Guided genetic relation algorithm on the adaptive asset allocation

Victor Parque Tenorio, 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 publicationProceedings of the SICE Annual Conference
    Pages173-178
    Number of pages6
    Publication statusPublished - 2011
    Event50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
    Duration: 2011 Sep 132011 Sep 18

    Other

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

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    Keywords

    • adaptive asset allocation
    • evolutionary computing
    • genetic relation algorithm

    ASJC Scopus subject areas

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

    Cite this

    Parque Tenorio, V., Mabu, S., & Hirasawa, K. (2011). Guided genetic relation algorithm on the adaptive asset allocation. In Proceedings of the SICE Annual Conference (pp. 173-178). [6060597]