Evolutionary learning of technical trading rules without data-mining bias

Files in This Item:
File Description SizeFormat 
Agapitos_PPSN2011_CameraReady.pdf170.7 kBAdobe PDFDownload
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
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
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
Appears in Collections:FMC² Research Collection
Computer Science Research Collection
Business Research Collection
CASL Research Collection

Show full item record

SCOPUSTM   
Citations 20

11
Last Week
0
Last month
checked on Jun 22, 2018

Page view(s) 20

130
checked on May 25, 2018

Download(s) 5

2,308
checked on May 25, 2018

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.