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

Recently, a new data mining methodology, Domain Driven Data Mining (DM), has been developed. On top of data-centered pattern mining, DM generally targets the actionable knowledge discovery under domainspecific 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 DM, 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 DM can satisfy business expectations and support business users to take actions for market surveillance.