Maziar Salahi; Tahereh Khodamoradi; Abdelouahed Hamdi
Abstract
The use of variance as a risk measure is limited by its non-coherentnature. On the other hand, standard deviation has been demonstrated as acoherent and effective measure of market volatility. This paper suggests theuse of standard deviation in portfolio optimization problems with cardinalityconstraints ...
Read More
The use of variance as a risk measure is limited by its non-coherentnature. On the other hand, standard deviation has been demonstrated as acoherent and effective measure of market volatility. This paper suggests theuse of standard deviation in portfolio optimization problems with cardinalityconstraints and short selling, specifically in the mean-conditional value-at riskframework. It is shown that, subject to certain conditions, this approach leadsto lower standard deviation. Empirical results obtained from experiments onthe SP index data set from 2016-2021 using various numbers of stocks andconfidence levels indicate that the proposed model outperforms existing modelsin terms of Sharpe ratios.
Samaneh Mohammadi Jarchelou; Kianoush Fathi Vajargah; Parvin Azhdari
Abstract
Investment is the selection of assets to hold and earn more pro t for greater prosperity in the future. The selection of a portfolio based on the theory of constraint is classical data covering analysis evaluation and ranking Sample function. The in vestment process is related to how investors act in ...
Read More
Investment is the selection of assets to hold and earn more pro t for greater prosperity in the future. The selection of a portfolio based on the theory of constraint is classical data covering analysis evaluation and ranking Sample function. The in vestment process is related to how investors act in deciding on the types of tradable securities to invest in and the amount and timing. Various methods have been proposed for the investment process, but the lack of rapid computational methods for determining investment policies in securities analysis makes performance appraisal a long term challenge. An approach to the investment process consists of two parts. Major is securities analysis and portfolio management. Securities analysis involvesestimating the bene ts of each investment, while portfolio management involves analyzing the composition of investments and managing and maintaining a set of investments. Classical data envelopment analysis (DEA) models are recognized as accurate for rating and measuring efficient sample performance. Unluckily, this perspective often brings us to get overwhelmed when it's time to start a project. When it comes to limiting theory, the problem of efficient sample selection using a DEA models to test the performance of the PE portfolio is a real discontinuous boundary and concave has not been successful since 2011. In order to solve this problem, we recommend a DEA method divided into business units based on the Markowitz model. A search algorithm is used to introduce to business units and prove their validity. In any business unit, the boundary is continuous and concave. Therefore, DEA models could be applied as PE evaluation. To this end, 25 companies from the companies listed on the Tehran Stock Exchange for the period 1394 to 1399 were selected as the sample size of statistics in data analysis. To analyzethe data, after classi cation and calculations were analysed by MATLAB software, the simulation results show that performance evaluation based on constraint theory based on DEA approach and the Markowitz model presented in this paper is efficient and feasible in evaluating the portfolio of constraint theory.
Shokouh Shahbeyk
Abstract
In this paper, we discuss some of the concepts of robustness for uncertain multi-objective optimization problems. An important factor involved with multi objective optimization problems is uncertainty. The uncertainty may arise fromthe estimation of parameters in the model, error of computation, the ...
Read More
In this paper, we discuss some of the concepts of robustness for uncertain multi-objective optimization problems. An important factor involved with multi objective optimization problems is uncertainty. The uncertainty may arise fromthe estimation of parameters in the model, error of computation, the structure of a problem, and so on. Indeed, some parameters are often unknown at the beginning of solving a multi-objective optimization problem. One of the mostimportant and popular approaches for dealing with uncertainty is robust optimization. Markowitz's portfolio optimization problem is strongly sensitive to the perturbations of input parameters. We consider Markowitz's portfolio optimization problem with ellipsoid uncertainty set and apply set-based minmax and lower robust efficiency to this problem. The concepts of robust efficiency are used in the real stock market and compared to each other. Finally, the increase and decrease effects of uncertainty set parameters on these robust efficient solutions are verified.
Asghar Abolhasani Hastiany; Alireza Zamanpour
Abstract
This study aims to optimize the portfolio using the genetic operator and network centralization. The statistical population of the study is the top 50 companies of Tehran Stock Exchange, in the first quarter of 2021, and to calculate the size of centrality, we used the difference in the overall performance ...
Read More
This study aims to optimize the portfolio using the genetic operator and network centralization. The statistical population of the study is the top 50 companies of Tehran Stock Exchange, in the first quarter of 2021, and to calculate the size of centrality, we used the difference in the overall performance of each company compared to all the top companies, based on a standard hybridization indicator. Then based on the companies’ performance in the capital market, the geometric mean of risk and return of efficient companies are determined, and given the real limitations of the budget, the requirements and expectations of the investors compared to the market’s performance and the risk-free investment, the problem of decision-making for the composition of the investment in the form of a multi-purpose paradigm is formulated. By using the modified optimization algorithm and the genetic algorithm with dual operators, we optimized the investment’s composition. Finally, we use the compound linear regression with data analysis approach to evaluate the effect of individual and systemic operators on determining the investment strategy, and the results represented the positive effect of these two operators.
