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Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30

Author

Listed:
  • Brian F Tivnan
  • David Rushing Dewhurst
  • Colin M Van Oort
  • John H Ring IV
  • Tyler J Gray
  • Brendan F Tivnan
  • Matthew T K Koehler
  • Matthew T McMahon
  • David M Slater
  • Jason G Veneman
  • Christopher M Danforth

Abstract

Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in calendar year 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity marketplace are relatively common and persist for longer than what physical constraints may suggest. Information feeds reported different prices for the same equity more than 120 million times, with almost 64 million dislocation segments featuring meaningfully longer duration and higher magnitude. During this period, roughly 22% of all trades occurred while the SIP and aggregated direct feeds were dislocated. The current market configuration resulted in a realized opportunity cost totaling over $160 million, a conservative estimate that does not take into account intra-day offsetting events.

Suggested Citation

  • Brian F Tivnan & David Rushing Dewhurst & Colin M Van Oort & John H Ring IV & Tyler J Gray & Brendan F Tivnan & Matthew T K Koehler & Matthew T McMahon & David M Slater & Jason G Veneman & Christopher, 2020. "Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-24, January.
  • Handle: RePEc:plo:pone00:0226968
    DOI: 10.1371/journal.pone.0226968
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    Cited by:

    1. Colin M. Van Oort & Ethan Ratliff-Crain & Brian F. Tivnan & Safwan Wshah, 2023. "Adaptive Agents and Data Quality in Agent-Based Financial Markets," Papers 2311.15974, arXiv.org.
    2. Ethan Ratliff-Crain & Colin M. Van Oort & James Bagrow & Matthew T. K. Koehler & Brian F. Tivnan, 2023. "Revisiting Cont's Stylized Facts for Modern Stock Markets," Papers 2311.07738, arXiv.org, revised May 2024.
    3. Yunus Santur, 2023. "A Novel Financial Forecasting Approach Using Deep Learning Framework," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1341-1392, October.

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