Domain-driven local exceptional pattern mining for detecting stock price manipulation

Ou, Y; Cao, L; Luo, C; Zhang, C

Domain-driven local exceptional pattern mining for detecting stock price manipulation

Ou, Y Cao, L

Luo, C Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5351 LNAI pp. 849 - 858
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Ou, Y	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Luo, C	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5351 LNAI pp. 849 - 858	en_US
dc.identifier.isbn	354089196X	en_US
dc.identifier.isbn	9783540891963	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10889
dc.description.abstract	Recently, a new data mining methodology, Domain Driven Data Mining (D 3M), has been developed. On top of data-centered pattern mining, D3M generally targets the actionable knowledge discovery under domain-specific circumstances. It strongly appreciates the involvement of domain intelligence in the whole process of data mining, and consequently leads to the deliverables that can satisfy business user needs and decision-making. Following the methodology of D3M, this paper investigates local exceptional patterns in real-life microstructure stock data for detecting stock price manipulations. Different from existing pattern analysis mainly on interday data, we deal with tick-by-tick data. Our approach proposes new mechanisms for constructing microstructure order sequences by involving domain factors and business logics, and for measuring the interestingness of patterns from business concern perspective. Real-life data experiments on an exchange data demonstrate that the outcomes generated by following D3M can satisfy business expectations and support business users to take actions for market surveillance. © 2008 Springer Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-540-89197-0_79	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Domain-driven local exceptional pattern mining for detecting stock price manipulation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	5351 LNAI	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Hanoi,Vietnam	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	closed_access
pubs.publication-status	Published	en_US
pubs.volume	5351 LNAI	en_US

Abstract:

Recently, a new data mining methodology, Domain Driven Data Mining (D 3M), has been developed. On top of data-centered pattern mining, D3M generally targets the actionable knowledge discovery under domain-specific circumstances. It strongly appreciates the involvement of domain intelligence in the whole process of data mining, and consequently leads to the deliverables that can satisfy business user needs and decision-making. Following the methodology of D3M, this paper investigates local exceptional patterns in real-life microstructure stock data for detecting stock price manipulations. Different from existing pattern analysis mainly on interday data, we deal with tick-by-tick data. Our approach proposes new mechanisms for constructing microstructure order sequences by involving domain factors and business logics, and for measuring the interestingness of patterns from business concern perspective. Real-life data experiments on an exchange data demonstrate that the outcomes generated by following D3M can satisfy business expectations and support business users to take actions for market surveillance. © 2008 Springer Berlin Heidelberg.

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http://hdl.handle.net/10453/10889