Maguire, Phil and O'Sullivan, Donal and Moser, Philippe and Dunne, Gavin (2012) Risk-adjusted portfolio optimisation using a parallel multi-objective evolutionary algorithm. In: IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) 2012. IEEE. ISBN 9781467318037

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Abstract

In this article we describe the use of a multi-objective evolutionary algorithm for portfolio optimisation based on historical data for the S&P 500. Portfolio optimisation seeks to identify manageable investments that provide a high expected return with relatively low risk. We developed a set of metrics for qualifying the risk/return characteristics of a portfolio's historical performance and combined this with an island model genetic algorithm to identify optimised portfolios. The algorithm was successful in selecting investment strategies with high returns and relatively low volatility. However, although these solutions performed well on historical data, they were not predictive of future returns, with optimised portfolios failing to perform above chance. The implications of these findings are discussed.

Item Type:	Book Section
Keywords:	commodity trading; genetic algorithms; investment; risk analysis;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	10331
Identification Number:	https://doi.org/10.1109/CIFEr.2012.6327805
Depositing User:	Phil Maguire
Date Deposited:	17 Dec 2018 15:36
Publisher:	IEEE
Refereed:	Yes
URI:	https://ieeexplore.ieee.org
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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