dc.contributor.authorPeng, Lei.
dc.date.accessioned2009-06-17T03:48:04Z
dc.date.available2009-06-17T03:48:04Z
dc.date.copyright2009en_US
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10356/17852
dc.description.abstractIn investment, it is highly desirable to maximize return or profit within a given risk level. Constructing a portfolio of investments to optimize the outcome is among the most significant financial decisions facing individuals and institutions. Essentially the standard portfolio optimization problem is to identify the optimal allocation of limited resources among a limited set of investments. Optimality is measured using a tradeoff between perceived risk and expected return. Expected future returns are based on historical data. Risk is measured by the variance of those historical returns. In this project, Genetic Algorithm is explored to tackle the multi-objective portfolio problem. GA is inspired from evolution process in which species evolve to improve themselves. This technique has received much attention in the past few years due to its powerful optimization and structure determining capabilities.en_US
dc.format.extent57 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleInvestment portfolio optimization using genetic algorithmen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipo (EEE)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeELECTRICAL and ELECTRONIC ENGINEERINGen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record