Mohammad Hosein Arman; Jamshid Salehi Sadaghiyani; Sara Mojdehi; Ali Nazarli
Volume 10, Issue 27 , January 2012, , Pages 94-117
Abstract
The Analytic Hierarchical Process (AHP) determines the relativeimportance of a set of alternatives in a multi-criteria decision problem.AHP has a hierarchial structure and based on pairwise comparisons ofthe project alternatives as well as pairwise comparisons of the multicriteria. Two separate algorithms ...
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The Analytic Hierarchical Process (AHP) determines the relativeimportance of a set of alternatives in a multi-criteria decision problem.AHP has a hierarchial structure and based on pairwise comparisons ofthe project alternatives as well as pairwise comparisons of the multicriteria. Two separate algorithms have been presented for measuringthe inconsistency ratioes of pairwise comparisons matrix andhierarchical structure. In this paper these algorithms and one of theapproximation methods, have been extended for measuring theinconsistency ratioes and alternatives’ weights in fuzzy AHP. Anumerical example is used to illustrate these methods.
Safar Fazli; Rasool Taghizadeh
Volume 8, Issue 19 , December 2010, , Pages 125-146
Abstract
Portfolio selection problem is an important field of capital assignment and budgeting in managerial finance and had proposed patterns for optimal selection of portfolio from the past. For this purpose we suggest a fuzzy ranking method with mathematical approach. This research is a survey in Tehran stock ...
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Portfolio selection problem is an important field of capital assignment and budgeting in managerial finance and had proposed patterns for optimal selection of portfolio from the past. For this purpose we suggest a fuzzy ranking method with mathematical approach. This research is a survey in Tehran stock exchange. Statistical population inclusive 50 superior companies of Tehran stock exchange in 1387. By survey of financial data of these companies, 20 companies have selected and weekly returns in 1387 have been calculated for them. In first stage, 20,000 random portfolios have been generated by a computer program. Each of these portfolios is composed of 20 companies that quantity of investment in each of them are between 0% and 100% and selected randomly. The uncertainty on the returns of each portfolio is approximated by means of a trapezoidal fuzzy number. A rank index that accounts for both expected return and risk is defined, allowing the decision-maker to compare different portfolios and select best portfolio. Conclusions showed that, according to risk aversion of investor, several optimal portfolios can be selected. In this article we suggested 3 optimal portfolios.