Portfolio Selection Using Genetic Algorithm

Slimane Sefiane and Mohamed Benbouziane ()

MPRA Paper from University Library of Munich, Germany

Abstract: The selection of optimal portfolios is the central problem of financial investment decisions. Mathematically speaking, portfolio selection refers to the formulation of an objective function that determines the weights of the portfolio invested in each asset as to maximize return and minimize risk. This paper applies the method of genetic algorithm (GA) to obtain an optimal portfolio selection. However, the GA parameters are of great importance in the procedure of convergence of this algorithm towards the optimal solution such as crossover. While, a five stock portfolio example is used in this paper to illustrate the applicability and efficiency of genetic algorithm method, GA method can also be used however for a larger number of portfolio compositions. The results obtained confirm previous research studies about the validity and efficiency of genetic algorithm in selecting optimal portfolios.

Keywords: portfolio optimization; objective function; artificial intelligence methods; genetic algorithm (search for similar items in EconPapers)
JEL-codes: G11 (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published in Journal of Applied Finance & Banking 4.2(2012): pp. 143-154

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/41783/1/MPRA_paper_41783.pdf original version (application/pdf)

Related works:
Journal Article: Portfolio Selection Using Genetic Algorithm (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:41783

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().