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High-frequency trading and networked markets

Author

Listed:
  • Federico Musciotto

    (Dipartimento di Fisica e Chimica, Università degli Studi di Palermo, I-90128 Palermo, Italy)

  • Jyrki Piilo

    (Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turun Yliopisto, Finland)

  • Rosario N. Mantegna

    (Dipartimento di Fisica e Chimica, Università degli Studi di Palermo, I-90128 Palermo, Italy; Complexity Science Hub Vienna, A1080 Vienna, Austria; Department of Computer Science, University College London, London WC1E 6BT, United Kingdom)

Abstract

Financial markets have undergone a deep reorganization during the last 20 y. A mixture of technological innovation and regulatory constraints has promoted the diffusion of market fragmentation and high-frequency trading. The new stock market has changed the traditional ecology of market participants and market professionals, and financial markets have evolved into complex sociotechnical institutions characterized by a great heterogeneity in the time scales of market members’ interactions that cover more than eight orders of magnitude. We analyze three different datasets for two highly studied market venues recorded in 2004 to 2006, 2010 to 2011, and 2018. Using methods of complex network theory, we show that transactions between specific couples of market members are systematically and persistently overexpressed or underexpressed. Contemporary stock markets are therefore networked markets where liquidity provision of market members has statistically detectable preferences or avoidances with respect to some market members over time with a degree of persistence that can cover several months. We show a sizable increase in both the number and persistence of networked relationships between market members in most recent years and how technological and regulatory innovations affect the networked nature of the markets. Our study also shows that the portfolio of strategic trading decisions of high-frequency traders has evolved over the years, adding to the liquidity provision other market activities that consume market liquidity.

Suggested Citation

  • Federico Musciotto & Jyrki Piilo & Rosario N. Mantegna, 2021. "High-frequency trading and networked markets," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(26), pages 2015573118-, June.
    • Handle: RePEc:nas:journl:v:118:y:2021:p:e2015573118
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    Citations

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    Cited by:

    1. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    2. Piero Mazzarisi & Adele Ravagnani & Paola Deriu & Fabrizio Lillo & Francesca Medda & Antonio Russo, 2022. "A machine learning approach to support decision in insider trading detection," Papers 2212.05912, arXiv.org.
    3. Jalshayin Bhachech & Arnab Chakrabarti & Taisei Kaizoji & Anindya S. Chakrabarti, 2022. "Instability of networks: effects of sampling frequency and extreme fluctuations in financial data," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(4), pages 1-14, April.
    4. Mario L'opez P'erez & Ricardo Mansilla, 2021. "Ordinal Synchronization and Typical States in High-Frequency Digital Markets," Papers 2110.07047, arXiv.org, revised Mar 2022.

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