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Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment -Empirical Study in the Japanese Stock Market-

Author

Listed:
  • Taiga Saito

    (Graduate School of Economics, University of Tokyo. Former research fellow at Financial Research Center in Financial Services Agency, Government of Japan.)

  • Takanori Adachi

    (BKC Research Organization of Social Sciences, Ritsumeikan University)

  • Teruo Nakatsuma

    (Department of Economics, Keio University)

  • Akihiko Takahashi

    (Graduate School of Economics, University of Tokyo)

  • Hiroshi Tsuda

    (Department of Mathematical Sciences, Doshisha University)

  • Naoyuki Yoshino

    (Financial Services Agency, Government of Japan. ADBI Institute.)

Abstract

In this study, we investigate ordering patterns of different types of market participants in Tokyo Stock Exchange (TSE) by examining order records of the listed stocks. Firstly, we categorize the virtual servers in the trading system of TSE, each of which is linked to a single trading participant, by the ratio of cancellation and execution in the order placement as well as the number of executions at the opening of the afternoon session. Then, we analyze ordering patterns of the servers in the categories in short intervals for the top 10 highest trading volume stocks. By classifying the intervals into four cases by returns, we observe how different types of market participants submit or execute orders in the market situations. Moreover, we investigate the shares of the executed volumes for the different types of servers in the swings and roundabouts of the Nikkei 225 index, which were observed in July, August, and September in 2015. The main findings of this study are as follows: Server type A, which supposedly includes non-market making proprietary traders with high-speed algorithmic strategies, executes and places orders along with the direction of the market. The shares of the execution and order volumes along with the market direction increase when the stock price moves sharply. Server type B, which presumably includes servers employing a market making strategy with high cancellation and low execution ratio, shifts its market making price ranges in the rapid price movements. We observe that passive servers in Server type B have a large share and buy at low levels in the price falls. Also, Server type B, as well as Server type A, makes profit in the price falling days and particularly, the aggressive servers in the server type make most of the profit. Server type C, which is assumed to include servers receiving orders from small investors, constantly has a large share of execution and order volume.

Suggested Citation

  • Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2017. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment -Empirical Study in the Japanese Stock Market-," CARF F-Series CARF-F-411, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    • Handle: RePEc:cfi:fseres:cf411
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    as
    1. Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2018. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment: Empirical Study in the Japanese Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 25(3), pages 179-220, September.
    2. Carrion, Allen, 2013. "Very fast money: High-frequency trading on the NASDAQ," Journal of Financial Markets, Elsevier, vol. 16(4), pages 680-711.
    3. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    4. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    5. Ekkehart Boehmer & Charles M. Jones & Xiaoyan Zhang, 2013. "Shackling Short Sellers: The 2008 Shorting Ban," The Review of Financial Studies, Society for Financial Studies, vol. 26(6), pages 1363-1400.
    6. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    7. Mauricio Labadie & Charles-Albert Lehalle, 2010. "Optimal algorithmic trading and market microstructure," Working Papers hal-00590283, HAL.
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    Cited by:

    1. Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2018. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment: Empirical Study in the Japanese Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 25(3), pages 179-220, September.
    2. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    3. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Ritesh Kumar Dubey & A. Sarath Babu & Rajneesh Ranjan Jha & Urvashi Varma, 2022. "Algorithmic Trading Efficiency and its Impact on Market-Quality," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 381-409, September.
    5. Taiga Saito & Akihiko Takahashi, 2018. "Online Supplement for "Stochastic Differential Game in High Frequency Market"," CIRJE F-Series CIRJE-F-1087, CIRJE, Faculty of Economics, University of Tokyo.
    6. Hayashi, Takaki & Nishide, Katsumasa, 2024. "Strategic liquidity provision in high-frequency trading," International Review of Financial Analysis, Elsevier, vol. 93(C).

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