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In financial markets, abnormal trading behaviors pose a serious challenge tomarket surveillance and risk management. What is worse, there is an increasingemergence of abnormal trading events that some experienced traders constitute acollusive clique and collaborate to manipulate some instruments, thus misleadother investors by applying similar trading behaviors for maximizing theirpersonal benefits. In this paper, a method is proposed to detect the hiddencollusive cliques involved in an instrument of future markets by firstcalculating the correlation coefficient between any two eligible unifiedaggregated time series of signed order volume, and then combining the connectedcomponents from multiple sparsified weighted graphs constructed by using thecorrelation matrices where each correlation coefficient is over auser-specified threshold. Experiments conducted on real order data from theShanghai Futures Exchange show that the proposed method can effectively detectsuspect collusive cliques. A tool based on the proposed method has beendeployed in the exchange as a pilot application for futures market surveillanceand risk management.

Detecting Collusive Cliques in Futures Markets Based on Trading Behaviors from Real Data