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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Cui, Tianxiang
Bai, Ruibin
Parkes, Andrew J
He, Fang
Qu, Rong
Li, Jingpeng
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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 In: 2015 IEEE Congress on Evolutionary Computation (CEC), Piscataway, NJ, USA: IEEE. 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, Japan, 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.
Status: Book Chapter: publisher version
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