جستجوی مقالات مرتبط با کلیدواژه "مدل میانگین واریانس" در نشریات گروه "مالی"

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

Since Markowitz's (1952) pioneering work on a single-period investment model, mean-variance portfolio optimization problem has become a cornerstone of investment management in both academic and industrial fields. Despite the presence of various theories and methods, the  model continues to be considered in the portfolio selection, because it doesn`t need to estimate other parameters in optimization and computes simply. The objective of this study was to investigate the performance of Model  in the portfolio.

In this paper, various models and methods have been used to select the optimal portfolios and to evaluate the performance of the portfolio. At the end of the paper, the ELECTRE multi-criteria decision-making method has been used to rank the portfolio selection models. Portfolio selection models in this paper include  model, mean-variance model, minimum-variance model and composition of the minimum-variance model and  model. In this paper, various criteria such as Sharpe ratio, Trainer ratio, Modigliani and Modigliani ratio, Sortino ratio, Information ratio have been used to measure portfolio performance.

Relatively, the performance of the  model was better in terms of Sharpe ratio and Modigliani and Modigliani ratio, the performance of the mean-variance model in terms of Trainer ratio, and the performance of the composition of the minimum-variance model and  model in terms of Sortino ratio and Information ratio.

Finally, the ELECTRE multi-criteria decision-making method has been used to rank the portfolio selection models. The results indicate that  model and minimum-variance model is superior to other models.

The 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.

Increasing the profits and reducing the risks have always been of the most important issues of concern to the investors in the financial markets. In recent years, many solutions and proposals have been suggested in respect to the frequency of portfolio optimization issue, with the highest return and the lowest possible risk. One of the most prominent suggestions is the Markowitz Model which is mostly known as the Modern Portfolio Theory. On the other hand, the TLBO algorithm which has been presented in 2010 is one of the most efficient meta-heuristic methods to solve the optimization problem. In this study, we are attempting to solve the portfolio optimization problem, according to the framework of the model introduced by Markowitz and using TLBO algorithm. For this purpose, the data related to the returns of 20 companies listed in TSE during the period 2012-2016 were collected. It is worth mentioning that four criteria including variance, mean absolute deviation, semi-variance and conditional value at risk (CvaR) were used in order to measure the risk level in this investigation.

Markowitz 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.

Short-selling prohibition has been one of the primary assumptions of Markowitz mean-variance model. Solving Markowitz quadratic model creates investment efficient frontier by considering only two return and budget constraints. In order to develop a more realistic portfolio selection model، in this paper، a new mathematical model is developed to allow short-selling under some practical constraints. Non-linear model offered is maped by using solved standard tools and constrained efficient frontier with using from 15 shares price information.