Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation

Authors:
Yuming Ou;Longbing Cao;Chao Luo;Chengqi Zhang
Affiliations:
Faculty of Information Technology, University of Technology, Sydney, Australia;Faculty of Information Technology, University of Technology, Sydney, Australia;Faculty of Information Technology, University of Technology, Sydney, Australia;Faculty of Information Technology, University of Technology, Sydney, Australia
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

Recently, a new data mining methodology, Domain Driven Data Mining (D3M), 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.