Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23609
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Cui, Tianxiang
Bai, Ruibin
Parkes, Andrew J
He, Fang
Qu, Rong
Li, Jingpeng
Contact Email: jli@cs.stir.ac.uk
Title: A Hybrid Genetic Algorithm for a Two-Stage Stochastic Portfolio Optimization With Uncertain Asset Prices
Citation: Cui T, Bai R, Parkes AJ, He F, Qu R & Li J (2015) A Hybrid Genetic Algorithm for a Two-Stage Stochastic Portfolio Optimization With Uncertain Asset Prices 2015 IEEE Congress on Evolutionary Computation (CEC), 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, Japan, Piscataway, NJ, USA: IEEE, pp. 2518-2525.
Issue Date: 2015
Conference Name: 2015 IEEE Congress on Evolutionary Computation (CEC2015)
Conference Location: Sendai, Japan
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.
Type: Conference Paper
Status: Book Chapter: publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URI: http://hdl.handle.net/1893/23609
URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7257198&tag=1
Affiliation: University of Nottingham Ningbo China
University of Nottingham Ningbo China
University of Nottingham
University of Nottingham
University of Nottingham
Computing Science - CSM Dept

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