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Quantifying immediate price impact of trades based on the $k$-shell decomposition of stock trading networks

Author

Listed:
  • Wen-Jie Xie

    (ECUST)

  • Ming-Xia Li

    (ECUST)

  • Hai-Chuan Xu

    (ECUST)

  • Wei Chen

    (SZSE)

  • Wei-Xing Zhou

    (ECUST)

  • H. E. Stanley

Abstract

Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into $k$ classes using the $k$-shell decomposition method. We investigate the influences of trading behaviors on the price impact by comparing a closed national market (A-shares) with an international market (B-shares), individuals and institutions, partially filled and filled trades, buyer-initiated and seller-initiated trades, and trades at different positions of a trading network. Institutional traders professionally use some trading strategies to reduce the price impact and individuals at the same positions in the trading network have a higher price impact than institutions. We also find that trades in the core have higher price impacts than those in the peripheral shell.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:1611.06666
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    References listed on IDEAS

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    1. Lada Adamic & Celso Brunetti & Jeffrey H. Harris & Andrei Kirilenko, 2017. "Trading networks," Econometrics Journal, Royal Economic Society, vol. 20(3), pages 126-149, October.
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    Cited by:

    1. Han, Rui-Qi & Li, Ming-Xia & Chen, Wei & Zhou, Wei-Xing & Stanley, H. Eugene, 2019. "Structural properties of statistically validated empirical information networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 747-756.
    2. 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.
    3. Xie, Wen-Jie & Li, Mu-Yao & Zhou, Wei-Xing, 2021. "Learning representation of stock traders and immediate price impacts," Emerging Markets Review, Elsevier, vol. 48(C).
    4. Zhao, Jingdong & Zhu, Hongliang & Li, Xindan, 2018. "Optimal execution with price impact under Cumulative Prospect Theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1228-1237.
    5. Ouyang, Fang-Yan & Zheng, Bo & Jiang, Xiong-Fei, 2019. "Dynamic fluctuations of cross-correlations in multi-time scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 515-521.
    6. Shan Lu & Jichang Zhao & Huiwen Wang, 2018. "The Power of Trading Polarity: Evidence from China Stock Market Crash," Papers 1802.01143, arXiv.org.
    7. Zhang, Ting & Gu, Gao-Feng & Zhou, Wei-Xing, 2019. "Order imbalances and market efficiency: New evidence from the Chinese stock market," Emerging Markets Review, Elsevier, vol. 38(C), pages 458-467.
    8. Huang, Qi-An & Zhao, Jun-Chan & Wu, Xiao-Qun, 2022. "Financial risk propagation between Chinese and American stock markets based on multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

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