جستجوی مقالات مرتبط با کلیدواژه « بهینه سازی سبد سهام » در نشریات گروه « اقتصاد »

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.

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.

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.

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.

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.

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.

Portfolio optimization is one of the main methods of investment and one of the main stages of portfolio construction. Since ptimization is the process of selecting the best option from a set of available options, given specific constraints, this is one of the most important challenges facing investors, financial managers and operations research modelers. In this paper, the optimal portfolio curvature with criteria of variance risk (MV), absolute deviation (MAD) and conditional value at risk (CVaR) for the five symbols Shasta, Kachad, Vepars, Khsapa and Shebandar is obtained from 1/09/1399 to 1/3/1400 and all four methods are compared. Accordingly, the effect of skewness and kurtosis on optimal portfolios in all four risk criteria is investigated by applying the dependence structure of copula functions using Monte Carlo simulation. In this regard, Pearson distribution system and Gaussian copula have been used to simulate the yields with different skewness and kurtosis and with standard deviation and mean of historical data, and finally it is shown that this process leads to a change in the optimal portfolios in all four methods of portfolio optimization, so that the change in the amount of presented risk causes the most change in the optimal portfolio of CVaR and the least change in the optimal portfolio MSV.

The purpose of this study is to optimize the portfolio in companies listed on the Iran capital market (Tehran Stock Exchange and Iran Farabours) as a multi-objective optimization problem. The first objective function includes risk minimization and the second objective function includes return maximization. The limitations of the model include the limitation of selecting companies individually as well as the limitation of budget. In order to solve the problem, two genetic metaheuristic algorithms and a gray wolf have been developed, which are analyzed using numerical examples taken from 491 companies listed on the Tehran Stock Exchange and the Iran Farabours market from April 26, 2016 to December 21, 2022 were subjected to numerical analysis.According to the numerical results, it can be seen that the gray wolf algorithm has a higher efficiency than the genetic algorithm in all examples. It is noteworthy, however, that in none of the numerical examples did the percentage of unwarranted responses in the algorithm improvement procedure exceed 10.2%. Also, the percentage improvement of the gray wolf algorithm compared to the genetic algorithm is reported to be between 3 and 11%.

In optimizing the portfolio, the main issue is the optimal selection of assets that can be bought with a certain amount of money. Although risk minimizing and revenue maximizing on investment seems simple, but in practice several approaches have been proposed for an optimal portfolio. In 1950, Harry Marquitz introduced his model in which proposed the optimization of the asset basket as a quadratic programing model with the aim of minimizing the variance of the asset set, provided that the expected return equals a constant value. In this research, the problem of three-objective optimization (i.e., maximizing stock returns, minimizing its risk and the third objective function, namely minimizing the number of assets) has been studied. Accordingly, investors, with admission a small amount of risk and a similar amount of return, will choose a basket of less assets. For this purpose, at first, genetic algorithms and multi- Particle Swarm Optimization algorithm were used to estimate the two-objective model of minimum variance and maximum return for better algorithm identification. Then, with regard to the better performance of the algorithm, this algorithm was used to estimate the three-objective model for maximizing stock returns, minimizing risk, and minimizing the number of assets.

The dynamics of capital markets is one of the main effective parameters of economy growth of each country. Selecting of optimal collection of properties is one of the capital market theories that has the certain importance in economics. The main aim of this research is solving stock portfolio-optimization using of Imperialist Competitive Algorithm. An applied pattern is developed model of mean-variance, mean-semi variance, mean-absolute deviations and Mean - Conditional Value at Riskthat its limitation has been added. In the purpose of solving problem of optimization of investment basket, we used of 25 daily accepted stock in Tehran Stock Exchange between 2009 -2016. The results of 4 patterns portfolio of research show that in the Imperialist Competitive Algorithm (ICA), mean - Conditional Value at Risk has high accuracy optimization in compare of others.

Modern Portfolio Theory is based on Harry Markowitz''s 1952 work on mean-variance portfolios. He stated that a rational investor should either maximize his expected return for a given level of risk، or minimize his risk for a given expected return. In this study the Markowitz model with cardinality constraints was studied. We extend the standard model to include cardinality constraints that limit a portfolio to have a specified number of assets، and to impose limits on the proportion of the portfolio held in a given asset (if any of the assets is held). Since considering the Markowitz model with cardinality constraints leads to NP-hard optimization problem، we introduce a Genetic Algorithm. In the usual manner، mean of the historical returns are used as inputs in the Markowitz model as rate of stock returns estimation. With studying the security prices، are shows that the rate of stock returns is difference with mean of historical returns، so with the aim of artificial neural networks، they were estimated. The proposed method was experienced on Tehran stock Exchange and the method was showed good results.