@inproceedings{9536675ae8364482939762f0508a59d2,
title = "A hybrid genetic algorithm for a two-stage stochastic portfolio optimization with uncertain asset prices",
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.",
keywords = "Genetic Algorithm, Hybrid Algorithm, Portfolio Optimization, Stochastic Programming",
author = "Tianxiang Cui and Ruibin Bai and Parkes, {Andrew J.} and Fang He and Rong Qu and Jingpeng Li",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Congress on Evolutionary Computation, CEC 2015 ; Conference date: 25-05-2015 Through 28-05-2015",
year = "2015",
month = sep,
day = "10",
doi = "10.1109/CEC.2015.7257198",
language = "English",
series = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2518--2525",
booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",
address = "United States",
}