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Clustering of Trade Prices by High-Frequency and Non–High-Frequency Trading Firms

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Listed:
  • Michael Goldstein
  • Ryan L. Davis
  • Bonnie F. Van Ness
  • Robert A. Van Ness

Abstract

We examine clustering of transaction prices in a sample that contains high-frequency trading firms’ transactions. We separate our sample into four categories: transactions with a high-frequency trading firm on both sides of the transaction, on only one side of the transaction (either liquidity provider or liquidity demander), or on neither side of the transaction. We find that transaction price clustering is less frequent when a high-frequency trading firm is on both sides of the transaction than when a high-frequency trading firm is on only one side of the transaction or if a high-frequency trading firm is not involved in the transaction. Further, we find that transactions where the liquidity providing order, which more likely dictates the price, is submitted by a high-frequency trading firm cluster less than when the liquidity providing order is submitted by a non–high-frequency trader. We thus conclude that the tendency of prices to cluster appears to be driven by a distinctly human bias.

Suggested Citation

  • Michael Goldstein & Ryan L. Davis & Bonnie F. Van Ness & Robert A. Van Ness, 2014. "Clustering of Trade Prices by High-Frequency and Non–High-Frequency Trading Firms," The Financial Review, Eastern Finance Association, vol. 49(2), pages 421-433, May.
    • Handle: RePEc:bla:finrev:v:49:y:2014:i:2:p:421-433
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    File URL: http://hdl.handle.net/10.1111/fire.12042
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    Cited by:

    1. Qin Wang & Jun Zhang, 2016. "Trade Size Clustering In The E-Mini Index Futures Markets," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 39(3), pages 247-262, September.
    2. Mishra, Ajay Kumar & Tripathy, Trilochan, 2018. "Price and trade size clustering: Evidence from the national stock exchange of India," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 63-72.
    3. Ahmed Baig & Nasim Sabah & Drew Winters, 2019. "Have Stock Prices become more Uniformly Distributed?," Economics Bulletin, AccessEcon, vol. 39(2), pages 1242-1250.
    4. Das, Sougata & Kadapakkam, Palani-Rajan, 2020. "Machine over Mind? Stock price clustering in the era of algorithmic trading," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    5. Zhang, Xiaotao & Liang, Junpeng & He, Feng, 2019. "Private information advantage or overconfidence? Performance of intraday arbitrage speculators in the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    6. Donglian Ma & Hisashi Tanizaki, 2022. "Intraday patterns of price clustering in Bitcoin," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.

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