Clustered stocks weighting with ant colony optimization in portfolio optimization

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

3 Citations (Scopus)

Abstract

Portfolio Optimization is an optimization problem in finance in which the aim is to maximize the return and minimize the risk of failure within the collection of assets, such as stocks, bonds, etc. This portfolio optimization problem has been of great concern to economic practitioners; since the risk cannot be completely eliminated, an appropriate risk management strategy for choosing and optimizing the portfolio, such that it will fulfill investors' expected risk criteria and returns, will be required. In this paper, we discuss stock portfolio optimization, where stock data based on financial parameters, such as P/E ratio and EPS ratio is converted to score parameters and then clustered by K-means clustering algorithm. After clustering, some stocks will be chosen for the portfolio. The weight of each stock portfolio will be determined such that the objective will be obtained. The Ant Colony Optimization algorithm is used to determine the weight of each stock. The portfolio performance will be evaluated based on some actual datasets. We show important facts that the value of a fitness function is key in choosing the corresponding weighted stock in a stock portfolio and the numerical results also suggest how to reduce the losses of the portfolio.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
CountryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • Ant Colony Optimization
  • K-means clustering
  • portfolio optimization
  • stocks

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