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 rules; Evolutionary 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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