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High-Frequency Trading Synchronizes Prices in Financial Markets

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  • Austin Gerig

Abstract

High-speed computerized trading, often called "high-frequency trading" (HFT), has increased dramatically in financial markets over the last decade. In the US and Europe, it now accounts for nearly one-half of all trades. Although evidence suggests that HFT contributes to the efficiency of markets, there are concerns it also adds to market instability, especially during times of stress. Currently, it is unclear how or why HFT produces these outcomes. In this paper, I use data from NASDAQ to show that HFT synchronizes prices in financial markets, making the values of related securities change contemporaneously. With a model, I demonstrate how price synchronization leads to increased efficiency: prices are more accurate and transaction costs are reduced. During times of stress, however, localized errors quickly propagate through the financial system if safeguards are not in place. In addition, there is potential for HFT to enforce incorrect relationships between securities, making prices more (or less) correlated than economic fundamentals warrant. This research highlights an important role that HFT plays in markets and helps answer several puzzling questions that previously seemed difficult to explain: why HFT is so prevalent, why HFT concentrates in certain securities and largely ignores others, and finally, how HFT can lower transaction costs yet still make profits.

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  • Austin Gerig, 2012. "High-Frequency Trading Synchronizes Prices in Financial Markets," Papers 1211.1919, arXiv.org.
  • Handle: RePEc:arx:papers:1211.1919
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    References listed on IDEAS

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    1. Austin Gerig & David Michayluk, 2010. "Automated Liquidity Provision and the Demise of Traditional Market Making," Papers 1007.2352, arXiv.org.
    2. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
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    Cited by:

    1. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    2. Donovan Platt & Tim Gebbie, 2016. "The Problem of Calibrating an Agent-Based Model of High-Frequency Trading," Papers 1606.01495, arXiv.org, revised Mar 2017.
    3. Lucio Maria Calcagnile & Giacomo Bormetti & Michele Treccani & Stefano Marmi & Fabrizio Lillo, 2015. "Collective synchronization and high frequency systemic instabilities in financial markets," Papers 1505.00704, arXiv.org.
    4. Fricke, Daniel & Gerig, Austin, 2014. "Liquidity Risk, Speculative Trade, and the Optimal Latency of Financial Markets," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100402, Verein für Socialpolitik / German Economic Association.
    5. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    6. Austin Gerig & David Michayluk, 2010. "Automated Liquidity Provision and the Demise of Traditional Market Making," Papers 1007.2352, arXiv.org.
    7. Ron Wallace, 2019. "Addressing the Malaise in Neoclassical Economics: A Call for Partial Models," Economic Thought, World Economics Association, vol. 8(1), pages 40-52, June.
    8. Benjamin Myers & Austin Gerig, 2013. "Simulating the Synchronizing Behavior of High-Frequency Trading in Multiple Markets," Papers 1311.4160, arXiv.org.

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