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The causal impact of algorithmic trading on market quality

Author

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  • 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. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    2. 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.
    3. 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.
    4. Moura, Marcelo L. & Pereira, Fatima R. & Attuy, Guilherme de Moraes, 2013. "Currency Wars in Action: How Foreign Exchange Interventions Work in an Emerging Economy," Insper Working Papers wpe_304, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    5. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    6. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    7. Hoffmann, Peter, 2012. "A dynamic limit order market with fast and slow traders," MPRA Paper 39855, University Library of Munich, Germany.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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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