Guided genetic relation algorithm on the adaptive asset allocation

Victor Parque, Shingo Mabu, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One important question in investment is how to build adaptive asset allocation strategies, i.e. portfolios which adjust to the changing conditions of the economic environments. This paper proposes an evolutionary approach for the adaptive asset allocation by using Guided Genetic Relation Algorithm(GRA-g), whose main role is to model and evolve the optimal adaptive portfolio structures. Simulations using asset classes in USA show that the proposed scheme offers competitive economic advantages. This paper suggests that the use of evolutionary computing techniques is an excellent tool to aid the asset allocation, whose advantages imply the usefulness to manage the exposure to risk.

Original languageEnglish
Title of host publicationSICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
PublisherSociety of Instrument and Control Engineers (SICE)
Pages173-178
Number of pages6
ISBN (Print)9784907764395
Publication statusPublished - 2011 Jan 1
Event50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
Duration: 2011 Sep 132011 Sep 18

Publication series

NameProceedings of the SICE Annual Conference

Other

Other50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011
CountryJapan
CityTokyo
Period11/9/1311/9/18

Keywords

  • adaptive asset allocation
  • evolutionary computing
  • genetic relation algorithm

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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