Volume 24, Issue 2 (IJIEPM 2013)                   2013, 24(2): 141-153 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Zare Mehrjerdi Y, Rasay H. Comparison of Metaheuristic Techniques for Portfolio Optimization under Semi-Variance Risk Criterion using t-test . Journal title 2013; 24 (2) :141-153
URL: http://ijiepm.iust.ac.ir/article-1-398-en.html
, yazm2000@yahoo.com
Abstract:   (13068 Views)
Abstract With the introduction of mean-variance model Markowitz took a giant step in modeling and optimizing portfolio type problems. But his model is based upon some assumptions that rarely they can hold in practice. For this reason, many researchers have taken steps both theoretical and practical to make some improvements to his standard mean-variance model. Up to now different risk criteria models such as semi-variance model, the absolute deviations mean model, and the variance with skewness model are introduced by various researchers. The most famous risk criterion is semi-variance model studied well and took into consideration again in this article as well. Here, authors have taken steps to optimize the semi-variance model taking the number of the portfolio shares and the ratio of portfolio in each share as the model constraints using simulated annealing and Tabu-Search meta-heuristic approaches for optimization purposes. The efficient frontier line of main constraint is drawn and the capability of these algorithms in drawing these lines using two-sampling t- test is investigated. The model uses the historical data from DAX, Hang Seng, and S&P100 from years 2007 through2009 as its input for calculation purposes as well as model analysis.
Full-Text [PDF 1678 kb]   (19029 Downloads)    
Type of Study: Research | Subject: Application and development of metaheurestic procedures
Received: 2010/10/12 | Accepted: 2013/09/28 | Published: 2013/09/28

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.