The applications of genetic algorithms in stock market data mining optimisation

Lin, L; Cao, L; Wang, J; Zhang, C

The applications of genetic algorithms in stock market data mining optimisation

Lin, L Cao, L

Wang, J Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Management Information Systems, 2004, 10 pp. 273 - 280
Issue Date:: 2004-12-01

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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	Wang, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	Management Information Systems, 2004, 10 pp. 273 - 280	en_US
dc.identifier.issn	1470-6326	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7024
dc.description.abstract	In the stock market, a technical trading rule is a popular tool for analysts and users to carry out their research and decide to buy or sell their shares. The key issue for the success of a trading rule is the selection of values for all parameters and their combinations. However, the range of parameters can vary in a large domain, so it is difficult for users to find the best parameter combination. In this paper, we present the Genetic Algorithm (GA) to overcome the problem in two steps: first, setting a sub-domain of the parameters with GA; second, finding a near optimal value in the sub domain with GA in a very reasonable time.	en_US
dc.relation.ispartof	Management Information Systems	en_US
dc.title	The applications of genetic algorithms in stock market data mining optimisation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Malaga, Spain	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	10	en_US

Abstract:

In the stock market, a technical trading rule is a popular tool for analysts and users to carry out their research and decide to buy or sell their shares. The key issue for the success of a trading rule is the selection of values for all parameters and their combinations. However, the range of parameters can vary in a large domain, so it is difficult for users to find the best parameter combination. In this paper, we present the Genetic Algorithm (GA) to overcome the problem in two steps: first, setting a sub-domain of the parameters with GA; second, finding a near optimal value in the sub domain with GA in a very reasonable time.

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http://hdl.handle.net/10453/7024