Recent Advances of Exception Mining in Stock Market

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Author(s): Chao Luo (University of Technology, Australia), Yanchang Zhao (University of Technology, Australia), Dan Luo (University of Technology, Australia), Yuming Ou (University of Technology, Australia)and Li Liu (University of Technology, Australia)
Copyright: 2010
Pages: 21
Source title: Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions
Source Author(s)/Editor(s): Pedro Nuno San-Banto Furtado (University of Coimbra, Portugal)
DOI: 10.4018/978-1-60566-816-1.ch010

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

This chapter aims to provide a comprehensive survey of the current advanced technologies of exception mining in stock market. The stock market surveillance is to identify market anomalies so as to provide a fair and efficient trading platform. The technologies of market surveillance developed from simple statistical rules to more advanced technologies, such as data mining and artificial intelligent. This chapter provides the basic concepts of exception mining in stock market. Then the recent advances of exception mining in this domain are presented and the key issues are discussed. The advantages and disadvantages of the advanced technologies are analyzed. Furthermore, our model of OMM (Outlier Mining on Multiple time series) is introduced. Finally, this chapter points out the future research directions and related issues in reality.

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Recent Advances of Exception Mining in Stock Market

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