Research on the Optimal Portfolio Based on Genetic Algorithms

DOI

10.2991/eeic-13.2013.21How to use a DOI?

Keywords

Genetic Algorithms; Portfolio; Utility Function

Abstract

Investment in securities is in an uncertain environment, any gains obtained are accompanied by certain risks. The essence of portfolio optimization is the optimal allocation of the limited assets in securities with different risk and return characteristics. In this paper, the portfolio decision-making utility function is established based on E-SV risk measure, through the analysis of Markowitz portfolio model, an improved portfolio selection criterion is obtained. Because what we solve is a more complex fractional programming portfolio selection model, the traditional algorithm can not guarantee to get global optimum. In view of this, the genetic algorithms of random simulation are introduced to conduct an in-depth study. Empirical analysis shows that the portfolio model and algorithm proposed in this paper is scientific and reasonable, which may provide investors with an effective theoretical guidance and basis for decision making.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Jun Han
PY  - 2013/12
DA  - 2013/12
TI  - Research on the Optimal Portfolio Based on Genetic Algorithms
BT  - Proceedings of the 3rd International Conference on Electric and Electronics
PB  - Atlantis Press
SP  - 90
EP  - 94
SN  - 1951-6851
UR  - https://doi.org/10.2991/eeic-13.2013.21
DO  - 10.2991/eeic-13.2013.21
ID  - Han2013/12
ER  -