Mining Domain-Driven Correlations in Stock Markets

UTSePress Research/Manakin Repository

Search UTSePress Research

Advanced Search


My Account

Show simple item record Lin, Li en_US Luo, Dan en_US Liu, Li en_US
dc.contributor.editor Zhang, S; Jarvis, R en_US 2009-11-09T02:45:24Z 2009-11-09T02:45:24Z 2005 en_US
dc.identifier 2005002983 en_US
dc.identifier.citation Lin Li, Luo Dan, and Liu Li 2005, 'Mining Domain-Driven Correlations in Stock Markets', Springer, Sydney, Australia, pp. 979-982. en_US
dc.identifier.issn 3-540-30462-2 en_US
dc.identifier.other E1 en_US
dc.description.abstract There have been many technical trading rules in stock market since the first stock exchange founded. Along with the developing of computer technology, the technical trading rules are playing more and more important roles in the stock market trading system. However, there are many problems also occurred, such as the huge database, inefficiency, etc. So, the in-depth data mining technology is becoming a powerful tool to overcome the shortage of the current technologies. In this paper, we give some applications of in-depth data mining method: to find the optimal range, to find the stock-rule pair and find the relationship between the number of pair and investment. This method can improve both efficiency and effectiveness. en_US
dc.publisher Springer en_US
dc.relation.isbasedon en_US
dc.title Mining Domain-Driven Correlations in Stock Markets en_US
dc.parent AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence Proceedings en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Sydney, Australia en_US
dc.identifier.startpage 979 en_US
dc.identifier.endpage 982 en_US FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.conference.location Sydney, Australia en_US
dc.for 080110 en_US
dc.personcode 10082206 en_US
dc.personcode 996795 en_US
dc.personcode 021010 en_US
dc.percentage 100 en_US Simulation and Modelling en_US
dc.classification.type FOR-08 en_US
dc.custom Australasian Joint Conference on Artificial Intelligence en_US 20051205 en_US
dc.location.activity Sydney, Australia en_US
dc.description.keywords Domain Driven, Optimization en_US
dc.staffid 021010 en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record