نتایج جستجو برای: variance approach portfolio optimization problem is a quadratic programming model and
تعداد نتایج: 20885636 فیلتر نتایج به سال:
We present a polynomial-time interior-point algorithm for a class of nonlinear saddle-point problems that involve semidefiniteness constraints on matrix variables. These problems originate from robust optimization formulations of convex quadratic programming problems with uncertain input parameters. As an application of our approach, we discuss a robust formulation of the Markowitz portfolio se...
This paper presents a multi-objective optimization model for the design of a waste management system consisting of customers, transfer stations, landfills and collection vehicles. The developed model aims to simultaneously minimize the total costs, greenhouse gas emissions and the rates of energy consumption. To tackle the multiple objectives in the problem, we utilize an interactive fuzzy prog...
Portfolio selection problem is one of the most important issues in the area of financial management in which is attempted to allocate wealth to different assets with controlling the return and risk. The aim of this paper is to obtain the optimum portfolio with regard to the cardinality and threshold constraints. In the paper, a novel multi-objective possibilistic programming model is developed ...
Credit allocation through the usage of Portfolio optimization mainly seeks tomaximize return and minimize the risk of the portfolio; but there are other importantissues including sustainable development which is important for government/publicsectors. This paper presents a novel credit allocation approach based on portfoliooptimization and investigates the effects of selected indicators of sust...
The worldwide rivalry of commerce leads organizations to focus on selecting the best project portfolio among available projects through utilizing their scarce resources in the most effective manner. To accomplish this, organizations should consider the intrinsic uncertainty in projects on the basis of an appropriate evaluation technique with regard to the flexibility in investment decision-maki...
In the paper, we consider three quadratic optimization problems which are frequently applied in portfolio theory, i.e, the Markowitz mean-variance problem as well as the problems based on the mean-variance utility function and the quadratic utility. Conditions are derived under which the solutions of these three optimization procedures coincide and are lying on the efficient frontier, the set o...
Conventionally, portfolio selection problems are solved with quadratic or linear programming models. However, the solutions obtained by these methods are in real numbers and difficult to implement because each asset usually has its minimum transaction lot. Methods considering minimum transaction lots were developed based on some linear portfolio optimization models. However, no study has ever i...
abstract compound is a word-formation process that are made two free morpheme (independent) and forms a new word with a new meaning that consists of meaning of both two component of compound. avestan language is one of the ancient iranian languages that is one of the indo-iranian languages. indo-iranian languages is one branch of indo-european languages. structure of compound noun and adjectiv...
This paper presents a novel metaheuristic method for solving an extended Markowitz portfolio selection model. In the extended model, the objective function has been modified to include realistic objectives and four additional sets of constraints, i.e., bounds on holdings, cardinality, minimum transaction lots, and liquidity constraints have been also included. The first set of constraints gua...
We modify two popular methods in machine learning, regularization and cross-validation, for the portfolio optimization problem. First, we introduce performance-based regularization (PBR), where the idea is to constrain the sample variances of the estimated portfolio risk and return. The goal of PBR is to steer the solution towards one associated with less estimation error in the performance. We...
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