A hybrid genetic algorithm for a two-stage stochastic portfolio optimization with uncertain asset prices

Tianxiang Cui, Ruibin Bai, Andrew J. Parkes, Fang He, Rong Qu, Jingpeng Li

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

4 Citations (Scopus)

Abstract

Portfolio optimization is one of the most important problems in the finance field. The traditional mean-variance model has its drawbacks since it fails to take the market uncertainty into account. In this work, we investigate a two-stage stochastic portfolio optimization model with a comprehensive set of real world trading constraints in order to capture the market uncertainties in terms of future asset prices. A hybrid approach, which integrates genetic algorithm (GA) and a linear programming (LP) solver is proposed in order to solve the model, where GA is used to search for the assets selection heuristically and the LP solver solves the corresponding sub-problems of weight allocation optimally. Scenarios are generated to capture uncertain prices of assets for five benchmark market instances. The computational results indicate that the proposed hybrid algorithm can obtain very promising solutions. Possible future research directions are also discussed.

Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2518-2525
Number of pages8
ISBN (Electronic)9781479974924
DOIs
Publication statusPublished - 10 Sep 2015
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: 25 May 201528 May 2015

Publication series

Name2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Conference

ConferenceIEEE Congress on Evolutionary Computation, CEC 2015
Country/TerritoryJapan
CitySendai
Period25/05/1528/05/15

Keywords

  • Genetic Algorithm
  • Hybrid Algorithm
  • Portfolio Optimization
  • Stochastic Programming

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Mathematics

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