Robust genetic network programming on asset selection

Victor Parque*, Shingo Mabu, Kotaro Hirasawa

*Corresponding author for this work

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


Financial innovation is continuously testing the asset selection models, which are the key both for building robust portfolios and for managing diversified risk. This paper describes a novel evolutionary based scheme for the asset selection using Robust Genetic Network Programming(r-GNP). The distinctive feature of r-GNP lies in its generalization ability when building the optimal asset selection model, in which several training environments are used throughout the evolutionary approach to avoid the over-fitting problem to the training data. Simulation using stocks, bonds and currencies in developed financial markets show competitive advantages over conventional asset selection schemes.

Original languageEnglish
Title of host publicationTENCON 2010 - 2010 IEEE Region 10 Conference
Number of pages6
Publication statusPublished - 2010 Dec 1
Event2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka, Japan
Duration: 2010 Nov 212010 Nov 24

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON


Other2010 IEEE Region 10 Conference, TENCON 2010

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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