8728

8089

كارشناسي ارشد

صنايع

اصفهان: دانشگاه صنعتي اصفهان، دانشكده صنايع و سيستم ها

1392

نه،93ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

فريماه مخاطب رفيعي

ناصر ملاوردي

27/1/93

رضا حجازي، جمشيد پرويزيان

مهندسي صنايع و سيستم ها

ID8089

94 Robust Portfolio Selection Model under Cardinality Constraints Transaction Cost and Minimum Transaction Lot and Solving Model with PSO Algorithm Vahid Dastaran v dastaran@in iut ac ir Date of Submission 2013 10 20 Department of Industrial Engineering Isfahan University of Technology Isfahan 8415683111 Iran Degree M Sc Language FarsiSupervisior Farimah Mokhatab Rafeie Farimah@cc iut ac irABSTRACTPortfolio selection is one of the most important problem in financial issues It is significant to apply bourse marketconditions in portfolio selection model in this thesis we added some important factors like cardinality constraints transaction costs and the minimum transaction lot simultaneously to get it more closer to the real conditions of theproblem Uncertainty of the model parameters is another very important factor to achieve optimum of the portfolioselection There are various approaches to encompass uncertain parameters so that the most important of themconsist of sensitivity analysis stochastic programming and robust optimization In this thesis we use robustoptimization to model the problem The problem shifts to the MIP mixed integer problem according to theminimum transaction lot and cardinality constraints and it is not possible to solve the problem by exact procedures Due to this characteristic of the problem we propose a meta heuristic procedure to solve the problemapproximately in this paper we use two different types of uncertainty sets for parameters cardinality and norm set introduced by Bertsimas to expose the effect of defined set type for uncertain parameters on the portfolio andcompare with each other At last several models of portfolio selection were compared with respect to variousuncertainty sets The results have shown that the efficient frontier of robust counterpart with respect to theuncertainty set with cardinality constraint in a similar level of probabilistic guarantee and expected return havehigher risk than the robust counterpart according to D Norm set In addition robust counterpart with respect to D Norm uncertainty set in a similar level has better turnover than probabilistic guarantee In other words it shows lesschanges about risk than other robust models KeywordsPortfolio Robust optimization Robust counterpart uncertainty set Particle swarm optimization

فريماه مخاطب رفيعي

ناصر ملاوردي

رضا حجازي، جمشيد پرويزيان