List of Articles Stock Portfolio Optimization

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  1 - Portfolio optimization with differential evolution and conditional value at risk approach
  Shahin Ramtinnia Romina Atrchi
  Portfolio selection, in order to maximize the profit from investment, is an important concern for minor and institutional investors.Therefore; efficient and secure optimization of financial assets is one of the most important new and modern, financial topics, trying to More

  Portfolio selection, in order to maximize the profit from investment, is an important concern for minor and institutional investors.Therefore; efficient and secure optimization of financial assets is one of the most important new and modern, financial topics, trying to improve the portfolio performance using modern approaches of other sciences. Accordingly, this article aimed to optimize the index returns of top 10 companies of Tehran Stock Exchange from 2011 to 2015 using portfolio risk minimization approach with the maximum yield according to conditional value at risk and differential evolution algorithm(DE-CVaR) on a monthly basis. The results showed that differential evolution algorithm with the conditional value at risk approach, had better Sharpe and returns ratios by CVaR value compared to the random algorithm. The results of posttest with monthly approach also showed that DE-CVaR was better than random algorithm in terms of the criteria for selecting the optimal portfolio. Manuscript profile
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  2 - Optimization of Network-Based Matrix Investment Portfolio and Comparison with Fuzzy Neural Combination Pattern and Genetic Algorithm(ANFIS)
  ALI SheidaeiNarmigi Fraydoon Rahnamay Roodposhti Reza Radfar
  Researchers have been researching portfolio optimization issues for several years. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, ie to minimize investment risk and maximize investment profit. The aim of More

  Researchers have been researching portfolio optimization issues for several years. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, ie to minimize investment risk and maximize investment profit. The aim of this study is to investigate the strategic capability of network matrix and fuzzy genetic neural model (ANFIS) in optimizing the investment portfolio among companies on the Tehran Stock Exchange. Grouping stocks by network matrix based on new variables including aggressive, indifferent and defensive stocks provided by Roodpashti (2009) and traditional variables including growth, growth-value and value stocks and classification of companies based on their market value and use. From the law of quarters and finally their weighting is considered in proportion to the return of that share. The design and presentation of a stock portfolio optimization model using adaptive fuzzy neural inference system and its combination with genetic algorithm (ANFIS) in which two different categories of technical and fundamental variables are used as model inputs. Research outputs show that these systems have the necessary ability to optimize the stock portfolio. Therefore, a combined model of neural networks and fuzzy reasoning theory with genetic algorithm has been used to weight the factors affecting stock portfolio optimization in the 7 years leading up to 1398. Manuscript profile
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  3 - A Combination of FSAW and DOE Method with an Application to Tehran Stock Exchange
  Salameh Barbat Mahnaz Barkhordariahmadi Vahid Momenaei Kermani
  10.22034/amfa.2021.1914207.1506
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  4 - 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
  10.22034/amfa.2021.1915292.1525
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  5 - Presenting a model for stock portfolio optimization based on a combination of GARCH-copula models in Tehran Stock Exchange
  Somayeh Rasekh Amir Mohammadzadeh Mohsen Seighali
  10.30495/qrm.2024.1994609.1043
  Therefore, 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 More

  Therefore, 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
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  6 - Stock portfolio optimization using prohibited search algorithms and itinerant trader
  fatemeh samadi fatemeh khosravi Hossein Eslami Mofid Abadi
  In this thesis, modeling and forecasting of stock market fluctuations using the combination of neural network and conditional variance patterns (case, Tehran Stock Exchange) have been used from April 2008 to April 2012. According to the predicted results, this hypothesi More

  In this thesis, modeling and forecasting of stock market fluctuations using the combination of neural network and conditional variance patterns (case, Tehran Stock Exchange) have been used from April 2008 to April 2012. According to the predicted results, this hypothesis is confirmed, but its accuracy is not as large as the composition of the neural network and the conditional variance pattern. In the return time series, both GRACH and ARCH conditional variances are rejected, but in the GRACH time series, ARCH is rejected. Given the artificial neural network simulation and conditional variance, the error value of the least squares is the return value of 18, that is, an error is required to estimate future returns. An important parameter of the opacifying factor is the dependence of our input and output at each stage, which indicates a number close to 1 and shows a complete dependence. According to the artificial neural network simulation and conditional variance, the least squares risk error value is 0.001, that is, to estimate the returns for the future, this error is error. Another important parameter of this regression table is R, which shows the dependence of our input and output in each stage, where 0 means a random relationship and 1 means dependence. Manuscript profile
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  7 - Optimizing the investment portfolio using ccc, dcc and Markowitz algorithm models : Evidence from the stock exchange
  zahra ghorbani Alireza Daghighi Asli Marjan Damankeshideh roya seifipour
  Extended Abstract This study investigates the impact of the capital market using multivariate GARCH models and the Markowitz algorithm to optimize the stock portfolio. The statistical population of this research includes stock exchange companies that were admitted More

  Extended Abstract This study investigates the impact of the capital market using multivariate GARCH models and the Markowitz algorithm to optimize the stock portfolio. The statistical population of this research includes stock exchange companies that were admitted to the stock exchange before 1395 and were active until the end of 1399 and had the following characteristics: The financial year of the companies should have ended on March 20th and the companies' shares should have been traded on the stock exchange during each year of the research period and the end-of-period price was available. In addition, the financial information of the companies must also be available. Considering the above characteristics, 4 top industries, including the automotive and parts manufacturing industry, the selected electrical machinery industry, the metal mining and oil products industry, were selected as the screening population in our portfolio based on a combination of stock liquidity, stock trading volume in the trading hall, stock trading frequency in the trading hall, and the company's impact on the market. The sample size is 800 and is daily during the period from 1395 to 1399. Purpose The results of this study show that the optimal weights are more allocated to stocks with less volatility in the stock return trend of that industry. In fact, lower weights are allocated to industries with more volatile returns among the four industries, namely the automotive and parts manufacturing and oil products industries. Conversely, the largest optimal average share of the portfolio among the four industries is for the non-metallic minerals industry with the least return volatility. Methodology The results of this study also show that industry stock return shocks have reciprocal effects on each other. For example, a positive shock to the stock return of the non-metallic minerals industry leads to a negative shock to the stock return of the automotive and parts manufacturing industry. In addition, the results of this study show that the CCC and DCC models have different results in estimating the optimal weights of the industries and risk-free assets that make up the investment portfolio. So that, the DCC model, compared to the CCC model, allocates less weight to the stocks of the automotive and parts manufacturing and oil products industries and, conversely, allocates more weight to the stocks of the non-metallic minerals industry. Finally, the results of this study show that the portfolio formed using the Markowitz optimization algorithms can track the risk-averse individual's utility to maximize profit. And Based on the results of this study, it is suggested that investors pay attention to the volatility of the stock return of that industry when selecting stocks for investment and allocate a greater share to stocks of industries with less return volatility. Finding It is also suggested that DCC models be used alongside CCC models to estimate the optimal weights of the investment portfolio. In addition, it is suggested that Markowitz optimization algorithms be used to form an investment portfolio that matches the risk-averse individual's utility. Now, let’s address the limitations of this study, that one of the limitations of this study is the use of daily stock return data. It is suggested that in future research, data with higher frequency such as hourly or minute data be used. Another limitation of this study is the non-consideration of other factors affecting stock returns, such as macroeconomic factors. It is suggested that in future research, these factors should also be considered. Conclusion The results of this study have important implications for investors and portfolio managers. The use of multivariate GARCH models and the Markowitz algorithm can help to optimize stock portfolios and improve risk-adjusted returns. Investors should consider the volatility of stock returns and the correlation between industries when making investment decisions. DCC models can be used to estimate optimal portfolio weights, and Markowitz optimization algorithms can be used to form portfolios that match the risk-averse individual's utility. Future research should focus on using higher frequency data and considering other factors affecting stock returns. Manuscript profile
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  8 - Optimization portfolio selection model with financial and ethical considerations
  elham fallahi ganzagh Farimah Mokhatab Rafiei
  The moral investment movement that began in the 1960s in the United States has recently led to a massive move around the world. Growing cases of corporate scams and scandals have pushed investors to consider the quality of corporate governance and the ethics of their be More

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