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Order flow dynamics around extreme price changes on an emerging stock market

Author

Listed:
  • Guo-Hua Mu

    (ECUST)

  • Wei-Xing Zhou

    (ECUST)

  • Wei Chen

    (SZSE)

  • Janos Kertesz

    (BME)

Abstract

We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the bid-ask spread, and the volume imbalance increase before the extreme events and decay slowly as a power law, which forms a well-established peak. The volume of buy market orders increases faster and the corresponding peak appears earlier than for sell market orders around positive events, while the volume peak of sell market orders leads buy market orders in the magnitude and time around negative events. When orders are divided into four groups according to their aggressiveness, we find that the behaviors of order volume and order number are similar, except for buy limit orders and canceled orders that the peak of order number postpones two minutes later after the peak of order volume, implying that investors placing large orders are more informed and play a central role in large price fluctuations. We also study the relative rates of different types of orders and find differences in the dynamics of relative rates between buy orders and sell orders and between individual investors and institutional investors. There is evidence showing that institutions behave very differently from individuals and that they have more aggressive strategies. Combing these findings, we conclude that institutional investors are more informed and play a more influential role in driving large price fluctuations.

Suggested Citation

  • Guo-Hua Mu & Wei-Xing Zhou & Wei Chen & Janos Kertesz, 2010. "Order flow dynamics around extreme price changes on an emerging stock market," Papers 1003.0168, arXiv.org.
  • Handle: RePEc:arx:papers:1003.0168
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    Cited by:

    1. Can Yilmaz Altinigne & Harun Ozkan & Veli Can Kupeli & Zehra Cataltepe, 2019. "An Empirical Study on Arrival Rates of Limit Orders and Order Cancellation Rates in Borsa Istanbul," Papers 1909.08308, arXiv.org.
    2. Oh, Gabjin & Kim, Ho-yong & Ahn, Seok-Won & Kwak, Wooseop, 2015. "Analyzing the financial crisis using the entropy density function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 464-469.
    3. Zhong, Li-Xin & Xu, Wen-Juan & Ren, Fei & Shi, Yong-Dong, 2013. "Coupled effects of market impact and asymmetric sensitivity in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2139-2149.
    4. Andor, György & Bohák, András, 2017. "Identifying events in financial time series – A new approach with bipower variation," Finance Research Letters, Elsevier, vol. 22(C), pages 42-48.
    5. 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.
    6. Xu, Hai-Chuan & Zhang, Wei & Liu, Yi-Fang, 2014. "Short-term market reaction after trading halts in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 103-111.
    7. Fei Ren & Li-Xin Zhong, 2011. "Price impact asymmetry of institutional trading in Chinese stock market," Papers 1110.3133, arXiv.org.
    8. Ren, Fei & Zhong, Li-Xin, 2012. "The price impact asymmetry of institutional trading in the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2667-2677.
    9. Dror Kenett & Shlomo Havlin, 2015. "Network science: a useful tool in economics and finance," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 14(2), pages 155-167, November.
    10. Paolo Mazza & Mikael Petitjean, 2019. "Testing the effect of technical analysis on market quality and order book dynamics," Applied Economics, Taylor & Francis Journals, vol. 51(18), pages 1947-1976, April.
    11. Zhi-Qiang Jiang & Wen-Jie Xie & Xiong Xiong & Wei Zhang & Yong-Jie Zhang & W. -X. Zhou, 2012. "Trading networks, abnormal motifs and stock manipulation," Papers 1301.0007, arXiv.org.
    12. Hai-Chuan Xu & Wei Zhang & Yi-Fang Liu, 2013. "Short-term Market Reaction after Trading Halts in Chinese Stock Market," Papers 1309.1138, arXiv.org, revised Jun 2014.
    13. Li-Xin Zhong & Wen-Juan Xu & Fei Ren & Yong-Dong Shi, 2012. "Coupled effects of market impact and asymmetric sensitivity in financial markets," Papers 1209.3399, arXiv.org, revised Jan 2013.
    14. Havran, Dániel & Erb, Tamás, 2015. "Mit veszítünk a piaci súrlódásokkal?. A pénzügyi piacok mikrostruktúrája [Trading mechanisms and market frictions. Microstructure of the financial markets]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 229-262.
    15. X. F. Jiang & T. T. Chen & B. Zheng, 2013. "Time-reversal asymmetry in financial systems," Papers 1308.0669, arXiv.org.
    16. 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).
    17. Jiang, X.F. & Chen, T.T. & Zheng, B., 2013. "Time-reversal asymmetry in financial systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5369-5375.
    18. Hai-Chuan Xu & Wei Chen & Xiong Xiong & Wei Zhang & Wei-Xing Zhou & H Eugene Stanley, 2016. "Limit-order book resiliency after effective market orders: Spread, depth and intensity," Papers 1602.00731, arXiv.org, revised Feb 2017.

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