Exception Mining on Multiple Time Series in Stock Market

This paper presents our research on exception mining on multiple time series data which aims to assist stock market surveillance by identifying market anomalies. Traditional technologies on stock market surveillance have shown their limitations to handle large amount of complicated stock market data. In our research, the outlier mining on multiple time series (OMM) is proposed to improve the effectiveness of exception detection for stock market surveillance. The idea of our research is presented, challenges on the research are analyzed, and potential research directions are summarized.

[1]  Marcello Minenna.  Insider trading, abnormal return and preferential information: Supervising through a probabilistic model , 2003 .

[2]  Girish Keshav Palshikar,et al.  Collusion set detection using graph clustering , 2008, Data Mining and Knowledge Discovery.

[3]  Jennifer Neville,et al.  Using relational knowledge discovery to prevent securities fraud , 2005, KDD '05.

[4]  Franklin Allen,et al.  Stock Price Manipulation, Market Microstructure and Asymmetric Information , 1991 .

[5]  Bradford Cornell,et al.  The Reaction of Investors and Stock Prices to Insider Trading , 1992 .

[6]  Michael Firth,et al.  The Effects of Insider Trading on Liquidity , 2005 .

[7]  Tak-Chung Fu,et al.  Stock time series pattern matching: Template-based vs. rule-based approaches , 2007, Eng. Appl. Artif. Intell..

[8]  Hongwei Qi,et al.  A model for mining outliers from complex data sets , 2004, SAC '04.

[9]  Srinivasan Parthasarathy,et al.  LOADED: link-based outlier and anomaly detection in evolving data sets , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[10]  Philip S. Yu,et al.  Correlating burst events on streaming stock market data , 2007, Data Mining and Knowledge Discovery.

[11]  Peter Goldschmidt,et al.  ALCOD IDSS: Assisting the Australian Stock Market Surveillance Team's Review Process , 1996, Appl. Artif. Intell..

[12]  Karl Felixson,et al.  Day end returns--stock price manipulation , 1999 .

[13]  H Eugene Stanley,et al.  Quantifying fluctuations in market liquidity: analysis of the bid-ask spread. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[15]  Henry C. Lucas.  Market expert surveillance system , 1993, CACM.