Genetic algorithms for robust optimization in financial applications

Lin, L; Cao, L; Zhang, C

Genetic algorithms for robust optimization in financial applications

Lin, L Cao, L

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IASTED International Conference on Computational Intelligence, 2005, 2005 pp. 387 - 391
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Lin, L	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Proceedings of the IASTED International Conference on Computational Intelligence, 2005, 2005 pp. 387 - 391	en_US
dc.identifier.isbn	0889864810	en_US
dc.identifier.isbn	9780889864818	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1683
dc.description.abstract	In stock market or other financial market systems, the technical trading rules are used widely to generate buy and sell alert signals. In each rule, there are many parameters. The users often want to get the best signal series from the in-sample sets, (Here, the best means they can get the most profit, return or Sharpe Ratio, etc), but the best one will not be the best in the out-of-sample sets. Sometimes, it does not work any more. In this paper, the authors set the parameters a sub-range value instead of a single value. In the sub-range, every value will give a better prediction in the out-of-sample sets. The improved result is robust and has a better performance in experience.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	Proceedings of the IASTED International Conference on Computational Intelligence	en_US
dc.title	Genetic algorithms for robust optimization in financial applications	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2005	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Calgary, Canada	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2005	en_US

Abstract:

In stock market or other financial market systems, the technical trading rules are used widely to generate buy and sell alert signals. In each rule, there are many parameters. The users often want to get the best signal series from the in-sample sets, (Here, the best means they can get the most profit, return or Sharpe Ratio, etc), but the best one will not be the best in the out-of-sample sets. Sometimes, it does not work any more. In this paper, the authors set the parameters a sub-range value instead of a single value. In the sub-range, every value will give a better prediction in the out-of-sample sets. The improved result is robust and has a better performance in experience.

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http://hdl.handle.net/10453/1683