Optimising a targeted fund of strategies using genetic algorithms

Evan Hurwitz, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

This paper examines the use of Genetic Algorithm in order to perform the task of continuously rebalancing a portfolio targeting specific risk and return characteristics. The portfolio is comprised of a number of arbitrarily performing trading strategies, plus a risk-free strategy in order to rebalance in a similar method to the traditional CAPM method of rebalancing portfolios. A format is proposed for designing a fitness function appropriate to the task, and evaluated through the final results. Results of targeting both risk and return are investigated and compared, as well as optimising the non-targeted variable in order to create efficient portfolios. The findings show that a Genetic Algorithm is indeed a viable tool for optimising a targeted portfolio, using the proposed fitness function.

Original languageEnglish
Title of host publicationProceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Pages2139-2143
Number of pages5
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: 14 Oct 201217 Oct 2012

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Country/TerritoryKorea, Republic of
CitySeoul
Period14/10/1217/10/12

Keywords

  • genetic algorithm
  • modern portfolio theory
  • portfolio optimisation
  • targeted return
  • targeted risk

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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