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Machines and Markets : Assessing the Impact of Algorithmic Trading on Financial Market Efficiency

Author

Listed:
  • Garg, Karan

    (University of Warwick)

Abstract

The rise of machine learning has revolutionised finance. Institutions across the world have increasingly turned to data science and machine learning to create trading models without the need for human intervention. This has had various implications for the financial markets that they operate in, including market efficiency. This paper simulates a financial market with agent-based modelling and Monte-Carlo style simulations, to motivate a qualitative discussion about the implications of increased algorithmic trading on financial market efficiency. It finds that algorithmic traders (ATs) can seemingly increase market efficiency through better liquidity management and more complete extraction of information from prices. However, this also comes with increased instability and potential convergence to an unstable equilibrium. The Adaptive Market Hypothesis (Lo, 2004) is suggested as an alternative framework for analysing AT behaviour.

Suggested Citation

  • Garg, Karan, 2021. "Machines and Markets : Assessing the Impact of Algorithmic Trading on Financial Market Efficiency," Warwick-Monash Economics Student Papers 11, Warwick Monash Economics Student Papers.
    • Handle: RePEc:wrk:wrkesp:11
    as

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    File URL: https://warwick.ac.uk/fac/soc/economics/research/wmesp/manage/11_-_karan_garg.pdf
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    References listed on IDEAS

    as
    1. Lo, Andrew W. & MacKinlay, A. Craig, 1989. "The size and power of the variance ratio test in finite samples : A Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 40(2), pages 203-238, February.
    2. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    3. Martin, Ian W.R. & Nagel, Stefan, 2022. "Market efficiency in the age of big data," Journal of Financial Economics, Elsevier, vol. 145(1), pages 154-177.
    4. Albert J. Menkveld & Boyan Jovanovic, 2010. "Middlemen in Limit Order Markets," 2010 Meeting Papers 955, Society for Economic Dynamics.
    5. Terrence Hendershott & Ryan Riordan, 2009. "Algorithmic Trading and Information," Working Papers 09-08, NET Institute, revised Aug 2009.
    6. Oliver Linton & Soheil Mahmoodzadeh, 2018. "Implications of High-Frequency Trading for Security Markets," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 237-259, August.
    7. Marc Oliver Rieger & Mei Wang & Thorsten Hens, 2017. "Estimating cumulative prospect theory parameters from an international survey," Theory and Decision, Springer, vol. 82(4), pages 567-596, April.
    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. Graham, John R. & Harvey, Campbell R., 2001. "The theory and practice of corporate finance: evidence from the field," Journal of Financial Economics, Elsevier, vol. 60(2-3), pages 187-243, May.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Neural Networks ; Agent-Based Modelling ; Efficient Market Hypothesis ; Stock Market Simulation ; Financial Regulation JEL Classification: C45 ; C53 ; G14 ; G17 ; G18;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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