Mining exceptional activity patterns in microstructure data

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

Mining exceptional activity patterns in microstructure data

Ou, Y Cao, L

Luo, C Liu, L

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, 2008, pp. 884 - 887
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	Liu, L	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, 2008, pp. 884 - 887	en_US
dc.identifier.isbn	9780769534961	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10830
dc.description.abstract	Market Surveillance plays an important role in maintaining market integrity, transparency and fairnesss. The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics. In the mean time, the existing market surveillance systems are facing challenges of misuse, mis-disclosure and misdealing of information, announcement and order in one market or crossing multiple markets. Therefore, there is a crucial need to develop workable methods for smart surveillance. To deal with such issues, we propose an innovative methodology - microstructure activity pattern analysis. Based on this methodology, a case study in identifying exceptional microstructure activity patterns is carried out. The experiments on real-life stock data show that microstructure activity pattern analysis opens a new and effective means for crucially understanding and analysing market dynamics. The resulting findings such as exceptional microstructure activity patterns can greatly enhance the learning, detection, adaption and decision-making capability of market surveillance. © 2008 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation	http://purl.org/au-research/grants/arc/DP0773412
dc.relation.ispartof	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008	en_US
dc.relation.isbasedon	10.1109/WIIAT.2008.160	en_US
dc.title	Mining exceptional activity patterns in microstructure data	en_US
dc.type	Conference Proceeding
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	University of Technology, Sydney, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Market Surveillance plays an important role in maintaining market integrity, transparency and fairnesss. The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics. In the mean time, the existing market surveillance systems are facing challenges of misuse, mis-disclosure and misdealing of information, announcement and order in one market or crossing multiple markets. Therefore, there is a crucial need to develop workable methods for smart surveillance. To deal with such issues, we propose an innovative methodology - microstructure activity pattern analysis. Based on this methodology, a case study in identifying exceptional microstructure activity patterns is carried out. The experiments on real-life stock data show that microstructure activity pattern analysis opens a new and effective means for crucially understanding and analysing market dynamics. The resulting findings such as exceptional microstructure activity patterns can greatly enhance the learning, detection, adaption and decision-making capability of market surveillance. © 2008 IEEE.

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http://hdl.handle.net/10453/10830