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The causal impact of algorithmic trading on market quality

Author

Listed:
  • Nidhi Aggarwal

    (Indira Gandhi Institute of Development Research)

  • Susan Thomas

    (Indira Gandhi Institute of Development Research)

Abstract

The causal impact of algorithmic trading on market quality has been difficult to establish due to endogeneity bias. We address this problem by using the introduction of co-location, an exogenous event after which algorithmic trading is known to increase. Matching procedures are used to identify a matched set of firms and set of dates that are used in a difference-in-difference regression to estimate causal impact. We find that securities with higher algorithmic trading have lower liquidity costs, order imbalance, and order volatility. There is new evidence that higher algorithmic trading leads to lower intraday liquidity risk and a lower incidence of extreme intraday price movements.

Suggested Citation

  • Nidhi Aggarwal & Susan Thomas, 2014. "The causal impact of algorithmic trading on market quality," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2014-023, Indira Gandhi Institute of Development Research, Mumbai, India.
    • Handle: RePEc:ind:igiwpp:2014-023
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    File URL: http://www.igidr.ac.in/pdf/publication/WP-2014-023.pdf
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    References listed on IDEAS

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    1. Foucault, Thierry, 1998. "Order Flow Composition and Trading Costs in Dynamic Limit Order Markets," CEPR Discussion Papers 1817, C.E.P.R. Discussion Papers.
    2. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    3. Susan Thomas, 2010. "Call auctions: A Solution to some difficulties in Indian finance," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2010-006, Indira Gandhi Institute of Development Research, Mumbai, India.
    4. Hendershott, Terrence & Riordan, Ryan, 2013. "Algorithmic Trading and the Market for Liquidity," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1001-1024, August.
    5. Albert J. Menkveld & Boyan Jovanovic, 2010. "Middlemen in Limit Order Markets," 2010 Meeting Papers 955, Society for Economic Dynamics.
    6. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    7. Carrion, Allen, 2013. "Very fast money: High-frequency trading on the NASDAQ," Journal of Financial Markets, Elsevier, vol. 16(4), pages 680-711.
    8. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    9. Álvaro Cartea & José Penalva, 2012. "Where is the Value in High Frequency Trading?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 1-46.
    10. Foucault, Thierry, 1999. "Order flow composition and trading costs in a dynamic limit order market1," Journal of Financial Markets, Elsevier, vol. 2(2), pages 99-134, May.
    11. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    12. Davies, Ryan J. & Kim, Sang Soo, 2009. "Using matched samples to test for differences in trade execution costs," Journal of Financial Markets, Elsevier, vol. 12(2), pages 173-202, May.
    13. Hoffmann, Peter, 2012. "A dynamic limit order market with fast and slow traders," MPRA Paper 39855, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. NIdhi Aggarwal & Venkatesh Panchapagesan & Susan Thomas, 2022. "When is the Order to Trade Ratio fee effective?," Working Papers 8, xKDR.
    2. Dubey, Ritesh Kumar & Chauhan, Yogesh & Syamala, Sudhakara Reddy, 2017. "Evidence of algorithmic trading from Indian equity market: Interpreting the transaction velocity element of financialization," Research in International Business and Finance, Elsevier, vol. 42(C), pages 31-38.
    3. Nidhi Aggarwal & Venkatesh Panchapagesan & Susan Thomas, 2019. "When do regulatory interventions work?," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2019-011, Indira Gandhi Institute of Development Research, Mumbai, India.
    4. Mestel, Roland & Murg, Michael & Theissen, Erik, 2018. "Algorithmic trading and liquidity: Long term evidence from Austria," Finance Research Letters, Elsevier, vol. 26(C), pages 198-203.
    5. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    6. Zhou, Hao & Kalev, Petko S. & Frino, Alex, 2020. "Algorithmic trading in turbulent markets," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    7. repec:grz:wpsses:2018-03 is not listed on IDEAS
    8. 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.
    9. Syamala, Sudhakara Reddy & Wadhwa, Kavita, 2020. "Trading performance and market efficiency: Evidence from algorithmic trading," Research in International Business and Finance, Elsevier, vol. 54(C).

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    More about this item

    Keywords

    Electronic limit order book markets; matching; difference-in-difference; efficiency; liquidity; volatility; flash crashes;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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