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Assessing the impact of algorithmic trading on markets: A simulation approach

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  • Gsell, Markus

Abstract

Innovative automated execution strategies like Algorithmic Trading gain significant market share on electronic market venues worldwide, although their impact on market outcome has not been investigated in depth yet. In order to assess the impact of such concepts, e.g. effects on the price formation or the volatility of prices, a simulation environment is presented that provides stylized implementations of algorithmic trading behavior and allows for modeling latency. As simulations allow for reproducing exactly the same basic situation, an assessment of the impact of algorithmic trading models can be conducted by comparing different simulation runs including and excluding a trader constituting an algorithmic trading model in its trading behavior. By this means the impact of Algorithmic Trading on different characteristics of market outcome can be assessed. The results indicate that large volumes to execute by the algorithmic trader have an increasing impact on market prices. On the other hand, lower latency appears to lower market volatility.

Suggested Citation

  • Gsell, Markus, 2008. "Assessing the impact of algorithmic trading on markets: A simulation approach," CFS Working Paper Series 2008/49, Center for Financial Studies (CFS).
    • Handle: RePEc:zbw:cfswop:200849
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gomber, Peter & Gsell, Markus, 2009. "Algorithmic trading engines versus human traders: Do they behave different in securities markets?," CFS Working Paper Series 2009/10, Center for Financial Studies (CFS).
    2. Gianluca Piero Maria Virgilio, 2019. "High-frequency trading: a literature review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(2), pages 183-208, June.
    3. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    4. Alexandru Mandes, 2020. "Impact of Electronic Liquidity Providers Within a High-Frequency Agent-Based Modeling Framework," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 407-450, February.
    5. Qixuan Luo & Yu Shi & Xuan Zhou & Handong Li, 2021. "Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1025-1049, December.
    6. Farjam, Mike & Kirchkamp, Oliver, 2018. "Bubbles in hybrid markets: How expectations about algorithmic trading affect human trading," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 248-269.
    7. Virgilio, Gianluca Piero Maria, 2020. "When spread bites fast – Volatility and wide bid-ask spread in a mixed high-frequency and low-frequency environment," Research in International Business and Finance, Elsevier, vol. 51(C).
    8. Le, Anh Tu & Le, Thai-Ha & Liu, Wai-Man & Fong, Kingsley Y., 2020. "Multiple duration analyses of dynamic limit order placement strategies and aggressiveness in a low-latency market environment," International Review of Financial Analysis, Elsevier, vol. 72(C).
    9. Alexandru Mandes, 2015. "Impact of inventory-based electronic liquidity providers within a high-frequency event- and agent-based modeling framework," MAGKS Papers on Economics 201515, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    10. Mahmoud Mahfouz & Tucker Balch & Manuela Veloso & Danilo Mandic, 2021. "Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets," Papers 2110.01325, arXiv.org, revised Oct 2021.
    11. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City University London.
    12. Paweł Mielcarz & Dmytro Osiichuk & Jarosław Cymerski, 2020. "Algorithmic Sangfroid? The Decline of Sensitivity of Crude Oil Prices to News on Potentially Disruptive Terror Attacks and Political Unrest," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    13. Qixuan Luo & Shijia Song & Handong Li, 2023. "Research on the Effects of Liquidation Strategies in the Multi-asset Artificial Market," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1721-1750, December.
    14. Virgilio, Gianluca, 2017. "Is high-frequency trading tiering the financial markets?," Research in International Business and Finance, Elsevier, vol. 41(C), pages 158-171.
    15. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).

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    More about this item

    Keywords

    Algorithmic Trading; Simulation; Double Auction;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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