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

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  • Junjie Wang
  • Shuigeng Zhou
  • Jihong Guan

Abstract

In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other investors by applying similar trading behaviors for maximizing their personal benefits. In this paper, a method is proposed to detect the hidden collusive cliques involved in an instrument of future markets by first calculating the correlation coefficient between any two eligible unified aggregated time series of signed order volume, and then combining the connected components from multiple sparsified weighted graphs constructed by using the correlation matrices where each correlation coefficient is over a user-specified threshold. Experiments conducted on real order data from the Shanghai Futures Exchange show that the proposed method can effectively detect suspect collusive cliques. A tool based on the proposed method has been deployed in the exchange as a pilot application for futures market surveillance and risk management.

Suggested Citation

  • Junjie Wang & Shuigeng Zhou & Jihong Guan, 2011. "Detecting Collusive Cliques in Futures Markets Based on Trading Behaviors from Real Data," Papers 1110.1522, arXiv.org.
    • Handle: RePEc:arx:papers:1110.1522
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    References listed on IDEAS

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    Cited by:

    1. Friedhelm Victor & Andrea Marie Weintraud, 2021. "Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges," Papers 2102.07001, arXiv.org.

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