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Robustness of multiple objective GP stock-picking in unstable financial markets: Real-world applications track

Hassan, G; Clack, CD; (2009) Robustness of multiple objective GP stock-picking in unstable financial markets: Real-world applications track. In: Rothlauf, F, (ed.) Proceedings of the 11th Annual conference on Genetic and evolutionary computation. (pp. pp. 1513-1520). ACM: Association for Computing Machinery: Montréal, QC, Canada. Green open access

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Abstract

Multiple Objective Genetic Programming (MOGP) is a promising stock-picking technique for fund managers, because the Pareto front approximates the risk/reward Efficient Frontier and simplifies the choice of investment model for a given client's attitude to risk. Unfortunately GP solutions don't work well if used in an environment that is different from the training environment, and the financial markets are notoriously unstable, often lurching from one market context to another (e.g. "bull" to "bear"). This turns out to be a hard problem -- simple dynamic adaptation methods are insufficient and robust behaviour of solutions becomes extremely important. In this paper we provide the first known empirical results on the robustness of MOGP solutions in an unseen environment consisting of real-world financial data. We focus on two well-known mechanisms to determine which leads to the more robust solutions: Mating Restriction, and Diversity Preservation. We introduce novel metrics for Pareto front robustness, and a novel variation on Mating Restriction, both based on phenotypic cluster analysis.

Type: Proceedings paper
Title: Robustness of multiple objective GP stock-picking in unstable financial markets: Real-world applications track
Event: GECCO’09
ISBN-13: 9781605583259
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/1569901.1570104
Publisher version: http://dx.doi.org/ 10.1145/1569901
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: multiobjective optimization, gp, portfolio optimisation, robustness, finance, dynamic environment
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10087089
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