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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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    Cited by:

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

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