomaram -
Open Access Article
26 - A Combination of FSAW and DOE Method with an Application to Tehran Stock Exchange
Salameh Barbat Mahnaz Barkhordariahmadi Vahid Momenaei Kermani -
Open Access Article
27 - Uncertain Entropy as a Risk Measure in Multi-Objective Portfolio Optimization
Mahsa mahmoodvandgharahshiran Gholamhossein Yari Mohammad Hassan BehzadiAs we are looking for knowledge of stock future returns in portfolio optimization, we are practically faced with two principal concepts: Uncertainty and Information about variables. This paper attempts to introduce a pragmatic bi-objective investment model based on unce MoreAs we are looking for knowledge of stock future returns in portfolio optimization, we are practically faced with two principal concepts: Uncertainty and Information about variables. This paper attempts to introduce a pragmatic bi-objective investment model based on uncertainty, instead of probability space and information theory, instead of variance and other moments as a risk measure for portfolio optimization. Not only is uncertainty space expected to be more in line with investment theory, but also, applying and learning this approach seems more straightforward and practical for novice investors. The proposed model simultaneously maximizes the uncertain mean of stock returns and minimizes uncertain entropy as a measure of portfolio risk. The uncertain zigzag distribution has been used for variables to avoid the complexity of fitting distributions for data. This uncertain mean-entropy portfolio optimization (UMEPO) has been solved by three meta-heuristic methods of multi-objective optimization: NSGA-II, MOPS, and MOICA. Finally, it was observed that the optimal portfolio obtained from the proposed model has a higher return and a lower entropy as a risk measure compared to the same model in the probability space. Manuscript profile -
Open Access Article
28 - Making Decision on Selection of Optimal Stock Portfolio Employing Meta Heuristic Algorithms for Multi-Objective Functions Subject to Real-Life Constraints
Ali Sepehri Hassan Ghodrati Ghazaani Hossein Jabbari Hossein Panahian -
Open Access Article
29 - Introduction of New Risk Metric using Kernel Density Estimation Via Linear Diffusion
Ahmad Darestani Farahani Mohammadreza Miri Lavasani Hamidreza Kordlouie Ghodratallah Talebnia -
Open Access Article
30 - Higher moments portfolio Optimization with unequal weights based on Generalized Capital Asset pricing model with independent and identically asymmetric Power Distribution
Bahman Esmaeili Ali Souri Sayyed Mojtaba Mirlohi -
Open Access Article
31 - Multiple portfolio optimization in Tehran Stock Exchange
Shadi Khalil Moghadam Farimah Mokhatab Rafiei Mohamad Ali Rastegar Hamed Aghayi Bejestan -
Open Access Article
32 - Portfolio optimization considering cardinality constraints and based on various risk factors using the differential evolution algorithm
Behnaz Ghadimi Mehrzad Minooei Gholamreza Zomorodian Mirfeiz FallahshamsAs the main achievement of the modern portfolio theory, portfolio diversifica-tion based on risk and return has attracted the attention of many researchers. The Markowitz mean-variance problem is a convex quadratic problem turned into a mixed-integer quadratic programmi MoreAs the main achievement of the modern portfolio theory, portfolio diversifica-tion based on risk and return has attracted the attention of many researchers. The Markowitz mean-variance problem is a convex quadratic problem turned into a mixed-integer quadratic programming problem when incorporating car-dinality constraints. Due to the high number of stocks in a market, this problem becomes an NP-hard problem. In this paper, a metaheuristic approach is pro-posed to solve the portfolio optimization problem with cardinality constraints using the differential evolution algorithm, while it is also intended to improve the solutions generated by the algorithm developed. In addition, variance, val-ue-at-risk, and conditional value-at-risk are assessed as risk measures. Candi-date models are solved for 50 top stocks introduced by the Tehran Stock Ex-change by considering the cardinality constraints of not more than five stocks within the portfolio and 24 trading periods. Finally, the obtained results are compared with the results of genetic algorithm. The results show that the pro-posed method has reached the optimal solution in a shorter time. Manuscript profile -
Open Access Article
33 - Portfolio optimization using gray wolf algorithm and modified Markowitz model based on CO-GARCH modeling
Fahime Jahanian Ahmad Mohammadi seyyed ali paytakhti oskooe Aliasghar MottaghiPortfolio optimization which means choosing the right stocks based on the highest return and lowest risk, is one of the most effective steps in making optimal investment decisions. Deciding which stock is in a better position compared to other stocks and deserves to be MorePortfolio optimization which means choosing the right stocks based on the highest return and lowest risk, is one of the most effective steps in making optimal investment decisions. Deciding which stock is in a better position compared to other stocks and deserves to be selected and placed in one's investment portfolio and how to allocate capital between these stocks, are complex issues. Theoretically, the issue of choosing a portfolio in the case of minimizing risk in the case of fixed returns can be solved by using mathematical formulas and through a quadratic equation; but in practice and in the real world, due to the large number of choices in capital markets, the mathematical approach used to solve this model, requires extensive calculations and planning. Considering that the behavior of the stock market does not follow a linear pattern, the common linear methods cannot be used and useful in describing this behavior. In this research, portfolio optimization using the gray wolf algorithm and the Markowitz model based on CO-GARCH modeling has been investigated. The statistical population of the current research included the information of 698 companies from the companies admitted to the Tehran Stock Exchange for the period of 2011 to 2020. First, the optimal investment model is presented based on the gray wolf algorithm, and After extracting the optimal model, the efficiency of the gray wolf algorithm is compared with the Markowitz model based on CO-GARCH modeling. Manuscript profile -
Open Access Article
34 - Application of meta-heuristic algorithms in portfolio optimization with capital market bubble conditions
Iman Mohammadi Hamzeh Mohammadi Khoshouei Arezo Aghaee chadeganiThe existence of bubbles in the market, especially the capital market, can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. On the other MoreThe existence of bubbles in the market, especially the capital market, can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. On the other hand, due to the goal of investors in achieving a portfolio of high returns with the least amount of risk, the need to pay attention to these markets increases. In this research, with the aim of maximizing return and minimizing investment risk, an attempt has been made to form an optimal portfolio in conditions where the capital market has a price bubble. According to the purpose, the research is of applied type, and in terms of data, quantitative and post-event, and in terms of type of analysis, it is of descriptive-correlation type. In order to identify the months with bubbles in the period from 2015 to 2021 in the Tehran Stock Exchange market, sequence tests and skewness and kurtosis tests were used. After identifying periods with bubbles, the meta-heuristic algorithms were used to optimize the portfolio. The results indicate the identification of 14 periods with price bubbles in the period under study. Also, in portfolio optimization, selected stock portfolios with maximum returns and minimum risk are formed. This research will be a guide for investors in identifying bubble courses and how to form an optimal portfolio in these conditions. Manuscript profile -
Open Access Article
35 - Visualized Portfolio Optimization of stock market: Case of TSE
Fatemeh Lakzaie Alireza Bahiraie saeed mohammadianAn investment portfolio is a collection of financial assets consisting of investment tools such as stocks, bonds, and bank deposits, among others, which are held by a person or a group of persons. In this research, we use the Markowitz model to optimize the stock portfo MoreAn investment portfolio is a collection of financial assets consisting of investment tools such as stocks, bonds, and bank deposits, among others, which are held by a person or a group of persons. In this research, we use the Markowitz model to optimize the stock portfolio and identify the minimum spanning tree (MST) structure in the portfolio consisting of 50 stocks traded in the TSE. The observable which is used to detect the minimum spanning tree (MST) of the stocks of a given portfolio is the synchronous correlation coefficient of the daily difference of logarithm of closure price of stocks. The correlation coefficient is calculated between all the possible pairs of stocks present in the portfolio in a given time course. The goal of the present study is to obtain the taxonomy of a portfolio of stocks traded in the TSE by using the information of time series of stock prices only. In this research, report results obtained by investigating the portfolio of the stocks used to compute 50 stocks of the Iran Stock Exchange in the time period from January 2012 to October 2022. Manuscript profile -
Open Access Article
36 - Application of Clayton Copula in Portfolio Optimization and its Comparison with Markowitz Mean-Variance Analysis
Roya Darabi Mehdi Baghban -
Open Access Article
37 - Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange
Seyed Alireza Miryekemami Ehsan Sadeh Zeinolabedin Sabegh -
Open Access Article
38 - Overview of Portfolio Optimization Models
Majid Zanjirdar -
Open Access Article
39 - Portfolio Optimization and the Momentum- Contrarian Strategy (MCS)- Based Performance: Evidence from Tehran Stock Exchange
Homayun Soltanzadeh Reza Keykhaei Abdolmajid Abdolbaghi Ataabadi Mohammad Hosein Arman -
Open Access Article
40 - Presentation a model for crediblistic multi-period portfolio optimization model whit bankruptcy control
snoor modaresi farshid kheirollah mehrdad ghanbari babak jamshidinavidIn this research, a mathematical model has been presented for optimizing multi-period portfolios with a bankruptcy control approach. The goals of optimizing the multi-period portfolios include: 1- maximizing the expected outflow of the investor 2- Minimizing accumulated MoreIn this research, a mathematical model has been presented for optimizing multi-period portfolios with a bankruptcy control approach. The goals of optimizing the multi-period portfolios include: 1- maximizing the expected outflow of the investor 2- Minimizing accumulated risk 3- Minimizing the uncertainty of the portfolio''''s returns during the investment period, that achievement of these three objectives has been evaluated by two limits of bankruptcy control and the maximum and minimum adjustments of investment amounts during the investment period. The Hybrid Particle Swarm Optimization (Hybrid PSO) algorithm has been considered as the proposed solution for solving the model and a practical example has been presented to illustrate the application of the proposed model, which includes a portfolio with 17 different types of stocks from the companies listed in Tehran Stock Exchange For the three-year period from 2014 to 2016, the daily returns of these companies have been used as inputs for the model. Three different modes for the weights of the goals of optimizing the portfolio of multi- period portfolios have been determined using the sensitivity analysis table. In the end, the state of investment, which the investor equates to all three goals of optimizing the weight, has been the most suitable state for optimizing a multi-period of portfolios. the results has been compared with other algorithms Experimental results have shown that the algorithm proposed by this research for solving the model has been more appropriate than other algorithm. Manuscript profile -
Open Access Article
41 - Multi-objective Portfolio Optimization Model by Fruit Fly Optimization Algorithm
Amir Amini alireza alinezhadOne 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 MoreOne 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
42 - Presenting a model for stock portfolio optimization based on a combination of GARCH-copula models in Tehran Stock Exchange
Somayeh Rasekh Amir Mohammadzadeh Mohsen SeighaliTherefore, in the present study, a model for stock portfolio optimization based on a combination of GARCH-copula models in the Tehran Stock Exchange was presented. The present study is in the group of descriptive-correlational researches in terms of practical purpose an MoreTherefore, in the present study, a model for stock portfolio optimization based on a combination of GARCH-copula models in the Tehran Stock Exchange was presented. The present study is in the group of descriptive-correlational researches in terms of practical purpose and data collection method. Also, the statistical sample of the study includes 50 more active companies in the fourth quarter of 1398. For this purpose, the monthly stock return information of these companies was studied over a period of 10 years between 2011 to 2020, and therefore the number 120 rows of observations for each company are the basis of the analysis. The findings of this study show that that the Garch-Copola EVT method has the necessary efficiency to form a portfolio. In terms of risk criteria, it can be seen that this method has presented the lowest risk among the existing methods, and these results confirm the relationship between risk and return in investment activities. Although in this method, a smaller return is obtained than other methods, but the risk will be lower for the investor. Therefore, it can be accepted that this method has been effective in order to optimize the stock portfolio. Comparing the performance of this method with the uniform weights method, it can be seen that the Sharpe ratio in the portfolio with uniform weights was significantly larger than this ratio in the Garch-Copola portfolio. Therefore, it seems that in terms of Sharpe's criterion, the uniform weights method performed better than the proposed method and this method did not have an acceptable efficiency in improving the performance of the portfolio compared to the uniform weights method. Although based on the Sharpe criterion, this method has shown the worst performance among the portfolio formation methods, but in terms of the risk criterion, it can be seen that the risk of this portfolio is significantly lower compared to other methods. Therefore, it can be accepted that the formation of the portfolio using the Garch-Copola EVT method has been able to reduce the portfolio risk compared to other methods. Manuscript profile -
Open Access Article
43 - Portfolio optimization based on return prediction using multiple parallel input CNN-LSTM
Hatef Kiabakht Mahdi Ashrafzadeh -
Open Access Article
44 - A Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection
farshad faezy razi Naeimeh Shadloo -
Open Access Article
45 - بهینه سازی سبد سهام با استفاده از الگوریتم Big Bang-Big Crunch
علیرضا علی نژاد سرمایهگذاری نقش تعیین کنندهای در رشد اقتصادی دارد. یکی از اهداف اساسی کشورها، دستیابی به رشد اقتصادی و توسعه ی پایدار میباشد. امروزه حجم قابل توجهی از کار مدیران سرمایه گذاری و همچنین به طور عموم سرمایه گذاران، ساختن پورتفوی کارآمدی از دارایی هاست که اهداف تقا More سرمایهگذاری نقش تعیین کنندهای در رشد اقتصادی دارد. یکی از اهداف اساسی کشورها، دستیابی به رشد اقتصادی و توسعه ی پایدار میباشد. امروزه حجم قابل توجهی از کار مدیران سرمایه گذاری و همچنین به طور عموم سرمایه گذاران، ساختن پورتفوی کارآمدی از دارایی هاست که اهداف تقاضا را برآورده سازد. در این تحقیق از مدل میانگین-واریانس مارکویتز به همراه محدودیت‏های عدد صحیح و همچنین یک رویکرد فرا ابتکاری جدید به نام الگوریتم Big Bang-Big Crunch برای تشکیل سبد سهام بهره گرفته شده است. الگوریتم مورد استفاده در این تحقیق با سایر الگوریتم‏های فراابتکاری نظیر الگوریتم شبیهسازی تبریدی، ژنتیک و... با استفاده از داده‏های سهام شاخصهای بورس هنگ کنگ، ایران و ژاپن مقایسه شده است و نتایج، حاکی از رقابتی بودن این الگوریتم برای حل مسأله بهینهسازی سبد سهام دارند. Manuscript profile -
Open Access Article
46 - Portfolio Optimization in Iran Stock Market: Reinforcement Learning Approach
mahdi esfandiar mohammadali keramati Reza Gholami Jamkarani Kashefy NeishabouriThe concepts of portfolio optimization and diversification have become a tool for developing and understanding financial markets and financial decision making. The purpose of this paper is to use algorithmic trading with a focus on reinforcement learning approach in ord MoreThe concepts of portfolio optimization and diversification have become a tool for developing and understanding financial markets and financial decision making. The purpose of this paper is to use algorithmic trading with a focus on reinforcement learning approach in order to optimize the portfolio of selected stocks. This research is applied in terms of purpose and in terms of data type, quantitative and in terms of method, descriptive and exploratory and from the perspective of research plan, it is a post-event. The statistical population of this study was 672 stock exchange companies in March 1400, of which five companies (statistical sample) were selected. The sampling method was selected by one-step cluster and then purposeful selection of a share from inside each cluster and the study period was from 2017 to 2021. The findings of the research in the upward and downward periods of the market have shown that the reinforcement learning approach in bullish and bearish markets is significantly superior to the buy and maintain approach and has provided better performance, and the results are in line with the performance of algorithms in the stock markets. Manuscript profile -
Open Access Article
47 - Stock portfolio optimization using prohibited search algorithms and itinerant trader
fatemeh samadi fatemeh khosravi Hossein Eslami Mofid AbadiIn 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 MoreIn 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
48 - Bi-Level Portfolio Optimization Considering Fundamental Analysis in Fuzzy Uncertainty Environments
Mohammad Parkhid Emran Mohammadi -
Open Access Article
49 - Robustness-based portfolio optimization under epistemic uncertainty
Md. Asadujjaman Kais Zaman -
Open Access Article
50 - Lexicographic goal programming approach for portfolio optimization
H Babaei M Tootooni K Shahanaghi A Bakhsha -
Open Access Article
51 - Optimizing the investment portfolio using ccc, dcc and Markowitz algorithm models : Evidence from the stock exchange
zahra ghorbani Alireza Daghighi Asli Marjan Damankeshideh roya seifipourExtended 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 MoreExtended 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
52 - Portfolio Optimization of Listed Industries in Tehran Stock Exchange using Orthogonal GARCH
sahar abedini esmaiel abounoori Gh. Reza Keshavarz HaddadAbstract 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 MoreAbstract 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
53 - Multivariate Portfolio Optimization under Illiquid Market Prospects
Nastaran Sarvipour fatemeh samadiThe aim of the current research is to optimize the multivariate portfolio optimization algorithms under illiquid market (commodity and financial) perspective. In this regard, an optimization model for portfolio risk-return assessment with LVaR constraints is investigate MoreThe aim of the current research is to optimize the multivariate portfolio optimization algorithms under illiquid market (commodity and financial) perspective. In this regard, an optimization model for portfolio risk-return assessment with LVaR constraints is investigated using reasonable financial and operational scenarios. This approach is achieved by minimizing LVaR. The research method is descriptive and correlational. The statistical population is the companies admitted to the Tehran stock exchange, which were selected by systematic elimination sampling (screening) of 100 companies that were present in the stock exchange during the financial years of 1392-1399.The required information was extracted through the new Rah Avard software and the official website related to the Tehran Stock Exchange Organization. The unit root test of the variables was investigated using the method of Lin and Chui, and the basics of econometrics were discussed, and the variables were investigated using the vector auto-regression method (VAR) using Eviews and MATLAB statistical software. Based on the results, it can be said; Liquidity affects commodity and financial markets. Also, the effect of optimization algorithms and modeling techniques on portfolio management and risk assessment was confirmed Manuscript profile -
Open Access Article
54 - Optimization portfolio selection model with financial and ethical considerations
elham fallahi ganzagh Farimah Mokhatab RafieiThe 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 MoreThe 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
55 - Higher moments Portfolio Optimization based on Generalized CAPM with asymmetric power distribution and fat tail
Ali Souri Saeid Fallahpour Bahman EsmaeiliEvery investor wants to select the optimal combination of return and risk in order to maximize their utility. In this study, an attempt was made to explain the optimal model for estimating returns and risk in cases where there is a financial crisis and the distribution MoreEvery investor wants to select the optimal combination of return and risk in order to maximize their utility. In this study, an attempt was made to explain the optimal model for estimating returns and risk in cases where there is a financial crisis and the distribution of return on assets does not follow the normal distribution.For this purpose, we use CAPM with independent and identically asymmetric power distribution (CAPM-IIAPD) and CAPM with independent identically saymmetric exponential power distribution with two tail parameters (CAPM-IAEPD) instead of traditional CAPM. When the assumption of normality is violated, higher moments are used to optimize the model. In the next step, using Polynomial Goal Programming, we calculate optimal portfolios with third and fourth moments.The time horizon of the research from 2011 to 2018 and the statistical population has been all the companies of Tehran Stock Exchange, among which 30 companies have been selected.The results show that CAPM-IIAPD Model is the best model among three models and the adjusted return on risk in optimized models with thirs and fourth moemnts in generalized CAPM models is significantly different from the traditional model and has a better performance. Manuscript profile -
Open Access Article
56 - Identify and rank the factors affecting stock portfolio optimization with fuzzy network analysis approach
Alireza Zamanpour Majid Zanjirdar Majid Davodi NasrThe Impact of Observing the Principles and Rules of Correct Communication on Project Management (Case Study: Karaj City) In recent years, many efforts have been made to guide investors in proper investment and numerous models have been offered. The concepts of portfolio MoreThe Impact of Observing the Principles and Rules of Correct Communication on Project Management (Case Study: Karaj City) In recent years, many efforts have been made to guide investors in proper investment and numerous models have been offered. The concepts of portfolio optimization and diversification have become tools for developing and understanding financial markets and financial decisions. In most optimization methods, the optimal answer and its accuracy are highly dependent on inputs to the extent that a more appropriate and accurate selection of input variables will be very important in stock portfolio optimization. In this research, through a regular and logical process based on the judgment method in a survey of 14 experts in the field of capital market investment and a quantitative and multivariate model of fuzzy network analysis, to assess the level of importance, ranking and refining the effective factors. Portfolio optimization was undertaken. Based on the analysis, the variables of profit volatility, return on capital, company value, market risk, stock profitability, financial structure, liquidity and survival index can be introduced as the most important factors affecting the optimization of the stock portfolio. Manuscript profile -
Open Access Article
57 - Portfolio Optimization Based on Robust Probablistic Planning Model Using Genetic Algorithm and Shuffled Frog-leaping Algorithm
MohammadSaeed Heidari Javad Validi Seyed Babak EbrahimiPortfolio 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 MorePortfolio 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
58 - Development a new ensemble learning approach for stock portfolio selection using multiclass SVM and genetic algorithm
nasrin bagheri mazraeh amir Daneshvar mehdi madanchi zajThe volume and speed of transactions in financial markets has increased significantly and has undergone extensive changes nowadays. Facing with increasing, decreasing or fluctuating trends in the stock market, determining the right trading strategy is very important. Th MoreThe volume and speed of transactions in financial markets has increased significantly and has undergone extensive changes nowadays. Facing with increasing, decreasing or fluctuating trends in the stock market, determining the right trading strategy is very important. Therefore, complex meta-heuristic models are used for choosing a suitable strategy. In this research, an attempt is made to develop a new method of selecting and optimizing the stock portfolio based on the ensemble learning algorithm and genetics in order to select the best trading strategy to achieve greater returns and less risk. A combination of a six-class support vector machine (SVM) algorithm is used to predict returns and receive a buying signal; besides, a dynamic genetic algorithm is used to optimize trading rules. In this study, collective learning methods including Bagging, one of the algorithms based on Ensemble Learning, have been used to improve the accuracy of classification of returns. Data related to each share and fundamental variables in a daily time interval between years 1390 to 1399 is used as training and test data. The obtained results, comparing to traditional methods, are promising. Manuscript profile -
Open Access Article
59 - 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 RadfarPortfolio 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 MorePortfolio 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
60 - 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 ShahverdianiIn 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 MoreIn 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
61 - Multi-Asset Portfolio Optimization based on Conditional Value at Risk using Artificial Bee Colony Algorithm
Somayeh Mousavi Abbasali Jafari-Nodoushan Marzieh Kazemi-Rashnani Mahsa MohammadtaheriMulti-asset portfolio management and optimization have always been of interest to investors. Due to the inflation in Iran market, different performance of the asset classes in different market conditions and the ability to earn more profit along with less risk by divers MoreMulti-asset portfolio management and optimization have always been of interest to investors. Due to the inflation in Iran market, different performance of the asset classes in different market conditions and the ability to earn more profit along with less risk by diversifying the types of assets, it seems necessary to select a portfolio consisting of stocks, foreign currency and commodities. In this paper, assets of the above categories, including Emami coins, American dollar, and 11 sector indices, are considered in the portfolio composition. Due to the importance of the risk measure in multi-asset portfolio optimization, a model with conditional value at risk, the historical simulation approach has been extended and its efficiency has been compared with the mean-variance model. The models have been solved using the artificial bee colony and imperialist competitive algorithms. The daily asset prices in the period 2013 to 2020 have been used to evaluate the models in Iran market. Results show that the mean-conditional value at risk model performs better than the mean-variance in the training and testing periods. Furthermore, optimized portfolios with the artificial bee colony algorithm could outperform the imperialist competitive algorithm based on the Sharpe ratio, conditional Sharpe ratio, and return on risk. Manuscript profile -
Open Access Article
62 - Long Memory usage in Portfolio Optimization using the Copula Functions: Empirical evidence of Iran and Turkey Stock Markets
Hasti Chitsazan Motahareh Moghadasi Reza Tehrani Mohsen MehraraThe main objective of this paper is to optimize and manage the portfolio by using copula functions. Copula function has been using as a powerful and flexible tool for the determination of dependency structure. Research data include the Iran stock market index and the Tu MoreThe main objective of this paper is to optimize and manage the portfolio by using copula functions. Copula function has been using as a powerful and flexible tool for the determination of dependency structure. Research data include the Iran stock market index and the Turkey stock market index. The present study seeks to find the effect of long memory on the structure of dependence between returns and optimal portfolio structure. In the first step, we compare the dependence structure between the net returns and the filter generated from the ARFIMA-GARCH process returns to investigate the impact of long memory on them. In the second step, the influence of the dependence structure between net returns and filtered returns on portfolio optimization has been investigated. The results indicated that the model can be fitted to the return of time series and the best pattern is the frank pattern. The results also indicated the existence of long memory in the mean and variance of stock return on the Iran stock market and the existence of long memory in the variance of the Turkey stock market. All models allocate more percentage of capital to Iran stock market and lower percent to Turkey stock market. Manuscript profile -
Open Access Article
63 - Designing a credit portfolio optimization model in the banking industry using a meta-innovative algorithm
ali asghar tehrani poor Ebrahim Abbasi Hosein Didehkhani arash naderianThe purpose of this study is to design a credit portfolio optimization model in the banking industry using a meta-innovative algorithm. Risk is one of the basic concepts in financial markets that has a certain complexity. Due to the lack of a clear picture of risk reali MoreThe purpose of this study is to design a credit portfolio optimization model in the banking industry using a meta-innovative algorithm. Risk is one of the basic concepts in financial markets that has a certain complexity. Due to the lack of a clear picture of risk realization, financial markets need risk control and management approaches. The present study is a descriptive survey in terms of data collection and applied in terms of purpose. The statistical population of this research includes all facility files of the last 10 years as well as the financial statements of Ansar Bank branches affiliated to Sepah Bank, which were selected by census method. The risk criteria used in the models are: fuzzy risk value, absolute value of fuzzy downward deviations and half entropy. Research models were implemented using multi-objective particle swarm optimization algorithm. The software used in conducting research is MATLAB software. The results show that the performance of the fuzzy risk-averaged model is better than the other two models in evaluating optimal portfolios. Therefore, the use of the above model in credit basket optimization is recommended. Manuscript profile -
Open Access Article
64 - Portfolio optimization in capital market bubble space, application of bee colony algorithm
Iman Mohammadi Hamzeh Mohammadi Khashoei arezoo aghaei chadeganiThe existence of bubbles in the market,especially the capital market,can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country.On the other han MoreThe existence of bubbles in the market,especially the capital market,can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country.On the other hand,due to the goal of investors in achieving a high return portfolio with the least amount of risk,it is necessary to pay more attention to these markets In this study,in order to maximize returns and minimize investment risk,an attempt was made to create an optimal portfolio in conditions where the capital market has a price bubble.According to the purpose,the research is of applied type,and in terms of data,quantitative and post-event,and in terms of analysis,is descriptive-correlation.In order to identify bubble months in the period from2015to2019in Tehran Stock Exchange,sequence tests and skewness and kurtosis tests were used and after identifying bubble periods,artificial bee colony algorithm was used to optimize the portfolio.The results indicate the identification of 10 periods with a price bubble in the study period.Also,in portfolio optimization, selected stock portfolios are formed with maximum returns and minimum risk.This research will be a guide for investors in identifying bubble courses and how to form an optimal portfolio in these conditions. Manuscript profile -
Open Access Article
65 - Two stage combination model for portfolio optimization via smart BETA strategies.
mohammad sharafi Nowrouz Nourollahzadeh fatemeh sarrafThe 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 MoreThe 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
66 - Performance Comparison of tcopula GARCH-LVaR with GARCH-VaR To optimize the portfolio in the Tehran Stock Exchange
Gholam Reza Taghizadegan , Gholamreza Zomorodian rasoul saadi, mirfeyz FallahThe aim of this research is to compare the performance of the value-at-risk model with the liquidity-t-copula approach with dynamic conditional correlation (t-copula-GARCH-LVaR) with the value-at-risk (VaR) model to optimize the portfolio in the Tehran Stock Exchange. I MoreThe aim of this research is to compare the performance of the value-at-risk model with the liquidity-t-copula approach with dynamic conditional correlation (t-copula-GARCH-LVaR) with the value-at-risk (VaR) model to optimize the portfolio in the Tehran Stock Exchange. In the current research, in order to test the desired hypotheses, the period is between 2001 and 2021. All the variables used in this research on a quantitative scale and observations in the form of time series are the daily logarithmic returns of 40 stock market indices, including 39 industry indices and one index of fixed-income bonds from the beginning of September 2011 to the end of July 2021. In this research, to perform the final analysis, all the calculations required for this research were done using the open-source software R 4.2.1. Our results showed that the t-copula-GARCH-LVaR optimization model performs better according to the Sharpe criterion based on Mann–Whitney U test at the 95% test level. Manuscript profile -
Open Access Article
67 - 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 GoodarziThe 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 MoreThe 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
68 - Stock Portfolio optimization: Effectiveness of particle swarm optimization and Markowitz model
Ali Bayat lida asadiThe purpose of the portfolio management is the portfolio selection, the portfolio that acts as guidance to investors in order to achieve to maximum efficiency. In this study for portfolio selection, particle swarm optimization and Markowitz model are used and a comparis MoreThe purpose of the portfolio management is the portfolio selection, the portfolio that acts as guidance to investors in order to achieve to maximum efficiency. In this study for portfolio selection, particle swarm optimization and Markowitz model are used and a comparison was made between them. Introducing the model to select a portfolio for investors who can make the right choice with evaluation of that model is of our objectives in this study. For this purpose, literature and various studies are verified and a set of measures with regard to the purpose of the research was collected. Among the companies listed on the Tehran Stock Exchange, 65 companies were selected as sample for the period 2009 to 2013 and were analyzed as a statistical sample. To analyze the data, first the data is collected and categorized in software EXCEL and after doing calculations were analyzed using MATLAB software.TThe results of this research showed that the particle swarm optimization has a fewer errors in the selection of optimal portfolio compared with Markowitz model. The most important suggestion for future research is to compare the particle swarm optimization with other models of optimization such as, colonial competition, meta-heuristic, arbitrage model and etc. Manuscript profile -
Open Access Article
69 - A Portfolio Optimization Model for a Private Equity Investment Company under Data Insufficiency Condition with an Artificial Bee Colony Meta-heuristic Approach
Fereydoun Rahnama Roodposhti Ehsan Sadeh Mirfeiz Fallahshams reza Ehteshamrasi jamil JalilianDifferent investors with different investment levels have a goal in common which is to reach a portfolio of assets which further to meeting the expected rate of return would have the least possible level of risk. In this study we aim to help an investment company to det MoreDifferent investors with different investment levels have a goal in common which is to reach a portfolio of assets which further to meeting the expected rate of return would have the least possible level of risk. In this study we aim to help an investment company to determine an optimized combination of assets containing the stocks of its subsidiary companies as well as other lower risk assets. One of the main challenges in investing in private companies’ stocks, is the lack of data related to their return and risk compared with public companies. In this paper we apply a simulation approach which is able to generate valid random numbers in data insufficiency condition to calculate the return and the risk of the private assets. Furthermore, defining the problem as a bi-objective optimization problem and regarding the fact that portfolio selection is an NP-Hard problem, we use a multi-objective covariance-based artificial bee colony algorithm to solve our problem. The results show that efficient portfolios are the ones have both high risk and low risk assets simultaneously. Manuscript profile -
Open Access Article
70 - Portfolio Optimization Using the Whale Algorithm with Expected Shortfall as the Measure of Risk
saeed fallahpour sepehr asefi sima fallahtafti MohammadReza BagherikazemabadPortfolio 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 MorePortfolio 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
71 - Portfolio optimization in an upside potential and downside risk (UPM-LPM) framework
ali saleh abadi Mohsen Sayar Mojtaba ShahryariIn the process of evolving portfolio theory, In order to eliminate the defects and basic assumptions limitation of the traditional model, the concept of downside risk and the Mean-LPM model has been introduced. The Lower Partial Moment (LPM) has been the downside risk MoreIn the process of evolving portfolio theory, In order to eliminate the defects and basic assumptions limitation of the traditional model, the concept of downside risk and the Mean-LPM model has been introduced. The Lower Partial Moment (LPM) has been the downside risk measure that is most commonly used in portfolio analysis. Its major disadvantage is that its underlying utility functions are linear above some target return. As a result, the upper partial moment (UPM)/lower partial moment (LPM) analysis has been suggested in the recent researches. The UPM-LPM framework is powerful because it implements the full richness of economic utility theory such as Morgenstern economic utility function and prospect theory. In this study, using by stock market Sector indexes over 3 years period since 2010 to 2012, the mean-variance and UPM/LPM optimal portfolio has been calculated in different degrees of potential and risk aversion. In the next step, the optimal portfolio performance of both model has been measured over second period from 2013 to 2015. This research used MATLAB software for optimizing and analyzing of portfolio selection models. The Jobson-Korkie test has been used to measure the portfolio performance difference between Mean-Variance and UPM-LPM model. It was found that there is significant difference between results of Sharp ratio in Markowitz portfolio and UPM-LPM portfolio, and in the different risk/potential aversion approaches the UPM-LPM portfolio are significantly better than the traditional Markowitz model Manuscript profile -
Open Access Article
72 - Developing a Fuzzy Multibjective Model for Multiperiod Portfolio Optimazation Considering Average Value at Risk
Amir Shiri Ghehi Hosein Didehkhani kaveh Khalili Damghani parviz SaeediThe purpose of the present research is to provide a multi-period portfolio optimization model in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’s risk was measured using the Average Value at Risk (A MoreThe purpose of the present research is to provide a multi-period portfolio optimization model in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’s risk was measured using the Average Value at Risk (AVaR) as a coherent risk measure. The model is designed in such a way that, in addition to considering transaction costs, the investor will have the opportunity to allocate part of his wealth to a risk-free asset. In designing the model, in addition to the cardinality constraints, constraints such as the minimum “proportion entropy” (as the portfolio of diversification degree) and the expected returns of the portfolio in each period are considered. The results of the model running by MOPSO algorithm indicated that the model objectives in the optimum portfolios were better suited than those when the model was run with random weights. The results also indicated that an increase in the portfolio diversification degree reduced the amount of the final wealth. Manuscript profile -
Open Access Article
73 - The Application of Robust Optimization and Goal Programming in Multi Period Portfolio Selection Problem
Saghar Homaeifar Emad RoghanianPortfolio 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 MorePortfolio 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
74 - Portfolio Optimization Using Chance Constrained Compromise Programming
mojtaba nouri Emran MohammadiOne 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 MoreOne 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
75 - Foster-Hart Optimal Portfolio
sepehr asefi reza eivazlu reza tehraniThis 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 MoreThis 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
76 - Presenting a fuzzy multi objective model for portfolio selection based on value at risk, semi-skewness and fuzzy credibility theory
Hosein Didehkhani Saeid HojjatiastaniIn finance, optimal portfolio selection, play's a crucial role for investor’s decisions. In practical cases the problem of optimal portfolio selection has some challenges. In a cases stocks are affected by various uncertain factors therefore, it is impossible to s MoreIn finance, optimal portfolio selection, play's a crucial role for investor’s decisions. In practical cases the problem of optimal portfolio selection has some challenges. In a cases stocks are affected by various uncertain factors therefore, it is impossible to simulate all of them properly. In this study, previous investigation about select and optimization of portfolio has been illustrated. For this purpose, credibility theory for calculating statistics moments such as Expected value, semi-skewness have been used. Also, the value at risk and Uncertainty is used for modeling in fuzzy Environment. For solving the model Matlab software run for solving Non-dominated sorting genetic algorithm "NSGA-II". And as result some of optimal pareto-front solutions have been obtained which were listed as optimal solution. To conclude Random portfolios has been created in order to compare with defined portfolios .the result indicate , defined models has more level of Satisfactory goals rather than Random portfolios. Manuscript profile -
Open Access Article
77 - Study of portfolio optimization based on downside risk, upside potential and behavioral variables efficiency
yavar mirabbasi hashem nikoumaram ali Saeidi Farideh HaghshenasWhile available models to measure the risk don’t consider the positive side of stock return probability distribution, this research tries to optimize the portfolio based on adjusted lower partial momentum (ALPM) with upside potential and behavioral variables to co MoreWhile available models to measure the risk don’t consider the positive side of stock return probability distribution, this research tries to optimize the portfolio based on adjusted lower partial momentum (ALPM) with upside potential and behavioral variables to compare the result with modern portfolio theory model which is one of the basic models in this area. This research studies 144 monthly portfolios of industry indices in Tehran Stock Exchange within 12 years and compute realized rate of return for those portfolios in next month. In the next stage the research make use of variance analysis between realized rates of return for portfolios made by two models. The present research determined that realized rate of return for portfolios made by ALPM are higher than modern portfolio theory model when investors are downside risk averse and upside potential lover. However in condition that investors are downside risk averse and upside potential averse there is not any difference between two model as well as when investors are downside risk averse and upside potential neutral. Manuscript profile -
Open Access Article
78 - When Behavioral Portfolio Theory meets mean-variance frontier
mohammad sajjad moghaddam Fereydon OhadiFinding the most optimize way to make a portfolio “ feasible “ has been ,and always will be a challenge and concern for those active in investment management industry. For several decades, Markowitz's (1952) Mean Variance Theory (MVT) has been considered as MoreFinding the most optimize way to make a portfolio “ feasible “ has been ,and always will be a challenge and concern for those active in investment management industry. For several decades, Markowitz's (1952) Mean Variance Theory (MVT) has been considered as the cornerstone of modern portfolio theory. The Behavioral Portfolio Theory (BPT) developed by Shefrin and Statman (2000) is often set against Markowitz's (1952) Mean Variance Theory (MVT). In this paper, we compare the asset allocations generated by BPT and MVT without restrictions. Using Tehran Securities Exchange stock prices from the TSE database for the 2012– 2017 period, A sample of 247 companies are listed on the Tehran Stock Exchange data for a period of 5 years was used for statistical analysis . stock prices contained in the TSE database to generate a possible asset allocations via bootstrap simulation.this paper is the study that empirically determines the BPT optimal portfolio.We show that Shefrin and Statman's (2000) optimal portfolio is Mean Variance (MV) efficient inmore than 70% of cases. Manuscript profile -
Open Access Article
79 - Developing Meta-heuristic AntLion-Genetic and PBILDE Algorithms to Portfolio Optimization in Tehran Stock Exchange
Mahdi Homayounfar Amir Daneshvar Jafar RahmaniIn 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 MoreIn 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 -
Open Access Article
80 - Pseudo-Triangular Entropy of Uncertain Variables: An Entropy-Based Approach to Uncertain Portfolio Optimization
Seyyed Hamed Abtahi Gholamhossein Yari Farhad Hosseinzadeh Lotfi Rahman Farnoosh -
Open Access Article
81 - Partial pseudo-triangular entropy of uncertain random variables with application to portfolio risk management
Seyyed Hamed Abtahi
Sanad
Sanad is a platform for managing Azad University publications
Links
Related Centers
Technical Support
Official pages
The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024