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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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    1. Robert A. Jarrow, 2008. "Market Manipulation, Bubbles, Corners, and Short Squeezes," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 6, pages 105-130, World Scientific Publishing Co. Pte. Ltd..
    2. Allen, Franklin & Gale, Douglas, 1992. "Stock-Price Manipulation," The Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 503-529.
    3. Shen, Huawei & Cheng, Xueqi & Cai, Kai & Hu, Mao-Bin, 2009. "Detect overlapping and hierarchical community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1706-1712.
    4. Xiao-Qian Sun & Hua-Wei Shen & Xue-Qi Cheng & Zhao-Yang Wang, 2012. "Degree-Strength Correlation Reveals Anomalous Trading Behavior," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-9, October.
    5. Li, Ming-Xia & Jiang, Zhi-Qiang & Xie, Wen-Jie & Xiong, Xiong & Zhang, Wei & Zhou, Wei-Xing, 2015. "Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 575-584.
    6. Hillion, Pierre & Suominen, Matti, 2004. "The manipulation of closing prices," Journal of Financial Markets, Elsevier, vol. 7(4), pages 351-375, October.
    7. Lee, Eun Jung & Eom, Kyong Shik & Park, Kyung Suh, 2013. "Microstructure-based manipulation: Strategic behavior and performance of spoofing traders," Journal of Financial Markets, Elsevier, vol. 16(2), pages 227-252.
    8. Sun, Xiao-Qian & Shen, Hua-Wei & Cheng, Xue-Qi & Zhang, Yuqing, 2017. "Detecting anomalous traders using multi-slice network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 1-9.
    9. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    10. Allen, Franklin & Gorton, Gary, 1992. "Stock price manipulation, market microstructure and asymmetric information," European Economic Review, Elsevier, vol. 36(2-3), pages 624-630, April.
    11. Jiang, Guolin & Mahoney, Paul G. & Mei, Jianping, 2005. "Market manipulation: A comprehensive study of stock pools," Journal of Financial Economics, Elsevier, vol. 77(1), pages 147-170, July.
    12. Xiao-Qian Sun & Xue-Qi Cheng & Hua-Wei Shen & Zhao-Yang Wang, 2011. "Distinguishing manipulated stocks via trading network analysis," Papers 1110.2260, arXiv.org.
    13. Sun, Xiao-Qian & Cheng, Xue-Qi & Shen, Hua-Wei & Wang, Zhao-Yang, 2011. "Distinguishing manipulated stocks via trading network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3427-3434.
    14. Wen-Jie Xie & Ming-Xia Li & Hai-Chuan Xu & Wei Chen & Wei-Xing Zhou & H. E. Stanley, 2016. "Quantifying immediate price impact of trades based on the $k$-shell decomposition of stock trading networks," Papers 1611.06666, arXiv.org, revised Dec 2016.
    15. Comerton-Forde, Carole & Putnins, Talis J., 2011. "Measuring closing price manipulation," Journal of Financial Intermediation, Elsevier, vol. 20(2), pages 135-158, April.
    16. Rajesh K. Aggarwal & Guojun Wu, 2006. "Stock Market Manipulations," The Journal of Business, University of Chicago Press, vol. 79(4), pages 1915-1954, July.
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