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The Impact of Algorithmic Trading in a Simulated Asset Market

Author

Listed:
  • Purba Mukerji

    (Department of Economics, Connecticut College, New London, CT 06320, USA)

  • Christine Chung

    (Department of Computer Science, Connecticut College, New London, CT 06320, USA)

  • Timothy Walsh

    (Department of Computer Science, Connecticut College, New London, CT 06320, USA)

  • Bo Xiong

    (Department of Computer Science, Connecticut College, New London, CT 06320, USA)

Abstract

In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1) directly analyze AT behavior to connect AT trading strategies to specific outcomes in the market; (2) measure the impact of AT on market quality; and (3) test the sensitivity of our findings to variations in market conditions and possible future events of interest. Examples of such variations and future events are the level of market uncertainty and the degree of algorithmic versus human trading. Our results show that liquidity increases initially as AT rises to about 10% share of the market; beyond this point, liquidity increases only marginally. Statistical arbitrage appears to lead to significant deviation from fundamentals. Our results can facilitate market oversight and provide hypotheses for future empirical work charting the path for developing countries where AT is still at a nascent stage.

Suggested Citation

  • Purba Mukerji & Christine Chung & Timothy Walsh & Bo Xiong, 2019. "The Impact of Algorithmic Trading in a Simulated Asset Market," JRFM, MDPI, vol. 12(2), pages 1-11, April.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:68-:d:224573
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    References listed on IDEAS

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    1. Kelejian, Harry H. & Mukerji, Purba, 2016. "Does high frequency algorithmic trading matter for non-AT investors?," Research in International Business and Finance, Elsevier, vol. 37(C), pages 78-92.
    2. Thierry Foucault & Albert J. Menkveld, 2008. "Competition for Order Flow and Smart Order Routing Systems," Journal of Finance, American Finance Association, vol. 63(1), pages 119-158, February.
    3. Michael Kearns & Alex Kulesza & Yuriy Nevmyvaka, 2010. "Empirical Limitations on High Frequency Trading Profitability," Papers 1007.2593, arXiv.org, revised Sep 2010.
    4. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    5. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    6. Froot, Kenneth A & Scharftstein, David S & Stein, Jeremy C, 1992. "Herd on the Street: Informational Inefficiencies in a Market with Short-Term Speculation," Journal of Finance, American Finance Association, vol. 47(4), pages 1461-1484, September.
    7. Azad, A.S.M. Sohel & Azmat, Saad & Fang, Victor & Edirisuriya, Piyadasa, 2014. "Unchecked manipulations, price–volume relationship and market efficiency: Evidence from emerging markets," Research in International Business and Finance, Elsevier, vol. 30(C), pages 51-71.
    8. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    9. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    10. 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.
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    Cited by:

    1. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.
    2. Lars Stentoft, 2020. "Computational Finance," JRFM, MDPI, vol. 13(7), pages 1-4, July.

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