Evolutionary learning of technical trading rules without data-mining bias

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Title: Evolutionary learning of technical trading rules without data-mining bias
Authors: Agapitos, Alexandros
O'Neill, Michael
Brabazon, Anthony
Permanent link: http://hdl.handle.net/10197/2735
Date: Sep-2010
Online since: 2011-01-20T16:24:26Z
Abstract: In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation literature, a comprehensive test for a rule’s statistical significance using Hansen’s Superior Predictive Ability is explicitly taken into account in the fitness function, and multi-objective evolutionary optimisation is employed to drive the search towards individual rules with better generalisation abilities. Empirical results on a spot foreign-exchange market index suggest that increased out-of-sample performance can be obtained after accounting for data-mining bias effects in a multi-objective fitness function, as compared to a single-criterion fitness measure that considers solely the average return.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): Springer-Verlag Berlin Heidelberg 2010
Keywords: Technical trading rulesEvolutionary learning
Subject LCSH: Evolutionary computation
Data mining
DOI: 10.1007/978-3-642-15844-5_30
Other versions: http://springerlink.com/content/3443w87r12777233/
Language: en
Status of Item: Peer reviewed
Is part of: Schaefer, R. ...et al. (eds.). Parallel Problem Solving from Nature – PPSN XI 11th International Conference, Kraków, Poland, September 11-15, 2010 : proceedings, part I
Conference Details: 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010), Krakow, Poland, September 11-15, 2010
ISBN: 978-3-642-15843-8
Appears in Collections:FMC² Research Collection
Computer Science Research Collection
Business Research Collection
CASL Research Collection

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