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Detect colluded stock manipulation via clique in trading network

Author

Listed:
  • Shi, Fa-Bin
  • Sun, Xiao-Qian
  • Shen, Hua-Wei
  • Cheng, Xue-Qi

Abstract

Market manipulation is one of the important issues that draw much attention from academia and industry. Many efforts have been made to detect manipulation in stock market. However, with the development of technology, the means of manipulation become more and more diversified and the effective detection methods remain to be an open problem. Here, we develop a generalized method for colluded traders detection based on transaction data. We investigate the clique of trading network, and find the number and weight of clique are greater in manipulated stocks than that in non-manipulated stocks. We further propose a method to detect colluded traders based on weight of cliques. Results demonstrate that our method is effective at distinguishing the manipulated stocks and the colluded traders.

Suggested Citation

  • Shi, Fa-Bin & Sun, Xiao-Qian & Shen, Hua-Wei & Cheng, Xue-Qi, 2019. "Detect colluded stock manipulation via clique in trading network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 565-571.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:565-571
    DOI: 10.1016/j.physa.2018.09.011
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    References listed on IDEAS

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    4. Vladimir Balash & Alexey Faizliev & Sergei Sidorov & Elena Chistopolskaya, 2021. "Conditional Time-Varying General Dynamic Factor Models and Its Application to the Measurement of Volatility Spillovers across Russian Assets," Mathematics, MDPI, vol. 9(19), pages 1-31, October.

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