Exception Mining on Multiple Time Series in Stock Market
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Outlier Mining on Multiple Time Series Data in Stock Market
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Detecting stock-price manipulation in an emerging market: The case of Turkey
Expert Systems with Applications: An International Journal
Mining minimal constrained flow cycles from complex transaction data
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
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Many mal-practices in stock market trading--e.g., circular trading and price manipulation--use the modus operandi of collusion. Informally, a set of traders is a candidate collusion set when they have "heavy trading" among themselves, as compared to their trading with others. We formalize the problem of detection of collusion sets, if any, in the given trading database. We show that naïve approaches are inefficient for real-life situations. We adapt and apply two well-known graph clustering algorithms for this problem. We also propose a new graph clustering algorithm, specifically tailored for detecting collusion sets. A novel feature of our approach is the use of Dempster---Schafer theory of evidence to combine the candidate collusion sets detected by individual algorithms. Treating individual experiments as evidence, this approach allows us to quantify the confidence (or belief) in the candidate collusion sets. We present detailed simulation experiments to demonstrate effectiveness of the proposed algorithms.